cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29910856
title: Identification of a prognostic signature consisting of three macrophage-related genes for glioblastoma based on bulk and single-cell transcriptomes analyses

description: Tumor-associated macrophages have been implicated in the progression and treatment resistance of glioblastoma (GBM). This study aimed to identify macrophage-related genes associated with prognosis and therapeutic response in GBM. Bulk RNA-seq data from 533 patients with GBM were downloaded from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Bioinformatic tools were used to detect the co-expression gene modules associated with the infiltration of immune cells, identify a prognostic macrophage-related gene signature, and explore their association with sensitivity to chemotherapeutic drugs and immune checkpoint blockade. Single-cell RNA-seq data and multiplexed immunofluorescence were used to validate ISG20 expression (a member of the identified gene signature) in macrophages. We detected gene modules associated with macrophages and identified a signature consisting of three macrophage-related genes (ISG20, PARP12 and IFIT5) in the discovery set (TCGA–GBM, <i>n</i> = 159), and validated its prognostic value in the validation set (CGGA–GBM, <i>n</i> = 374). This gene signature demonstrated favorable accuracy in predicting prognosis and resistance of immuno- and chemo-therapy. The co-expression of ISG20 and PD-1 in macrophages was verified by single-cell RNA-seq data and multiplex immunofluorescence. This study presents a macrophage-related gene signature to predict prognosis and therapeutic response in GBM. ISG20, PARP12 and IFIT5 are interferon-stimulated genes, and further investigations may provide new insights into the interplay between macrophages and interferon signaling in GBM.
DOI: 10.6084/m9.figshare.29910856.v1
title: Identification of a prognostic signature consisting of three macrophage-related genes for glioblastoma based on bulk and single-cell transcriptomes analyses

description: Tumor-associated macrophages have been implicated in the progression and treatment resistance of glioblastoma (GBM). This study aimed to identify macrophage-related genes associated with prognosis and therapeutic response in GBM. Bulk RNA-seq data from 533 patients with GBM were downloaded from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Bioinformatic tools were used to detect the co-expression gene modules associated with the infiltration of immune cells, identify a prognostic macrophage-related gene signature, and explore their association with sensitivity to chemotherapeutic drugs and immune checkpoint blockade. Single-cell RNA-seq data and multiplexed immunofluorescence were used to validate ISG20 expression (a member of the identified gene signature) in macrophages. We detected gene modules associated with macrophages and identified a signature consisting of three macrophage-related genes (ISG20, PARP12 and IFIT5) in the discovery set (TCGA–GBM, <i>n</i> = 159), and validated its prognostic value in the validation set (CGGA–GBM, <i>n</i> = 374). This gene signature demonstrated favorable accuracy in predicting prognosis and resistance of immuno- and chemo-therapy. The co-expression of ISG20 and PD-1 in macrophages was verified by single-cell RNA-seq data and multiplex immunofluorescence. This study presents a macrophage-related gene signature to predict prognosis and therapeutic response in GBM. ISG20, PARP12 and IFIT5 are interferon-stimulated genes, and further investigations may provide new insights into the interplay between macrophages and interferon signaling in GBM.
DOI: 10.6084/m9.figshare.30516882
title: Using a coloring activity to identify children’s development of visual–motor integration: an application of artificial intelligence

description: Visual–motor integration (VMI) is an important indicator in children with learning disabilities. We aimed to use performance in a coloring activity to identify children’s VMI developmental status. A sample of 505 preschool children (mean = 57.64, SD = 11.10) were recruited. Among them, data from 404 and 101 children were used as the training and testing data, respectively. The Beery–Buktenica Developmental Test of Visual–motor Integration, fourth Edition, (VMI-4) was used as an indicator for the model of artificial intelligence (AI). The total scores of the VMI-4 were calculated, and then based on the children’s age, the total scores were transferred into standard scores and the developmental status of visual–motor integration. The AI model comprised a regression model and classification model to predict the developmental status rated by the VMI-4. In the training data, we found that the AI model comprising the support vector machine (SVM) regression model and eXtreme Gradient Boostin (XGBoost) classification model exhibited the best performance (accuracy: 86.2%; sensitivity: 84.7%; and specificity, 85.4%). The results of the trained AI model on the testing data indicated good performance, with accuracy, sensitivity, and specificity of 80.20%, 73.68%, and 81.71%, respectively. Combining the coloring activity with the AI technique has great potential as a screening tool to identify children’s VMI developmental status.
DOI: 10.6084/m9.figshare.30516882.v1
title: Using a coloring activity to identify children’s development of visual–motor integration: an application of artificial intelligence

