bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gut microbiota and autism spectrum disorders: a bidirectional Mendelian randomization study
Authors: Li Zhi , Liu Shuai , Liu Fang , Dai Nannan , Liang Rujia , Lv Shaoguang , Bao Lisha 
Published: December 2023

BackgroundIn recent years, observational studies have provided evidence supporting a potential association between autism spectrum disorder (ASD) and gut microbiota. However, the causal effect of gut microbiota on ASD remains unknown.MethodsWe identified the summary statistics of 206 gut microbiota from the MiBioGen study, and ASD data were obtained from the latest Psychiatric Genomics Consortium Genome-Wide Association Study (GWAS). We then performed Mendelian randomization (MR) to determine a causal relationship between the gut microbiota and ASD using the inverse variance weighted (IVW) method, simple mode, MR-Egger, weighted median, and weighted model. Furthermore, we used Cochran’s Q test, MR-Egger intercept test, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and leave-one-out analysis to identify heterogeneity and pleiotropy. Moreover, the Benjamin-Hochberg approach (FDR) was employed to assess the strength of the connection between exposure and outcome. We performed reverse MR analysis on the gut microbiota that were found to be causally associated with ASD in the forward MR analysis to examine the causal relationships. The enrichment analyses were used to analyze the biological function at last.ResultsBased on the results of IVW results, genetically predicted family Prevotellaceae and genus Turicibacter had a possible positive association with ASD (IVW OR=1.14, 95% CI: 1.00-1.29, P=3.7×10−2), four gut microbiota with a potential protective effect on ASD: genus Dorea (OR=0.81, 95% CI: 0.69-0.96, P=1.4×10−2), genus Ruminiclostridium5 (OR=0.81, 95% CI: 0.69-0.96, P=1.5×10−2), genus Ruminococcus1 (OR=0.83, 95% CI: 0.70-0.98, P=2.8×10−2), and genus Sutterella (OR=0.82, 95% CI: 0.68-0.99, P=3.6×10−2). After FDR multiple-testing correction we further observed that there were two gut microbiota still have significant relationship with ASD: family Prevotellaceae (IVW OR=1.24; 95% CI: 1.09-1.40, P=9.2×10-4) was strongly positively correlated with ASD and genus RuminococcaceaeUCG005 (IVW OR=0.78, 95% CI: 0.67-0.89, P=6.9×10−4) was strongly negatively correlated with ASD. The sensitivity analysis excluded the influence of heterogeneity and horizontal pleiotropy.ConclusionOur findings reveal a causal association between several gut microbiomes and ASD. These results deepen our comprehension of the role of gut microbiota in ASD’s pathology, providing the foothold for novel ideas and theoretical frameworks to prevent and treat this patient population in the future.
Magnetocardiography at rest predicts cardiac death in patients with acute chest pain
Authors: Wessel N. , Kim J. S. , Joung B. Y. , Ko Y. G. , Dischl D. , Gapelyuk A. , Lee Y. H. , Kim K. W. , Park J. W. , Landmesser U. 
Published: December 2023

