bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Transformative Potential of Large Language Models in Mining Electronic Health Records Data
Authors: Zurita Amadeo Wals , del Rio Héctor Miras , de Aguirre Nerea Ugarte Ruiz , Navarro Cristina Nebrera , Jiménez María Rubio , Carmona David Muñoz , Sánchez Carlos Míguez 
Published: March 2024

AbstractIntroductionIn this study, we evaluate the accuracy, efficiency, and cost-effectiveness of Large Language Models (LLMs) in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators.MethodsWe implemented a script using the OpenAI API to extract structured information in JSON format from comorbidities reported in 250 personal history reports. These reports were manually reviewed in batches of 50 by five specialists in radiation oncology. We compared the results using metrics such as Sensitivity, Specificity, Precision, Accuracy, F-value, Kappa index, and the McNemar test, in addition to examining the common causes of errors in both humans and GPT models.ResultsThe GPT-3.5 model exhibited slightly lower performance compared to physicians across all metrics, though the differences were not statistically significant (McNemar’s test p = 0.79). GPT-4 demonstrated clear superiority in several key metrics (McNemar’s test p < 0.001). Notably, it achieved a sensitivity of 96.8%, compared to 88.2% for GPT-3.5 and 88.8% for physicians. However, physicians marginally outperformed GPT-4 in precision (97.7% vs. 96.8%). GPT-4 showed greater consistency, replicating the exact same results in 76% of the reports across 10 repeated analyses, compared to 59% for GPT-3.5, indicating more stable and reliable performance. Physicians were more likely to miss explicit comorbidities, while the GPT models more frequently inferred non-explicit comorbidities, sometimes correctly, though this also resulted in more false positives.ConclusionThis study demonstrates that, with well-designed prompts, the LLMs examined can match or even surpass medical specialists in extracting information from complex clinical reports. Their superior efficiency in time and costs, along with easy integration with databases, makes them a valuable tool for large-scale data mining and real-world evidence generation.
Follow-up after post-exposure prophylaxis before and during the COVID-19 pandemic in Brazil
Authors: Matsuda Elaine Monteiro , de Campos Ivana Barros , Brígido Luís Fernando de Macedo 
Published: March 2024

ABSTRACTAlthough post-exposure prophylaxis (PEP) is a powerful tool to abort HIV infection within 72 hours of exposure, blocking the establishment of chronic infection, follow-up metrics of this intervention are scarce. As antiretroviral use delays diagnosis biomarkers, the moment to perform serological evaluations must be considered this to avoid missed diagnosis opportunities. We assessed the return adherence after PEP dispensation in service in the Sao Paulo metropolitan area and reviewed the literature, both showing limited adherence to current protocols and leading to difficulties in diagnosing early HIV infection. The current proposed date for the first return after PEP is associated with low adherence and may have limited capability to detect antibodies if the infection is present. Guidelines should allow a longer time after PEP discontinuation along with message reminders to encourage adherence and avoid false negative results that can be detrimental both to the patient and to the community.
Association between Time-Weighted Remnant Cholesterol and Incident Cancer: A Population-Based Chinese Cohort Study
Authors: Li Lifang , Zhang Nan , Yang Yifan , Him Pui Hugo Hok , Hei Leung Bosco Kwok , In Chou Oscar Hou , Chang Carlin , Chung Wai Abraham Ka , Lip Gregory , Tse Gary , Liu Tong , Zhou Jiandong 
Published: March 2024