description: Visual–motor integration (VMI) is an important indicator in children with learning disabilities. We aimed to use performance in a coloring activity to identify children’s VMI developmental status. A sample of 505 preschool children (mean = 57.64, SD = 11.10) were recruited. Among them, data from 404 and 101 children were used as the training and testing data, respectively. The Beery–Buktenica Developmental Test of Visual–motor Integration, fourth Edition, (VMI-4) was used as an indicator for the model of artificial intelligence (AI). The total scores of the VMI-4 were calculated, and then based on the children’s age, the total scores were transferred into standard scores and the developmental status of visual–motor integration. The AI model comprised a regression model and classification model to predict the developmental status rated by the VMI-4. In the training data, we found that the AI model comprising the support vector machine (SVM) regression model and eXtreme Gradient Boostin (XGBoost) classification model exhibited the best performance (accuracy: 86.2%; sensitivity: 84.7%; and specificity, 85.4%). The results of the trained AI model on the testing data indicated good performance, with accuracy, sensitivity, and specificity of 80.20%, 73.68%, and 81.71%, respectively. Combining the coloring activity with the AI technique has great potential as a screening tool to identify children’s VMI developmental status.
DOI: 10.6084/m9.figshare.30635846
title: Machine learning-based preliminary screening tool for clinical pregnancy prediction: towards management of IVF/ICSI stages

description: Accurate prediction of pregnancy outcomes in assisted reproductive technology (ART) remains a clinical challenge due to the complexity and heterogeneity of IVF/ICSI cycles. Existing models often focus on isolated treatment stages and rely on linear statistical assumptions, limiting their ability to support personalized care throughout the entire treatment process. This retrospective study included 1,062 women who underwent IVF/ICSI between 2016 and 2021, with an additional temporal validation cohort of 250 patients treated in 2022. Two machine learning (ML) models were developed to predict clinical pregnancy outcomes during the pre-treatment and treatment phases. Model performance was evaluated using metrics including precision-recall curves, F1 score, calibration, Brier score, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to enhance interpretability, and restricted cubic spline (RCS) analysis explored nonlinear relationships. Both models were deployed as interactive web calculators to facilitate clinical use. Both models demonstrated favorable performance in internal and external validation. Key predictors identified for the pre-treatment phase included female age, antral follicle count (AFC), and body mass index (BMI). For the treatment phase, important predictors comprised serum progesterone level on HCG day, gonadotropin dosage, and endometrial thickness on HCG day. RCS and subgroup analyses revealed significant nonlinear threshold effects of these variables on pregnancy probability. We developed and validated dual-phase ML models for clinical pregnancy prediction across IVF/ICSI stages. Through improved interpretability and online accessibility, our models offer a practical and individualized decision-support tool to optimize ART strategies in real-world clinical settings.
DOI: 10.6084/m9.figshare.30635846.v1
title: Machine learning-based preliminary screening tool for clinical pregnancy prediction: towards management of IVF/ICSI stages

description: Accurate prediction of pregnancy outcomes in assisted reproductive technology (ART) remains a clinical challenge due to the complexity and heterogeneity of IVF/ICSI cycles. Existing models often focus on isolated treatment stages and rely on linear statistical assumptions, limiting their ability to support personalized care throughout the entire treatment process. This retrospective study included 1,062 women who underwent IVF/ICSI between 2016 and 2021, with an additional temporal validation cohort of 250 patients treated in 2022. Two machine learning (ML) models were developed to predict clinical pregnancy outcomes during the pre-treatment and treatment phases. Model performance was evaluated using metrics including precision-recall curves, F1 score, calibration, Brier score, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to enhance interpretability, and restricted cubic spline (RCS) analysis explored nonlinear relationships. Both models were deployed as interactive web calculators to facilitate clinical use. Both models demonstrated favorable performance in internal and external validation. Key predictors identified for the pre-treatment phase included female age, antral follicle count (AFC), and body mass index (BMI). For the treatment phase, important predictors comprised serum progesterone level on HCG day, gonadotropin dosage, and endometrial thickness on HCG day. RCS and subgroup analyses revealed significant nonlinear threshold effects of these variables on pregnancy probability. We developed and validated dual-phase ML models for clinical pregnancy prediction across IVF/ICSI stages. Through improved interpretability and online accessibility, our models offer a practical and individualized decision-support tool to optimize ART strategies in real-world clinical settings.
DOI: 10.6084/m9.figshare.30811381
title: Nonlinear association of a body shape index and hyperlipidemia: an American population-based cross-sectional study

