bluuebunny/crossref_metadata_2025_split huggingface dataset:
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
Authors: Chen Bo , Bei Zhilei , Cheng Xingyi , Li Pan , Tang Jie , Song Le 
Published: June 2024

AbstractMultiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous information to construct high-quality MSA. Although various methods have been proposed to generate virtual MSA under these conditions, they fall short in comprehensively capturing the intricate co-evolutionary patterns within MSA or require guidance from external oracle models. Here we introduce MSAGPT, a novel approach to prompt protein structure predictions via MSA generative pre-training in the low-MSA regime. MSAGPT employs a simple yet effective 2D evolutionary positional encoding scheme to model the complex evolutionary patterns. Endowed by this, its flexible 1D MSA decoding framework facilitates zero-or few-shot learning. More-over, we demonstrate that leveraging the feedback from AlphaFold2 can further enhance the model’s capacity via Rejective Fine-tuning (RFT) and Reinforcement Learning from AF2 Feedback (RLAF). Extensive experiments confirm the efficacy of MSAGPT in generating faithful virtual MSA to enhance the structure prediction accuracy (up to +8.5% TM-Score on few-shot scenarios). The transfer learning capabilities also highlight its great potential for facilitating other protein tasks.
THE HYBRID HISTORY OF ZEBRAFISH
Authors: McCluskey Braedan M. , Batzel Peter , Postlethwait John H. 
Published: June 2024

AbstractSince the description of the zebrafishDanio rerioin 1822, the identity of its closest living relative has been unclear. To address this problem, we sequenced the exomes of ten species in genusDanioand used them to infer relationships across the 25 chromosomes of the zebrafish genome. The majority of relationships withinDaniowere remarkably consistent across all chromosomes. Relationships of chromosome segments, however, depended systematically upon genomic location within zebrafish chromosomes. Regions near chromosome centers identifiedD. kyathitand/orD. aesculapiias the closest relative of zebrafish, while segments near chromosome ends supported onlyD. aesculapiias the zebrafish sister species. Genome-wide comparisons of derived character states revealed that danio relationships are inconsistent with a simple bifurcating species history and support an ancient hybrid origin of theD. reriolineage. We also found evidence of more recent gene flow limited to the high recombination ends of chromosomes and several megabases of chromosome 20 with a history distinct from the rest of the genome. The additional insight gained from incorporating genome structure into a phylogenomic study demonstrates the utility of such an approach for future studies in other taxa. The multiple genomic histories of species in the genusDaniohave important implications for comparative studies in these species and for our understanding of the hybrid evolutionary history of zebrafish.
Effectiveness of Radiation Therapy for Low- to Intermediate-Grade Neuroendocrine Tumors
Authors: Koenig Julie L. , Carter Justin N. , Aggarwal Sonya , Chang Daniel T. , Loo Billy W. , Diehn Maximilian , Koong Albert C. , Kunz Pamela L. , Gensheimer Michael F. 
Published: June 2024

AbstractSurgery is the primary treatment for localized neuroendocrine tumors (NETs). Grade 1-2 NETs traditionally have been considered radioresistant due to their indolent nature but data regarding a role for radiation therapy (RT) are limited to old, small retrospective studies. We performed a retrospective review of patients with grade 1-2 NETs treated with RT at a large academic center to assess response and local failure rates. Radiographic response was evaluated with logistic regression. Local failure was assessed with cumulative incidence rates and competing risk regressions. We identified 35 patients with 78 treated lesions between 1974 and 2017. Most tumors originated from the pancreas (n=13) and bronchus/lung (n=11). Nine (26%) patients had grade 1 tumors, 16 (46%) had grade 2 tumors, and ten (29%) had grade 1-2 tumors. The median biologically effective dose (BED10) was 50.7 Gy (range, 20.0-106.5). The median follow-up was 13.5 months (range, 0.5 to 140.6 months). 20 of 21 (95%) patients had palliation of symptoms. Of 52 intact lesions, response was complete in 7 (13%), partial in 14 (27%), stable in 25 (48%), and progressive in 6 (12%) lesions. Higher BED10was associated with a response (odds ratio/Gy 1.06; 95% CI, 1.02-1.11; p=0.008). Of 59 intact or resected lesions, the 2-year cumulative incidence of local failure was 26.4%. Grade 2 lesions were associated with local failure (hazard ratio 7.70; 95% CI, 1.22-48.8; p=0.03). We show that grade 1-2 NETs often respond radiographically and symptomatically to RT. RT should be considered in the management of grade 1-2 NETs.
Is execute attention affected by environmental lighting conditions?
Authors: Wessolowski Nino , Rahim Raschida Jasmin 
Published: June 2024

