cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28606658
title: Improvement of endocrine and metabolic conditions in patients with polycystic ovary syndrome through acupuncture and its combined therapies: a systematic review and meta-analysis

description: Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder among women of reproductive age that significantly impacts their reproductive health. Acupuncture and its combined therapies may have beneficial effects on the endocrine and metabolic states of women with PCOS. This systematic review and meta-analysis evaluated the treatment effects and potential mechanisms of acupuncture and its combined therapies compared to oral metformin in treating PCOS patients. The evaluation focused on three sets of outcomes: hormonal indicators, metabolic indicators, and body weight indicators. Studies that involved additional therapies beyond the specified interventions or included patients with other diseases were excluded. Additionally, data mining methods were used, including frequency statistics to analyze the frequency of acupuncture points and the meridians involved, and the Apriori algorithm to perform association rule analysis for the most effective interventions. The study included 46 articles (51 studies) involving six interventions: acupuncture combined with metformin, acupuncture treatment, acupuncture with Chinese herbal medicine and metformin, acupuncture with Chinese herbal medicine, acupuncture combined with cupping, and auricular acupuncture combined with metformin showed significant improvements in all evaluated indicators. Data mining revealed the Stomach meridian of foot yangming was the most frequently used, and the most commonly used combination of points included CV4, SP6, and ST36. This study suggests that acupuncture and its combined therapies may benefit PCOS. However, risk of bias and heterogeneity observed were noted. Future high-quality, rigorously designed randomized controlled trials are needed to confirm these findings and provide stronger clinical recommendations for acupuncture in PCOS treatment.
DOI: 10.6084/m9.figshare.28606658.v1
title: Improvement of endocrine and metabolic conditions in patients with polycystic ovary syndrome through acupuncture and its combined therapies: a systematic review and meta-analysis

description: Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder among women of reproductive age that significantly impacts their reproductive health. Acupuncture and its combined therapies may have beneficial effects on the endocrine and metabolic states of women with PCOS. This systematic review and meta-analysis evaluated the treatment effects and potential mechanisms of acupuncture and its combined therapies compared to oral metformin in treating PCOS patients. The evaluation focused on three sets of outcomes: hormonal indicators, metabolic indicators, and body weight indicators. Studies that involved additional therapies beyond the specified interventions or included patients with other diseases were excluded. Additionally, data mining methods were used, including frequency statistics to analyze the frequency of acupuncture points and the meridians involved, and the Apriori algorithm to perform association rule analysis for the most effective interventions. The study included 46 articles (51 studies) involving six interventions: acupuncture combined with metformin, acupuncture treatment, acupuncture with Chinese herbal medicine and metformin, acupuncture with Chinese herbal medicine, acupuncture combined with cupping, and auricular acupuncture combined with metformin showed significant improvements in all evaluated indicators. Data mining revealed the Stomach meridian of foot yangming was the most frequently used, and the most commonly used combination of points included CV4, SP6, and ST36. This study suggests that acupuncture and its combined therapies may benefit PCOS. However, risk of bias and heterogeneity observed were noted. Future high-quality, rigorously designed randomized controlled trials are needed to confirm these findings and provide stronger clinical recommendations for acupuncture in PCOS treatment.
DOI: 10.6084/m9.figshare.28743008
title: Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study

description: Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival. We aimed to investigate independent predictors of PCNC, develop machine learning models, and construct a predictive nomogram for PCNC in patients undergoing thoracic surgery for lung cancer. This study used data from a previous retrospective study of 16,368 patients with lung cancer (training set: 11,458; validation set: 4,910) with American Standards Association physical statuses I–IV who underwent surgery. Postoperative information was collected from electronic medical records to help build models based on cause-and-effect and statistical data, potentially revealing hidden dependencies between factors and diseases in a big data environment. The optimal model was analyzed and filtered using multiple machine-learning models (Logistic regression, eXtreme Gradient Boosting, Random forest, Light Gradient Boosting Machine and Naïve Bayes). A predictive nomogram was built and receiver operating characteristics were used to assess the validity of the model. The discriminative power and clinical validity were assessed using calibration and decision-making curve analyses. Multivariate logistic regression analysis revealed that age, surgery duration, intraoperative intercostal nerve block, postoperative patient-controlled analgesia, bronchial blocker use and sufentanil use were independent predictors of PCNC. Random forest was identified as the optimal model with an area under the curve of 0.898 in the training set and 0.752 in the validation set, confirming the excellent prediction accuracy of the nomogram. All the net benefits of the five machine-learning models in the training and validation sets demonstrated excellent clinical applicability, and the calibration curves showed good agreement between the predicted and observed risks. The combination of machine-learning models and nomograms may contribute to the early prediction and reduction in the incidence of PCNC.
DOI: 10.6084/m9.figshare.28743008.v1
title: Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study

