U.S. nursing homes provide long-term care to over 1.2 million older adults, 60% of whom were physically frail and 68% had moderate or severe cognitive impairment. Limited research has examined the longitudinal experience of these two conditions in older nursing home residents.n = 266,001). Physical frailty was measured by FRAIL-NH and cognitive impairment by the Brief Interview for Mental Status. Separate sets of group-based trajectory models were fitted to identify the trajectories of physical frailty and trajectories of cognitive impairment, and to estimate the association between older residents’ characteristics at admission with each set of trajectories. A dual trajectory model was used to quantify the association between the physical frailty trajectories and cognitive impairment trajectories.This national longitudinal study included newly-admitted non-skilled nursing care older residents who had Minimum Data Set (MDS) 3.0 (2014–16) assessments at admission, 3 months, and 6 months , “Consistently Pre-frail” (29.0%), “Worsening Frailty” (7.6%), “Improving Frailty” (5.5%), and “Consistently Robust” (4.8%)] and three cognitive impairment trajectories  were identified. One in five older residents simultaneously followed the trajectories of “Consistently Frail” and “Consistently Severe Cognitive Impairment”. Characteristics associated with higher odds of the “Improving Frailty”, “Worsening Frailty”, “Consistently Pre-frail” and “Consistently Frail” trajectories included greater at-admission cognitive impairment, age ≥ 85 years, admitted from acute hospitals, cardiovascular/metabolic diagnoses, neurological diagnoses, hip or other fractures, and presence of pain. Characteristics associated with higher odds of the “Consistently Moderate Cognitive Impairment” and “Consistently Severe Cognitive Impairment” included worse at-admission physical frailty, neurological diagnoses, hip fracture, and receipt of antipsychotics.Findings provided information regarding the trajectories of physical frailty, the trajectories of cognitive impairment, the association between the two sets of trajectories, and their association with residents’ characteristics in older adults’ first six months post-admission to U.S. nursing homes. Understanding the trajectory that the residents would most likely follow may provide information to develop a comprehensive care approach tailored to their specific healthcare goals.The online version contains supplementary material available at 10.1186/s12877-022-03012-8. Physical frailty, characterized by decreased physical reserve and increased vulnerability to external stressors, and cognitive impairment, manifested as dysfunctions on at least one cognitive domain, are common geriatric conditions. In community-dwelling older adults living in the United States (U.S.), 6% have physical frailty only, 26% have cognitive impairment only, and 9% experience both conditions . PhysicaChanges in the prevalence of physical frailty and cognitive impairment occur in the first three months of nursing home residence : 23% of Physical frailty and cognitive impairment are highly correlated , 14–17. This study examined the trajectories of physical frailty and cognitive impairment in a national cohort of U.S. older nursing home residents. We focused on the first six months post admission because this is a critical window during which older residents adjust to clinical care and living environment changes , 29. TheWe used the national data repository for all residents in U.S. Medicaid-/Medicare-certified nursing homes, the Minimum Data Set (MDS) 3.0. The MDS 3.0 assessments are completed at admission, quarterly and annually post-admission to collect data through self-report and/or staff-administered questionnaires on residents’ demographic characteristics, cognitive functioning, mood, behavioral status, physical functioning, bladder and bowel conditions, diagnoses, health conditions, nutritional status, medications and other treatment .We included residents who were 1) aged ≥ 65 years at admission; (2) newly admitted (defined as no previous stay in a nursing home in the 90 days before the current admission) between 01/01/2014 and 06/30/2016; (3) had a prognosis of life expectancy ≥ 6 months at admission, determined by answering “no” to the MDS 3.0 question, “Does the resident have a condition or chronic disease that may result in a life expectancy of less than 6 months?” , 32; (4) aged ≥ 6FRAIL-NH was developed to assess physical frailty in nursing home residents using items readily available in MDS 3.0 , 34. As The BIMS focuses on temporal orientation and ability to recall and is included on Section C  of the MThe following characteristics were drawn from the admission assessment: age , sex , race/ethnicity , location of the nursing homes , sources of admission , active diagnoses, any presence of pain (yes/no), and receipt of medications in the 7 days before the MDS 3.0 assessment . Active diagnoses included cancer, heart failure, hypertension, diabetes mellitus, Alzheimer’s disease, cerebrovascular accident/transient ischemic attack(TIA)/stroke, non-Alzheimer’s/other dementia , multiple sclerosis, Parkinson’s disease, seizure disorder/epilepsy, arthritis, osteoporosis, hip fracture, other fracture, asthma/chronic obstructive pulmonary disease (COPD)/chronic lung disease, anxiety disorder, and depression.Sample characteristics at admission were first described in percentages.To address the first two study objectives, two sets of group-based trajectory models (GBTM) were fitted respectively for: 1) physical frailty , and 2) cognitive impairment , with time measured by the length of stay (in days) when each of the respective assessments occurred. We then estimated the association between sample characteristics at admission and the identified trajectories. We followed the three-step model building process recommended by Nagin  and repoWe first identified the trajectories. We used the same approach for the physical frailty and cognitive impairment. GBTM with two to six trajectory groups were fitted to the data with all groups in quadratic shape, the highest possible order for 3 time points. We compared the fit statistics and graphic depictions of these models to see if adding another trajectory identified a new unique experience or if it overlapped with the existing ones. Fit statistics included (1) Bayesian information criterion (BIC): the model with the greater BIC was preferred; (2) Group average posterior probability (AvePP) of assignment: An AvePP >0.7 for all trajectory groups was indicative of good certainty of group assignments; (3) Odds of correct classification (OCC): A model was considered to have high assignment accuracy with OCC>5 for all trajectory groups. From these models, predicted values of FRAIL-NH (or BIMS) scores were graphed as solid lines to depict the trajectories with 95% confidence bands (shown with dashed lines). After we determined the optimal number of trajectories, we optimized the shapes of each trajectory (quadratic or linear) to further improve the model fit. The model that best fit the data was identified to represent the trajectories of physical frailty or the trajectories of cognitive impairment. We assigned qualitative labels to each trajectory guided by validated cutoffs for physical frailty  and cognWe then selected the residents’ characteristics to be included in each trajectory model. We first “hard-assigned” residents into the trajectory to which they had the highest posterior probability of belonging. The distributions of demographic and clinical characteristics were calculated by the assigned trajectory groups. Then, we used multinomial logistic regression models to estimate and compare the crude and adjusted associations between demographic and clinical characteristics and the assigned trajectory groups. These models helped us identify the final set of characteristics to be included in the GBTMs in Step 3. The We then estimated the association between demographic and clinical characteristics and the trajectories. We included cognitive impairment at admission and the selected demographic and clinical characteristics (Step 2) in the final physical frailty GBTM model (Step 1) to estimate the association between the characteristics and the identified physical frailty trajectories. Similarly, physical frailty at admission and the selected demographic and clinical characteristics (Step 2) were included in the final cognitive impairment GBTM (Step 1) to estimate the association between the characteristics and the identified cognitive impairment trajectories. From these models, we derived adjusted odds ratios (aOR) and corresponding 95% confidence intervals (CI).To address the third study objective, a dual trajectory model was used. This model allowed us to examine the association between the identified physical frailty trajectories and the identified cognitive impairment trajectories. Three sets of conditional probabilities linked membership across the physical frailty and cognitive impairment trajectory groups . Expliciggplot2 [The analysis used SAS 9.4  with PROC TRAJ for GBTM . We creaggplot2 .Of the 266,001 eligible older residents, nearly half were aged ≥ 85 years, two thirds were women, less than one in five belonged to a racial/ethnic minority group, three quarters entered nursing homes located in urban settings, 35.7% were admitted from the community, and 38.8% from acute hospitals. At admission, 54.1% older adults were frail as measured by the FRAIL-NH scale, and 36.7% had severe cognitive impairment according to BIMS. The top five diagnoses were hypertension (75.9%), Non-Alzheimer’s/Other dementia (42.5%), depression (39.8%), diabetes mellitus (30.2%), and arthritis (29.3%). Pain was documented in over one third of residents. In the past seven days, 19.0% received antipsychotics and 46.9% received antidepressants. . All models showed good certainty and accuracy of group assignment. , the “Improving Frailty” trajectory  and the “Consistently Frail” trajectory .Most diagnoses were consistently associated with higher odds of the four trajectories relative to the “Consistently Robust” trajectory. Notably, the aORs for the “Consistently Frail” trajectory were higher than the other three trajectories for cerebrovascular accident/TIA/stroke , multiple sclerosis , Parkinson’s Disease , and hip fracture . Older residents with presence of pain had greater odds of being in the “Consistently Frail” , “Improving Frailty”  and “Consistently Pre-frail”  trajectories. Older residents who received antipsychotics or hypnotics in the seven days were less likely to follow the “Improving Frailty”, “Consistently Pre-frail” or “Consistently Frail” trajectories, while those who received antidepressants were more likely to do so.Following a similar model building and selection process (Supplement Table S.Older residents were then assigned to the cognitive impairment trajectory group to which they had the highest posterior probability of belonging (Step 2). At admission, about 57.8% of the older residents assigned to the “Consistently Severe Cognitive Impairment Trajectory” were frail, which was 8% higher than those assigned to the “Consistently Intact/Mild Cognitive Impairment Trajectory”. The distribution of demographic and clinical characteristics are summarized in Supplement Table S.As shown in Supplement Table S.Older adults with advancing age, from racial/ethnic minority groups, or who were residing in rural nursing homes had greater odds of being in the “Consistently Moderate Cognitive Impairment” or “Consistently Severe Cognitive Impairment” trajectories, while those admitted from acute hospitals or other non-community sources were less likely to do so. Female residents were less likely than males to belong to the “Consistently Moderate Cognitive Impairment” trajectory. There were no sex differences for the “Consistently Severe Cognitive Impairment” trajectory.Alzheimer’s disease, cerebrovascular accident/TIA/stroke, non-Alzheimer’s/other dementia, seizure disorder/epilepsy or hip fracture were associated with greater odds for the “Consistently Moderate Cognitive Impairment” or “Consistently Severe Cognitive Impairment” trajectories. Conversely, cancer, heart failure, hypertension, diabetes mellitus, multiple sclerosis, Parkinson’s disease, arthritis, osteoporosis, asthma/COPD/chronic lung disease, anxiety disorder, depression or presence of pain were consistently associated with lower odds. Older residents who received antipsychotics or