description: This study aimed to evaluate the association between A Body Shape Index (ABSI) and hyperlipidemia, and assess nonlinear relationships <i>via</i> restricted cubic spline (RCS) analysis. Data from 21,013 adults in the National Health and Nutrition Examination Survey (NHANES, 2005–2020) were analyzed. Hyperlipidemia was defined using National Cholesterol Education Program (NCEP) criteria. ABSI was calculated as waist circumference/(BMI^(2/3) × H^(1/2)) and stratified into quartiles (Q1–Q4). Multivariable logistic regression models, adjusted for demographic characteristics, health behaviors, and clinical features, assessed the association between ABSI and hyperlipidemia. RCS and subgroup analyses explored nonlinearity and interactions, with sensitivity analyses excluding lipid-lowering medication users and participants who died within two years. Higher ABSI was significantly associated with increased hyperlipidemia risk across models. In the fully adjusted model, each ABSI unit increase correlated with a 24% higher risk (OR=1.24, 95% CI:1.17-1.31). Quartile analysis showed gradually elevated risk, with Q4 (highest) having 68% higher odds than Q1 (lowest; OR=1.68, 95% CI:1.46-1.94, P-trend&lt;0.001). RCS revealed a significant nonlinear inverse U-shaped relationship (P for nonlinearity&lt;0.001) with a threshold of 0.084; participants with ABSI&gt;0.084 had a 28% higher risk (OR=1.28, 95% CI:1.13-1.44) versus ABSI≤0.084. Subgroup analyses identified significant interactions with age (P&lt;0.001), marital status (P=0.028), hypertension (P=0.005), and diabetes (P=0.001). Sensitivity analyses confirmed result robustness. This study provides the first comprehensive evidence of a significant ABSI-hyperlipidemia association. The threshold effect suggests ABSI may serve as a valuable anthropometric tool for hyperlipidemia risk stratification. Future studies should validate causality and explore mechanisms underlying the observed nonlinear dynamics.
DOI: 10.6084/m9.figshare.30811381.v1
title: Nonlinear association of a body shape index and hyperlipidemia: an American population-based cross-sectional study

description: This study aimed to evaluate the association between A Body Shape Index (ABSI) and hyperlipidemia, and assess nonlinear relationships <i>via</i> restricted cubic spline (RCS) analysis. Data from 21,013 adults in the National Health and Nutrition Examination Survey (NHANES, 2005–2020) were analyzed. Hyperlipidemia was defined using National Cholesterol Education Program (NCEP) criteria. ABSI was calculated as waist circumference/(BMI^(2/3) × H^(1/2)) and stratified into quartiles (Q1–Q4). Multivariable logistic regression models, adjusted for demographic characteristics, health behaviors, and clinical features, assessed the association between ABSI and hyperlipidemia. RCS and subgroup analyses explored nonlinearity and interactions, with sensitivity analyses excluding lipid-lowering medication users and participants who died within two years. Higher ABSI was significantly associated with increased hyperlipidemia risk across models. In the fully adjusted model, each ABSI unit increase correlated with a 24% higher risk (OR=1.24, 95% CI:1.17-1.31). Quartile analysis showed gradually elevated risk, with Q4 (highest) having 68% higher odds than Q1 (lowest; OR=1.68, 95% CI:1.46-1.94, P-trend&lt;0.001). RCS revealed a significant nonlinear inverse U-shaped relationship (P for nonlinearity&lt;0.001) with a threshold of 0.084; participants with ABSI&gt;0.084 had a 28% higher risk (OR=1.28, 95% CI:1.13-1.44) versus ABSI≤0.084. Subgroup analyses identified significant interactions with age (P&lt;0.001), marital status (P=0.028), hypertension (P=0.005), and diabetes (P=0.001). Sensitivity analyses confirmed result robustness. This study provides the first comprehensive evidence of a significant ABSI-hyperlipidemia association. The threshold effect suggests ABSI may serve as a valuable anthropometric tool for hyperlipidemia risk stratification. Future studies should validate causality and explore mechanisms underlying the observed nonlinear dynamics.
DOI: 10.5281/zenodo.18329860
title: Base de datos del TFG Análisis de la calidad universitaria a través de datos públicos