description: Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival. We aimed to investigate independent predictors of PCNC, develop machine learning models, and construct a predictive nomogram for PCNC in patients undergoing thoracic surgery for lung cancer. This study used data from a previous retrospective study of 16,368 patients with lung cancer (training set: 11,458; validation set: 4,910) with American Standards Association physical statuses I–IV who underwent surgery. Postoperative information was collected from electronic medical records to help build models based on cause-and-effect and statistical data, potentially revealing hidden dependencies between factors and diseases in a big data environment. The optimal model was analyzed and filtered using multiple machine-learning models (Logistic regression, eXtreme Gradient Boosting, Random forest, Light Gradient Boosting Machine and Naïve Bayes). A predictive nomogram was built and receiver operating characteristics were used to assess the validity of the model. The discriminative power and clinical validity were assessed using calibration and decision-making curve analyses. Multivariate logistic regression analysis revealed that age, surgery duration, intraoperative intercostal nerve block, postoperative patient-controlled analgesia, bronchial blocker use and sufentanil use were independent predictors of PCNC. Random forest was identified as the optimal model with an area under the curve of 0.898 in the training set and 0.752 in the validation set, confirming the excellent prediction accuracy of the nomogram. All the net benefits of the five machine-learning models in the training and validation sets demonstrated excellent clinical applicability, and the calibration curves showed good agreement between the predicted and observed risks. The combination of machine-learning models and nomograms may contribute to the early prediction and reduction in the incidence of PCNC.
DOI: 10.6084/m9.figshare.28956820.v1
title: Development of a preliminary multivariable model predicting hamstring strain injuries during preseason screening in soccer players: a multidisciplinary approach

description: Reducing the incidence of hamstring strain injuries (HSIs) is a priority for soccer clubs. However, robust multifactorial predictive models are lacking and potential predictors such as sprint kinematics, performance fatigability, and psychological variables have been overlooked. Thus, the aim of this study was to develop a preliminary parsimonious multifactorial model to predict players at risk of HSI through preseason screening. Psychological, physiological, kinematic, performance fatigability and health-related variables were collected for 120 regional and national soccer players during the 2022 preseason. HSIs were prospectively recorded over the entire soccer season. After variable selection, logistic regressions with the Wald backward stepwise method were used to refine the model. The predictive abilities of the model and of the individual variables were determined using the area under the receiver operating characteristic curve (AUC). Twenty-nine players sustained an HSI during the follow-up period. The final model included eight variables: age, sex, HSI history, knee flexor performance fatigability, sprint performance (best sprint time and maximal theoretical velocity V<sub>0</sub>), perceived vulnerability to injury, and subjective norms in soccer. While its model was preliminary, it showed good fit indices and strong predictive performance (true positive rate: 79%, AUC = .82). None of the variables evaluated independently demonstrated satisfactory performance in predicting HSI (AUC≤.65). Using a multidisciplinary approach and measurements of only a few variables during preseason screening, the current model tends to demonstrate high accuracy in identifying soccer players at risk of HSI. The study introduces a preliminary multifactorial model derived from preseason screening, effectively predicting 79% of subsequent HSIs.The performance fatigability of the knee flexors, perceived injury vulnerability and internalization of subjective norms about fatigue and pain in soccer play crucial roles in predicting HSI occurrence, alongside other predictors such as age, HSI history and sprint performance.Individual HSI predictors, assessed independently, show limited predictive power. The study introduces a preliminary multifactorial model derived from preseason screening, effectively predicting 79% of subsequent HSIs. The performance fatigability of the knee flexors, perceived injury vulnerability and internalization of subjective norms about fatigue and pain in soccer play crucial roles in predicting HSI occurrence, alongside other predictors such as age, HSI history and sprint performance. Individual HSI predictors, assessed independently, show limited predictive power.
DOI: 10.6084/m9.figshare.29405470
title: Identification of GBP6 as a potential prognostic biomarker in esophageal carcinoma by integrated bioinformatics analysis and experiment validation

