bluuebunny/crossref_metadata_2025_split huggingface dataset:
Translation, adaptation, and measurement properties of the Muscle-Strengthening Exercise Questionnaire among university students in Indonesia
Authors: Ashari Reza Setyono , Sabirin Rahmaningsih Mara , Merlinda Stevani Tia Bella , Pratiwi Dilia Ananda , Tsania Melati Nilna , Wibowo Rakhmat Ari 
Published: May 2024

ABSTRACTBackgroundDespite the abundant evidence showing the benefits of muscle-strengthening exercise (MSE), no epidemiological tool is available for assessing MSE among Indonesian university students. This study aimed to adapt the Muscle-Strengthening Exercise Questionnaire (MSEQ) into the Indonesian context and test the validity and reliability of the MSEQ Indonesian version.MethodsTranslation and cultural adaptation, content validity studies, concurrent validity studies, and reliability studies were conducted with a total of 121 respondents. The concurrent validity study compared the results of measuring MSE frequency, intensity, duration, and volume with those of the 7-day diary and relative handgrip strength. For the reliability study, the respondents were asked to fill the MSEQ Indonesian version twice with a time interval of 7 days.ResultsModerate-to-strong correlations were observed between the results for the weekly frequency, duration, intensity, and volume of MSE and those of the 7-day diary and hand grip strength. Test–retest reliabilities were good to excellent for machine weight, holistic, and overall MSE but poor for bodyweight MSE. In assessing the target muscle group, the MSEQ Indonesian version showed good test–retest reliability for machine-weight MSE but poor-to-very good test–retest reliability for bodyweight, free weight, and holistic MSE.ConclusionsOur study showed acceptable validity and reliability of the MSEQ Indonesian version to be used for assessing MSE among university students in Indonesia. Further studies are warranted to ensure the generalizability of our findings among the adult population.
Performance of empirical and model-based classifiers for detecting sucrase-isomaltase inhibition using the13C-sucrose breath test
Authors: Van Wyk Hannah , Lee Gwenyth O. , Schillinger Robert J. , Edwards Christine A. , Morrison Douglas J. , Brouwer Andrew F. 
Published: May 2024

AbstractBackgroundEnvironmental enteric dysfunction (EED) is a syndrome characterized by epithelial damage including blunting of the small intestinal villi and altered digestive and absorptive capacity which may negatively impact linear growth in children. The13C-sucrose breath test (13C-SBT) has been proposed to estimate sucrase-isomaltase (SIM) activity, which is thought to be reduced in EED. We previously showed how various summary measures of the13C-SBT breath curve reflect SIM inhibition. However, it is uncertain how the performance of these classifiers is affected by test duration.MethodsWe leveraged SBT data from a cross-over study in 16 adults who received 0, 100, and 750 mg of Reducose, a natural SIM inhibitor. We evaluated the performance of a pharmacokinetic-model-based classifier,ρ, and three empirical classifiers (cumulative percent dose recovered at 90 minutes (cPDR90), time to 50% dose recovered, and time to peak dose recovery rate), as a function of test duration using receiver operating characteristic curves. We also assessed the sensitivity, specificity, and accuracy of consensus classifiers.ResultsTest durations of less than 2 hours generally failed to accurately predict later breath curve dynamics. The cPDR90 classifier had the highest area-under-the-curve and, by design, was robust to shorter test durations. For detecting mild SIM inhibition,ρhad a higher sensitivity.ConclusionsWe recommend SBT tests run for at least a 2-hour duration. Although cPDR90 was the classifier with highest accuracy and robustness to test duration in this application, concerns remain about its sensitivity to misspecification of CO2production rate. More research is needed to assess these classifiers in target populations.
Apathy and Physical Activity: A Systematic Review and Meta-Analysis
Authors: Farajzadeh Ata , Hébert Alexe , Lahart Ian M. , Bilodeau Martin , Boisgontier Matthieu P. 
Published: May 2024

