bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ethnic differences in the rate pressure product to dance aerobic exercise with African Canadian and South Asian cardiac patients
Authors: Tomlinson Sherldine , Naemi Roozbeh 
Published: April 2024

Abstract

Introduction: The rate pressure product (RPP) is a non-invasive method used as an index to measure myocardial oxygen consumption (MVO2) on physiological responses. It is the product of heart rate (HR) and systolic blood pressure (SBP) (RPP = HR x SBP/100). Past studies have investigated the RPP on exercise response and produced positive outcomes. However, the data is limited for racialised groups such as individuals of black African descent and South Asians. This is despite their experiencing similar cardiovascular risk.
Objective: This study sought to accomplish two key goals: 1) to evaluate and contrast the responses of blood pressure (BP), HR, and RPP following ten sessions of low-impact dance aerobics in African Canadian and South Asian cardiac patients, and 2) to assess the degree of health improvements within these demographic groups.
Methods: 160 patients, divided evenly between African Canadians and South Asians, all with hypertension or associated conditions, were encouraged to participate in 10 exercise sessions. A range of variables were recorded before and after the exercise sessions. Subsequently, a multitude of statistical methodologies were leveraged to dissect the collected data.
Results: The study unveiled substantial differences in SBP and HR (p < 0.05) between the two groups both prior to and after the exercise sessions, although the shifts in RPP (p > 0.05) did not yield statistical significance. Improvements were also observed in mean arterial pressure (MAP) and pulse pressure (PP) measurements from the baseline to the point after exercise.
Conclusion: Our findings demonstrated enhancements in BP and various cardiovascular functionalities following exercise in African Canadian and South Asian participants. These results underline the importance of acknowledging the nuanced variances in BP and RPP responses to exercise across different ethnicities. This understanding allows pinpointing specific groups predisposed to a heightened risk of severe cardiovascular ailments. Tailored exercise interventions can then be formulated to cater to these particular groups' needs, thereby contributing to the overall reduction of the cardiovascular disease burden.
Study the Impact of Fuel Cell Integration in Hybrid Autonomous Systems on Technical-Economic Performance
Authors: BAYOUD Mebarka , GHOUDELBOURK Sihem , BOUZIDI Belgacem Mohamed Nassim 
Published: April 2024

Abstract

Renewable resources are increasingly acknowledged as a crucial solution to address escalating energy demands and combat heightened greenhouse gas emissions within conventional energy systems. This research delves into the techno-economic feasibility of incorporating fuel cell systems into stand-alone hybrid systems tailored to electrify a community load situated in Annaba, northeastern Algeria. This load averages 6.9 kW of power and requires 165.59 kWh/day annually. Employing HOMER software, a comprehensive comparative analysis of four distinct hybrid power system configurations—namely PV/BAT, PV/BAT/DG, PV/BAT/FC, and PV/BAT/FC/DG—was conducted to pinpoint the most optimal solution in meeting energy demands. Parameters such as net present cost, energy cost, energy surplus, load satisfaction, and CO2 emissions were considered. Additionally, the study addressed the environmental impact and evaluated the impact of varying storage battery fleet sizes on the total cost of the power system. Among the configurations scrutinized, the PV/BAT/FC/DG hybrid system emerged as the most favorable option, striking a well-balanced compromise between PV penetration efficiency, energy cost, and system stability. Moreover, the findings highlight the benefits of integrating fuel cell technology into the hybrid system, including minimizing excess energy, reducing the necessity for DG interventions, and optimizing the size of the battery bank.
The Energetics of the 1998 Pacific Decadal Transition
Authors: Mauget Steven 
Published: April 2024

