Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can exacerbate previous headache disorders or change the type of pain experienced from headaches. This study aimed to investigate the clinical features of Long COVID headaches.n = 50; without previous headache, n = 52) with long COVID and headache complaints. The Migraine Disability Assessment Test and Visual Analog Pain Scale were used to collect participants' headache data according to a standardized protocol.This was a cross-sectional, descriptive, and analytical observational study that included 102 patients , exhibited partial improvement in headache symptoms with analgesics (p = 0.045), and had a duration of long COVID of <1 year (p = 0.030). Patients with moderate or severe disability and those classified as having severe headaches in the long COVID phase were highly likely to develop chronic headaches. Hospital admission , back pain , insomnia , and paraesthesia  were associated with headache in these patients.The patients in this study who reported experiencing headaches before COVID-19 had longer headache duration in the long COVID phase than that in the pre-long COVID phase (Headache is a disabling condition in patients with long COVID-19, exacerbating the conditions of those with headaches prior to contracting COVID-19. After the acute period of coronavirus disease 2019 (COVID-19), some symptoms persist for months, or new symptoms appear, such as muscle fatigue, arthralgia, myalgia, cognitive decline, and anxiety, among others  and inclTo diagnose chronic headache attributed to systemic viral infection , the IntThe pathophysiology of long COVID headache is still not completely known. However, it has been hypothesized that the direct invasion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into the central nervous system affects neuronal pathways . For exaThe exact pathophysiological mechanism underlying headaches associated with long COVID, as well as the clinical features in these patients, represents a gap in scientific knowledge that remains to be filled. Thus, this study aimed to assess the clinical characteristics of long COVID headache, including its intensity and effect on disability, as well as its associated epidemiological factors.This study was approved by the Ethics Committee and for Research with Human Beings at the Center for Biological Health Sciences of the University of the State of Pará . All participants provided written consent to participate after receiving information about the study. This study was conducted in strict compliance with the principles of the Declaration of Helsinki.This was an observational, cross-sectional, analytical-descriptive, and quantitative study reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.The diagnostic criteria for long COVID included a previous diagnosis of COVID-19, presentation of symptoms at least 4 weeks after the acute phase of the disease, and the presence of symptoms for at least 3 months, with symptoms appearing no later than 2 months after infection. COVID-19 was diagnosed using positive polymerase chain reaction tests and was characterized by fatigue, shortness of breath, cough, joint pain, chest pain, muscle pain, headache, and other symptoms that cannot be attributed to any other cause (n = 50) and without a prior history of headache (n = 52); (ii) duration of long COVID: up to 12 months (n = 57) and 12 months or longer (n = 45); (iii) migraine disability assessment (MIDAS) questionnaire results: minimal or mild disability (n = 33) and moderate or severe disability (n = 69); (iv) visual analog scale for pain (VAS) score: ≤5 (n = 35) and >5 (n = 67); (v) for patients with a prior history of headache (n = 50): characteristics of headache before and after contracting COVID-19  of whom were excluded for not having headaches associated with long COVID. Thus, the final sample consisted of 102 patients with long COVID symptoms. The patients were divided into different groups based on the following factors: (i) prior history of headache: with a prior history of headache , and current headache data . In addition, the MIDAS questionnaire  was usedThe collected data were analyzed using GraphPad Prism™ version 5.0 software . To assess the distribution of epidemiological characteristics of the collected data, simple descriptive and inferential statistical methods were applied. Categorical variables were analyzed using Fisher's exact test and reported as the frequency and percentage. The association between the individual exposure factors of patients and clinical outcome (headache associated with long COVID) was evaluated using logistic regression, from which the raw odds ratio (for each exposure factor) and respective confidence interval values were calculated. An α level of 0.05 was adopted to reject the null hypothesis.Long COVID headache was observed most frequently in women aged more than 39 years and in individuals with more than 9 years of education. The average duration of long COVID in the patients was 321.46 days; more than half of the patients did not have a history of headaches prior to contracting COVID-19, and those with a prior history of headache reported experiencing worsening pain in the long COVID phase. The characteristics of long COVID headache consisted of the following: a “pressure” or “squeezing” headache, frequency between two and five times each week, duration more than 6 h, and pain in the bilateral temporal cranial region. Additionally, the major associated symptoms were photophobia and phonophobia .Headaches with an intensity of higher than five points on the VAS were observed in 65.68% of the patients (67/102), and the highest reported VAS score was eight points (25.49%). Severe disability was observed in 47.05% of the patients , B.p = 0.005). However, in the long COVID phase, they experienced the following: an increased frequency of pain both daily (p = 0.022) and two to five times each week (p = 0.012), and only partial improvement in the pain condition after taking medications (p = 0.002).The pain characteristics before and after contracting COVID-19 in patients with a prior history of headache are presented in p = 0.031) and only partial improvement in pain with medications (p = 0.045) compared to those without previous headache. The other variables for long COVID headache demonstrated no differences between patients with previous headache and those without (Patients who had a history of headache prior to contracting COVID-19 reported having a longer duration of long COVID headache ( without .p = 0.004) and a greater intensity of pain, as assessed by the VAS (p = 0.014), than patients with minimal or mild disability. Patients with minimal or mild disability had a shorter pain duration than those with moderate or severe disability , and only 7.41% of them reported an improvement in pain without the use of medications (p = 0.000). This group of patients also presented with symptoms associated with headache (p = 0.000) and moderate or severe disability according to the MIDAS  and exhibited an improvement in pain without the use of medications (p = 0.010) compared to patients with a long COVID duration of more than or equal to 1 year , while other patients may experience pain with migraine characteristics  . Our In Similar results to those found in our study were reported by a cross-sectional study of 201 individuals in the city of Madrid in which no difference in the prevalence rates of headache was found between patients with long COVID with migraine prior to contracting COVID-19 and those without migraine prior to contracting COVID-19  leads to an imbalance between the pro- and anti-inflammatory pathways, additionally leading to overactivation of the renin–angiotensin system. Overactivated angiotensin receptor (AT1R) results in cytokine storm syndrome and overproduction of proinflammatory cytokines, which can lead to vasoconstriction, inflammation, oxidative stress, and neurological dysfunction .Tryptophan metabolism is altered in patients with long COVID. During COVID-19 infection, it is possible that there is an increase in the levels of kynurenine, whose function is the enzymatic degradation of tryptophan . An imbaAmong the limitations of this study, it should be noted that prior headaches were evaluated according to retrospective reports of patients, which could introduce a memory bias. However, headache is a common disease, and its carriers are, in most cases, clear about their individual characteristics. The MIDAS is a questionnaire designed to assess migraine, the use of which could introduce instrumentation bias; however, we used it to assess other headaches due to the absence of another validated instrument that was equally capable of measuring the disability brought about by headaches.In this study, we investigated headache symptoms in patients with long COVID for up to 15 months, and this is the first study in the Brazilian Amazon demonstrating the chronicity of headache in long COVID. However, further studies that follow up with patients with COVID-19 for a period longer than that in the present study (15 months) should be conducted. Additionally, cohort studies assessing and reassessing the signs and symptoms of long COVID headache to define the changes in the clinical findings of headache and disability over time in long COVID are needed, as this still represents an important gap in knowledge to be filled.In conclusion, long COVID headache is a disabling and intense manifestation that persists for months after the acute period of COVID-19, exhibiting a chronification tendency. Patients with a history of headache report a greater exacerbation of pain and, sometimes, different pain characteristics between headaches occurring during the long COVID phase and headaches occurring prior to this phase. Notably, hospitalization, paraesthesia, back pain, and insomnia were associated with headaches in patients with long COVID.The results of the present study can be explained by the involvement of multiple organs and systems, both before and during COVID-19 infection, as this can directly or indirectly alter the central nervous system and cause headache. This study is the first in the Brazilian Amazon to describe headache associated with long COVID and its long-term involvement in the exacerbation of symptoms. Although the results of the present research are important, we suggest performing further prospective studies to assess and monitor the signs and symptoms of headache in patients with long COVID, in addition to studies involving pain therapy, as this is still an important gap in scientific knowledge in this field.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethical Committee in Research of State University of Pará. The patients/participants provided their written informed consent to participate in this study.AR and LF: conceptualization, investigation, figures, writing—original draft, and writing—review and editing. TB, BB, CS, and NS: writing—original draft and writing—review and editing. AP, AD, and ES: writing—review and editing. JQ and LF: supervision and project administration. All authors contributed to the article and approved the submitted version.