antidepressants were more likely to follow the “Consistently Moderate Cognitive Impairment” or “Consistently Severe Cognitive Impairment” trajectories.The conditional probabilities linking trajectories of physical frailty and trajectories of cognitive impairment are shown in Fig. In older residents following a “Consistently Intact/Mild Cognitive Impairment” trajectory, over a third followed the “Consistently Pre-frail” trajectory. The probability of following a “Consistently Frail” trajectory was highest (60.9%) in those with a “Consistently Severe Cognitive Impairment” trajectory.In residents who followed a “Consistently Robust” or “Improving Frailty” physical frailty trajectory, around half followed a “Consistently Intact/Mild Cognitive Impairment” trajectory. Over 40% of those in the “Consistently Frail” trajectory followed a “Consistently Severe Cognitive Impairment” trajectory.In their six months after nursing home admission, 21.5% of older residents followed “Consistently Frail” and “Consistently Severely Cognitive Impairment” trajectories.To the best of our knowledge, this is the first study to quantify the trajectories of physical frailty and cognitive impairment in U.S. nursing home residents on the national level. The use of GBTM with continuous measures of physical frailty and cognitive impairment enabled us to describe the “natural history” of these two prominent conditions, identify the demographic and clinical characteristics associated with the respective trajectories, and examine the relationship between the trajectories of physical frailty and trajectories of cognitive impairment in older adults’ first six months of nursing home residence.Consistent with prior studies that found heterogeneous progression trajectories in community-dwelling older adults , five diAt-admission physical frailty level appeared to be a strong determinant of older residents’ physical frailty trajectory in the next six months. An exception to this observation was those following the “Improving Frailty” trajectory, who had a baseline predicted FRAIL-NH score comparable to the “Consistently Pre-frail” trajectory. Several characteristics may help distinguish between the two: seizure disorder/epilepsy, other fracture, and presence of pain were associated with higher odds to be in the “Improving Frailty” trajectory than the “Consistently Pre-frail” trajectory. But perhaps the strongest factors to differentiate these two trajectories was severe cognitive impairment, admission from acute hospitals, and age ≥ 85 years.The experience of older residents while living in the nursing home may influence the trajectory of physical frailty. In addition to nursing home admission , 42, socThe “Intact/Mild Cognitive Impairment” trajectory was consistent with prior research that mostly identified a subgroup of older adults who started with a high cognitive function and remained relatively stable over time . HoweverComorbid conditions were associated with reduced odds to follow the “Consistently Moderate Cognitive Impairment” or “Consistently Severe Cognitive Impairment” trajectories. However, this finding does not suggest that older adults with greater comorbidities were less likely to follow the more adverse cognitive impairment trajectories. Instead, residents with comorbid conditions likely entered nursing homes for care needs related to dependencies related to activities of daily living and other physical functions and physical illnesses , 49, ratRegarding the relationship between the two sets of trajectories, one previous study in community-dwelling older adults identified four trajectory groups of physical frailty and cognitive impairment, including one marked by the simultaneous accelerated worsening of both conditions which was named “cognitive frailty” . AnotherWe note a few limitations. We focused on the first six months post admission to nursing home admission. This early period is a critical window when functional impairment could impact older adults’ adjustments to changes in care setting and living environment, as well as long-term health outcomes , 56. We In conclusion, in older adults’ first six months in a U.S. nursing home, five physical frailty trajectories were identified. While half of older residents were consistently frail, some showed improvement or decline in their physical frailty. Three cognitive impairment trajectories were identified with minimal changes over time. Trajectories of physical frailty were strongly associated with trajectories of cognitive impairment. More than 1 in 5 residents followed the trajectories of being consistently frail and severely cognitive impaired. Older residents may benefit from comprehensive care management approaches tailored to the distinctive trajectories that they were following. The identified sociodemographic and clinical characteristics that were associated with the identified physical frailty and cognitive impairment trajectories can inform our efforts to triage older residents upon admission for proper care.Additional file 1: Supplement Figure S.1. Sample flowchart. Supplement Figure S.2a. Graphic depictions of group-based trajectory model with two to six groups for physical frailty. Supplement Figure S.2b. Graphic depictions of group-based trajectory model with two to six groups for cognitive impairment. Supplement Table S.1. The FRAIL-NH scale. Supplement Table S.2a. Fit statistics for trajectory models for physical frailty over the first six months of nursing home stay. Supplement Table S.2b. Fit statistics for trajectory models for cognitive impairment over the first six months of nursing home stay. Supplement Table S.3a. At-admission cognitive impairment, demographic and clinical characteristics by assigned physical frailty trajectories. Supplement Table S.3b. At-admission physical frailty, demographic and clinical characteristics by assigned cognitive impairment trajectories. Supplement Table S.4a. Association between demographic and clinical characteristics at admission and physical frailty trajectory groups. Supplement Table S.4b. Association between demographic and clinical characteristics at admission and cognitive impairment trajectory groups. Method Supplement: Model Building Step 2.


One hour plasma glucose concentration (1hPG) during an oral glucose tolerance test (OGTT) may be an alternative to 2-h plasma glucose concentration (2hPG) in the identification of individuals at increased risk of hyperglycaemia, although its role is not fully understood.mellitus diagnosis, in a sample of patients with morbid obesity.We aim to investigate the relationship of these measures with other glucose parameters, as well as their relationship with cardiometabolic risk markers and the level of agreement for prediabetes mellitus diagnosis.We retrospectively evaluated 656 patients with morbid obesity without diagnosed diabetes. To define prediabetes with 2hPG, 2022 American Diabetes Association guidelines criteria were used, while for 1hPG, glucose ≥ 155 mg/dL was considered. Cohen’s Kappa coefficient was used to assess the agreement between both measures of prediabetes ρ = 0.281, p < 0.001), fasting plasma glucose (FPG) , glycated haemoglobin (Hb1AC)  and were negatively correlated with homeostatic model assessment for cell-β function (HOMA-β) . 2hPG were also correlated with the same parameters, except for HOMA-β.A Cohen’s Kappa coefficient of 0.405 (p < 0.001) was obtained. The 1hPG were positively correlated with homeostatic model assessment for insulin resistance (HOMA-IR) (A fair agreement between 1 and 2hPG was verified. 1hPG criteria may be a useful indicator of β-cell dysfunction and insulin resistance in patients with morbid obesity without diabetes diagnosis. Systolic (SBP) and diastolic blood pressure (DBP) were also retrieved, being measured using a mercury sphygmomanometer. The 2022 American Diabetes Association guidelines criteria and glucose ≥ 155 mg/dL were used to define pre-T2DM, for 2hPG and 1hPG, respectively. Data on smoking habits was also retrieved.In addition to clinical parameters, data on HbA1c, serum insulin, creatinine, transaminases, as well on lipid profile , were also taken from the hospital clinical files. Values of HOMA-IR [ × /405] were used as a measure of insulin resistance (IR)  and valur) correlation coefficient or the Spearman (ρ) correlation coefficient, as appropriate. Statistical analysis was performed using SPSS Statistics 25.0 . A significance level of 5% was considered.Continuous variables were described as mean and standard deviation (SD), or as median and interquartile range (IQR), and were compared using Student’s T-test or the Mann–Whitney test, as appropriate. Categorical variables were summarised as counts and proportions. The Cohen’s Kappa coefficient was computed to evaluate the agreement between the two measures of pre-T2DM diagnosis—1hPG and 2hPG (primary endpoint). For the secondary objective, correlations between continuous variables (the relationship of 1hPG and 2hPG with parameters of glucose metabolism and cardio-metabolic risk) were carried out using the Pearson  and had a mean ± SD age of 40.3 ± 10.1 years (Table p < 0.001), corresponding to a fair agreement between 1 and 2hPG , fasting glucose  and HbA1c , and negatively correlated with HOMA-β . Values of 2hPG were also correlated with the same variables, except HOMA-β . The insulinogenic index showed to be negatively correlated with both 1hPG  and 2hPG  , HbA1c , HOMA-IR  and of C-Peptide , and lower HOMA-β  (Table p = 0.402) . No significant differences were observed regarding HDL, LDL, total cholesterol, high sensitivity C-reactive protein (hsCRP), creatinine, HOMA-IR or HOMA-β  identifying individuals at lower risk than the other criteria, which are currently in use . 1hPG se.[Regarding our secondary objectives, we observed that 1hPG values were negatively correlated with β-cell function and insulinogenic index and were positively correlated with IR, fasting glucose and HbA1c. Similar results were obtained for 2hPG, except for HOMA-β. Looking for the correlation coefficients, overall, the correlations seemed to be stronger for 1hPG than 2hPG. Bergman et al. showed t.. Other s., 28 haveThe insulinogenic index on the OGTT can be used as an index of early phase insulin secretion , 30. It According to our findings, 1hPG were significantly and positively correlated with triglycerides, and negatively correlated with HDL-C. This is in accordance with the observation that dyslipidaemia in T2DM patients is characterised by high triglyceride levels and decreased HDL-C, which can be observed many years before the onset of clinically-relevant hyperglycaemia , 33. TheAlthough it has been previously demonstrated that 1hPG is associated with inflammatory markers (e.g.: hsCRP)  this wasThis study has limitations that should be mentioned. The included patients came from a tertiary hospital, and they were referenced for BS, which can skew the results as it is a very specific population, moreover, these results may not be generalizable to populations of different traits. Therefore, further studies including a more diversified sample of patients should be taken to corroborate our findings. Furthermore, the impact of the found changes in each method on the progression to T2DM after BS has not been evaluated, which limit the conclusions concerning the accuracy of the methods in predicting T2DM progression. Moreover, data on carotid echocardiography, ABI, vascular endothelial function that would help on identifying potential cardiometabolic risk factors, were not available for this study population, and should also be considered a potential study limitation. Additionally, although most of the recuited patients were female (86.9%) this percentage is in agreement with the the sex distribution of patients who attended bariatric appointments.This study showed that 1hPG could be an alternative for the diagnosis of prediabetes in morbidly obese patients. Considering the paucity of research regarding the agreement between 1 and 2hPG in people with morbid obesity, this study provided relevant insights on this regard. Also, this study was strengthened by the large number of subjects included.A fair agreement between 1 and 2hPG was found. 1hPG may be useful for evaluating β-cell dysfunction and IR in morbidly obese patients without diagnosed diabetes. Abnormalities in 1hPG could represent a more severe metabolic perturbation, which is characterised by higher glycaemic, lower insulin sensitivity, and a markedly reduced β-cell function.