Abstract

To trace the energetics of the 1998 shift in Pacific decadal variability (PDV), monthly ocean assimilation, atmospheric reanalysis, and satellite-derived cloud cover and radiative data was analyzed. Data during 1980-2021 showed evidence of the Pacific Walker circulation abruptly forcing the equatorial Pacific Ocean into a persistently elevated energy state after 1998. This occurred as a step-like shift to stronger trade winds, positive SOI, and increased central equatorial Pacific (CEP) 500 mb subsidence (w500), accompanied by a significant 25% increase in mean equatorial Pacific oceanic available potential energy (OAPE). Covarying (|r| > 0.6) CEP w500, fractional cloud cover, top-of-atmosphere albedo, and surface short-wave absorbed radiative energy (SWARS) indicate a control of CEP Walker subsidence on CEP SWARS via a subsidence-albedo effect. Similarly correlated CEP SWARS and OAPE (r = 0.670), and a 71 exa Joule (eJ) increase in mean CEP SWARS after 1998 that is two orders of magnitude greater than the increase in mean OAPE (0.507 eJ), suggests the post-1998 strong Walker PDV phase was radiatively driven. Together, this is interpreted as evidence of a positive Walker radiative-dynamic feedback over the equatorial Pacific after 1998. The 1998 shift’s circumstances suggest the influence of persistent 1998-2001 La Niña conditions in tipping PDV from a climatically weak-Walker phase to a strong-Walker phase. Based on these results, and previous work that studied the 1976-1977 PDV transition, a preliminary hypothesis is proposed of PDV as a bi-stable radiative climate mechanism governed by Walker-driven subsidence-albedo variation and ENSO-related tipping events.
Determinants of Low Fifth Minute Apgar Score among Newborn Delivered at health facilities in Ethiopia,systematic review and meta-analysis
Authors: Anagaw Tadele Fentabel , Fenta Eneyew Talie , Adal Ousman , Tareke Abiyu Abadi , Kebede Natnael , Delie Amare Mebrat , Bogale Eyob Ketema 
Published: April 2024

Abstract

Introduction: Apgar score is the most consistently used measure of neonatal health and it provides an accepted and convenient method for reporting the status of newborn infants in the few minutes after birth. Apgar score at 5th minute are better predictor neonatal survival. There is no evidence on pooled magnitude and determinant factors associated with low 5th minute Apgar score among newborns delivered in Ethiopia.Objective This review aimed to estimate the pooled magnitude and associated factors with low 5th minute Apgar score among newborns delivered at health facilities of EthiopiaMethod Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist guideline was followed for this review and meta-analysis. Electronic data base (PubMed, EMBASE, Midline, Cochrane library, Web of science, Google scholar and Science direct) were used to retrieve studies. Data were extracted using Microsoft Excel and analyzed using STATA version 17. Publication bias was checked by funnel plot, Beggs and Egger’s regression test. Pooled analysis was conducted using a weighted inverse variance of random effect model. Subgroup and sensitivity analysis was done.Result Nine papers were included from the total of 1982 articles that were retrieved using various electronic databases in order to determine the aggregate magnitude of low five-minute APGAR scores. A total of 23% (95% CI 16–30) of newborns delivered at Ethiopian health facilities had a low 5-minute APGAR score. Factors associated with low Apgar score were Skin incision time > 3 minutes (AOR:4.76,95%CI:1.28–8.25),gestational hypertension (AOR = 5.71, 95%CI: 0.89–10.53), birth weight < 2500 gram (AOR:4.82,95%CI:3.72–5.91) ,induced/augmented labor (AOR:3.21, 95%CI:2.27–4.15),meconium-stained liquor (AOR: 5.36,95%CI: 3.45–7.27), Presentation of fetus non vertex (AOR:3.15,95% CI:1.31–5.01), no ANC follow up (AOR:11.46,95%CI: 3.94–18.97), cesarean section delivery(AOR:15.98,95%CI:14.76–17.22),APH/PPH (AOR:8.92,95%CI:7.87–9.98),and Prolonged labor duration > 24hours (AOR:13.92,95%CI:10.95-16).Conclusion This review revealed that approximately 3 in 10 newborns delivered at health facilities of Ethiopia had low 5th minute Apgar score. Therefore, Comprehensive intervention including enhancing ANC follow up, early identification danger sign and complication of pregnancy and nutritional coursing were recommended for healthcare workers and concerned stakeholders working on maternal, newborn, and child healthcare.
A semi-classical Floquet-NEGF approach to model photon-assisted tunneling in quantum-well devices
Authors: De Sutter Nathan , Vanderstraeten Emile , Ginste Dries Vande 
Published: April 2024