We conducted this Systematic Review to create an overview of the currently existing Artificial Intelligence (AI) methods for Magnetic Resonance Diffusion-Weighted Imaging (DWI)/Fluid-Attenuated Inversion Recovery (FLAIR)—mismatch assessment and to determine how well DWI/FLAIR mismatch algorithms perform compared to domain experts. We searched PubMed Medline, Ovid Embase, Scopus, Web of Science, Cochrane, and IEEE Xplore literature databases for relevant studies published between 1 January 2017 and 20 November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Five studies fit the scope of this review. The area under the curve ranged from 0.74 to 0.90. The sensitivity and specificity ranged from 0.70 to 0.85 and 0.74 to 0.84, respectively. Negative predictive value, positive predictive value, and accuracy ranged from 0.55 to 0.82, 0.74 to 0.91, and 0.73 to 0.83, respectively. In a binary classification of ±4.5 h from stroke onset, the surveyed AI methods performed equivalent to or even better than domain experts. However, using the relation between time since stroke onset (TSS) and increasing visibility of FLAIR hyperintensity lesions is not recommended for the determination of TSS within the first 4.5 h. An AI algorithm on DWI/FLAIR mismatch assessment focused on treatment eligibility, outcome prediction, and consideration of patient-specific data could potentially increase the proportion of stroke patients with unknown onset who could be treated with thrombolysis. Magnetic resonance imaging (MRI) is the primary imaging modality for stroke detection and classification in patients with an unknown onset and wake-up stroke (WUS), using Diffusion-weighted imaging (DWI) and T2-Weighted Fluid-Attenuated Inversion Recovery (FLAIR) sequences . DWI seqBased on results from randomized clinical trials, alteplase or recombinant tissue plasminogen activator (rtPA) can be administered up to 4.5 h after stroke symptoms onset, as the risk of hemorrhage and poor outcomes increases with time ,7,8,9. FOver recent years, the number of artificial intelligence (AI) algorithms applicable for radiological purposes has increased markedly ,13,14. MField experts, e.g., neuroradiologists, perform the current clinically used binary mismatch assessment, but it has been shown to have poor inter- and intra-observer agreement ,7,8. An We conducted this Systematic Review to discover the currently existing AI algorithms for automated DWI/FLAIR mismatch assessment and determine how well DWI/FLAIR mismatch algorithms perform as compared to experienced radiologists and neurologists.This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement . The stuLiterature was searched between 26 October 2022, and 20 November 2022, in PubMed MEDLINE, Ovid Embase, IEEE Xplore, Web of Science, Elsevier Scopus, and Cochrane. We searched articles published between 1 January 2017, and 20 November 2022, within the clinical and technical scope of the review.The preliminary search string was done in PubMed MEDLINE. Afterward, it was converted and edited with the polyglot tool Systematic Review-accelerator  . This maThe literature was extracted and collected for the reference tool Endnote 20 . Duplicates were removed with Endnote’s internal duplicate search function.The references were exported to Covidence  to screen and review articles. The internal duplicate tool in Covidence removed duplicates not caught by Endnote 20. Only studies focusing on machine learning methods to automatically identify DWI and FLAIR lesions on structural brain MRI were included. The studies had to be peer-reviewed and in English. Editorials, case series, letters, conference proceedings, reviews, and inaccessible papers were also excluded. The main inclusion and exclusion criteria for the screening process are shown in The two reviewers, a medical doctor (C.M.O.) and a medical student (J.S.), independently screened all records based on titles and abstracts, followed by a full-text review of the potentially relevant papers for final inclusion. Any conflicts during the screening process were resolved by consulting a 3rd and 4th reviewer (K.S. and J.F.C.).Three reviewers  extracted data from the included articles. The extracted study characteristics composed of the following: The number of patients, size of training and test data set, population demographics, National Institutes of Health Stroke Scale (NIHSS) score upon admission, time from onset to MRI, and the percentage of the dataset with an onset to MRI within 4.5 h from symptom onset. In addition, scanner characteristics, the type of scanner, and the used artificial intelligence methods were extracted. Performance data for the algorithms and used comparator was extracted and composed of; Area under the curve (AUC), Sensitivity (Sens), Specificity (Spec), Accuracy (Acc), Precision or positive predictive value (PPV), and negative predictive value (NPV).If multiple performance data were mentioned for different algorithms, only the performance data from the best algorithm was selected unless otherwise stated.The two reviewers used the questionnaire Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool to perform the quality assessment of the included studies . The QUAThe literature search resulted in 3273 articles. Duplicates and conference abstracts accounted for 1595. The total number of titles and abstracts to screen was then 1678. Eighteen articles were selected relevant for full-text assessment. In five articles, the AI algorithm did not perform a DWI-FLAIR mismatch assessment. One was not in English. Two had outcomes not relevant for this study, i.e., a focus on prediction accuracy for upper extremity motor outcomes 90 days post-stroke and a focus on detecting FLAIR and DWI lesions independently and not as a mismatch ,21. One All five included studies were retrospective. They used image data from different hospitals or university databases from the period between 2011 and 2021. Study characteristics and scanner specifications are summarized in The study population sizes varied from 268 to 587 patients diagnosed with stroke. The study conducted by Polson et al. had two study populations, one internal and one external . The extThe median population age covering all five studies ranged from 63 to 70 years. The maximal difference in NIHSS scores upon admission between training and test populations in the included articles was minor. Polson et al. and Zhang et al. had a higher NIHSS score upon admission in the training population compared to the test population ,24. Lee Zhu et al. had a very high average time from onset to MRI of 10.3 h . The medFour out of five studies used stroke neuroradiologists or neurologists for mismatch assessment as the comparator for their AI algorithm ,23,24,25Lee et al., Jiang et al., and Zhu et al. used a DL algorithm in conjunction with conventional machine learning (ML) algorithms in a two-step model for the assessment of TSS ,25,26. TAfter the segmentation, all three studies used different ML techniques for the TSS classification. All three studies tested and compared three to seven various ML models to find the best-performing ML model. In Lee et al. and Jiang et al., the best-performing ML models were Random Forest and a support vector machine with a radial kernel , respectively ,26. Zhu For this review, the performance measures for the AI models were registered, and missing values were calculated from the existing data when possible. The best performance metrics and preferentially from out-of-distribution external validation datasets are the performance results for the TSS classification reported in The area under the curve (AUC) was reported in all five studies. It ranged from 0.74 to 0.90 for the AI models. The AUC was not reported for the comparators, as these assessments were dichotomous. The sensitivity, also known as the true positive rate, and the specificity, also known as the true negative rate, were reported in all five studies. It ranged from 0.70 to 0.82 and from 0.74 to 0.84, respectively. Evaluation of the best performances showed that the specificity was a little higher than the sensitivity in all five studies. Results are shown in For Lee et al., Jiang et al., Polson et al., and Zhu et al., the positive predictive value (PPV) and negative predictive value (NPV) were reported or calculated, ranging from 0.74 to 0.91 and 0.65 to 0.80, respectively. In the same four studies, the accuracy ranged from 0.73 to 0.83 ,24,25,26Performance measures for the comparators were obtained in all studies except for Jiang et al. The sensitivity and specificity ranged from 0.49 to 0.82 and 0.59 to 0.91, respectively. The accuracy ranged from 0.61 to 0.74 ,23,24,25These results suggest that AI sensitivity, NPV, and accuracy in the determination of TSS were better than for the radiologists’ but the AI specificity and PPV were comparable to that of the radiologists’ in a binary classification of TSS ±4.5 h. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was applied to all included studies of this review. The results of the risk of bias/concern for applicability analysis are presented in Cohort sizes were reasonably large in all five included studies and comparable to the cohort sizes in studies included in another systematic review with a focus on the potential of automated segmentation of stroke lesions in MRI images . Sampled study cohorts, description of inclusion and exclusion criteria, and processing of the images for optimization of the AI algorithm performances were assessed.The studies description of the comparator process and methods used for the segmentations also affected the evaluation.None of the studies were assessed with a low risk of bias and concern for applicability in all domains. No meta-analysis was conducted due to the small number of included articles.The currently existing AI-based DWI/FLAIR mismatch assessment uses FLAIR lesion visibility as a surrogate marker of TSS with a binary decision, i.e., ±4.5 h since stroke onset. Because of the non-trivial correlation between FLAIR changes and TSS, it is not recommended that FLAIR lesion visibility is used for the determination of time since stroke onset within the first 4.5 h [Ebinger et al. have shown that AI algorithms can register several features in the images, such as size, homogeneity, gradient, and intensity . This crIn the five included studies, we found that the used AI algorithms performed equivalent to or even better than neuroradiologists and neurologists in the binary classification of time since onset ±4.5 h from DWI/FLAIR images. The automated registration of several features could explain these results.Thomalla et al. and Ebinger et al. have found that even though there is a relationship between the time clock and tissue progression, there is no significant correlation between relative signal intensity on FLAIR imaging and TSS ,8. The FThe sensitivities and specificities reported for the AI algorithms were more consistent across the different studies than for the neuroradiologists and neurologists, with the exception of one study, showing the opposite. This supports the idea that automated segmentations could provide a standardized assessment of lesions and potentially decrease inter-rater variability. In a study on fully automatic acute ischemic lesion segmentation on DWI, they argue that domain experts could save time and effort if the manual segmentations were based on automated segmentation .In a study from 2022 on automated segmentation, they tested whether the combination of T2w-FLAIR and DWI sequences could lead to a better segmentation of stroke lesions than single modal modality approaches, i.e., only DWI .Similarly, in a study on infarct segmentation on DWI, ADC, and low b-value-weighted images from 2019, they found that the combination of sequences led to an improved segmentation of lesions compared to only diffusion maps and could produce results comparable with manual lesions segmented by domain experts . These studies suggest that automated segmentations could assist neuroradiologists in the assessment of infarction lesions and maybe neurologists in the treatment decision of patients with acute ischemic stroke.To conduct the FLAIR segmentation, three of the included articles, Zhu et al., Jiang et al., and Lee et al., used the DWI segmentation data and features, such as location, to improve their FLAIR segmentation ,25,26. LOther studies on FLAIR segmentation have highlighted the difficulties in segmenting FLAIR