description: Conjunto de datos construido para el Trabajo de Fin de Grado “Análisis de la calidad universitaria a través de datos públicos”. Integra posiciones en rankings universitarios internacionales y variables institucionales complementarias para análisis multivariante y visualización. Incluye base en formato Excel y documentación asociada.
DOI: 10.6084/m9.figshare.29606070
title: Relatively low plasma cortisol levels in parturients are associated with epidural-related maternal fever

description: Epidural analgesia (EA) may suppress the stress response or cortisol production during vaginal delivery by blocking pain transmission. However, there is no evidence that epidural analgesia contributes to epidural-related maternal fever (ERMF) by inhibiting the hypothalamic pituitary adrenal (HPA) axis. This study aimed to evaluate the relationship between maternal cortisol levels and ERMF during vaginal delivery. Prospective cohort study. The venous blood samples were collected from full-term pregnant women undergoing vaginal delivery and categorized into control group (&lt;37.5 °C without EA), normal group (&lt;37.5 °C with EA), mild group (37.5–37.9 °C with EA) or fever group (≥38 °C with Epidural analgesia (EA)). Then, plasma cortisol and pro-inflammatory cytokine levels (IL-6, TNF-α, IL-2 and IFN-γ) were assessed to determine the impact of EA on the stress response. We also detected hormones related to the HPA axis, including CRH and ACTH plasma concentration. Additionally, general adrenal function was evaluated by measuring renin, aldosterone, testosterone, and DHEA-s concentrations in the maternal venous blood. Clinical evidence of adrenal dysfunction was also investigated using medical records. Epidural analgesia induced high levels of cytokines IL-6 and TNF-α in all parturients, but ERMF was observed only in those with lower cortisol levels. Correlation analysis revealed a positive relationship between IL-6 (or TNF-α) and cortisol concentration ratio and maximum maternal temperature. Elevated ACTH, β-endorphin and αMSH levels were observed in parturients with ERMF, indicating primary adrenal dysfunction. Medical records showed no significant differences in symptoms, signs, or laboratory results related to adrenal function among the four groups. There was a relationship between relatively low maternal cortisol levels and ERMF. ChiCTR2200058359 obtained on April 7, 2022.
DOI: 10.6084/m9.figshare.29606070.v1
title: Relatively low plasma cortisol levels in parturients are associated with epidural-related maternal fever

description: Epidural analgesia (EA) may suppress the stress response or cortisol production during vaginal delivery by blocking pain transmission. However, there is no evidence that epidural analgesia contributes to epidural-related maternal fever (ERMF) by inhibiting the hypothalamic pituitary adrenal (HPA) axis. This study aimed to evaluate the relationship between maternal cortisol levels and ERMF during vaginal delivery. Prospective cohort study. The venous blood samples were collected from full-term pregnant women undergoing vaginal delivery and categorized into control group (&lt;37.5 °C without EA), normal group (&lt;37.5 °C with EA), mild group (37.5–37.9 °C with EA) or fever group (≥38 °C with Epidural analgesia (EA)). Then, plasma cortisol and pro-inflammatory cytokine levels (IL-6, TNF-α, IL-2 and IFN-γ) were assessed to determine the impact of EA on the stress response. We also detected hormones related to the HPA axis, including CRH and ACTH plasma concentration. Additionally, general adrenal function was evaluated by measuring renin, aldosterone, testosterone, and DHEA-s concentrations in the maternal venous blood. Clinical evidence of adrenal dysfunction was also investigated using medical records. Epidural analgesia induced high levels of cytokines IL-6 and TNF-α in all parturients, but ERMF was observed only in those with lower cortisol levels. Correlation analysis revealed a positive relationship between IL-6 (or TNF-α) and cortisol concentration ratio and maximum maternal temperature. Elevated ACTH, β-endorphin and αMSH levels were observed in parturients with ERMF, indicating primary adrenal dysfunction. Medical records showed no significant differences in symptoms, signs, or laboratory results related to adrenal function among the four groups. There was a relationship between relatively low maternal cortisol levels and ERMF. ChiCTR2200058359 obtained on April 7, 2022.
DOI: 10.5281/zenodo.18329859
title: Base de datos del TFG Análisis de la calidad universitaria a través de datos públicos

