bluuebunny/crossref_metadata_2025_split huggingface dataset:
Admission and discharge profiles of people with MS accessing in-patient rehabilitation in Canada
Authors: Mate Kedar K. V. , Ow Nikki , Hum Stanley , Mayo Nancy 
Published: June 2023

AbstractBackgroundRehabilitation is the mainstay of management for people with disabilities of neurological origin to maximize function and reduce disability. Access to in-patient rehabilitation is usually reserved for people after crises or those who are discharged from acute care such as in stroke or trauma. Access to people with Multiple Sclerosis (MS) differs across countries and unknown for Canada. With the progression of MS, quality of life (QOL) becomes more closely coupled with independence and hence timely access to rehabilitation is important. The objectives of this paper are (i) to characterize the disability profiles of people with MS admitted to in-patient rehabilitation in Canada; and (ii) to estimate the extent to which disability profiles differ from admission to discharge.MethodsA longitudinal study of a rehabilitation admission-to-discharge cohort of 3500 people with MS was conducting using latent class analysis on the five Functional Independence Measure (FIM) subscales for Self-care, Transfers, Locomotion, Bladder/Bowel, and Cognition. The extent to which age, sex, and calendar time was associated with latent class membership, at both admission and discharge, was estimated using ordinal logistic regression, and proportional odds model was calculated for each age and sex.ResultsA five-class model fit the data at admission and a four-class model fit the data at discharge determined using likelihood ratio G2, Akaike’s Information Criterion, and Bayesian Information Criterion.ConclusionAt admission, the disability profiles showed a hierarchical progression across the FIM subscales. The least disabled profile was characterized by locomotion dependency only; the most disabled profile was characterized by dependencies in all subscales except cognition. At discharge, the least disabled class, representing 28% of discharges, was characterized by no dependencies; the most disabled class remained with dependencies (23%) in all areas. The study highlights the importance of reserving scarce rehabilitation services to those with more disability.
Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy
Authors: Upadhyaya Dipak P. , Prantzalos Katrina , Thyagaraj Suraj , Shafiabadi Nassim , Fernandez-BacaVaca Guadalupe , Sivagnanam Subhashini , Majumdar Amitava , Sahoo Satya S. 
Published: June 2023

AbstractThe rapid adoption of machine learning (ML) algorithms in a wide range of biomedical applications has highlighted issues of trust and the lack of understanding regarding the results generated by ML algorithms. Recent studies have focused on developing interpretable ML models and establish guidelines for transparency and ethical use, ensuring the responsible integration of machine learning in healthcare. In this study, we demonstrate the effectiveness of ML interpretability methods to provide important insights into the dynamics of brain network interactions in epilepsy, a serious neurological disorder affecting more than 60 million persons worldwide. Using high-resolution intracranial electroencephalogram (EEG) recordings from a cohort of 16 patients, we developed high accuracy ML models to categorize these brain activity recordings into either seizure or non-seizure classes followed by a more complex task of delineating the different stages of seizure progression to different parts of the brain as a multi-class classification task. We applied three distinct types of interpretability methods to the high-accuracy ML models to gain an understanding of the relative contributions of different categories of brain interaction patterns, including multi-focii interactions, which play an important role in distinguishing between different states of the brain. The results of this study demonstrate for the first time that post-hoc interpretability methods enable us to understand why ML algorithms generate a given set of results and how variations in value of input values affect the accuracy of the ML algorithms. In particular, we show in this study that interpretability methods can be used to identify brain regions and interaction patterns that have a significant impact on seizure events. The results of this study highlight the importance of the integrated implementation of ML algorithms together with interpretability methods in aberrant brain network studies and the wider domain of biomedical research.
FM3VCF: A Software Library for Accelerating the Loading of Large VCF Files in Genotype Data Analyses
Authors: Zuo Zhen , Li Qi , Li Zhuo , Tang You , Huang Meng 
Published: June 2023