lesions in stroke and mixed pathology cases, yielding mediocre DICE values ranging from 0.58 to 0.79 ,31. ThisTo improve FLAIR segmentation, the intensity correction by an algorithm, the use of an automated filter to remove inhomogeneity, and the negative influence of high-intensity regions were tested in a study from 2019. The FLAIR lesion segmentation method showed promising results in a follow-up FLAIR imaging dataset on lesion volume estimations .In a study on FLAIR visibility and 90-day outcome prediction but no DWI/FLAIR mismatch assessment, they found that patients with an early visible FLAIR lesion within 4.5 h and who were given intravenous (IV) thrombolysis had a poorer 90-day outcome than those who had a later development of visible FLAIR lesions .Even though FLAIR lesion visibility cannot serve as a surrogate marker for TSS, FLAIR changes could resemble a biological marker of stroke progression/tissue salvage-ability, though the influence of the blood-brain barrier (BBB) permeability and vascular recanalization in subacute ischemic stroke is still questioned ,8,9,33.The risks of bias and concern for applicability seen in the four domains of the QUADAS-2 assessment were mainly due to a limited description of the human comparator process, convenience sampled cohorts, insufficient description of inclusion and exclusion criteria, and/or extensive exclusion of images which could limit the use in a daily clinical setting.Only two of the included studies transparently specified their stroke patient selection with inclusion and exclusion criteria. The exclusion criteria were small or unsegmented lesions, DWI lesions on sites with extensive leucoaraiosis on FLAIR, and heavy artifacts on DWI or FLAIR sequences ,26. ThesThe split size of the training set and test set in all five studies followed the general trend and ranged from 70/30 to 90/10. Consideration of sample size is important as deep-learning-based algorithms have higher recognition accuracy on larger sample data sets .The search process was performed according to PRISMA guidelines. Given the speed and interest to develop AI algorithms for neuroradiology purposes, there is a probability that newer and relevant studies are not included in this systematic review when published.One of the inclusion criteria was to only include algorithms on DWI and FLAIR sequences, based on the present process to determine treatment eligibility for patients with unknown symptom onset. In the preliminary search process, we found a study on stroke onset determination with the use of perfusion-weighted imaging . This shThe continued advancement of radiology AI methods offers new opportunities to improve the detection, diagnosis, and prediction of patient outcomes. Still, even if the results of a study seem promising, a method can sometimes perform poorer than expected when tested on an external dataset with a larger, consecutive cohort. External validation is, therefore, important, and a validation and evaluation framework could be necessary .For remote hospitals and in countries where the MRI scanner density is low, the use of an approved AI algorithm could assist regarding diagnosis, decrease unnecessary transportation to other clinics, and improve treatment decisions and, thereby, patient outcomes .Time is of the essence in the treatment of acute ischemic stroke. A rapid and efficient workflow is, therefore, essential. The exclusion of hemorrhage, stroke mimics, and the estimation of stroke volume on imaging is important to identify patients eligible for rtPA-treatment . The dooAnother way to improve MRI workflow was tested in a study on a synthetic FLAIR sequence automatically developed from DWI sequences. The synthetic FLAIR sequence was tested against the usual FLAIR sequence in DWI/FLAIR mismatch assessment by neuroradiologists. It showed comparable diagnostic performances .A number of AI algorithms for outcome prediction of stroke patients based on MRI have used modified Rankin scales as a reference to predict stroke outcomes and were able to predict the likelihood of hemorrhagic transformation ,43,44,45In a Meta-analysis on examination of delayed thrombolysis among patients selected according to mismatch criteria, they found that patients who received delayed thrombolysis, i.e., after 3 h, was associated with increased reperfusion/recanalization, but the relation between delayed thrombolysis in mismatch patients and the post-treatment outcome was not established . SeveralIn WAKE-UP, a randomized, placebo-controlled trial of thrombolysis in stroke with an unknown time of symptom onset, they used MRI criteria to determine patients’ eligibility for thrombolysis. In a report from the WAKE–UP study on systematic image interpretation, the physicians went through systematic image training before rating the images. The results showed a high level of consistency amongst the raters in the assessment of the different imaging criteria, e.g., the presence of an acute ischemic lesion and the extent of infarction . The DWIAdding stroke treatment and patient-specific data, such as the location of lesions, symptoms, and comorbidities, will improve the interpretation of the relationship between imaging features and functional outcomes. A reliable algorithm to serve as assistance to determine patient eligibility for rtPA treatment could prove to provide a more standardized evaluation, be less time-consuming, and overall improve the treatment of patients admitted to the hospital under suspicion of stroke.However, the creation of an AI method that will lower the DTN time, be accurate, and also bring predictive knowledge of the influenced tissues still comprise a challenge .In all of the reviewed studies and in the daily clinical practice, some patients are misclassified in regard to the cut-off time of 4.5 h. In Polson et al., the DL model identified 29/37 (78%), and the radiologists identified 23/37 (62%) of the evaluation set patients with known onset time within 4.5 h . The AI To be able to improve the analysis of the tissue status and stroke progression and identify those cases currently misclassified, further research is required. The development of AI methods and awareness in daily clinical practice could be amplified through new research in the field with a status of the currently existing AI methods. R. Karthik and R. Menaka et al. propose the development of an end-to-end automatic framework to identify both stroke lesions and prediction of the outcome of the influenced tissues to increase awareness of existing computer-aided detection frameworks .This review elucidates the existing AI methods on DWI/FLAIR mismatch assessment, creates awareness, and focuses on the next potential steps towards an improvement in the current standard analysis and treatment selection for rtPA.In a binary classification of ±4.5 h, the machine learning and deep learning algorithms in this review performed equivalent to or even better than domain experts, but surveyed AI methods are most likely not able to determine TSS only using DWI and FLAIR sequences. Furthermore, the relation between time since stroke onset and increasing visibility of FLAIR hyperintensity lesions is not recommended for the determination of TSS within the first 4.5 h. An AI DWI/FLAIR mismatch assessment focused on treatment eligibility and outcome prediction, adding patient-specific data, could pave the road for improving stroke treatments.


Diet modification may contribute to the prevention of age-related cognitive decline. The association between dairy product consumption and cognitive function in older people remains unknown. We investigated whether cheese intake is associated with lower cognitive function (LCF) in community-dwelling older adults. This cross-sectional study included 1503 adults aged over 65 years. The analyzed data were obtained through face-to-face interviews and functional ability measurement. Cognitive function was assessed using the mini-mental state examination (MMSE), and a score ≤23 was defined as LCF. The prevalence of LCF was 4.6%, and this group had smaller calf circumference, slower usual walking speed, and a more frequent history of anemia than subjects with MMSE scores >23. After adjusting for confounding factors, logistic regression analysis revealed cheese intake (odds ratio (OR) = 0.404, 95% confidence interval (CI) = 0.198–0.824), age , usual walking speed  and calf circumference  to be significant factors associated with LCF. Although the present study was an analysis of cross-sectional data of Japanese community-dwelling older adults, the results suggest that cheese intake is inversely associated with LCF. Dementia prevention and treatment are major strategies for achieving good health and longevity. The proportion of the older population with dementia, and the percentage of people requiring nursing care due to dementia, continue to increase; as such, dementia prevention has become an urgent issue ,2.The effective prevention of dementia requires preserving as much cognitive function as possible and preventing cognitive decline as early as possible. A variety of information obtained from longitudinal and cross-sectional research provides insight into the factors that affect cognitive function. A synopsis of the results of previous studies has indicated that physical activity, consumption of a Mediterranean diet, dairy intake in midlife, and moderate consumption of wine are effective in delaying or preventing dementia and cognitive decline ,4,5,6. OIn particular, there is increased interest in the relationship between food intake and cognitive function. Previous studies have shown that a dietary pattern characterized by a high intake of soybean products, vegetables, seaweed, milk, and dairy products, together with a low intake of grain products, is associated with reduced risk of developing dementia ; moreoveThe purpose of this study was to elucidate the relationship between cheese intake and cognitive function, evaluated based on MMSE scores in community-dwelling older people, using cross-sectional data.This study was conducted as part of a comprehensive geriatric survey that aimed to identify factors related to geriatric syndromes, such as cognitive impairment, falls, urinary incontinence, frailty, and sarcopenia. Participants were recruited from the participant pool of a comprehensive geriatric survey conducted by our team once per year or every two years. Participants were included in this study if (i) they were community-dwelling older adults aged 65–99 years and (ii) they had provided written informed consent. Participants were excluded from the analyses if (i) they did not consent to their data being used; (ii) they had an unstable medical condition, a severe disease, or were not permitted to participate in this study by a study physician, or (iii) had participated in other cohort studies.This study analyzed the cross-sectional data of two different cohorts. Cohort 1 consisted of 759 people from a previous 2017 cohort (see below for details) who had participated in the follow-up survey in 2019. The data of this cohort from the 2019 survey were used as the cross-sectional data for this study. Cohort 2 consisted of 757 people who had participated in a new cohort survey in 2019. The total number of subjects was 1516. The recruitment processes for the two cohorts are described below.Cohort 1: This cross-sectional study utilized data from a cohort of subjects who had participated in a comprehensive health examination, “The Otassha Study 2017 Cohort”, conducted at the Tokyo Metropolitan Institute of Gerontology [n = 6788) in 2017. After excluding 422 women who had participated in another cohort study, we sent invitation letters to 6366 candidates. A total of 1365 women participated in the health examination in 2017. Of these subjects, 759 women (55.6%) completed the follow-up examination in 2019 .Cohort 2: This study recruited volunteer participants from 18 neighborhoods near the Tokyo Metropolitan Institute of Gerontology, Itabashi, Tokyo, Japan. In 2019, the names and addresses of all individuals aged 75–85 years and registered in the Basic Resident Registry for this area were extracted (n = 4233). After excluding 88 participants who had been enrolled in other cohorts, invitations were sent to 4145 candidates. In total, 757 individuals (275 men and 482 women) participated in this study (Baseline survey in 2019 and once-per-year follow-up survey for a period of 10 years).Out of 1516 subjects in