AbstractBright daylight has various positive influences, such as the long-term synchronization of circadian rhythms and an associated alertness that creates optimal conditions for attentional performance. However, the short-term effects of light on attention have not yet been sufficiently researched. Studies of these short-term effects on selective attention also showed partly contradictory effects. To investigate these short-term effects, 95 participants completed the Attention Network Test, under conditions of bright daylight or warm-white light. The focus of the present study was on executive attention, because this construct comes closest to the ANT short form of selective attention used. A significant enhancement of executive attention was observed under bright daylight conditions. This specifically means a short term effect of bright daylight on executive attention and thus selective attention in young adults.
Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes
Authors: Gevertz Jana L. , Wares Joanna R. 
Published: June 2024

AbstractVirtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.
Interactive Learning with ChatGPT: Hands-On Practice and Real-Time Feedback in Health Sciences Education for SMART Goal Writing
Authors: Speer Julie E. , Parker Sara M. , Williams Brittany L. 
Published: June 2024

AbstractPhysical therapy (PT) students often fail to master documentation skills, such as goal writing, because they struggle to engage in the material early in the curriculum. Therefore, we sought to leverage ChatGPT to create an active learning experience with personalized feedback and hands-on practice. During the activity, students (n=48) learned to use ChatGPT and employed these techniques to learn about goal writing in PT and the SMART (Specific, Measurable, Attainable, Relevant, Time-bound) framework. Next, students engaged in a clarifying lecture before using ChatGPT to generate scenarios for which they drafted or edited SMART goals and asked ChatGPT for feedback. Pre-tests and post-tests were used to measure the impacts of the experience and to capture student perspectives. As a function of the activity, students were better able to recognize the purposes of SMART goals (p<0.0001) and write better goals (p<0.0001). They also showed increased confidence in their abilities (p<0.0001). Furthermore, student responses suggested that they enjoyed the activity (m=3.5/4) and found it helpful (m=3.7/4). Following the activity, 27 students continued to use ChatGPT to practice or study. This activity represents a novel approach for using generative artificial intelligence (AI) in the classroom to help students actively explore the topic of goal writing. Additionally, this modeled responsible use of AI for health care applications. This well-received activity can be easily scaled to include more complex tasks or group discussions, or adapted for use as an asynchronous assignment related to documentation or topics across health science education.
State-level disparities in cervical cancer prevention and outcomes in the U.S.: A modeling study
Authors: Alarid-Escudero Fernando , Gracia Valeria , Wolf Marina , Zhao Ran , Easterly Caleb W. , Kim Jane J. , Canfell Karen , de Kok Inge M.C.M. , Barnabas Ruanne V. , Kulasingam Shalini 
Published: June 2024

AbstractBackgroundDespite HPV vaccines’ availability for over a decade, coverage across the US varies. While some states have tried to increase HPV vaccination coverage, most model-based analyses focus on national impacts. We evaluated hypothetical changes in HPV vaccination coverage at the national and state levels for California, New York, and Texas using a mathematical model.MethodsWe developed a new mathematical model of HPV transmission and cervical cancer, creating US and state-level models, incorporating country- and state-specific vaccination coverage and cervical cancer incidence and mortality. We quantified the national and state-level impact of increasing HPV vaccination coverage to 80% by 2025 or 2030 on cervical cancer outcomes and the time to elimination defined as <4 per 100k women.ResultsIncreasing vaccination coverage to 80% in Texas over ten years could reduce cervical cancer incidence by 50.9% (95% credible interval [CrI]:46.6-56.1%) by 2100, from 1.58 (CrI:1.19-2.09) to 0.78 (CrI:0.57-1.02) per 100,000 women. Similarly, New York could see a 27.3% (CrI:23.9-31.5%) reduction, from 1.43 (CrI:0.93-2.07) to 1.04 (Crl:0.66-1.53) per 100,000 women, and California a 24.4% (CrI:20.0-30.0%) reduction, from 1.01 (Crl:0.66-1.44) to 0.76 (Crl:0.50-1.09) per 100,000 women. Achieving 80% coverage in five years will provide slightly larger and sooner reductions. If the vaccination coverage levels in 2019 continue, cervical cancer elimination could occur nationally by 2051 (Crl:2034-2064), but state timelines may vary by decades.ConclusionTargeting an HPV vaccination coverage of 80% by 2030 will disproportionately benefit states with low coverage and higher cervical cancer incidence. Geographically focused analyses can better inform priorities.
Sex-differences in Newly Diagnosed Severe Aortic Stenosis in British Columbia (B.C.) between 2012 - 2022
Authors: Roshan Aishwarya , Yim Jeffrey , Lakhani Shamikh , Wang Jennifer , Sidhu Aamiya , Sayre Eric C. , Humphries Karin , Sathananthan Janarthanan , Wood David , Tsang Michael Y.C. , Yeung Darwin F. , Luong Christina , Nair Parvathy , Gin Kenneth , Jue John , Webb John G. , Tsang Teresa S.M. 
Published: June 2024

