bluuebunny/crossref_metadata_2025 huggingface dataset:
New Insights on Catalysts-Supported In-Situ Upgrading of Heavy Oil and Hydrogen Generation during In-Situ Combustion Oil Recovery
Authors: Fassihi M. R. , Moore R. G. , Pereira Almao P. , Mehta S. A. , Ursenbach M. G. , Mallory D. G.
Published: April 2024

As part of greenhouse gas reduction initiatives, there have been many publications on carbon sequestration, reducing the carbon footprint of oil and gas operations, and generating carbonless fuel [e.g., hydrogen (H2)] by means of in-situ processes. In-situ upgrading (ISU) can help with these aspects by converting bitumen and heavy oil into low sulfur, low N2, and low asphaltene products, generating fewer emissions and producing hydrogen as a byproduct, thus helping with utilization of vast resources of energy that would otherwise be wasted due to extreme measures of no fossil fuel policies. In addition, such processes could produce more valuable products, enhanced shipping/pipelining, and less demanding downstream processing.

In this paper, we provide new insights into the results of several combustion tube tests that were performed for Alberta Ingenuity Centre for In Situ Energy, using different heavy oils with fresh supported catalysts. The catalysts were placed in the production end of the combustion tube so oil would pass over the catalyst bed before being produced. In practice, solid catalyst particles could be placed into the oil-bearing formation adjacent to the producing wellbore, ensuring that crude oil will flow over the catalysts during oil production. In this paper, we use many laboratory results that have never been published before. The objective is to understand whether using catalysts has merit in our future oil production activities under the current environmental restrictions. A commercial Ni/Mo catalyst was used in these tests. The results of these tests indicated at least temporary significant occurrence of reactions such as hydroprocessing (HP) and hydrotreating reactions, such as hydrocracking, hydrodesulfurization (HDS), hydrodenitrogenation (HDN), and hydrodeoxygenation. They also generated a significant volume of hydrogen in situ.

We will discuss the impact of pressure, temperature, water injection, and dispersed vs. supported catalysts on the degree of oil upgrading. Also, the key parameters that could impact in-situ hydrogen generation will be presented. Specifically, the role of reactions such as aquathermolysis, thermal cracking, water-gas shift (WGS, defined later) reaction, and coke gasification will willbe discussed. Note that the products of these reactions could undergo additional methanation (ME) reactions, which could reduce the H2 concentration in the produced gas. Finally, methods of upscaling these results to the field conditions will be presented.
Open-label randomised clinical trial investigating whether Robot-Assisted Kidney Transplantation can reduce surgical complications compared to Open Kidney Transplantation (ORAKTx): Study protocol for a randomised clinical trial
Authors: Ortved Milla , Dagnæs-Hansen Julia , Stroomberg Hein Vincent , Kistorp Thomas , Rohrsted Malene , Sørensen Søren Schwartz , Røder Andreas
Published: April 2024

Background: Kidney transplantation is the ultimate treatment for end-stage kidney disease. Function of the kidney graft is not only dependent on medical factors but also a complication-free surgical procedure. In the event of major surgical complications, the kidney graft is potentially lost and the patient will return to the waiting list which may be long. To optimise peri-operative care and reduce complications, robot-assisted kidney transplantation (RAKT) has been introduced as an alternative to open kidney transplantation (OKT), but to our knowledge no randomised clinical trials are comparing RAKT to OKT. We will explore whether robot-assisted surgery can reduce 30-day surgical complications compared to open surgery in kidney transplantation.
Methods: This a single-site, open-label, randomised clinical trial comparing RAKT to OKT. Participants are adult recipients of kidney transplantation and all participants are recruited from Copenhagen University Hospital – Rigshospitalet, Denmark. The study plans to include 106 participants who will be randomised in a 1:1 manner between OKT and RAKT. Primary outcomes are vascular- and major surgical complications at 30 days post-operatively. Participants will be followed for two years to evaluate secondary outcomes including recovery, late complications and kidney graft function. This is designed as a superiority trial and planned analyses are as intention-to-treat.
Discussion: Studies indicate RAKT can reduce several surgical complications, but the lack of RCTs limits the extrapolation of these results to justify the replacement of an open approach with a robot-assisted one. Ultimately, the introduction of new surgical techniques should be as vigorously tested as any other new treatments. However, reducing surgical complications that compromise graft viability could lead to improved patient care and survival.
Trial registration: The trial was prospectively registered on February 15th 2023 on clinicaltrials.gov with NCT-identifier 05730257.
Transcriptome- and DNA methylation-based cell-type deconvolutions produce similar estimates of differential gene expression and differential methylation
Authors: Hannon Emily R , Marsit Carmen J , Dent Arlene E , Embury Paula , Ogolla Sidney , Midem David , Williams Scott M , Kazura James W
Published: April 2024

