bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving the quality of neonatal health care in Ethiopia: a systematic review
Authors: Belay Demeke Mesfin , Erku Daniel , Bayih Wubet Alebachew , Kassie Yohannes Tesfahun , Minuye Birhane Binyam , Assefa Yibeltal 
Published: May 2024

BackgroundEnsuring high-quality healthcare for newborns is essential for improving their chances of survival within Ethiopia's healthcare system. Although various intervention approaches have been implemented, neonatal mortality rates remain stable. Therefore, the present review seeks to identify initiatives for enhancing healthcare quality, their effects on neonatal wellbeing, and the factors hindering or supporting these Quality Improvement (QI) efforts' success in Ethiopia.MethodsWe searched for original research studies up to June 23, 2023, using PubMed/Medline, WHO-Global Health Library, Cochrane, Clinical Trials.gov, and Hinari. After selecting eligible studies, we assessed their quality using a mixed-method appraisal tool. Quality of care refers to how healthcare services effectively improve desired outcomes for individuals and patient populations. It encompasses vital principles such as safety, effectiveness, timeliness, efficiency, equity, and patient-centeredness.ResultsWe found 3,027 publication records and included 13 studies during our search. All these interventions primarily aimed to provide safe healthcare, with a strong focus on Domain One, which deals with the evidence-based routine upkeep and handling of complications, and Domain Seven, which revolves around ensuring staff competency, emerged as a frequent target for intervention. Many interventions aimed at improving quality also concentrate on essential quality measure elements such as processes, focusing on the activities that occur during care delivery, and quality planning, involving distributing resources, such as basic medicine and equipment, and improving infrastructure. Moreover, little about the facilitators and barriers to QI interventions is investigated.ConclusionsThis review highlights the significance of introducing QI initiatives in Ethiopia, enhancing the healthcare system's capabilities, engaging the community, offering financial incentives, and leveraging mobile health technologies. Implementing QI interventions in Ethiopia poses difficulties due to resource constraints, insufficient infrastructure, and medical equipment and supplies shortages. It necessitates persistent endeavors to improve neonatal care quality, involving ongoing training, infrastructure enhancement, the establishment of standardized protocols, and continuous outcome monitoring. These efforts are crucial to achieving the optimal outcomes for newborns and their families.
From obese to lean curriculum: exploring students’ experiences about developing competencies in medical education
Authors: Karami Morteza , Hashemi Nooriyah , Van Merrienboer Jeroen 
Published: May 2024

IntroductionSince the beginning of the 21st century, competency-based education has been proposed as an approach to education in many disciplines including the medical sciences and it has become a dominant approach in many countries. We aimed to explore the lived experiences of general medical students about developing competencies in the academic curriculum.MethodsWe conducted a phenomenology method to study lived experiences of general medical students through selecting participants via a purposeful sampling strategy. Snowballing and maximum variation samplings were also applied to recruit additional participants. The study was conducted at a Medical School in Iran. Three successive phases of qualitative data analysis, namely, data reduction by coding, data structuring by categorization, and data interpretation by discussion were applied to analyze the interviews.ResultsThe results of the research showed that students’ lived experiences fall under 4 main themes with 9 subthemes. The main themes show that (1) the compartmentalized curriculum in basic courses is experienced as the missing parts in a puzzle, (2) the physiopathology curriculum is experienced as swimming on land, (3) the externship is experienced as touring a mysterious land, (4) the internship is experienced as unleashed arrows.DiscussionOur findings reveal that despite the changes already made in the curriculum, its compartmentalization is still a main obstacle to achieving competency-based medical education. A strict requirement for leaving the discipline-based curriculum behind is to use an integrated approach, in which basic science courses are connected with clinical cases, and physiopathology courses are connected with externships and internships.
Association of clinical, imaging and laboratory parameters with adverse effects of glucocorticoid therapy in patients with giant cell arteritis
Authors: Schweiger Leyla , Hafner Franz , Meinitzer Andreas , Brodmann Marianne , Dejaco Christian , Jud Philipp 
Published: May 2024

