cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28077511
title: Biologics for eosinophilic oesophagitis: a systematic review and meta-analysis

description: Advancing the understanding of the pathophysiology of eosinophilic oesophagitis (EoE) and other eosinophilic gastrointestinal diseases (EGIDs) has spurred research into targeted biological therapies, while the conclusive therapeutic efficacy of biologics remains uncertain. In this review, we conducted a meta-analysis of all RCTS of biologics in the treatment of EoE to evaluate their efficacy and safety and discussed their treatment of non-EoE EGIDs. We searched the PubMed, EMBASE, Cochrane Library, and Web of Science databases. Double-blind randomized controlled trials comparing biologics with placebo in patients with EoE and non-EoE EGIDs were collected and further screened for inclusion and exclusion. The caliber of the included literature was evaluated using the Cochrane risk assessment tool findings. Data extraction and meta-analysis were conducted using RevMan 5.4 and Stata 17.0. Clinical response and histological remission were the major endpoints. Our search retrieved 3,237 articles. There were seven trials in total, comprising 792 people with EoE. Key outcomes of this meta-analysis include the following: Anti-IL-5 biologics exhibited statistically significant benefits in histological remission (RR 2.03 [CI 1.45–2.85]; <i>p</i> &lt; 0.0001) compared to the placebo, but there was no significant difference in symptom relief (RR 1.06 [CI 0.88 to 1.28]; <i>p</i> = 0.53); anti-IL-4/13 biologics had significant effects on histologic improvement (RR 10.48 [CI 5.54–19.82]; <i>p</i> &lt; 0.00001) and symptom related score reduction (RR 1.44 [CI 1.08–1.93]; <i>p</i> = 0.01), with a better outcome for endoscopic remission than with placebo (SMD–1.06 [CI–1.26–0.86], <i>p</i> &lt; 0.00001); no statistically significant differences in adverse effects were observed between the intervention and control groups. Our findings suggest that the biologics currently being investigated are considered safe and effective treatments for EoE, while their efficiency varies. However, the discussion of biologics in non-pharyngitis EGID is hampered by a lack of research, necessitating more research in high-quality trials. We performed a meta-analysis using high-quality randomized controlled trials to confirm the clinical efficacy and safety of biologic therapies. The significant superiority of biologic therapies over placebo controls and the high safety profile of biologics confirm their value for clinical use and dissemination. At present, research results on biologics for the treatment of non-eoe EGID are still lacking and deserve to be confirmed by further studies.
DOI: 10.6084/m9.figshare.28077511.v1
title: Biologics for eosinophilic oesophagitis: a systematic review and meta-analysis

description: Advancing the understanding of the pathophysiology of eosinophilic oesophagitis (EoE) and other eosinophilic gastrointestinal diseases (EGIDs) has spurred research into targeted biological therapies, while the conclusive therapeutic efficacy of biologics remains uncertain. In this review, we conducted a meta-analysis of all RCTS of biologics in the treatment of EoE to evaluate their efficacy and safety and discussed their treatment of non-EoE EGIDs. We searched the PubMed, EMBASE, Cochrane Library, and Web of Science databases. Double-blind randomized controlled trials comparing biologics with placebo in patients with EoE and non-EoE EGIDs were collected and further screened for inclusion and exclusion. The caliber of the included literature was evaluated using the Cochrane risk assessment tool findings. Data extraction and meta-analysis were conducted using RevMan 5.4 and Stata 17.0. Clinical response and histological remission were the major endpoints. Our search retrieved 3,237 articles. There were seven trials in total, comprising 792 people with EoE. Key outcomes of this meta-analysis include the following: Anti-IL-5 biologics exhibited statistically significant benefits in histological remission (RR 2.03 [CI 1.45–2.85]; <i>p</i> &lt; 0.0001) compared to the placebo, but there was no significant difference in symptom relief (RR 1.06 [CI 0.88 to 1.28]; <i>p</i> = 0.53); anti-IL-4/13 biologics had significant effects on histologic improvement (RR 10.48 [CI 5.54–19.82]; <i>p</i> &lt; 0.00001) and symptom related score reduction (RR 1.44 [CI 1.08–1.93]; <i>p</i> = 0.01), with a better outcome for endoscopic remission than with placebo (SMD–1.06 [CI–1.26–0.86], <i>p</i> &lt; 0.00001); no statistically significant differences in adverse effects were observed between the intervention and control groups. Our findings suggest that the biologics currently being investigated are considered safe and effective treatments for EoE, while their efficiency varies. However, the discussion of biologics in non-pharyngitis EGID is hampered by a lack of research, necessitating more research in high-quality trials. We performed a meta-analysis using high-quality randomized controlled trials to confirm the clinical efficacy and safety of biologic therapies. The significant superiority of biologic therapies over placebo controls and the high safety profile of biologics confirm their value for clinical use and dissemination. At present, research results on biologics for the treatment of non-eoe EGID are still lacking and deserve to be confirmed by further studies.
DOI: 10.6084/m9.figshare.30928094
title: Impact of sodium-glucose cotransporter-2 inhibitors on aging biomarkers and plasma ceramide levels in type 2 diabetes: beyond glycemic control