Background
Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs).
Methods
Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children (n = 8) before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modeling with Cibersortx or IDOL, respectively.
Results
Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60–0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils.
Conclusion
Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.
Multivariate Analysis of Water Quality in the Dhaleshwari River, Bangladesh: Identifying Pollution Sources and Environmental Implications
Authors: Ali Md. Sohrab , Begum Shaheda , Rabbi Fahim Muntasir , Sumaia Mrs , Hasan Md. Kamrul , Rahman Md. Alinur , Rahman Mohammad Mahfuzur , Rahaman Md. Hasibur
Published: April 2024

The Dhaleshwari river, coursing through the central part of Bangladesh, is significantly impacted by rampant industrialization and various anthropogenic activities. This study aims to evaluate the current water quality status of the Dhaleshwari river, considering seasonal variations, and identify potential pollution sources using physicochemical parameters, multivariate statistical analysis, and the Weight Arithmetic Water Quality Index method. Sampling was conducted at six locations along the Dhaleshwari river from 2021 to April 2022, encompassing both dry and wet seasons. Eleven physicochemical parameters were tested following standard procedures. Correlation coefficients revealed strong positive relationships between EC, TDS, TS, BOD5, COD, and Chloride. Additionally, strong negative relationships were observed between DO, BOD5, COD, and TSS. The calculated WQI values demonstrated that the overall water quality of the Dhaleshwari river is very poor. Seasonal variation in water quality was significant, with the worst quality observed during the dry season; however, water quality during the wet season was also unsatisfactory. Cluster analysis grouped the six sampling stations into four distinct clusters based on water quality similarities across both seasons. Geospatial analysis shows the distribution of water quality in the study locations. Principal Components Analysis (PCA) indicated that direct wastewater discharge from surrounding industries, particularly tanneries, as well as municipal wastewater, were the major pollution sources. In conclusion, this study sheds light on the deteriorating water quality of the Dhaleshwari river due to industrialization and human activities. The findings underscore the urgent need for targeted interventions to mitigate pollution sources and improve the overall health of the river ecosystem.
Hybrid deep learning combined with traditional financial models: Application of RNN models and GARCH-Family Model for Natural Gas Price Volatility Forecasting
Authors: Chen Yufeng , Fan Xingang
Published: April 2024