Abstract

The non-equilibrium Green’s function formalism is often employed to model photon-assisted tunneling processes in opto-electronic quantum well devices. For this purpose, self-consistent schemes based on a quantum electrodynamical description of light-matter interactions have been proposed before. However, these schemes are typically computationally very demanding. Therefore, in this work, a novel semi-classical method based on Floquet-Green theory is proposed, which strongly mitigates the computational costs. By comparison to results obtained with a traditional, purely quantum mechanical technique, the new approach is validated and shown to be faster and exhibits superior convergence properties. Finally, a two-band model for superlattice structures is constructed to further illustrate the advantages of the novel, advocated method.
Forest Bathing and the Mental Health of Medical Students
Authors: Mazzarino Jane M. , Périco Eduardo , Both Maria Eduarda Stefani , Agostinetto Airton 
Published: April 2024

Abstract

Introducion: The Shinrin-yoku exercise or forest bath is an oriental therapeutic technique for human interaction with nature. Through this practice, it is possible to obtain benefits for both the physical and mental health of the individual. Due to the growing pressure related to the need to improve performance, the academic community has gradually been experiencing adversities regarding their mental health, especially medical students, who have grueling study days in a highly competitive environment.
Objectivo: Thus, the objective of this article is to evaluate the mood states of these students before and after the forest bathing technique, using the Brunel Mood Scale (BRUMS).
Method: The participants, medical students who were in their first and fourth semesters of graduation, were tested in terms of sex, semester and yoga practice.
Results: After the forest bathing sessions, the positive mood indicator (vigor) increased, while the negative indicators (tension, confusion, fatigue, depression and anger) decreased significantly (p < 0.001), pointing to positive aspects of this practice. With regard to gender, girls were more confused, and there were no differences related to yoga practice.
Model the effect of different Hole Transport Inorganic materials on performance of perovskite solar cells
Authors: Eisa Altaib , Abdalla Taha. A. , Ahmed Amel. A. 
Published: April 2024

Abstract
Effect of different Hole transport material inorganic materials on performance of perovskite solar cells is investigated using Comsol Multiphysics by solving the Helmholtz equation derived from Maxwell’s equation. The study aims to highlight the change in perovskite solar cell performance caused by using different kinds of inorganics materials as hole transporting materials such as (MoO_3 ,Cu_2 O ,CuI ,CuSCN ,NiO_x). The J -V characteristics for different inorganic materials as hole transporting materials are simulated. The simulation results show that perovskite solar cells based on MoO_3 as hole transporting materials has higher Voc , better fill factor (FF) and an overall power conversion efficiency (18.15%). While, CuSCN has the lowest efficiency 10.34 % due to the high potential barrier faced by hole transporting materials in CuSCN/CH3 NH3 PbI3 interface. Thus, inserting MoO_3 in perovskite solar cell design may possibly be considered as novel designing for future perovskite solar cells.
A Machine Learning Algorithm for the Detection of Paroxysmal Nocturnal Haemoglobinuria (PNH) in UK Primary Care Electronic Health Records
Authors: Worker Amanda , Mahon Hadley , Sams Jack , Boardman-Pretty Freya , Marchini Elena , Dubis Rand , Warren Alan , Varones Liz , Ollerenshaw Daniel , Grant Calum , Fish Peter , Kelly Richard J 
Published: April 2024