IntroductionSudden cardiac arrest is a major cause of morbidity and mortality worldwide and remains a major public health problem for which better non-invasive prediction tools are needed. Primary preventive therapies, such as implantable cardioverter defibrillators, are not personalized and not predictive. Most of these devices do not deliver life-saving therapy during their lifetime. The individual relationship between fatal arrhythmias and cardiac function abnormalities in predicting cardiac death risk has rarely been explored.MethodsWe retrospectively analyzed the measurements at rest for 191 patients with acute chest pain (ACP) magnetocardiographically. Our recently introduced analyses are able to detect inhomogeneities of the depolarization and repolarization. Moreover, electrically silent phenomena—intracellular ionic currents as well as vortex currents—can be measured and quantified. All included ACP patients were recruited in 2009 at Yonsei University Hospital and were followed up until 2022.ResultsDuring half of the follow-up period (6.5 years), 11 patients died. Out of all the included nine clinical, eight magnetocardiographical, and nine newly introduced magnetoionographical parameters we tested in this study, three parameters revealed themselves to be outstanding at predicting death: heart rate-corrected QT (QTc) prolongation, depression of repolarization current IKr + IKs, and serum creatinine (all significant in Cox regression, p &lt; 0.05). They clearly predicted cardiac death over the 6.5 years duration (sensitivity 90.9%, specificity 85.6%, negative predictive accuracy 99.4%). Cardiac death risk was more than ninefold higher in patients with low repolarization reserve and QTc prolongation in comparison with the remaining patients with ACP (p &lt; 0.001). The non-parametric Kaplan–Meier statistics estimated significantly lower survival functions from their lifetime data (p &lt; 0.001).DiscussionTo the best of our knowledge, these are the first data linking magnetocardiographical and magnetoionographical parameters and subsequent significant fatal events in people, suggesting structural and functional components to clinical life-threatening ventricular arrhythmogenesis. The findings support investigation of new prevention strategies and herald those new non-invasive techniques as complementary risk stratification tools.
Whole-exome sequencing reveals novel variants of monogenic diabetes in Tunisia: impact on diagnosis and healthcare management
Authors: Kheriji Nadia , Dallali Hamza , Gouiza Ismail , Hechmi Meriem , Mahjoub Faten , Mrad Mehdi , Krir Asma , Soltani Manel , Trabelsi Hajer , Hamdi Walid , Bahlous Afef , Ben Ahmed Melika , Jamoussi Henda , Kefi Rym 
Published: December 2023

Introduction: Monogenic diabetes (MD) accounts for 3%–6% of all cases of diabetes. This prevalence is underestimated due to its overlapping clinical features with type 1 and type 2 diabetes. Hence, genetic testing is the most appropriate tool for obtaining an accurate diagnosis. In Tunisia, few cohorts of MD have been investigated until now. The aim of this study is to search for pathogenic variants among 11 patients suspected of having MD in Tunisia using whole-exome sequencing (WES).Materials and methods: WES was performed in 11 diabetic patients recruited from a collaborating medical center. The pathogenicity of genetic variation was assessed using combined filtering and bioinformatics prediction tools. The online ORVAL tool was used to predict the likelihood of combinations of pathogenic variations. Then, Sanger sequencing was carried out to confirm likely pathogenic predicted variants among patients and to check for familial segregation. Finally, for some variants, we performed structural modeling to study their impact on protein function.Results: We identified novel variants related to MD in Tunisia. Pathogenic variants are located in several MODY and non-MODY genes. We highlighted the presence of syndromic forms of diabetes, including the Bardet–Biedl syndrome, Alström syndrome, and severe insulin resistance, as well as the presence of isolated diabetes with significantly reduced penetrance for Wolfram syndrome-related features. Idiopathic type 1 diabetes was also identified in one patient.Conclusion: In this study, we emphasized the importance of genetic screening for MD in patients with a familial history of diabetes, mainly among admixed and under-represented populations living in low- and middle-income countries. An accurate diagnosis with molecular investigation of MD may improve the therapeutic choice for better management of patients and their families. Additional research and rigorous investigations are required to better understand the physiopathological mechanisms of MD and implement efficient therapies that take into account genomic context and other related factors.
One-month assessment of Th-cell axis related inflammatory cytokines, IL-17 and IL-22 and their role in alcohol-associated liver disease
Authors: Sagaram Manasa , Frimodig Jane , Jayanty Danielle , Hu Huirong , Royer Amor J. , Bruner Ryne , Kong Maiying , Schwandt Melanie L. , Vatsalya Vatsalya 
Published: December 2023

