bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Hybrid News Recommendation Approach Based on Title-Content Matching
Authors: Jiang Shuhao , Lu Yizi , Song Haoran , Lu Zihong , Zhang Yong 
Published: April 2024

Abstract

Personalized news recommendation can alleviate the information overload problem, and accurate modeling of user interests is the core of personalized news recommendation. Existing news recommendation methods integrate the titles and contents of news articles that users have historically browsed to construct user interest models. However, this method ignores the phenomenon of "title-content mismatching" in news articles, which leads to the lack of precision in user interest modeling. Therefore, a hybrid news recommendation method based on title-content matching is proposed in this paper(1)An interactive attention network is employed to model the correlation between title and content contexts, thereby enhancing the feature representation of both; (2) The degree of title-content matching is computed using a Siamese Neural Network, constructing a user interest model based on title-content matching; and (3) Neural Collaborative Filtering (NCF) based on Factorization Machines (FM) is integrated, taking into account the perspective of the potential relationships between users for recommendation, leveraging the insensitivity of neural collaboration to news content to alleviate the impact of title-content mismatching on user feature modeling. The proposed model was evaluated on a real dataset, and the experimental results demonstrate that the proposed method effectively improved the performance of news recommendation.
Comparison of Outcomes Between Kidney Transplantation with Immunological High-Risk Living Donor Versus Deceased Donor Transplantation
Authors: Banjongjit Athiphat , Tiankanon Kanitha , Udomkarnjananun Suwasin , Wuttiputhanun Thunyatorn , Jutivorakool Kamonwan , Watanaboonyongcharoen Phandee , Sutherasan Methee , Praditpornsilpa Kearkiat , Avihingsanon Yingyos , Tantiyavarong Pichaya , Townamchai Natavudh 
Published: April 2024

Abstract

ABO-incompatible (ABOi) and human leukocyte antigen (HLA)-incompatible (HLAi) kidney transplantations are known as immunological high-risk transplantation. Patients with incompatible living donors have to choose either undergoing desensitization and then transplantation or waiting for deceased donor kidney transplantation (DDKT). Studies on the outcome of ABOi and HLAi living donor kidney transplantations (LDKTs) compared to waiting for and receiving DDKT outside the United States and European countries remain scarce. This single-center retrospective study was conducted in patients who underwent ABOi, HLAi, and DDKT between January 2008 and November 2021. The patient survival rate was 97.7%, 92.5%, and 82.6% at 5, 10, and 15 years, respectively, in the DDKT group compared to 96.8% at 5 and 10 years in the ABOi group (P = 0.84) and 94.8% at 5 years in the HLAi group (P = 0.91). The death-censored graft survival was 95.6%, 83.1%, and 63.8% at 5, 10, and 15 years in the DDKT group, as compared to 90.3% at 5 and 10 years in the ABOi group (P = 0.73) and 92.1% at 5 years in the HLAi group (P = 0.53). Antibody-mediated rejection occurred significantly higher in the HLAi group with a hazard ratio of 2.77 (95% confidence interval: 1.31–5.88; P = 0.008) compared to the DDKT group. ABOi and HLAi KT did not increase rates of BK and cytomegalovirus (CMV) infection compared to DDKT. In summary, ABOi and HLAi transplant recipients had comparable patient, graft survival, and CMV and BK infections to DDKT. Our study emphasizes the usefulness of ABOi and HLAi LDKT to reduce patients’ waiting time and improve their quality of life.
Identification of the biomarkers of ACS co-morbid T2DM in human serum through UHPLC/MS study
Authors: Duan Mingyu , Zhang Zhihan , Deng Ruicheng , He Ruhua , Zhang Yameng , Fu Meijiao , Zhang Xueying , Dilshat Miradil , He Jun 
Published: April 2024