AbstractBackgroundThe increasing size of genotype data has led to the loading of VCF files becoming a computational bottleneck in various analyses, including imputation and genome-wide association studies (GWAS). To address this issue, we developed a software library, FM3VCF (fast M3VCF), that utilizes multiple CPU threads to accelerate this process.FindingsFM3VCF can convert VCF files into the exclusive data format of MINIMAC4[1], M3VCF[1], and efficiently read and parse data from VCF files. In comparison to m3vcftools[1], FM3VCF is approximately 20 times faster for compressing VCF files to M3VCF format. Furthermore, FM3VCF is approximately 3 times faster than HTSlib[2], including decompressing and parsing, for reading compressed VCF files. FM3VCF is written in C and is open-source, available for download fromhttps://github.com/Oliver-111/m3vcfunder the MIT/BSD license.ConclusionFM3VCF is a powerful tool for accelerating the loading of large VCF files in genotype data analyses. By fully utilizing multiple CPU threads, FM3VCF can significantly reduce the computational burden in various genomic analyses.
The role of binocular disparity and attention in the neural representation of multiple moving stimuli in the visual cortex
Authors: Chakrala Anjani Sreeprada , Xiao Jianbo , Huang Xin 
Published: June 2023

AbstractSegmenting visual scenes into distinct objects and surfaces is a fundamental visual process, with stereoscopic depth and motion serving as crucial cues. However, how the visual system uses these cues to segment multiple objects is not fully understood. We investigated how neurons in the middle-temporal (MT) cortex of macaque monkeys represent overlapping surfaces at different depths, moving in different directions. Neuronal activity was recorded from three male monkeys during discrimination tasks under varying attention conditions. We found that neuronal responses to overlapping surfaces showed a robust bias toward the binocular disparity of one surface over the other. The disparity bias of a neuron was positively correlated with the neuron’s disparity preference for a single surface. In two animals, neurons preferring near disparities of single surfaces (near neurons) showed a near bias for overlapping stimuli, while neurons preferring far disparities (far neurons) showed a far bias. In the third animal, both near and far neurons displayed a near bias, though the near neurons showed a stronger near bias. All three animals exhibited an initial near bias across neurons relative to the average of the responses to the individual surfaces. Although attention modulated neuronal responses, the disparity bias was not caused by attention. We also found that the effect of attention was consistent with object-based, rather than feature-based attention. We proposed a model in which the pool size of the neuron population that weighs the responses to individual stimulus components can be variable. This model is a novel extension of the standard normalization model and provides a unified explanation for the disparity bias across animals. Our results reveal how MT neurons encode multiple stimuli moving at different depths and present new evidence of response modulation by object-based attention. The disparity bias allows subgroups of neurons to preferentially represent individual surfaces of multiple stimuli at different depths, thereby facilitating segmentation.
What works and for whom in treating depression in older adults in deprived communities in Brazil: Findings from a causal mediation analyses of the PROACTIVE trial that overlapped with the COVID-19 pandemic
Authors: Seward Nadine , Nakamura Carina Akemi , Peters Tim J , Loh Wen Wei , McMillan Dean , Gilbody Simon , Scazufca Marcia , Araya Ricardo 
Published: June 2023

