cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30675962.v1
title: A machine-learning model to identify concurrent vascular disease in symptomatic patients with chronic obstructive pulmonary disease

description: Chronic obstructive pulmonary disease (COPD) is a complex, heterogeneous syndrome often accompanied by vascular diseases that worsen prognosis and quality of life. This study aimed to develop a machine learning model to identify concurrent vascular diseases in symptomatic COPD patients. We retrospectively analyzed data from 6,274 COPD patients treated between July 2010 and July 2018. Patients were randomly split into training and validation sets (7:3). After feature selection using LASSO regression, eight machine learning algorithms—including Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Convolutional Neural Network, AdaBoost, and Stacked Generalization (Stacking)—were applied to develop and validate predictive models. Performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA). The Stacking model achieved the highest AUC (0.867; 95% CI: 0.852–0.882), with 79.4% accuracy, 74.9% sensitivity, and 84.0% specificity. It also demonstrated excellent calibration and, on DCA, provided the highest net clinical benefit within the threshold probability range of 0.1–0.5. At a 0.2 threshold, the model could prevent approximately 35% of unnecessary interventions compared to a "treat-all" approach, while identifying about 75% of high-risk patients relative to a "treat-none" strategy. The Stacking machine-learning model showed superior performance in identifying concurrent vascular disease among symptomatic COPD patients, offering strong discriminative ability, calibration, and clinical utility. It may serve as an effective decision-support tool to optimize diagnostic evaluation in this high-risk subgroup.
DOI: 10.6084/m9.figshare.29554275
title: Comparison of the burden of musculoskeletal disorders between China and worldwide data using the global burden of disease dataset from 1990 to 2021

description: This study was to compare the worldwide burden of musculoskeletal (MSK) disorders with the age and gender-specific trends of MSK disorders in China and globally between 1990 and 2021. Using publicly available data from the Global Burden of Disease (GBD) database from 1990 to 2021. Examined the features of the burden of MSK disorders in China and globally, including age and gender-specific trends in incidence, prevalence, mortality, disability-adjusted life years (DALYs), and related age-standardized measures of MSK disorders. To represent the trends in the burden of MSK disorders, the average annual percentage change (AAPC) was computed using Joinpoint. Age, gender were important parameters that were used a comparative study of the disparities in the burden of MSK disorders between China and the global. The worldwide ASIR of MSK disorders rose from 4641.50 to 4358.54 between 1990 and 2021, and China fell from 4039.13 to 3634.09 per 100,000. The worldwide ASPR rose from 19178.47 to 19836.76, China, went from 16966.24 to 17358.70 per 100,000. The ASMR in China declined from 1.22 to 1.10, while the worldwide decreased from 1.55 to 1.47 per 100,000. The ASDR in China declined from 1615.73 to 1578.71, while the worldwide ASDR decreased from 1886.22 to 1916.21 per 100,000. The worldwide AAPC of ASIR, ASPR, ASMR, and ASDR was −0.21%, 0.11%, −0.25%, and 0.04%, respectively. China was −0.34%, 0.09%, −0.41%, and −0.07%, respectively. The burden of MSK disorders was influenced similarly by age and gender. The burden of MSK disorders rose in China and globally between 1990 and 2021, and it varies with age. Women are more prone than men to acquire MSK disorders. Because of its vast and aging population, MSK disorders continue to be a major public health concern in China and globally.
DOI: 10.6084/m9.figshare.29554275.v1
title: Comparison of the burden of musculoskeletal disorders between China and worldwide data using the global burden of disease dataset from 1990 to 2021