A 68-year-old man, without a family history of cancer, was treated for primary cutaneous melanoma of the scalp. Two years later, a right lateral cervical lymph recurrence was observed and he was treated with lymphadenectomy and adjuvant nivolumab for 1 year. Four years from the initial melanoma diagnosis, a computer tomography scan showed a solid nodular lesion of 26 × 40 × 75 mm inside the previously known inguinoscrotal hernia. A new recurrence of melanoma was the most probable diagnosis and a right inguinal hernioplasty was performed. Notably, the histopathological examination revealed a mesenteric fibromatosis with the typical immunohistochemical pattern (strong nuclear staining of β-catenin). Interestingly, this represents the first case of a patient with a mesenteric desmoid tumour presenting as an inguinal hernia masking a cutaneous melanoma recurrence. Aggressive fibromatosis, also known as desmoid tumour (DT), is a benign, locally invasive, non-metastasising neoplasm with a very high rate of recurrence. This entity represents 0.03% of all neoplasms and less than 3% of all soft tissue tumours , 2.DTs can arise in virtually any part of the body, and according to their main localisations, they are classified as extra-abdominal (60%), abdominal wall (25%) and intra-abdominal (8%–15%) , 4.Mesenteric fibromatosis represent 0.73% of all abdominal tumours and accounts for up to 8% of all desmoid neoplasms . InteresHere, we report the first case of a patient, with prior diagnosis of melanoma, with a mesenteric DT presenting as an inguinal hernia. In this case report, Case Reports (CARE) guidelines were followed and the patient’s authorisation was obtained before manuscript submission.A 68-year-old man, without a family history of cancer, was diagnosed with a pT2N0M0 BRAF  wild type primary cutaneous melanoma of the scalp in April 2017. The treatment approach included a wide tumour resection and a sentinel lymph node biopsy. After that, the proposed follow-up scheme was physical examination and computer tomography (CT) scans every 6 months. Of note, in the baseline CT scan, a small right inguinal hernia with adipose content was evidenced.Two years after the initial diagnosis, an enlargement of a right lateral cervical lymph node was detected. A core needle biopsy confirmed the presence of melanoma recurrence. At this time, the patient underwent a right cervical lymph node dissection, obtaining 2 out of 23 lymph nodes with melanoma metastases. Adjuvant treatment with nivolumab was completed for 1 year with excellent tolerance.In September 2021, 4 years after the initial melanoma diagnosis, a follow-up CT scan showed a solid nodular lesion of 26 × 40 × 75 mm inside the already-known inguinoscrotal hernia . In thisDuring the last follow-up exam (December 2021), the patient remained with no evidence of the disease.DTs appear to be of aponeurotic origin, have a benign histological appearance and result from an aggressive fibroblast–myofibroblast proliferation . John MaHowever, the real aetiology of mesenteric fibromatosis remains unknown. Most cases have been reported in association with Gardner’s syndrome, previous trauma and prolonged oestrogen exposure, but mesenteric fibromatosis can occur as a primary condition in the absence of any predisposing factors .Atypical localisation of DT represents a diagnostic challenge. Watchful waiting is usually the first recommended approach.et al  OR  OR “abdominal fibromatosis”[All Fields] OR (“mesenteric”[All Fields] AND “fibromatosis”[All Fields]) OR “mesenteric fibromatosis”[All Fields] OR  OR “aggressive fibromatosis”[All Fields] OR (“desmoid”[All Fields] AND “tumor”[All Fields]) OR “desmoid tumor”[All Fields]) OR  OR “aggressive fibromatosis”[All Fields] OR (“aggressive”[All Fields] AND “fibromatosis”[All Fields])) AND  OR “inguinal hernia”[All Fields] OR )).


Carbon values greater than −28.6‰ and hydrogen values greater than −71‰ may indicate the addition of synthetic curcuminoids to the natural ones.Curcuminoid complex, a mixture of curcumin, demethoxycurcumin and didemethoxycurcumin, is one of the most popular antioxidants of natural origin, and it has a multitude of other health benefits. It is threatened by the proliferation of counterfeit products on the market containing synthetic curcuminoids whose addition is difficult to identify as they present the three curcuminoid forms in the correct ratios. Consequently, the necessity to detect this fraudulent practice is escalating. Carbon-14 analysis is the most effective available method, but it is also expensive and difficult to implement. This paper describes the first attempt to characterize natural curcuminoids and their synthetic form, used as an adulterant, through the analysis of stable isotope ratios of carbon and hydrogen (expressed as  Curcuma longa and other Curcuma species (spp.) [w/w) in the purified extract. The extract, known as “Curcuminoids” or simply “Curcumin,” is a mixture of curcumin  [w/w), demethoxycurcumin (about 15–20% w/w), and didemethoxycurcumin, also known as bisdemethoxycurcumin (below 5% w/w) [Curcuminoids are natural polyphenolic compounds isolated from turmeric roots (s (spp.) ), a peres (spp.) . While cs (spp.) , they co 5% w/w) ,9. The iAs reported by Girme et al., the natural curcuminoid extract is increasingly purposely blended with the cheaper synthetic curcumin to obtain a product that is less expensive to produce . This ad10B), using inductively coupled plasma mass spectrometry (ICP-MS) [Two methods have recently been proposed to identify the fraudulent addition of synthetic curcumin to the natural extract. The first is based on the high-performance thin-layer chromatography (HPTLC) and high-performance liquid chromatography-photodiode array (HPLC-PDA) quantification of CIMP-1 -5-hydroxy-1-(4-hydroxy-3-methoxyphenyl) hexa-1,4-dien-3-one, a key intermediate in the synthesis of curcumin of synthetic origin . The sec14C radiocarbon dating, which is expensive, not easily accessible and provided by a limited number of laboratories [The United States Pharmacopeia lists specific ratios between the concentrations of curcumin, demethoxycurcumin, and didemethoxycurcumin as a means for controlling curcuminoids . Theoretratories . The dat13C/12C), nitrogen (15N/14N), sulfur (34S/32S), oxygen (18O/16O) and hydrogen (2H/1H). Chemically identical molecules may have completely different isotope ratios, that vary based on several factors. The carbon stable isotope ratio (δ13C) is influenced by the C3, C4 or CAM photosynthetic cycle of the plant from which the molecule derives, or by the fossil source used as a precursor for the chemical synthesis of the molecule. On one hand, as reported by O’Leary at al., while C4 plants have δ13C values between −14 and −12‰, C3 plants range from −29 to −25‰ [δ13C ranging from −42.5‰ to −25.5‰ [δ2H), which has significantly more positive values in non-natural molecules  than in natural ones  [In recent years, stable isotope ratio analysis (SIRA) found widespread use in food science , not onl to −25‰ . On the o −25.5‰ . Factors14C analysis was performed using a fluid scintillation counter [This study investigates, for the first time, the possibility of identifying the fraudulent dilution of natural antioxidant curcuminoids with synthetic curcuminoids by using this powerful analytical approach. Samples of authentic natural curcuminoids, adulterant synthetic curcuminoids, and natural curcuminoids spiked with the synthetic form were analyzed for C and H stable isotope ratios using an isotope ratio mass spectrometer (IRMS) interfaced with an elemental analyzer (EA) and a pyrolyzer (P). Moreover, to ensure the natural and synthetic origin of the curcuminoids used and the percentage of synthetic adulterant added in the spike samples, a  counter .Twenty-one samples of authentic  curcuminoids were provided by the producer INDENA S.p.A., Milan, Italy. The samples were produced in different years between 2009 and 2017. Six synthetic curcuminoids samples were purchased on the market. The extremely small number of global producers, all located in India, justifies this sample size. In addition, seven curcuminoid complexes spiked with different concentrations of synthetic curcuminoids were produced in the laboratory. The samples were lyophilized and ground into a powder.v/v) (solvent 1) and acetonitrile (solvent 2). The percentage of solvent 2 at the start of the gradient was 40% and was increased to 60% (6 min) and then again to 40% up to the end of the analytical run. The flow rate was 0.8 mL/min.The three curcuminoid components  were measured following the method reported by Sahu et al. . In brieA mixture of acetonitrile and water (50:50) was used as a sample diluent. Methanol was used for the preparation of primary stocks solutions (1 mg/mL), accurately weighed curcumin, demethoxycurcumin and didemethoxycurcumin. These were used to prepare a series of calibration standards with appropriate concentrations, working standard (100 mg/mL) and quality control (QC) samples. Calibration curves (six points) were constructed between the concentration range of 10 and 80 mg/mL.Fifteen mg of each curcuminoid sample under analysis were transferred to a 15 mL Borosil glass centrifuge tube and centrifuged in acetonitrile at 1500 rpm for 30 min. The supernatant layer (10 mL) was used for direct injection into the HPLC. An electronic balance , a centrifuge  and an ultra sonicator  were used for sample preparation.The content of curcumin, demethoxycurcumin and didemethoxycurcumin was expressed as a percentage.14C content of pulverized food material was performed in accordance with DIN EN 16640:2017-05 [The determination of biobased carbon content by analyzing the :2017-05 .2. The CO2 was cleaned for additional oxygen and other gaseous components by using cold traps and dissolving in absorber fluid. After scintillator fluid had been added to the pure CO2 (cocktail with 6.8 g CO2), the sample cocktail was measured in two glass bottles in a liquid scintillation counter (LSC), i.e., Packard Canberra Tri-Carb 2770 TR/SL, as a repeated measure. The measurement time was approximately 1600 min. Each measurement lasted approximately 50 min, and each sample was measured approximately 10 to 20 times. The measurement was calibrated using 14C standards of 14C-free CO2 and CO2 of known 14C content. The results were determined by the background count rate and counting efficiency. The result is given in pmC, which can be converted to the content of biogenic carbon using the correction factor of 0.985 in accordance with DIN EN 16640:2017-05. The uncertainty of the method  was 5%-modern.Approximately 20 g of the sample material was combusted in a bomb calorimeter. The sample’s organic carbon content was completely oxidized to CO13C/12C ratio was measured using an isotope mass spectrometer  after complete combustion in an elemental analyzer . The 2H/1H ratio was measured using an IRMS  coupled with a pyrolizer . The amount introduced into the above-mentioned instruments to analyze the samples was 0.5 and 0.2 mg, respectively.The 13C/12C and 2H/1H values are expressed in the delta scale (δ‰) against the international V-PDB (Vienna PeeDee Belemnite) standard according to Equation (1):ref is the international measurement standard, the sample is the analyzed sample, and iE/jE is the ratio of heavier to lighter isotopes. The delta values were multiplied by 1000 and expressed in “per mil” (‰) units or, according to the International System of Units (SI), in unit ‘milliurey’ (mUr).Based on the IUPAC protocol , 13C/12Cδ13C was calculated against working in-house standards (caseins), which were themselves calibrated against international reference materials: fuel oil NBS-22 with δ13C = −30.03‰, sucrose IAEA-CH-6 with δ13C = −10.45‰ , and L-glutamic acid USGS 40 with δ13C = −26.39‰  for 13C/12C.The isotopic value δ2H, we left the sample to equilibrate with the laboratory air for two days; it was subsequently placed inside a vacuum dryer with P2O5 for 48 h and finally placed in a zero-blank autosampler to comply with the principle of identical treatment [δ2H = −157 ± 2‰) and KHS  from the U.S. Geological Survey were used to obtain 2H/1H values through the creation of a linear equation and by adopting a comparative equilibration procedure [δ13C and 4.0‰ for δ2H values.Before we carried out the analysis of reatment . Keratinrocedure . We usedp) values of less than 0.05 were used to indicate a significance level.XLSTAT  was used to statistically evaluate the data. The existence of differences was verified by regression analysis with a confidence level of 95%. A one-way ANOVA was performed to determine the spatially significant differences between variables. Tukey’s honest significant difference (HSD) test for unequal sample sizes was implemented to evaluate significant differences due to geographical origin. Probability (14C (14C-org.) results, all the samples matched the claimed content   . Given tAll the samples analyzed, regardless of their synthetic or natural origin, had not dissimilar contents in the three forms: curcumin, demethoxycurcumin and didemethoxycurcumin see . The quaδ13C of the natural curcuminoid complex ranged between −30.7‰ and −29.0‰, with an average value of −29.8‰. This behavior is in line with the matrix botanical origin. Indeed, turmeric (Curcuma spp.) belongs to C3 plants, whose δ13C typically ranges from −29 to −25‰ [Curcuma domestica [Curcuma longa L. to be −27.77 ± 0.63 [The  to −25‰ . Regardiomestica , whereas7 ± 0.63 .