bluuebunny/crossref_metadata_2025_split huggingface dataset:
Glycosylation profiling of triple-negative breast cancer: clinical and immune correlations and identification of LMAN1L as a biomarker and therapeutic target
Authors: Yu Qianru , Zhong Hanyi , Zhu Xinhao , Liu Chang , Zhang Xin , Wang Jiao , Li Zongyao , Shi Songchang , Zhao Haoran , Zhou Cixiang , Zhao Qian 
Published: January 2025

IntroductionBreast cancer (BC) is the most prevalent malignant tumor in women, with triple-negative breast cancer (TNBC) showing the poorest prognosis among all subtypes. Glycosylation is increasingly recognized as a critical biomarker in the tumor microenvironment, particularly in BC. However, the glycosylation-related genes associated with TNBC have not yet been defined. Additionally, their characteristics and relationship with prognosis have not been deeply investigated.MethodsTranscriptomic analyses were used to identify a glycosylation-related signature (GRS) associated with TNBC prognosis. A machine learning-based prediction model was constructed and validated across multiple independent datasets. The model's predictive capability was extended to evaluate the prognosis of TNBC individuals, tumor immune microenvironment and immunotherapy response. LMAN1L (Lectin, Mannose Binding 1 Like) was identified as a novel prognostic marker in TNBC, and its biological effects were validated through experimental assays.ResultsThe GRS showed significant prognostic relevance for TNBC patients. The risk model effectively predicted molecular features, including immune cell infiltration and potential responses to immunotherapy. Experimental validation confirmed LMAN1L as a novel glycosylation-related prognostic gene, with low expression significantly inhibiting TNBC cell proliferation and migration.DiscussionOur GRS risk model demonstrates robust predictive capability for TNBC prognosis and immunotherapy response. This model offers a promising strategy for personalized treatment and improved clinical outcomes in TNBC.
Inhibition SIRT1 to regulate FOXP3 or RORγt can restore the balance of Treg/Th17 axis in ulcerative colitis and enhance the anti-inflammatory effect of moxibustion
Authors: Zhu Yuanbing , Wang Yuemei , Zuo Xiaotong , Liu Shuqing , Cao Lishuang , Wang Junmeng , Yang Qingqing , Huang Qianhui , Huang Qin , Tian Muqiu , Ping Yanling , Wu Qiaofeng 
Published: January 2025

IntroductionUlcerative colitis (UC) is a chronic inflammatory disease. Patients with UC typically exhibit disruption of the Treg/Th17 immune axis, but its exact mechanism is still unclear.MethodsThis study first analyzed RNA- seq data from public databases of humans and mice, and in vitro cytology experiments were conducted to induce or inhibit the expression of SIRT1. In vivo, UC mice were treated with moxibustion and SIRT1 inhibitor EX-527 to confirm the changes in the transcription factors identified through analysis of the datasets.ResultsThe results show that Treg/Th17 axis disruption is an important feature of UC. Differential gene expression and immune infiltration analysis showed that upstream transcription factors, including Forkhead box P3 (FOXP3), were significantly disrupted. In vitro cytology experiments, the results indicate that SIRT1 is activated in LPS induced inflammation, subsequently perturbing the Treg/Th17 immune balance axis. Finally, in vivo studies, the results have shown that administering EX-527 to inhibit SIRT1 leads to an increasing in FOXP3 expression and a decreasing in RORγt expression in UC colon tissue. In addition, the results indicate that traditional Chinese moxibustion can down regulate the expression of SIRT1, directly affecting the balance of Th17/Treg axis, and the combined use of EX-527 further improves the therapeutic effect of moxibustion.ConclusionOur research shows that inhibition SIRT1 can regulate Treg and Th17 immune balance axis. This finding indicates a new important potential target for the treatment of UC.
Developing and validating a machine learning model to predict multidrug-resistant Klebsiella pneumoniae-related septic shock
Authors: Pan Shengnan , Shi Ting , Ji Jinling , Wang Kai , Jiang Kun , Yu Yabin , Li Chang 
Published: January 2025

BackgroundMultidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.MethodsA retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024. The cohort was randomly divided into a training set (n = 969) and a validation set (n = 416). Feature selection was performed using LASSO regression and the Boruta algorithm. Seven machine learning algorithms were evaluated, with logistic regression chosen for its optimal balance between performance and robustness against overfitting.ResultsThe overall incidence of MDR-KP-associated septic shock was 16.32% (226/1,385). The predictive model identified seven key risk factors: procalcitonin (PCT), sepsis, acute kidney injury, intra-abdominal infection, use of vasoactive medications, ventilator weaning failure, and mechanical ventilation. The logistic regression model demonstrated excellent predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.906 in the training set and 0.865 in the validation set. Calibration was robust, with Hosmer-Lemeshow test results of P = 0.065 (training) and P = 0.069 (validation). Decision curve analysis indicated substantial clinical net benefit.ConclusionThis study presents a validated, high-performing predictive model for MDR-KP-associated septic shock, offering a valuable tool for early clinical decision-making. Prospective, multi-center studies are recommended to further evaluate its clinical applicability and effectiveness in diverse settings.
Diagnostic accuracy of Bellavere’s score in cardiac autonomic neuropathy among chronic kidney disease patients: a study of prevalence and dialysis impact
Authors: Toshniwal Saket Satyasham , Kumar Sunil , Acharya Sourya , Ghali Abhishek , Raut Sarang , Deolikar Vinit , Reddy Harshitha , Kotak Palash , Wanjari Anil , Bawankule Shilpa 
Published: January 2025

BackgroundCardiac autonomic neuropathy (CAN) is a significant complication in chronic kidney disease (CKD), leading to increased morbidity and mortality. Early detection is essential for managing CKD patients effectively, especially those on hemodialysis. This study evaluated the prevalence CAN in CKD and diagnostic accuracy of Bellavere’s Score in predicting CAN in CKD patients, including those undergoing hemodialysis.MethodsThis prospective observational study included 200 CKD patients. Cardiac autonomic neuropathy was assessed using Bellavere’s Score, calculated through a series of autonomic function tests including heart rate variability and blood pressure responses. Bellavere’s Score was measured pre-and post-dialysis in hemodialysis patients. The diagnostic performance of the score was evaluated using receiver operating characteristic (ROC) curves to determine sensitivity, specificity, and likelihood ratios.ResultsAmong the patients, 60% were diagnosed with CAN, with 35% having early CAN and 24% severe CAN. Bellavere’s Score showed high diagnostic accuracy across CKD stages, with sensitivity ranging from 75 to 89.29% and specificity from 69.09 to 96%. In CKD stage III patients, the sensitivity was 78.57% and specificity 91.4%. In stage V, sensitivity increased to 89.29%, though specificity dropped to 69.09%. For hemodialysis patients, Bellavere’s Score exhibited a sensitivity of 79.78% and specificity of 79.28%. The prevalence of CAN decreased significantly from 79.8% pre-dialysis to 64% post-dialysis (p &lt; 0.01).ConclusionBellavere’s Score provides a reliable and non-invasive approach for diagnosing CAN in CKD patients, with strong diagnostic performance across different disease stages and in hemodialysis. Larger studies are warranted to further validate its utility.
Predicting functional outcomes of patients with spontaneous intracerebral hemorrhage based on explainable machine learning models: a multicenter retrospective study
Authors: Pan Bin , Li Fengda , Liu Chuanghong , Li Zeyi , Sun Chengfa , Xia Kaijian , Xu Hong , Kong Gang , Gu Longyuan , Cheng Kaiyuan 
Published: January 2025