description: This study was to compare the worldwide burden of musculoskeletal (MSK) disorders with the age and gender-specific trends of MSK disorders in China and globally between 1990 and 2021. Using publicly available data from the Global Burden of Disease (GBD) database from 1990 to 2021. Examined the features of the burden of MSK disorders in China and globally, including age and gender-specific trends in incidence, prevalence, mortality, disability-adjusted life years (DALYs), and related age-standardized measures of MSK disorders. To represent the trends in the burden of MSK disorders, the average annual percentage change (AAPC) was computed using Joinpoint. Age, gender were important parameters that were used a comparative study of the disparities in the burden of MSK disorders between China and the global. The worldwide ASIR of MSK disorders rose from 4641.50 to 4358.54 between 1990 and 2021, and China fell from 4039.13 to 3634.09 per 100,000. The worldwide ASPR rose from 19178.47 to 19836.76, China, went from 16966.24 to 17358.70 per 100,000. The ASMR in China declined from 1.22 to 1.10, while the worldwide decreased from 1.55 to 1.47 per 100,000. The ASDR in China declined from 1615.73 to 1578.71, while the worldwide ASDR decreased from 1886.22 to 1916.21 per 100,000. The worldwide AAPC of ASIR, ASPR, ASMR, and ASDR was −0.21%, 0.11%, −0.25%, and 0.04%, respectively. China was −0.34%, 0.09%, −0.41%, and −0.07%, respectively. The burden of MSK disorders was influenced similarly by age and gender. The burden of MSK disorders rose in China and globally between 1990 and 2021, and it varies with age. Women are more prone than men to acquire MSK disorders. Because of its vast and aging population, MSK disorders continue to be a major public health concern in China and globally.
DOI: 10.6084/m9.figshare.28702675
title: Amylin inhibits gastric cancer progression by targeting CCN1 and affecting the PI3K/AKT signalling pathway

description: <b>Background: </b>After glucagon and insulin, amylin is the third most significant and active islet hormone. Amylin and insulin act synergistically to suppress postprandial blood glucose levels. Research has shown that insulin plays a vital role in the growth and progression of cancer; however, the relationship between amylin and gastric cancer (GC) is unclear. This study used a combination of <i>in vitro</i> and <i>in vivo</i> experiments to investigate the role of amylin in the progression of GC. The expression of amylin in GC and its clinical correlation were evaluated using 38 pairs of GC and healthy human clinical samples. <i>In vitro</i> studies, human GC cell lines were treated with amylin to evaluate the effects of amylin on the proliferation, apoptosis and migration of GC cells. In <i>in vivo</i> studies, xenograft mouse models were established by subcutaneous injection of GC cells into nude mice, followed by treatment with amylin to assess tumor growth. Finally, Next-Generation Sequencing Technology (RNA-seq) was used to explore the potential mechanism of amylin on GC. We found that amylin expression was reduced in GC compared to adjacent normal gastric tissues and that elevated amylin expression was negatively correlated with adverse pathological factors (<i>p</i> &lt; 0.05). Additionally, we demonstrated that amylin impeded the growth, invasion, migration, and colony formation of GC cells and suppressed the epithelial-to-mesenchymal transformation of these cells (<i>p</i> &lt; 0.05). Tumour xenograft model experiments confirmed the tumour-suppressive effect of amylin in subcutaneous tumours in nude mice (<i>p</i> &lt; 0.05). Transcriptome sequencing (RNA-seq) revealed that amylin significantly down-regulated CCN1 gene expression in GC cells (<i>p</i> &lt; 0.001). Further intervention targeting CCN1 verified its significance as a target of amylin’s anti-carcinogenic function in GC. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that amylin exerted its oncogenic effects by inhibiting the PI3K/Akt signalling pathway (<i>p</i> &lt; 0.05). Our findings demonstrate that amylin plays a crucial role in suppressing gastric cancer progression by targeting CCN1 and inhibiting the PI3K/Akt signalling pathway. These results suggest that amylin could serve as a potential therapeutic agent for GC treatment.
DOI: 10.6084/m9.figshare.28702675.v1
title: Amylin inhibits gastric cancer progression by targeting CCN1 and affecting the PI3K/AKT signalling pathway