two cohorts, 1504 subjects with no missing values for cheese intake and MMSE were analyzed.The study protocol was approved by the Clinical Research Ethics Committee of the Tokyo Metropolitan Institute of Gerontology (TMIG) (ID R2-25 and R2-18). The procedures were fully explained to all participants, and written informed consent was obtained.Interview. Face-to-face interviews were conducted to assess the participants’ history of falls, urinary incontinence, and frequencies of food intake , and chronic conditions, such as heart disease, hyperlipidemia, dyslipidemia, diabetes, osteoporosis, osteoarthritis (OA), and anemia. The dietary variety score was calculated using the intake frequencies of 9 food categories . For each participant, a score of 1 was given for each food category if it was eaten every day, and a score of 0 was given if it was eaten once every 2 days, once or twice a week, or not at all; the total score (0 to 9) was then calculated [lculated .Anthropometric and physical function measures. Measurements of height and weight were converted to body mass index (BMI). Muscle mass, body fat mass, and percentage of body fat were determined using bioelectrical impedance analysis (BIA) . Blood pressure was measured using automatic blood pressure monitors  prior to the measurement of physical function. The calf circumference of the non-dominant leg was measured with the subject in a seated position, with the knee and ankle at right angles and the foot resting on the floor. Measurements were taken at the level of the widest circumference, and subcutaneous tissue was not compressed. The grip strength of the dominant hand was measured twice using a hand-held Smedley-type dynamometer, and the highest value of the two trials was used for analysis. Usual walking speed was measured twice on a flat walking path of 11 m with markers placed at 3 m and 8 m. A stopwatch was used to measure the time taken to walk 5 m between the markers, and the fastest time of the two trials was recorded. The use of assistive walking devices was allowed in the measurement of walking speed if the participant expressed concern about walking without a device or if the investigators suspected a risk of falling.Blood indicators. Non-fasting blood samples were collected in a seated position at baseline. Analyses were carried out centrally in one laboratory . Lipid levels  cholesterol, and triglycerides) were determined. Serum albumin was measured via the bromocresol green method, glycated hemoglobin (HbA1c) via latex agglutination assay, and serum creatinine via an enzymatic assay.Cognitive function. Global cognitive status was assessed using MMSE. Lower cognitive function was operationally defined as an MMSE score ≤23 [core ≤23 .Descriptive statistics are expressed as mean and standard deviation or as a number (%). Participants were divided into a cheese intake group (N = 1230) and a non-cheese intake group (N = 287).t-test for continuous variables and using a chi-square test for categorical variables. Multiple logistic regression analyses were used to analyze the factors associated with lower cognitive function. Model I included only the participants’ cheese intake. Model II was Model I adjusted for age, physical function, and physique factors. Model III was Model II adjusted for medical history, blood pressure, number of teeth, blood variables, urinary incontinence, milk intake, and dietary variety score. Significant and nonsignificant variables were entered into the multiple logistic regression models to obtain the odds ratio (OR) and 95% confidence interval (CI).The subjects were also classified into a group with MMSE scores ≤ 23 and a group with MMSE scores > 23. The two groups were compared using Student’s p-values of less than 0.05 were considered statistically significant. All analyses were performed using SPSS software, Windows version 25.0 .The As shown in In the present study, we conducted an analysis of cross-sectional data on the relationship between cheese intake and cognitive function in community-dwelling older people, and the results suggest that cheese intake is inversely associated with the risk of lower cognitive function, defined as an MMSE score of 23 or below, even after adjusting for multiple confounding factors. The significance of the results of this research will be discussed from various points of view.p = 0.03) but not vascular dementia (p = 0.14), even after adjusting for many potential confounders including age, sex, years of education, stroke, hypertension, diabetes, total cholesterol level, BMI, smoking, and exercise [p = 0.003) and a multiple logistic regression analysis  after adjusting for socio-demographic factors and other dietary factors, leading to the conclusion that increased cheese intake was associated with decreased cognitive impairment (p = 0.0034) [Many studies on the relationship between food intake and the development of cognitive decline and dementia have been reported in multiple research fields. Among 1081 people aged 60 and above without dementia at baseline, who were assessed for 17 years 1988–2005) in the Hisayama study, 303 people subsequently developed dementia (including 166 patients with Alzheimer’s dementia and 98 patients with vascular dementia). When the relationship between milk and dairy product intake and dementia was analyzed in this cohort, it was found that milk and dairy product intake significantly reduced the risk of developing Alzheimer’s dementia (exercise . Anotherexercise . However88–2005 iexercise . Another 0.0034) . The out 0.0034) . A large 0.0034) .To explore the relationship between cheese intake and cognitive function in detail, we conducted multiple regression analyses using three models. The results shown in Although the present study shows an inverse association of cheese intake with lower cognitive function, it is not possible to elucidate the cause of this association from the results obtained from the cross-sectional data. However, a previous study reported an association between higher dietary diversity and better cognitive function . In the In this study, the factors, other than cheese intake, that were associated with lower cognitive function were age, usual walking speed, and calf circumference. Many previous studies have reported that age is associated with cognitive decline and dementia, and this finding was validated in this study. The association of usual walking speed with cognitive decline and lower cognitive function has been demonstrated and verified in longitudinal and cross-sectional studies ,30, and This study has several limitations. First, the present finding of an inverse relationship between cheese intake and lower cognitive function was obtained from an analysis of cross-sectional data. Whether cheese intake contributes to a reduced risk of cognitive decline cannot be elucidated from the present result, and should be further investigated in a longitudinal study. Second, information on the status of cheese intake was based on self-reporting during the interview, and was not quantified by an objective method. Third, although many cut-off values have been proposed for defining lower cognitive function, the cut-off value used in the present study was operationally determined as an MMSE score of 23 or below, according to that proposed by O’Bryant et al. . FinallyAlthough the present study was an analysis of cross-sectional data of Japanese community-dwelling older adults, the results suggest that cheese intake is inversely associated with lower cognitive function even after adjusting for multiple confounding factors. In the future, a large-scale longitudinal analysis is needed to elucidate the causal relationship.


The cognitive-interpersonal model of anorexia nervosa (AN) posits that cognitive and interpersonal traits contribute to the development and maintenance of AN. We investigated cognitive and interpersonal factors put forward by the model in a sample of 145 adolescent inpatients with AN using network analysis. Our main outcomes included core eating disorder symptoms, cognitive style, socio-affective factors, and mood symptoms. We estimated a cross-sectional network using graphical LASSO. Core and bridge symptoms were identified using strength centrality. Goldbricker was used to reduce topological overlap. The node with the highest strength centrality was Concern over Mistakes, followed by Eating Preoccupation, Social Fear, and Overvaluation of Weight and Shape. The nodes with the highest bridge strength were Concern over Mistakes, Doubt about Actions, Overvaluation of Weight and Shape, and Depression. Notably, both performance on a cognitive flexibility task and BMI were not connected to any other nodes and were subsequently removed from the final network. We provide partial support for the cognitive-interpersonal model while also supporting certain premises put forward by the transdiagnostic cognitive-behavioral model. The high centrality of Concern over Mistakes and Social Fear supports the theory that both cognitive and interpersonal difficulties contribute to AN, particularly in adolescence. Anorexia nervosa (AN) is a serious psychiatric disorder characterized by a fear of weight gain, distorted body image, and extreme weight-control behaviors . AN is aThe cognitive-interpersonal model of AN, proposed in the mid-2000s, posits that obsessive-compulsive (OC) and avoidant traits are predisposing factors to the development of AN that also maintain the disorder by fostering pro-anorexia beliefs and behaviors ,6. For eA theory at the forefront of eating disorder (ED) research is the transdiagnostic cognitive-behavioral model, proposed several years before the cognitive-interpersonal model . In contObsessive-compulsive disorders and EDs are highly comorbid, particularly in AN . CognitiPerfectionism is a personality trait that reflects the tendency to have very high personal standards, be overly critical of oneself when these standards are not achieved, have an extreme preoccupation with mistakes, doubt when considering personal achievements, and have an excessive focus on organization and precision . Higher Cognitive flexibility is the ability to adaptively shift between mental processes in response to environmental demands and make subsequent appropriate behavioral adjustments. Poor cognitive flexibility tends to go hand in hand with difficulty balancing detail-oriented and global information processes  . Lack ofDifficulty with interpersonal relationships is a well-established symptom of AN and is thought to be a maintaining factor of the disorder . AccordEvidence suggests that interpersonal difficulties in AN are linked to alexithymia ,21, defiIn addition, individuals with AN report high rates of psychiatric comorbidity. More than 50% of adolescents with AN have at least one psychiatric comorbidity, with depression and anxiety being the most commonly reported ,26. ThesNetwork analysis (NA) has been increasingly applied to the study of EDs to model the connections between symptoms and overall network structure ,29,30. NNA has been used in several studies to investigate the transdiagnostic cognitive-behavioral model of eating disorders ,34,35,36Although both the cognitive-behavioral and cognitive-interpersonal models posit that people with EDs experience a wide range of symptoms, including perfectionism and interpersonal difficulties, the majority of network models examine only core ED symptoms. Networks are highly sensitive to included variables, and the inclusion of general psychopathology symptoms in addition to core ED symptoms may affect the centrality of the results ,39. MoreMost network studies examine mixed-age and transdiagnostic samples, with a few exceptions. One NA comparing ED symptoms in adolescent and adult patients with AN and BN found similarities in network structure across ages and diagnoses, showing that drive for thinness, ineffectiveness, perfectionism, and emotion dysregulation are potentially important treatment targets . AnotherWe aim to investigate cognitive and interpersonal factors posited by the cognitive-interpersonal model using NA in a sample of adolescent inpatients with AN. Our definition of adolescence in the current analysis is in line with recent evidence indicating that biological and social adolescent growth correspond more closely with the period between 10 and 24 years old, rather than 10–19 years old . We hypoThis study is part of a longitudinal multicentred trial called EVHAN . A subset of 145 inpatients