The natural gas market has significant commonalities with the general financial market, especially its time series data are often non-stationary and show different fluctuation characteristics due to different market conditions. Therefore, accurate forecasting of natural gas price volatility requires a correct handling of the unique characteristics of its time series. In this paper, GARCH model and TGARCH model are specially selected to capture the volatility heteroscedasticity generated in different market scenarios, and IGARCH model is used to ensure that the model can still maintain high prediction accuracy when the time series is non-stationary. In order to deal with the long-term dependence of natural gas prices on time series, this paper introduces the LSTM model and the GRU model, both of which are variants of recurrent neural network (RNN). Thus we obtain the GARCH-IGARCH-TGARCH-LSTM/GRU model. It is worth noting that this model is applied to the field of natural gas price volatility prediction for the first time, which provides a new research perspective for in-depth understanding and accurate prediction of natural gas market volatility. We use the natural gas futures price index from June 2013 to June 2023 for the simulation test. Using 100 repeated experiments, we verify the robustness of the GARCH-IGARCH-TGARCH-GRU model in volatility forecasting and demonstrate its superior forecasting accuracy with a mean square error (MSE) of 0.22 and a mean absolute error (MAE) of 0.13. In the face of market breaks and extreme events, the integrated model shows higher adaptability and robustness. This study not only provides a powerful volatility forecasting tool for natural gas market participants, but also provides a strong demonstration of the universality of this type of model.
Developing a Modified Deep Belief Network with metaheuristic optimization Algorithm for predicting Alzheimer disease using Electroencephalogram
Authors: Jayagopal Prabhu , Mohan Prakash , Rajasekar Vijay Anand , SathishKumar Sree Dharinya , Mathivanan Sandeep Kumar , Mallik Saurav , Qin Hong
Published: April 2024

A neurological brain disorder that progresses over time is Alzheimer's disease. Alzheimer's disease can take years to identify, comprehend, and manifest—even in cases where signs are obvious. On the other hand, technological developments like imaging methods aid in early detection. But frequently, the results are unreliable, which delays the course of treatment. By dividing resting-state electroencephalography (EEG) signals into three groups—AD, healthy controls, and mild cognitive impairment (MCI)—this work offers a novel perspective on the diagnosis of Alzheimer's disease (AD). In order to overcome data limits and the over-fitting issue with deep learning models, we looked at augmenting the one-dimensional EEG data of 100 patients (49 AD participants, 37 MCI subjects, and 14 HC subjects) with overlapping sliding windows. Better results and early intervention could arise from this for persons afflicted with the illness. This research has the potential to significantly advance the early diagnosis of Alzheimer's disease and lay the groundwork for the creation of more precise and trustworthy diagnostic instruments for this debilitating condition. This study presents a Modified Deep Belief Network (MDBN) with a metaheuristic optimization method for detecting face expression and Alzheimer's disease using EEG inputs. The recommended method extracts significant features from EEG data in a novel way by applying the Improved Binary Salp Swarm Algorithm (IBSSA), which combines the MDBN and the metaheuristic optimization algorithm. The performance of the suggested technique MDBN-IBSSA for Alzheimer's disease diagnosis is evaluated using two publicly available datasets. The proposed technique's capacity to discriminate between healthy and ill patients is proved by the MDBN-IBSSA accuracy of 98.13%, f-Score of 96.23%, sensitivity of 95.89%, precision of 95.671%, and specificity of 97.13%. The experimental results of this study show that the MDBN-IBSSA algorithm proposed for AD diagnosis is effective, superior, and applicable.
Overexpression of ATF3 enhanced the therapeutic effects of human adipose-derived stem cells on erectile dysfunction associated with cavernous nerve injury
Authors: Lin Jiajing , Lu Yawen , Lin Zelong , Wu Yunlin , Jin Long , Fan Wenbei , Chen Bohao , An Geng , Li Chaohui , Fan Yong
Published: April 2024