ABSTRACTPurposePatient motivation is essential for the success of their rehabilitation. Apathy, a state of primary motivational deficiency, may therefore hinder physiotherapists’ interventions, such as those aimed at increasing patients’ physical activity. This study aims to examine the relationship between apathy and physical activity and to identify the factors that influence this relationship.MethodsSix databases were searched for articles including both a measure of apathy and physical activity. Two reviewers screened articles for inclusion, assessed risk of bias, and extracted data from each study. Pearson product-moment correlations were pooled from eligible studies using the generic inverse pooling and random effects method to examine the relationship between apathy and physical activity. Subgroup meta-analyses were conducted to examine the differences between studies that included participants with different health conditions and used different types of physical activity outcomes and apathy measures. Meta-regressions were conducted to examine the moderating effects of age and gender.ResultsTwenty-eight articles were included in the systematic review and 22 studies (n = 12,541 participants) in the main meta-analysis. Results showed a negative correlation between apathy and physical activity (r = -0.13; 95% CI: -0.18 to -0.09; p < 0.0001 based on Pearson’s r values; r = - 0.40; 95% CI: -0.68 to -0.02; p = 0.043 based on Spearman’s rho values). A subgroup meta-analysis revealed that the correlation was statistically significant in patients with Parkinson’s disease and in older adults who were healthy, depressed, fallers, or had mild cognitive impairment. A meta-regression showed an effect of age, with a stronger correlation between apathy and physical activity in older adults compared to younger adults (p = 0.003).ConclusionOur results suggest that higher levels of apathy are associated with lower levels of physical activity and that this negative association is stronger with age. Therefore, apathy may limit exercise therapy efficacy and carry prognostic implications for patients whose condition requires physical activity.
A polygenic predictor of baseline QTc is associated with sotalol-induced QT prolongation
Authors: Lancaster Megan C. , Davogustto Giovanni , Prifti Edi , Perret Claire , Funck-Brentano Christian , Roden Dan M. , Salem Joe-Elie 
Published: May 2024

AbstractBackgroundDrug-induced QT interval prolongation raises the risk of fatal arrhythmia and is a central issue in safety pharmacology. Yet individual QT drug response is highly variable, and risk stratification remains a challenge. We hypothesized that genetic factors underlying the baseline QT influence this variation in QT drug response.MethodsA cohort of 990 healthy subjects was prospectively challenged with a single 80 mg oral sotalol dose. Exclusion criteria included abnormal ECG, cardiac disease, and QT-prolonging drug use. ECGs were obtained at baseline and 3 hours post-dose. QT response was defined as absolute change in Fridericia heart-rate corrected QT (ΔQTc). Plasma sotalol was measured 3 hours post-dose. Subjects were genotyped on the MEGA array. Principal component analysis of genotypes was used to define genetic ancestry. A polygenic risk score (PRS) for the baseline QTc comprised of 465,399 common variants was calculated from assayed and imputed genotypes. The difference in PRS between subjects with high QT response (ΔQTc≥60 ms) and those with ΔQTc<60ms was assessed by Mann Whitney test. A multivariable regression model was used to assess the association between PRS and ΔQTc after covariate adjustment.Results:Of the 990 subjects, 978 (99%) passed genomic quality control and were included in analysis. 62% were female (n=607), with median age 23 (interquartile range (IQR) 21-33). The median ΔQTc 3 hours post-sotalol was 21 ms (IQR 12-30). Ten subjects (1%) had ΔQTc≥60 ms, and their PRS was significantly higher compared to those with ΔQTc<60 ms (P=0.0082). In the regression model, PRS was positively associated with ΔQTc (P=0.0002).ConclusionCommon genetic variants associated with the baseline QT also capture part of the repolarization response to sotalol. This adds to the emerging concept that the genetic architecture of a trait can predict drug response.
Colistin resistance among the Gram-negative nosocomial pathogens in India: A systematic review and meta-analysis
Authors: Dwibedy Sambit K. , Padhy Indira , Panda Aditya K. , Mohapatra Saswat S. 
Published: May 2024