with AN was included in the present analysis. Inclusion criteria for the current study were as follows: being hospitalized for AN due to a high risk to life, either physical or psychological , having an admission BMI < 15 and/or sudden and rapid weight loss, consent for participation in the study, and affiliation with the French Social Security health coverage system. Exclusion criteria were refusal to participate, insufficient command of the French language, and presence of a potentially confounding medical pathology . For the present analysis, two additional criteria were added. First, participants had to be aged between 13 and 24 years, as patients younger than 13 years were not assessed with the same test battery. Next, participants must have completed the Test of Attentional Performance (TAP)—Flexibility subtest.Participants were recruited from 11 inpatient eating disorder treatment facilities in France  between April 2009 and May 2011. This research was approved by the Comité de Protection des Personnes Ile-de-France III on 25 March 2008.Data were collected within two weeks of the patient’s hospital admission for AN, including sociodemographic data, age of AN onset, self-report questionnaires, neuropsychological evaluation, and nutritional status. Patients were diagnosed with current AN based on DSM-IV-TR criteria. The BMI criterion was <10th percentile up to 17 years of age and BMI < 17.5 for 17 years of age and above. For all self-report measures, subscale or total scores were included in the network. Including single items would have led to a large number of nodes and thus reduced the accuracy of our network estimation given the present sample size . More inEating Disorder Examination Questionnaire (EDEQ). Eating disorder symptoms were evaluated using a French translation of the Eating Disorder Examination Questionnaire EDEQ; ,49), a 2, a 249])Frost Multidimensional Perfectionism Scale (FMPS). The FMPS  (French Bermond–Vorst Alexithymia Questionnaire (BVAQ). The BVAQ measures alexithymia, defined as a reduction or incapacity to experience, verbalize, fantasize, and/or think about one’s emotions FMPS Parental Expectations and FMPS Parental Criticism: both dimensions measure the patient’s perceived parental influence on maladaptive perfectionismThe goldbricker function revealed seven “bad pairs” see . After cn = 2500) were used to estimate the stability of centrality indices. The correlation stability (CS) coefficient measures the stability of centrality indices by indicating the percentage of our sample that can be dropped to maintain, with a 95% confidence interval, a correlation value equal to or above r = 0.7. The CS coefficient is considered acceptable when above 0.25 and good from 0.5 [Network Stability. To estimate edge and centrality stability, we used the bootnet package version 1.5 . Non-parfrom 0.5 .Network estimation, centrality, and predictability. A regularized partial correlation network was estimated for the entire sample, resulting in an undirected, weighted network. Each edge represents a conditional dependence between two nodes, and an absent edge signifies conditional independence. Edges represent partial correlations. The thickness of the edge represents the strength of the correlation between variables. The network was visualized with the qgraph version 1.9.2 , which uTo inspect centrality measures, we used centralityPlot and centralityTable functions, including the strength  and expected influence , as other centrality indices such as betweenness and closeness have been shown to be less reliable in psychological networks . As we amgm version 1.2-13. Predictability refers to how well each node is explained by all other nodes in the network by computing each node’s R2 [Network predictability was assessed with R-package ode’s R2 .Bridge nodes. Nodes that connect different clusters of symptoms in psychopathological networks are called bridge nodes. For example, bridge nodes could connect eating disorder symptoms (cluster 1) to depressive symptoms (cluster 2). The bridge function in the package network tools was used to calculate bridge strength  and bridge EI .n = 145  inpatients diagnosed with AN. The percentage of male participants is consistent with other network ED studies  participants with AN binge purge subtype (AN-BP) and n = 67 (46.2%) with AN-R. Sixteen percent (n = 22) of the sample had a premenarchal onset of AN. The average age at hospital admission was 18.2 (SD = 3.1) years old, and n = 78 (53.8%) of the sample was under the age of 18. The average age of AN onset was 15.3 (SD = 3.2) years old, and the average duration of illness was 2.9 (SD = 2.5) years.Descriptive statistics are presented in s  and genr = 0.72), Restraint and Eating (part r = 0.67), and Fear and Avoidance (part r = 0.63). The strongest connections to the central node Concern over Mistakes were Personal Standards (part r = 0.67), Doubt about Actions (part r = 0.56), Overvaluation of Weight and Shape (part r = 0.5), and Parental Criticism and Expectations (part r = 0.49).Bootstrapped difference tests showed that Overvaluation of Weight and Shape had significantly higher strength centrality than Restraint. There was no significant difference in strength centrality between Overvaluation of Weight and Shape and Eating.In our post hoc exploratory analysis of AN subtypes, the CS coefficient was <0.25. Therefore, the results of this analysis could not be interpreted and are not included here.The CS coefficient for bridge strength was 0.44, indicating that centrality indices could be interpreted. As all correlations were positive, only bridge strength is discussed. Bridge strength is shown in r = 0.4), and from cluster three, the strongest pathway was between Concern over Mistakes and Overvaluation of Weight and Shape (part r = 0.5).Regarding bridge pathways, the strongest pathway to core ED symptoms from cluster two was between Depression and Eating , interpersonal , and mood  symptoms. Concern over Mistakes, Preoccupation with Eating, Social Fear, and Overvaluation of Weight and Shape were the most central nodes.Eating and Overvaluation of Weight and Shape emerged as the most central ED symptoms. The high centrality of Eating Preoccupation is in line with the cognitive-interpersonal model, which posits that AN patients experience constant thoughts or images related to food and eating as a result of dietary restriction. Our results are also in line with the majority of network studies of both AN and transdiagnostic samples, which indicate that Overvaluation of Weight and Shape is a central ED symptom in both adults and adolescents ,36,39,46Evidence from previous NAs do suggest that Restraint plays an important role in EDs. In a sample of adult females with ED , EDEQ Restraint had the highest bridge strength between ED symptoms and nodes related to social cognition . AnotherOur finding that Restraint was not a central symptom in an inpatient sample with AN is surprising. At the same time, just because a node is not central does not indicate that it is less clinically important. It is possible that Restraint indirectly contributes to AN by leading to Eating Preoccupation, which subsequently develops into a maladaptive emotion regulation strategy. It is also possible that Restraint connects to other variables not included in our network, such as difficulties in emotion regulation. Future research should investigate the role of Restraint in AN and its potential association with Eating Preoccupation and broader psychopathological variables, such as difficulties in emotion regulation.Concern over Mistakes was the most central symptom in our network. Further, Doubt about Actions was also highly central, in line with evidence that perfectionism plays an important role in AN . One NA Fear related to social anxiety (SA) was also highly central. It has been well established that EDs are highly comorbid with SA, which is associated with more severe ED psychopathology . FurtherInterestingly, BMI was not at all connected to the network and was therefore removed from the final network and only included descriptively. One possible explanation is that in this sample of inpatients with severe AN, weight status was relatively homogeneous. At the same time, our finding that BMI is not central to AN is in line with previous literature. One NA of a transdiagnostic mixed adult and adolescent sample found that for all diagnostic subgroups , BMI had the lowest centrality strength index . In a saWhen performing bridge analysis, three clusters were detected: (1) core ED symptoms, (2) negative affectivity symptoms, and (3) cognitive and socio-affective symptoms. Of note, cognitive and socio-affective symptoms were grouped together in the same cluster. Upon visual inspection of the network, Family Dynamic and Parental Expectations and Concerns connected more strongly with Alexithymia, while perfectionism outcomes (excluding Doubt about Actions) connected more strongly with each other, mirroring the cognitive and interpersonal factors in the model.The strongest bridge connection from the cognitive and socio-affective cluster to the ED cluster was between Concern over Mistakes and Overvaluation of Weight and Shape. For individuals with EDs, success in achieving weight and shape goals can define most, if not all, of their self-worth. Therefore, the strong association of Overvaluation of Weight and Shape with Concern over Mistakes may reflect the fear of “failing” in the adherence to strict dietary rules to maintain or achieve a certain weight or shape goal. As posited in the cognitive-interpersonal model, people with AN have extremely perfectionistic standards in regard to food consumption and adhere to self-imposed rules rigidly to avoid failure and strong negative emotion ,6. HowevThe node with the highest bridge centrality connecting the cognitive and socio-affective cluster to negative affectivity was Doubt about Actions. Bridge nodes are suspected to be key nodes that spread activation from one disorder to another . This reDepression was a bridge node between negative affectivity and ED symptoms. Specifically, Depression connected most strongly to Preoccupation with Eating. This connection is unsurprising, considering the association between rumination and depression, a link that has also been found in individuals with AN . While nOur study is the first study to test the cognitive-interpersonal model of AN using NA. This study contributes to the growing body of work using NA to not only examine core ED symptoms, but also their complex relationships with cognitive , interpersonal , and mood symptoms  e.g., ,39,41). ,41. 39,4n = 9) reported a maximum BMI in the overweight/obese range, indicating that our sample may have included participants with atypical AN, who may have different cognitive and clinical profiles compared to “typical” AN. Unfortunately, we lacked statistical power and the data on the medical history of body mass dynamics to analyze this issue separately. At the same time, as this group of patients was small, it is unlikely that they significantly impacted our overall results.This study is not without limitations. First, as data are cross-sectional, insight into the temporal relationship between symptoms cannot be determined. Second, as previously mentioned, one hospital center failed to collect data for the LSAS Avoidance subscale, potentially affecting results. Further, we used only self-report data, which may not be fully representative; for example, when describing family dynamics, it is the patient’s perception that is measured and not objective dysfunction. We were also unable to conduct an analysis comparing AN subtypes to test the hypothesis posited by the cognitive-interpersonal model that AN-R may have a distinct phenotype. In addition, as our study investigates symptoms in adolescent inpatients, we are not able to draw generalized conclusions regarding anorexia in young children or adults, nor in less severe or subclinical cases. Finally, a small proportion of our participants  play an important role in AN. Further, in line with previous literature, Overvaluation of Weight and Shape was also central to the network. The high centrality of Concern over Mistakes and social anxiety supports the theory that both cognitive and interpersonal difficulties contribute to AN, particularly in adolescence. Our results suggest a potential compatibility between the cognitive-behavioral theory and the cognitive-interpersonal model.


