Objectives: To identify and compare the cerebrospinal fluid (CSF) parameters that predict the presence of neonatal bacterial meningitis using optimal cutoff values, and to derive and compare predictive profiles based on a combination of individual parameters for the same purpose.Study Design: The retrospective component of the Shanghai Neonate Meningitis Cohort included all term neonates who underwent lumbar puncture between 2000 and 2017. Those with severe neurological diseases, histories of ventricular drainage, or traumatic lumbar punctures were excluded. Reference ranges were determined for non-bacterial meningitis neonates based on the 5th, 25th, 50th, 75th, and 95th CSF parameter quantiles, and their relationships with age were calculated using generalized additive models that tested for linear relationships. The optimal cutoff value for each measured CSF parameter was calculated using receiver operating characteristic analysis and by deriving the Youden's index. Parameters with good diagnostic efficacies were combined to produce predictive profiles using logistic regression. The diagnostic efficacies of the single parameters and profiles were compared in neonates with confirmed bacterial meningitis.Results: White blood cells (WBCs) in CSF showed a higher diagnostic ability for neonatal bacterial meningitis than CSF protein, glucose, lactate dehydrogenase, or chloride. The sensitivity and specificity of the diagnostic cutoff value for WBCs (20 × 106/L) were 95.1 and 98.7%, respectively. Profiles based on CSF parameter combinations improved the specificities slightly to 99.0–99.7%. However, employing predictive profiles did not improve sensitivities, which remained at 95.1–96.0%.Conclusions: Profiles for predicting neonatal bacterial meningitis improve the sensitivity and specificity of diagnosis slightly, although not appreciably, compared to the single parameter of CSF WBC alone. Bacterial meningitis more commonly occurs in the first month of life than at any other time . AlthougSince the rate of bacterial meningitis-positive cerebrospinal fluid (CSF) cultures is quite low, diagnosing this condition still relies on deeper interpretation of CSF parameters. Some studies proposed that combined these parameters to build a predictive profile may improve the efficacy of diagnosing bacterial meningitis in neonates –10. HoweThe Shanghai Neonate Meningitis Cohort is a multi-centered ambispective group that encompasses almost all term neonates who underwent lumbar puncture (LP) in Shanghai. The Cohort was derived from four tertiary class A pediatric hospitals that cover >95% of child meningitis admissions in Shanghai: Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Children's Medical Center Affiliated to Shanghai Jiao Tong University School of Medicine, Children's Hospital of Shanghai Jiao Tong University, and Children's Hospital of Fudan University. Our study only used retrospective data from the cohort and included all 2,802 neonates who underwent LP between January 2000 and December 2017. Approval for the study and data sharing with the coordinating institution was granted by the Institutional Review Board of each hospital . The informed consent requirement was waived owing to the study's retrospective nature.6/L red blood cells in their CSF); (2) the neonates were >28 days at the onset of bacterial meningitis; and (3) a history of other severe neurological diseases or ventricular drainage was present  the neonates experienced traumatic LP  counts as well as neutrophil, protein, glucose, lactate dehydrogenase (LDH), and chloride levels were obtained from the first LP after admission. Under 5% of the patients were missing data on WBCs, proteins, glucose, LDH, and chloride. Early-onset bacterial meningitis was defined as the onset of symptoms within the first 72 h of life.2 test, Fisher's exact test, Kruskal-Wallis test, or independent t-test, as appropriate. The correlations between CSF parameters and age were calculated using generalized additive models for linear relationships.Descriptive statistics were used to summarize the baseline characteristics; values are presented as the median as well as the 5th, 25th, 75th, and 95th quantiles. The differences in the clinical characteristics of the bacterial and non-bacterial meningitis groups were assessed using the χP < 0.20) were selected as potential profile components. Finally, we developed four profile groups (Y1–Y4) each based on up to five parameters using the final multivariable logistic regression formula. Each single parameter was multiplied by its regression coefficients, and the sum of all these products as well as the constant term was deemed the final combined profile. We also calculated the Youden's index for different CSF parameter cutoff values, and applied the Delphi method to determine the threshold values for diagnosis (We performed receiver operating characteristic (ROC) analyses with the data bootstrapped 500 times; the areas under the curve (AUCs) for CSF WBCs, protein, glucose, LDH, and chloride in neonates with and without bacterial meningitis were compared. To establish diagnostic profiles, we first considered five CSF parameters by using crude logistic regression models. All parameters significantly associated with bacterial meningitis in the crude models  and the R programming software. The graphs and figures were created using GraphPad Prism 5.0  and SPSS 20.P < 0.001), and their average stay in the hospital was much longer . The top three reported clinical symptoms of neonates with bacterial meningitis were fever (84.8%), neurological symptoms (50.5%), and feeding difficulties (38.1%)  of the neonates with bacterial meningitis were born of local Shanghai residents; 3.2% of mothers received antibiotics before or during labor and approximately one-third (35.8%) of neonates were treated with antibiotics before admission. Twelve of the patients (11.4%) had early-onset disease. Neonates with bacterial meningitis were older than those without bacterial meningitis  and protein concentration than older neonates, indicating that these values decreased with age during the neonatal period. In particular, protein levels were markedly higher in younger neonates (960 mg/L) than in older counterparts   .6/L was chosen for CSF WBCs using the Delphi method. The sensitivities and specificities of the CSF protein, glucose, and LDH values were lower  were remarkably better than for other parameters. Considering the practicality of acquiring WBC counts in the clinic, a threshold value of 20 × 10er Table . Proteiner Table .The CSF parameters were combined to produce four diagnostic profiles (Y1–Y4). While combining the protein, glucose, LDH, and/or chloride improved the sensitivity to 95.1–96.0% and the specificity to 99.1–99.7%, these changes were not appreciable.In this large-scale multi-centered cohort, CSF WBC counts showed a high sensitivity and specificity in diagnosing bacterial meningitis. However, CSF protein, glucose, LDH, and chloride levels showed considerable overlap in patients with and without bacterial meningitis. Combining WBCs, protein, glucose, LDH, and chloride to create a diagnostic profile improved the specificity to some degree, but not sensitivity.6/L for CSF WBCs. In some single center studies with small sample sizes, a WBC cutoff of 21 × 106/L was considered the best upper limit  was higher than −1.7032, there was 99.7% certainty that the neonate had bacterial meningitis; however, the sensitivity of this method was barely different from that of WBC alone. Protein concentrations are relatively constant in children and adolescents owing to their compact blood-brain barriers. When meningitis occurs, the disruption in the blood-brain barrier leads to increased CSF protein concentrations ; thus, pGlucose is a frequently used indicator when diagnosing bacterial meningitis in adults, but compared to WBC, CSF glucose concentrations are more inconsistent in neonates , 23. As There were some limitations to our study. First, it was retrospective in nature, leading to inevitable bias when collecting clinical data from medical documents. To reduce recall bias, all medical files were proofread using the Delphi method. Second, we only included patients who underwent LP; hence, those with mild cases may have been missed. Although the patients were all supposed to receive follow-up for at least 6 months, this was rarely implemented; besides, including patients with mild bacterial meningitis would have only increased the sensitivities and specificities of the diagnostic cutoff values.6/L. Combining the diagnostic parameters of WBCs, protein, glucose, LDH, and chloride boosted the specificity to 99.7%, but did not change the sensitivity.In conclusion, we found that the WBC count was a useful parameter for diagnosing bacterial meningitis in full-term neonates, with the infection likely to be present in neonates with WBCs above the cutoff of 20 × 10The manuscript was written by HH, JT, and XG. Clinical data were collected by HH, LW, and MX. Clinical files were proofread by JT, JL, and XG. LH and XZ were in charge of data analysis and YZ was responsible for the coordination of this clinical research.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Although renal dysfunction is associated with a higher incidence of malignancies, there is no research on the incidence of specific types of digestive cancer in pre-dialytic chronic kidney disease (CKD) patients compared to the general population. This study was conducted on newly diagnosed pre-dialytic CKD patients  between 2003 and 2013 using the National Health Insurance Service-National Sample Cohort in Korea. The risk of digestive cancer development in pre-dialytic CKD patients was calculated as the standardized incidence ratio (SIR). During a median follow-up of 54.9 months, the risk of digestive cancer in CKD patients was significantly higher than in the cohort population , the SIR of pancreatic cancer was 2.21, and the SIRs of hepatoma, colorectal cancer (CRC), bile duct cancer, and gastric cancer were 2.01, 1.60, 1.40, and 1.25, respectively. Moreover, in CKD patients younger than 40 years, the incidence ratios of hepatoma and CRC were remarkably larger compared with the cohort population of the same age . However, the incidence of specific types of digestive cancer seemed to be similar, irrespective of sex. In conclusion, digestive cancers were more frequently observed in CKD-diagnosed patients compared with a cohort population in Korea, which suggests that physicians should closely monitor their patients for the incidence of digestive cancer when they are diagnosed with CKD. Renal function impairment has been shown to be involved in multiple organ diseases . Decreas14. In Korea, the total estimated prevalence of CKD for adults ≥ 20 years is 8.2% . Moreover, the SIRs of colorectal, gastric, hepatoma, pancreatic, and bile duct cancers in CKD patients were 1.60, 1.25, 2.01, 2.21, and 1.40, respectively. However, there were no significant differences in incidence ratio for gall bladder, esophageal, and small bowel cancers between the CKD-diagnosed population and the cohort population .Next, we investigated the relative incidence risk of digestive cancer between men and women. The overall incidence ratios of digestive cancer in men and women