bluuebunny/crossref_metadata_2025_split huggingface dataset:
Polymorphic ventricular tachycardia and cardiac arrest from abiraterone-induced hypokalemia: a case report
Authors: Mao Jessica , Chang Allison Komatsu , Chin Stephen , Preet Komal , Torosyan Nare , Sarkissian Sarmen , Ebinger Joseph 
Published: April 2024

Abstract
Background
Polymorphic ventricular tachycardia (PMVT) is an unstable and often fatal cardiac tachyarrhythmia. While there are many causes of this rhythm, including electrolyte imbalances, ischemia, and genetic disorders, iatrogenic etiologies are important to recognize. Abiraterone is an androgen synthesis antagonist effective in treating prostate cancer, but here we describe a case of severe hypokalemia secondary to abiraterone resulting in polymorphic ventricular tachycardia and cardiac arrest. While this is a potential adverse effect of the medication, severe hypokalemia causing polymorphic ventricular tachycardia and cardiac arrest, as seen in our patient’s case, has not been described.

Case presentation
A 78-year-old African-American man with history of prostate cancer presents with polymorphic ventricular tachycardia and cardiac arrest. After resuscitation, he was found to be severely hypokalemic and refractory to large doses of repletion. Evaluation of secondary causes of hypokalemia identified the likely culprit to be adverse effects from prostate cancer treatment.

Conclusion
A broad differential diagnosis for polymorphic ventricular tachycardia is essential in identifying and treating patients presenting in this rhythm. Here we present a case of iatrogenic polymorphic ventricular tachycardia secondary to oncologic treatment.
Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors
Authors: Du Ming , Xie XingRan , Luo Jing , Li Jin 
Published: April 2024

Abstract
Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective, which are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge to the research and development of machine learning-based kinase inhibitors' active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we present a novel Meta-learning-based inductive logistic matrix completion method for the Prediction of Kinase Inhibitors (MetaILMC). MetaILMC adopts a meta-learning framework to learn a well-generalized model from tasks with sufficient samples, which can fast adapt to new tasks with limited samples. As MetaILMC allows the effective transfer of the prior knowledge learned from kinases with sufficient samples to kinases with a small number of samples, the proposed model can produce accurate predictions for kinases with limited data. Experimental results show that MetaILMC has excellent performance for prediction tasks of kinases with few-shot samples and is significantly superior to the state-of-the-art multi-task learning in terms of AUC, AUPR, etc., various performance metrics. Case studies also provided for two drugs to predict Kinase Inhibitory scores, further validating the proposed method's effectiveness and feasibility.

Scientific contribution
Considering the potential correlation between activity prediction tasks for different kinases, we propose a novel meta learning algorithm MetaILMC, which learns a prior of strong generalization capacity during meta-training from the tasks with sufficient training samples, such that it can be easily and quickly adapted to the new tasks of the kinase with scarce data during meta-testing. Thus, MetaILMC can effectively alleviate the data scarcity problem in the prediction of kinase inhibitors.
Severity of gambling behaviors: exploring associations with venues, legality, and substance use
Authors: Bonny-Noach Hagit 
Published: April 2024

Abstract
Introduction
The COVID-19 pandemic has been linked to an increase in gambling behaviors, potentially leading to Gambling Disorder (GD) and adverse health and social consequences. Problematic gambling has received little research attention over the years in Israeli society and the issue of gambling is not high on the list of priorities of Israeli policymakers. The present study examined gambling behavior in Israel on a continuum of severity and its association with venues where the gambling occurs, legality, attitude toward the legalization of casinos and poker, and substance use.

Methods
The study questionnaires were distributed to approximately 15,000 Jewish-Israeli adults via internet panel. From 3,088 Israeli adults who answered the questionnaire, 1,251 (40.5%) reported gambling in the last year and were included in the analyses.

Results
Based on the Problem Gambling Severity Index, 60% of participants were classified as non-problem gamblers, 25% as at low-risk for a gambling problem, 11% as at moderate risk, and 4% as having a gambling problem. Most online gambling was practiced by non-problem gamblers (40%) and most illegal gambling was by low-risk gamblers (34%). The more severe the gambling behavior was on the continuum, the more it was associated with illegal substance use and positive attitudes toward legalized casinos in Israel. Logistic regression showed the odds of developing moderate and problem gambling were 3.8 times higher for online gamblers (OR = 3.8; CI 2.6–5.4; p < 0.000) and 3.3 times higher for illegal gamblers (OR = 3.3; CI 2.2–4.9; p < 0.000).