Hynobius salamander in Fujian province, China, we worked on understanding its relationship with other species and ultimately describing it. Please welcome the Fujian Bamboo Salamander to science, a segregated species based on genetics and morphology. While it is related to other southern mainland Chinese species, it may have diverged earlier and share some similarities with morphology and behavior with the Anji salamander. The Fujian Bamboo Salamander is special as it produces vocalization when under threat. The species is, however, incredibly rare, fitting the definition of Critically Endangered in the IUCN Red List of Threatened Species.What does not have a name is difficult to understand and protect. Upon the unexpected discovery of an Hynobius salamanders in southern China. Here, we unexpectedly sampled Hynobius individuals in Fujian province, China, and then worked on determining their taxonomic status. We describe Hynobius bambusicolus sp. nov. based on molecular and morphological data. The lineage is deeply divergent and clusters with the other southern Chinese Hynobius species based on the concatenated mtDNA gene fragments (>1500 bp), being the sister group to H. amjiensis based on the COI gene fragment, despite their geographic distance. In terms of morphology, the species can be identified through discrete characters enabling identification in the field by eye, an unusual convenience in Hynobius species. In addition, we noted some interesting life history traits in the species, such as vocalization and cannibalism. The species is likely to be incredibly rare, over a massively restricted distribution, fitting the definition of Critically Endangered following several lines of criteria and categories of the IUCN Red List of Threatened Species.It is important to describe lineages before they go extinct, as we can only protect what we know. This is especially important in the case of microendemic species likely to be relict populations, such as  Similarly, species that have not yet been described are going extinct before being documented [Anthropogenisation of landscapes, and other human activities, have brought the world to the sixth great mass extinction ,2. The tcumented  and withcumented  (as seencumented ). Biodivcumented , and concumented . Howevercumented , and we www.iucnredlist.org; accessed on 3 May 2023), amphibians are the most threatened animal class, with habitat loss being one of the principal drivers of species decline [With 41% of species listened as threatened by the IUCN Red List of Species . We do not provide precise GPS coordinates here to protect the species from harvesting for the pet trade. The two individuals were found in a bamboo forest (Phyllostachys cf. edulis) about 1500 m above sea level. As ten days (24–26 January and 4–10 August) of fieldwork during adequate sampling seasons and times of day resulted in only two adult individuals, and the population size at this location, and likely for this species, is likely lower than 200 individuals, we followed the IUCN recommendation on ethical sampling [We found two individuals of the genus sampling . We did sampling .All applicable international, national, and/or institutional guidelines for the care and use of animals were strictly followed. All animal sample collection protocols complied with the current laws of the People’s Republic of China. All observations and experiments conducted in this study are in agreement with the ethical recommendations of the College of Biology and the Environment at Nanjing Forestry University . We did not collect adult individuals as voucher specimens, in line with the IUCN recommendation on research involving species at risk of extinction . Instead2), 0.1 unit/μL of Ex taq , and double distilled water added to make up the final volume. The PCR thermal profiles for each primer fragment are described in We extracted the total genomic DNA for all three samples using the Qiagen DNeasy Blood & Tissue kit  according to the manufacturer’s protocol. We then performed standard Polymerase Chain Reaction (PCR) amplifications for all 15 samples in 20 µL of total reaction per tube, containing 35 to 50 ng/μL of template DNA . The finHynobius individuals, and aligned them with their most homologous sequences retrieved from Genbank , (ii) protein-coding Cytochrome b (Cytb) gene , (iii) protein-coding cytochrome c oxidase subunit I (COI) gene , and (iv) concatenated 16S rRNA, Cytb and COI  inferred from the COI gene fragment from individuals distributed across continental East Asia. Furthermore, we selected the COI alignment dataset due to its adequacy in the number of taxa for robust analysis in comparison to the concatenated dataset. We assigned the sequences to their respective species group and generated the haplotypes using DNAsp v.6.12.03  [Next, we constructed a haplotype network to identify the inheritance pattern between the sequences of our candidate species and its homologous sequences. Due to the strong posterior probability for the candidate species in the terature , we used, Spain) . We then, Spain) , impleme, Spain) . Lastly,, Spain) .b, and COI using MEGA v.11.0.13  [Finally, to estimate the evolutionary divergence over sequence pairs between species, we analyzed 29 sequences of concatenated 16S rRNA, CytPA, USA) . We assiPA, USA) .Hynobius species. See For the morphological analyses, we measured the characters listed by Shen et al. , Lai andH. amjiensis (see phylogenetic results). However, due to (1) the rarity of H. amjiensis, for instance, Chen et al. [H. maoershanensis [Ideally, a species description should include a morphological comparison between the new species and the most closely related one. Here, it would be a two-by-two comparison between the species described in this paper and n et al.  found 16hanensis  and two hanensis ; howeverH. chinensis species complex, and ranging on the Chinese mainland, are H. amjiensis, H. guabangshanensis, H. chinensis, H. maoershanensis, and H. yiwuensis. We did not include individuals from northeast China, Taiwan Island, or the Japanese archipelago in the morphological comparisons as they were not closely related in the phylogenetic analyses. We then created a dichotomic key to enable the morphological identification of the species and the determination of discrete characters.The species within the In addition, with the development of “technoecology” , 3D prinH. chinensis species complex, we documented the development of the species before the eggs hatched and after hatching at 16, 69, 74, 77, and 87 days. We aimed to document development between stages 29 and 37 when embryos are in the egg sac; tadpoles between stages 40 and 49 when free swimming with balancers; between stages 50 and 59, when the larvae are not yet using their developing hind legs and once after stage 60 when the larvae use their hind legs [To ascertain that the development of the species does not deviate from that of the ind legs . The salWhen probed, the individual captured in January sometimes emitted a vocalization. We managed to record the individual only once with a linear PCM recorder  using the built-in microphone. The call was recorded at a sampling rate of 44.1 kHz with a 16-bit resolution. We analyzed the call properties following the method in Prasad et al.  using RaHynobius amjiensis  H. maoershanensis vs. H. guabangshanensis, mean = 0.021; (iii) H. chinensis vs. H. guabangshanensis, mean = 0.032; and (iv) H. maoershanensis vs. H. chinensis , we obtained 55 haplotypes (h) from 567 sites representing 16 species of Hynobiid salamanders distributed across East Asia, with a haplotype diversity (Hd) of 0.98 : [“partim): ” pp. 218HolotypeVoucher 22HyF006; sub-adult collected by Zhenqi Wang and Zhixin Qiu on 26 January 2022 in Quxi village, Liancheng county, People’s Republic of China . This salamander is named after a main character from Jin Yong’s swordsman fiction “The semi gods and semi devils” [The species was first found in Quxi village, Liancheng county, in the west of Fujian province in China. The name  devils” , with XuIdentity, diagnosis, and distributionHynobius larvae in shape and color and do not differ from other Hynobius species in the region in their development  under LSID: urn:lsid:zoobank.org:pub: 9047D736-3394-4B36-AE97-CEEB3359B36D. The new species name Nomenclatural actsThe electronic edition of this article conforms to the requirements of the amendedhttp://zoobank.org/”. The LSID for this publication is urn:lsid:zoobank.org:pub:9047D736-3394-4B36-AE97-CEEB3359B36D. The electronic edition of this work was published in a journal with an ISSN and has been archived and is available from the following digital repositories: PubMed Central.International Code of Zoological Nomenclature, and hence the new names contained herein are available under that code from the electronic edition of this article. This published work and the nomenclatural act it contains have been registered in ZooBank, the online registration system for the ICZN. The ZooBank LSIDs (Life Science Identifiers) can be resolved, and the associated information viewed through any standard web browser by appending the LSID to the prefix “Habitat and behaviorHynobius bambusicolus breeds in shallow pools in bamboo forests  and of low frequency (peak frequency of 129.20 Hz). The vocalization was composed of four strong harmonics , with a termedia . In thisHynobius bambusicolus, the Fujian Bamboo Salamander, based on deeply divergent molecular analyses, non-overlapping morphological characters, and vastly segregated distribution. Field identification of Hynobius individuals based on morphology is not always easy [H. bambusicolus  is dated to c. 20 mya [H. bambusicolus is, therefore, an ancient lineage, likely to have seen its distribution regularly shift, expand, and contract with paleogeographic and climatic variations [H. bambusicolus and other genera, such as Pachytriton and Paramesotriton, is not impossible.Our results, through the phylogenetic trees, haplotype network, and comparative pairwise genetic distance, show that . 20 mya , and H. riations . The areH. bambusicolus is also a negative point to the survival of the species. The species is micro-endemic, presumably composed of a single known relic population with an apparently incredibly small population size, similar to other south Chinese Hynobius species . The ges , even considering this number as a constant for each water body, it would be a maximum of 20 breeding females, and therefore a population size likely to be well below 200 breeding individuals, matching with the criteria B1, B2, and C2 for Critically Endangered following the recommendation of the IUCN Red List of Threatened Species . Due to Due to the rarity of this new species, we urge all hobbyists to avoid collecting this salamander or divulging local information and resisting any trade. The breeding site is within a planted bamboo forest, surrounded by mixed forest, and the distribution of the species is likely to have been impacted by bamboo plantation and harvest over the last decades or centuries. A site where the species was found about 10 years ago by a ranger on the opposite side of the valley is now entirely dry and unlikely to support a breeding population, making habitat loss the main threat to the species. Climate change is also most likely to impact the species, as seen in other Hynobiids , and we