description: Aging is a complex biological process marked by the decline of physiological functions and heightened susceptibility to chronic illnesses, notably cardiometabolic disorders. Ceramides (Cer) are lipid derivatives linked to aging and metabolic diseases. Sodium-Glucose Cotransporter-2 inhibitors (SGLT2i), widely used in managing type 2 diabetes, have an unclear impact on aging biomarkers and Cer profiles. This study explored the association between SGLT2i use, plasma Cer levels (CerC16:0, CerC18:0, CerC22:0, CerC24:0, and CerC24:1), and aging biomarkers—Human Insulin-Like Growth Factor 1 (IGF-1), mammalian target of rapamycin (mTOR), 5-Methylcytosine (5MC), and Human H2AFX (Histone H2AX) in patients with type 2 diabetes mellitus (T2DM). In this retrospective study, 95 participants were divided into three groups: patients on SGLT2i (<i>n</i> = 34), patients on non-SGLT2i anti-diabetic treatments (<i>n</i> = 36), and healthy controls (<i>n</i> = 25). Plasma Cer and aging biomarkers were quantified using Liquid Chromatography with tandem mass spectrometry (LC–MS–MS) and ELISA, respectively. Principal component analysis (PCA) assessed group-based clustering, while ANCOVA evaluated group differences with confounder adjustment. SGLT2i-treated patients showed significantly lower CerC16:0, CerC22:0, and CerC24:1 levels (<i>p</i> &lt; 0.01) and decreased 5MC and H2AX (<i>p</i> &lt; 0.05) compared to non-SGLT2i patients. IGF-1 was significantly elevated in the SGLT2i group (<i>p</i> &lt; 0.01), suggesting a possible protective effect on metabolic health. PCA distinguished control from diabetic groups but revealed overlap between SGLT2i and non-SGLT2i groups. Beyond glucose control, SGLT2i may improve plasma Cer and aging markers in diabetic patients, supporting their broader therapeutic potential in aging and age-related diseases. Further large-scale studies are warranted to confirm these effects and underlying mechanisms.
DOI: 10.6084/m9.figshare.28107554
title: Single-cell transcriptomics reveals an abnormal immune microenvironment in plasma cell mastitis

description: Plasma cell mastitis (PCM) is a non-bacterial inflammatory breast disorder affecting young and middle-aged nonpregnant and nonlactating women. However, a comprehensive and systematic analysis of PCM has not been conducted. Comprehensive cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) analysis was performed on 10,185 cells derived from PCM tissue to generate a highly detailed transcriptomic atlas. Thirteen distinct clusters were identified, and eight cell types were successfully annotated, including endothelial cells, common myeloid progenitor cells and neutrophils. Additionally, the overall alterations in immune cell composition in PCM patients were evaluated. Furthermore, differentially expressed genes between normal and PCM samples were assessed, and functional enrichment analysis was conducted. Finally, cellular communication and interaction were analysed to determine cell-to-cell communications. This study significantly contributes to the field of PCM, presenting a systematic and valuable perspective that serves as a fundamental resource for further exploration of the cellular and molecular mechanisms, pathogenesis of associated diseases and potential interventions.
DOI: 10.6084/m9.figshare.28107554.v1
title: Single-cell transcriptomics reveals an abnormal immune microenvironment in plasma cell mastitis