Conclusion
Though more research attention should be paid to gambling behaviors, harm reduction gambling interventions should be made available to all categories on the continuum of severity of gambling behaviors. The present study provides evidence-based information to promote health policies that aim to prevent and reduce harm for Israeli gamblers.
Temporally-correlated massive access: joint user activity detection and channel estimation via vector approximate message passing
Authors: Xiong Yueyue , Li Wei 
Published: April 2024

AbstractIn the paper, we investigate a massive machine-type communication (mMTC), where numerous single-antenna users communicate with a single-antenna base station while being active. However, the status of user can undergoes multiple transitions between active and inactive states across whole consecutive intervals. Then, we formulate the problem of joint user activity detection and channel estimation within the dynamic compressed sensing (DCS) framework, considering the temporally-correlated user activity across the entire consecutive intervals. To be specific, we introduce a new hybrid vector approximate message passing algorithm for DCS (HyVAMP-DCS). The proposed algorithm comprises a VAMP block for estimating channel and a loopy belief propagation (LBP) block for detecting user activity. Moreover, these two blocks can exchange messages, enhancing the performance of both channel estimation and user activity detection. Importantly, compared to the fragile GAMP algorithm, VAMP is robust and applicable to a much broader class of large random matrices. Furthermore, the fixed points of VAMP’s state evolution align with the replica prediction of the minimum mean-squared error. The simulation results illustrate the superiority of HyVAMP-DCS, demonstrating its significant outperformance over HyGAMP-DCS.
HE4-based nomogram for predicting overall survival in patients with idiopathic pulmonary fibrosis: construction and validation
Authors: Tian Mi , Zhu Xiaohui , Ren Lijun , Zhou Xuan , GU Lina , Meng Kaifang , Tian Yaqiong , Cai Hourong , Liu Xiaoqin , Ding Jingjing 
Published: April 2024

AbstractIdiopathic pulmonary fibrosis (IPF) is a life-threatening interstitial lung disease. Identifying biomarkers for early diagnosis is of great clinical importance. The epididymis protein 4 (HE4) is important in the process of inflammation and fibrosis in the epididymis. Its prognostic value in IPF, however, has not been studied. The mRNA and protein levels of HE4 were used to determine the prognostic value in different patient cohorts. In this study, prognostic nomograms were generated based on the results of the cox regression analysis. We identified the HE4 protein level increased in IPF patients, but not the HE4 gene expression. The increased expression of HE4 correlated positively with a poor prognosis for patients with IPF. The HR and 95% CI were 2.62 (1.61–4.24) (p < 0.001) in the training set. We constructed a model based on the risk-score = 0.16222182 * HE4 + 0/0.37580659/1.05003609 (for GAP index 0–3/4–5/6–8) + (− 1.1183375). In both training and validation sets, high-risk patients had poor prognoses (HR: 3.49, 95%CI 2.10–5.80, p = 0.001) and higher likelihood of dying (HR: 6.00, 95%CI 2.04–17.67, p = 0.001). Analyses of calibration curves and decision curves suggest that the method is effective in predicting outcomes. Furthermore, a similar formulation was used in a protein-based model based on HE4 that also showed prognostic value when applied to IPF patients. Accordingly, HE4 is an independent poor prognosis factor, and it has the potential to predict IPF patient survival.
Investigation on the Micro-Grinding Induced Crystallographic Variations of Nine Different Clinkers
Authors: Kang Hyunuk , Lee Jihoon , Park Jun-Boum , Moon Juhyuk 
Published: April 2024