were higher in CKD-diagnosed patients compared with those in the cohort population . AdditioWhen we stratified these patients into three groups based on age , the SIR for digestive cancer was 3.25 in the patients less than 40 years, 1.54 in the patients between 40 to 64 years, and 1.52 in the patients 65 years and older . MoreoveWhen the CKD subjects were divided into two groups (early detection vs. late detection), based on the follow-up duration from CKD diagnosis (1-year), the observed number of digestive cancer cases in the early- and late-detection groups was 483 (33.5%) and 960 (66.5%), respectively . In addiThe results of our study showed that the incidence risk of digestive cancer in CKD patients was significantly higher than that in the cohort population. In addition, the SIRs of hepatoma and colorectal cancers were relatively higher in CKD patients less than 40 years compared with those patients 40 years and over. However, the incidence risk for specific types of digestive cancer seemed to be similar irrespective of sex. To date, there are many studies on the association between decreased renal function and cancer incidence –12. HoweAlthough the precise biological reason why digestive cancer risk was higher in CKD patients compared to the general population cannot be clearly explained, previous research has indicated that it may be associated with vitamin D deficiency , 20, ureIn contrast, radiation contrast agent for computerized tomography (CT) is not generally recommended when patients are suffering from CKD –29 to avBased on these data, the incidence risk for specific types of digestive cancer was not different between men and women. However, the SIRs for hepatoma and colorectal cancers were greater in CKD patients younger than 40 years than those in CKD patients 40 years or over. It is difficult to explain the reason why they were higher in this age group, but it may be because the observed number was significantly smaller in this age group than the other age groups . Therefoth revision of the international classification of diseases (ICD-10) code in their recently published papers, and we defined CKD using the same method in this study. In addition, the diagnostic codes for digestive cancer were also used in previously published manuscripts [There were several limitations to our study. First, the NHIS-NSC was established based on administrative data from health insurance claims instead of clinical data that includes disease progress. Therefore, we were unable to investigate patients based on stratified CKD stage. Second, the diagnosis of CKD and digestive cancer was based on a number of diagnostic codes, and it was not easy to verify the diagnoses from the codes. However, Kim  and Yoo uscripts , 33. Altases ICD- code in uscripts , SIR wasIn conclusion, digestive cancers were more frequently observed in CKD-diagnosed patients compared with a cohort population in Korea. In particular, hepatoma and colorectal cancer were more frequently observed in CKD-diagnosed patients less than 40 years compared with the CKD diagnosed patients 40 years and over, indicating that physicians likely pay closer attention to the increased incidence risk of digestive cancer when their patients are suffering from CKD.The investigation was performed using data from the NHIS-NSC in the Republic of Korea. The cohort was composed of 2.2% of the total eligible Korean population , which was selected as a representative sample in 2002 using systematic stratified random sampling. The data contained information for demographics such as age groups , sex, income levels, healthcare utilization, prescriptions, and diagnostic codes based on the ICD-10. Information related to deaths was also provided from the Korea National Statistical Office, with follow-up data from 2002 to 2013 available. Detailed information about the NHIS-NSC can be found in a previously published paper . This stember 2013 . However, those who were diagnosed with CKD in 2002 were not included in the current study, because the information about their disease history was inaccurate . Moreover, we excluded cases who had already had cancer or been diagnosed with a malignancy before 2003 and investigated the patients who were diagnosed with new digestive cancers during the years 2003 to 2013. Of those patients, anyone diagnosed with ESRD (n = 710) before diagnosis of digestive cancer was also excluded from the study. ESRD was identified when subjects had insurance claims with a regular dialysis treatment code for 90 ± 7 days after the date of the first dialysis. Additionally, subjects diagnosed with any cancer before the diagnosis of CKD  and who were under 20 years of age at the time of CKD diagnosis (n = 624), and 104 patients who underwent KT before diagnosis of digestive cancer were excluded. Finally, a total of 35,443 patients were included in the present study , 'N19' (unspecified kidney failure), 'I12' , 'I13' , 'E10.2' , 'E11.2' , 'E13.2' , and 'E14.2'  from 1 January 2002 to 31 Decnt study .To apply a precise definition of digestive cancer, we defined it in detail as follows: esophageal cancer (‘C15’), gastric cancer (‘C16’), small bowel cancer (‘C17’), colorectal cancer (‘C18-20’), hepatoma (‘C22.0’), bile duct cancer (‘C22.1’ and ‘C24’), gall bladder cancer (‘C23’), and pancreatic cancer (‘C25’).In general, the National Cancer Registry (NCR) reports on the annual incidence rate of 61 types of cancer but does not report sub-categorization with decimal places. For that reason, estimations of the SIRs according to the detailed definition of the cancer using NCR data was limited. Therefore, to calculate the SIRs, we investigated the cancer rates of the complete NHIS-NSC data (cohort population) for adults aged 20 years and older from 2003 to 2013, which were considered as reference cancer rates and compared them with those in patients with CKD. Because the information on risk factors in all cohort subjects was incomplete, we could not use Cox and Poisson regression. In addition, an indirect method, such as SIR, is considered particularly useful either when stratum-specific risks or rates are missing in one of the groups under comparison or when the study group(s) is (are) small . Thus, wWe estimated SIRs with 95% CIs to assess the incidence risk of digestive cancer among patients with pre-dialytic CKD. SIRs were calculated as the observed number of cancer cases divided by the expected number of cases. The expected number of cancer cases was estimated as the age-  and calendar year-specific incidence rates of the cohort population multiplied by the number of persons at risk.p-value <0.05 in two-sided tests was considered statistically significant.Moreover, SIRs were estimated after stratification by age group  or sex at baseline. We also estimated SIRs with 95% CIs for the subtypes of digestive cancer, and all the statistical analyses were carried out using SAS version 9.4 . A S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.S5 Table(DOC)Click here for additional data file.


Oscillospira, Dorea, Blautia, Bacteroides, Coprococcus, and Collinsella, and a significantly higher abundance of Roseburia and Faecalibacterium genera during the E82 phase compared with the basal phase. The consumption of low-gliadin bread E82 by NCGS subjects induced potentially positive changes in the gut microbiota composition, increasing the butyrate-producing bacteria and favoring a microbial profile that is suggested to have a key role in the maintenance or improvement of gut permeability.The study evaluated the symptoms, acceptance, and digestibility of bread made from transgenic low-gliadin wheat, in comparison with gluten free bread, in Non-coeliac gluten sensitivity (NCGS) patients, considering clinical/sensory parameters and gut microbiota composition. This study was performed in two phases of seven days each, comprising a basal phase with gluten free bread and an E82 phase with low-gliadin bread. Gastrointestinal clinical symptoms were evaluated using the Gastrointestinal Symptom Rating Scale (GSRS) questionnaire, and stool samples were collected for gluten immunogenic peptides (GIP) determination and the extraction of gut microbial DNA. For the basal and E82 phases, seven and five patients, respectively, showed undetectable GIPs content. The bacterial 16S rRNA gene V1-V2 hypervariable regions were sequenced using the Illumina MiSeq platform and downstream analysis was done using a Quantitative Insights into Microbial Ecology (QIIME) pipeline. No significant differences in the GSRS questionnaires were observed between the two phases. However, we observed a significantly lower abundance of some gut genera  There is an increasing demand for wheat in new markets, especially in countries where wheat is not grown. Wheat grain comprises three major groups of components: Starch, proteins, and cell wall polysaccharides, accounting for about 90% of the dry weight [Triticeae tribe, which include barley and rye.Globally, wheat is the cereal most used for bread. Although only 35% of all wheat production is consumed by humans, it dominates the international grain trade as 110 of the 250 million tons of traded grain is wheat , Non-coeliac gluten sensitivity (NCGS), Wheat allergy, and Wheat-dependent exercise-induced anaphylaxis (WDEIA). CD is a systemic immune-mediated disorder elicited by gluten from wheat, barley, and rye in genetically susceptible individuals. Prevalence of CD varies worldwide but it ranges from 0.3 to 2.4% in western countries . NCGS isBifidobacterium, B. longum, and Lactobacillus), while populations of potentially unhealthy bacteria increase, parallel to reductions in the intake of polysaccharides after following the GFD [For both CD and NCGS patients, symptoms improve or disappear entirely, when gluten is withdrawn from the diet. Therefore, a gluten free diet (GFD) is at present the most effective treatment available for both patients. However, the nutritional profile of GFDs is of concern, as GFDs are characterized by an unbalanced intake of nutrients, with an excess of saturated and hydrogenated fatty acids; low quantities of alimentary fiber, micronutrients, and vitamins; low contribution to the recommended daily protein intake; and a high carbohydrate content, yielding an increase in the glycemic index . At the  the GFD .The down-regulation of immunodominant gluten peptides by RNA interference (RNAi) in wheat is an excellent approach for reducing gluten toxicity. Protein extracts from flour made from wheat lines where all gliadin fractions have been down-regulated show a reduction in the extracts capacity to stimulate the human leukocyte antigen (HLA)DQ2- and DQ8- restricted T-cell clones in CD patients . Among aOur body is colonized by trillions of microorganisms in a symbiotic state, and these populations have fundamental roles in health and disease. Microbial communities in the gut are involved in the well-being of the innate and adaptive immune system. This nexus between nutrient metabolism and the immune system occurs at different levels, from the sensing of nutrients to endocrine signaling. The host microbiome has been associated with vitamin production, lipid metabolism regulation, the regulation of gene expression, and energy extraction from food ,20,21. IDysbiosis in NCGS causes gut