description: Plasma cell mastitis (PCM) is a non-bacterial inflammatory breast disorder affecting young and middle-aged nonpregnant and nonlactating women. However, a comprehensive and systematic analysis of PCM has not been conducted. Comprehensive cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) analysis was performed on 10,185 cells derived from PCM tissue to generate a highly detailed transcriptomic atlas. Thirteen distinct clusters were identified, and eight cell types were successfully annotated, including endothelial cells, common myeloid progenitor cells and neutrophils. Additionally, the overall alterations in immune cell composition in PCM patients were evaluated. Furthermore, differentially expressed genes between normal and PCM samples were assessed, and functional enrichment analysis was conducted. Finally, cellular communication and interaction were analysed to determine cell-to-cell communications. This study significantly contributes to the field of PCM, presenting a systematic and valuable perspective that serves as a fundamental resource for further exploration of the cellular and molecular mechanisms, pathogenesis of associated diseases and potential interventions.
DOI: 10.6084/m9.figshare.28060522
title: Severe interstitial lung disease risk prediction in anti-melanoma differentiation-associated protein 5 positive dermatomyositis: the STRAD-Ro52 model

description: Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5<sup>+</sup>DM-ILD) often leads to acute respiratory failure and endangers lives. This study quantitatively analysed chest high-resolution computed tomography (HRCT) images to assess MDA5<sup>+</sup>DM-ILD and establish a risk prediction model for severe ILD within six months. We developed a ‘Standardized Threshold Ratio Analysis & Distribution’ (STRAD) to analyse lung HRCT images. In this retrospective study, 51 patients with MDA5<sup>+</sup>DM-ILD were included and divided into severe-ILD and non-severe-ILD groups based on the occurrence of acute respiratory failure within six months post-diagnosis of MDA5<sup>+</sup>DM. The STRAD parameters, clinical indicators and treatments were compared between the two groups. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal STRAD parameters. Multivariate analysis selected clinical factors to be further combined with STRAD to enhance the predictive performance of the final model (STRAD-Ro52 model). Significant differences were observed between the two groups in STRAD parameters, anti-Ro52 antibody titers, presence of anti-Ro52 antibodies, age, ESR, ALB, Pa/FiO<sub>2</sub>, IgM and IL-4 levels. The STRAD parameters were significantly correlated with demographic, inflammatory, organ function and immunological indicators. Lasso logistic regression analysis identified the −699 to −650 HU lung tissue proportion (%V7) as the optimal parameter for predicting severe ILD and S6·%V7, and the distribution of %V7 in the mid lungs was the optimal space parameter. Multifactorial regression of clinical indicators showed that the presence of anti-Ro52 antibodies was an independent risk factor for severe ILD, leading to the establishment of the STRAD-Ro52 model. The STRAD-Ro52 model assists in identifying MDA5<sup>+</sup>DM patients at risk of developing severe ILD within six months, further optimizing precise disease management and clinical research design. Standardized Threshold Ratio Analysis & Distribution (STRAD) was developed to analyse lung high-resolution computed tomography (HRCT) images.Significant differences were observed between the severe-ILD and non-severe-ILD groups in STRAD parameters.The STRAD-Ro52 model, utilizing HRCT images and anti-Ro52 antibody, accurately predicts severe-ILD risk in patients with MDA5<sup>+</sup>DM-ILD. Standardized Threshold Ratio Analysis & Distribution (STRAD) was developed to analyse lung high-resolution computed tomography (HRCT) images. Significant differences were observed between the severe-ILD and non-severe-ILD groups in STRAD parameters. The STRAD-Ro52 model, utilizing HRCT images and anti-Ro52 antibody, accurately predicts severe-ILD risk in patients with MDA5<sup>+</sup>DM-ILD.
DOI: 10.6084/m9.figshare.28060522.v1
title: Severe interstitial lung disease risk prediction in anti-melanoma differentiation-associated protein 5 positive dermatomyositis: the STRAD-Ro52 model