AbstractAlthough clinker has been used for many years, complicated mineralogical properties of clinker pose challenges for the precise quantification. In this study, the mineralogical and crystallographic properties of nine different clinkers according to grinding procedures were investigated. With the dry-grinding for 2 h, particle size reduction to 3 μm of median particle size with a substantial phase transition to an amorphous phase observed, to which alite (C3S) mainly contributed to the transition. Meanwhile, the crystallographic properties of the clinker phases were barely changed during the wet-grinding. In the wet-grinding program, the amount of ferrite solid solution (C4AF) with a high linear absorption coefficient was not underestimated. Furthermore, well-corrected preferred orientation effect on C3S was positively contributed to the analysis result of clinkers with the wet-grinding. Hence, it was suggested that the crystallographic effects observed in the wet-grinding program could produce more reliable results in phase analysis for the clinkers.
Clinging to the top: natal dispersal tracks climate gradient in a trailing-edge population of a migratory songbird
Authors: Gaya Heather E. , Cooper Robert J. , Delancey Clayton D. , Hepinstall-Cymerman Jeffrey , Kurimo-Beechuk Elizabeth A. , Lewis William B. , Merker Samuel A. , Chandler Richard B. 
Published: April 2024

Abstract
Purpose
Trailing-edge populations at the low-latitude, receding edge of a shifting range face high extinction risk from climate change unless they are able to track optimal environmental conditions through dispersal.

Methods
We fit dispersal models to the locations of 3165 individually-marked black-throated blue warblers (Setophaga caerulescens) in the southern Appalachian Mountains in North Carolina, USA from 2002 to 2023. Black-throated blue warbler breeding abundance in this population has remained relatively stable at colder and wetter areas at higher elevations but has declined at warmer and drier areas at lower elevations.

Results
Median dispersal distance of young warblers was 917 m (range 23–3200 m), and dispersal tended to be directed away from warm and dry locations. In contrast, adults exhibited strong site fidelity between breeding seasons and rarely dispersed more than 100 m (range 10–1300 m). Consequently, adult dispersal kernels were much more compact and symmetric than natal dispersal kernels, suggesting adult dispersal is unlikely a driving force of declines in this population.

Conclusion
Our findings suggest that directional natal dispersal may mitigate fitness costs for trailing-edge populations by allowing individuals to track changing climate and avoid warming conditions at warm-edge range boundaries.
Hunting mode and habitat selection mediate the success of human hunters
Authors: Gaynor Kaitlyn M. , McInturff Alex , Abrahms Briana L. , Smith Alison M. , Brashares Justin S. 
Published: April 2024

Abstract
Background
As a globally widespread apex predator, humans have unprecedented lethal and non-lethal effects on prey populations and ecosystems. Yet compared to non-human predators, little is known about the movement ecology of human hunters, including how hunting behavior interacts with the environment.

Methods
We characterized the hunting modes, habitat selection, and harvest success of 483 rifle hunters in California using high-resolution GPS data. We used Hidden Markov Models to characterize fine-scale movement behavior, and k-means clustering to group hunters by hunting mode, on the basis of their time spent in each behavioral state. Finally, we used Resource Selection Functions to quantify patterns of habitat selection for successful and unsuccessful hunters of each hunting mode.

Results
Hunters exhibited three distinct and successful hunting modes (“coursing”, “stalking”, and “sit-and-wait”), with coursings as the most successful strategy. Across hunting modes, there was variation in patterns of selection for roads, topography, and habitat cover, with differences in habitat use of successful and unsuccessful hunters across modes.

Conclusions
Our study indicates that hunters can successfully employ a diversity of harvest strategies, and that hunting success is mediated by the interacting effects of hunting mode and landscape features. Such results highlight the breadth of human hunting modes, even within a single hunting technique, and lend insight into the varied ways that humans exert predation pressure on wildlife.
Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study
Authors: Tian Zhen , Cheng Yifan , Zhao Shuai , Li Ruiqi , Zhou Jiajie , Sun Qiannan , Wang Daorong 
Published: April 2024

Abstract
Background
Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection.

Methods
A total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors.

Results
The comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649–0.923] vs. 0.822 [0.692–0.952] vs. 0.733 [0.573–0.892] vs. 0.511 [0.359–0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance.

Conclusions
The novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population.