AbstractBackgroundRemnant cholesterol (RC) is becoming an increasingly well-recognized risk factor for cardiometabolic diseases. However, no study has explored the predictive role of RC in new-onset cancer. This study aimed to examine the associations between RC and time-weighted RC with incident cancer in the general population.MethodsThis was a retrospective population-based study enrolling patients attending family medicine clinics in Hong Kong between 1st January 2000 and 31st December 2003 with at least three RC measurements during follow-up visits. The primary outcome was new-onset cancer. The secondary outcome was cancer-related mortality. Multivariable Cox regression was used to evaluate associations between baseline RC and time-weighted RC with outcomes.ResultsA total of 75,342 adults (39.7% males, mean age: 62.5 years old) were included. During a median follow-up of 16.8 years, 8335 (11.1%) incident cancer and 4349 (5.7%) cancer-related deaths were observed. After adjusting for potential confounders, one mmol/L increased of time-weighted RC was associated with 41% and 62% higher risk of incident cancer (HR, 1.41; 95%CI, 1.26-1.57; p<0.0001) and cancer-related mortality (HR, 1.62; 95%CI, 1.43-1.85; p<0.0001), respectively. However, no significant association between baseline RC with risk of new-onset cancer (HR, 1.04; 95%CI, 0.82-1.31; p=0.768) and cancer-related mortality (HR, 0.85; 95%CI, 0.61-1.17; p=0.315) in the adjusted model. The association between time-weighted RC and incident cancer was significant regardless of age, gender, and remained consistent amongst those with baseline use of most cardiometabolic agents, as well as those complicated with most comorbidities.ConclusionsHigher time-weighted RC was associated with increased risk of new-onset cancer and cancer-related mortality amongst the general population.
Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions
Authors: Fanelli Giuseppe , Franke Barbara , Fabbri Chiara , Werme Josefin , Erdogan Izel , Witte Ward De , Poelmans Geert , Hyun Ruisch I. , Reus Lianne Maria , van Gils Veerle , Jansen Willemijn J. , Vos Stephanie J.B. , Alam Kazi Asraful , Martinez Aurora , Haavik Jan , Wimberley Theresa , Dalsgaard Søren , Fóthi Ábel , Barta Csaba , Fernandez-Aranda Fernando , Jimenez-Murcia Susana , Berkel Simone , Matura Silke , Salas-Salvadó Jordi , Arenella Martina , Serretti Alessandro , Mota Nina Roth , Bralten Janita 
Published: March 2024

AbstractThe co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer’s disease, bipolar disorder, and Tourette’s syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such asSLC39A8andHLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.HighlightsLocal genetic correlations found even in the absence of global correlations.Both positive and negative local correlations found for IR-neuropsychiatric pairs.Enrichment for immune, and insulin signalling pathways, among others.Pinpointed shared likely causal variants within 10 genomic regions.Identified therapeutic targets, e.g., SLC39A8 and HLA-DRB1, for drug repurposing.
Leucine Rich Repeat Kinase 2 is not required for lysozyme expression in intestinal Paneth cells
Authors: Tasegian Anna , Dikovskaya Dina , Scott Molly , Helps Tom , Meus Tosca , McLean Mairi H. , Swamy Mahima 
Published: March 2024

AbstractGenetic variants in Leucine-rich Repeat Kinase 2 (LRRK2)gene have been linked to Parkinson’s disease (PD) and are also associated with inflammatory bowel diseases (IBD), specifically Crohn’s disease (CD), a transmural inflammation that can affect the entire length of the gastrointestinal tract and is commonly seen in the ileum1. In ileal CD, defects in specialized intestinal epithelial cells known as Paneth cells are believed to drive disease pathogenesis2,3. Paneth cells contribute to mucosal defense by secreting antimicrobial peptides including lysozyme and to the maintenance of intestinal stem cells by secreting growth factors. A previous article published by Zhanget al4inNature Immunologyidentified a key role for LRRK2 in selective sorting and secretion of lysozyme in Paneth cells. However, after extended analyses, we find that LRRK2 is not required for lysozyme expression in the murine gut and is not expressed in either murine or human Paneth cells.
Dense, Continuous Membrane Labeling and Expansion Microscopy Visualization of Ultrastructure in Tissues
Authors: Shin Tay Won , Wang Hao , Zhang Chi , An Bobae , Lu Yangning , Zhang Elizabeth , Lu Xiaotang , Karagiannis Emmanouil D. , Kang Jeong Seuk , Emenari Amauche , Symvoulidis Panagiotis , Asano Shoh , Lin Leanne , Costa Emma K. ,   IMAXT Grand Challenge Consortium, Marblestone Adam H. , Kasthuri Narayanan , Tsai Li-Huei , Boyden Edward S. 
Published: March 2024

AbstractLipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines a novel membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validated the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supported the imaging of membranes and proteins at a resolution of ∼60 nm on a confocal microscope. We demonstrated the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yielded ∼35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.
GelBox: Open-source software to improve rigor and reproducibility when analyzing gels and immunoblots
Authors: Gulbulak Utku , Wellette-Hunsucker Austin G. , Campbell Kenneth S. 
Published: March 2024

ABSTRACTGelBox is open-source software that was developed with the goal of enhancing rigor, reproducibility, and transparency when analyzing gels and immunoblots. It combines image adjustments (cropping, rotation, brightness, and contrast), background correction, and band-fitting in a single application. Users can also associate each lane in an image with metadata (for example, sample type). GelBox data files integrate the raw data, supplied metadata, image adjustments, and band-level analyses in a single file to improve traceability. GelBox has a user-friendly interface and was developed using MATLAB. The software, installation instructions, and tutorials, are available athttps://campbell-muscle-lab.github.io/GelBox/.
k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations
Authors: Chen Lexin , Roe Daniel R. , Kochert Matthew , Simmerling Carlos , Miranda-Quintana Ramón Alain 
Published: March 2024

AbstractOne of the key challenges ofk-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such ask-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex datasets such as those obtained from molecular simulation,k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors ofk-means++ will lead to a lack of reproducibility.K-meansN-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficientn-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helpingk-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse datasets and be used as a standalone tool or as part of our MDANCE clustering package.
Color-gradient-based phase-field equation for multiphase flow
Authors: Haghani Reza , Erfani Hamidreza , McClure James E. , Flekkøy Eirik Grude , Berg Carl Fredrik 
Published: March 2024

In this paper, the underlying problem with the color-gradient (CG) method in handling density-contrast fluids is explored. It is shown that the CG method is not fluid invariant. Based on nondimensionalizing the CG method, a phase-field interface-capturing model is proposed which tackles the difficulty of handling density-contrast fluids. The proposed formulation is developed for incompressible, immiscible two-fluid flows without phase-change phenomena, and a solver based on the lattice Boltzmann method is proposed. Coupled with an available robust hydrodynamic solver, a binary fluid flow package that handles fluid flows with high density and viscosity contrasts is presented. The macroscopic and lattice Boltzmann equivalents of the formulation, which make the physical interpretation of it easier, are presented. In contrast to existing color-gradient models where the interface-capturing equations are coupled with the hydrodynamic ones and include the surface tension forces, the proposed formulation is in the same spirit as the other phase-field models such as the Cahn-Hilliard and the Allen-Cahn equations and is solely employed to capture the interface advected due to a flow velocity. As such, similarly to other phase-field models, a so-called mobility parameter comes into play. In contrast, the mobility is not related to the density field but a constant coefficient. This leads to a formulation that avoids individual speed of sound for the different fluids. On the lattice Boltzmann solver side, two separate distribution functions are adopted to solve the formulation, and another one is employed to solve the Navier-Stokes equations, yielding a total of three equations. Two series of numerical tests are conducted to validate the accuracy and stability of the model, where we compare simulated results with available analytical and numerical solutions, and good agreement is observed. In the first set the interfacial evolution equations are assessed, while in the second set the hydrodynamic effects are taken into account.

Published by the American Physical Society
2024
Effectiveness of pictorial warnings on food fat content: consumers’ perception and neurological responses
Authors: Piper Luigi , de Cosmo Lucrezia Maria , Prete M. Irene , Mileti Antonio , Guido Gianluigi 
Published: March 2024