Abstract
Type 2 diabetes mellitus (T2DM) is closely associated with an increased risk and adverse event of acute coronary syndrome (ACS). The present study aims to identify metabolic biomarker, and discuss the association between metabolic phenotype and clinical phenotype in ACS co-morbid T2DM patients. Serum metabolic study was conducted in ACS co-morbid T2DM patients by using ultra-high performance liquid chromatography-mass spectrometry (UHPLC/MS). Association between the differential metabolites and serum fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), Syntax score I and major adverse cardiovascular event (MACE) were analyzed using Spearman's correlation. COX regression and Kaplan-Meier survival curve were employed to recognize the risk factors of MACE, time-dependent receptor operating characteristic (ROC) curve was used to assess the predictive value of the risk factors.The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was applied for metabolic pathway identification. We found that serum 7-Ketocholesterol, L-Dopa and 2-furanacrolein are potential biomarkers of ACS co-morbid T2DM. These three biomarkers synergize to demonstrate high predictive value (AUC = 0.953) for the occurrence of MACE. This implies that in ACS co-morbid T2DM patients, there is a close correlation between the metabolic phenotype and the clinical phenotype.
Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models
Authors: Mulugeta Getahun , Zewotir Temesgen , Tegegne Awoke Seyoum , Juhar Leja Hamza , Muleta Mahteme Bekele 
Published: April 2024

Abstract

Introduction:
Renal transplantation is a critical treatment that can save the lives of individuals who are suffering from end-stage renal disease (ESRD), but graft failure remains a significant concern. Accurate prediction of graft survival after renal transplantation is crucial as it enables clinicians to identify patients at higher risk of graft failure. This study aimed to develop clinical prognostic models for predicting graft survival after renal transplantation and compare the performance of various statistical and machine learning models.
Methodology:
The study utilized data from a retrospective cohort of renal transplant recipients at the Ethiopian National Kidney Transplantation Center from September 2015 to February 2022. Various statistical and machine learning models were evaluated based on their discrimination, calibration, and interpretability. The comparison of models included standard Cox, Lasso-Cox, Ridge-Cox, Elastic net-Cox, Random Survival Forest, and Stochastic Gradient Boosting. The prognostic predictors of graft survival were selected based on the significance and relative importance of variables in different models.
Results
The study analyzed a total of 278 completed cases and observed the event of graft failure in 21 patients. The median graft survival time was 33 months, and the mean hazard of graft failure was 0.0755. The results revealed that the 1-year, 3-year, and 5-year graft survival rates are 0.936, 0.924, and 0.914 respectively. The study found that the Random Survival Forest and Stochastic Gradient Boosting models demonstrated the best calibration and discrimination performance shown by an equal AUC of 0.97 and the overlapped calibration plots. On the other hand, the Cox proportional hazards model has the highest interpretability and established superior accuracy in estimating survival probabilities, as evidenced by its lowest Brier score of 0.000071. The current study indicates that an episode of chronic rejection, recipient residence, an episode of acute rejection, post-transplant urological complications, post-transplant nonadherence, blood urea nitrogen level, and number of post-transplant admissions were consistently identified as the top significant prognostic predictors of renal graft survival.
Conclusions
The Random Survival Forest and Stochastic Gradient Boosting models demonstrated superior calibration and discrimination performance, while the Cox proportional hazards model offered accurate estimation of survival probabilities and interpretability. Clinicians should consider the trade-off between performance and interpretability when choosing a model. Incorporating these findings into clinical practice can improve risk stratification, enable early interventions, and inform personalized management strategies for kidney transplant recipients.
Analysis of eye tracking in inclusive mathematics video lessons in Brasilian high school
Authors: Batista Leonardo dos Santos , Benitez Priscila , Kumada Kate Mamhy Oliveira , Moura Thainá Letícia Dourado , Soares André , Cavalcante Tardelly A. 
Published: April 2024

Abstract

Large-scale assessments indicate that 68% of the Brasilians evaluated, with and without disabilities, who are at the end of basic education, have a mathematics performance below the minimum level necessary to exercise citizenship. Studies indicate that visually enriched teaching strategies are more effective than the traditional model, however, they still lack metrics to evaluate visual materials. Thus, the objective of this study was to verify the feasibility of eye tracking as an auxiliary measure in the evaluation of inclusive teaching materials in the area of ​​mathematics. Specifically, the concepts of multiplication, greatest common divisor and distribution were covered in the inclusive video lessons. To this end, we adopted the methodology of an exploratory research, having the teaching procedure evaluated using multiple baseline experimental design between mathematical concepts. The research involved the participation of 11 people with varying biopsychosocial conditions. As main results, we obtained that eye tracking provides sufficient data to assist in the evaluation of complex teaching material, managing, together with other measures, to produce a detailed report on the individual, scoring their change in behavior and detailing the barriers faced by him during the intervention. So, gaze measurements can support decision-making about educational planning.
Improved Outcomes and Reduced Costs: Exclusive Human Milk Diet for Very Low Birth Weight Infants at a Single Center
Authors: Mudekunye Gabriella , Tullius Zoe , Guevara Cynthia , Timmerman Amanda , Moorthy Gyan , Lee Martin , Reddy Siresha , Chheda Sadhana 
Published: April 2024