description: Conjunto de datos construido para el Trabajo de Fin de Grado “Análisis de la calidad universitaria a través de datos públicos”. Integra posiciones en rankings universitarios internacionales y variables institucionales complementarias para análisis multivariante y visualización. Incluye base en formato Excel y documentación asociada.
DOI: 10.6084/m9.figshare.30633112
title: Dietary salt intake and cardiovascular outcomes: an umbrella review of meta-analyses and dose-response evidence

description: Excessive salt intake is a known cardiovascular disease (CVD) risk factor, but the health impacts of both high and low sodium intake remain debated. This study synthesize meta-analytic evidence on dietary salt intake and cardiovascular outcomes, including subgroup and dose–response analyses. PubMed, Embase, Web of Science, and Cochrane Library were searched through August 28, 2024, for meta-analyses of randomized controlled trials and observational studies. 21 meta-analyses comprising 91 outcomes were included. Low sodium intake was associated with reduced risks of CVD mortality (RR = 0.83, 95% CI: 0.73–0.95), stroke mortality (RR = 0.74, 95% CI: 0.57–0.95), and all-cause mortality (RR = 0.88, 95% CI: 0.82–0.93), as well as systolic blood pressure (MD = −3.39 mmHg) and diastolic blood pressure (MD = −1.54 mmHg), improved vascular elasticity, and increased heart rate. Urinary sodium excretion and Na/K ratio decreased, while urinary potassium, calcium, and serum potassium increased. No adverse effects on lipid profiles were observed. High salt intake was associated with increased risks of CVD (RR = 1.13, 95% CI: 1.06–1.20), hypertension (OR = 1.33, 95% CI: 1.24–1.42), stroke (OR = 1.34, 95% CI: 1.19–1.51), and stroke mortality (OR = 1.40, 95% CI: 1.21–1.63). Each 1 g/day sodium increase raised systolic blood pressure by 0.60 mmHg and CVD and stroke risks by 4% and 6%, respectively. High salt intake increases cardiovascular risk, while moderate reduction provides protective benefits without adverse lipid effects. Tailored strategies are needed based on regional, sex-specific, and methodological differences.
DOI: 10.6084/m9.figshare.30633112.v1
title: Dietary salt intake and cardiovascular outcomes: an umbrella review of meta-analyses and dose-response evidence

description: Excessive salt intake is a known cardiovascular disease (CVD) risk factor, but the health impacts of both high and low sodium intake remain debated. This study synthesize meta-analytic evidence on dietary salt intake and cardiovascular outcomes, including subgroup and dose–response analyses. PubMed, Embase, Web of Science, and Cochrane Library were searched through August 28, 2024, for meta-analyses of randomized controlled trials and observational studies. 21 meta-analyses comprising 91 outcomes were included. Low sodium intake was associated with reduced risks of CVD mortality (RR = 0.83, 95% CI: 0.73–0.95), stroke mortality (RR = 0.74, 95% CI: 0.57–0.95), and all-cause mortality (RR = 0.88, 95% CI: 0.82–0.93), as well as systolic blood pressure (MD = −3.39 mmHg) and diastolic blood pressure (MD = −1.54 mmHg), improved vascular elasticity, and increased heart rate. Urinary sodium excretion and Na/K ratio decreased, while urinary potassium, calcium, and serum potassium increased. No adverse effects on lipid profiles were observed. High salt intake was associated with increased risks of CVD (RR = 1.13, 95% CI: 1.06–1.20), hypertension (OR = 1.33, 95% CI: 1.24–1.42), stroke (OR = 1.34, 95% CI: 1.19–1.51), and stroke mortality (OR = 1.40, 95% CI: 1.21–1.63). Each 1 g/day sodium increase raised systolic blood pressure by 0.60 mmHg and CVD and stroke risks by 4% and 6%, respectively. High salt intake increases cardiovascular risk, while moderate reduction provides protective benefits without adverse lipid effects. Tailored strategies are needed based on regional, sex-specific, and methodological differences.