description: To evaluate the role of guanylate-binding protein 6 (GBP6) in esophageal cancer (ESCA). We comprehensively evaluated the expression pattern, clinical features, prognostic role, biological functions, and immunological role of GBP6 based on the public databases. Immunohistochemistry was used to validate the results in an ESCA tissue microarray. The proliferative activity of ESCA cells was evaluated using cell counting kit 8 assay. The cell cycle changes were determined using flow cytometry. We also analyzed potential compounds for combination therapy with GBP6. Among the GBPs family, we found that GBP6 was only specifically expressed in the normal oropharyngeal tract, while its expression was downregulated in ESCA tissues. Patients with low expression of GBP6 was associated with poor prognosis, worse grade and TNM stage. However, no significant relationship between GBP6 and the immune microenvironment in ESCA. Enrichment analysis showed that GBP2-high expression group was significantly enriched in the p53 signaling pathway. Then, we found that knockdown of GBP6 expression inhibited the proliferative of ESCA cells and induced the cell cycle arrest. Moreover, the expression of p53, p21, and p27 were also downregulated in the GBP6 knockdown group. Finally, drug response prediction revealed that several small molecule compounds may have better anti-cancer effects in samples with high expression of GBP6. The GBP6 could be a potential prognostic therapeutic target for ESCA treatment.
DOI: 10.6084/m9.figshare.29405470.v1
title: Identification of GBP6 as a potential prognostic biomarker in esophageal carcinoma by integrated bioinformatics analysis and experiment validation

description: To evaluate the role of guanylate-binding protein 6 (GBP6) in esophageal cancer (ESCA). We comprehensively evaluated the expression pattern, clinical features, prognostic role, biological functions, and immunological role of GBP6 based on the public databases. Immunohistochemistry was used to validate the results in an ESCA tissue microarray. The proliferative activity of ESCA cells was evaluated using cell counting kit 8 assay. The cell cycle changes were determined using flow cytometry. We also analyzed potential compounds for combination therapy with GBP6. Among the GBPs family, we found that GBP6 was only specifically expressed in the normal oropharyngeal tract, while its expression was downregulated in ESCA tissues. Patients with low expression of GBP6 was associated with poor prognosis, worse grade and TNM stage. However, no significant relationship between GBP6 and the immune microenvironment in ESCA. Enrichment analysis showed that GBP2-high expression group was significantly enriched in the p53 signaling pathway. Then, we found that knockdown of GBP6 expression inhibited the proliferative of ESCA cells and induced the cell cycle arrest. Moreover, the expression of p53, p21, and p27 were also downregulated in the GBP6 knockdown group. Finally, drug response prediction revealed that several small molecule compounds may have better anti-cancer effects in samples with high expression of GBP6. The GBP6 could be a potential prognostic therapeutic target for ESCA treatment.
DOI: 10.6084/m9.figshare.30928064
title: Efficacy and safety of compound glycyrrhizin in patients with alopecia areata: a systematic review and meta-analysis