IntroductionChanges in the expression of cyto- and chemokines due to alcohol-associated liver disease (ALD) have been reported to be both protective and pathogenic. This study examined plasma levels of two key cytokines, Il-17 and Il-22, which construct the proinflammatory vs. anti-inflammatory axes across the spectrum of alcohol use disorder (AUD) and ALD including alcohol-associated hepatitis (AH) to determine the underlying status of the inflammation.MethodsForty-two males and females aged 25-63 yrs. were grouped as healthy controls (HV[n=8]), AUD with no liver injury (AUDNLI [n=8]), AUD with liver injury (AUDLI [n=8]), non-severe alcohol-associated hepatitis (NSAH [n=9]), and severe alcohol-associated hepatitis (SAH [n=9]). Demographic, drinking, and clinical data were collected. Blood samples were collected at baseline (BL, all subjects) and during week 4 (W4, only patients) for IL-17 and IL-22; and statistically analyzed.ResultsIL-17 was highly elevated in the SAH group both at BL and post-SOC. LTDH and BL IL-22 in non-severe AH patients were associated significantly. LTDH significantly predicted W4 IL-22 levels, positively (increasing) in NSAH and inversely (lowering) in SAH patients. BL and W4 IL-22 levels were significantly higher (4-fold, p≤0.001) in all AH patients compared to all AUD patients (AUROC=0.988, p≤0.001). IL-22 showed significant affinity with AST, AST: ALT ratio, total bilirubin, INR, and PT both at BL and W4. IL-22 was inversely associated with IL-1β; and positively with TNF-α and IL-8 both at BL, and W4. BL IL-17 showed a positive correlation with MELD (p=0.017) in all AH patients. In SAH, &gt; 2-fold W4 IL-17 level compared to BL showed significant within subjects’ effects, p=0.006. In AUD patients without AH, the drop in IL-17 at W4 vs. BL showed a significant within subjects’ effect, p=0.031.DiscussionDrinking chronicity predicted opposite effects in IL-22 levels in NSAH (antiinflammatory) and SAH (pro-inflammatory) patients at post-SOC. BL IL-22 levels differentiated AH patients robustly from the AUD patients (with or without liver injury); and showed corresponding increases stepwise with the stages of ALD. IL-22 was closely associated with progression and injury markers of the liver; and response to the cytokines of pro-inflammatory nature. Pro-inflammatory indicator of IL-17 cell axis, IL-17 showed a strong positive association with MELD, a severity indicator of AH.
Development and management of gastrointestinal symptoms in long-term COVID-19
Authors: He Kai-Yue , Lei Xin-Yuan , Zhang Lei , Wu Dan-Hui , Li Jun-Qi , Lu Li-Yuan , Laila Umm E. , Cui Cui-Yun , Xu Zhi-Xiang , Jian Yong-Ping 
Published: December 2023

BackgroundEmerging evidence reveals that SARS-CoV-2 possesses the capability to disrupt the gastrointestinal (GI) homeostasis, resulting in the long-term symptoms such as loss of appetite, diarrhea, gastroesophageal reflux, and nausea. In the current review, we summarized recent reports regarding the long-term effects of COVID-19 (long COVID) on the gastrointestine.ObjectiveTo provide a narrative review of abundant clinical evidence regarding the development and management of long-term GI symptoms in COVID-19 patients.ResultsLong-term persistent digestive symptoms are exhibited in a majority of long-COVID patients. SARS-CoV-2 infection of intestinal epithelial cells, cytokine storm, gut dysbiosis, therapeutic drugs, psychological factors and exacerbation of primary underlying diseases lead to long-term GI symptoms in COVID-19 patients. Interventions like probiotics, prebiotics, fecal microbiota transplantation, and antibiotics are proved to be beneficial in preserving intestinal microecological homeostasis and alleviating GI symptoms.ConclusionTimely diagnosis and treatment of GI symptoms in long-COVID patients hold great significance as they may contribute to the mitigation of severe conditions and ultimately lead to the improvement of outcomes of the patients.
Antibiotic resistance profiles of gut microbiota across various primate species in Guangxi
Authors: Huang Hongli , Pang Xianwu , Que Tengcheng , Chen Panyu , Li Shousheng , Wu Aiqiong , He Meihong , Qiu Hong , Hu Yanling 
Published: December 2023

