bluuebunny/crossref_metadata_2025_split huggingface dataset:
Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease
Authors: Luo Hanyu , Wang Benlong , Cao Rui , Feng Jun 
Published: December 2024

BackgroundTo investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method.MethodsWe selected 575 elderly patients with CHD admitted to the Affiliated Lu’an Hospital of Anhui Medical University from January 2020 to January 2023. Based on whether patients were readmitted within 3 years, they were divided into two groups: those readmitted within 3 years (215 patients) and those not readmitted within 3 years (360 patients). Lasso regression and multivariate logistic regression were used to compare the predictive value of these models. XGBoost, LR, RF, KNN and DT algorithms were used to build prediction models for readmission risk. ROC curves and calibration plots were used to evaluate the prediction performance of the model. For external validation, 143 patients who were admitted between February and June 2023 from a different associated hospital in Lu'an City were also used.ResultsThe XGBoost model demonstrated the most accurate prediction performance out of the five machine learning techniques. Diabetes, Red blood cell distribution width (RDW), and Triglyceride glucose-body mass index (TyG-BMI), as determined by Lasso regression and multivariate logistic regression. Calibration plot analysis demonstrated that the XGBoost model maintained strong calibration performance across both training and testing datasets, with calibration curves closely aligning with the ideal curve. This alignment signifies a high level of concordance between predicted probabilities and observed event rates. Additionally, decision curve analysis highlighted that both decision trees and XGBoost models achieved higher net benefits within the majority of threshold ranges, emphasizing their significant potential in clinical decision-making processes. The XGBoost model's area under the ROC curve (AUC) reached 0.903, while the external validation dataset yielded an AUC of 0.891, further validating the model's predictive accuracy and its ability to generalize across different datasets.ConclusionTyG-BMI, RDW, and diabetes mellitus at the time of admission are the factors affecting readmission of elderly patients with coronary artery disease, and the model constructed based on the XGBoost algorithm for readmission risk prediction has good predictive efficacy, which can provide guidance for identifying high-risk patients and timely intervention strategies.
Cardiovascular risk profile with SCORE2 and SCORE2-OP: comparing Portugal, Spain, Italy, and France using the new European predictive models
Authors: Fontainhas Mariana , Gavina Cristina , Miranda Joana , Pereira-Silva Raquel , Guichard João , Seixas Daniela , Araújo Francisco 
Published: December 2024

IntroductionThis study aims to characterize the cardiovascular risk profile in countries with low-to-moderate cardiovascular mortality risk (Italy, Portugal, France, and Spain) using the SCORE2 and SCORE2-OP models. It also examines regional variations and the involvement of healthcare professionals in performing risk assessments.MethodsA retrospective observational study was conducted using data from 24,434 cardiovascular risk assessments performed between December 2022 and July 2023 through a digital application used by physicians. The assessments used the SCORE2 model for individuals aged 40–69 and the SCORE2-OP model for those aged 70 and older. Risk stratification into “low-to-moderate,” “high,” and “very high” categories was analyzed based on individual risk factors such as age, smoking habits, systolic blood pressure, and cholesterol levels.ResultsApproximately 50%–60% of individuals in these countries were classified as having “high” or “very high” cardiovascular risk. The highest proportions were observed in Portugal (62.44%) and Italy (64.05%), while lower proportions were found in Spain (46.65%) and France (52.74%). Regional analysis identified areas with the highest cardiovascular risk, such as Portalegre in Portugal and Apulia in Italy. Key risk factors included older age, smoking, high systolic blood pressure, and high non-HDL cholesterol. General practitioners were the primary healthcare professionals conducting these assessments.DiscussionThe study highlights a significant proportion of individuals with “high” or “very high” cardiovascular risk in countries with low-to-moderate mortality risk. These findings underscore the need for targeted cardiovascular disease prevention strategies and the crucial role of general practitioners in managing cardiovascular risk.
Exploring lower secondary school teachers' perceptions of integrating simulations into chemistry instruction
Authors: Batamuliza Jane , Habinshuti Gonzague , Nkurunziza Jean Baptiste 
Published: December 2024