BackgroundSpontaneous intracerebral hemorrhage (SICH) is the second most common cause of cerebrovascular disease after ischemic stroke, with high mortality and disability rates, imposing a significant economic burden on families and society. This retrospective study aimed to develop and evaluate an interpretable machine learning model to predict functional outcomes 3 months after SICH.MethodsA retrospective analysis was conducted on clinical data from 380 patients with SICH who were hospitalized at three different centers between June 2020 and June 2023. Seventy percent of the samples were randomly selected as the training set, while the remaining 30% were used as the validation set. Univariate analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Pearson correlation analysis were used to screen clinical variables. The selected variables were then incorporated into five machine learning models: complementary naive bayes (CNB), support vector machine (SVM), gaussian naive bayes (GNB), multilayer perceptron (MLP), and extreme gradient boosting (XGB), to assess their performance. Additionally, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model, and global and individual interpretive analyses were conducted using importance ranking and Shapley additive explanations (SHAP).ResultsAmong the 380 patients, 95 ultimately had poor prognostic outcomes. In the validation set, the AUC values for CNB, SVM, GNB, MLP, and XGB models were 0.899 (0.816–0.979), 0.916 (0.847–0.982), 0.730 (0.602–0.857), 0.913 (0.834–0.986), and 0.969 (0.937–0.998), respectively. Therefore, the XGB model performed the best among the five algorithms. SHAP analysis revealed that the GCS score, hematoma volume, blood pressure changes, platelets, age, bleeding location, and blood glucose levels were the most important variables for poor prognosis.ConclusionThe XGB model developed in this study can effectively predict the risk of poor prognosis in patients with SICH, helping clinicians make personalized and rational clinical decisions. Prognostic risk in patients with SICH is closely associated with GCS score, hematoma volume, blood pressure changes, platelets, age, bleeding location, and blood glucose levels.
Serum metabolomic signatures of patients with rare neurogenetic diseases: an insight into potential biomarkers and treatment targets
Authors: Wijekoon Nalaka , Gonawala Lakmal , Ratnayake Pyara , Sirisena Darshana , Gunasekara Harsha , Dissanayake Athula , Amaratunga Dhammika , Steinbusch Harry W. M. , Hathout Yetrib , Hoffman Eric P. , Dalal Ashwin , Mohan Chandra , de Silva K. Ranil D. 
Published: January 2025

IntroductionTo further advance our understanding of Muscular Dystrophies (MDs) and Spinocerebellar Ataxias (SCAs), it is necessary to identify the biological patterns associated with disease pathology. Although progress has been made in the fields of genetics and transcriptomics, there is a need for proteomics and metabolomics studies. The present study aimed to be the first to document serum metabolic signatures of MDs (DMD, BMD, and LGMD 2A) SCAs (SCA 1-3), from a South Asian perspective.MethodsA total of 28 patients (SCA 1-10, SCA 2-2, SCA 3-2, DMD-10, BMD-2, LGMD-2) and eight controls (aged 8–65 years) were included. Metabolomic analysis was performed by Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS), with support from the Houston Omics Collaborative.Results and discussionAmino acid metabolism was the primary altered super pathway in DMD followed by carbohydrate metabolism and lipid metabolism. In contrast, BMD and LGMD 2A exhibited a more prominent alteration in lipid metabolism followed by amino acid metabolism. In SCAs, primarily lipid, amino acid, peptide, nucleotide, and xenobiotics pathways are affected. Our findings offer new insights into the variance of metabolite levels in MD and SCA, with substantial implications for pathology, drug development, therapeutic targets and clinical management. Intriguingly, this study identified two novel metabolites associated with SCA. This pilot cross-sectional study warrants further research involving larger groups of participants, to validate our findings.
Triglyceride-glucose index as a marker for visceral obesity in patients with gastric cancer
Authors: Zuo Junbo , Huang Zhenhua , Ge Yan , Ding Xin , Wang Xiuhua , Huang Yan 
Published: January 2025

BackgroundThe triglyceride-glucose (TyG) index has emerged as a validated and cost-effective indicator of insulin resistance (IR). Given the significant association between visceral obesity and IR, this study aimed to investigate the utility of the TyG index in estimating visceral obesity in patients with gastric cancer (GC).MethodsThe visceral fat area (VFA), subcutaneous fat area (SFA), and VFA-to-SFA ratio (VSR) were determined through the analysis of CT images at the lumbar 3 level. The definition of visceral obesity was established as VFA ≥ 100 cm2. The association between the TyG index and visceral obesity was assessed using logistic regression analysis and restricted cubic splines. The diagnostic performance for identifying visceral obesity was evaluated by calculating the area under the Receiver Operating Characteristics curve (AUC).ResultsThe cross-sectional study enrolled a total of 314 patients with GC, among whom 159 (50.64%) were identified as having visceral obesity. The TyG index was positively correlated with VFA (r = 0.45, p &lt; 0.001), SFA (r = 0.23, p &lt; 0.001), and VSR (r = 0.35, p &lt; 0.001). However, subsequent multivariate linear regression analysis demonstrated that the TyG index was significantly associated with VFA and VSR, but not SFA. After adjusting for potential confounding factors, the TyG index remained independently associated with visceral obesity (OR = 2.54, 95% CI: 1.32–4.89, p = 0.005) and demonstrated a significantly positive linear correlation with visceral obesity in patients with GC (p-value for non-linearity = 0.116). TyG-BMI, the combination index of TyG and BMI, showed the highest predictive power in identifying visceral obesity in GC patients (AUC = 0.849, 95% CI: 0.807–0.890, p &lt; 0.001). The subgroup analysis revealed a significantly stronger positive association between the TyG index and visceral obesity in patients with BMI ≥ 25 kg/m2 (p for interaction = 0.049).ConclusionThe TyG index exhibited a significant association with visceral obesity and proved to be a valuable predictor for visceral obesity when combined with BMI in patients with GC.
Survival analysis of recurrent ovarian cancer under different PARP inhibitor treatment patterns: a single-center retrospective study
Authors: Shen Jingtian , Wang Xi , Mpano Olivier , Wang Ying , Shan Yihan , Lou Xinning , Ye Piaopiao , Yan Xiaojian 
Published: January 2025