Abstract

Background Paroxysmal Nocturnal Haemoglobinuria (PNH) is an ultra rare acquired clonal haematopoietic disorder. It is difficult to diagnose due to a variety of common symptoms, heterogeneity between patients and lack of awareness and recognition in primary care settings. As a result many PNH patients experience a long diagnostic odyssey and misdiagnoses. To improve diagnostic rates, this study aimed to develop a machine learning-based clinical case-finding tool which identifies patients at high risk of having undiagnosed PNH.Methods The study uses data from the Optimum Patient Care Research Database, which contains electronic health records from general practitioner (GP) practices across the United Kingdom. A PNH group was identified as records with an existing code for a PNH diagnosis and non-PNH patients were identified as records without an existing code for a PNH diagnosis. Clinically driven features including symptoms, diagnoses, healthcare utilisation and exclusionary features, from 131 PNH patients and 593,838 non-PNH patients, were inputted to a tree-based XGBoost machine learning model to classify patients.Results 60.4% of cases classified as positive by the final model were PNH cases (positive predictive value) and 27% of PNH cases were correctly classified (recall) and specificity was 99.9%. The adjusted positive predictive value (PPV) was 19.59 and can be interpreted as nearly 1 in 5 cases flagged by the algorithm may be confirmed as PNH. The five most important clinical features in the model were aplastic anaemia, pancytopenia, haemolytic anaemia, myelodysplastic syndrome, and Budd-Chiari. Other features that contribute as top features in the model are haematology referrals, urology referrals, and haptoglobin tests.Conclusion This is the first study to combine expert understanding of the clinical presentation of PNH with machine learning to develop a clinically-useful algorithm which uses real-world electronic health records to identify possible undiagnosed PNH patients. This algorithm has the potential to expedite diagnosis of PNH and improve outcomes for both patients and clinicians.
Epidemiological Parameters of Clostridiodes difficile Acquisition and Transmission for Mathematical Modeling: a Systematic Review
Authors: Olufadewa Isaac , West Hal , Latimer Harrison , Chen Shi 
Published: April 2024

Abstract

Clostridiodes difficile infections (CDI) are a major threat to patient, healthcare providers, and public health with approximately half a million cases and 30,000 deaths reported annually in the United States (Lessa et al 2015; Zhang et al 2021). CDI, which is one of the most common HAIs, has had multiple major outbreaks in the US and internationally (Bartlett et al 2006; Lakkasani et al 2020) at various periods and in a wide range of settings such as hospitals, nursing homes, and intensive care units. C.difficile is an endospore-forming bacterium that is primarily transmitted through the fecal-oral route. CDI is the leading cause of infectious diarrhea which can be mild to severe in severity. It can also be life-threatening requiring surgery (Lawson et al 2016; Stewart & Hollenbeak, 2011). In 2019, the U.S. Centers for Disease Control and Prevention (CDC) 2019 Antimicrobial Resistance Threat Report designated CDI as one of the major five urgent threats to public health and it was estimated to contribute an economic burden of over 1 billion US dollars in healthcare cost in the U.S. (CDC, 2019).
The value and need for more open and FAIR data sharing to bolster biodiversity conservation in Southeast Asia
Authors: Tanalgo Krizler 
Published: April 2024

Abstract

The tropical Southeast Asian region, with its unique geographical characteristics and features, is home to a multitude of distinct species found in 11 nations. However, the availability of biodiversity data for several taxonomic groups remains unclear. In this study, I examined 17,126,598 occurrence records from Southeast Asia in the GBIF database to compare species data turnover in the region and compare them based on taxonomic groups. The analysis revealed significant disparities in data availability and usage between countries and taxonomic groups within SEA using GBIF. Moreover, there were weak and no correlations among species occurrence records, country size, and GDP. Based on my research, I would argue for a more concerted effort to incorporate and implement FAIR data-sharing approaches in biodiversity research in Southeast Asia. This should become a standard in the scientific community and be integrated into existing biodiversity agendas and frameworks, especially for funded research projects. This will enable a better understanding of biodiversity patterns and will facilitate the development of effective conservation strategies in Southeast Asia. This adaptive management approach is critical for responding to emerging threats and ensuring the long-term sustainability of conservation efforts. In conclusion, these initiatives play a crucial role in enhancing biodiversity conservation in SEA by promoting access to information, fostering collaboration, enabling evidence-based decision-making, engaging local communities, and supporting adaptive management practices.
Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study
Authors: Yoo Richard M , Viggiano Ben T , Pundi Krishna N , Fries Jason A , Zahedivash Aydin , Podchiyska Tanya , Din Natasha , Shah Nigam H 
Published: April 2024

Abstract

Background
With the capability to render prediagnoses, consumer wearables have the potential to affect subsequent diagnoses and the level of care in the health care delivery setting. Despite this, postmarket surveillance of consumer wearables has been hindered by the lack of codified terms in electronic health records (EHRs) to capture wearable use.