description: Although compound glycyrrhizin (CG) has been widely used to alopecia areata (AA) in China, its efficacy and safety remain unclear. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of CG for AA. Eight literature databases were retrieved from their inceptions to 29 February 2024 to identify the eligible randomized controlled trials comparing CG plus conventional treatments with conventional treatments alone for the treatment of AA. Risk ratio (RR), mean difference and 95% confidence interval (CI) were used to estimate the pooled results. RevMan 5.4 (Cochrane Collaboration, Copenhagen, Denmark) and Stata 12.0 software (StataCorp., College Station, TX) were used for statistical analysis. A total of 23 eligible studies with 2219 patients were included. The pooled results revealed that CG plus conventional treatments was superior to conventional treatments alone in cure rate (RR = 1.60, 95%CI [1.47, 1.74], <i>p</i> &lt; .001), total efficacy rate (RR = 1.37, 95%CI [1.29, 1.45], <i>p</i> &lt; .001) and the Severity of Alopecia Tool (SALT) score, regardless of different conventional treatments, treatment courses and doses of CG. In terms of safety, a few patients suffered from adverse events (AEs), including oedema, elevated blood pressure and gastrointestinal tract discomfort, and the incidence of oedema was higher in the patients receiving CG (RR = 2.53, 95%CI [1.04, 6.19], <i>p</i> = .04). The combination of CG and conventional treatments was effective and safe for patients with AA, and CG could promote hair regrowth with mild AEs.
DOI: 10.6084/m9.figshare.30928064.v1
title: Efficacy and safety of compound glycyrrhizin in patients with alopecia areata: a systematic review and meta-analysis

description: Although compound glycyrrhizin (CG) has been widely used to alopecia areata (AA) in China, its efficacy and safety remain unclear. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of CG for AA. Eight literature databases were retrieved from their inceptions to 29 February 2024 to identify the eligible randomized controlled trials comparing CG plus conventional treatments with conventional treatments alone for the treatment of AA. Risk ratio (RR), mean difference and 95% confidence interval (CI) were used to estimate the pooled results. RevMan 5.4 (Cochrane Collaboration, Copenhagen, Denmark) and Stata 12.0 software (StataCorp., College Station, TX) were used for statistical analysis. A total of 23 eligible studies with 2219 patients were included. The pooled results revealed that CG plus conventional treatments was superior to conventional treatments alone in cure rate (RR = 1.60, 95%CI [1.47, 1.74], <i>p</i> &lt; .001), total efficacy rate (RR = 1.37, 95%CI [1.29, 1.45], <i>p</i> &lt; .001) and the Severity of Alopecia Tool (SALT) score, regardless of different conventional treatments, treatment courses and doses of CG. In terms of safety, a few patients suffered from adverse events (AEs), including oedema, elevated blood pressure and gastrointestinal tract discomfort, and the incidence of oedema was higher in the patients receiving CG (RR = 2.53, 95%CI [1.04, 6.19], <i>p</i> = .04). The combination of CG and conventional treatments was effective and safe for patients with AA, and CG could promote hair regrowth with mild AEs.
DOI: 10.6084/m9.figshare.29378180
title: A validated predictive model for mid- and long-term mortality risk assessment after elective endovascular repair in abdominal aortic aneurysm patients