SummaryBackgroundThe PROACTIVE trial was a task-shared, collaborative care, psychosocial intervention that was highly effective at improving recovery from depression in older adults in Brazil that overlapped with the COVID-19 pandemic. Here we investigate mediators of the intervention’s effectiveness.MethodsCausal mediation analysis using interventional indirect effects, decomposed the total effect of PROACTIVE on recovery from depression (PHQ-9<10), into multiple indirect effects including: dose of intervention (number of sessions and number of activities completed); social support measured through Luben Social Network Scale; perceived loneliness through the three-item UCLA questionnaire; conditions associated with frailty; and extra sessions offered to participants who did not respond to the intervention.FindingsOf the intervention’s total effect (difference in probability of recovery from depression between the intervention and control arms 0·211 [bias-corrected 95% CI: 0·139, 0·274]): 14% was mediated through improved conditions associated with frailty 0·030 [0·003, 0·065]); 6% through reduced loneliness (0·013 [0·001, 0·028]); and 20% through attending extra sessions for participants who did not respond to the intervention (0·042 [0·007, 0·105]).InterpretationOur findings emphasise the importance of a home-based intervention to improve depression outcomes where participants are encouraged to self-select activities to mitigate against loneliness and are referred to primary care to manage health issues relating to frailty. Importantly, our findings suggest that offering extra sessions to participants who did not respond to the intervention shows promise in ensuring a sustained recovery from depression.FundingSão Paulo Research Foundation and Joint Global Health Trials UK.
Meta-analysis of CYP2C19 and CYP2D6 metabolic activity on antidepressant response from 13 clinical studies using genotype imputation
Authors: Li Danyang , Pain Oliver , Fabbri Chiara , Wong Win Lee Edwin , Lo Chris Wai Hang , Ripke Stephan , Cattaneo Annamaria , Souery Daniel , Dernovsek Mojca Z. , Henigsberg Neven , Hauser Joanna , Lewis Glyn , Mors Ole , Perroud Nader , Rietschel Marcella , Uher Rudolf , Maier Wolfgang , Baune Bernhard T. , Biernacka Joanna M. , Bondolfi Guido , Domschke Katharina , Kato Masaki , Liu Yu-Li , Serretti Alessandro , Tsai Shih-Jen , Weinshilboum Richard ,   the GSRD Consortium,   the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, McIntosh Andrew M. , Lewis Cathryn M. 
Published: June 2023

AbstractCytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. The association of CYP2C19 and CYP2D6 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR=1.46, 95% CI [1.03, 2.06], p=0.033, heterogeneity I2=0%, subgroup difference p=0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19 and CYP2D6, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. CYP2D6 structural variants cannot be imputed from genotype data, limiting inference of pharmacogenetic effects. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression measures, and diverse ancestry studies, would more fully capture the influence of metabolic phenotypes.
Early life seizures and epileptic spasms inSTXBP1-related disorders
Authors: Thalwitzer Kim M , Xian Julie , deCampo Danielle , Parthasarathy Shridhar , Magielski Jan , Sullivan Katie Rose , Goss James , Rigby Charlene Son , Boland Michael , Prosser Ben , Ruggiero Sarah M , Syrbe Steffen , Helbig Ingo 
Published: June 2023

ABSTRACTBackground and ObjectivesIndividuals with disease-causing variants inSTXBP1frequently have epilepsy onset in the first year of life with a variety of seizure types, including epileptic spasms. However, the impact of early-onset seizures and anti-seizure medication (ASM) on the risk of developing epileptic spasms and impact on their trajectory is poorly understood, limiting informed and anticipatory treatment, as well as trial design.MethodsWe retrospectively reconstructed seizure and medication histories in weekly intervals for individuals withSTXBP1-related disorders with epilepsy onset in the first year of life and quantitatively analyzed longitudinal seizure histories and medication response.ResultsWe included 61 individuals with early onset seizures, 29 of whom had epileptic spasms. Individuals with neonatal seizures were likely to have continued seizures after the neonatal period (25/26). The risk of developing epileptic spasms was not increased in individuals with neonatal seizures or early infantile seizures (21/41 vs. 8/16; OR 1, 95% CI 0.3-3.9,p= 1). We did not find any ASM associated with the development of epileptic spasms following prior seizures. Individuals with prior seizures (n = 16/21, 76%) had a higher risk to develop refractory epileptic spasms (n = 5/8, 63%, OR =1.9, 95% CI 0.2-14.6,p= 0.6). Individuals with refractory epileptic spasms had a later onset of epileptic spasms (n = 20, median 20 weeks) compared to individuals with non-refractory epileptic spasms (n = 8, median 13 weeks;p= 0.08). When assessing treatment response, we found that clonazepam (n = 3, OR 12.6, 95% CI 2.2-509.4;p< 0.01), clobazam (n=7, OR 3, 95% CI 1.6-6.2;p< 0.01), topiramate (n=9, OR 2.3, 95% CI 1.4-3.9;p< 0.01), and levetiracetam (n=16, OR 1.7, 95% CI 1.2-2.4;p< 0.01) were more likely to reduce seizure frequency and/or to maintain seizure freedom with regards to epileptic spasms than other medications.DiscussionWe provide a comprehensive assessment of early-onset seizures inSTXBP1-related disorders and show that the risk of epileptic spasms is not increased following a prior history of early-life seizures, nor by certain ASM. Our study provides baseline information for targeted treatment and prognostication in early-life seizures inSTXBP1-related disorders.
The effect of residential aged care facility COVID-19 lockdowns on resident mortality
Authors: Uribe Gabriela , Fotheringham Penelope , Moore Corey 
Published: June 2023