Abstract

Preterm infants born at very low birth weight (VLBW) are at increased risk of developing many highly morbid conditions such as necrotizing enterocolitis (NEC), periventricular leukomalacia (PVL), and retinopathy of prematurity (ROP). This leads to a significant emotional and cost burden for their families and the healthcare system. Previous literature has established that an exclusive human milk diet (EHMD) when compared to a diet containing bovine milk is associated with decreased incidence of several of these life-threatening conditions. This single center retrospective review at a hospital on the US-Mexico Border compares outcomes of VLBW infants pre- and post- initiation of an EHMD. Infants following an EMHD feeding protocol began fortification on average three days earlier and had their first feeds earlier. The EHMD group had lower incidences of both surgical and medical necrotizing enterocolitis (NEC), and fewer total parenteral nutrition (TPN), central venous line (CVL), and antibiotic days and fever blood transfusions with comparable growth outcomes. In aggregate, this yielded a substantial cost savings.
MEDITRON: Open Medical Foundation Models Adapted for Clinical Practice
Authors: Chen Zeming , Romanou Angelika , Bonnet Antoine , Hernández-Cano Alejandro , Alkhamissi Badr , Matoba Kyle , Salvi Francesco , Pagliardini Matteo , Fan Simin , Köpf Andreas , Mohtashami Amirkeivan , Sallinen Alexandre , Swamy Vinitra , Sakhaeirad Alireza , Krawczuk Igor , Bayazit Deniz , Marmet Axel , Mi Li , Boillat-Blanco Noémie , Keitel Kristina , Elkin Javier , Robert Blaise , Montariol Syrielle , Bressan Silvia , Chen David , Demers Vincent , Emery Nina , Glasson Nicolas , Mensah Paulina , Miauton Alix , Roemer Ségolène , Siebert Johan , Starvaggi Carl , Suttels Véronique , Tan Rainer , Taylor R. , du Toit Jacques , Hartley Mary-Anne , Jaggi Martin , Bosselut Antoine 
Published: April 2024

Abstract
Large language and multimodal models (LLMs and LMMs) will transform access to medical knowledge and clinical decision support. However, the current leading systems fall short of this promise, as they are either limited in scale, which restricts their capabilities, closed-source, which limits the extensions and scrutiny that can be applied to them, or not sufficiently adapted to clinical settings, which inhibits their practical use. In this work, we democratize large-scale medical AI systems by developing MEDITRON: a suite of open-source LLMs and LMMs with 7B and 70B parameters adapted to the medical domain. MEDITRON extends pretraining on a comprehensively curated medical corpus that includes biomedical literature and internationally recognized clinical practice guidelines. Evaluations using standard medical reasoning benchmarks show significant improvements over all current open-access models and several state-of-the-art commercial LLMs that are orders of magnitude larger, more expensive to host, and closed-source. Enhanced with visual processing capabilities, our MEDITRON-V model also outperforms all open-access models and much larger closed-source models on multimodal reasoning tasks for various biomedical imaging modalities. Beyond traditional benchmarks, we also create a novel and physician-driven adversarial question dataset grounded in real-world clinical settings, and a comprehensive 17-metric evaluation rubric to assess alignment and contextualization to real-world clinical practice. Applying this framework to MEDITRON-70B's responses, sixteen independent physicians found a high level of alignment across all metrics, including medical accuracy, safety, fairness, communication, and interpretation. The MEDITRON suite is a significant step forward in closing the technological gap between closed- and open-source medical foundation models. By releasing our methodologies, models, and real-world clinical practice benchmarks, we aim to drive the open-source development of more capable, representative, accessible, and transparent medical AI assistants.
Transformative Resilience: Harnessing ICT for Trauma Recovery and Community Empowerment among Eritrean Refugees
Authors: Kidane Selam 
Published: April 2024