PurposeThis paper delves into evaluating the effectiveness of warning messages as a deterrent against excessive fat consumption. It examines how consumers perceive the fat content of food products when presented with two distinct label types: (1) a textual warning, providing succinct information about the fat content, and (2) a pictorial warning, offering a visual representation that immediately signifies the fat content.Design/methodology/approachTwo quantitative studies were carried out. Study 1 employed a questionnaire to evaluate the efficacy of textual and pictorial warning messages on high- and low-fat food products. Similarly, Study 2 replicated this comparison while incorporating a neuromarketing instrument to gauge participants’ cerebral reactions.FindingsResults indicate that pictorial warnings on high-fat foods significantly deter consumers’ purchasing intentions. Notably, these pictorial warnings stimulate the left prefrontal area of the cerebral cortex, inducing negative emotions in consumers and driving them away from high-fat food items.Originality/valueWhile the influence of images over text in shaping consumer decisions is well understood in marketing, this study accentuates the underlying mechanism of such an impact through the elicitation of negative emotions. By understanding this emotional pathway, the paper presents fresh academic and managerial perspectives, underscoring the potency of pictorial warnings in guiding consumers towards healthier food choices.Highlights Textual warnings do not seem to discourage high-fat product consumption.A pictorial warning represents the fat content of an equivalent product.Pictorial warnings decrease the intention to purchase a high-fat product.Pictorial warnings determine an increase in negative emotions.
Time management profiles of college students and its relationship to sociodemographic and psychological factors
Authors: Faure-Carvallo Adrien , Nieto-Fernández Sergio , Calderon Caterina , Gustems Josep 
Published: March 2024

PurposeThe objectives of this research are to analyze the sociodemographic and personality profiles most related to good academic time management among 845 students from different faculties at the University de Barcelona (UB) and to identify the explanatory factors of effective academic time management.Design/methodology/approachPoor time management is a common behavior among university students and an explanatory factor for academic failure. A sociodemographic questionnaire, the Procrastination Assessment Scale-Student (PASS), the Academic Time Management (ATM), the Brief Symptom Inventory (BSI-18) and the Big Five Inventory-10 (BFI-10) were administered.FindingsThe results reveal that female students, education majors and those with high academic performance show better time management than the rest of the student body. Additionally, students who have better academic time management are also more neurotic, more open to experience, more responsible and less prone to procrastination. The factors established as explanatory of good academic time management are neuroticism, openness to experience and low procrastination.Originality/valueThe implications of the results for promoting academic time management in university studies through specific actions are discussed.
A multidimensional lens of environmental consciousness: towards an environmentally conscious theory of planned behavior
Authors: Laheri Vishal Kumar , Lim Weng Marc , Arya Purushottam Kumar , Kumar Sanjeev 
Published: March 2024

Purpose
The purpose of this paper is to examine the purchase behavior of consumers towards green products by adapting and extending the theory of planned behavior with the inclusion of three pertinent environmental factors posited to reflect environmental consciousness in the form of environmental concern, environmental knowledge and environmental values.

Design/methodology/approach
The data was collected from 410 consumers at shopping malls with retail stores selling green and non-green products in a developing country using cluster sampling and analyzed using covariance-based structural equation modeling.

Findings
The findings of this study indicate that environmental factors reflecting environmental consciousness positively influence consumers’ attitude towards purchasing green products, wherein consumers’ environmental values have a stronger influence than their environmental concern and environmental knowledge. The findings also reveal that subjective norm, attitude and perceived behavioral control toward purchasing green products positively shape green purchase intention. The same positive effect is also witnessed between green purchase intention and behavior. However, perceived behavioral control towards purchasing green products had no significant influence on green purchase behavior.

Practical implications
This study suggests that green marketers should promote environmental consciousness among consumers to influence and shape their planned behavior towards green purchases. This could be done by prioritizing efforts and investments in inculcating environmental values, followed by enhancing environmental knowledge and finally inducing environmental concern among consumers. Green marketers can also leverage subjective norm and perceptions of behavioral control toward purchasing green products to reinforce green purchase intention, which, in turn, strengthens green purchase behavior. This green marketing strategy should also be useful to address the intention–behavior gap as seen through the null effect of perceived behavioral control on purchase behavior toward green products when this strategy is present.

Originality/value
This study contributes to theoretical generalizability by reaffirming the continued relevance of the theory of planned behavior in settings concerning the environment (e.g. green purchases), and theoretical extension by augmenting environmental concern, environmental knowledge and environmental values with the theory of planned behavior, resulting in an environmentally conscious theory of planned behavior. The latter is significant and noteworthy, as this study broadens the conceptualization and operationalization of environmental consciousness from a unidimensional to a multidimensional construct.