description: <b>Background: </b>After glucagon and insulin, amylin is the third most significant and active islet hormone. Amylin and insulin act synergistically to suppress postprandial blood glucose levels. Research has shown that insulin plays a vital role in the growth and progression of cancer; however, the relationship between amylin and gastric cancer (GC) is unclear. This study used a combination of <i>in vitro</i> and <i>in vivo</i> experiments to investigate the role of amylin in the progression of GC. The expression of amylin in GC and its clinical correlation were evaluated using 38 pairs of GC and healthy human clinical samples. <i>In vitro</i> studies, human GC cell lines were treated with amylin to evaluate the effects of amylin on the proliferation, apoptosis and migration of GC cells. In <i>in vivo</i> studies, xenograft mouse models were established by subcutaneous injection of GC cells into nude mice, followed by treatment with amylin to assess tumor growth. Finally, Next-Generation Sequencing Technology (RNA-seq) was used to explore the potential mechanism of amylin on GC. We found that amylin expression was reduced in GC compared to adjacent normal gastric tissues and that elevated amylin expression was negatively correlated with adverse pathological factors (<i>p</i> &lt; 0.05). Additionally, we demonstrated that amylin impeded the growth, invasion, migration, and colony formation of GC cells and suppressed the epithelial-to-mesenchymal transformation of these cells (<i>p</i> &lt; 0.05). Tumour xenograft model experiments confirmed the tumour-suppressive effect of amylin in subcutaneous tumours in nude mice (<i>p</i> &lt; 0.05). Transcriptome sequencing (RNA-seq) revealed that amylin significantly down-regulated CCN1 gene expression in GC cells (<i>p</i> &lt; 0.001). Further intervention targeting CCN1 verified its significance as a target of amylin’s anti-carcinogenic function in GC. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that amylin exerted its oncogenic effects by inhibiting the PI3K/Akt signalling pathway (<i>p</i> &lt; 0.05). Our findings demonstrate that amylin plays a crucial role in suppressing gastric cancer progression by targeting CCN1 and inhibiting the PI3K/Akt signalling pathway. These results suggest that amylin could serve as a potential therapeutic agent for GC treatment.
DOI: 10.6084/m9.figshare.28382265
title: Prognostic and clinicopathological value of soluble programmed cell death ligand-1 (sPD-L1) in patients with peripheral T-cell lymphoma: a meta-analysis

description: Previous studies have explored whether soluble programmed cell death ligand-1 (sPD-L1) can be used to predict the prognosis of patients with peripheral T-cell lymphoma (PTCL); however, no consistent results have been obtained. Consequently, we conducted the present meta-analysis to identify the precise significance of sPD-L1 in predicting the prognosis of PTCL. We searched PubMed, Embase, Web of Science, and the Cochrane Library until July 31, 2024. The value of sPD-L1 in predicting PTCL prognosis was examined by combining the hazard ratios (HRs) with 95% confidence intervals (CIs). Seven articles involving 445 patients were included in this study. Based on our pooled findings, increased sPD-L1 was associated with dismal overall survival (OS) (HR = 4.22, 95%CI = 1.89–9.43, <i>p</i> &lt; 0.001) and worse progression-free survival (PFS) (HR = 2.57, 95%CI = 1.35–4.90, <i>p</i> = 0.004) in PTCL. Furthermore, higher sPD-L1 levels were correlated with male sex (OR = 1.80, 95%CI = 1.06–3.03, <i>p</i> = 0.029), International Prognostic Index (IPI) score ≥2 (OR = 4.32, 95%CI = 2.10–8.89, <i>p</i> &lt; 0.001), elevated lactate dehydrogenase (LDH) level (OR = 5.15, 95%CI = 1.94–13.71, <i>p</i> = 0.001), presence of B symptoms (OR = 2.56, 95%CI = 1.45–4.52, <i>p</i> = 0.001), and ECOG PS ≥2 (OR = 7.41, 95%CI = 1.49–36.92, <i>p</i> = 0.015) in PTCL. According to the present meta-analysis, higher sPD-L1 levels were significantly correlated with poor OS and inferior PFS in patients with PTCL. Additionally, high sPD-L1 levels were also associated with clinical features representing the development of PTCL. The present meta-analysis has first explored the impact of sPD-L1 on forecasting PTCL prognosis.Higher sPD-L1 level was significantly correlated with dismal OS and inferior PFS of PTCL patients.High sPD-L1 was also connected to clinical features representing the disease development of PTCL. The present meta-analysis has first explored the impact of sPD-L1 on forecasting PTCL prognosis. Higher sPD-L1 level was significantly correlated with dismal OS and inferior PFS of PTCL patients. High sPD-L1 was also connected to clinical features representing the disease development of PTCL.
DOI: 10.6084/m9.figshare.28382265.v1
title: Prognostic and clinicopathological value of soluble programmed cell death ligand-1 (sPD-L1) in patients with peripheral T-cell lymphoma: a meta-analysis