Abstract
This study investigates the effectiveness of the TRUST intervention, delivered through Information and Communication Technology (ICT), in addressing trauma and enhancing resilience among Eritrean refugees in impoverished camps. The research aims to explore whether the comprehensive TRUST program, compared to a shorter psycho-education session, can reduce traumatic stress levels and improve social and economic resilience and social capital, indicating a reduction in collective trauma. Ethical approval was obtained, and participants were randomly assigned to intervention groups. Livelihood support availability was assessed, and psychometric tests were administered before and after the intervention. Results indicate a significant decrease in traumatic stress levels and improvements in social and economic resilience among participants receiving the full TRUST intervention. Interestingly, the availability of livelihood support did not significantly impact trauma levels or associated improvements. Challenges in delivering therapy via ICT, such as connectivity issues, were identified. This study underscores the potential of ICT-enabled interventions in addressing mental health challenges in resource-constrained environments but highlights the importance of addressing connectivity issues for effective implementation.
A longitudinal study on the development trajectory of auditory processing and its relationship with language development in Chinese preschool children with autism spectrum disorder: Study protocol
Authors: Liu Panting , Zhou Jia , Sun Yuying , Zhang Lei , Ji Hui , Xu Jing , Xu Qu , Yao Mengmeng , Ge Yujia , Chi Xia , Qian Jun , Hong Qin 
Published: April 2024

Abstract

Background：It was reported that more than 96% of autism spectrum disorder (ASD) children are accompanied with different degrees of sensory processing abnormalities, and up to 50% of ASD children exhibit abnormal auditory response. Studies have confirmed that some ASD children’s abnormal auditory response may be related to their abnormal auditory processing. Prior research demonstrated that ASD children’s auditory processing has high heterogeneity, thus, ASD children’s auditory processing may have different developmental trajectories. However, no study has concentrated on the developmental trajectories of ASD children’s auditory processing. In addition, auditory processing plays a crucial role in ASD children’s language development, thus, ASD children’s different language development outcomes may be related to different auditory processing development tracks. Therefore, this study aims to explore the developmental trajectory of auditory processing in ASD children and analyze the relationship between different developmental trajectories of auditory processing and language impairment.
Methods/Design：In this study, 220 ASD children aging 3 years and 0 months to 4 years and 11 months are recruited as the research objects, and their demographic characteristics are collected. The subjects are tested for peripheral hearing, intelligence, and autism symptoms. Furthermore, ASD children’s auditory processing and language development are evaluated at baseline, 1 year, and 2 years later. In addition, ASD children’s auditory processing is evaluated by electrophysiological test and the Preschool Auditory Processing Assessment Scale. Moreover, ASD children’s language skills are assessed using the Language Development Assessment Scale for Children Aged 1-6. The various categories of the developmental trajectory of ASD children’s auditory processing are examined through the latent category growth model. Additionally, a hierarchical regression model is developed to analyze the predictive impact of different auditory processing development trajectories on language impairment in ASD children.
Discussion：This longitudinal study will explore the categories of auditory processing developmental trajectories in ASD children, and analyze the relationship between different categories of auditory processing developmental trajectories and language development, providing new ideas and targeted targets for the rehabilitation training of language impairment in ASD children, as well as promoting early and accurate interventions for ASD children.
The potential misuse of AI in online medical admission interviews
Authors: Hampshire Mandy , Howard Joshua , James David 
Published: April 2024