IntroductionTeachers play a crucial role in guiding students to enhance their learning and achieve academic goals. Understanding their perspectives on educational technology is essential to fostering effective integration into classroom instruction. This study investigates the perceptions of lower secondary school chemistry teachers regarding the ease of use and usefulness of integrating ICBLS into teaching.MethodsA multiple case study design was employed, involving eight chemistry teachers from four schools where ICBLS had been implemented. Data were collected through semi-structured interviews, group discussions, professional workshops, and classroom observation. The data were transcribed and analyzed thematically using NVivo 14 software.ResultsThe findings indicate that teachers hold positive perceptions of ICBLS, citing benefits such as enhanced safety, collaboration learning, and hands-on activities. However, challenges were identified, including limited access to computers and insufficient professional training.DiscussionThe study underscores the importance of addressing technical and training barriers to optimize the use of ICBLS. Recommendations include strengthening professional development, fostering peer collaboration, aligning curricula with ICBLS, and ensuring administrative support and provision of adequate resources.
Development and validation of a risk score to predict adverse birth outcomes using maternal characteristics in northwest Ethiopia: a retrospective follow-up study
Authors: Anteneh Rahel Mulatie , Tesema Getayeneh Antehunegn , Lakew Ayenew Molla , Feleke Sefineh Fenta 
Published: December 2024

BackgroundAdverse birth outcomes are unfavorable outcomes of pregnancy that are particularly common in low- and middle-income countries. At least one ultrasound is recommended to predict adverse birth outcomes in early pregnancy. However, in low-income countries, imaging equipment and trained manpower are scarce. According to our search of the literature, there is no validated risk prediction model for predicting adverse birth outcomes in Ethiopia. Hence, we developed and validated a model and risk score to predict adverse birth outcomes using maternal characteristics during pregnancy for use in resource-limited settings.MethodsA retrospective follow-up study was conducted from 1 January 2016 to 31 May 2021, and a total of 910 pregnant women were included in this study. Participants were selected using a simple random sampling technique. Stepwise, backward multivariable analysis was conducted. The model's accuracy was assessed using density plots, discrimination, and calibration. The developed model was assessed for internal validity using bootstrapping techniques and evaluated for clinical utility using decision curve analysis across various threshold probabilities.ResultsPremature rupture of Membrane, number of fetuses, residence, pregnancy-induced hypertension, antepartum hemorrhage, hemoglobin level, and labor onset remained in the final multivariable prediction model. The area under the curve of the model was 0.77 (95% confidence interval: 0.73–0.812). The developed risk prediction model had a good performance and was well-calibrated and valid. The decision curve analysis indicated the model provides a higher net benefit across the ranges of threshold probabilities.ConclusionIn general, this study showed the possibility of predicting adverse birth outcomes using maternal characteristics during pregnancy. The risk prediction model using a simplified risk score helps identify high-risk pregnant women for specific interventions. A feasible score would reduce neonatal morbidity and mortality and improve maternal and child health in low-resource settings.
Stigma related to podoconiosis in Ethiopia: a systematic review
Authors: Abiso Temesgen Lera , Kerbo Amene Abebe , Woticha Eskinder Wolka , Koyira Mengistu Meskele 
Published: December 2024

