cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30635858
title: Operative versus nonoperative management of acute Achilles tendon rupture: a systematic review and meta-analysis of clinical outcomes from randomized controlled trials

description: The objective of this study was to compare the differences in re-rupture rates, complications, and functional assessments of Achilles tendon ruptures (ATRs) treated operatively or nonoperatively to guide clinical treatment choices. A literature search was performed in the PubMed, Cochrane Library, and Embase databases up to March 1, 2025, for randomized controlled trials (RCTs) involving patients with ATR receiving operative and nonoperative therapies. Primary outcomes included re-rupture rates, complications, and functional assessment. Meta-analysis of the extracted data was carried out using Review Manager 5.3 and Stata 17.0. A total of 14 RCTs were included in the meta-analysis, comprising 1,628 participants. The meta-analysis results revealed a considerably lower re-rupture rate in both the minimally invasive (MI) group (risk ratio [RR], 0.28; 95% confidence interval [CI]: 0.11 to 0.74) and the open group (RR, 0.30; 95% CI: 0.19 to 0.50). For complications, subgroup analysis showed no significant difference between the MI and nonoperative groups (RR, 2.40; 95% CI: 0.52 to 10.98), whereas the open group had a higher complication rate (RR, 3.03; 95% CI: 1.75 to 5.26) than nonoperative groups. There was no significant difference in the functional assessment between operative and nonoperative groups. Regarding return to work, the MI group returned to work earlier compared to the nonoperative group. Open operative treatment significantly reduces the rate of re-rupture compared to nonoperative treatment but is accompanied by a higher risk of complications. MI treatment offers both of these advantages, along with superiority in return to work.
DOI: 10.6084/m9.figshare.30748640
title: Ezetimibe and the risk of new-onset type 2 diabetes: a systematic review and meta-analysis

description: Statins reduce cardiovascular risk but may increase new-onset type 2 diabetes mellitus (NO-T2DM). Ezetimibe, a cholesterol absorption inhibitor, is often added to statins to improve lipid control, yet its impact on NO-T2DM remains uncertain. This systematic review evaluated moderate-intensity statin plus ezetimibe dual therapy versus high-intensity statin monotherapy for NO-T2DM risk. Five databases were searched to identify eligible studies. Random-effects meta-analyses generated pooled relative risks (RR) quantifying the effect of ezetimibe plus moderate-intensity statins on NO-T2DM. The Attributable Risk Fraction (ARF) was quantified utilizing the pooled estimate. Ten observational studies and four clinical trials were included. In four cohort studies, ezetimibe plus moderate-intensity statin compared to high-intensity statin monotherapy was significantly linked to 18% reduced risk of NO-T2DM (pooled RR: 0.82; 95% CI: 0.77–0.87; I2 = 0.0%; p &lt; 0.001). In three methodologically similar studies, compared to moderate-intensity statin monotherapy, adding ezetimibe to moderate-intensity statin dual therapy showed non-statistically (p &gt; 0.05) significant 4% increased risk of NO-T2DM development (pooled RR: 1.04; 95% CI: 0.94–1.14, I2= 0.0%). Compared with patients receiving high-intensity statin therapy, 22% of NO-T2DM cases could potentially be averted with dual therapy (moderate-intensity statin plus ezetimibe). In four studies involving 5,072 patients on high-intensity statins who developed NO-T2DM, 1,115 patients (812–1,420) could have been prevented with ezetimibe plus moderate-intensity statin dual therapy. Incorporating ezetimibe with moderate-intensity statins, rather than relying solely on high-intensity statins, may reduce the risk of NO-T2DM in patients with dyslipidemia and elevated cardiovascular disease risk. CRD42024518630.
DOI: 10.6084/m9.figshare.30748640.v1
title: Ezetimibe and the risk of new-onset type 2 diabetes: a systematic review and meta-analysis