Abstract

Background
Over half UK Medical Schools used online interviews for 2024 entry. Artificial intelligence (AI) is widely used medicine. However, there are no publications of its use by applicants to undergraduate degrees to improve their chances of selection.
We report a pilot study to test the hypothesis that the use of AI by candidates participating in online interviews for Nottingham Medical School would enhance their performance.
Methods
Three historic scenarios and associated questions from the University of Nottingham (UoN) Medicine course selection interviews were submitted to three AI platforms as a potential candidate could during an interview.
For each scenario, the setting of the task (priming statement), the details and instructions of the scenario, and three questions were submitted in turn.
Entry and responses times and how these could be optimized were studied using two approaches for text entry, typing directly into the AI platform and recording directly into the AI platform using a second device.
The quality of the AI responses was scored independently by the authors using a ‘traffic light’ scoring system where ‘green’ was ‘the response was satisfactory/appropriate for an 18-year-old candidate’.
Results
Entry and response times: Typing into the AI platform was associated with delays before the AI response started. Direct entry using a second device had no such delays.
All three AI platforms started responding when as the text was being entered. But median times for completion of the response varied between 14sec and 41 sec.
The quality of the AI responses: Only the minority of the responses, if read verbatim, scored ‘green’.
Conclusions
AI could potentially be used by an applicant for the UoN Undergraduate Medicine course to ‘enhance’ their online interview performance, but it would be difficult in practice. The candidate would have to use a second device, use the response to the scenario details/instructions to formulate prompt and more natural answers to the questions rather than reading the AI question responses verbatim and hope there were no technical problems. We think that candidates could perform better by using AI before the interview day to generate answers that are polished, confident and natural.
The association of HER-2 expression with clinicopathological characteristics and clinical outcomes in patients with localized prostate cancer after radical prostatectomy
Authors: Wang Shuo , yang xiao , You Ruijian , Du Peng , Liu Yiqiang , Ji Yongpeng , Zhao Qiang , Cao Yudong , Ma Jinchao , Yu Ziyi , Yang Yong 
Published: April 2024

Abstract

Purpose
To investigate the association between HER-2 expression and clinicopathological characteristics, biochemical recurrence (BCR) rate and BCR free survival in localized prostate cancer (PCa) patients after radical prostatectomy (RP).
Methods
Between January 2014 and December 2019, 44 patients with pathological confirmed localized PCa who underwent RP were included into this study. According to the expressed level of HER-2 protein, patients were divided into 4 cohorts, cohort-1 (HER-2 0+), cohort-2 (HER-2 1 + or 2+), cohort-3 (HER-2 0 + or 1+) and cohort-4 (HER-2 2+), the clinicopathological and clinical outcomes were analyzed and compared between cohort-1 and cohort-2, cohort-3 and cohort-4, respectively. Univariable, multivariable COX regression model and Kaplan-Meier curves were used to determine the association between HER-2 expression and clinicopathological outcomes including Gleason Score (GS), pathological T (pT) stage, positive surgical margins (PSM) and BCR free survival, respectively.
Results
The median follow-up time was 43 months (IQR 35–49), 20 patients (45.45%) of tumors exhibited HER-2 immuno-reactivity, 14 (31.82%) had HER-2 1+, 6 (13.64%) had HER-2 2 + and 0 (0%) had HER-2 3 + immunostaining. The proportion of PSM in patients with HER-2 0 + was significant lower compared with 1 + or 2+ (41.7% vs 70%, P = 0.045). By using multivariable logistics regression models revealed that HER-2 1 + or 2 + was the independent risk factor strongly associated with higher proportion of PSM (HR, 2.691; 95% CI, 0.619–11.71, P = 0.042). 24 (54.55%) patients developed BCR after surgery, including 8 (33.3%) in cohort-1, 16 (80%) in cohort-2, P = 0.029 while 18 (47.4%) in cohort-3, 6 (100%) in cohort-4, P = 0.025. According to Kaplan-Meier analysis, results showed that in cohort-1, the BCR free survival was significantly longer than in cohort-2, P = 0.000; while in cohort-3, the BCR free survival was significantly longer than in cohort-4, P = 0.000. By using multivariable Cox regression model revealed that HER-2 1+, 2+ (HR, 17.002; 95% CI, 1.378-210.216, P < 0.001), HER-2 2+ (HR, 2.849; 95% CI, 1.234–3.246, P = 0.004) were all significant independent factors associated with BCR after surgery
Conclusion
HER-2 expression was a common phenomenon in PCa, nearly half proportion of localized PCa had HER-2 1 + or 2+, but the cases expressed HER-2 3 + were rare. Cases with HER-2 1 + or 2 + were more likely developed BCR compared with HER-2 0+. The HER-2 expression was closely associated with shorter BCR free survival and higher proportion of PSM, but not associated with GS and pT stage. HER-2 could be considered as a potential method predicting clinical outcomes in patients after RP.