AbstractAimsThis perspective is exploring the effect of residential aged care facility COVID-19 lockdowns on resident mortality.RelevancePeople living in residential aged care facilities (RACFs) are at increased risk of COVID-19 infection and death.In Australia, COVID-19 infections in RACF residents represented seven percent of recorded infections and the case fatality rate (CFR) was 33%. RACFs were categorised as high-risk settings for COVID-19 and a number of measures to reduce the risk of acquiring COVID-19 were implemented at the National and State and Territory levels, such as strict infection control, monitoring, testing, vaccination and increased clinical capacityMethodsAll COVID-19 infections and deaths in the 12-months from 15 October 2021 to 14 October 2022 for the cohort of residents living in an Australian RACF (except Western Australia) at the beginning of this period were analysed.Key pointsLockdowns of RACFs were successful in substantially reducing the number of COVID-19 infections and deaths in RACF residents. This was largely because the infection and mortality rates of COVID-19 were high in 2020 and 2021.The findings of this study could be used to evaluate and inform national and local states and territories’ guidelines on the management of COVID-19 in RACFs.
Harmonizing Healthy Cohorts to Support Multicenter Studies on Migraine Classification using Brain MRI Data
Authors: Yoon Hyunsoo , Schwedt Todd J. , Chong Catherine D. , Olatunde Oyekanmi , Wu Teresa 
Published: June 2023

AbstractMulticenter and multi-scanner imaging studies might be needed to provide sample sizes large enough for developing accurate predictive models. However, multicenter studies, which likely include confounding factors due to subtle differences in research participant characteristics, MRI scanners, and imaging acquisition protocols, might not yield generalizable machine learning models, that is, models developed using one dataset may not be applicable to a different dataset. The generalizability of classification models is key for multi-scanner and multicenter studies, and for providing reproducible results. This study developed a data harmonization strategy to identify healthy controls with similar (homogenous) characteristics from multicenter studies to validate the generalization of machine-learning techniques for classifying individual migraine patients and healthy controls using brain MRI data. The Maximum Mean Discrepancy (MMD) was used to compare the two datasets represented in Geodesic Flow Kernel (GFK) space, capturing the data variabilities for identifying a “healthy core”. A set of homogeneous healthy controls can assist in overcoming some of the unwanted heterogeneity and allow for the development of classification models that have high accuracy when applied to new datasets. Extensive experimental results show the utilization of a healthy core. One dataset consists of 120 individuals (66 with migraine and 54 healthy controls) and another dataset consists of 76 (34 with migraine and 42 healthy controls) individuals. A homogeneous dataset derived from a cohort of healthy controls improves the performance of classification models by about 25% accuracy improvements for both episodic and chronic migraineurs.HighlightsThe harmonization method was established by Healthy Core Construction.The inclusion of a healthy core addresses intrinsic heterogeneity that exists within a healthy control cohort and in multicenter studies.The utilization of a healthy core can increase the accuracy and generalizability of brain imaging-based classification models.The proposed harmonization method offers flexible utilities for multicenter studies.
Prognostic impact of atrial cardiomyopathy: Long-term follow-up of patients with and without low-voltage areas following atrial fibrillation ablation
Authors: Masuda Masaharu , Matsuda Yasuhiro , Uematsu Hiroyuki , Sugino Ayako , Ooka Hirotaka , Kudo Satoshi , Fujii Subaru , Asai Mitsutoshi , Okamoto Shin , Ishihara Takayuki , Nanto Kiyonori , Tsujimura Takuya , Hata Yosuke , Higashino Naoko , Nakao Sho , Mano Toshiaki 
Published: June 2023