ObjectiveTo compare the effects of different treatment modes containing PARPis and traditional treatment modes on the survival of patients with recurrent ovarian cancer.MethodsFrom December 2012 to December 2023, 131 recurrent ovarian cancer patients were screened. The patients were followed up retrospectively, and the relevant data was collected and analyzed.ResultsEighty-three patients used PARPis throughout the treatment process, and the median OS was not reached. Forty-eight patients did not use PARPis, and the median OS was 45.4 months. The two groups ‘ BRCA gene status, NACT, postoperative residual disease status, and PFI differ (P &lt; 0.05). There was no significant difference in recurrence characteristics between the PARPis use and non-use groups in first-line maintenance therapy (P &lt; 0.05). The use of PARPis, CA125 level and PFI were the independent influencing factors of OS in patients with recurrent ovarian cancer (P &lt; 0.05). The median OS of patients with PARPis maintenance treatment in the single-line, second-line and last-line has not been reached. The median OS in the multi-line group was 69.5 months.ConclusionThe use of PARPis, CA125 level and PFI were independent influencing factors of OS in patients with recurrent ovarian cancer. The first-line maintenance use of PARPis will not cause differences in disease recurrence characteristics. Compared with the patients without PARPis, patients with recurrent ovarian cancer receiving PARPis maintenance therapy have longer OS. The group of patients with PARPis maintenance treatment in the second and last lines showed better OS (P &lt; 0.05). However, OS was not significantly different between the second-line and last-line groups (P &lt; 0.05). There was no significant difference in OS between the multiple-line use PARPis and single-line use PARPis groups.
CA19-9-related macrophage polarization drives poor prognosis in HCC after immune checkpoint inhibitor treatment
Authors: Du Tingting , Zou Jialin , Yang Yunying , Xie Honghui , Pang Hui , Zhuang Wenquan , Wang Shutong , Wei Guangyan 
Published: January 2025

BackgroundElevated levels of carbohydrate antigen 19-9 (CA19-9) levels are known to worsen outcomes in various tumors by influencing immune responses. However, the role of CA19-9 in immunotherapy for hepatocellular carcinoma (HCC) remains poorly understood.MethodsThis study included 621 patients treated with anti-PD-1/PD-L1 treatment at the First Affiliated Hospital of Sun Yat-sen University from January 2017 to March 2023. During immunotherapy, CA19-9 levels were measured and classified as either elevated (≥35 U/mL) or normal (&lt;35 U/mL) for clinical analysis.ResultsPatients with elevated CA19-9 levels had significantly worse progression-free survival (PFS) and overall survival (OS). The 1-year and 2-year PFS rates were 53.3% and 29.1% in the normal CA19-9 group compared to 16.9% and 11.3% in the elevated group (p &lt; 0.001). Similarly, the 1-year and 2-year OS rates were 90.5% and 75.5% in the normal group versus 64.0% and 36.5% in the elevated group (p &lt; 0.001). Multivariate analysis confirmed CA19-9 was an independent prognostic factor for both PFS and OS. Bioinformatic analysis indicated that FUT3, a key gene in CA19-9 synthesis, correlated with increased macrophage infiltration. And increased M2 macrophage levels and reduced M1 macrophage levels were noted in HCC samples with elevated CA19-9 levels. Further in vivo experiments indicated blocking CA19-9 improved the efficacy of PD-1 treatment through inducing the M1-like polarization of macrophages.ConclusionsOur findings demonstrate that elevated CA19-9 levels during immunotherapy are associated with poor survival outcomes in HCC patients. These findings highlight the crucial role of CA19-9 in shaping the tumor immune environment, particularly through its effect on macrophage polarization, and suggest that targeting CA19-9 may improve immunotherapy outcomes.
Case Report: Combination of sirolimus and endoscopic lauromacrogol sclerotherapy in the management of blue rubber bleb nevus syndrome with gastric tract bleeding
Authors: Liu Lu , Wang Liyuan , Hu Fan 
Published: January 2025