δ13C value of Curcuma spp. and the natural curcuminoid complex could be explained by the fact that the different compounds have a characteristic isotope composition due to the different metabolic pathways involved in their synthesis [The gap between the typical ynthesis . The samynthesis .δ13C analysis of synthetic samples are presented in δ13C values, it is necessary to comprehend this molecule production technique. Synthetic curcumin and curcuminoids are synthesized starting from petroleum-derived compounds. Currently, many synthetic production routes to curcumin are variations of Pabon’s method , while acetylacetone derived from fossils probably exhibits the typical variability range between −32.5‰ and −23.3‰, as reported by Yeh et al. [δ13C values (average = −26.5‰) that are significantly higher (p < 0.01) than those typical of natural curcuminoids, others have overlapping values (average = −28.8‰). Therefore, δ13C analysis does not always detect fraudulent additions of this adulterant to natural curcuminoids, but it could serve as a simple and rapid screening method. Furthermore, few laboratories still routinely carry out the δ2H analysis and it is therefore necessary to provide reference δ2H intervals in addition to δ13C ones, to carry out a more accurate data interpretation. Using a 95% probability level, it is possible to identify a threshold value of −28.6‰ for the δ13C of the natural curcuminoid complex. Higher δ13C values may indicate the addition of synthetic curcuminoids.As reported by various authors ,35,36, th et al. . The isoδ2H values found in the two sample types  are significantly different and can be used to clearly differentiate between the two products. The natural curcuminoid complex has highly negative values ranging from −120‰ to −82‰  contains monthly weighted average precipitation (δ2Hp and δ18Op) from all continents and islands between 1960 and the present. The temperature and the amount of precipitation significantly influence the δ2H values of the water. Consequently, based on geographical parameters , the δ2H can vary in the precipitation from approximately −60 to +1% worldwide [δ2H values range from −70 to −38‰ in China, they range from −38‰ to −6‰ in India. This data can explain the low δ2H value of natural curcuminoids and, in particular, the lower value found in Chinese samples (average −106‰) compared to Indian samples (average −87‰). In order to proceed with the percentage estimate of natural curcuminoids adulterated with synthetic curcuminoids, the evaluation of the geographical origin must be taken into consideration, as discussed in more detail in The  to −82‰ . The δ2Ht et al. , the isosurement . The GNIorldwide . While δδ2H values, ranging between +42‰ and +58‰. Considering the synthesis reaction illustrated in Synthetic curcuminoids had very high and positive p < 0.01) in hydrogen isotopic composition between natural curcuminoids and their adulterant makes this parameter the most promising for detecting the fraudulent addition of synthetic curcuminoids to the natural product.The significant difference , the δ13C  does notδ13C . TherefoFinally, in samples containing substantial additions of synthetic curcuminoids (about 70%), both parameters demonstrated their effectiveness in identifying the presence of synthetic ingredients with significant deviations from the proposed limits.14C analysis, despite its undeniable effectiveness, requires a large amount of sample (about 20 g), it is time-consuming  and it is performed by few accredited laboratories worldwide only. The isotope ratios of hydrogen and, in some cases, carbon, exhibit significantly different ranges of variability between natural curcuminoids and their synthetic adulterant. These outcomes allow for the identification of not only the sample origin , but also for the fraudulent addition of synthetic products to the natural complex (spiked samples). Consequently, the analysis of hydrogen isotope ratios can be an effective response because it is relatively quick, inexpensive, and routinely performed in laboratories around the world.The protection of natural antioxidant curcuminoids against a recent new type of adulteration consisting in the addition of synthetic curcuminoids requires the development of increasingly sophisticated, yet rapid and cost-effective, analytical techniques. The


N = 916) was randomly assigned to 1 of 4 conditions: (1) write gratitude letters and do not share them (private gratitude), (2) share gratitude one-to-one with benefactors via text (1-to-1 gratitude), (3) share gratitude publicly on social media (public gratitude), or (4) track daily activities (control). Participants were asked to complete their assigned activity four times with different people (as applicable) over the course of about a week. Overall, participants assigned to any digital gratitude intervention experienced improvements in state gratitude, positive emotions, negative emotions, elevation, connectedness, support, and loneliness, relative to controls. Relative to all other conditions, participants assigned to text their benefactors showed the biggest boosts in social connectedness and support. Our findings show that easily scalable digital gratitude interventions can advance the well-being of young college students.Numerous investigations to date have established the benefits of expressing gratitude for improved psychological well-being and interpersonal relationships. Nevertheless, the social dynamics of gratitude remain understudied. Do the effects of gratitude differ when it is expressed privately, communicated directly to the benefactor one-to-one, or shared publicly? We tested this question in a preregistered intervention study. An ethnically and economically diverse sample of undergraduate students (The online version contains supplementary material available at 10.1007/s42761-022-00150-5. No amount of anxiety changes the future. But any amount of gratitude changes the present.” —Marc & Angel ChernoffMost people want to be happy, and gratitude interventions may be an effective path towards this goal  gratitude “to,” which is prompted by actions performed by benefactors. Recently, the actor-target-witness framework proposed that gratitude within dynamic social networks involves distinct roles  and processes . PAIs are simple, low-cost cognitive behavioral strategies that involve mirroring the thoughts and behaviors of naturally happy people  to enhance well-being  intervention that originated in the seminal studies of Emmons and McCullough  instructds = .20 to .46) and well-being (ds = .14 to .46), as well as reduce depression and anxiety (g = −.29), although some researchers note the effects are small  experiments that predate new open science practices prompted by the replication movement  and a cognitive component (life satisfaction) that assesses overall quality of life  to one of four conditions that directed them to (1) share gratitude with a benefactor face-to-face, (2) share gratitude by video call, (3) share gratitude by text, or (4) control. The study took place in four parts, with each time point spaced 3–5 days apart. All three gratitude conditions boosted well-being, relative to control, but differed little from each other—with the exception that texting gratitude had slightly weaker effects on some outcomes . Another study conducted by Koay et al.  to express gratitude on Instagram, and found higher levels of state gratitude, but no effects on life satisfaction, relative to control. Notably, neither study was preregistered and both had small sample sizes. By using a large, high-powered sample and preregistering our hypotheses in advance, we endeavored to test gratitude interventions using open science practices.Finally, with the rise of social and digital media , (2) share gratitude with a benefactor via text (1-to-1 gratitude), (3) share gratitude with a benefactor on social media , or (4) track their daily activities (control). Participants were asked to complete their assigned activity four times with different people (as applicable) over the course of about a week. We predicted that participants assigned to any gratitude condition would experience improvements in well-being outcomes, relative to controls. We also expected that sharing gratitude one-to-one might be the most impactful activity because private gratitude does not involve any kind of social interaction  write a gratitude letter and do not share it . Preregistration is available on the OSF at control condition, those in the gratitude  conditions will experience greater increases in state gratitude, positive emotions, social emotions, life satisfaction, elevation, connectedness, and support, as well as greater decreases in negative emotions and loneliness.Relative to participants in the 1-to-1 gratitude group will experience the biggest increases in state gratitude, positive emotions, social emotions, life satisfaction, elevation, connectedness, and support, as well as the biggest decreases in negative emotions and loneliness.Relative to participants assigned to all other conditions, participants in the N = 800), but overrecruited to allow for preregistered data exclusions. Following preregistered criteria (and to ensure credibility of responses), 116 participants were excluded for reporting that they did not do the assigned activity, 74 were excluded for putting no effort into the activity, 19 were excluded for completing a survey in less than 2 min, and 13 were excluded for entering the same response more than 15 times in a row. Some participants were excluded for two or more criteria. All exclusions were noted in the preregistration form in either the manipulation check or exclusion criteria sections. Attrition across time points was also relatively low: 4.4% at time 2 (T2), 6.8% at T3, and 8.9% at T4 (the end of the intervention). Dropout across conditions from T1 to T4 was relatively comparable: 7.4% for private gratitude, 9.3% for 1-to-1 gratitude, 9.7% for public gratitude, and 9.1% for control.We recruited 1,105 undergraduate students from a large public university and compensated them with course credit. Eligibility criteria required that they were at least 18 years old, fluent in English, own and use a smartphone, regularly use social media, and have access to an email account they check regularly. We aimed to recruit at least 200 participants per condition  self-reported their genders as 67.7% female, 31.7% male, and 0.7% other . Their ethnicities were 42.4% Asian, 33.6% Hispanic, 8.8% White, 3.4% Black, 7.8% more than one race, and 4.0% other. They also came from a range of household incomes: 19.7% reported that their families earned less than $25,000 a year; 23.1% earned $25,000 to $50,000; 26.7% $50,000 to 100,000; 16.6% earned $100,000 to 150,000; 5.2% earned $150,000 to $200,000; and 8.9% earned over $200,000. Most were unpartnered singles (65.6%), but a sizable share were in a romantic relationship of some kind . Many also worked part-time (27.5%).The final sample of 916 participants (T1), participants logged-in to an undergraduate research participation system and were directed to a Qualtrics survey where