The COVID-19 pandemic has exacerbated the trends of childhood overweight, obesity, and malnutrition, as well as increased psychological stress and family conflicts among family members. It is important to explore the relationship between changes in the family environment during the COVID-19 on child nutrition.This study aims to analyze the nutritional status of Chinese children during the COVID-19 pandemic and its relationship with family diet, family environment, and parental anxiety, in order to provide evidence for further interventions in children’s nutritional status.This study included 7,645 primary and secondary school students and their parents from five schools in Chengdu, China. Chi-square tests were used to analyze the categorical variables of children’s malnutrition, overweight, obesity, and parental anxiety. T-tests were used to assess changes in the continuous variable of family environment between two rounds of follow-up surveys. Multiple logistic regression analysis was employed to examine the impact of changes in family diet during the COVID-19 pandemic on children’s nutritional status. Generalized estimating equations were used to analyze the effects of family environment and parental anxiety on childhood obesity.The prevalence of malnutrition and obesity decreased from 11.64% and 11.60% in wave 1 to 4.96% and 10.50% in wave 2, and the rate of overweight increased from 13.11% in wave 1 to 13.73% in wave 2. Children whose families reduced consumption of staple foods during the COVID-19 were more likely to be frail, and families increased consumption of sugary drinks, take-out or meal delivery services, living in towns, family environmental barriers, and parental anxiety were risk factors for overweight obesity. Mother’s education level in middle and high school and low age were protective factors for overweight obesity.The physical environment of the family, the emotions of family members, and children’s perceptions of the family’s soft environment can influence children’s eating behaviors, children’s nutritional intake, and malnutrition and obesity in children under public health emergencies, and family-based dietary interventions may be effective. Parents can increase consumption of healthy foods and improve the family environment, which improve their growth. AccordiThe outbreak of the COVID-19 pandemic has brought challenges to the stability of the family environment and the emotions of family members. According to reports, during the outbreak in China, about 25 million rural migrant workers were unemployed or waiting to return to work, and nearly one-fourth of the urban employed population was economically affected . FinanciThe daily calorie intake of children at home accounts for about 65%–72% of their total intake, making the family food environment crucial for their dietary intake and nutritional development , 16. In The nutrition of children is not only related to the family’s dietary consumption, but also to the family’s environmental atmosphere. In Chinese family culture, great importance is placed on family meals, where family members come together to share food, engage in emotional communication, and pass on healthy knowledge to children. This determines that the family environment in Chinese families plays an important role in children’s diet . A positThe theory of the family’s healthy ecosystem suggests that the family is a complex system composed of multiple interactive relationships and roles, with parents being considered as core members who guide and educate children , 27–29. The family is considered the most fundamental and important unit. The COVID-19 pandemic has had a wide-ranging impact on family members’ eating behaviors, childhood overweight and obesity, and intra-family relationships. These aspects have become hot topics in research, but there are still many unknown areas that require further investigation regarding changes in household food consumption as a unit, changes in the Chinese family environment, and the impact of these changes on children’s nutritional status. This study investigated the nutritional status of children after 6 and 18 months of continuous COVID-19 pandemic through a longitudinal study, enriching the understanding of the epidemiological trends of childhood overweight/obesity and malnutrition during the pandemic. Previous research has primarily focused on household dietary consumption during routine periods, but this study examined families as units to complement the understanding of changes in household dietary consumption during the COVID-19 pandemic. In China, family culture emphasizes family cohesion and close relationships, thus this study utilized the Chinese Family Assessment Instrument to assess family atmosphere and explore the relationship between family environment and child nutrition. By analyzing the association between household dietary consumption, family environment, parental anxiety, and child nutrition during the COVID-19 pandemic, this study filled gaps in relevant knowledge and can contribute to the development of effective, family-based interventions during significant public health events.2.2.1.Chengdu Positive Child Development (CPCD)  is a lonThis program began with a baseline survey in December 2019. The wave 1 data (W1) was collected in June 2020 and the wave 2 data (W2) was collected in June 2021, with each wave lasting 1 month. This study used data from the first and second rounds, with a sample of 7- to 15-year-old primary and secondary school students and their parents. A total of 7,877 individuals participated in the study. Due to missing matching information between parents and students’ questionnaires, as well as a significant amount of missing data (≥20%) for some questionnaire items, the final sample consisted of 7,645 children aged 7–15.The study was conducted in accordance with the Helsinki Declaration, and all parents and children participating in the study provided informed consent prior to inclusion. Children also obtained parental informed consent before being included in the study. The research protocol was approved by the Medical.2.2.A basic information questionnaire was used to collect demographics about the subjects and their parents, including the gender, grade, and age of the elementary school students, and education level, age, and current place of residence.The nutritional status of children is measured by BMI with reference to the Chinese children’s standard. Due to the restrictions on the aggregation of people during the pandemic, the basic information of students’ height and weight was reported by their parents in both waves of the survey. Overweight, obesity, and malnutrition was defined according to BMI, which was calculated by dividing weight (kg) by the square of height (m) , 34. AccRegarding family diet consumption, Parents were asked since June 2020 (the wave 1 survey) about the changes in meal patterns and consumption of major foods during the pandemic, including” going outside to buy groceries,” “using take-out or meal delivery services,” “dine out in the restaurants,” “cooking at home”etc., and check the corresponding box, Including: “decreased,” “decreased a little,” “no change,” “increased a little,” and “increased.” Parents were also asked whether their household consumption of “staple food,” “vegetables,” “fruits,” and “sugary drinks and desserts,” “increased,” “remained the same,” or “decreased” during the COVID-19 pandemic.Family environment variables were measured using the Chinese Family Assessment Instrument (C-FAI) scale developed by Shek and Ma . There wParental anxiety variables were measured using the Anxiety Self-Assessment Scale (SAS) scale developed by Zung , the SAS2.3.The data was input using EpiData 3.2 and statistical analysis was performed using SPSS 26.0 to calculate the rates among the variables. Chi-square test or T test was used to compare whether the changes among the variables in the two waves of the survey were statistically significant. Multiple logistic regression was used to analyze the effect of changes in family diet consumption on the nutrition status of children and adolescents. A generalized estimation model was used to analyze the effects of family environment and parental anxiety on childhood overweight and obesity. A two-sided test with a test level of 0.05 was used.3.3.1.A total of 7,645 students were included in the study, of whom 3,770 (49.31%)were boys and 3,875 (50.69%)were girls; 3,167 (43.43%)were students aged 7–9 years, 3,308 (43.27%)were students aged 10–12 year, and 1,170 (15.30%)were students aged 13–15 years. 5,085 (66.51%)students were living in urban areas and 2,560 (33.49%) students were living in rural areas. 3,368 (44.05%) father’s education level was at junior high school or below, 4,277 (55.95%) at high school and above. 3,471 (45.40%) mother’s education level was at junior high school or below, and 4,714 (54.60%)at high school or above see .3.2.p < 0.05). As the duration of the COVID-19 pandemic increases, the prevalence of malnutrition and obesity among children has decreased . With the increasing duration of the COVID-19 pandemic, the degree of family environmental barriers decreased in obese children, and the level of parental anxiety decreased in malnutrition children , higher rates of overweight prevalence among children in families with increased take-out and meal delivery behavior  compared to families with constant consumption of take-out and meal delivery services. Families with increased consumption of sugary drinks had higher prevalence rates of childhood obesity compared to families with unchanged sugary drink consumption  see .3.5.p < 0.05)  see .4.The aim of this study was to investigate the nutritional status of children in China during the COVID-19 pandemic and its association with family dietary consumption, family environment, and parental anxiety. The findings revealed that increased consumption of unhealthy food by families, chaotic or dysfunctional family environments, and heightened parental psychological stress were risk factors for childhood overweight and obesity. Furthermore, reduced consumption of staple foods by families was identified as a risk factor for childhood malnutrition.Research has found that during the initial outbreak of the COVID-19 pandemic, the rates of childhood overweight, malnutrition, parental anxiety, and levels of family environmental disruption were higher compared to the period when the pandemic was prevalent. The drastic changes in the environment during the early stages of the COVID-19 outbreak disrupted many systems that children and parents rely on for health, livelihood, education, diet, and exercise , 44. TheDuring the COVID-19 pandemic, reducing staple food consumption in households is a risk factor for child undernutrition. Staple foods are the main source of energy intake. A cohort study has shown that government lockdown measures significantly altered food intake . In 2020Increasing consumption of take-out meals, sugary beverages, and desserts in households is a risk factor for child overweight and obesity. Take-out meals tend to be more energy-dense and have poorer nutrition, often containing high amounts of unhealthy ingredients such as fat, salt, and sugar, which are associated with weight gain and various negative health outcomes , 53. ComOur research found that family dysfunction and parental anxiety are risk factors for childhood obesity. Epidemiological studies have shown that family functioning plays a significant role in children’s diet and weight status . A chaot5.Firstly, gaining a better understanding of the nutritional status of children during the ongoing COVID-19 pandemic has highlighted the changes in childhood obesity and malnutrition. This prompts us to not only focus on the issue of childhood obesity during major public health events but also pay attention to the problem of childhood malnutrition. This is important for informing the development of public health policies, which may involve food safety regulations, dietary education, social support systems, and other aspects, in order to protect the nutritional health of children.This study provides limited evidence on the relationship between changes in household dietary consumption, family environment, parental anxiety, and child nutrition. The study suggests that dietary rules and food accessibility within the family, as well as parental emotions and parenting behaviors, can influence children’s dietary choices and quality. By further applying the theory of the family health ecosystem to research on family nutrition, the study enriches the theoretical understanding in this field. Additionally, it provides a theoretical basis for further analyzing