AbstractBackgroundDespite its high prevalence, little is known about the effect of sex on the management and outcomes of aortic stenosis (AS). We sought to characterize the effect of sex on the clinical evaluation for and provision of aortic valve replacement (AVR), including surgical (SAVR) and transcatheter aortic valve replacement (TAVR), and subsequent morbidity and mortality outcomes.MethodsA comprehensive chart review was conducted on all patients with a first diagnosis of severe aortic stenosis (AS) at Vancouver General and University of British Columbia Hospitals from 2012 to 2022. Exact chi-square and Kruskal-Wallis tests were used to evaluate variables of interest.ResultsA total of 1794 studies met inclusion criteria, comprising 782 females (44%) and 1012 males (56%). Females were significantly older than males at the time of first diagnosis (79 versus 75 years, p<0.001). Females were significantly less likely to be evaluated by the TAVR clinic, cardiac surgeon, or to receive aortic valve intervention (p-value≤0.001).Females were significantly more likely to be rejected for TAVR due to older age (OR 0.23 (0.07, 0.59)), comorbid conditions (OR 0.68 (0.47, 0.97)), and frailty (OR 0.23 (0.07, 0.59)). Females were significantly more likely to be rejected for SAVR on the basis of frailty (OR 0.66 (0.46, 0.94)). Females also had significantly higher rates of 1-year mortality, hospitalization, and heart failure hospitalization compared to males (p-values < 0.05).ConclusionOur data suggest significant sex-based discrepancies in the management of AS. Females with severe AS are diagnosed later in life and are less likely to be evaluated for valve intervention. They are less likely to receive intervention due to older age, frailty, and multimorbid conditions. Further research is warranted for more effective identification and follow up of aortic stenosis as well as timely referral for AVR, where appropriate, especially of females.
Stroke Risk Reduction in Migraine Patients Using Propranolol: Evidence from Two Large-Scale Real-World Data Analyses
Authors: Jeong Eugene , Mogos Mulubrhan F. , Chen You 
Published: June 2024

AbstractBackground:Propranolol, a non-selective beta-blocker, is commonly used for migraine prevention, but its impact on stroke risk among migraine patients remains controversial. Using two large electronic health records-based datasets, we examined stroke risk differences between migraine patients with- and without- documented use of propranololMethods:This retrospective case-control study utilized EHR data from the Vanderbilt University Medical Center (VUMC) and the All of Us Research Program. Migraine patients were first identified based on the International Classification of Headache Disorders, 3rd edition (ICHD-3) criteria using diagnosis codes. Among these patients, cases were defined as those with a primary diagnosis of stroke following the first diagnosis of migraine, while controls had no stroke after their first migraine diagnosis. Logistic regression models, adjusted for potential factors associated with stroke risk, assessed the association between propranolol use and stroke risk, stratified by sex and migraine subtype. A Cox proportional hazards regression model was used to estimate the hazard ratio (HR) for stroke risk at 1, 2, 5, and 10 years from baseline.Results:In the VUMC database, 378 cases and 15,209 controls were identified, while the All of Us database included 267 cases and 6,579 controls. Propranolol significantly reduced stroke risk in female migraine patients (VUMC: OR=0.52, p=0.006; All of Us: OR=0.39,p=0.007), but not in males. The effect was more pronounced for ischemic stroke and in females with migraines without aura (MO) (VUMC: OR=0.60, p=0.014; All of Us: OR=0.28, p=0.006). The Cox model showed lower stroke rates in propranolol-treated female migraine patients at 1, 2, 5, and 10 years (VUMC: HR=0.06-0.55, p=0.0018-0.085; All of Us: HR=0.23, p=0.045 at 10 years).Conclusions:Propranolol is associated with a significant reduction in stroke risk, particularly ischemic stroke, among female migraine without aura patients. These findings suggest that propranolol may benefit stroke prevention in high-risk populations.
ChatGPT takes the FCPS exam in Internal Medicine
Authors: Qazi Hina , Ali Syed Ahsan , Irfan Muhammad , Siddiqui M. A. Rehman 
Published: June 2024