inflammation, diarrhea, constipation, visceral hypersensitivity, abdominal pain, a dysfunctional metabolic state, and issues with peripheral immune and neuro-immune communication . In the The aim of the present study was to evaluate the acceptability, digestibility, and safety of a bread made using flour from the E82 line with all gliadins strongly downregulated, in volunteer NCGS patients, in comparison with a GFD. In addition, gut microbiota shifts after the consumption of both GF and E82 breads were also investigated.Eleven patients with non-celiac gluten sensitivity (NCGS) that fulfilled the Salerno criteria  and had All study participants provided informed consent, and the study design was approved by the local ethics committee of the Hospital of León .The study was performed in two phases , each of which had a duration of seven days.Basal phase: Strict gluten-free diet (GFD) for seven days. During this phase, the subjects consumed the gluten-free bread which they habitually eat at home (100–150 g bread daily). At the end of this phase, Clinical Questionnaires based on Gastrointestinal Symptom Rating Scale (GSRS) and Sensory Questionnaires on palatability and acceptability of the bread were completed, and stool samples  were collected.E82 phase: Continuation of the basal GFD, but with the substitution of the gluten-free bread with the low-gliadin bread E82 for seven days. Patients were instructed to consume a minimum of 100 g and a maximum of 150 g of the E82 bread daily. At the end of this phase, the Clinical and Sensory Questionnaires were completed, as in the Basal phase, and stool samples (E82 phase samples) were collected.The GSRS questionnaire is a validated, self-administered questionnaire that includes 15 questions, which assess severity of gastrointestinal (GI) symptoms using a 7-point Likert scale in five domains: Indigestion, diarrhea, constipation, abdominal pain, and reflux . The sevTriticum aestivum cv BW208) was obtained as reported in Reference [http://gmoinfo.jrc.ec.europa.eu/gmp_report.aspx?CurNot1/B/ES/13/20), according to the legislation for the release of genetically modified higher plants.The low-gliadin wheat line E82  and white flour was vacuum stored at 4 °C till the preparation of the breads.The ingredients for the low-gliadin E82 bread provided in the E82 phase were: flour (2.4 kg), water (1.6 L), and salt (12 g). The ingredients were mixed for 10 min and allowed to stand at 15 °C for 14 h and then at 25 °C for 4 h. For bread making, the mixture, yeast (48 g), and salt (36 g) were mixed to form a dough and then it was left to rest for 45 min at room temperature. The fermentation and baking process was carried out as described in Reference .After cooling, the loaves were cut into slices and frozen in portions. The bread, stored at the Hospital of León, was made available to the subjects for the E82 phase. The breads were supplied frozen to the test subjects for defrosting immediately before consumption.The gliadin fractions from 100 mg samples of flour and bread were extracted and analyzed by Reverse-phase high-performance liquid chromatography as described in Reference . BrieflyFlour and bread samples from the low-gliadin wheat were analyzed for gluten content (parts per million) at the Centro Nacional de Biotecnología (CNB-CSIC) using a competitive ELISA, based on the monoclonal antibody R5 as described in Reference . The assSubjects collected 2–4 g stool sample in a sealed container after recording food intake at the end of both the basal phase and the E82 phase. Then, the stool samples were delivered within the next two hours after deposition and stored at −80 °C until processing. All samples were identified and labelled with a randomized numeric code.The MoBioPowerSoil  kit was used to extract the DNA from faecal samples according to the manufacturer’s instructions, including an initial high shaking step to improve lysis with a TissueLyser II machine (QIAGEN). DNA was quantified using a NanoDrop ND-1000 spectrophotometer .For the study, a total of 20 stool samples were analyzed from the 10 subjects. The extraction and quantification method used was as described in Reference . Each saThe bacterial 16S rRNA gene V1-V2 hypervariable regions of the 36 samples (nine patients with four samples each: one for each phase of the study and two DNA repetitions for each phase), were amplified in polymerase chain reactions (PCR) using the universal bacterial primers 8F (AGAGTTTGATCMTGGCTCAG) and 357R (CTGCTGCCTYCCGTA), complemented with 8nt index and Illumina adapter sequences. The PCR amplification and single-end sequencing of the V2 hypervariable region was carried out according to Reference [http://qiime.org/)) with default parameters [The 16S rRNA sequences obtained were analyzed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline . A paired t-Test was used to compare symptoms and global acceptance.Differences in alpha diversity between both phases were estimated using a non-parametric test and Monte Carlo permutations, for each of the three alpha diversity estimators . We also estimated the existence of statistically significant differences between groups for the beta diversity unweighted and weighted UNIFRAC distances, using a PERMANOVA test.To assess whether specific differences occurred in some bacterial taxa between phases, we selected the taxa that were present in at least 80% of the samples per each phase. The non-parametric Kruskal-Wallis test was used to compare the existence of differences in the relative abundance of the bacterial taxa between phases using the PASW statistical software, version 20.0 .Bread derived from the low-gliadin line E82 was made using a simple formulation with no additives and long fermentation times. The final bread product b was prop = 0.9075). Furthermore, the five domains analyzed by the GSRS  showed similar scores after consuming the low-gliadin E82 bread compared with the basal GF bread. Moreover, the patients scored both diets similarly, except patient #1, whose gave a higher score to the E82 bread, and patient #11, who scored the GF bread higher , the presence of GIP was studied in the faecal samples. During the basal phase, three patients showed values for GIPs that were higher than the limit of quantification, with a maximum of 2.93 μg/g feces. For the E82 phase, all patients showed GIP levels below 0.69 μg/g feces, with five patients showing an undetectable GIPs content b. The rep = 1). All aspects, except for appearance, in the Sensory Questionnaires were scored higher for the E82 bread, though with no significant difference with the basal phase , Actinobacteria (3.5%), Proteobacteria (0.8%), and Bacteroidetes (0.2%). At the family level, the most abundant were Ruminococcaceae (30.9%), Lachnospiraceae (23.6%) Erysipelotrichaceae (12.3%), Veillonellaceae (10.4%), Clostridiaceae (3.8%), Streptococcaceae (2.8%), and Coriobacteriaceae (2.4%). At the genera level, the most abundant were Dialister (4.7%), Catenibacterium (4.61%), Ruminococcus (3.1%), and Faecalibacterium (3.0%). Finally, at the species level, the most abundant were Faecalibacterium prausnitzii (3.0%), Eubacterium biforme (1.1%), and Collinsella aerofaciens (0.9%). Other taxa that comprise the gut microbiota, for example, the phyla Fusobacteria, Verrucomicrobia, and Cyanobacteria, were also detected but the relative abundance of the taxa was negligible (less than 0.1%).In total, 8 phyla, 16 classes, 20 orders, 33 families, 46 genera, and 22 species, were identified in all the samples. On average, the phyla with the highest abundance were p = 0.357), chao1 (p = 0.232), and observed OTUS or Richness (p = 0.265)   . Similarctively) .Actinobacteria phylum in the E82 phase compared to the basal phase (p = 0.031) but observed no differences between the phases for any other phylum , as well as the Bacteroidales and the Coriobacteriales orders   .Veillonellaceae (p = 0.036) and Ruminococcaceae (p = 0.035) in the E82 phase   .Oscillospira (p = 0.038), Dorea, Blautia, Bacteroides, Coprococcus , and Collinsella (p = 0.013) genera was observed. Conversely, we observed a higher abundance in the E82 phase in the Roseburia and Faecalibacterium genera compared to the basal phase   .Collinsella aerofaciens (p = 0.013) and a higher abundance in Faecalibacterium prausnitzii (p = 0.022) in the E82 phase compared to the basal phase  showed that the NCGS patients’ scores were similar between both phases. Only patient #1 gave a higher score to the E82 bread . Three aBifidobacterium and Lactobacillus, as well as an increase in the production of short chain fatty acids (SCFAs). The first study was based on a batch-culture fermentation system, which simulated the distal region of the human large intestine, whilst the second study used a continuous colonic model in three stages imitating the proximal, transverse, and distal human colon. Although both studies opened a new horizon to explore using gut simulating systems, they presented some limitations such as the number of samples and that these were in vitro studies, making it essential to validate the results in human intervention studies in vivo.Previous in vitro studies ,44, descFaecalibacterium, specifically of the species F. prausnitzii, and a decrease in the relative abundance of other genera, such as Bacteroides, Blautia, Dorea, Coprococcus, and Collinsella . This microbial profile has been previously described in several studies [Our present study evaluated in vivo the effect of bread E82, which could be considered a GFB, on the composition of the intestinal microbiota in eleven patients with NCGS. Our results based on the intestinal microbiota composition showed that the E82 bread consumption induced an increase in the relative abundance of the genus  studies ,46,47 to studies ,54,55,56Roseburia and Faecalibacterium genera (F. prausnitzii) [Another interesting finding in this study was the increase of the most efficient butyrate-producing bacteria, such as the snitzii) ,58. Butysnitzii) ,60. Thessnitzii) ,62.Faecalibacterium, an anti-inflammatory genus, and the decrease of Bacteroides a proinflammatory genus, thereby improving the gut inflammation characteristics of NCGS patients [Our results suggest that the E82 bread consumption favored a less inflammatory phenotype that could be related to the increase of patients ,63. The patients . The meaOur study may have the limitation that the intestinal fungal microbiome, also known as the mycobiome, has not been addressed, as well as the relationship between the bacterial and fungal species of the gut microbiota in these patients. Currently, the study of the mycobiome has been limited, but recently it has been demonstrated that it is a key player in the state of health and some human diseases, especially modulating the immune system and producing secondary metabolites in synergy with the intestinal bacteria ,65. TherIn conclusion, our results suggest that the consumption of the E82 bread could be a positive component of the GFD for NCGS patients, since it produces no adverse clinical symptoms, has acceptable sensory parameters, and it induces positive changes in the gut microbiota composition, increasing the butyrate-producing bacteria and favoring a bacterial profile with a key role in the maintenance or improvement of the gut permeability in NCGS patients.