IntroductionUnderstanding the gut microbiota and antibiotic resistance gene (ARG) profiles in non-human primates (NHPs) is crucial for evaluating their potential impact on human health and the environment.MethodsIn this study, we performed metagenomic analysis of 203 primate fecal samples, including nine NHP species and humans, to comprehensively characterize their gut microbiota and ARGs.ResultsOur study reveals the prevailing phyla in primates as Firmicutes, Bacteroidetes, Euryarchaeota, and Proteobacteria. The captive NHPs exhibited higher ARG abundance compared to their wild counterparts, with tetracycline and beta-lactam resistance genes prevailing. Notably, ARG subtypes in Trachypithecus leucocephalus (T. leucocephalus) residing in karst limestone habitats displayed a more dispersed distribution compared to other species. Interestingly, ARG profiles of NHPs clustered based on geographic location and captivity status. Co-occurrence network analysis revealed intricate correlations between ARG subtypes and bacterial taxa. Procrustes analysis unveiled a significant correlation between ARGs and microbial phylogenetic community structure. Taxonomic composition analysis further highlighted differences in microbial abundance among NHPs and humans.DiscussionOur study underscores the impact of lifestyle and geographical location on NHP gut microbiota and ARGs, providing essential insights into the potential risks posed by NHPs to antibiotic resistance dissemination. This comprehensive analysis enhances our understanding of the interplay between NHPs and the gut resistome, offering a critical reference for future research on antibiotic resistance and host-microbe interactions.
Association between the triglyceride glucose index and cognitive impairment and dementia: a meta-analysis
Authors: Wang Huan , Ling Qin , Wu Yifan , Zhang Mingjie 
Published: December 2023

BackgroundThe triglyceride and glucose (TyG) index is an alternative index of insulin resistance (IR). We aimed to clarify the relationship between the TyG index and cognitive impairment and dementia.MethodsWe conducted a comprehensive search of the PubMed, Cochrane Library, and Embase databases until February 2023 to identify relevant studies. Random-effects models were used to pool effect sizes, and the Grading of Recommendations Assessment, Development, and Evaluation system (GRADE) was used to assess the quality of the evidence.ResultsTen studies were included, with seven of which investigated the relationship between the TyG index and cognitive impairment and three exploring the association between the TyG index and dementia. When the TyG index was described as a categorical variable, it was positively associated with the risk of cognitive impairment (OR = 2.32; 95% CI 1.39–3.87) and dementia (OR = 1.14, 95% CI 1.12–1.16). The association of the TyG index with the risk of cognitive impairment (OR = 3.39, 95% CI 1.67–6.84) and dementia (OR = 1.37, 95% CI 1.03–1.83) remained significant for per 1 unit increment in the TyG index. The GRADE assessment indicated a very low certainty for cognitive impairment. Low certainty and moderate certainty were observed for dementia when the TyG index was analyzed as a categorical variable and as a continuous variable, respectively.ConclusionThe TyG index is associated with an increased risk of cognitive impairment and dementia. Further prospective research is warranted to confirm these findings.Systematic review registration: https://www.crd.york.ac.uk/, Protocol registration number: CRD42023388028.
A modelling study to dissect the potential role of voltage-gated ion channels in activity-dependent conduction velocity changes as identified in small fiber neuropathy patients
Authors: Maxion Anna , Kutafina Ekaterina , Dohrn Maike F. , Sacré Pierre , Lampert Angelika , Tigerholm Jenny , Namer Barbara 
Published: December 2023

ObjectivePatients with small fiber neuropathy (SFN) suffer from neuropathic pain, which is still a therapeutic problem. Changed activation patterns of mechano-insensitive peripheral nerve fibers (CMi) could cause neuropathic pain. However, there is sparse knowledge about mechanisms leading to CMi dysfunction since it is difficult to dissect specific molecular mechanisms in humans. We used an in-silico model to elucidate molecular causes of CMi dysfunction as observed in single nerve fiber recordings (microneurography) of SFN patients.ApproachWe analyzed microneurography data from 97 CMi-fibers from healthy individuals and 34 of SFN patients to identify activity-dependent changes in conduction velocity. Using the NEURON environment, we adapted a biophysical realistic preexisting CMi-fiber model with ion channels described by Hodgkin-Huxley dynamics for identifying molecular mechanisms leading to those changes. Via a grid search optimization, we assessed the interplay between different ion channels, Na-K-pump, and resting membrane potential.Main resultsChanging a single ion channel conductance, Na-K-pump or membrane potential individually is not sufficient to reproduce in-silico CMi-fiber dysfunction of unchanged activity-dependent conduction velocity slowing and quicker normalization of conduction velocity after stimulation as observed in microneurography. We identified the best combination of mechanisms: increased conductance of potassium delayed-rectifier and decreased conductance of Na-K-pump and depolarized membrane potential. When the membrane potential is unchanged, opposite changes in Na-K-pump and ion channels generate the same effect.SignificanceOur study suggests that not one single mechanism accounts for pain-relevant changes in CMi-fibers, but a combination of mechanisms. A depolarized membrane potential, as previously observed in patients with neuropathic pain, leads to changes in the contribution of ion channels and the Na-K-pump. Thus, when searching for targets for the treatment of neuropathic pain, combinations of several molecules in interplay with the membrane potential should be regarded.
Factor analysis based on SHapley Additive exPlanations for sepsis-associated encephalopathy in ICU mortality prediction using XGBoost — a retrospective study based on two large database
Authors: Guo Jiayu , Cheng Hongtao , Wang Zicheng , Qiao Mengmeng , Li Jing , Lyu Jun 
Published: December 2023