they consented and completed outcome measures. They were then randomly assigned to one of four conditions. In the private gratitude condition, we asked participants to “write a letter of gratitude to someone who has done something for which you are extremely grateful. Please do not share your letter with this person or anyone else.” In the 1-to-1 gratitude condition, participants were instructed to “use your smartphone to text someone who has done something for which you are extremely grateful, and thank them for their kind act(s).” In the public gratitude condition, we asked participants to “use social media to reach out to someone who has done something for which you are extremely grateful, and thank them for their kind act(s).” Finally, participants in the control condition were asked to “keep track of all the activities you do” and “keep a brief log.” See Supplemental Materials for full instructions for all conditions. After receiving instructions, they were asked to go off and complete their assigned activity that same day.The study took place over four time points that were completed within about a week  a day after completing the T1 survey. On the second (T2) survey, we asked participants to report on their experiences and complete additional outcome measures. We then provided similar activity instructions for them to perform again that day. This process repeated for two more time points (T3 and T4). Because we aimed to compare three positive interventions with potentially subtle differences, participants were instructed to repeat their assigned activity 3 times over 4 time points (as a way of maximizing effects). We also asked participants in the gratitude conditions to thank a different benefactor at each time point because we wanted the gratitude activity to stay novel and varied, which past research and theory suggests may slow down hedonic adaptation and maximize well-being effects  until 24 h after they completed the previous survey , the study duration  varied slightly by participant, lasting from 3 to 15 days .Participants were emailed another survey (strongly disagree) to 7 (strongly agree). We computed scale reliabilities using Cronbach’s alpha .Gratitude was measured with the three social  items from the Gratitude Questionnaire 6-Item ; negative emotions included items like “angry/hostile,” “depressed/blue,” and “dull/bored” ; and social emotions included socially oriented negative emotion items like “uncomfortable,” “guilty,” “indebted,” and “ashamed” . Participants rated how much they felt each emotion “today,” with response choices ranging from 1  to 7 (extremely).Emotions (aka affect) were measured with a modified 17-item Affect-Adjective Scale . T1α = .83; T2α = .85; T3α = .85; T4α = .88.Life satisfaction was measured with 3 items from the Comprehensive Inventory of Thriving  to 7 (feel very strongly). T1α = .84; T2α = .87; T3α = .89; T4α = .89.Elevation was assessed with the 6-item elevation questionnaire  to 5 (much agreement). Example items include “I felt a sense of contact with people who care for me, and whom I care for” and “I felt a strong sense of intimacy with the people I spent time with.” T1α = .84; T2α = .88; T3α = .90; T4α = .90.Connectedness (aka relatedness) was measured with 3 items from the Balanced Measure of Psychological Needs  to 5 (strongly agree). Example items include “There are people I can depend on to help me” and “There are people who appreciate me as a person.” T1α = .86; T2α = .88; T3α = .85; T4α = .83.Support was measured with the 3-item support subscale from the CIT . T1α = .74; T2α = .81; T3α = .82; T4α = .83.Finally, loneliness was assessed with the 3-item loneliness subscale of the CIT  were coded as the reference group. Next, we examined and plotted the means and standard errors of each condition at each time point to determine what type of model  was most appropriate , the control condition was coded as the reference group. For the pseudovariate used to test Hypothesis 2 (1-to-1 > Others), the other conditions , positive emotions , life satisfaction , elevation , connectedness , and support , as well as significant decreases in loneliness  and marginal decreases in negative emotions . Interestingly, for some outcomes , all of the gratitude conditions improved relative to control, while for other outcomes , the gratitude conditions stayed relatively stable while the control condition actually decreased .Hypothesis 1 was mostly supported. Relative to the control group, participants in the gratitude conditions reported significant increases in gratitude  and support . However, they did not report greater increases in gratitude, positive emotions, social emotions, life satisfaction, and elevation, or greater decreases in negative emotions and loneliness across time .Hypothesis 2 was mostly unsupported, with two exceptions. Relative to all other conditions (collapsed), the ds = .12 to .36), but small effects can meaningfully aggregate over time , these effects are likely relatively reliable and replicable. Given that students showed significant improvements within about 4 days, longer digital gratitude interventions may be even more powerful. For example, a recent 4-week gratitude intervention study showed larger effects (d = .33) on life satisfaction than did the present study  experienced improvements in gratitude, positive emotions, negative emotions, elevation, connectedness, support, and loneliness relative to controls. Additionally, out of all conditions, students assigned to text their benefactors showed the biggest boosts in social connectedness and support. The effect sizes were relatively small . In other words, those in the gratitude conditions stayed relatively happy throughout the intervention, but those in the control condition became unhappier. These results are consistent with previous observations that students tend to become less happy as the semester/quarter progresses , past research suggests that allowing people to choose the positive activity that suits them may enhance its well-being benefits  emerged, we found no significant differences in well-being. These social benefits are unsurprising, given that the 1-to-1 condition was the only one that required a personal social interaction , digital medium , and target/benefactor (romantic partners vs. parents vs. friends). For example, writing private letters of indiscriminate length may have required more time and effort than sharing gratitude on social media (or vice versa). Additionally, the assigned digital medium may have influenced whom participants chose as targets. For instance, those sharing gratitude via social media may have been less likely to thank their grandparents, who may not have or use social media. In sum, in testing whether different types of digital gratitude interventions differentially impact well-being, it is unavoidable to create conditions that differ in more than one factor, because digital behaviors themselves differ in more than one factor. Nonetheless, future investigators may seek creative ways to address these issues.We also gauged participants’ adherence to their assigned intervention instructions with self-report items , which may be a less reliable approach than using other more objective criteria. Notably, other prominent positive activity interventions  also rely on self-reported adherence items  than samples matched to U.S. census data  benefited participants similarly. However, those assigned to text their benefactors directly reported the biggest boosts in feelings of social connection and support. By examining how digital gratitude interventions do (or do not) affect well-being, we hope this study informs researchers and practitioners about how to cultivate and customize future well-being interventions. Namely, future studies could expand on our approach to determine whether such interventions are similarly beneficial in school districts, companies, governmental organizations, and health care settings. Because digital gratitude interventions can be easily implemented online and feasibly delivered to thousands of individuals, they present a potentially useful tool for both researchers and practitioners.ESM 1(DOCX 440 kb)


Correction: Int J Equity Health 21, 82 (2022)https://doi.org/10.1186/s12939-022-01678-5After publication of this article , the aut(1) In the Results section of the Abstract the penultimate sentence should have read “Additionally, we estimated that children whose mothers had no formal education were 27%  less likely to be fully vaccinated than those whose mother had primary level, or above, education.” instead of “Additionally, we estimated that children whose mothers had no formal education were 28%  less likely to be fully vaccinated than those whose mother had primary level, or above, education.”(2) In the fourth paragraph of the Discussion section, the sentence “We estimated that children were 27%  more likely to be fully vaccinated if their mother had primary level education or above, compared to no education.” should have read “We estimated that children were 27%  less likely to be fully vaccinated if their mother had no formal education, compared to primary education or above.”The original article  has been


With an objective to accelerate much-needed genetic analyses and breeding applications, draft genome assemblies were generated in several legume crops; many of them are not high quality because they are mainly based on short reads. However, the superior quality of genome assembly is crucial for a detailed understanding of genomic architecture, genome evolution, and crop improvement.Legume crops are an important source of protein and oil for human health and in fixing atmospheric NPresent study was undertaken with an objective of developing improved chromosome-length genome assemblies in six different legumes followed by their systematic investigation to unravel different aspects of genome organization and legume evolution.in situ Hi-C data to improve the existing draft genomes and performed different evolutionary and comparative analyses using improved genome assemblies.We employed A. duranensis and A. ipaensis). A comprehensive comparative analysis of these genome assemblies offered improved insights into various evolutionary events that shaped the present-day legume species. We highlighted the expansion of gene families contributing to unique traits such as nodulation in legumes, gravitropism in groundnut, and oil biosynthesis in oilseed legume crops such as groundnut and soybean. As examples, we have demonstrated the utility of improved genome assemblies for enhancing the resolution of “QTL-hotspot” identification for drought tolerance in chickpea and marker-trait associations for agronomic traits in pigeonpea through genome-wide association study. Genomic resources developed in this study are publicly available through an online repository, ‘Legumepedia’.We have developed chromosome-length genome assemblies in chickpea, pigeonpea, soybean, subterranean clover, and two wild progenitor species of cultivated groundnut (This study reports chromosome-length genome assemblies of six legume species and demonstrates the utility of these assemblies in crop improvement. The genomic resources developed here will have significant role in accelerating genetic improvement applications of legume crops. The subfamily Papilionoideae is the largest of the three subfamilies  and contains species of immense economic value globally as grain and forage legume crops. Being important commodities that provide protein for human consumption and fix atmospheric NCicer arietinum, CaGA v1.0 Cajanus cajan, C.cajan_V1.0 Glycine max, glyma Lee gnm1 Trifolium subterraneum, TSUd_r1.1 Arachis hypogaea) - Arachis ipaensis , Arachis duranensis  - were also developed mainly based on short sequence reads Due to advances in next-generation sequencing (NGS) technologies, genome assemblies, though fragmented, have been developed for several legume crops. For instance, N50 (scaffolds) values were 39.99 Mb, 0.51 Mb, 50.39 Mb, and 0.28 Mb in chickpea , pigeonpea (Cajca.Asha_v2.0), soybean (Glyma.Lee_v2.0), wild groundnut relatives A. duranensis (Aradu.V14167_v2.0) and A. ipaensis (Araip.K30076_v2.0), and subterranean clover . We used these chromosome-length genome assemblies to evaluate evolutionary divergence among