The Japanese government has developed and promoted a national nutrient-based dietary guideline for preventing lifestyle-related chronic disease. However, its impact in a real-life setting has never been evaluated. We performed a critical appraisal of the guideline by examining the association between adherence to the guideline and lifestyle-related outcome indicators.This is a retrospective observational cross-sectional study using nationally representative data on health and nutrition characteristics from the 2016 National Health and Nutrition Survey of Japan. We considered 3,861 participants aged ≥20 years, with evidence of low health risks of diabetes, hypertension, hyperlipidemia, and obesity. Five health outcome indicators (hemoglobin A1c (HbA1c), systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, and body mass index), were employed. A summary score was developed to reflect adherence to the recommended intake of seven nutrients defined by the guideline, including proteins, fat, saturated fatty acid, carbohydrate, dietary fiber, sodium, and potassium. Multivariate quartile regression approaches were employed to examine the association between the adherence score and the health outcome indicators, adjusting for the covariates.Overall, the proportion of those who adhered to the guideline (adherence rate) for all seven nutrients was only 0.3%. There was considerable variation in the adherence rate between the different nutrients, from 24.2% to 61.8%. After adjustment for covariates, in most health outcome indicators, regardless of age category and quartile, there was no clear association between the guideline adherence score and indicators.There is plenty of scope for improving the guideline. Nutrient impact on health may not necessarily depend on the amount of each nutrient in the diet. The significance and contribution of synergies between nutrients and complex interactions within foods to health outcomes need to be explored in future guideline updates. Japan is one of the most successful countries in the world on account of excellent health outcomes . The decAccording to Nomura and colleagues 2017), behavioral risk factors made a greater contribution to the overall disease burden, which included mortality and morbidity (25.2%) in 2015 than metabolic risks 16.4%), or environmental and occupational risks (4.4%)  6.37) and 60.70 (interquartile range [IQR] 56.30–64.90). Scores ranged from 24.9 to 70.0. Compared to those with a lower adherence score, those with a higher score were older and more likely to be women, be engaged in primary industry, and have a higher total energy intake; and were less likely to be current smokers or to drink alcohol frequently (≥3 days/week) .Intakes of the seven nutrients among the study population are presented by quantile adherence scores in With the sensitivity analyses, the regression coefficients of adherence score to the health outcome indicators were also estimated by two different BMI categories  in termsTo our knowledge, the present study is the first to perform a critical appraisal of the DRIs by examining the association between adherence to the TDGs and lifestyle-related chronic outcome indicators. Overall, only 0.3% of the study population adhered to TDGs for all seven nutrients studied; and there was a large range in the adherence rate between the different nutrients from 24.2% to 61.8%. We found that a higher adherence score was related to older age and female gender . A similWhile there was a clear tendency that a group with higher adherence score had the greater adherence rate for each nutrient, a poor adherence rate for sodium was observed in a group with higher adherence score . FurtherThe daily salt intake of Japanese is known to be approximately 9.2 gram for women and 10.8 gram for men in 2016 , and mosImportantly, the findings of the multivariate regression analyses imply that TDG adherence based on the seven nutrients has limited beneficial impact (and an adverse effect on some stratified groups) on the indicators, after adjusting for covariates. These findings support existing nutrition science understandings that the level of each nutrient, independent of the source of the nutrients, may not necessarily explain healthy diet , 24. NutOur study findings highlight that the scope for improving the DRIs/TDGs is wide. As Jacobs et al. (2009) have reported, foods are more than just a collection of nutrients they contain . There aOur analyses are subject to similar limitations as are described for other studies concerning dietary adherence , 9. FirsSecond, we could not rule out the confounding effects of unmeasured predictors of negative health outcomes, such as low physical activity and functional decline including frailty and sarcopenia among older adults . AlthougThird, the different nutrients have not been weighted for adherence score calculation. Adherence to TDGs for each nutrient was scored from a maximum of 10 to a minimum of 0 for each individual, and scores for the seven nutrients were simply summed up to obtain the total score ranging from 0 (the lowest adherence) to 70 (the highest adherence). How the "best" adherence score  is actually defined requires a clear understanding of what the optimal weight is and how it could be optimized. This is a large study subject in itself , 35, andFourth, the cross-sectional nature of this study limits the drawing of inferences about causation. Importantly, the potential for reverse causation , which refers to a direction of cause-and-effect that is the opposite of the expected outcome, could be a challenge in determining a causal relationship between dietary patterns and health outcome indicators. This tends to be inevitable in observational studies . In thisFifth, we made a number of comparisons, and some statistically significant associations may have occurred owing to chance events. In this study, the regression approach theoretically assumed the independence of the set of participants between regression models (for the different outcomes). Of course, these models were built with identical participants and hence, this assumption was not fully met. Therefore, the inflation of the alpha error by multiple comparisons can actually be a problem. For example, we observed increased levels of HbA1c among those the 20–39 and 40–59 years age categories who had a higher adherence score . This waSixth, the decision to choose HbA1c, SBP, DBP, HDL-C, and BMI as health outcome indicators (the dependent variables) was based on data availability. Future studies should explore outcomes as a whole to attempt to comprehensively evaluate the health impact of TDGs.Finally, as a consequence of this exclusion of people who have disease risks, the potential impact of poor TDGs adherence on the health outcome indicators might be reduced or eliminated because of a healthier population. However, our sensitivity analyses, i.e. the regression analyses of those who failed to meet the eligibility criteria, demonstrated similar results, indicating that the influence of this limitation may be marginal.S1 TableHbA1c: hemoglobin A1c; SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-C: high-density lipoprotein-cholesterol; BMI: body mass index; CI: Confidence interval; adjusted for sex, occupation, smoking status, alcohol consumption status, and total energy intake as restricted cubic spline; * poor: <18.5 for 18–49 years old, <20.0 for 50–69 years old, <21.5 for 70 years old or more. The 'mean' indicates the parameter estimates of the conventional linear regression, while '25%', '50%', and '75%' show the parameter estimates of the quantile regressions at the 25th, 50th, and 75th percentiles of the adherence score distribution, respectively.(DOCX)Click here for additional data file.S2 TableHbA1c: hemoglobin A1c; SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-C: high-density lipoprotein-cholesterol; BMI: body mass index; CI: Confidence interval; adjusted for sex, occupation, smoking status, alcohol consumption status, and total energy intake as restricted cubic spline; * proper: 18.5–24.9 for 18–49 years old, 20.0–24.9 for 50–69 years old, 21.5–24.9 for 70 years old or more. The 'mean' indicates the parameter estimates of the conventional linear regression, while '25%', '50%', and '75%' show the parameter estimates of the quantile regressions at the 25th, 50th, and 75th percentiles of the adherence score distribution, respectively.(DOCX)Click here for additional data file.S3 TableEstimated coefficients of adherence score using quantile/conventional regression with no eligibility criteria for participants: (A) for score quantile of 25% and 50%, (B) for 75% and mean. HbA1c: hemoglobin A1c; SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-C: high-density lipoprotein-cholesterol; BMI: body mass index; CI: Confidence interval; adjusted for sex, occupation, smoking status, alcohol consumption status, and total energy intake as restricted cubic spline. The 'mean' indicates the parameter estimates of the conventional linear regression, while '25%', '50%', and '75%' show the parameter estimates of the quantile regressions at the 25th, 50th, and 75th percentiles of the adherence score distribution, respectively.(DOCX)Click here for additional data file.


Eucomis autumnalis (Mill.) Chitt. subspecies autumnalis is a popular African plant that is susceptible to population decline because the bulbs are widely utilized for diverse medicinal purposes. As a result, approaches to ensure the sustainability of the plants are essential. In the current study, the influence of smoke-water (SW) and karrikinolide (KAR1 isolated from SW extract) on the phytochemicals and antioxidant activity of in vitro and greenhouse-acclimatized Eucomis autumnalis subspecies autumnalis were evaluated. Leaf explants were cultured on Murashige and Skoog (MS) media supplemented with SW  or KAR1  and grown for ten weeks. In vitro regenerants were subsequently acclimatized in the greenhouse for four months. Bioactive phytochemicals in different treatments were analyzed using ultra-high performance liquid chromatography (UHPLC-MS/MS), while antioxidant potential was evaluated using two chemical tests namely: DPPH and the β-carotene model. Smoke-water and KAR1 generally influenced the quantity and types of phytochemicals in in vitro regenerants and acclimatized plants. In addition to eucomic acid, 15 phenolic acids and flavonoids were quantified; however, some were specific to either the in vitro regenerants or greenhouse-acclimatized plants. The majority of the phenolic acids and flavonoids were generally higher in in vitro regenerants than in acclimatized plants. Evidence from the chemical tests indicated an increase in antioxidant activity of SW and KAR1-treated regenerants and acclimatized plants. Overall, these findings unravel the value of SW and KAR1 as potential elicitors for bioactive phytochemicals with therapeutic activity in plants facilitated via in vitro culture systems. In addition, it affords an efficient means to ensure the sustainability of the investigated plant. Nevertheless, further studies focusing on the use of other types of antioxidant test systems (including in vivo model) and the carry-over effect of the application of SW and KAR1 for a longer duration will be pertinent. In addition, the safety of the resultant plant extracts and their pharmacological efficacy in clinical relevance systems is required. In anglobally ,4,5,6. Gglobally . Presentglobally . Both SWher PGRs  and ofteher PGRs . The useher PGRs ,11,12. Ther PGRs ,14,15. Iher PGRs . As a reher PGRs .Eucomis, including Eucomis autumnalis subspecies autumnalis (family: Asparagaceae), is a popular remedy against a variety of diseases [Eucomis due to their endemic nature, indiscriminate harvesting, and wide utilization, particularly the underground parts such as the bulbs and roots [In folk medicine, the genus diseases . For insdiseases . Recentlnd roots . The potnd roots ,20. Howend roots ,22.Eucomis autumnalis subspecies autumnalis following treatment with SW and KAR1. The current study was guided by the following research questions:In most cases, extensively sought-after plants are often collected from the wild, resulting in a decline in their natural populations . Researc1 influence the phytochemical pool and antioxidant activity of Eucomis autumnalis subspecies autumnalis?(1) How does the application of SW and KAR(2) What are the dynamics of the aforementioned parameters in in vitro and acclimatized plants?