AbstractBackgroundAtrial cardiomyopathy is known as an underlying pathophysiological factor in the majority of AF patients. Left atrial low-voltage areas (LVAs) are reported to coincide with fibrosis, and to likely represent atrial cardiomyopathy. This study aimed to delineate differences in the long-term prognosis of patients stratified by the size of LVAs.MethodsThis observational study included 1,488 consecutive patients undergoing initial ablation for AF. LVAs were defined as regions with a bipolar peak-to-peak voltage of < 0.50 mV. The total study population was divided into 3 groups stratified by LVA size: patients with no LVAs (n=1136), those with small (< 20 cm2, n=250) LVAs, and those with extensive (≥ 20 cm2, n=102) LVAs. Composite endpoints of death, heart failure, and stroke were followed for up to 5 years.ResultsComposite endpoints developed in 105 (7.1%) of 1488 patients, and AF recurrence occurred in 410 (27.6%). Composite endpoints developed more frequently in the order of patients with extensive LVAs (19.1%), small LVAs (10.8%), and no LVAs (5.1%; p for trend<0.0001). Multivariable analysis revealed that LVA presence was independently associated with higher incidence of composite endpoints, irrespective of AF recurrence (modified hazard ratio=1.73, 95% confidence interval=1.13-2.64, p=0.011)ConclusionsLVA presence and its extent were both associated with poor long-term composite endpoints of death, heart failure, and stroke, irrespective of AF recurrence or other confounders. Underlying atrial cardiomyopathy appears to define a poor prognosis after AF ablation.Clinical perspectiveWhat is KnownPatients with left atrial low-voltage area has high incidence of atrial fibrillation recurrence after ablationWhat the study addsPresence of low-voltage areas was associated with poor prognosis including death, heart failure hospitalization, and stroke.Graphic abstract
Focusing antibody responses to the fusion peptide in rhesus macaques
Authors: Cottrell Christopher A. , Pratap Payal P. , Cirelli Kimberly M. , Carnathan Diane G. , Enemuo Chiamaka A , Antanasijevic Aleksandar , Ozorowski Gabriel , Sewall Leigh M. , Gao Hongmei , Greene Kelli M. , Allen Joel D. , Ngo Julia T. , Choe Yury , Nogal Bartek , Silva Murillo , Bhiman Jinal , Pauthner Matthias , Irvine Darrell J. , Montefiori David , Crispin Max , Burton Dennis R. , Silvestri Guido , Crotty Shane , Ward Andrew B. 
Published: June 2023

AbstractImmunodominance of antibodies targeting non-neutralizing epitopes and the high level of somatic hypermutation within germinal centers (GCs) required for most HIV broadly neutralizing antibodies (bnAbs) are major impediments to the development of an effective HIV vaccine. Rational protein vaccine design and non-conventional immunization strategies are potential avenues to overcome these hurdles. Here, we report using implantable osmotic pumps to continuously deliver a series of epitope-targeted immunogens to rhesus macaques over the course of six months to elicit immune responses against the conserved fusion peptide. Antibody specificities and GC responses were tracked longitudinally using electron microscopy polyclonal epitope mapping (EMPEM) and lymph node fine-needle aspirates, respectively. Application of cryoEMPEM delineated key residues for on-target and off-target responses that can drive the next round of structure-based vaccine design.