and addressing child nutrition issues. However, it is important to note that the evidence is still limited, and further research is needed to validate and gain a deeper understanding of these relationships. Lastly, exploring the importance of the social environment in child nutrition has significant implications for policymakers and the design of intervention measures.6.Research promotes the development of intervention measures, and family diet can influence children’s eating habits and intake. Family-based dietary interventions may be effective, as families can increase the consumption of healthy foods and reduce the consumption of obesity-promoting foods such as sugary beverages. At the same time, parents can improve their children’s nutritional status by establishing a supportive family environment, improving their own mental well-being, and maintaining family stability. These factors are crucial for promoting healthy eating and overall growth in children.Research promotes family nutrition education, and the influence of family factors on child nutrition is undoubtedly important. The family is a microcosm of society, and China has its unique cultural background. For example, parents express love through food, and grandparents tend to overfeed children. When intervening in child nutrition, cultural differences should be taken into account . ChineseThe research findings can provide recommendations for governments and relevant organizations to take measures to mitigate the negative impact of the COVID-19 pandemic on children’s nutrition status. This may include providing economic assistance, improving the food supply chain, promoting healthy eating campaigns, and implementing other measures to ensure children have access to adequate nutrition during the pandemic.7.In this study, a large sample of children and adolescents was used, focusing not only on overweight and obesity, but also on malnutrition. The study focused on the impact of “family-based” eating behaviors on children and adolescents, which was conducted 2 years into the COVID-19 pandemic, helping to complement the impact of families on the nutritional status of children and adolescents in the ongoing pandemic.However, there are some limitations to the research. Firstly, the study sample was limited to the Sichuan province, which may reduce the generalizability of the findings. Future research could consider recruiting participants from other regions in China to have a more comprehensive understanding of the nutritional status and weight-related behaviors of Chinese children. Secondly, due to the restrictions of the COVID-19 pandemic, the height and weight of children were reported by parents rather than measured by professionals, which may affect the accuracy of the measurements. Future research could consider using professional measurements of height and weight to improve the accuracy of the data. Thirdly, the survey on household food consumption was relatively simple and may not cover the comprehensive aspects of dietary consumption. Future research could design more detailed and scientific questionnaires to have a more comprehensive understanding of household dietary consumption patterns and changes in the consumption of food types. Lastly, there may be other confounding factors that could influence the associations found in the study. Future research could consider other confounding factors such as genetics, seasonality, weather, physical conditions, and lifestyle factors to enhance the reliability of the research findings.8.In summary, the material environment of the family, the emotions of family members, and children’s perceptions of the family environment can all influence children’s eating behaviors and nutritional intake. Research findings indicate that the prevalence of malnutrition and obesity among children may be more severe during the COVID-19 pandemic. As the duration of the pandemic progresses, the rate of malnutrition in children decreases significantly, while the rate of obesity slightly decreases and the rate of overweight slightly increases. Therefore, the issue of childhood overweight and obesity should not be overlooked. The COVID-19 pandemic has also led to changes in family food consumption, family functioning, and parental emotions. Reduced consumption of staple foods by families is a risk factor for child under-nutrition, while increased consumption of take-out food and sugary beverages is a risk factor for child overweight and obesity. Family dysfunction and parental anxiety are also risk factors for childhood obesity. Family food consumption, family environment, and parental emotions are all factors that influence children’s nutrition. This study provides additional insights into the changing trends of childhood overweight, malnutrition, and other related issues during the ongoing COVID-19 pandemic. It also provides a theoretical basis for family-based intervention measures.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Medical Ethics Committee of Sichuan University ethics number: K2020025. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.LZ: funding acquisition. LP and RH: methodology. LZ and LJ: project administration and resources. LP: software and writing—original draft. RH, YF, WS, LZ, and LJ: supervision. LP, RH, YF, and WS: writing—review and editing. All authors contributed to the article and approved the submitted version.This work was supported by National Natural Science Foundation of China, Approval No.: 82273748.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


IntroductionDiabetes mellitus (DM) encompasses a group of heterogeneous, chronic, and non-communicable diseases characterized by an increase in blood glucose levels. As it has become easily accessible for patients to know about their symptoms and treatment of diseases, it is of utmost importance that reliable information is conveyed on the internet. If not managed appropriately, it may result in the dissemination of false information, leading to risky practices and incorrect treatment, further resulting in detrimental consequences.AimTo assess the quality and reliability of information related to DM on YouTube.MethodologyA cross-sectional observational study was conducted in April 2023, wherein top YouTube videos related to 'diabetes' were analyzed for baseline characteristics, type of uploader, as well as quality and reliability using Global Quality Score (GQS) and Reliability Score (DISCEN), respectively. Statistical analysis was performed using SPSS software.ResultsA total of 87 videos were evaluated in the study. Unfortunately, only 21% of those were uploaded by doctors. The median Video Power Index (VPI) for videos uploaded by other sources was the highest (184.7), and the lowest was for videos uploaded by hospitals (12.6), and this was statistically significant (p = 0.038). The median GQS was highest for videos uploaded by doctors (4) and lowest for videos uploaded by others (3.5). The reliability score was higher in videos uploaded by healthcare organizations (4), which was not significant (p > 0.05).ConclusionsVideos uploaded by physicians and healthcare organizations contained reliable information with a high global quality score. Videos uploaded by sources other than doctors and healthcare professionals should consult physicians, as self-diagnosis or self-treatment can lead to potential harm to patients. Diabetes mellitus (DM) encompasses a group of heterogeneous, chronic, and non-communicable diseases characterized by an increase in blood glucose levels . This coYouTube has become an easily accessible and fast-paced resource for many patients seeking to learn about their symptoms and treatment of diabetes . HoweverThis web-based, cross-sectional observational study conducted in April 2023 does not involve human participation. The study was conducted on a single day, April 19. First, a questionnaire was created on Google Forms that included predetermined criteria related to videos such as the number of likes, dislikes, views, source of video uploaded, and comments. Then, we assessed the content of the videos that were found using keywords like diabetes, diabetes treatment, diabetes prevention, diabetes cause, diabetes cure, and diabetes diet.All the authors evaluated 15 videos, and any repeated entries were deleted. Then, screening for inclusion and exclusion criteria was done. In order to be included, videos should have specifically contained information about diabetes and should have been presented in the English language. All the videos that did not contain information about diabetes or were presented in any language other than English were excluded. Also, videos that were shorter than one minute or longer than 15 minutes in duration were excluded. The next step involved assessing the quality and reliability of the videos using GQS and Reliability Score . The resA total of 87 videos were evaluated in this study. After meticulous screening for inclusion and exclusion criteria and the deletion of duplicates, 62 videos were included in the study. The total number of views was 28,243,110, the total number of likes was 426,667, the dislikes were 15,480, and the comments were 29,695. Over 87% of the videos were uploaded more than a year ago. The majority of the videos (35.5%) were posted by sources other than doctors, hospitals, healthcare organizations, and news agencies. Unfortunately, only 21% of the videos were posted by doctors. The detailed characteristics of the analyzed videos are depicted in Table The Global Quality Score (GQS), Reliability Score (RS), and Video Power Index (VPI) are compared based on the uploading of the video in Table . The medDiabetes mellitus (DM) is a complex condition that requires patients to make several daily choices encompassing their diet, exercise routine, and medication. In addition, it demands that individuals possess a range of self-management abilities . The easIn the context of this study, we utilized the GQS, RS, and VPI scores as tools for assessing the quality, reliability, and popularity of the content from each of the uploaders. Osman et al. reported that the quality of health-related content on YouTube was below average . ConversPertaining to the quality of videos based on the type of uploader, we found that videos uploaded by doctors and hospitals had the highest quality. Consistent with the findings of our study, Diers et al., in their evaluation of the usefulness of YouTube videos on asthma, found that videos posted by doctors had the highest quality . SimilarAccording to our analysis, VPI was higher in videos posted by sources other than doctors, healthcare organizations, hospitals, and news agencies (184.71) compared to those posted by doctors (84.01), healthcare organizations (26.49), hospitals (12.595), and news agencies (49.4). A similar study on rotator cuff repair by Celik et al. found that VPI was higher in videos posted by doctors . FurtherRegarding RS, we found that the reliability index was higher in videos by hospitals and healthcare organizations. In contrast to our study findings, existing literature reports that content posted by doctors has the highest quality and reliability across social media and web-based platforms ,16.LimitationsOne of the main limitations of our study was that we only analyzed videos in English. Second, the YouTube platform is very dynamic, and the results may change by adding new videos or removing old ones. Finally, the limited sample size could be another limitation; however, it is unusual for any viewer to go through 50+ videos on YouTube.Responsible individuals or organizations should upload verified information that is easily understandable by the general population and provides all relevant and necessary information about diabetes. The videos should contain reliable information with a high global quality score. Additionally, it is important to mention in these videos that self-diagnosis and self-treatment can potentially harm the patient, and one should always consult a doctor or physician for proper diagnosis and treatment.