(3) What are the differences in terms of the phytochemical and antioxidant activities of the aboveground and underground parts of acclimatized plants?1 were obtained from the laboratory stock; details of their preparations have been extensively described [Solutions of SW and KARescribed ,24. We pEucomis autumnalis subspecies autumnalis clones maintained on PGR-free media served as source of leaf explants for the current study. Mother stock of Eucomis autumnalis subspecies autumnalis was obtained from University of KwaZulu-Natal  Botanical Garden, and was positively identified (Voucher no: Masondo 2) by the curator of the Bews Herbarium, UKZN, South Africa.1 concentrations . A control lacking SW and KAR1 was included in the experiment. Each treatment had 15 explants and the experiments was conducted in duplicate. The conditions of the growth room were similar to the previous setup as described by Masondo et al. [Leaf explants excised from stock plants were surface sterilized and cultured in tissue-culture screw-cap jars with 30 mL Murashige and Skoog (MS) media . In ordeo et al. . After 1Eucomis autumnalis subspecies autumnalis after 10 weeks and 4 months, respectively. In vitro (whole plantlets) and acclimatized plants separated into aerial (leaves) or underground (bulbs and roots) parts were oven-dried at 50 ± 2 ℃ for seven days and milled into powder form.Plant material was harvested from in vitro regenerants and acclimatized In triplicates, 20 mg of the ground plant samples were homogenized with 80% methanol (MeOH) using an oscillation ball mill  at a frequency of 27 Hz for 3 min. Deuterium-labelled internal standards  acid and salicylic  acid) were added to the extraction solvent prior to plant material homogenization. The resultant supernatant was centrifuged at 48,297.6 g for 10 min and retained for the quantification of the phytochemicals. Supernatants were filtered through 0.45 µm nylon membrane filters  and analyzed using a UHPLC–MS/MS  linked to a Micromass Quattro microTM API benchtop triple quadrupole mass spectrometer , operating in a negative ion mode, as described by Gruz et al. . AuthentEucomis autumnalis subspecies autumnalis were extracted in 50% MeOH using 100 mg per 10 mL in an ultrasonic sonicator . This was sonicated for 20 min while maintaining the temperature with the use of ice-cold water. Following filtration, the resultant filtrates were dried to constant weight under a fan. The dried extracts were re-suspended in 50% MeOH and evaluated at a final concentration of 0.5 mg/mL in two test systems, namely, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and β-carotene acid model, as detailed previously [β-carotene assays, respectively. In addition, 50% MeOH was included as the solvent control. Each experiment was done in triplicate.Ground samples from in vitro and acclimatized eviously . Ascorbi®, version 16.0, Chicago, IL, USA). Where there was statistical significance (p ≤ 0.05), the mean values were further separated using the Duncan’s Multiple Range Test (DMRT).Experiments were conducted in completely randomized designs. Phytochemical content and antioxidant activity data were subjected to analysis of variance (ANOVA) using the SPSS software package for Windows  significantly enhanced the eucomic acid accumulated in the leaves of greenhouse-acclimatized plants  regenerants and acclimatized  plants. The levels of these two derivatives were several times higher in in vitro plantlets than in the acclimatized plants. However, derivatives such as vanillic and syringic acids were specific to the in vitro regenerants and acclimatized (leaves) plants, respectively.Four derivatives of hydroxybenzoic acids were accumulated in the different plant parts of tumnalis . Only pr1 (10−9 M) significantly enhanced the concentrations of protocatechuic and p-hydroxybenzoic acids. Likewise, in the acclimatized plants, both derivatives were significantly higher in KAR1 treatment for the leaves (10−7 M), bulbs, and roots (10−8 and 10−9 M) when compared to the control.Among the in vitro regenerants, SW (1:500 and 1:1500 dilutions) and KAREucomis autumnalis subspecies autumnalis  and KAR1 (10−9 M) treatments of the in vitro regenerants.When compared to the control, treatments with either SW or KAR1 .After acclimatization, the concentrations of caffeic and coumaric acids were generally low (<1 μg/g DW), regardless of the treatments. In addition, ferulic acid concentrations ranged from 0.17–0.39 and 0.78–1.89 μg/g DW for the leaves and underground parts, respectively. The higher concentrations of ferulic acid observed in the underground parts were facilitated by the presence of SW  and KAREucomis autumnalis subspecies autumnalis, their concentrations were generally low (0.1–3.4 μg/g DW), and only observed in a few treatments for both the in vitro and acclimatized plants -treated in vitro regenerants had the highest antioxidant activity, i.e., approximately two-fold that of the control  and 33% (roots and bulbs) of the ex vitro plants maintained a higher antioxidant activity than the control plants  had higher antioxidant activity than the control. About 67% of the leaves of ex vitro plants treated with SW or KAR1 had significantly higher antioxidant activity than the control, while there was no difference in the antioxidant activity of the roots and bulbs with or without the respective treatments  treatments, is noteworthy, given that this plant is used for inflammation-related conditions.Secondary metabolites in natural resources, including medicinal plants, have been widely explored for their biological properties ,30, and hemicals . Globallhemicals ,18,20. Themicals ,22. Evenhemicals , the usehemicals . The pot species , Tulbagh species , Isatis digotica , and Aloorescens . In the ablished ,37. For ablished , and thiMerwilla plumbea [Eucomis autumnalis subspecies autumnalis [Artemisia judaica [Eucomis autumnalis subspecies autumnalis, the concentrations of approximately 70% of the hyroxybenzoic  and hydroxycinnamic  derivatives were several times higher in the in vitro regenerants than in the acclimatized plants. However, eucomic acid was generally higher in the acclimatized plants, while no discrete pattern was observed with the concentrations of flavonoids at different plant stages.The presence of an elicitor is known to activate genes related to defense-systems which often trigger the biosynthesis and accumulation of secondary metabolites . This is plumbea , Eucomistumnalis , and Art judaica . Among tEucomis autumnalis [Opuntia ficus-indica [Cryptostephanus vansonii [Lotus japonicus; this was considered a potential leaf-opening factor (LOF) in this species. Likewise, the growth inhibitory potential of eucomic acid isolated from Cattleya trianaei was demonstrated at varying concentrations [Eucomis. Although no significant increase in the levels of eucomic acid was observed in in vitro plantlets treated with SW and KAR1, the leaves of the acclimatized plants, especially those from SW (1:500 and 1:1500) treatment, had significantly higher eucomic acid contents relative to the control. Despite the occurrence of eucomic acid in a large number of plants, especially in members of the genus Eucomis, evidence of their specific biological activities remains speculative.Eucomic acid has been quantified in medicinal plants such as tumnalis ,42, Opuns-indica , and Cryvansonii . Previouvansonii  isolatedtrations . In the Eucomis autumnalis subspecies autumnalis were evaluated using DPPH and β-carotene assays in order to establish the influence of SW and KAR1 applications under diverse conditions. In the current study, the extracts generally demonstrated higher antioxidant potential in the β-carotene model than in the DPPH assay. The highest antioxidant power (β-carotene model) was observed in KAR1-treated in vitro regenerants, i.e., almost two-fold higher than the control. Based on the responses in the in vitro and acclimatized plants, whereby majority of the extracts had higher antioxidant activity in β-carotene model when compared to the DPPH assay, the mechanism of the antioxidant activity of Eucomis autumnalis subspecies autumnalis is more likely to be inclined towards HAT than SET. Similar findings were also exhibited by the extracts of micropropagated and acclimatized plants that were treated with different PGRs at varying concentrations [Several pharmacological activities, including antioxidant potency, are often attributed to the quality and quantity of phenolic acids and flavonoids in plants ,30,37. Atrations ,12,49. Ptrations , and thitrations ,12,40,49Eucomis autumnalis subspecies autumnalis provides a valuable alternative to the underground plant parts which are widely utilized in traditional medicine. The current findings also suggest that the biological effects of medicinal plants often differ based on the plant part investigated [In the current study, the variation observed in the antioxidant activity of different parts of acclimatized plants is important from a conservation perspective. For instance, the use of alternative plant organs, with a minimal detrimental effect on the survival of the whole plant, has been strongly recommended by researchers . This mestigated ,18,44.1 as potential elicitors for bioactive phytochemicals was demonstrated in Eucomis autumnalis subspecies autumnalis. This may provide an alternative approach for the production of secondary metabolites with therapeutic potential. Based on the use of UHPLC, the phytochemical profiles of Eucomis autumnalis subspecies autumnalis treated with SW and KAR1 was established for in vitro and acclimatized plants. Generally, in vitro regenerants accumulated higher concentrations of phytochemicals which significantly decreased after plants underwent prolonged periods of continuously-changing climatic conditions in the greenhouse. Among the nine phenolic acids in the in vitro regenerants, coumaric acid was the major (23-52 μg/g DW) bioactive compound. Acclimatized plants had only six types of phenolic acids, including syringic acid, which was absent in the in vitro stage. Likewise, the levels of a number of flavonoids were generally low, and different types were accumulated in in vitro and acclimatized plants. The levels of eucomic acid, which can be considered a diagnostic compound in Eucomis species, was significantly accumulated in the leaves of SW (1:1500) treatment after acclimatization. Antioxidant activity was relatively higher in the acclimatized plants when compared to the in vitro regenerants. Given the limitations associated with the two test systems used in the current study, the antioxidant activity demonstrated by the extract may be considered to be of low clinical significance. Thus, other test systems, especially in vivo systems, will be essential to reach a valid conclusion about the antioxidant potential of SW and KAR1-treated plants. From a conservation perspective, the current findings provide preliminary evidence of the value of SW and related technology as a potentially-viable method for the biosynthesis of phytochemicals of therapeutic importance in medicinal plants. Nevertheless, it will be necessary to establish the carry-over effect of SW and KAR1 for a longer duration (>1 year) on the phytochemical pools and other pharmacological activities (besides antioxidant), as well as to determine the overall safety of plant extracts. In addition, it will be necessary to investigate other medicinal plants in order to reach a valid conclusion about the overall potential of the tested compounds.The importance of SW and KAR