Background Erectile dysfunction (ED) frequently arises as a prevalent complication following prostatectomy. Local administration of stem cells via penile injection has shown effectiveness in addressing erectile dysfunction stemming from bilateral cavernous nerve injury (BCNI-ED). Moreover, stem cell therapy, incorporating targeted genetic alterations, has the capacity to improve therapeutic results. This investigation involved the utilization of human Adipose-Derived Stem Cells (hASCs) that were genetically modified to overexpress activating transcription factor 3 (ATF3) and subsequently injected into the cavernous bodies of rat penises to enhance the therapeutic efficacy in cases of BCNI-ED. Methods Human adipose-derived stem cells (hASCs) were isolated, cultured, and characterized using flow cytometry, as well as subjected to osteogenic and adipogenic induction. Subsequently, the hASCs were transfected with lentivirus and assessed for the expression of nerve-associated growth factors. Following this, the treated hASCs were administered into the penises of rats with bilateral cavernous nerve injury-induced erectile dysfunction (BCNI-ED) to assess their therapeutic efficacy. The intracorporeal pressure/mean arterial pressure ratio (ΔICP/MAP) was utilized as a measure of erectile function. In vivo small animal imaging was conducted to monitor the distribution of the hASCs. Results hASCs showed increased expression of CD90, CD73, CD105 and decreased expression of CD45. ATF3-modified hASCs expressed nerve growth-related factors and had improved therapeutic potential in improving ΔICP/MAP ratio and α-SMA content while decreasing Collagen I content. In vivo imaging of small animals showed hASCs residing within the cavernous, with no group differences. Conclusions ATF3 enhanced neural-related trophic factor expression in hASCs, boosting their therapeutic potential of adipose stem cells on erectile dysfunction associated with cavernous nerve injury.
Genome resequencing and genome-wide polymorphisms in mosquito vectors Aedes aegypti and Aedes albopictus from south India
Authors: Acharya Preeti , Singh Upasana Shyamsunder , Rajamannar Veeramanoharan , Muniaraj Mayilsamy , Nayak Binata , Das Aparup
Published: April 2024

Aedes aegypti and Aedes albopictus mosquitoes spread major vector-borne viral diseases in tropical and sub-tropical regions of the globe. In this study, we sequenced the genome of Indian Ae. aegypti and Ae. albopictus and mapped to their reference genomes. Comparative genomics were performed between our strain and the reference strains. A total of 14,416,484 single nucleotide polymorphisms (SNPs) and 156,487 insertions and deletions (InDels) were found in Ae. aegypti, and 28,940,433 SNPs and 188,987 InDels in Ae. albopictus. A special emphasis was given to gene families involved in mosquito digestion, development and innate immunity that could be putative candidates for vector control. Serine protease cascades and their inhibitors called serpins, play a central role in these processes. We extracted high-impact variants in genes associated with serine proteases and serpins. This study reports for the first time a high coverage genome sequence data of an Indian Ae. albopictus mosquito. The results from this study will provide insights into Indian Aedes specific polymorphisms and the evolution of immune related genes in mosquitoes, that can serve as a resource for future comparative genomics and for those pursuing development of targeted biopesticides for effective mosquito control strategies.
Deep Learning-based Study on Assessment and Enhancement Strategy for Geological Disaster Emergency Evacuation Capacity in Changbai Mountain North Scenic Area
Authors: Zheng Erzong , Zhang Yichen , Zhang Jiquan , Zhu Jiale , Yan Jiahao , Liu Gexu
Published: April 2024

This research focuses on enhancing emergency evacuation capabilities under geological disasters in the northern scenic area of Changbai Mountain. The research leverages advanced technology, employing the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), significantly enhancing image clarity. It is then combined with deep learning methods for a comprehensive assessment of emergency evacuation capacity, facilitating a more accurate understanding of the current scenic area situation. Introducing a 3D model based on elevation, slope, and slope direction enhances the geographic overview, offering a 360° view for a more detailed understanding of terrain and geomorphological features. Considering geographic and climatic factors, the study proposes targeted improvements tailored to the specific characteristics of the scenic area. The innovation of this study lies in its successful resolution of remote sensing image blurring using Real-ESRGAN, introducing an advanced method for small-area studies. Through these integrated tools and methods, the study significantly enhances the accuracy of data processing, assessment, and decision support. It demonstrates an integrated approach to scenic area research, contributing to geohazard management, emergency planning, and the overall safety of the Changbai Mountain scenic area.
A Hybrid News Recommendation Approach Based on Title-Content Matching
Authors: Jiang Shuhao , Lu Yizi , Song Haoran , Lu Zihong , Zhang Yong
Published: April 2024