BackgroundPodoconiosis, one of the neglected tropical diseases (NTDs), affects barefoot people in impoverished regions and contributes to poverty by having negative impacts on economic output, education, and disability. Podoconiosis has enormous social, psychological, and economic implications for the affected individuals. Social stigmatization of people with the disease is widespread, and patients are restricted from schools, local meetings, mosques, and churches and not allowed to marry into unaffected families. Therefore, this study aimed to systematically review the stigma related to podoconiosis in Ethiopia.MethodsThe Preferred Items for Systematic Reviews and Meta-analysis (PRISMA-2020) guidelines were followed. PubMed/Medline, Scopus, Web of Science, Google Scholar, and Google databases were searched. Articles published in the English language from inception to 2023 were included. Data were extracted by using a pre-prepared Excel sheet, and a narrative synthesis of the findings was done. This review was registered in the PROSPERO International Prospective Register of Systematic Reviews with registration number CRD42024510091.ResultsA total of 19 studies were included in this systematic review. This systematic review identified different types of stigmas reported from individuals with podoconiosis. The types of stigmas identified were enacted, felt, and internalized stigma and social stigmas, including exclusion from different social occurrences such as idir, meetings, funerals, and churches/mosques. Stigma also prevented patients with podoconiosis from visiting health institutions for the treatment of the disease and using health services for other cases.ConclusionsStigma among podoconiosis patients is widely distributed in Ethiopia. Different types of stigmas were identified among the patients. Extensive work needs to be done to prevent the disease and stigma. Strengthening works on community awareness about the disease help to ensure that it does not get transmitted from affected individuals to others.Systematic review registrationPROSPERO, identifier CRD42024510091.
Circulating miR-134 in mesial temporal lobe epilepsy: implications in hippocampal sclerosis development and drug resistance
Authors: Guerra Leal Bárbara , Carvalho Cláudia , Santos Cristina , Samões Raquel , Martins-Ferreira Ricardo , Teixeira Catarina , Rodrigues Diana , Freitas Joel , Lemos Carolina , Chorão Rui , Ramalheira João , Lopes João , Martins da Silva António , Pinho e Costa Paulo , Chaves João 
Published: December 2024

AimmiR-134 has been widely reported as upregulated in experimental and human studies of Mesial Temporal Lobe Epilepsy the most common drug-resistant epilepsy (DRE). Studies have shown that the use of antagomirs, anti-miR-134, may be a promising therapeutic approach to these epilepsies. However, data on miR-134 in other epileptic syndromes is scarce. In this study, we aimed to quantify serum levels of miR-134 in a cohort of patients with Mesial Temporal Lobe Epilepsy-Hippocampal Sclerosis (MTLE-HS) and with Genetic Generalized Epilepsies (GGE). Additionally, we explored the correlation between miR-134 serum levels and clinical parameters, such as age at onset or febrile seizures antecedents, to evaluate its potential as a biomarker and therapeutic target in epilepsy.MethodsmiR-134 levels were evaluated in cell-free serum of 131 patients with epilepsy (75 women, 56 men; age 41.10 ± 13.12 years; 72 with DRE) and 42 healthy individuals (25 women, 17 men; age 42.40 ± 9.80 years). The epilepsy cohort included 77 MTLE-HS patients and 54 GGE patients.ResultsPatients with elevated miR-134 circulating levels were at higher risk of drug-resistant epilepsy (OR [95% CI] = 2.246 [1.111–4.539], p = 0.021). Other risk factors included an older age (OR [95% CI] = 1.032 [1.004–1.061], p = 0.025), history of febrile seizures (OR [95% CI] = 2.994 [1.385–6.471], p = 0.005) and higher disease duration (OR [95% CI] = 1.038 [1.011–1.066], p = 0.006). The strongest predictor of DRE was hippocampal sclerosis (OR [95% CI] = 10.338 [4.566–23.404], p &lt; 0.001). Circulating miR-134 levels were significantly higher in MTLE-HS patients compared to controls (p &lt; 0.05) and GGE patients (p &lt; 0.05). However, the clinical utility of miR-134 in discriminating MTLE-HS patients from controls was only moderated (AUC = 0.651 ± 0.051 95% CI 0.551–0.751, p = 0.007).ConclusionWe show that miR-134 circulating levels are associated with DRE, especially in MTLE-HS, a syndrome characterized by severe hippocampal damage, consistent with activity-regulated miR-134 expression. This overexpression likely contributes to disease progression and our results support the potential of targeting miR-134 as a novel therapeutic approach for refractory epilepsy.
Neuroethical considerations and attitudes about neurostimulation as a fatigue countermeasure among emergency responders
Authors: Cabrera Laura Y. , Munoz Alejandro , Mehta Ranjana K. 
Published: December 2024