ObjectiveSepsis-associated encephalopathy (SAE) is strongly linked to a high mortality risk, and frequently occurs in conjunction with the acute and late phases of sepsis. The objective of this study was to construct and verify a predictive model for mortality in ICU-dwelling patients with SAE.MethodsThe study selected 7,576 patients with SAE from the MIMIC-IV database according to the inclusion criteria and randomly divided them into training (n = 5,303, 70%) and internal validation (n = 2,273, 30%) sets. According to the same criteria, 1,573 patients from the eICU-CRD database were included as an external test set. Independent risk factors for ICU mortality were identified using Extreme Gradient Boosting (XGBoost) software, and prediction models were constructed and verified using the validation set. The receiver operating characteristic (ROC) and the area under the ROC curve (AUC) were used to evaluate the discrimination ability of the model. The SHapley Additive exPlanations (SHAP) approach was applied to determine the Shapley values for specific patients, account for the effects of factors attributed to the model, and examine how specific traits affect the output of the model.ResultsThe survival rate of patients with SAE in the MIMIC-IV database was 88.6% and that of 1,573 patients in the eICU-CRD database was 89.1%. The ROC of the XGBoost model indicated good discrimination. The AUCs for the training, test, and validation sets were 0.908, 0.898, and 0.778, respectively. The impact of each parameter on the XGBoost model was depicted using a SHAP plot, covering both positive (acute physiology score III, vasopressin, age, red blood cell distribution width, partial thromboplastin time, and norepinephrine) and negative (Glasgow Coma Scale) ones.ConclusionA prediction model developed using XGBoost can accurately predict the ICU mortality of patients with SAE. The SHAP approach can enhance the interpretability of the machine-learning model and support clinical decision-making.
Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis
Authors: Miyamoto Nobukazu , Ueno Yuji , Yamashiro Kazuo , Hira Kenichiro , Kijima Chikage , Kitora Naoki , Iwao Yoshihiko , Okuda Kayo , Mishima Shohei , Takahashi Daisuke , Ono Kazuto , Asari Mika , Miyazaki Kazuki , Hattori Nobutaka 
Published: December 2023

Background and aimsIt is important to diagnose cerebral infarction at an early stage and select an appropriate treatment method. The number of stroke-trained physicians is unevenly distributed; thus, a shortage of specialists is a major problem in some regions. In this retrospective design study, we tested whether an artificial intelligence (AI) we built using computer-aided detection/diagnosis may help medical physicians to classify stroke for the appropriate treatment.MethodsTo build the Stroke Classification and Treatment Support System AI, the clinical data of 231 hospitalized patients with ischemic stroke from January 2016 to December 2017 were used for training the AI. To verify the diagnostic accuracy, 151 patients who were admitted for stroke between January 2018 and December 2018 were also enrolled.ResultsBy utilizing multimodal data, such as DWI and ADC map images, as well as patient examination data, we were able to construct an AI that can explain the analysis results with a small amount of training data. Furthermore, the AI was able to classify with high accuracy (Cohort 1, evaluation data 88.7%; Cohort 2, validation data 86.1%).ConclusionIn recent years, the treatment options for cerebral infarction have increased in number and complexity, making it even more important to provide appropriate treatment according to the initial diagnosis. This system could be used for initial treatment to automatically diagnose and classify strokes in hospitals where stroke-trained physicians are not available and improve the prognosis of cerebral infarction.