Especially high contributions to the models were observed from the default mode network. These findings highlight the potential of using functional connectivity at rest as a task-free alternative for predicting activation in the VS, offering a possibility to estimate reward response in the broader sampling of subject populations.Reward anticipation is essential for directing behavior toward positively valenced stimuli, creating motivational salience. Task-related activation of the ventral striatum (VS) has long been used as a target for understanding reward function. However, some subjects may not be able to perform the respective tasks because of their complexity or subjects’ physical or mental disabilities. Moreover, task implementations may differ, which results in limited comparability. Hence, developing a task-free method for evaluating neural gain circuits is essential. Research has shown that fluctuations in neuronal activity at rest denoted individual differences in the brain functional networks. Here, we proposed novel models to predict the activation of the VS during gain anticipation, using the functional magnetic resonance imaging data of 45 healthy subjects acquired during a monetary incentive delay task and under rest. In-sample validation and held-out data were used to estimate the generalizability of the models. It was possible to predict three measures of reward activation  from resting-state functional connectivity (Pearson’s  Reward processing is an essential function of adaptive behavior in our everyday lives . The abiThe MID task allows for investigating the different stages of reward processing as well the different incentive amounts . SpecifiResting-state fMRI (rsfMRI) has attracted attention as it does not require additional interfaces like a keyboard or screen compared to task-fMRI and allows investigating a broader sample participants almost irrespective of their physical and mental capabilities. Research has shown that rsfMRI captures each subject’s individual variability  and coulHere, we aimed to assess whether functional connectivity of the brain at rest allows for prediction of task-evoked VS activation during reward anticipation with the MID task. It might allow for predicting VS activation during reward anticipation without the need to acquire task-fMRI, potentially opening the doors to a useful approach to investigate neural reward function in various populations. We aimed to predict VS activation in regard to reward sensitivity, maximal reward, and average reward based on rsfMRI scans. Our focus was the prediction of reward sensitivity, as we previously reported that VS reward sensitivity could be a potential indicator for motivational anhedonia . Reward The subjects were recruited in three different locations as part of the healthy control cohorts in separate studies. For group 1 (Center of KANSEI Innovation), 17 participants were enrolled from the local community via a newspaper and public notice. Group 2  included 15 participants who were similarly recruited as part of a previous study . Group 3Functional brain images were collected using three different MRI scanners. All scans were acquired with echo planar imaging sequences. Details on MRI acquisition are summarized in Participants performed a modified MID task as described by post hoc analysis. The behavioral data analysis was done using SPSS statistics 21 software .As the behavioral data were skewed, we used non-parametric descriptive statistics. Friedman test was used to compare the effect of the reward amount  on subjective motivational levels and reaction times. The Dunn–Bonferroni correction was used as 1, the scans acquired during the first 10 s were discarded; the first four volumes (TR = 2500 ms) according to the standardized pipeline. All imaging data were preprocessed using slice-timing correction and subsequently two-pass realignment to the mean image. Then, using the normalization parameters obtained through the segmentation of the structural image which was previously aligned with the mean functional image, the data were normalized to the standard template on the Montreal Neurological Institute (MNI) reference brain, and resampled to 2 × 2 × 2 mm3 voxels. The MID task data were then smoothed using an 8 mm full-width at half-maximum (FWHM) Gaussian kernel. In order to standardize the length of all rsfMRI scans between cohorts, the data from group 1 were shortened to the first 5 min for the subsequent analysis. We created a gray matter mask by combining and thresholding the respective compartments of Harvard-Oxford atlas and SPM tissue probability map at 50%, which was applied to the rsfMRI images. Afterward, artifacts were further removed using the Brain Wavelet toolbox2  software . We discarded the first five volumes of each run for the MID task to ensure magnetic signal equilibrium. For each rsfMRI data in groups 2 and 3, the scans acquired during the first 14 s were discarded; the first seven volumes for group 2 (TR = 2000 ms) and five for group 3 . As the data in group 1 belonged to the Brain Mapping by Integrated Neurotechnologies for Disease Studiestoolbox2  with theFor groups 1 and 3, we modeled 24 regressors that included the 8 reward anticipation conditions [amount of money ], 2 control events , and 14 outcome phase activities [six conditions  × two outcome (hit/miss) and two conditions (± ¥0)]. For group 2, the first-level included 14 regressors that modeled anticipating reward in the four reward cue conditions [amount of money ], eight feedback phase activities [four conditions ] × two outcome (hit/miss)], and two control events . In detail, the following aspects of the experiment were modeled: For group 2, there was hit or miss feedback for the 0 yen condition. For groups 1 and 3, there was just one common feedback for both, hit and miss, outcomes of ±0 yen. We used these feedbacks to improve the stability of the model for groups 1 and 3, in which the trial numbers for one block were less than those of group 2. All condition regressors were convolved with canonical hemodynamic response function. The six realignment parameters for each subject were furthermore included as nuisance regressors. Further details are provided in our previous papers .small reward anticipation: 20 yen anticipation vs. 0 yen anticipation, medium reward anticipation: 100 yen anticipation vs. 0 yen anticipation, maximal reward anticipation: 500 yen anticipation vs. 0 yen anticipation, and average reward anticipation: 20,100, and 500 yen vs. 0 yen anticipation.Since we focused on gain anticipation, individual parameter estimates were generated for four contrasts: x/y/z = 12/10/−4, −10/10/−6 in MNI space. We defined spheres around these maxima with a radius of 6 mm and merged the bilateral ventral striata into one ROI using MarsBaR  correction. For this analysis, the group mask was constructed by combining the gray matter mask (see the section “fMRI Data Preprocessing”) and all task-fMRI volumes of all subjects as an inclusive mask.For extraction of the VS activation, we used the peak coordinates of gain anticipation areas from a meta-analysis of the MID task , which a MarsBaR . The oveBased on our previous study  we paramY represents the VS activation, R the reward rank , b1 the slope, and b0 the constant term. Of note, we used the rank instead of reward magnitude which was used in the previous paper, due to the more linear relationship between the rank and the VS activation. The slope indicates the sensitivity to benefit size, where a larger slope parameter (b1) indicates higher reward sensitivity.p ≤ 0.05.After the common preprocessing, further rsfMRI-specific processing comprised artifact reduction by motion, tissue, and frequency regression, and estimation of the FC. White matter (WM) and cerebrospinal fluid (CSF) masks were created using the respective compartments of the Harvard-Oxford atlas with a 95% threshold. Nuisance regressors were defined as the first three principal components of WM and CSF  and the 3 (n = 30) and held-out samples (n = 15) equally distributed over all cohorts in order to show generalizability of our results. The generalizability of the model was estimated using a 10-fold cross-validation (10-FCV), repeatedly splitting the estimation sample into training (n = 27) and test sets (n = 3) for model estimation. We trained one model for each measure of the VS activation to learn associations between the patterns of functional connectivity at rest and the targeted VS measure. The individual predictors were calculated using the weight and individual functional connectivity matrices for each subject. A regression model was employed to correct the scaling of the predicted and actual value, which are not automatically accounted for by PRoNTo. The weight matrix averaged over the folds and the complete estimation set was used for this. The individual predictors were fed into the resulting regression model. We report Pearson’s correlation coefficient and the mean squared error (MSE) as measures of similarity between the predicted and actual values of the VS measures. An MSE of 0 would indicate no error between the predicted and actual values. For the performance on the training runs, the significances were estimated by the permutation test implemented in PRoNTo with 10,000 runs. Resulting p-values < 0.05 were considered significant. We also present the nodes and edges with the highest weight vector values that contribute the most to estimations. The ROIs were grouped to seven functional networks according to We aimed to predict three different measures of VS activation: reward sensitivity, maximal reward, and average reward. In order to build a model, we used relevance vector regression (RVR)  implemen3 . In the 2(3) = 42.067, p < 0.01]. Post hoc tests revealed significant differences between all conditions (p < 0.05) after Bonferroni adjustment, indicating that participants were motivated more by increasing monetary rewards. There was a significant difference in reaction times on the amount of money . Dunn–Bonferroni post hoc tests were carried out and there were significant differences between 0 and 100 yen, 0 and 500 yen, or 20 and 500 yen (p < 0.01). The difference between 20 and 100 yen is marginally significant (p = 0.054). There was no significant difference between other amount of money (p > 0.1), which might be due to the flooring effect for 100 and 500 yen. The median response time and motivation ratings are shown in There was a significant difference in subjective motivational levels on the amount of money . For the average reward model, the predictive power of the performance on the validation sample (Pearson’s r = 0.50) is much higher than that of training runs (Pearson’s r = 0.19) and comparable to that of the maximal reward model (Pearson’s r = 0.54). This might be caused by greater heterogeneity within the training than the validation sample and indicates potential predictive capabilities of the model beyond significance.For the maximal reward and reward sensitivity models, the predictive power of the performance on the validation sample is comparable to that of training runs [Pearson’s As for the reward sensitivity and maximal reward models, the node with the highest contribution to prediction of the VS measures was located in the DMN. In detail, the node corresponds to the TPJ, which is a heteromodal associative region with structural connections to the limbic system, the prefrontal cortex, the cingulate gyrus, the putamen, and the thalamus . This enFor all models, most of the edges with the highest weights correspond to the functional connections within the DM, and between the DM and other task-positive networks such as the DA and the VA networks. This suggests that the DMN itself contributes highly to the prediction of VS measures from functional connectivity at rest. The weight matrix by itself does not tell whether these networks have increased or decreased functional connectivity in relation to the VS measures. Still, these findings may help us to understand a comprehensive role of the DMN to interact with other networks for predicting VS measures during anticipation.Considering the high correspondence between resting state connectivity and task activation , the facThe idea that brain connectivity at rest can predict activation during a task has previously been reported by using the Human Connectome Project dataset . The autThe proportional response in the VS to increasing amount of gain (reward sensitivity) is disrupted in depression . If geneA previous study tried to predict neural activation in the striatum with a reinforcement learning task through the use of structural connectivity . The autA major merit of the current study is the possibility of in-sample as well as out-of-sample validation. Although it is common to use cross-validation only, ideally a trained model should be tested with independent samples. In addition, given that participants were scanned at multiple sites with different scanners, we showed that the model could learn a predictive pattern across datasets.Our results need to be interpreted in the light of methodological limitations. The length of the resting state scans is comparably short and the repetition times are relatively long. Even though the overall analyzed resting state period is within the