IntroductionFirst responders play a pivotal role in ensuring the wellbeing of individuals during critical situations. The demanding nature of their work exposes them to prolonged shifts and unpredictable situations, leading to elevated fatigue levels. Modern countermeasures to fatigue do not provide the best results. This study evaluates the acceptance and ethical considerations of a novel fatigue countermeasure using transcranial Direct Current Stimulation (tDCS) for fire and emergency medical services (EMS) personnel.MethodsTo better understand first responders' perceptions and ethical concerns about this novel fatigue countermeasure in their work, we conducted semi-structured interviews with first responders (N = 20). Interviews were transcribed into text and analyzed using qualitative content analysis.ResultsOver half of responders (59%) were interested, but over a third had a cautionary stand. Half of the participants seemed to have positive views regarding acceptability; a few were more cautionary or hesitant. A main area of consideration was user control (75%), with the majority wanting to retain some control over when or whether to accept the stimulation. Just above half of the participants (64%) mentioned privacy concerns. Another relevant consideration, raised by 50% of participants, was safety and the potential impact of stimulation (e.g., side effects, long-term effects). Overall, participants thought they needed to understand the system better and agreed that more education and training would be required to make people more willing to use it.DiscussionOur exploration into combating fatigue among first responders through tDCS has revealed promising initial reactions from the responder community. Findings from this study lay the groundwork for a promising solution, while still in a nascent design stage, to improve the effectiveness and resilience of first responders in fatiguing shifts and critical situations.
Drug-induced coagulopathies: a real-world pharmacovigilance study using the FDA adverse event reporting system
Authors: Lu Yanjun , Xu Qian , Zhu Shita 
Published: December 2024

BackgroundThis study aims to investigate adverse drug reaction signals associated with coagulopathies through data mining using the Adverse Event Reporting System (FAERS) of the US Food and Drug Administration. Prompt identification of high-risk drugs provides a valuable basis for enhancing clinical drug safety.MethodsThe adverse event reports related to coagulopathies from Q1 2004 to Q2 2024 were extracted from the ASCII data packages in FAERS. The reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) were used to identify adverse drug reaction signals associated with coagulopathies.ResultsDuring the reporting period, 40,545 reports were retrieved, with a slightly higher proportion of females than males. Among the top 30 drugs associated with the occurrence of coagulopathies, 24 drugs exhibited positive signals in risk analysis. Based on the individual drug reporting odds ratio (95% confidence interval) as a measure of risk signal strength, the top five drugs are as follows: gemcitabine [ROR (95% CI):16.87 (15.83–17.98)], busulfan [ROR (95% CI):15.51 (13.69–17.58)], anti-thymocyte globulin [ROR (95% CI):15.49 (13.49–17.78)], tacrolimus [ROR (95% CI):12.7 (11.57–13.95)], etonogestrel and ethinylestradiol vaginal ring [ROR (95% CI):11.88 (10.95–12.89)]. After categorizing the drugs, the strongest risk signal is sex hormones and modulators of the genital system [ROR (95% CI):11.88 (10.95–12.89)], followed by analgesics [ROR (95%CI): 6.73 (6.38–7.1)], immunosuppressants [ROR (95% CI):3.91 (3.76–4.05)], antineoplastic agents [ROR (95% CI):3.33 (3.22–3.45)], corticosteroids for systemic use [ROR (95% CI): 2.94 (2.73–3.18)], antiepileptics [ROR (95% CI):1.93 (1.71–2.18)], drugs used in diabetes [ROR (95% CI):1.5 (1.34–1.67)], antibacterials for systemic use [ROR (95% CI):1.46 (1.28–1.68)].ConclusionOur findings indicate that multiple drugs are associated with an increased risk of coagulopathies. From the pharmacovigilance perspective, proactive analysis of these drugs aids in clinical monitoring and enhances risk identification of coagulopathies.
PM2.5-induced oxidative stress upregulates PLA2R expression in the lung and is involved in the pathogenesis of membranous nephropathy through extracellular vesicles
Authors: Zhang Wang , Chen Jiating , Yuan Ye , Luo Jiao , Zhou Zhanmei , Wang Guobao 
Published: December 2024