description: Risk scoring systems for open surgical repair of abdominal aortic aneurysm (AAA) may overestimate mortality after endovascular aneurysm repair (EVAR). A model for mid-term and long-term mortality after EVAR is still lacking. MEDLINE, Embase and WOS were searched from January 1, 2000 to December 31, 2022. Hazard ratios and 95% confidence intervals (CI) for mortality-related risk factors were extracted and synthesized in a meta-analysis. The C-statistics, IDI, NRI and DCA were used to assess the stability. A predictive model incorporating independent meta-analytic variables was developed, validated in a clinical cohort and compared with the Giles model. 35 studies containing 49272 patients were analyzed. A prediction model was established, including age, gender, aneurysm diameter, American Society of Anesthetists score, chronic obstructive pulmonary disease, cardiac disease, renal disease, cerebrovascular disease, diabetes, peripheral vascular disease, statins, aspirin, and smoker. The model had a C-statistic of 0.738 (95%CI:0.697, 0.779) in validation cohort, comprising 537 patients after EVAR. The sensitivities were 0.765, 0.796 and 0.756, and the specificities were 0.744, 0.652 and 0.668 at 1/3/5 years. In contrast, Giles model had a C-statistic of 0.657 (95%CI:0.608, 0.706). Integrated discrimination improvement (0.03, <i>p</i> &lt; 0.001; 0.045, <i>p</i> = 0.01; 0.062, <i>p</i> &lt; 0.001) and net reclassification index (0.342, <i>p</i> &lt; 0.001; 0.306, <i>p</i> &lt; 0.001; 0.356, <i>p</i> &lt; 0.001) indicated improved predictive performance by the new model over Giles model. This meta-analysis-derived AAA long-term mortality prediction model employs precision risk stratification to enhance clinical decision-making and implement personalized follow-up protocols, thereby delivering evidence-based support for clinical practice. Although multiple risk stratification systems have been established for prognostic assessment following endovascular aneurysm repair (EVAR), these models exhibit notable limitations. Current paradigms predominantly focus on short-term prognostic evaluation within 30 days to 1 year postoperatively, while demonstrating insufficient capability to predict long-term or all-cause mortality and clinical outcomes. To address these gaps, this study developed a novel predictive model for long-term mortality risk after EVAR through systematic analysis of multicenter, large-scale clinical datasets and incorporation of a comprehensive array of potential prognostic determinants. This model provides clinicians with a more reliable decision-support tool for risk stratification, which may ultimately contribute to enhanced long-term survival rates and improved quality of life in AAA patients.
DOI: 10.6084/m9.figshare.29378180.v1
title: A validated predictive model for mid- and long-term mortality risk assessment after elective endovascular repair in abdominal aortic aneurysm patients

description: Risk scoring systems for open surgical repair of abdominal aortic aneurysm (AAA) may overestimate mortality after endovascular aneurysm repair (EVAR). A model for mid-term and long-term mortality after EVAR is still lacking. MEDLINE, Embase and WOS were searched from January 1, 2000 to December 31, 2022. Hazard ratios and 95% confidence intervals (CI) for mortality-related risk factors were extracted and synthesized in a meta-analysis. The C-statistics, IDI, NRI and DCA were used to assess the stability. A predictive model incorporating independent meta-analytic variables was developed, validated in a clinical cohort and compared with the Giles model. 35 studies containing 49272 patients were analyzed. A prediction model was established, including age, gender, aneurysm diameter, American Society of Anesthetists score, chronic obstructive pulmonary disease, cardiac disease, renal disease, cerebrovascular disease, diabetes, peripheral vascular disease, statins, aspirin, and smoker. The model had a C-statistic of 0.738 (95%CI:0.697, 0.779) in validation cohort, comprising 537 patients after EVAR. The sensitivities were 0.765, 0.796 and 0.756, and the specificities were 0.744, 0.652 and 0.668 at 1/3/5 years. In contrast, Giles model had a C-statistic of 0.657 (95%CI:0.608, 0.706). Integrated discrimination improvement (0.03, <i>p</i> &lt; 0.001; 0.045, <i>p</i> = 0.01; 0.062, <i>p</i> &lt; 0.001) and net reclassification index (0.342, <i>p</i> &lt; 0.001; 0.306, <i>p</i> &lt; 0.001; 0.356, <i>p</i> &lt; 0.001) indicated improved predictive performance by the new model over Giles model. This meta-analysis-derived AAA long-term mortality prediction model employs precision risk stratification to enhance clinical decision-making and implement personalized follow-up protocols, thereby delivering evidence-based support for clinical practice. Although multiple risk stratification systems have been established for prognostic assessment following endovascular aneurysm repair (EVAR), these models exhibit notable limitations. Current paradigms predominantly focus on short-term prognostic evaluation within 30 days to 1 year postoperatively, while demonstrating insufficient capability to predict long-term or all-cause mortality and clinical outcomes. To address these gaps, this study developed a novel predictive model for long-term mortality risk after EVAR through systematic analysis of multicenter, large-scale clinical datasets and incorporation of a comprehensive array of potential prognostic determinants. This model provides clinicians with a more reliable decision-support tool for risk stratification, which may ultimately contribute to enhanced long-term survival rates and improved quality of life in AAA patients.