Trial registration
: Not applicable.
Attenuating Muscle Damage Biomarkers and Muscle Soreness After an Exercise-Induced Muscle Damage with Branched-Chain Amino Acid (BCAA) Supplementation: A Systematic Review and Meta-analysis with Meta-regression
Authors: Salem Atef , Ben Maaoui Khouloud , Jahrami Haithem , AlMarzooqi Mezna A. , Boukhris Omar , Messai Balsam , Clark Cain C. T. , Glenn Jordan M. , Ghazzaoui Hadeel A. , Bragazzi Nicola Luigi , Ammar Achraf , Trabelsi Khaled , Chtourou Hamdi 
Published: April 2024

Abstract
Background
Branched-chain amino acid (BCAA) supplementation is one of the most popular strategies used by the general population and athletes to reduce muscle soreness and accelerate the recovery process of muscle damage biomarkers after an intense exercise or training session.

Objectives
This systematic review and meta-analysis investigated the effects of BCAA supplementation on muscle damage biomarkers and muscle soreness after exercise-induced muscle damage (EIMD).

Methods
The systematic literature search for randomized controlled trials was conducted using seven databases, up to September 13th, 2022. The eligibility criteria for selecting studies were as follows: studies performed on healthy active participants, using BCAA at least once, controlled with a placebo or control group, performing resistance or endurance exercises, and followed up at least once post-EIMD. The methodological quality of the studies was assessed using the “SIGN RCT checklist”. Random-effects meta-analyses were processed to compute the standardized mean difference (Hedges’ g). Meta-regression analyses were completed with daily and total dosage and supplementation as continuous moderator variables.

Results
Of the 18 studies included in this meta-analysis, 13 were of high quality and five were of acceptable quality. Our results revealed BCAA supplementation elicits a significant effect on reducing creatine kinase (CK) levels immediately (g = − 0.44; p = 0.006) and 72 h (g = − 0.99; p = 0.002), but not 24 h, 48 h, and 96 h post-EIMD. Additionally, a significant effect on delayed onset of muscle soreness (DOMS) was identified at 24 h (g = − 1.34; p < 0.001), 48 h (g = − 1.75; p < 0.001), 72 h (g = − 1.82; p < 0.001), and 96 h (g = − 0.82; p = 0.008), but not immediately post-EIMD. No significant effect was found on lactate dehydrogenase (LDH) levels at any time point. Meta-regression indicated higher daily and total dosages of BCAA, and longer supplementation periods were related to the largest beneficial effects on CK (total dosage and supplementation period) at 48 h, and on DOMS at 24 h (only daily dosage).

Conclusion
The overall effects of BCAA supplementation could be considered useful for lowering CK and DOMS after EIMD, but not LDH. The longer supplementation period prior to the EIMD could be more effective for CK and DOMS reduction.
Reading processes of public and private middle school and high school students
Authors: de Oliveira Adriana Marques , Santos Jair Lício Ferreira , Capellini Simone Aparecida 
Published: April 2024

Abstract
Introduction
Reading has been widely discussed, mainly due to the published results of international performance tests of schoolchildren. The gaps generated in literacy hinder the development of basic skills necessary for reading, which will have a negative impact on the teaching-learning process from elementary school to high school. This study aimed to compare the reading performance of the students in public and private schools through tests of the Brazilian reading processes-PROLEC-SE-R.

Methods
Cross-sectional study. The Brazilian adaptation of the PROLEC-SE-R was administered to 436 students: 221 from the state school (G1 6th year, n = 30; G2 7th year, n = 33; G3 8th year, n = 35; G4 9th year, n = 31; G5 1st year, n = 32; G6 2nd year, n = 30; G7 3rd year, n = 30) e 215 private schools (G8 6th year, n = 31; G9 7th year, n = 31; G10 8th year, n = 30; G11 9th year, n = 31; G12 1st year, n = 30; G13 2nd year, n = 31; G14 3rd year, n = 31). Tools of descriptive and bivariate analysis were used.

Results
Superior performance of the private school students on spelling tests helps their reading as evidenced by their scores for syntactic and semantic processes. When the knowledge of the use of the word in text, extraction of meaning and its understanding was needed, the difficulty of access to the mental lexicon of the studied population became evident.

Conclusion
The PROLEC-SE-R, in addition to establishing the reading profile of elementary and high school students, shows that the gaps in teaching and learning, which exist between public and private education in the literacy period, accompany students throughout the basic education cycle. Knowing the reading profile and in which process there is a disruption is important so that the teaching of specific strategies can be promoted throughout the entire schooling process, especially in primary and secondary education.