BackgroundGiant cell arteritis (GCA) is characterized by inflammation of large and medium vessels. First-line therapy for the treatment of GCA are glucocorticoids, which are effective while potential adverse effects should be considered, especially during long-term use. The aim was to investigate the incidence of glucocorticoids’ adverse effects and potential predictors for them.Materials and methods138 GCA patients were retrospectively evaluated for newly developed glucocorticoid adverse effects in 2020. Potential predictors, defined as initial glucocorticoid pulse therapy, relapse of GCA and concomitant polymyalgia rheumatica as well as parameters of inflammation and endothelial dysfunction, including pulse-wave velocity and intima-media-thickness, were measured in 2012.ResultsPotential new glucocorticoid adverse effects per patient was 1 (25th-75th 0–3) of which chronic kidney disease progression (29%), bone fractures (23.2%), cataracts (18.1%), dementia, and arterial hypertension (each at 12.3%) were most commonly recorded. Significant associations were found between occurrence of any relapse and new diabetes mellitus and between initial glucocorticoid pulse therapy and new dementia (all with p &lt; 0.05). In multivariate regression analysis, any relapse was a predictor for developing diabetes mellitus (OR 9.23 [95% CI 1.33–64.05], p = 0.025). However, no correlations were observed between endothelial dysfunction or inflammatory parameters and development of new glucocorticoid adverse effects.ConclusionGCA relapses may be associated for development of diabetes mellitus potentially by increasing glucocorticoid doses. Parameters of inflammation and endothelial dysfunction are not suited predictors for glucocorticoid adverse effects.
GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation
Authors: Zhou Qingqing , Liu Can , Duan Yuchen , Sun Kaijie , Li Yu , Kan Hongxing , Gu Zongyun , Shu Jianhua , Hu Jili 
Published: May 2024

IntroductionLarge Language Models (LLMs) play a crucial role in clinical information processing, showcasing robust generalization across diverse language tasks. However, existing LLMs, despite their significance, lack optimization for clinical applications, presenting challenges in terms of illusions and interpretability. The Retrieval-Augmented Generation (RAG) model addresses these issues by providing sources for answer generation, thereby reducing errors. This study explores the application of RAG technology in clinical gastroenterology to enhance knowledge generation on gastrointestinal diseases.MethodsWe fine-tuned the embedding model using a corpus consisting of 25 guidelines on gastrointestinal diseases. The fine-tuned model exhibited an 18% improvement in hit rate compared to its base model, gte-base-zh. Moreover, it outperformed OpenAI’s Embedding model by 20%. Employing the RAG framework with the llama-index, we developed a Chinese gastroenterology chatbot named “GastroBot,” which significantly improves answer accuracy and contextual relevance, minimizing errors and the risk of disseminating misleading information.ResultsWhen evaluating GastroBot using the RAGAS framework, we observed a context recall rate of 95%. The faithfulness to the source, stands at 93.73%. The relevance of answers exhibits a strong correlation, reaching 92.28%. These findings highlight the effectiveness of GastroBot in providing accurate and contextually relevant information about gastrointestinal diseases. During manual assessment of GastroBot, in comparison with other models, our GastroBot model delivers a substantial amount of valuable knowledge while ensuring the completeness and consistency of the results.DiscussionResearch findings suggest that incorporating the RAG method into clinical gastroenterology can enhance the accuracy and reliability of large language models. Serving as a practical implementation of this method, GastroBot has demonstrated significant enhancements in contextual comprehension and response quality. Continued exploration and refinement of the model are poised to drive forward clinical information processing and decision support in the gastroenterology field.
Multi-sequence generative adversarial network: better generation for enhanced magnetic resonance imaging images
Authors: Li Leizi , Yu Jingchun , Li Yijin , Wei Jinbo , Fan Ruifang , Wu Dieen , Ye Yufeng 
Published: May 2024

IntroductionMRI is one of the commonly used diagnostic methods in clinical practice, especially in brain diseases. There are many sequences in MRI, but T1CE images can only be obtained by using contrast agents. Many patients (such as cancer patients) must undergo alignment of multiple MRI sequences for diagnosis, especially the contrast-enhanced magnetic resonance sequence. However, some patients such as pregnant women, children, etc. find it difficult to use contrast agents to obtain enhanced sequences, and contrast agents have many adverse reactions, which can pose a significant risk. With the continuous development of deep learning, the emergence of generative adversarial networks makes it possible to extract features from one type of image to generate another type of image.MethodsWe propose a generative adversarial network model with multimodal inputs and end-to-end decoding based on the pix2pix model. For the pix2pix model, we used four evaluation metrics: NMSE, RMSE, SSIM, and PNSR to assess the effectiveness of our generated model.ResultsThrough statistical analysis, we compared our proposed new model with pix2pix and found significant differences between the two. Our model outperformed pix2pix, with higher SSIM and PNSR, lower NMSE and RMSE. We also found that the input of T1W images and T2W images had better effects than other combinations, providing new ideas for subsequent work on generating magnetic resonance enhancement sequence images. By using our model, it is possible to generate magnetic resonance enhanced sequence images based on magnetic resonance non-enhanced sequence images.DiscussionThis has significant implications as it can greatly reduce the use of contrast agents to protect populations such as pregnant women and children who are contraindicated for contrast agents. Additionally, contrast agents are relatively expensive, and this generation method may bring about substantial economic benefits.
Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study
Authors: Liu Wei , Jia Longbin , Xu Lina , Yang Fengbing , Guo Zixuan , Li Jinna , Zhang Dandan , Liu Yan , Xiang Han , Cheng Hongjiang , Hou Jing , Li Shifang , Li Huimin 
Published: May 2024