description: Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5<sup>+</sup>DM-ILD) often leads to acute respiratory failure and endangers lives. This study quantitatively analysed chest high-resolution computed tomography (HRCT) images to assess MDA5<sup>+</sup>DM-ILD and establish a risk prediction model for severe ILD within six months. We developed a ‘Standardized Threshold Ratio Analysis & Distribution’ (STRAD) to analyse lung HRCT images. In this retrospective study, 51 patients with MDA5<sup>+</sup>DM-ILD were included and divided into severe-ILD and non-severe-ILD groups based on the occurrence of acute respiratory failure within six months post-diagnosis of MDA5<sup>+</sup>DM. The STRAD parameters, clinical indicators and treatments were compared between the two groups. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal STRAD parameters. Multivariate analysis selected clinical factors to be further combined with STRAD to enhance the predictive performance of the final model (STRAD-Ro52 model). Significant differences were observed between the two groups in STRAD parameters, anti-Ro52 antibody titers, presence of anti-Ro52 antibodies, age, ESR, ALB, Pa/FiO<sub>2</sub>, IgM and IL-4 levels. The STRAD parameters were significantly correlated with demographic, inflammatory, organ function and immunological indicators. Lasso logistic regression analysis identified the −699 to −650 HU lung tissue proportion (%V7) as the optimal parameter for predicting severe ILD and S6·%V7, and the distribution of %V7 in the mid lungs was the optimal space parameter. Multifactorial regression of clinical indicators showed that the presence of anti-Ro52 antibodies was an independent risk factor for severe ILD, leading to the establishment of the STRAD-Ro52 model. The STRAD-Ro52 model assists in identifying MDA5<sup>+</sup>DM patients at risk of developing severe ILD within six months, further optimizing precise disease management and clinical research design. Standardized Threshold Ratio Analysis & Distribution (STRAD) was developed to analyse lung high-resolution computed tomography (HRCT) images.Significant differences were observed between the severe-ILD and non-severe-ILD groups in STRAD parameters.The STRAD-Ro52 model, utilizing HRCT images and anti-Ro52 antibody, accurately predicts severe-ILD risk in patients with MDA5<sup>+</sup>DM-ILD. Standardized Threshold Ratio Analysis & Distribution (STRAD) was developed to analyse lung high-resolution computed tomography (HRCT) images. Significant differences were observed between the severe-ILD and non-severe-ILD groups in STRAD parameters. The STRAD-Ro52 model, utilizing HRCT images and anti-Ro52 antibody, accurately predicts severe-ILD risk in patients with MDA5<sup>+</sup>DM-ILD.
DOI: 10.6084/m9.figshare.30297044
title: Response to letter regarding “integrating systemic inflammation and liver biomarkers:prognostic implications of the ferritin index in heart failure”

description: Response to letter regarding “integrating systemic inflammation and liver biomarkers:prognostic implications of the ferritin index in heart failure”
DOI: 10.6084/m9.figshare.30297044.v1
title: Response to letter regarding “integrating systemic inflammation and liver biomarkers:prognostic implications of the ferritin index in heart failure”

description: Response to letter regarding “integrating systemic inflammation and liver biomarkers:prognostic implications of the ferritin index in heart failure”
DOI: 10.6084/m9.figshare.29483730
title: Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease

description: Crohn’s disease (CD) is a chronic inflammatory bowel disease, with infliximab (IFX) commonly used for treatment. However, no clinically applicable model currently exists to predict the response of patients with CD to IFX therapy. Given the strong association between sarcopenia and IFX treatment outcomes, this study developed computerized tomography radiomics-based machine learning (ML) models, utilizing psoas muscle volume as a proxy for skeletal muscle mass, to predict the response of patients with CD to IFX therapy. In this retrospective study, patients with CD from two institutions were recruited between January 2010 and January 2023, following stringent inclusion and exclusion criteria. Regions of interest were delineated using 3D Slicer software, and radiomics features were extracted with the Pyradiomics package in Python. <i>Z</i> score standardization and independent sample <i>t</i> test were applied to identify optimal predictive features, which were then utilized in seven ML algorithms for training and validation. Model performance was assessed through receiver-operating characteristic curves, precision–recall curves, and calibration curve analyses, evaluating accuracy and clinical applicability. Binary logistic regression was employed to identify predictors of IFX treatment response. A total of 134 patients were included, divided into a training cohort (<i>n</i> = 84) and a validation cohort (<i>n</i> = 50). Twenty differential radiomics features were selected for integration into the ML models. All models demonstrated strong predictive performance in the validation cohort, with a mean area under the curve of 0.849. The eXtreme Gradient Boosting algorithm outperformed others, achieving an area under the curve of 0.910. Psoas computerized tomography radiomics-based ML models effectively predict the response of patients with CD to IFX therapy, with the eXtreme Gradient Boosting model exhibiting superior performance.
DOI: 10.6084/m9.figshare.29483730.v1
title: Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease

description: Crohn’s disease (CD) is a chronic inflammatory bowel disease, with infliximab (IFX) commonly used for treatment. However, no clinically applicable model currently exists to predict the response of patients with CD to IFX therapy. Given the strong association between sarcopenia and IFX treatment outcomes, this study developed computerized tomography radiomics-based machine learning (ML) models, utilizing psoas muscle volume as a proxy for skeletal muscle mass, to predict the response of patients with CD to IFX therapy. In this retrospective study, patients with CD from two institutions were recruited between January 2010 and January 2023, following stringent inclusion and exclusion criteria. Regions of interest were delineated using 3D Slicer software, and radiomics features were extracted with the Pyradiomics package in Python. <i>Z</i> score standardization and independent sample <i>t</i> test were applied to identify optimal predictive features, which were then utilized in seven ML algorithms for training and validation. Model performance was assessed through receiver-operating characteristic curves, precision–recall curves, and calibration curve analyses, evaluating accuracy and clinical applicability. Binary logistic regression was employed to identify predictors of IFX treatment response. A total of 134 patients were included, divided into a training cohort (<i>n</i> = 84) and a validation cohort (<i>n</i> = 50). Twenty differential radiomics features were selected for integration into the ML models. All models demonstrated strong predictive performance in the validation cohort, with a mean area under the curve of 0.849. The eXtreme Gradient Boosting algorithm outperformed others, achieving an area under the curve of 0.910. Psoas computerized tomography radiomics-based ML models effectively predict the response of patients with CD to IFX therapy, with the eXtreme Gradient Boosting model exhibiting superior performance.
DOI: 10.6084/m9.figshare.29635924
title: Perceptions of research experience during internal medicine training: insights from a national survey

description: There is limited insights into how internal medicine (IM) residents perceive research. This study aimed to assess residents’ perceptions across five domains: well-being, research competence, research support, effect on training, and research quality. In 2022, a survey instrument was developed to assess residents’ perceptions of research across five domains. All IM programs listed in Fellowship and Residency Electronic Interactive Database (FREIDA) were contacted by email, and residents were invited to participate. A total of 530 residents from 67 IM programs completed our survey. Just over 80% of respondents had participated in research during residency. Residents were dissatisfied with research opportunities and infrastructure but viewed research funding more positively. Negative views were held regarding the impact of publication pressure on well-being and the quality of research produced. Publication pressure was perceived to compromise clinical work and limit career paths. Some residents acknowledged that research enriched their training. Residents who had already matched into a fellowship program reported higher research competency and better impressions of the impact of research on their medical training compared to those who had not yet or did not plan to match into a fellowship. Residents who planned to prioritize research in their future careers held positive perceptions of research across multiple domains. This study provides valuable insights into the impact of publication pressure on IM residents. The findings suggest a need to realign expectations for research productivity or target interventions to better support research for all residents, regardless of track or career goals.