legumes, re-date major evolutionary events, and describe massive gene loss and gain events. We performed comparative analyses across the Papilionoideae family to identify species-specific and expanded gene families. We have demonstrated the utility of these chromosome-length genome assemblies to enhance precision and resolution in the fine mapping of drought tolerance in chickpea and marker-trait associations for agronomic traits in pigeonpea. We have provided all these datasets through an online repository called “Legumepedia” (https://cegresources.icrisat.org/legumepedia/index.php).Here, we used In situ Hi-C was performed as described previously A. duranensis V14167, A. ipaensis K30076 and subterranean clover cv. Daliak. Before harvesting, mature, dry seeds were grown for 2–3 weeks in sterilized potting mix and dark treated for 2–3 days. We constructed one in situ library each for chickpea, subterranean clover and A. ipaensis; two in situ libraries each for pigeonpea, soybean, and A. duranensis (Table S1). These libraries were sequenced using the HiSeq X Ten and NextSeq 500 instruments. The generated Hi-C reads were used to anchor, order, orient, and correct misjoins in the existing draft genome assemblies (“D assemblies”) of six legumes  were aligned to the respective “C assemblies” using Hisat2 From each of the “C assemblies”, gene models were predicted by integrating homology-based prediction, Arabidopsis and rice using BLASTN (1E-05). The transfer RNA (tRNA) genes were identified using the tRNAscan-SE v2.0 Functional annotations were assigned to the genes using BLASTP (1E-05) according to the best hits to the NCBI non-redundant, Swiss-Prot, and TrEMBL databases A. duranensis, A. ipaensis together with Medicago, lotus, cultivated groundnut, mungbean, adzuki bean, common bean, red clover, Arabidopsis, and rice were analyzed using OrthoFinder v2.3.7 https://www.megasoftware.net/) with 1,000 bootstraps. To detect known transcription factors (TFs) in the genomes of the six studied legumes, we used the Plant Transcription Factor Database The predicted protein sequences from chickpea, pigeonpea, soybean, subterranean clover, Vitis vinifera) Chromosomes from within a genome or different genomes were compared using the predicted gene models for all the legumes described earlier Synonymous nucleotide substitutions on synonymous sites (Ks) were estimated using the Nei-Gojobori approach QTL-hotspot” region. The methodology described in Kale et al. A RIL population (RIL3) developed by crossing ICC 4958 (a drought-tolerant genotype) and ICC 1882 (a drought-sensitive genotype) was used for linkage mapping 2 mapping population derived by crossing ICPA 2039 × ICPL 87119 was used for high-density linkage map construction in the present study. A total of 336 progenies, along with parental lines, were genotyped using the “Axiom Cajanus SNP Array”, which resulted in the identification of 11,697 polymorphic markers P-value < 1E-04). The recombination frequencies were calculated using est.rf function, while grouping was done using formLinkageGroups function. Finally, the marker distance was estimated using the kosambi mapping function An FThe WGS data of pigeonpea Arachis ipaensis) to 106.34X (soybean) coverage were generated for six legume genomes (Table S1). The generated sequencing data in each species were used to anchor, order, orient, and correct misjoins in the “D assemblies”, thereby generating chromosome-length genome assemblies (“C assemblies”)  (A. ipaensis). In agreement with the pattern in most other plant genomes Table S3). The GC content in “C assemblies” of the studied legumes varied from 31.03% in chickpea to 36.84% in A. ipaensis (To improve existing draft genome assemblies (“D assemblies”) paensis) . The repipaensis ; Fig. S5ab initio prediction, and mRNA expression evidence, we predicted a total of 25,105 protein-encoding gene models in chickpea; 29,482 in pigeonpea; 51,839 in soybean; 37,474 in subterranean clover; 33,810 in A. duranensis; and 38,783 in A. ipaensis (b and 2b). The average mRNA, CDS, intron, and exon lengths were similar in all of the studied legumes . In addition to protein-coding genes, we also identified a range of 87 to 231 miRNA, 108 to 2,126 rRNA, 725 to 1,115 tRNA, and 509 to 9,350 snoRNA genes (Table S6). We also investigated pseudogenes in “C assemblies” and found that the maximum number was predicted in A. ipaensis  and the minimum in chickpea (565)  . The numTable S7).The “C assemblies” and predicted gene models were evaluated for their completeness using Benchmarking Universal Single-Copy Orthologs Table S8). The comparison of Hi-C contact matrices for “C” and “D” assemblies for a given species are indicative of significantly improved “C” assemblies (a and 2a). While comparing the improvement in “C assemblies” over “D assemblies”, we found distinct superiority in the new assemblies for the characteristics described below.We compared the “C assemblies” with the previous “D assemblies” for a range of features to assess quality improvement. In all cases, the “C assemblies” were superior. For instance, the genome sequences anchored to chromosomes in “C assemblies” increased from 40.86% and 65.24% to 91.35% and 92.59% in pigeonpea and chickpea, respectively  without any guidance from “C” or “D” assemblies (Data S1). A comparison of this map with both assemblies showed an assignment of 97.64% of loci to the 11 appropriate pseudomolecules in the “C assembly” compared to only 64.30% in the “D assembly”. Three pseudomolecules in the “C assembly” that did not have colinearity to the previous assembly were named based on the linkage groups of the genetic map developed. A comparison of the genetic map and Hi-C guided pseudomolecules showed excellent colinearity, indicating better quality of the “C assembly” (Data S1). We found that CcLG02 of the “D assembly” was split into two different pseudomolecules (CcLG02_v2.0 and CcLG05_v2.0), suggesting CcLG02 was incorrectly joined in the “D assembly”. Additionally, CcLG05 and CcLG09 of the “D assembly” are now part of a single pseudomolecule (CcLG09_v2.0) in the “C assembly”  was observed in pigeonpea. Interestingly, CcLG05_v2.0 has increased by 707.39% compared to the “D assembly”. In contrast, subterranean clover and soybean exhibited average increase of 12.16% across eight pseudomolecules and 1.83% across 20 pseudomolecules length, respectively (Table S8).Correcting misjoins: The comparison of “C assemblies” with “D assemblies” highlighted numerous assembly errors  in short-read based “D assemblies”  (Table S9). Some of the significant corrections in terms of size included inversions of ∼ 42 Mb and ∼ 19 Mb in pseudomolecules 6 and 7, respectively, of chickpea  and ∼ 13 Mb (pseudomolecule 9) in the case of A. duranensis and ∼ 16 Mb (in pseudomolecules 4 and 10) in A. ipaensis were corrected (emblies” . The maxchickpea . Similarorrected .Tables S10 and S11).Genomic colinearity is a direct reflection of the structure of the ancestral genome. Based on six sets of high-quality genomic data, we were able to identify genomic colinearity (as colinear genes) within each legume genome, between each pair of genomes, and between them and the grape genome, which was used as an outgroup reference. Homologous blocks with more than 4, 10, 20, and 50 colinear genes were identified and recorded (Table S10). By characterizing sequence divergence between colinear genes and relating them to different events, polyploidization, or speciation, we managed to infer orthologous and outparalogous genes between different genomes (Tables S12 and S13). Outparalogs, produced by polyploidization shared by legumes, would often have higher divergence than orthologs. Among the six studied legumes, subterranean clover has a maximum number  of colinear orthologs in chickpea, and the two species share 4,034 outparalogs which were duplicated due to the eudicot-common hexaploid (ECH) and legume-common tetraploid (LCT) events that occurred in the common ancestors of both genomes.Among the studied legumes, the number of within-species colinear genes was lowest in subterranean clover. We identified 4,268 colinear genes in 475 duplicated blocks in subterranean clover, each having at least four colinear genes. The subterranean clover genome shared appreciable gene colinearity with the other genomes, having 16,097 (grape) to 45,404 (soybean) colinear genes. As expected, subterranean clover shares the most colinear genes with soybean, which was affected by an extra whole-genome duplication and theoretically doubled the number of colinear blocks with the other legumes (Table S10). Similarly, chickpea colinear genes exhibited nearly 40% increase, from 4,376 to 5,992 and the two wild groundnut diploid genomes showed more than 25% increase. Despite the reduction in total genes predicted in the “C assemblies” (Table S8), a significant increase in the number of colinear genes indicated that the increase was a result of improved contiguity of the “C assemblies”.Genome-wide comparison of “C assemblies” showed much improved gene colinearity in the six legume genomes. For instance, the number of identified pigeonpea colinear genes showed a >200% increase, from a previously measured 2,086 using the “D assembly” Table S13). This shows an accumulated effect of genome fractionation after the split from other non-legume eudicots. Moreover, by referring to a legume relative, Medicago truncatulaTable S14).After a polyploidization event, many duplicated genes are subsequently lost by the non-random process called genome fractionation Medicago, we counted the number of unmatched or non-colinear gene numbers between colinear genes and found that the numbers followed a geometric distribution (Table S15), with a further gene deletion rate of ∼0.30–0.42. The further gene deletion rate means that when a DNA strand breaks, DNA deletion has a probability of 0.30–0.42 to extend to involve the following gene. To be more precise, a single gene may be deleted at a probability of 30–42%, two neighboring genes at 9–16%, and three genes at 2.7–6.4%. This suggests that most genes were deleted in relatively short DNA segments.Gene deletion may occur in a segmental manner. That is, neighboring genes could be deleted from an ancestral chromosomal region at the same time, or accumulated small  deletions might result in a similar observation Medicago genomes as a reference. In subterranean clover, we inferred 2,216 ECH-produced and 3,612 LCT-produced colinear genes, respectively. The number of identified LCT-produced gene pairs in pigeonpea increased from 464 in the “D assembly” to 2,783 in the “C assembly”. The “C assemblies” of other legumes also showed an increase of these event-related colinear genes as compared to “D assemblies” Table S16).We attempted to infer homologous genes related to different polyploidization events of speciation by referring to intra- and inter-genomic homologous gene dot plots . We infeMedicago genome as reference was also constructed similarly to show relatedness between legume chromosomes . The occurrence of the LCT at ∼ 51.42 mya (48.27–54.57) agrees with previous estimates by different groups Lotus japonicus), Medicago, subterranean clover) and indigoferoid/millettioid (adzuki bean (Vigna angularis), common bean (Phaseolus vulgaris), mungbean (Vigna radiata), pigeonpea, soybean) lineages split to form two large legume subgroups. The subterranean clover lineage split from the Medicago lineage ∼ 19.93 mya (18.71–21.15). As per our analysis, the SST occurred less than 12.15 mya (11.40–12.89) because the dating of ancestral genome divergences provides a maximum time after which the polyploidy occurred. In the case of Arachis species, while Ad (A. duranensis) and Bd (A. ipaensis) genome lineages were calculated to have diverged 2.10 (1.97–2.23) mya, the divergence of Ad-At  and Bd-Bt  appears to have occurred about 0.74 (0.70–0.79) and 0.35 (0.33–0.37) mya, respectively (b). These latter values support the proposal by Zhuang and colleagues With colinear genes identified in the “C assemblies”, we dated the speciation between legumes and the polyploid events that occurred by using a normal fitting distribution. Using the ECH event at 115–130 mya ectively b. These MedicagoTrifolium pratense) Arabidopsis and rice (Oryza sativa) as the outgroup species. Reciprocal pairwise