BackgroundEarly neurological deterioration (END) is a frequent complication in patients with perforating artery territory infarction (PAI), leading to poorer outcomes. Therefore, we aimed to apply machine learning (ML) algorithms to predict the occurrence of END in PAI and investigate related risk factors.MethodsThis retrospective study analyzed a cohort of PAI patients, excluding those with severe stenosis of the parent artery. We included demographic characteristics, clinical features, laboratory data, and imaging variables. Recursive feature elimination with cross-validation (RFECV) was performed to identify critical features. Seven ML algorithms, namely logistic regression, random forest, adaptive boosting, gradient boosting decision tree, histogram-based gradient boosting, extreme gradient boosting, and category boosting, were developed to predict END in PAI patients using these critical features. We compared the accuracy of these models in predicting outcomes. Additionally, SHapley Additive exPlanations (SHAP) values were introduced to interpret the optimal model and assess the significance of input features.ResultsThe study enrolled 1,020 PAI patients with a mean age of 60.46 (range 49.11–71.81) years. Of these, 30.39% were women, and 129 (12.65%) experienced END. RFECV selected 13 critical features, including blood urea nitrogen (BUN), total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), atrial fibrillation, loading dual antiplatelet therapy (DAPT), single antiplatelet therapy (SAPT), argatroban, the basal ganglia, the thalamus, the posterior choroidal arteries, maximal axial infarct diameter (measured at &lt; 15 mm), and stroke subtype. The gradient-boosting decision tree had the highest area under the curve (0.914) among the seven ML algorithms. The SHAP analysis identified apoB as the most significant variable for END.ConclusionOur results suggest that ML algorithms, especially the gradient-boosting decision tree, are effective in predicting the occurrence of END in PAI patients.
Impulse control disorders and use of dopamine agonists in early onset Parkinson’s disease
Authors: Turcano Pierpaolo , Jacobson Jessie , Ghoniem Khaled , Mullan Aidan , Camerucci Emanuele , Stang Cole , Piat Capucine , Bower James H. , Savica Rodolfo 
Published: May 2024

IntroductionImpulse control disorders (ICDs) are defined as excessive and repetitive behaviors that may affect Parkinson’s disease (PD) patients exposed to dopamine agonists. Current data on ICDs in patients with early-onset Parkinson’s disease (EOPD) is lacking. In this study we aim to assess the frequency of use of dopamine agonists, the prevalence of ICDs, and to explore potential factors associated with their development in patients with EOPD.MethodsWe used the Mayo Clinic Data Explorer system to investigate a population-based cohort of EOPD patients between 1990 and 2022 at Mayo Clinic, Rochester, MN. We used ICD coding for parkinsonism; then, we reviewed all the clinical records and included only those patients with a clinical diagnosis of PD with symptoms onset at or before the age of 50, and who developed ICDs after using therapeutic doses of dopamine agonists.ResultsA total of 831 (513 males and 318 females) patients with EOPD were included with a median age at symptom onset of 42 years of age (CI: 37–46). Dopamine agonists were used in 49.7% of all patients; of these, only 14.5% developed symptoms of one or more ICDs. Hypersexuality was the most commonly observed ICD (38.3%), and the only one having a statistically significant male predominance (p = 0.011).ConclusionICDs are common in EOPD, particularly when associated with the use of dopamine agonists.
Changes in cortical grey matter volume with Cognitive Orientation to daily Occupational Performance intervention in children with developmental coordination disorder
Authors: Malik Myrah Anum , Weber Alexander Mark , Lang Donna , Vanderwal Tamara , Zwicker Jill G. 
Published: May 2024