description: Statins reduce cardiovascular risk but may increase new-onset type 2 diabetes mellitus (NO-T2DM). Ezetimibe, a cholesterol absorption inhibitor, is often added to statins to improve lipid control, yet its impact on NO-T2DM remains uncertain. This systematic review evaluated moderate-intensity statin plus ezetimibe dual therapy versus high-intensity statin monotherapy for NO-T2DM risk. Five databases were searched to identify eligible studies. Random-effects meta-analyses generated pooled relative risks (RR) quantifying the effect of ezetimibe plus moderate-intensity statins on NO-T2DM. The Attributable Risk Fraction (ARF) was quantified utilizing the pooled estimate. Ten observational studies and four clinical trials were included. In four cohort studies, ezetimibe plus moderate-intensity statin compared to high-intensity statin monotherapy was significantly linked to 18% reduced risk of NO-T2DM (pooled RR: 0.82; 95% CI: 0.77–0.87; I2 = 0.0%; p &lt; 0.001). In three methodologically similar studies, compared to moderate-intensity statin monotherapy, adding ezetimibe to moderate-intensity statin dual therapy showed non-statistically (p &gt; 0.05) significant 4% increased risk of NO-T2DM development (pooled RR: 1.04; 95% CI: 0.94–1.14, I2= 0.0%). Compared with patients receiving high-intensity statin therapy, 22% of NO-T2DM cases could potentially be averted with dual therapy (moderate-intensity statin plus ezetimibe). In four studies involving 5,072 patients on high-intensity statins who developed NO-T2DM, 1,115 patients (812–1,420) could have been prevented with ezetimibe plus moderate-intensity statin dual therapy. Incorporating ezetimibe with moderate-intensity statins, rather than relying solely on high-intensity statins, may reduce the risk of NO-T2DM in patients with dyslipidemia and elevated cardiovascular disease risk. CRD42024518630.
DOI: 10.6084/m9.figshare.28245622
title: Risk factors for hepatocellular carcinoma: an umbrella review of systematic review and meta-analysis

description: Numerous meta-analyses have identified various risk factors for hepatocellular carcinoma (HCC), prompting a comprehensive study to synthesize evidence quality and strength. This umbrella review of meta-analyses was conducted throughout PubMed, EMBASE, Web of Science, and Cochrane Database of Systematic Reviews. Evidence strength was evaluated according to the evidence categories criteria. We identified 101 risk factors throughout 175 meta-analyses. 31 risk factors were classified as evidence levels of class I, II, or III. HBV and HCV infections increase HCC risk by 12.5-fold and 11.2-fold, respectively. These risks are moderated by antiviral treatments and virological responses but are exacerbated by higher HBsAg levels, anti-HBc positivity, and co-infection. Smoking, obesity, non-alcoholic fatty liver disease, diabetes, low platelet, elevated liver enzymes and liver fluke infection increase HCC risk, while coffee consumption, a healthy diet, and bariatric surgery lower it. Medications like metformin, glucagon-like peptide-1 receptor agonists (GLP-1 RAs), aspirin, statins, and selective serotonin reuptake inhibitors reduce HCC risk, while acid suppressive agents, particularly proton pump inhibitors, elevate it. Blood type O reduces the risk of HCC, while male gender and older age increase the risk. HBV and HCV are major HCC risk factors, with risk mitigation through antiviral treatments. Lifestyle habits such as smoking and alcohol use significantly increase HCC risk, highlighting the importance of cessation. Certain drugs like aspirin, statins, GLP-1 RAs, and metformin may reduce HCC occurrence, but further research is needed to confirm these effects.
DOI: 10.6084/m9.figshare.28245622.v1
title: Risk factors for hepatocellular carcinoma: an umbrella review of systematic review and meta-analysis