AbstractBackgroundThe rapid rise of nosocomial infections and the growing ineffectiveness of frontline antibiotics against Gram-negative bacteria (GNB) have put the healthcare sector under unprecedented stress. In this scenario, colistin, an antibiotic of the polymyxin class, has become the last resort treatment option. The unrestricted use of colistin in the preceding decades has led to the emergence of colistin-resistant (ColR) bacterial strains. Unfortunately, comprehensive data on the prevalence of ColRnosocomial pathogens in India are lacking. This study was conducted to address this information gap and to determine the prevalence of ColRamong the nosocomial GNB species in India.MethodsA systematic review and meta-analysis were conducted to determine the prevalence of ColRamong the nosocomial GNB species in India and their geographical distribution. A systematic search of the online databases was performed and eligible studies meeting the inclusion criteria were used for qualitative synthesis. The combined event rate and 95% confidence interval were estimated using forest plot with a random-effect model. Cochrane Q statistics and I2statistics were used to detect possible heterogeneity.ResultsFrom a total of 1865 retrieved records from 4 databases, 36 studies were included in the study. Among the most common nosocomial pathogens, K. pneumoniaeshowed a rate of ColRat 17.4%, followed byP. aeruginosa(14.1%),E. coli(12.3%), andA. baumannii(12.2%). Interestingly, our analysis revealed thatE. cloacaehas the highest rate of ColRat 28.5%.ConclusionsThe level of resistance displayed byK. pneumoniae,P. aeruginosa, and to a lesser extent,E. coliandA. baumanniiin the Indian subcontinent poses a challenge for public health management. Though prevalence may differ among regions and over time, continued surveillance, and efforts to curb the spread of resistance are crucial to ensure the continued effectiveness of this critical antibiotic.
The interactive effect of intra-beat and inter-beat blood pressure variability on neurodegeneration in older adults
Authors: Lohman Trevor , Shenasa Fatemah , Sible Isabel , Kapoor Arunima , Engstrom Allison C , Dutt Shubir , Head Elizabeth , Sordo Lorena , M. Alitin John Paul , Gaubert Aimee , Nguyen Amy , Nation Daniel A 
Published: May 2024

AbstractBlood pressure variability (BPV) and arterial stiffness are age-related hemodynamic risk factors for neurodegenerative disease, but it remains unclear whether they exert independent or interactive effects on brain health. When combined with high inter-beat BPV, increased intra-beat BPV indicative of arterial stiffness could convey greater pressure wave fluctuations deeper into the cerebrovasculature, exacerbating neurodegeneration. This interactive effect was studied in older adults using multiple markers of neurodegeneration, including medial temporal lobe (MTL) volume, plasma neurofilament light (NfL) and glial fibrillary acidic protein (GFAP). Older adults (N=105) without major neurological or systemic disease were recruited and underwent brain MRI and continuous BP monitoring to quantify inter-beat BPV through systolic average real variability (ARV) and intra-beat variability through arterial stiffness index (ASI). Plasma NfL and GFAP were assessed. The interactive effect of ARV and ASI on MTL atrophy, plasma NfL, and GFAP was studied using hierarchical linear regression. Voxel-based morphometry (VBM) was used to confirm region-of-interest analysis findings. The interaction between higher ARV and higher ASI was significantly associated with left-sided MTL atrophy in both the region-of-interest and false discovery rate-corrected VBM analysis. The interactive effect was also significantly associated with increased plasma NfL, but not GFAP. The interaction between higher ARV and higher ASI is independently associated with increased neurodegenerative markers, including MTL atrophy and plasma NfL, in independently living older adults. Findings could suggest the increased risk for neurodegeneration associated with higher inter-beat BPV may be compounded by increased intra-beat variability due to arterial stiffness.
Alterations in aperiodic and periodic EEG activity in young children with Down syndrome
Authors: Geiger McKena , Hurewitz Sophie R. , Pawlowski Katherine , Baumer Nicole T. , Wilkinson Carol L. 
Published: May 2024