BackgroundParticulate matter (PM2.5) has been implicated in the development of membranous nephropathy (MN), but the underlying mechanism has yet to be fully understood. Oxidative stress is an essential factor of PM2.5-related toxicity and plays a significant role in the exposure of target antigenic epitopes in MN. This study aims to explore the pathogenic effects of PM2.5 in facilitating the crosstalk between the lung and kidney in MN.MethodWe examined oxidative stress indicators and the circulating levels of extracellular vesicles (EVs) in patients diagnosed with MN. Additionally, we assessed the expression of M-type phospholipase A2 receptor (PLA2R) in human lung tissue ex vivo. To verify the impact of PM2.5 on PLA2R expression in the lung and the kidney, we stimulated human bronchial epithelial cells (Beas-2B) with lipopolysaccharide (LPS) or PM2.5. We then treated podocytes in vitro with the supernatants from PM2.5-exposed Beas-2B cells, intervening with GW4869, an inhibitor of EV release, to explore the role of EV-mediated cell-cell interactions.ResultsWe found that elevated serum markers of oxidative stress and increased levels of PLA2R + EVs correlated positively with anti-PLA2R antibody levels in the serum of patients with idiopathic MN (IMN). Notably, PLA2R expression was significantly higher in the lung tissue of smokers, suggesting a possible link between PLA2R and oxidative stress. In vitro experiments demonstrated that PLA2R expression in Beas-2B cells was upregulated upon stimulation with LPS and PM2.5, an effect that could be reversed by the antioxidant glutathione (GSH). Furthermore, the supernatants from PM2.5-exposed Beas-2B cells were found to induce PLA2R overexpression and injury in podocytes, with this effect being mitigated by GW4869, an inhibitor of EVs release.ConclusionOur study contributes new knowledge to the understanding of how environmental pollutants, such as PM2.5, cause kidney damage through oxidative stress and EV-mediated signaling. The findings pave the way for further research into therapeutic strategies targeting oxidative stress and EVs, which could potentially improve patient outcomes of MN, particularly in high-risk populations like smokers and those exposed to air pollution.
Monitoring of the trough concentration of valproic acid in pediatric epilepsy patients: a machine learning-based ensemble model
Authors: Chen Yue-Wen , Lin Xi-Kai , Chen Si , Zhang Ya-Lan , Wu Wei , Huang Chen , Rao Xin , Lu Zong-Xing , Liu Zhou-Jie 
Published: December 2024

AimsFew personalized monitoring models for valproic acid (VPA) in pediatric epilepsy patients (PEPs) incorporate machine learning (ML) algorithms. This study aimed to develop an ensemble ML model for VPA monitoring to enhance clinical precision of VPA usage.MethodsA dataset comprising 366 VPA trough concentrations from 252 PEPs, along with 19 covariates and the target variable (VPA trough concentration), was refined by Spearman correlation and multicollinearity testing (366 × 11). The dataset was split into a training set (292) and testing set (74) at a ratio of 8:2. An ensemble model was formulated by Gradient Boosting Regression Trees (GBRT), Random Forest Regression (RFR), and Support Vector Regression (SVR), and assessed by SHapley Additive exPlanations (SHAP) analysis for covariate importance. The model was optimized for R2, relative accuracy, and absolute accuracy, and validated against two independent external datasets (32 in-hospital and 28 out-of-hospital dataset).ResultsUsing the R2 weight ratio of GBRT, RFR and SVR optimized at 5:2:3, the ensemble model demonstrated superior performance in terms of relative accuracy (87.8%), absolute accuracy (78.4%), and R2 (0.50), while also exhibiting a lower Mean Absolute Error (9.87) and Root Mean Squared Error (12.24), as validated by the external datasets. Platelet count (PLT) and VPA daily dose were identified as pivotal covariates.ConclusionThe proposed ensemble model effectively monitors VPA trough concentrations in PEPs. By integrating covariates across various ML algorithms, it delivers results closely aligned with clinical practice, offering substantial clinical value for the guided use of VPA.