description: Numerous meta-analyses have identified various risk factors for hepatocellular carcinoma (HCC), prompting a comprehensive study to synthesize evidence quality and strength. This umbrella review of meta-analyses was conducted throughout PubMed, EMBASE, Web of Science, and Cochrane Database of Systematic Reviews. Evidence strength was evaluated according to the evidence categories criteria. We identified 101 risk factors throughout 175 meta-analyses. 31 risk factors were classified as evidence levels of class I, II, or III. HBV and HCV infections increase HCC risk by 12.5-fold and 11.2-fold, respectively. These risks are moderated by antiviral treatments and virological responses but are exacerbated by higher HBsAg levels, anti-HBc positivity, and co-infection. Smoking, obesity, non-alcoholic fatty liver disease, diabetes, low platelet, elevated liver enzymes and liver fluke infection increase HCC risk, while coffee consumption, a healthy diet, and bariatric surgery lower it. Medications like metformin, glucagon-like peptide-1 receptor agonists (GLP-1 RAs), aspirin, statins, and selective serotonin reuptake inhibitors reduce HCC risk, while acid suppressive agents, particularly proton pump inhibitors, elevate it. Blood type O reduces the risk of HCC, while male gender and older age increase the risk. HBV and HCV are major HCC risk factors, with risk mitigation through antiviral treatments. Lifestyle habits such as smoking and alcohol use significantly increase HCC risk, highlighting the importance of cessation. Certain drugs like aspirin, statins, GLP-1 RAs, and metformin may reduce HCC occurrence, but further research is needed to confirm these effects.
DOI: 10.6084/m9.figshare.28264479
title: Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis

description: This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model. Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. Immune cell infiltration, immune escape and drug sensitivity disparities between high- and low-risk groups were assessed using CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) methods. High- and low-risk cell populations were discerned based on the expression of prognostic genes in BRCA scRNA-seq data. M0 and M1 macrophages significantly promote the metastasis of breast cancer (BRCA). The developed prognostic model demonstrates good predictive performance for patient survival at 1, 3 and 5 years, with AUC values of 0.654, 0.602 and 0.635, respectively. Compared to the low-risk group, the high-risk group exhibits increased immune cell infiltration and higher levels of immune evasion. scRNA-seq data reveal that high-risk cells have significantly higher risk scores and exhibit notable differences in signalling pathways and intercellular communication patterns. This study presents a novel risk score model based on exosome-related genes, validated by comprehensive analyses including differential expression, survival analysis and external dataset validation. The model’s clinical significance is reinforced through its ability to stratify patients into high- and low-risk groups with distinct survival outcomes and immune landscape characteristics. The integration of RNA-seq and scRNA-seq data highlights the predictive accuracy of the model and underscores its potential for identifying novel therapeutic targets and improving patient prognosis.
DOI: 10.6084/m9.figshare.28264479.v1
title: Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis

description: This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model. Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. Immune cell infiltration, immune escape and drug sensitivity disparities between high- and low-risk groups were assessed using CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) methods. High- and low-risk cell populations were discerned based on the expression of prognostic genes in BRCA scRNA-seq data. M0 and M1 macrophages significantly promote the metastasis of breast cancer (BRCA). The developed prognostic model demonstrates good predictive performance for patient survival at 1, 3 and 5 years, with AUC values of 0.654, 0.602 and 0.635, respectively. Compared to the low-risk group, the high-risk group exhibits increased immune cell infiltration and higher levels of immune evasion. scRNA-seq data reveal that high-risk cells have significantly higher risk scores and exhibit notable differences in signalling pathways and intercellular communication patterns. This study presents a novel risk score model based on exosome-related genes, validated by comprehensive analyses including differential expression, survival analysis and external dataset validation. The model’s clinical significance is reinforced through its ability to stratify patients into high- and low-risk groups with distinct survival outcomes and immune landscape characteristics. The integration of RNA-seq and scRNA-seq data highlights the predictive accuracy of the model and underscores its potential for identifying novel therapeutic targets and improving patient prognosis.
DOI: 10.6084/m9.figshare.29503784
title: Multiparameter MRI-based automatic segmentation and diagnostic models for the differentiation of intracranial solitary fibrous tumors and meningiomas