BackgroundBlue rubber bleb nevus syndrome (BRBNS) is a rare venous malformation disorder. Currently, there is no standard therapy for this disease. However, lauromacrogol, a sclerosant extensively utilized in the management of vascular malformations, has been applied in the treatment of BRBNS. Research on the combined therapy of sirolimus and lauromacrogol for the treatment of BRBNS remains limited.Case summaryHere, we report the case of a 12-year-old girl diagnosed with BRBNS. The patient presented with chronic anemia and skin “hemangioma.” The examinations showed severe anemia, along with decreased serum iron and ferritin levels. Magnetic resonance imaging showed abnormal nodular lesions in various parts of the intestine. The patient was treated with a combination of endoscopic sclerotherapy using lauromacrogol and oral sirolimus. After 1 year of treatment, the patient showed no signs of anemia or gastrointestinal tract bleeding.ConclusionBRBNS is a rare disorder that is often misdiagnosed, especially by inexperienced pediatricians. The combination of oral sirolimus with endoscopic lauromacrogol has demonstrated efficacy in reducing lesion size and elevating hemoglobin levels.
Overweight and glucose/lipid metabolism abnormality associated with SSRIs: a pharmacovigilance study based on the FDA adverse event reporting system
Authors: Cao Jinming , Chen Zhicong , Wang Yan , Ma Yunpeng , Yang Zhen , Cai Jian , Xiao Zhijun , Xu Feng 
Published: January 2025

BackgroundIn the past few decades, selective serotonin reuptake inhibitors (SSRIs) became widely used antidepressants worldwide. Therefore, the adverse reactions of patients after SSRI administration became a public and clinical concern. In this study, we conducted a pharmacovigilance study using the Adverse Event Reporting System (FAERS) database of the US Food and Drug Administration. Our main goal was to evaluate adverse events related to SSRIs, with a particular focus on abnormal weight gain and glucose/lipid metabolism disorders.MethodThe adverse event data for representative SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline) was extracted from the FAERS database from 2004Q1 to 2023Q4. The reporting odds ratio and proportional reporting ratio were employed to explore relevant adverse event reports (ADEs) signals. Univariate logistic regression analysis was utilized to explore factors associated with glucose/lipid metabolism abnormality following SSRIs treatment.ResultsWe identified 143,744 ADE reports associated with SSRIs and revealed significant abnormal signals related to weight gain and glucose/lipid metabolism in depressed patients. Variations were observed among different SSRIs medications. Specifically, citalopram was associated with abnormal weight gain (ROR: 4, 95% CI: 3.1-5.2) and hepatic steatosis (ROR: 2.8, 95% CI: 2.1-3.6); escitalopram was correlated with gestational diabetes (ROR: 9.1, 95% CI: 6.6-12.4) and cholestasis (ROR: 2.4, 95% CI: 1.75-3.38); fluoxetine was associated with obesity (ROR: 2.8, 95% CI: 2.08-3.78); fluvoxamine was linked to arteriospasm coronary (ROR: 13.87, 95% CI: 4.47-43.1); and sertraline was implicated in neonatal jaundice (ROR: 16.1, 95% CI: 12.6-20.6). Females and younger age are important risk factors for the development of associated adverse effects.ConclusionOur study screened for adverse effects associated with abnormal glucose/lipid metabolism, such as abnormal body weight and fatty liver, in depressed patients taking selective serotonin reuptake inhibitors by utilizing FAERS database. This provides valuable insights for healthcare professionals in accepting and managing patients treated with SSRIs.