AbstractDown syndrome is the most common cause of intellectual disability, yet little is known about the neurobiological pathways leading to cognitive impairments. Electroencephalographic (EEG) measures are commonly used to study neurodevelopmental disorders, but few studies have focused on young children with DS. Here we assess resting state EEG data collected from toddlers/preschoolers with DS (n=29, age 13-48 months old) and compare their aperiodic and periodic EEG features with both age-matched (n=29) and cognitive-matched (n=58) comparison groups. DS participants exhibited significantly reduced aperiodic slope, increased periodic theta power, and decreased alpha peak amplitude. A majority of DS participants displayed a prominent peak in the theta range, whereas a theta peak was not present in age-matched participants. Overall, similar findings were also observed when comparing DS and cognitive-matched groups, suggesting that EEG differences are not explained by delayed cognitive ability.
Uncovering COVID-19 Transmission Tree: Identifying Traced and Untraced Infections in an Infection Network
Authors: Lee Hyunwoo , Choi Hayoung , Lee Hyojung , Lee Sunmi , Kim Changhoon 
Published: May 2024

ABSTRACTWe present a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. This network, illustrating infector-infectee relationships, provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder the conventional analysis of such networks from epidemiological surveillance. To address this challenge, our research suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. Furthermore, the study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods. The four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings of this study is the identification of notable transmission patterns in specific age groups, particularly in those aged 20–29, 40–69, and 0–9, based on the four type classifications. Moreover, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities.
Moderating heritability with genomic data
Authors: Benstock Sarah E. , Prom-Wormley Elizabeth , Verhulst Brad 
Published: May 2024

AbstractEnvironmental moderators may amplify or suppress the heritability (i.e., the proportion of genetic variation) of a phenotype. This genetic sensitivity to the environment is called gene-environment interaction (GxE). Existing GxE methods struggle to identify replicable interactions because they focus on the interaction coefficients. We propose a novel method for estimating GxE heritability using genetic marginal effects from GxE genome-wide analyses and LD Score Regression (LDSC). We demonstrate the effectiveness of our method for body mass index (BMI) treating biological sex (binary) and age (continuous) as moderators. We find robust, interpretable evidence for GxE that is not detected by existing methods.
Task-Relevant Stimulus Design Improves P300-Based Brain-Computer Interfaces
Authors: Kim Jongsu , Cho Yang Seok , Kim Sung-Phil 
Published: May 2024

AbstractObjectiveIn the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands.ApproachIn the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses.Main ResultsWith the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieves the accuracy of 91.2% and the information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing the accuracy of 91.7% and the ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields the accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus.SignficanceUsing a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.
Multiomics analysis of narcolepsy T cells: global hypomethylation linked to T cell proliferation
Authors: Shimada Mihoko , Honda Makoto , Honda Yoshiko , Kodama Tohru , Hitomi Yuki , Tokunaga Katsushi , Miyagawa Taku 
Published: May 2024

SummaryNarcolepsy type 1 (NT1) is a chronic sleep disorder caused by a loss of orexin-producing cells in the brain and involves autoimmune mechanisms, including the presence of autoreactive T cells. We performed a genome-wide DNA methylation analysis using CD4+and CD8+T cells of NT1 patients. Analysis of differentially methylated regions as well as multiomics analysis with genomic and transcriptomic data obtained from the same samples indicated that cell chemotaxis pathways are implicated as a cause in the pathogenesis of NT1. Additionally, we found global hypomethylation in both the T cells of NT1 cases (CD4+:P= 1.69E-67; CD8+:P= 4.83E-12). These NT1-associated hypomethylated sites were significantly more abundant in solo-WCGW (sequences without neighboring CpGs, where W is an A or T base;P= 9.87E-194). Solo-WCGW tends to lose DNA methylation over the course of cell divisions, suggesting enhanced T cell proliferation in NT1.
Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions
Authors: Omelchenko Alisa A. , Siwek Jane C. , Chhibbar Prabal , Arshad Sanya , Nazarali Iliyan , Nazarali Kiran , Rosengart AnnaElaine , Rahimikollu Javad , Tilstra Jeremy , Shlomchik Mark J. , Koes David R. , Joglekar Alok V. , Das Jishnu 
Published: May 2024

AbstractThe explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences.SlidingWindowInteractionGrammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM’s representations are used as features.SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally.To further evaluate SWING’s generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.