description: Intracranial solitary fibrous tumors (SFTs) and meningiomas are meningeal tumors with different malignancy levels and prognoses. Their similar imaging features make preoperative differentiation difficult, resulting in high misdiagnosis rates. Thus, accurately distinguishing SFTs from meningiomas preoperatively is vital for surgical planning and treatment strategies. A total of 252 patients (56 SFTs and 196 meningiomas) data from January 2014 to May 2024 were used to train our models . A VB-Net deep learning network was employed to refine automatic segmentation. To identify SFTs and meningiomas, the segmented data were analyzed using machine learning to construct single-sequence and multi-sequence MRI models and combined with clinical/radiological features to develop a fusion index-related model.To enhance clinical applicability, we constructed a four-category model using the predictive probabilities from secondary classification as input features. The VB-Net segmentation model performed well in both the tumor cores and the whole tumor, with DSCs of 0.87 (±0.17) and 0.79 (±0.26), respectively. The integration of clinical and radiological data enhanced the model’s AUC to 0.957. Stratified analysis showed that the weighted AUC value reached 0.846 in the validation set. The comprehensive system integrating automatic segmentation with diagnostic models can differentiate SFTs from meningiomas precisely.
DOI: 10.6084/m9.figshare.29503784.v1
title: Multiparameter MRI-based automatic segmentation and diagnostic models for the differentiation of intracranial solitary fibrous tumors and meningiomas

description: Intracranial solitary fibrous tumors (SFTs) and meningiomas are meningeal tumors with different malignancy levels and prognoses. Their similar imaging features make preoperative differentiation difficult, resulting in high misdiagnosis rates. Thus, accurately distinguishing SFTs from meningiomas preoperatively is vital for surgical planning and treatment strategies. A total of 252 patients (56 SFTs and 196 meningiomas) data from January 2014 to May 2024 were used to train our models . A VB-Net deep learning network was employed to refine automatic segmentation. To identify SFTs and meningiomas, the segmented data were analyzed using machine learning to construct single-sequence and multi-sequence MRI models and combined with clinical/radiological features to develop a fusion index-related model.To enhance clinical applicability, we constructed a four-category model using the predictive probabilities from secondary classification as input features. The VB-Net segmentation model performed well in both the tumor cores and the whole tumor, with DSCs of 0.87 (±0.17) and 0.79 (±0.26), respectively. The integration of clinical and radiological data enhanced the model’s AUC to 0.957. Stratified analysis showed that the weighted AUC value reached 0.846 in the validation set. The comprehensive system integrating automatic segmentation with diagnostic models can differentiate SFTs from meningiomas precisely.
DOI: 10.6084/m9.figshare.29653263
title: Unravelling the myriad physiologic roles of transthyretin: critical considerations for treating transthyretin amyloidosis

description: Transthyretin (TTR) is a highly conserved protein with crucial and broadly protective physiologic roles across organ systems and diseases. Evidence shows that TTR contributes to neuroprotection, cognition, glucose regulation, pregnancy, muscle development, and bone mineralization. In several disease states, including diabetes, Alzheimer’s disease, Lewy body dementia, cerebrovascular disease, and osteoporosis, higher TTR levels may be protective. Numerous studies have shown that low levels of TTR are associated with increased mortality overall and in relation to cardiovascular disease and several malignancies. There is a growing portfolio of approved and investigational transthyretin amyloidosis (ATTR) treatments that differ in their mechanisms and effects on circulating TTR. When selecting an ATTR therapy, clinicians must decide whether to stabilize and preserve TTR and its functions or knockdown and drastically reduce TTR. This review summarizes the vital physiologic roles of TTR in health and disease. We consider the potential effects on normal biologic pathways that may occur while therapeutically suppressing TTR and discuss clinical decisions concerning ATTR therapies in the context of the summarized literature. TTR is essential for a broad range of physiologic processes and may confer clinically protective effects in neurologic and other organ systems. While a link between low TTR and severe disease and mortality is well established, it remains unclear whether long-term TTR suppression <i>via</i> ATTR therapies increases risk of disease. Clinical decisions in ATTR, however, should reflect the current understanding of the roles of TTR and the patient’s clinical history. TTR serves vital physiologic roles across organ systems. Given its clinically protective properties, continued investigation into the potential long-term impact of TTR suppression <i>via</i> knockdown or gene editing therapies is prudent. ATTR treatment selection should reflect an awareness of the physiologic importance of TTR, as well as consideration of the potential long-term impact of chronic TTR suppression.
DOI: 10.6084/m9.figshare.29653263.v1
title: Unravelling the myriad physiologic roles of transthyretin: critical considerations for treating transthyretin amyloidosis