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

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

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

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

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

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

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

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

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

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

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.
Development and evaluation of a clinical nursing decision support system for the prevention of neonatal hypoglycaemia
Authors: Liu Qiaoyan , Sun Lulu , Yang Jie , Yin Wei , Cao Songmei
Published: April 2024

Background: Hypoglycaemia is one of the most common complications during the neonatal period.Frequent hypoglycaemia can lead to neurodevelopmental impairment, and even sudden death. Evidence suggests that health care professionals should determine whether a newborn is at high risk for hypoglycaemia immediately after birth and make the most appropriate preventive decisions. Therefore, this study aimed to develop a clinical nursing decision support system for neonatal hypoglycaemia prevention based on the prediction model for neonatal hypoglycaemia risk that was developed in a previous study，and to evaluate its effects.
Methods: Nursing process as the theoretical framework, based on evidence-based nursing, standardized nursing language, and clinical decision support technology, the neonatal hypoglycaemia prevention nursing decision support system was developed.The system was used in the neonatology department of a Grade III Class A general hospital from September 1 to 30, 2023.The application effect of the system was evaluated and compared through the incidence of neonatal hypoglycaemia, hypoglycaemia-related adverse outcomes in neonates,and the experience of nurses after the use of the system.
Results: The incidence of neonatal hypoglycaemia decreased after the system was implemented, and the difference was statistically significant (X2=4.522, P=0.033). None of the neonates experienced adverse outcomes during hospitalization. The rate of hypoglycaemia risk assessment in neonates after system implementation was 92.16%. The total Clinical Nursing Information System Effectiveness Evaluation Scale score was 104.36 ± 1.96.
Conclusion: The neonatal hypoglycaemia prevention nursing decision support system realizes neonatal hypoglycaemia risk assessment, intelligent decision-making, and effect evaluation, effectively reduces the incidence of neonatal hypoglycaemia, and improves the standardization of neonatal hypoglycaemia management.
Mesenchymal stem cell-derived protein extract induces periodontal regeneration
Authors: Peng Yihao , Iwasaki Kengo , Taguchi Yoichiro , Ishikawa Isao , Umeda Makoto
Published: April 2024

Background
Periodontal disease is characterized by chronic inflammation and destruction of supporting periodontal tissues, ultimately leading to tooth loss. In recent years, “cell-free treatment” without stem cell transplantation has attracted considerable attention for tissue regeneration. This study investigated the effects of extracts of mesenchymal stem cells (MSC-extract) and their protein components (MSC-protein) on the proliferation and migration of periodontal ligament (PDL) cells and whether MSC-protein can induce periodontal regeneration.
Methods
MSC-extract and MSC-protein were obtained by subjecting mesenchymal stem cells (MSCs) to freeze–thaw cycles and acetone precipitation. Cell proliferation was examined using a WST-8 assay and Ki67 immunostaining, and cell migration was examined using Boyden chambers. The MSC-protein content was analyzed using liquid chromatography-mass spectrometry, protein arrays, and enzyme-linked immunosorbent assays (ELISAs). Gene expression in MSC-protein-treated PDL cells was examined using RNA-sequencing and Gene Ontology analyses. The regenerative potential of MSC-protein was examined using micro-computer tomography (CT) and histological analyses after transplantation into a rat periodontal defect model.
Results
MSC-extract and MSC-protein promoted the proliferation and migration of PDL cells. Protein array and ELISA revealed that MSC-protein contained high concentrations of basic fibroblast growth factor (bFGF) and hepatocyte growth factor (HGF). Exogenous bFGF promoted the proliferation and migration of PDL cells. Furthermore, the transplantation of MSC-protein enhanced periodontal tissue regeneration with the formation of new alveolar bone and PDLs.
Conclusions
These results indicate that the MSC-protein promotes the proliferation and migration of PDL cells and induces significant periodontal tissue regeneration, suggesting that the MSC-protein could be used as a new cell-free treatment for periodontal disease.