appropriate range according to a recent study (from 5 up to 13 min) , longer The data that support the findings of this study are available from the corresponding author upon reasonable request.All the studies were carried out in accordance with the recommendations of Ethical Guidelines for Medical and Health Research Involving Human Subjects, the Ethics Committee of Hiroshima University with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki and received financial reimbursement for participation. The protocols were approved by the Ethics Committee of Hiroshima University.AM, MK, GO, RL, YO, SYa, and SK contributed to the conception and design of the overall analysis which was coordinated by AM and MK. MK, AM, and MT performed the statistical analysis planned by RL, MK, AM, MR, and AH. AM, GO, KT, SYo, CS, and AY performed the recruitment. AM, GO, MT, KT, SYo, NI, CS, and AY conducted the initial medical screening. AM, GO, MT, KT, SYo, NI, CS, and AY recorded the neuroimaging data. AM, GO, MT, and CS performed the quality control of the data. AM, CS, and AY provided the medical assistance and subject monitoring. GO, YO, and SYa provided the medical supervision. AM, MK, MR, MS, GO, MT, GG, AH, RL, SK, SYo, MM, YO, PH, and PM interpreted the data. AM composed the overall manuscript. MK provided parts of the sections “Materials and Methods” and “Results.” All authors have critically revised the manuscript, and read and approved the submitted version of the manuscript.SK received grants and research support, consulting fees, and/or honoraria within the last 3 years from Angelini, AOP Orphan Pharmaceuticals AG, Celgene GmbH, Eli Lilly, Janssen-Cilag Pharma GmbH, KRKA-Pharma, Lundbeck A/S, Mundipharma, Neuraxpharm, Pfizer, Sanofi, Schwabe, Servier, Shire, Sumitomo Dainippon Pharma Co. Ltd., and Takeda. RL received conference speaker honorarium within the last 3 years from Shire and support from the Siemens Healthcare regarding clinical research using PET/MR. MS has received travel grants from Janssen, Eli Lilly, Austroplant, and AOP Orphan Pharmaceuticals AG; speaker honoraria from Janssen and Austroplant; and workshop participation from Eli Lilly. She is also the recipient of a NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (23741). YO has received lecture fees from Otsuka, Dainippon-Sumitomo, Astellas, Pfizer, Eli Lilly, Janssen, Meiji, Mochida, Yoshitomi, Eisai, and GSK. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Sarco-osteopenia (SOP) is a new type of geriatric syndrome, resulting from the combination of sarcopenia (SP) and osteoporosis (OP). Xianling Gubao capsule (XLGBC), made from several traditional Chinese medicine, is reported to have a therapeutic effect on diseases of bones and joints. This protocol will be designed to assess the efficacy of XLGBC in the treatment of SOP.Relevant randomized controlled trial literatures evaluating the effect of XLGBC on patients with SOP will be obtained by searching the following 7 electronic databases: Cochrane Library, PubMed, Embase, Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP), Chinese Biomedical and Medical Database (CBM), and Wanfang Database, from inception to March 2019. The primary outcomes will be bone mineral density, skeletal muscle mass index, handgrip strength, and gait speed. Stata V.13.0 software will be used for data synthesis and analysis, sensitivity analysis, subgroup analysis, and risk of bias assessment. Reporting bias will be evaluated utilizing a funnel, with Egger tests assessing funnel plot symmetries. Quality of evidence will be evaluated according to guidance of the Recommendations Assessment, Development, and Evaluation guideline.This study will provide a rational synthesis of current evidences for XLGBC on SOP.The conclusion of this study will provide evidence to judge the effectiveness and safety of XLGBC on SOP.This systematic review will be contributed to peer-reviewed publications, aiming to provide evidence about efficacy of XLGBC on SOP.CRD42019128223. SP is diagnosed as the decline in skeletal muscle mass and function, which has serious influence on patient's activities and diet. OP is defined as a systemic bone disease characterized by age-related fracture risk. Loss of bone and muscle strength combine into a single diagnosis of “sarco-osteopenia” (SOP). SOP is a major disease for elderly people, which is considered as a higher risk for falls and fractures.–4 Among elderly people, SOP is associated with significant morbidity and mortality as well as a decline in the quality of life. After 50 years, muscle mass decreases by 0.5% to 2% annually and muscle strength by 1.5% to 3%. This decrease is greater in the sedentary population and in males, in whom a prevalence of double resistance is found. Also, for women, bone mass begins to decrease by 0.5% from 30 years with a rapid point decrease in postmenopausal women, this decrease being stable in men.,7 These changes together with multiple contributing factors, such as sedentary lifestyle, malnutrition, chronic diseases, and some pharmacological treatments, finally producing OP and SP, even SOP.Because of the aging group, this fraction epidemic is predicted to exponentially escalate by 2050.1.2 Myostatin (inhibitor of muscle development) is one of the therapeutic targets that are currently studied. A study showed that administration of ACVR2B-Fc (myostatin receptor recombinant protein) resulted in an increase in lean body mass and markers of bone formation in postmenopausal women. The antibody LY2495655 has been associated with a moderate increase in muscle mass and strength. Other pharmacological therapies such as testosterone, inhibitors of angiotensin-converting enzyme, ghrelin, growth hormone, and insulin-like factor 1 have also been evaluated to treat both OP and SP, but no clear evidence of their benefits has been found so far. Chinese medicine has good clinical efficacy in the treatment of OP. And Xianling gubao is the well-known Chinese patent medicine. The recipe is based on the modern prescriptions of the Miao people. A new type of national medicine was developed. The prescription was collected, sorted, and screened by a famous orthopedics expert, Professor Shi Guangda, and eventually consisted of 9 Chinese herbs, such as epimedium, psoralen, psoralen, Radix Rehmanniae, Salvia miltiorrhiza, and Zhimu, which are mainly used in the treatment of OP, fracture, osteoarthritis, aseptic necrosis, etc. There are a lot of literatures showing that Xianling Gubao capsule (XLGBC) has a good effect on the treatment of bone diseases.–15 Thus, the purpose of this review is to summarize clinical researches on XLGBC for SOP and findings of this review will be reliable within evidence of clinical studies.As for treatments for SOP, new pharmacological treatments are developed for the management of low body mass index (BMI) and SP.22.1The protocol has been registered on the International Prospective Register of Systematic Reviews (PROSPERO)  basing on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement guidelines , balance ability, pain, and adverse events.2.32.3.1Seven databases including Cochrane Library, PubMed, Embase, Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP), Chinese Biomedical and Medical Database (CBM), and Wanfang Database will be searched with a time span from their inception to March 2018. The search strategy in PubMed database is as follows: “Xianling Gubao Capsule” AND “Sarco-Osteopenia” or (“sarcopenia” and “osteoporosis”). Chinese translation of the above retrieval strategy will be used for Chinese database search.2.3.2In order to obtain the potential nonelectronic literatures, relevant magazines and medical journals will also be filtered for further search.2.3.3With the help of Endnote V.X7 (Columbia CP Ltd), 2 reviewers will carry out the first round of selection by preliminarily screening the title and abstract of the obtained articles. Duplicate publications, nonclinical researches, and irrelevant articles will be excluded. Another round of selection will be conducted through reading the full text of the documents to decide the final inclusions. Any disagreement will be settled by discussion the judgement of a third reviewer. The study selection procedure will be showed by a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart Fig. . Microso2.3.4If any article lacks the necessary data, we will contact authors of the article by the contact information we can get. If we fail in this way, the potential impact that the missing data would probably bring about will be analyzed in the discussion part.2.3.5According to suggestions from Cochrane Handbook for Systematic Reviews of Interventions, risk of bias of each included trial will be assessed in the following aspects: random sequence generation, allocation concealment, implementation of the blind method, incomplete data, selective report, and other bias. Risk of bias will be evaluated as low, high, or unclear (unclear or unknown risk of bias).2.4I2 test statistics firstly. The evaluation criteria are as follow: I2 < 25% means no significant heterogeneity, I2 = 25% to 50% indicates moderate heterogeneity, and I2 > 50% represents strong heterogeneity. Then, basing on the I2 value, the random-effects model  or the fixed-effect model  will be chosen.Meta-analysis of the included studies will be performed by statistical softwares, respectively  and Stata 12.0 software ). Mean difference (MD) or standard mean difference (SMD) will be used for continuous variables, and rate ratio (RR) will be chosen for dichotomous variables. Besides, 95% confidence intervals (95% CIs) for both variables will also be measured. As for data synthesis, the heterogeneity of each included studies will be assessed by 2.5I2 ≥ 50%) and the trials included are adequate, we will perform subgroup analysis and sensitivity analysis to seek the probable source of the heterogeneity, in consideration of different study characteristics, such as participants characteristics, sample size, interventions, controls, outcome measures, and so on.Once heterogeneity appears significant (2.6When the number of the included studies exceeds 10, reporting bias will be assessed by funnel plots drawn through Egger regression test. Symmetry of funnel plots indicates no reporting bias. On the contrary, if the points of the funnel plot appear to be dispersed and asymmetrical, reporting bias is considered to be existent and the reliability is low.2.7With the guidance of the Recommendations Assessment, Development, and Evaluation guideline, the quality of evidence will be evaluated by 2 reviewers. We will take into account limitations of the study, inconsistencies, indirect evidence, imprecision, and publication bias. Levels of evidence quality will be rated as high, moderate, low, or very low.2.8Ethical approval will not be needed, as the data extracted for our study are derived from published literature and will not cause invasion of participant privacy. We aim to publish this systematic review, evaluating the effects of XLGBC on SOP.3 The current treatments are single treatments for one of these conditions. May be beneficial to the muscles at the same time, and therefore extended to treat the muscles less serious. It mainly includes anabolic hormones, vitamin D, receptor agonists, and growth hormones.Although currently in SOP drug research has made great progress, a common target for “muscle-osteoporosis” therapeutic drugs is still less, and the efficacy is not exact.To the best of our knowledge, it will be the first systematic review and meta-analysis on XLGBC in the treatment of SOP. First, the results of this review will provide objective statistics for further researches on SOP. Second, the results will offer reliable references for clinicians and patients in the treatment of SOP with XLGBC. Third, the results may introduce an alternative therapy of SOP to policy makers to decrease the burden of public health.Hongxing Huang conceived the study idea. Qunqun Chen was responsible for the design of this systematic review. Junxiang Zeng and Yi Chen contributed to the data analysis plan. Yalan Yang and Tian Zhang drafted the manuscript and Tao Luo edited it. All authors provided feedback and approved the final manuscript.Data curation: Qunqun Chen.Formal analysis: Qunqun Chen.Investigation: Junxiang Zeng.Methodology: Yi Chen.Software: Yalan Yang, Tian Zhang.Writing – original draft: Tao Luo.Writing – review & editing: Hongxing Huang.