description: Transthyretin (TTR) is a highly conserved protein with crucial and broadly protective physiologic roles across organ systems and diseases. Evidence shows that TTR contributes to neuroprotection, cognition, glucose regulation, pregnancy, muscle development, and bone mineralization. In several disease states, including diabetes, Alzheimer’s disease, Lewy body dementia, cerebrovascular disease, and osteoporosis, higher TTR levels may be protective. Numerous studies have shown that low levels of TTR are associated with increased mortality overall and in relation to cardiovascular disease and several malignancies. There is a growing portfolio of approved and investigational transthyretin amyloidosis (ATTR) treatments that differ in their mechanisms and effects on circulating TTR. When selecting an ATTR therapy, clinicians must decide whether to stabilize and preserve TTR and its functions or knockdown and drastically reduce TTR. This review summarizes the vital physiologic roles of TTR in health and disease. We consider the potential effects on normal biologic pathways that may occur while therapeutically suppressing TTR and discuss clinical decisions concerning ATTR therapies in the context of the summarized literature. TTR is essential for a broad range of physiologic processes and may confer clinically protective effects in neurologic and other organ systems. While a link between low TTR and severe disease and mortality is well established, it remains unclear whether long-term TTR suppression <i>via</i> ATTR therapies increases risk of disease. Clinical decisions in ATTR, however, should reflect the current understanding of the roles of TTR and the patient’s clinical history. TTR serves vital physiologic roles across organ systems. Given its clinically protective properties, continued investigation into the potential long-term impact of TTR suppression <i>via</i> knockdown or gene editing therapies is prudent. ATTR treatment selection should reflect an awareness of the physiologic importance of TTR, as well as consideration of the potential long-term impact of chronic TTR suppression.
DOI: 10.6084/m9.figshare.30231168
title: Prevalence of fibromyalgia in asthma and its impact on asthma control: a case-control study

description: Asthma and fibromyalgia are chronic conditions that significantly impair quality of life and share common inflammatory and neurophysiological mechanisms. This study aimed to investigate the prevalence of fibromyalgia in asthma patients and examine its impact on asthma control. In this case-control study, 120 patients diagnosed with asthma and 120 age- and sex-matched healthy controls were enrolled. Fibromyalgia was diagnosed based on the 2016 revised ACR criteria using the Widespread Pain Index (WPI) and Symptom Severity Scale (SSS). Asthma control was assessed using the Asthma Control Test (ACT). The relationship between fibromyalgia and asthma control was analyzed using independent t-tests, chi-square tests, logistic regression, and ROC analysis. The prevalence of fibromyalgia in the asthma group was 16.7% compared to 3.3% in controls (<i>p</i> &lt; 0.001). Asthma patients with fibromyalgia had significantly lower ACT scores than those without (18.2 ± 2.9 vs. 20.1 ± 2.4; <i>p</i> = 0.004). The proportion of patients with uncontrolled asthma was higher among those with fibromyalgia (60% vs. 35%). Logistic regression showed that fibromyalgia was associated with increased odds of uncontrolled asthma (OR = 2.16), although this was not statistically significant (<i>p</i> = 0.103). ACT scores showed no significant correlation with WPI or SSS. ROC analysis revealed that the ACT score had no discriminatory power in identifying fibromyalgia (AUC = 0.50). Fibromyalgia is a common comorbidity in asthma patients and may adversely affect perceived asthma control. However, the ACT score alone may not be a reliable indicator of fibromyalgia presence, suggesting the need for multidimensional assessment tools in routine asthma care.