Objective
We sought to develop a weak supervision–based approach to demonstrate the feasibility and efficacy of EHR-based postmarket surveillance on consumer wearables that render atrial fibrillation (AF) prediagnoses.

Methods
We applied data programming, where labeling heuristics are expressed as code-based labeling functions, to detect incidents of AF prediagnoses. A labeler model was then derived from the predictions of the labeling functions using the Snorkel framework. The labeler model was applied to clinical notes to probabilistically label them, and the labeled notes were then used as a training set to fine-tune a classifier called Clinical-Longformer. The resulting classifier identified patients with an AF prediagnosis. A retrospective cohort study was conducted, where the baseline characteristics and subsequent care patterns of patients identified by the classifier were compared against those who did not receive a prediagnosis.

Results
The labeler model derived from the labeling functions showed high accuracy (0.92; F1-score=0.77) on the training set. The classifier trained on the probabilistically labeled notes accurately identified patients with an AF prediagnosis (0.95; F1-score=0.83). The cohort study conducted using the constructed system carried enough statistical power to verify the key findings of the Apple Heart Study, which enrolled a much larger number of participants, where patients who received a prediagnosis tended to be older, male, and White with higher CHA2DS2-VASc (congestive heart failure, hypertension, age ≥75 years, diabetes, stroke, vascular disease, age 65-74 years, sex category) scores (P<.001). We also made a novel discovery that patients with a prediagnosis were more likely to use anticoagulants (525/1037, 50.63% vs 5936/16,560, 35.85%) and have an eventual AF diagnosis (305/1037, 29.41% vs 262/16,560, 1.58%). At the index diagnosis, the existence of a prediagnosis did not distinguish patients based on clinical characteristics, but did correlate with anticoagulant prescription (P=.004 for apixaban and P=.01 for rivaroxaban).

Conclusions
Our work establishes the feasibility and efficacy of an EHR-based surveillance system for consumer wearables that render AF prediagnoses. Further work is necessary to generalize these findings for patient populations at other sites.
Using ChatGPT in Psychiatry to Design Script Concordance Tests in Undergraduate Medical Education: Mixed Methods Study
Authors: Hudon Alexandre , Kiepura Barnabé , Pelletier Myriam , Phan Véronique 
Published: April 2024

Abstract

Background
Undergraduate medical studies represent a wide range of learning opportunities served in the form of various teaching-learning modalities for medical learners. A clinical scenario is frequently used as a modality, followed by multiple-choice and open-ended questions among other learning and teaching methods. As such, script concordance tests (SCTs) can be used to promote a higher level of clinical reasoning. Recent technological developments have made generative artificial intelligence (AI)–based systems such as ChatGPT (OpenAI) available to assist clinician-educators in creating instructional materials.

Objective
The main objective of this project is to explore how SCTs generated by ChatGPT compared to SCTs produced by clinical experts on 3 major elements: the scenario (stem), clinical questions, and expert opinion.

Methods
This mixed method study evaluated 3 ChatGPT-generated SCTs with 3 expert-created SCTs using a predefined framework. Clinician-educators as well as resident doctors in psychiatry involved in undergraduate medical education in Quebec, Canada, evaluated via a web-based survey the 6 SCTs on 3 criteria: the scenario, clinical questions, and expert opinion. They were also asked to describe the strengths and weaknesses of the SCTs.

Results
A total of 102 respondents assessed the SCTs. There were no significant distinctions between the 2 types of SCTs concerning the scenario (P=.84), clinical questions (P=.99), and expert opinion (P=.07), as interpretated by the respondents. Indeed, respondents struggled to differentiate between ChatGPT- and expert-generated SCTs. ChatGPT showcased promise in expediting SCT design, aligning well with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria, albeit with a tendency toward caricatured scenarios and simplistic content.

Conclusions
This study is the first to concentrate on the design of SCTs supported by AI in a period where medicine is changing swiftly and where technologies generated from AI are expanding much faster. This study suggests that ChatGPT can be a valuable tool in creating educational materials, and further validation is essential to ensure educational efficacy and accuracy.