comparisons of the proteins of 15 species led to the identification of 36,854 gene families (Table S18). The analysis identified 535 gene families that are specific to Papilionoideae (a). These families were enriched in genes involved in defense response, nodulation, TOR signaling, flavonol biosynthesis, calcium ion homeostasis, response to symbiotic bacterium, arbuscular mycorrhizal association, nitrogen fixation, and gravitropism (a). Within the studied legumes, the number of gene families specific to galegoid , milletioid , and dalbergioid  lineages was 85, 72, and 1,272, respectively (a). Cultivated groundnut shared a higher number of gene families with its B-progenitor  as compared to its A-progenitor .Predicted proteins from the six newly annotated genomes were compared to those already annotated in other members of the Papilionoideae family, including onoideae a. These itropism a. Withinectively a. CultivP-value < 0.05; Fig. 4b). In the case of chickpea, 237 and 357 families were considerably expanded and contracted, respectively. Functional annotation of expanded gene families suggested enrichment for genes involved in short-day photoperiodism, cellulose catabolism, xyloglucan biosynthesis, and red, far-red light phototransduction. The members of dalbergioid clade and soybean demonstrated expansion of genes mainly involved in oil biosynthesis , auxin biosynthesis, and gravitropism (Data S2).The identified gene families were studied for expansion/contraction during legume evolution. A total of 131/35 families were significantly expanded/contracted in Papilionoideae . The species tree based on concatenated sequences of nodulation-related genes highlighted that both the Arachis species formed one clade, and remaining legumes were part of another clade indicating the presence of a unique nodulation mechanism among the members of Arachis species and needs to be investigated further (c).Nodulation is a characteristic feature of legumes that can furnish them with a competitive growing advantage in nitrogen-poor soils compared with non‐legume plants. Multiple genes are involved in the formation and development of root nodules. We identified and investigated such genes in the studied legumes . Among the identified RGAs, RLKs were the most abundant class, followed by NBS-encoding genes. We identified 140 (in chickpea) to 532 (in subterranean clover) NBS-encoding genes in studied genomes. In chickpea (35.00%), pigeonpea (29.74%), subterranean clover (24.06%), A. duranensis (34.91%), and A. ipaensis (21.59%), a high number of RGAs were CC-NBS-LRR (CNL) genes in contrast to soybean (21.91%), where TIR-NBS-LRR (TNL) genes were most abundant (Table S21). In chickpea, of the total NBS-encoding genes, 137 (97.86%) were mapped to one of the eight pseudomolecules with significantly biased distribution among the pseudomolecules ; ∼33.57% were located on pseudomolecule Ca5_v2.0  harbored a significantly high number of NBS genes  such as NBS-encoding proteins, receptor-like proteins (RLPs), and receptor-like protein kinases (RLKs). Among the studied legumes, the number of RGAs ranged from 828 in chickpea to 1,698 in soybean (BS genes .Table S23). In each of the studied legumes, bHLH, MYB, ERF, FAR1, C2H2, WRKY, and NAC were the most abundant TF families , given their role in the regulation of skotomorphogenesis and photomorphogenesis in higher plants An average of 2,282 putative transcription factors (TFs) belonging to 58 families were predicted from the six legumes. These TFs constitute 5.17% (subterranean clover) to 7.16% (soybean) of the predicted protein-coding genes  population developed from the ICC 4958 (drought-tolerant) × ICC 1882 (drought-sensitive) cross QTL-hotspot” region, each RIL was sequenced at 1X coverage, and by aligning these sequencing data with the “D assembly”, a recombination breakpoints-based genetic map was developed with 53,223 SNPs in 1,610 bins QTL-hotspot” region into two sub-regions namely “QTL-hotspot_a” (139.22 kb) and “QTL-hotspot_b” (153.36 kb). To assess the utility of the “C assembly” for enhancing the resolution of QTLs, the sequencing data of the population was mapped to the “C assembly” that provided 85,598 high-quality SNPs (∼61% higher SNPs identified as compared to the “D assembly”). Based on these data, an improved recombination breakpoints-based genetic map with 2,495 bins and spanning 700.14 cM genetic distance was developed (Data S3). This improved bin map showed higher colinearity with the genome assembly compared to the earlier bin map developed using the “D assembly” . Moreover, the new bin map shows expected properties, like a low recombination rate in centromeric regions while higher recombination in telomeric regions. QTL analysis by using this bin map and the above-mentioned phenotyping data identified 137 QTL, including 40 major QTL as compared to 71 major QTL described previously In order to make chickpea a more resilient crop, a “eveloped ; Data S3Fig. 5a; . Moreoveb). These regions are exactly colinear with 13158245–13170667 (12.42 kb) and 13313432–13392516 (79.08 kb) regions on Ca4 in the “D assembly”. While analysing “QTL-hotspot_a” (139.22 kb) and “QTL-hotspot_b” (153.36 kb) with the “C assembly”, we identified 68.87 kb upstream region of “QTL-hotspot_a” matching with 12.42 kb (12997237–13009659), and 1.13 kb upstream region of “QTL-hotspot_b” matching with 79.08 kb (13152424–13231508) in the “C assembly”, which coincides with the “QTL-hotspot_a”. Therefore, the QTL analysis using bin map based on “C assembly” shortened the sizes of “QTL-hotspot_a” from 139.22 kb to 12.42 kb and “QTL-hotspot_b” from 153.36 kb to 79.08 kb (48.43%). The re-defined “QTL-hotspot” region harbored eight candidate genes compared to 26 genes in the previous “QTL-hotspot” region (b). This detailed analysis indicated the presence of two additional genes (Ca_v2.0_09671 and Ca_v2.0_09672) in the re-defined “QTL-hotspot” region. Functional annotation suggested the eight candidate genes mainly encoded for stress-responsive proteins such as serine/threonine-protein kinase, TIFY 4A-like, and epidermal patterning factor.In the present study, the topmost QTL for each trait were located in two hotspot regions, 12997237–13009659 (12.42 kb) and 13152424–13231508 (79.08 kb) on Ca4_v2.0 in the “C assembly” b. These ” region b. This dTable S24). After identifying genome-wide SNPs across pigeonpea lines, SNP loci with minor allele frequencies less than 5% and more than 20% heterozygosity were eliminated from the analysis. Therefore, 731,585 SNPs and 317,120 SNPs from the “C” and “D” assemblies, respectively, were used for GWAS with phenotyping data for nine agronomic traits collected from three locations over two years. In total, 132 marker-trait associations (MTAs) with the “C assembly” and 97 MTAs with the “D assembly” were identified . Out of 132 MTAs identified with the “C assembly”, 82 were located on newly assembled contigs . Of the total MTAs detected in the “C” and “D” assemblies, three and two MTAs were found to be associated with more than one trait, respectively (Table S26). From these three MTAs identified from “C assembly”, two MTAs, i.e., CcLG01_v2.0pos43089516.1 and CcLG08_v2.0pos9028689.1 were found to be associated with days to 50% flowering (DF) and days to 75% maturity (DM), and one MTA CcLG10_v2.0pos11613531.1 was associated with the number of pods per plant (PODSPP) and seed yield (SY). Interestingly, all three of these MTAs in the “C assembly” were located on newly assembled contigs. The only MTA (CcLG05_v2.0pos35533065.1) found consistent across two years at one location for DF was identified with the “C assembly”. The remaining MTAs identified through the “C assembly” and all MTAs identified through “D assembly” were associated with only one dataset for target traits. From the above mentioned total MTAs, 10 MTAs were identified with both genome assemblies. Further, the “C assembly” with newly assembled contigs has discovered new functional variants associated with traits. For instance, MTA CcLG01_v2.0pos32391886.1 detected on CcLG01_v2.0, associated with DF, causing missense mutation was present on the unassembled Scaffold129730 in the “D assembly”. Similarly, CcLG11_v2.0pos28601229.1 associated with the number of seeds per pod (SEEDSPP) causing missense mutation was present on the unassembled Scaffold137823 in “D assembly”. These old and new MTAs identified and mapped with the “C assembly” will be valuable in developing high yielding and early maturing varieties in pigeonpea.Yield is one of the most important, variable and complex traits across crop species. It can be enhanced many fold by improving yield attributing or contributing traits. In order to discover genes/genomic segments associated with yield attributing traits in pigeonpea, a genome-wide association study (GWAS) was performed with whole-genome sequencing (WGS) data and multi-location/years traits phenotyping data on 292 pigeonpea lines. To compare the utility of the “C assembly” over the “D assembly” for GWAS analysis, WGS data on 292 pigeonpea lines A. duranensis, and A. ipaensis using the Hi-C analysis. Hi-C is a popular approach for studying how genomes fold inside the nucleus in 3D and has been used to improve genome assemblies of several crop species A high‐quality reference genome is pre-requisite to understand genome organization, to describe evolutionary events and to precisely identify genomic regions/genes associated with agronomically important traits. Therefore, in the present study, we have improved reference genomes of six legume species, namely, chickpea, pigeonpea, soybean, subterranean clover, Hi-C relies on the density and proximity of cross-linked chromatin interactions to orient and order the contigs, and it can resolve the errors introduced due to the limitations of genetic maps Gossypium-common decaploidization Better genome assemblies and better gene colinearity ensure the inference of thousands of credible homologs produced in an evolutionary event, polyploidization or speciation. Homologs in colinearity were much likely produced simultaneously in the corresponding event. This provides a precious opportunity to determine if divergently evolved genes were the ones under natural selection Vaccinium corymbosum), where a more contiguous assembly provided a higher number of significant SNPs with enhanced precision QTL-hotspot” region with the new assembly delimited ∼300 kb region to ∼235 kb and prioritised candidate genes from 26 to 8. It could be attributed to more anchored sequences in the respective region in the “C assembly” thereby increasing the number of markers and recombination bins compared to the “D assembly”.Reference-based variant detection methods are vastly dependent on the quality of the reference genome used for variant calling because the artifacts present in the assembly are passed on to the variants called using them. In this study, we identified more SNPs (19.31%) and MTAs (36.08%) with the “C assembly” as compared to the “D assembly” of pigeonpea that helped improve GWAS results for yield and yield-related traits. Similar observations were reported for blueberry (https://cegresources.icrisat.org/legumepedia/index.php”. Legumepedia is designed to be highly interactive, adaptive, and expandable. We have incorporated the genome assemblies, predicted gene models, and annotations for all of the legumes presented in the current study. A user can use the ‘search’ option to retrieve information about any gene/locus. The repository offers JBrowse, a visualization tool to view the different genomic features of each of the six genomes.We have made all datasets reported here public via an online repository - “Legumepedia”. It is freely available at “In summary, this study reports high-quality genome assemblies and genome features of six legume species and demonstrates their utility for basic genetics research and plant breeding applications. The chromosome-length assemblies of these legumes amplify the genomic resources available to the legume community and are potential springboards for accelerating crop improvement via genomics-assisted breeding or genome editing technologies such as CRISPR.This article does not contain any studies with human or animal subjects.Vanika Garg: Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Olga Dudchenko: Formal analysis, Investigation. Jinpeng Wang: Formal analysis, Investigation. Aamir W. Khan: Formal analysis, Investigation. Saurabh Gupta: Formal analysis, Investigation. Parwinder Kaur: Resources, Funding acquisition. Kai Han: Formal analysis. Rachit K. Saxena: Formal analysis. Sandip M. Kale: Formal analysis. Melanie Pham: Investigation. Jigao Yu: Formal analysis. Annapurna Chitikineni: Resources. Zhikang Zhang: Formal analysis. Guangyi Fan: Formal analysis. Christopher Lui: Investigation. Vinodkumar Valluri: Formal analysis. Fanbo Meng: Formal analysis. Aditi Bhandari: Formal analysis. Xiaochuan Liu: Formal analysis. Tao Yang: Resources. Hua Chen: Resources. Babu Valliyodan: Resources. Manish Roorkiwal: Formal analysis. Chengcheng Shi: Formal analysis. Hong Bin Yang: Resources. Neva C. Durand: Investigation. Manish K. Pandey: Formal analysis. Guowei Li: Resources. Rutwik Barmukh: Formal analysis. Xingjun Wang: Resources. Xiaoping Chen: Resources. Hon-Ming Lam: Resources. Huifang Jiang: Resources. Xuxiao Zong: Resources. Xuanqiang Liang: Resources. Xin Liu: Resources. Boshou Liao: Resources. Baozhu Guo: Resources. Scott Jackson: Resources. Henry T. Nguyen: Resources. Weijian Zhuang: Resources, Funding acquisition. Wan Shubo: Resources, Supervision. Xiyin Wang: Supervision, Funding acquisition. Erez Lieberman Aiden: Supervision, Funding acquisition. Jeffrey L. Bennetzen: Writing – review & editing, Supervision. Rajeev K. Varshney: Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Coronavirus disease 2019 (COVID-19) pandemic has continued relentlessly for over one and half years now, causing a threat to life, fear of falling sick, helplessness, anxiety, depression and, pessimism about the future. There has been an increasing concern over student mental health in higher education. Our study was designed to measure current mental health status and its relationship with sociodemographic variables and level of knowledge about COVID-19 in Saudi Arabia.A cross-sectional survey was conducted among laboratory medicine students of Makkah city, Saudi Arabia from October, 2020 to January, 21. A semi-structured questionnaire was circulated through mail and What’s App. Data collected included sociodemographic details and level of knowledge towards the COVID-19 among the students. Depression anxiety and stress-21 item (DASS 21) was used to assess psychological status.p = 0.0003) and depression scores (p = 0.04). Respiratory droplet as a mode of transmission revealed higher scores on anxiety subscale (p = 0.007), whereas surface contamination reported high score of stress (p = 0.004) and anxiety (p = 0.002). Knowing that COVID-19 can also clinically present with gastrointestinal symptoms was found to show high stress (p = 0.005) and anxiety (p = 0.01) scores than any other way of clinical presentation.Our study reported 51.4% depressive symptoms, 57.9% anxiety symptoms, and 48.5% stress in the study participants. History of being hospitalized with COVID-19 and ICU reported high anxiety (COVID-19 is likely to cause negative effect on the psychological health of students. The novel coronavirus disease 2019 (“COVID-19”) has gripped the world, causing alarm and dread across the world. Since the start of the pandemic through December 1st, 2020, there have been 347,157 confirmed (Covid-19) cases in Saudi Arabia, with 5907 fatalities  of the gHealth education transition from face-to-face sessions to video lectures or live streams  lead to Adequate knowledge about pandemic may show reduced negative impact (anxiety and stress) in vulnerable population , 20. RecA cross-sectional study through an online survey was conducted at Umm-Al-Qura University between October 1st to December 1st, 2020. The questionnaires were distributed to the target sample of university students through social media platforms using snowball-sampling technique. Platforms including Facebook, WhatsApp, and Twitter, as well as personal e-mail, were used for the recruitment and dissemination. The participants of this survey were mainly young people aged 18–25 years.We calculated the sample size based on the assumption prevalence of 24% psychological impact according to Alkhamees et al.  at 5% abThe inclusion criteria were current university students in Saudi Arabia who spoke and understood English and had access to the online questionnaire.A semi-structured study questionnaire was developed by researchers to obtain general information of the participants as such as gender, age, education, marital status, occupation, income. The Latter half of the questionnaire included questions related to general and mental health, “COVID 19” exposure and, knowledge regarding “COVID-19”. Knowledge questions included ten items inquiring about knowledge regarding mode of spread, ways of prevention and, symptoms. The participants could select “yes or no” options. A pilot study was done on 50 participants to check the validity and reality of the scale.Psychological outcomes were assessed using Depression Anxiety Stress Scales DASS-21) The DASS-21 is a screening tool used for the general population for screening depression, anxiety and, stress. It is a self-administered 21-item instrument, which screens for depression, anxiety and, stress based on the recommended severity thresholds for the depression, anxiety, stress subscales  The DASS. ParticiAll the data collected through google forms were converted to excel spreadsheets and analysis was performed using IBM SPSS Statistics for Windows, version 22.0 . The data were cleaned, sorted, and processed before the commencement of analyses. The survey’s answers fields were designed to be mandatory to be filled before proceeding to the next section, options such as “None” or “I don’t know” were provided wherever necessary to minimize missed data. Kolmogorov–Smirnov (K–S) test was used to check normality of the data distribution.Descriptive analyses were performed for sample characteristics, history of “COVID-19” in self or family, history of contact with “COVID-19” positive patient, knowledge towards various aspects of “COVID-19” and concerns, and precautionary measures. The results of these analyses were presented as frequencies and percentages for categorical variables and as means and standard deviations for continuous variables.t-test to compare two groups. All tests of associations were carried out at a significance level of < 0.05.The psychological burden of the “COVID-19” pandemic was measured using scores on the three subscales of the DASS; the scores were presented as means with standard deviations. Bivariate analyses were performed to find the relationship between the individual variables and scores on each of the three scales  using independent Out of the 400 students who were invited to participate in the survey through the online link, 369 agreed to participate and completed the study questionnaire giving a response rate of 92.2%. Among the participants 269 (72.8%) belonged to department of laboratory medicine and 100 (27.1%) to department of medicine. The demographic information for the 369 participants  is shown in Table Regarding the knowledge on various aspects of “COVID-19”, the majority (94.6%) of the participants knew correctly that “COVID-19” can be prevented by the use of face mask, social distancing, and handwashing and 94.0% knew its mode of transmission, whereas only 39.6% of the participants knew that 56 °C for 30 min, diethyl ether, 75% ethanol, peracetic acid and, other lipid solvents can effectively kill the virus causing “COVID-19”. The Majority of the participants exhibited good knowledge about general symptoms related to “COVID-19” illness (91.6%) including the knowledge about breathlessness and low oxygen saturation (85.9%), whereas 74.3% of the participants knew about gastrointestinal symptoms as the presentation of “COVID-19”. Awareness about psychological balance and its relation with improved immunity was found in 77.8% Table .Table 2Rn = 179) , anxiety symptoms in 214 (57.9%) and, stress in 179 (48.5%) study participants. The overall mean DASS-21 depression subscale score was 11.25 ± 10.86. Among the 190 students who screened positive for depressive symptoms, 22.6% scored mild, 38.4% moderate, 15.8% severe and, 23.2% extremely severe on the DASS sub-score for depressive symptoms. The overall mean DASS-21 anxiety symptoms subscale score was 10.16 ± 9.62. Of the 214 patients who screened positive for anxiety, 13.6% of them showed mild anxiety, 38.8% moderate, 11.2% severe and, 36.4% extremely severe anxiety symptoms. In terms of the DASS-21 stress subscale, the overall mean score was 11.55 ± 10.36. Stress was mild in 49.7%, moderate in 27.9%, severe in 16.2% and, extremely severe in 6.2% participants screened positive for stress (p = 0.001), anxiety symptoms (p = 0.01) and, stress (p = 0.04) symptoms. Positive family history of psychiatric illness was significantly related with a high score on depressive symptoms (p = 0.03), anxiety (p = 0.005) and, stress (p = 0.02) symptoms. While the history of chronic medical illness has shown high scores of stress symptoms (p = 0.01). History of contracting “COVID-19” was related with high scores on anxiety (p = 0.004) and depressive symptoms (p = 0.02) subscale while the factors such as family member getting infected with “COVID-19”, history of quarantine and close contact with “COVID”-positive case had not shown any significant change in DASS-21 scores. Participants hospitalized with “COVID-19” has shown a significantly high anxiety symptom (p = 0.0003). Participants admitted to ICU reported significantly high anxiety scores (p = 0.0003) and stress scores (p = 0.04)  and respiratory droplet as a mode of transmission was associated with higher scores on the anxiety subscale (p = 0.007). Knowledge regarding the surface contamination as a mode of spread was associated with a significantly high score of stress (p = 0.004) and anxiety (p = 0.002). Knowing that “COVID-19” can also clinically present with gastrointestinal symptoms showed significantly high scores on stress (p = 0.005) and anxiety (p = 0.01) subscale than any other way of clinical presentation  about “COVID-19” for most of the knowledge variables when compared to a Chinese study (50–60%) . The knoAdequate pandemic-related knowledge, teaching psychological strategies  and social support help reduce negative psychological affect –43. BesiOur study had few limitations worth mentioning. Since the study design is cross-sectional, the causal association cannot be established. Thus, findings need to be supported by well-designed longitudinal studies. Further studies about the impact of COVID-19 with follow-up these students may help shed some light on the psychological effect among students. Since the data collection was online, the reliability and accuracy of information provided may be limited. Furthermore, the recruitment of subjects via social media could also induce a selection bias of those students that experience more than average mental health issues. There is a possibility of self-rating bias and issues of subjectivity and reliability since the measure used to assess psychological impact was a self-rating scale. The higher scores were not further evaluated by a psychiatrist to confirm the presence of a psychiatric disorder. Moreover, the questionnaire and scale were not translated in Arabic.Our study findings reflect the increased mental health burden among the university students due to the “COVID-19” pandemic. The Government should consider incorporating mental health and psychological intervention within the “COVID-19” outbreak prevention and mitigation program for vulnerable groups such as health care professionals and students. To alleviate the mental health burden of the “COVID-19” pandemic, mental health interventions and professionally trained counsellors shall be made available within the campus. Future research should be aimed at targeting multiple universities for longer periods to identify the full spectrum and course of student mental disorders. Longitudinal studies in the future may help answer whether direct or indirect exposure to the “COVID-19” virus added to mental health disorders. Continuation of face-to-face academic activities and social interaction may be of paramount importance to protect the psychological wellbeing of university students.