ABSTRACTLarge language models (LLMs) have exhibited remarkable proficiency in clinical knowledge, encompassing diagnostic medicine, and have been tested on questions related to medical licensing examinations. ChatGPT has recently gained popularity because of its ability to generate human-like responses when presented with exam questions. It has been tested on multiple undergraduate and subspecialty exams and the results have been mixed. We aim to test ChatGPT on questions mirroring the standards of the FCPS exam, the highest medical qualification in Pakistan.We used 111 randomly chosen MCQs of internal medicine of FCPS level in the form of a text prompt, thrice on 3 consecutive days. The average of the three answers was taken as the final response. The responses were recorded and compared to the answers given by subject experts. Agreement between the two was assessed using the Chi-square test and Cohen’s Kappa with 0.75 Kappa as an acceptable agreement. Univariate regression analysis was done for the effect of subspeciality, word count, and case scenarios in the success of ChatGPT.. Post-risk stratification chi-square and kappa statistics were applied.ChatGPT 4.0 scored 73% (69%-74%). Although close to the passing criteria, it could not clear the FCPS exam. Question characteristics and subspecialties did not affect the ChatGPT responses statistically. ChatGPT shows a high concordance between its responses indicating sound knowledge and a high reliability.This study’s findings underline the necessity for caution in over-reliance on AI for critical clinical decisions without human oversight. Creating specialized models tailored for medical education could provide a viable solution to this problem.Author SummaryArtificial intelligence is the future of the world. Since the launch of ChatGPT in 2014, it become one of the most widely used application for people in all fields of life. A wave of excitement was felt among the medical community when the chatbot was announced to have cleared the USMLE exams. Here, we have tested ChatGPT on MCQs mirroring the standard of FCPS exam questions. The FCPS is the highest medical qualification in Pakistan. We found that with a vast data base, ChatGPT could not clear the exam in all of the three attempts taken by it. ChatGPT, however, scored a near passing score indicating a relatively sound knowledge.We found ChatGPT to be a consistent LLM for complex medical scenarios faced by doctors in their daily lives irrespective of the subspecialty, length or word count of the questions. Although ChatGPT did not pass the FCPS exam, its answers displayed a high level of consistency, indicating a solid understanding of internal medicine. This demonstrates the potential of AI to support and improve medical education and healthcare services in near future.
Prediction of binding sites of GPCRs based on 3D convolutional neural networks
Authors: Yu Junfeng , Zhang Ying , Lv Jun 
Published: June 2024

AbstractG protein-coupled receptors are a class of receptor proteins located on the cell membrane, regulating the perception and response of cells to various external signals. Identifying the binding sites of G protein-coupled receptors plays a crucial role in understanding their allosteric modulation mechanisms. However, obtaining the crystal structure of the complex through experimental means and subsequently identifying the binding sites require substantial resources. With the development of computer-aided computation, deep learning can effectively predict the binding sites between proteins and ligands. This study predicted the binding sites of G protein-coupled receptors based on 3D convolutional neural network. A total of 108 G protein-coupled receptors recorded in the scPDB database were collected for this study, and a 3D convolutional neural network model was established based on these three-dimensional structures. Firstly, the PDB file of the protein is voxelized and segmented into different channels according to the type of atoms in a certain region. Then, 3D convolutional neural network is employed to predict the binding site through traversal, and the optimal voxel box size and model parameters are determined based on performance evaluation metrics. The established 3D convolutional neural network accurately predicts the binding site of G protein-coupled receptors, with an accuracy as high as 0.942, precision of 0.678, and recall rate of 0.532. Additionally, using the backpropagation algorithm, the gradients of the input data are calculated, and the importance of input elements on the final classification result is analyzed.
Gradient matching accelerates mixed-effects inference for biochemical networks
Authors: van Oppen Yulan B. , Milias-Argeitis Andreas 
Published: June 2024

SummarySingle-cell time series data frequently display considerable variability across a cell population. The current gold standard for inferring parameter distributions across cell populations is the Global Two Stage (GTS) approach for nonlinear mixed-effects (NLME) models. However, this method is computationally intensive, as it makes repeated use of non-convex optimization that in turn requires numerical integration of the underlying system. Here, we propose the Gradient Matching GTS (GMGTS) method as an efficient alternative to GTS. Gradient matching offers an integration-free approach to parameter estimation that is particularly powerful for dynamical systems that are linear in the unknown parameters, such as biochemical networks modeled by mass action kinetics. Here, we harness the power of gradient matching by integrating it into the GTS framework. To this end, we significantly expand the capabilities of gradient matching via uncertainty propagation calculations and the development of an iterative estimation scheme for partially observed systems. Through comparisons of GMGTS with GTS in different inference setups, we demonstrate that our method provides a significant computational advantage, thereby facilitating the use of complex NLME models in systems biology applications.