Water pollution is a major threat to public health worldwide. The health risks of ingesting trace elements in drinking water were assessed in the provinces of Punjab and Khyber Pakhtunkhwa, Pakistan. Eight trace elements were measured in drinking water, using Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and compared with permissible limits established by the World Health Organization (WHO) and the Pakistan Environmental Protection Agency (Pak EPA). In addition, health risk indicators such as the chronic daily intake (CDI) and the health risk index (HRI) were calculated. Our results showed that the concentrations of chromium (Cr), nickel (Ni), and manganese (Mn) were 2593, 1306, and 695 ng/g, respectively, in Lahore and Jhang, while the concentrations of arsenic (As) in Lahore, Vehari, Multan, and Jhang were 51, 50.4, 24, and 22 ng/g, respectively, which were higher than the permissible limits suggested by the WHO. The values of CDI were found to be in the order of Cr > Ni > Mn > Cu > As > Pb > Co > Cd. Similarly, the health risk index (HRI) values exceeded the safe limits (>1) in many cities . The aforementioned analysis shows that consumption of trace element-contaminated water poses an emerging health danger to the populations of these localities. Furthermore, inter-metal correlation and principal component analysis (PCA) showed that both anthropogenic and geologic activities were primary sources of drinking water contamination in the investigated areas. Water is an essential element for life. Freshwater comprises 3% of the total water on Earth. Only a small percentage 0.01%) of this freshwater is available for human use % of this. AlthougTaking into account the health hazards to the population, researchers in Pakistan have taken an interest in the level and extent of contact with such contaminants . Being aOwing to limited financial resources, people in Pakistan are facing a serious problem in the supply of clean and safe drinking water because they directly use underground water for domestic and other applications. In the past, varied and even conflicting results on the safety of groundwater were reported in different population-enriched areas in Pakistan, which highlights the great concern about groundwater quality and the need for further investigation on groundwater contamination and the potential pollution sources. For instance, the Pakistan Council for Research in Water Resources (PCRWR) has declared that Punjab province cities, such as Lahore, Vehari, Multan, Sheikhopura, Bahawalpur, Gujranwala, and Kasur are the most-affected areas with respect to elevated trace metal concentrations . Despite2, and consists of varying geological features. The geology of Pakistan ranges from high mountains in the north to alluvial plains and even deserts in the south, with five rivers of different origins that irrigate the land and affect sedimentation. The study area is comprised of two provinces: Punjab and Khyber Pakhtunkhwa (KPK). Punjab, the second largest province of Pakistan, mostly consists of fertile alluvial plains of the Indus River. It is a heavily irrigated area, and canals are found throughout the province. In Punjab, most areas experience extremely foggy winters and hot summers, and temperatures range from 2 to 45 °C. KPK sits primarily on the Iranian plateau and has huge Hindu Kush mountains, and its major parts are typically dry. Multiple samples were collected from each site to span the whole area. Between any two sampling points, a minimum distance of 1 km was maintained.Pakistan is situated in South Asia, covers 796,095 kmRepresentative samples were collected without changing the element concentrations between sampling and analysis. Sterilized, recyclable, disposable conical tubes that had locking screw caps of a 15 mL volume were used to collect samples. Millipore syringe filters with a 0.2 μm pore size were used for the filtration of samples. To eliminate microbial activity, several drops of nitric acid were also added to the samples (decreasing pH to 2). Information regarding sampling date, time, and ID code was given to each sample label. A separate field pro forma was designed in order to record the field observations of each sample and every sampling site. Information that was gathered regarding sampling site contained observations about potential As sources around the sampling range and the prominent health effects of As amongst the locals. A geographical position system (GPS) was used to record the coordinates of sampling sites.Agilent 7500 cx ICP-MS  was used in determining trace elements concentrations in water samples. Serial dilution of standard stock solution , obtained from the NCATN , was used to prepare calibration solutions. Operating parameters for ICP-MS were set as follows: Carrier gas: 1.1 L/min; radio-frequency (RF) power: 1510 W; nebulizer pump: 0.1 rps; helium gas: 3.5 mL/min; makeup gas: 0.10 L/min.As a quality control, a representative of all samples containing 20 μL of every sample was prepared and injected after every 30 samples. The variation in metal concentrations was <10% for QC samples. In addition to that, standards were calibrated twice, at the start and at the end. Each sample was injected three times by the instrument, and an average was given as a result during every test. All samples were tested thrice, i.e., a pure sample once and fivefold-diluted samples twice, in order to make sure that the results were precise.For human health risk assessment, the health risk index (HRI) for all elements in each area was also calculated. Using chronic daily intake (CDI) and the reference dose (RfD) for each element, the HRI was set by the Agency for Toxic Substances and Disease Registry (ATSDR) or the Environmental Protection Agency (EPA). The values for CDI through water ingestion were calculated using a modified equation from the EPA .CDI = CUnivariate and multivariate statistical analyses such as inter-metal correlation were performed using SPSS . However, to compare different trace element concentrations in different cities, analysis of variance (ANOVA) was performed using Statistix 8 (version 8.1) and principal component analysis (PCA) was performed in an R environment (R version 3.5.1). The concentration maps of metals in the study area were prepared using the Arc geographic information system (version 10.3.1). Furthermore, the Spearman correlation coefficient matrix for all the selected elements was used.4) [In the present study, drinking water quality has been assessed on the basis of the WHO  and the 4) . The Mn 4) , Besham 4) , Sialkot4) . The ran4) ,43. Ni i4) . The Ni 4) , Peshawa4) , and Fai4) . Lead al4) . Its hig4) . Previou4) , while a4) . The Cr 4) , Faisala4) , and Pes4) . Cr is u4) .The chronic daily intake (CDI) values and health risk indices (HRI) of the selected trace elements are shown in p < 0.05) between different locations, which indicated that different locations contribute differently to the mean trace element concentration in the water (p < 0.05) in the drinking water samples collected from the different areas. Statistically remarkable differences were found between Lahore–Jhang, Lahore–Multan, Lahore–Vehari, Lahore–Peshawar, and Lahore–Swabi. However, Jhang–Swabi, Multan–Vehari and Peshawar–Swabi showed non-significant relationships. Correspondingly for Mn, statistically notable differences were found between Jhang–Multan, Jhang–Lahore, Jhang–Peshawar, Jhang–Swabi, Jhang–Vehari, Vehari–Peshawar and Vehari–Swabi. However no significant difference (p < 0.05) was found for Cu concentration among different localities. Overall, As concentrations in drinking water samples in Jhang, Lahore, and Vehari showed statistically significant differences (p < 0.05). However, Lahore–Vehari, Jhang–Multan, and Peshawar–Swabi showed no significant differences. Likewise, Ni concentration was significantly higher in Lahore as compared to other cities. Pb, Cd, and Co concentrations also showed significant differences.We performed a statistical analysis on the comparison of selected trace elements in different locations. The analyzed outcomes show significant variation  was used to compare the compositional and spatial patterns between the examined water samples and the identified latent factors, and to find the possible sources of the trace metals in the water samples. PCA has been widely applied to identify the contribution of anthropogenic and natural sources . The datIn the case of South Punjab, the total cumulative variance of the two Factors was 58.50%, in which Factor 1 contributed 39% and Factor 2 contributed 20%, with high loadings on Cu (r = 0.9), Cd (r = 0.9), and Pb (r = 0.9) in Factor 1. The Cd level may be due to geological activities, while Pb could be influenced by the plumbing systems in Vehari and Multan. Moreover, Mn (r = 0.7) and Co (r = 0.6) were efficiently loaded in Factor 2. Arsenic showed negative loading in both Factors. The results show that Factor 1 and 2 may indicate both anthropogenic and geologic sources.When investigating the results of KPK, it was found that the total cumulative variance for the two factors in KPK was 61%. Factor 1 contributed 35% of the total variance, with high loadings of Ni (r = 0.8), Pb (r = 0.8), and Cd (r = 0.7). Moreover, Mn and arsenic were loaded in Factor 2, showing that both may have anthropogenic and geological contributions. The high Ni level could be due to the weathering of rocks.After thorough investigation of the drinking water samples collected from groundwater of the investigated areas, the highest concentration was found for Chromium (Cr), followed by Ni > Mn > Cu > As > Pb > Co > Cd. The concentrations of As, Mn, Ni, Cr and Pb were higher than their respective permissible limits set by the Pak-EPA and the WHO, while Cu, Co, and Cd concentrations were within their permissible limits. The HRI and CDI values of As showed that the water is not safe for drinking in Lahore, Vehari, Multan, and Jhang. Similarly, Cr and Ni also exceeded the safe limits in Lahore. However, for Mn, Cu, Co, Cd, and Pb, the HRI values were within the limits. ANOVA analysis indicated that trace element contaminations in different areas varied significantly. The Lahore area has contributed high levels of contamination. It has been observed from the univariate and multivariate statistical analyses that both anthropogenic  and geologic sources (As) were responsible for higher trace element concentrations. To sum off, despite being an essential part of life, As, Cr, and Ni contaminated water may pose a serious health threat to some of the investigated areas. Therefore, it is strongly recommended that water from polluted areas should not be used for drinking purposes without proper treatment. Industrial wastewater disposal in big cities such as Lahore and Multan should be strictly monitored, and all industries should be directed to adapt and follow wastewater treatment measures.