IntroductionCognitive Orientation to daily Occupational Performance (CO-OP) is a cognitive-based, task-specific intervention recommended for children with developmental coordination disorder (DCD). We recently showed structural and functional brain changes after CO-OP, including increased cerebellar grey matter. This study aimed to determine whether CO-OP intervention induced changes in cortical grey matter volume in children with DCD, and if these changes were associated with improvements in motor performance and movement quality.MethodsThis study is part of a randomized waitlist-control trial (ClinicalTrials.gov ID: NCT02597751). Children with DCD (N = 78) were randomized to either a treatment or waitlist group and underwent three MRIs over 6 months. The treatment group received intervention (once weekly for 10 weeks) between the first and second scan; the waitlist group received intervention between the second and third scan. Cortical grey matter volume was measured using voxel-based morphometry (VBM). Behavioral outcome measures included the Performance Quality Rating Scale (PQRS) and Bruininks-Oseretsky Test of Motor Proficiency-2 (BOT-2). Of the 78 children, 58 were excluded (mostly due to insufficient data quality), leaving a final N = 20 for analyses. Due to the small sample size, we combined both groups to examine treatment effects. Cortical grey matter volume differences were assessed using a repeated measures ANOVA, controlling for total intracranial volume. Regression analyses examined the relationship of grey matter volume changes to BOT-2 (motor performance) and PQRS (movement quality).ResultsAfter CO-OP, children had significantly decreased grey matter in the right superior frontal gyrus and middle/posterior cingulate gyri. We found no significant associations of grey matter volume changes with PQRS or BOT-2 scores.ConclusionDecreased cortical grey matter volume generally reflects greater brain maturity. Decreases in grey matter volume after CO-OP intervention were in regions associated with self-regulation and motor control, consistent with our other studies. Decreased grey matter volume may be due to focal increases in synaptic pruning, perhaps as a result of strengthening networks in the brain via the repeated learning and actions in therapy. Findings from this study add to the growing body of literature demonstrating positive neuroplastic changes in the brain after CO-OP intervention.
Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs
Authors: Liu Tianyu , Wu Yu , Ye An , Cao Lei , Cao Yongnian 
Published: May 2024

BackgroundChannel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems.MethodsIn TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.ResultsThe performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.ConclusionThe proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.
DTI-ALPS index decreased in patients with Type 2 Diabetes Mellitus
Authors: Yu Shuncheng , Jiang Hongjun , Yu Langxuan , Liu Tieli , Yang Chun , Cao Jiajun , Song Qingwei , Miao Yanwei , Wang Weiwei 
Published: May 2024

BackgroundsType 2 Diabetes Mellitus (T2DM) has become a significant global public health issue, characterized by a rising prevalence and associated deficits across multiple organ systems. Our study aims to utilize the DTI-ALPS technique to assess the change of ALPS index in T2DM patients, and to explore whether such changes are correlated with cognition level and diffusion parameters.MethodsThe study involved 41 patients with T2DM (mean age, 60.49 ± 8.88 years) and 27 healthy controls (mean age, 58.00 ± 7.63 years). All subjects underwent MRI examination, cognitive assessment, and laboratory tests. Tract-based spatial statistics (TBSS) was used to evaluate white matter changes. GLM was performed to check the DTI-ALPS index difference between T2DM and HC groups. Spearman correlation analysis and partial correlation analysis were used to analyze the correlation between the DTI-ALPS index and diffusion properties &amp; cognitive scores.ResultsThe results show that the ALPS index was lower in T2DM patients. MoCA score was significantly correlated with the ALPS index. Patients with T2DM had a significant increase in both mean diffusivity (MD) and radial diffusivity (RD) and decrease in fractional anisotropy (FA) compared to the HC group.ConclusionThe results suggest that the ALPS index is decreased in T2DM patients and associates with cognitive level.
Adolescent binge ethanol impacts H3K9me3-occupancy at synaptic genes and the regulation of oligodendrocyte development
Authors: Brocato Emily R. , Easter Rachel , Morgan Alanna , Kakani Meenakshi , Lee Grace , Wolstenholme Jennifer T. 
Published: May 2024

IntroductionBinge drinking in adolescence can disrupt myelination and cause brain structural changes that persist into adulthood. Alcohol consumption at a younger age increases the susceptibility of these changes. Animal models to understand ethanol’s actions on myelin and white matter show that adolescent binge ethanol can alter the developmental trajectory of oligodendrocytes, myelin structure, and myelin fiber density. Oligodendrocyte differentiation is epigenetically regulated by H3K9 trimethylation (H3K9me3). Prior studies have shown that adolescent binge ethanol dysregulates H3K9 methylation and decreases H3K9-related gene expression in the PFC.MethodsHere, we assessed ethanol-induced changes to H3K9me3 occupancy at genomic loci in the developing adolescent PFC. We further assessed ethanol-induced changes at the transcription level with qPCR time course approaches in oligodendrocyte-enriched cells to assess changes in oligodendrocyte progenitor and oligodendrocytes specifically.ResultsAdolescent binge ethanol altered H3K9me3 regulation of synaptic-related genes and genes specific for glutamate and potassium channels in a sex-specific manner. In PFC tissue, we found an early change in gene expression in transcription factors associated with oligodendrocyte differentiation that may lead to the later significant decrease in myelin-related gene expression. This effect appeared stronger in males.ConclusionFurther exploration in oligodendrocyte cell enrichment time course and dose response studies could suggest lasting dysregulation of oligodendrocyte maturation at the transcriptional level. Overall, these studies suggest that binge ethanol may impede oligodendrocyte differentiation required for ongoing myelin development in the PFC by altering H3K9me3 occupancy at synaptic-related genes. We identify potential genes that may be contributing to adolescent binge ethanol-related myelin loss.