description: Previous studies have explored whether soluble programmed cell death ligand-1 (sPD-L1) can be used to predict the prognosis of patients with peripheral T-cell lymphoma (PTCL); however, no consistent results have been obtained. Consequently, we conducted the present meta-analysis to identify the precise significance of sPD-L1 in predicting the prognosis of PTCL. We searched PubMed, Embase, Web of Science, and the Cochrane Library until July 31, 2024. The value of sPD-L1 in predicting PTCL prognosis was examined by combining the hazard ratios (HRs) with 95% confidence intervals (CIs). Seven articles involving 445 patients were included in this study. Based on our pooled findings, increased sPD-L1 was associated with dismal overall survival (OS) (HR = 4.22, 95%CI = 1.89–9.43, <i>p</i> &lt; 0.001) and worse progression-free survival (PFS) (HR = 2.57, 95%CI = 1.35–4.90, <i>p</i> = 0.004) in PTCL. Furthermore, higher sPD-L1 levels were correlated with male sex (OR = 1.80, 95%CI = 1.06–3.03, <i>p</i> = 0.029), International Prognostic Index (IPI) score ≥2 (OR = 4.32, 95%CI = 2.10–8.89, <i>p</i> &lt; 0.001), elevated lactate dehydrogenase (LDH) level (OR = 5.15, 95%CI = 1.94–13.71, <i>p</i> = 0.001), presence of B symptoms (OR = 2.56, 95%CI = 1.45–4.52, <i>p</i> = 0.001), and ECOG PS ≥2 (OR = 7.41, 95%CI = 1.49–36.92, <i>p</i> = 0.015) in PTCL. According to the present meta-analysis, higher sPD-L1 levels were significantly correlated with poor OS and inferior PFS in patients with PTCL. Additionally, high sPD-L1 levels were also associated with clinical features representing the development of PTCL. The present meta-analysis has first explored the impact of sPD-L1 on forecasting PTCL prognosis.Higher sPD-L1 level was significantly correlated with dismal OS and inferior PFS of PTCL patients.High sPD-L1 was also connected to clinical features representing the disease development of PTCL. The present meta-analysis has first explored the impact of sPD-L1 on forecasting PTCL prognosis. Higher sPD-L1 level was significantly correlated with dismal OS and inferior PFS of PTCL patients. High sPD-L1 was also connected to clinical features representing the disease development of PTCL.
DOI: 10.6084/m9.figshare.28770615
title: A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles

description: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method for predicting the clinical efficacy of GC treatment based on immune variables detected via flow cytometry. We collected 394 tumour tissues from GC patients for flow cytometry analysis and gating analysis of tumour-infiltrating immune cells (TIICs). Unsupervised consensus clusters were generated from the cohort to classify patients into different phenogroups, and their clinical characteristics were examined. The derived model was evaluated via principal component analysis and <i>t</i>-distributed stochastic neighbourhood embedding analysis. Kaplan–Meier’s curves were used to determine the prognosis during a 920-day-long median follow-up period (interquartile range: 834–1071 days). Adjusted multivariate Cox regression analysis was used to evaluate the association of clusters with disease-free survival (DFS) and recurrence. All patients were classified based on their TIIC profiles into the C1 (characterized by low CD45 negative cell, high lymphocyte, high neutrophil and low CD3 + T cell levels), C2 (characterized by high CD8 + CD279+ cell and low CD4+ Th and CD8+ Tc cell numbers) and C3 (characterized by high CD4 + CD25+ and Treg cell levels) phenogroups. Patients from the three clusters had varied pathologies, MMR statuses and TIIC distribution patterns (<i>p</i> &lt; .05). Kaplan–Meier’s analysis showed that the prognosis of C3 was inferior compared to C1 and C2 (<i>p</i> = .0025). Adjusted Cox proportional hazard models helped us identify that C1 and C2 exhibited a favourable factor of recurrence after surgery, compared to C3. Kaplan–Meier’s analysis showed that C1 and C2 were associated with a better DFS than C3 in some GC patient subgroups. The machine learning model developed was found to be effective model at predicting the prognosis of patients with GC and their TIIC profiles for risk stratification in clinical settings.
DOI: 10.6084/m9.figshare.28770615.v1
title: A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles

description: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method for predicting the clinical efficacy of GC treatment based on immune variables detected via flow cytometry. We collected 394 tumour tissues from GC patients for flow cytometry analysis and gating analysis of tumour-infiltrating immune cells (TIICs). Unsupervised consensus clusters were generated from the cohort to classify patients into different phenogroups, and their clinical characteristics were examined. The derived model was evaluated via principal component analysis and <i>t</i>-distributed stochastic neighbourhood embedding analysis. Kaplan–Meier’s curves were used to determine the prognosis during a 920-day-long median follow-up period (interquartile range: 834–1071 days). Adjusted multivariate Cox regression analysis was used to evaluate the association of clusters with disease-free survival (DFS) and recurrence. All patients were classified based on their TIIC profiles into the C1 (characterized by low CD45 negative cell, high lymphocyte, high neutrophil and low CD3 + T cell levels), C2 (characterized by high CD8 + CD279+ cell and low CD4+ Th and CD8+ Tc cell numbers) and C3 (characterized by high CD4 + CD25+ and Treg cell levels) phenogroups. Patients from the three clusters had varied pathologies, MMR statuses and TIIC distribution patterns (<i>p</i> &lt; .05). Kaplan–Meier’s analysis showed that the prognosis of C3 was inferior compared to C1 and C2 (<i>p</i> = .0025). Adjusted Cox proportional hazard models helped us identify that C1 and C2 exhibited a favourable factor of recurrence after surgery, compared to C3. Kaplan–Meier’s analysis showed that C1 and C2 were associated with a better DFS than C3 in some GC patient subgroups. The machine learning model developed was found to be effective model at predicting the prognosis of patients with GC and their TIIC profiles for risk stratification in clinical settings.
DOI: 10.6084/m9.figshare.29456283
title: Comparative efficacy of glioma treatment strategies: an umbrella review of meta-analyses

description: To assess the quality of evidence, identify potential biases, and verify the validity of existing studies on glioma treatment outcomes. We extracted and analyzed data from systematic reviews and meta-analyses assessing therapies in glioma patients. Our search included PubMed, Embase, Web of Science, and the Cochrane Database up to July 2024. Two authors independently evaluated each meta-analysis for methodological quality using the AMSTAR tool, following established evidence classification guidelines. This study evaluated 68 meta-analyses and 389 outcomes from 4,243 articles, classifying 193 outcomes as ‘very low’ quality, 96 as ‘low’ quality, 28 (7.2%) as ‘high’ quality, and 72 (18.5%) as ‘moderate’ quality, 156 as NS, 231 (59.4%) as Class IV, and 2 (0.005%) as Class III, respectively. High-quality evidence demonstrated that combining Traditional Chinese and Western medicine, adding bevacizumab (BVZ) to chemoradiotherapy (CRT), and other specific treatments improved overall survival (OS). Molecularly targeted drugs combined with temozolomide (TMZ) and radiotherapy (RT), as well as standard therapy augmented with anti-vascular endothelial growth factor or BVZ, prolonged progression-free survival (PFS). Additionally, treatments involving RT with TMZ or TMZ alone increased adverse events, whereas high-quality evidence showed that integrated Traditional Chinese and Western medicine, compared to Western medicine alone, reduced bone marrow suppression and gastrointestinal issues, enhancing treatment efficacy. High-quality evidence indicates that targeted therapy, immunotherapy, CRT, integrated Chinese and Western medicine, gene therapy, and combinations of targeted therapies with CRT or chemotherapy (CT) can extend OS and PFS in gliomas. However, CT, CRT, and targeted therapies, with or without additional CT or CRT, may increase adverse events. Integrated Chinese and Western medicine alone may reduce adverse effects.