bluuebunny/crossref_metadata_2025_split huggingface dataset:
3D spatial transcriptomics reveals the molecular domain structure of the mouse olfactory bulb
Authors: Klimpert Nell , Kollo Mihaly , Brann David H. , Tan Chichun , Barry David , Ma Ying , Schaefer Andreas T. , Fleischmann Alexander 
Published: February 2025

ABSTRACTA core organizing principle of the vertebrate brain is its symmetry along multiple axes. However, the structure of these axes, and the precision with which neurons, circuit modules, and brain regions align to them, remain poorly understood. Here, we used 3D spatial transcriptomics to reconstruct the anatomical and molecular organization of the mouse olfactory bulb. We mapped the positions of nearly one thousand molecularly distinct glomeruli, the structural and functional units of odor processing, revealing highly symmetric organization across brain hemispheres. Within each bulb, we defined a curved axis of symmetry that divides pairs of sister glomeruli. Gene expression programs in olfactory sensory neurons predicted glomerular position with near-glomerular resolution. However, glomerular symmetry was disrupted in deeper layer mitral and granule cells, suggesting a reorganization of olfactory bulb output pathways. Our findings provide the first comprehensive map of the molecular domain structure of the olfactory bulb.
Sensory Cues in Assistive Technologies: a Comparative Study of the BuzzClip and NOA
Authors: Pittet Claire E. , Fabien Maël 
Published: February 2025

AbstractElectronic travel aids have the potential to significantly improve the security and independence of blind or visually impaired people when traveling. However, it is critical that such devices convey relevant information without overwhelming users. This study examines the performance of two assistive technologies in obstacle detection: the BuzzClip, which uses ultrasonic sensors to provide haptic feedback, and NOA, which utilizes infrared cameras and an AI-powered computer to deliver audio feedback. One participant completed four different mobility routes with each route tested separately using both devices, and the number of auditory or haptic stimuli was recorded. According to the data collected, the BuzzClip produced significantly more sensory feedback than NOA. This difference can be attributed to the devices’ detection methods and the ability to filter obstacles. These findings highlight the differences between certain electronic travel aids, underlining the importance of creating tools that provide sufficient information to users while avoiding the potential sensory overload caused by excessive stimuli. Such considerations are crucial in designing mobility tools that effectively enhance mobility while offering an agreeable experience for users.
Food Deserts and Food Insecurity in Indian Medical Colleges: A Mixed Methods Study Exploring Myths and Realities
Authors: Joykutty Mekha , Reshma V. R , John Anjum 
Published: February 2025

AbstractIntroductionLack of consistent access to sufficient, nutritious food is a pressing issue globally. Resilient food systems amalgamating multiple sectors like agriculture, animal husbandry, healthcare, and education are essential to resolve food insecurity. College students, particularly in medical colleges, are vulnerable to food insecurity due to limited access to healthy food, lack of resources (time, money, and knowledge), resulting in unhealthy eating habits. This study aims to investigate the presence of food deserts within medical colleges in Kerala, analyze factors influencing student food preferences, and explore the consequences of food insecurity on their health and academic performance.Methods and AnalysisA cross-sectional study will be conducted at a private medical college in Central Kerala over 8 months. Food outlets within a one-mile radius of the campus will be identified and assessed for affordability, variety, and nutritional quality. Data will be collected from medical students (medical, dental, nursing, laboratory) throughout all years using structured questionnaires, focus group discussions, and interviews with food outlet operators. Stratified random sampling will ensure representation across different academic years, and disciplines. Observations of food outlets will evaluate food variety, pricing, and hygiene standards. Quantitative data will be analyzed using descriptive and inferential statistics, while qualitative data will be thematically analyzed to gain deeper insights.Ethics and DisseminationEthical approval has been obtained, and informed consent will be collected from participants. Data confidentiality will be strictly maintained. Findings will be disseminated through academic conferences, publications, and policy briefs using de-identified data to advocate for healthier food environments on campuses.Key MessagesFood insecurity among medical college students is a relatively under-researched issue, particularly in India, with limited studies on food deserts in such settings.This study identifies food deserts around a private medical college in Kerala and examines how limited access to nutritious food affects students’ health and academic performance.Through a mixed-methods design, combining quantitative surveys and qualitative focus group discussions, it provides a comprehensive understanding of the food environment, including the affordability and quality of food outlets.The findings highlight the need for policy interventions to improve food access and quality on campuses and suggest this research could guide future studies and influence public health and campus food policies.While this study is groundbreaking for the region, its findings may be limited in generalizability to other institutions or regio
Persons with multiple sclerosis reveal distinct kynurenine pathway metabolite patterns: a multinational cross-sectional study
Authors: Kupjetz M. , Langeskov-Christensen M. , Riemenschneider M. , Inerle S. , Ligges U. , Gaemelke T. , Patt N. , Bansi J. , Gonzenbach R. , Reuter M. , Rosenberger F. , Meyer T. , McCann A. , Ueland P.M. , Eskildsen S.F. , Nygaard M.K.E. , Joisten N. , Hvid L.G. , Dalgas U. , Zimmer P. 
Published: February 2025

AbstractNeuroaxonal damage in multiple sclerosis (MS) results from an interplay of neurotoxic pathomechanisms combined with a reduced neuroprotective capacity of neurons and glia to resist neurotoxic damage. Kynurenine pathway (KP) imbalance resembles some of the molecular mechanisms central to the incompletely understood MS pathophysiology. To study the role of the KP in MS, we performed targeted metabolomics on serum samples from 353 persons with MS and 111 healthy individuals, and detected MS-specific differences in concentrations of most kynurenines. Using exploratory factor analysis, we then identified two distinct KP metabolite patterns: the inflammation-driven neurotoxic pattern „NeuroTox“ and the neuroprotective pattern „NeuroPro“. Our results show that greater „NeuroTox“ and lower „NeuroPro“ were associated with higher disease severity. The novelty of our data-driven approach that identified distinct KP metabolite patterns in MS advocates for future studies using comparable approaches to investigate whether KP imbalance follows similar disease-specific patterns in diseases other than MS.
Comparison of Large Language Models’ Performance on Neurosurgical Board Examination Questions
Authors: Andrade Nicholas S. , Donty Surya 
Published: February 2025

AbstractBackgroundMultiple-choice board examinations are a primary objective measure of competency in medicine. Large language models (LLMs) have demonstrated rapid improvements in performance on medical board examinations in the past two years. We evaluated five leading LLMs on neurosurgical board exam questions.MethodsWe evaluated five LLMs (OpenAI o1, OpenEvidence, Claude 3.5 Sonnet, Gemini 2.0, and xAI Grok2) on 500 multiple-choice questions from the Self-Assessment in Neurological Surgery (SANS) American Board of Neurological Surgery (ABNS) Primary Board Examination Review. Performance was analyzed across 12 subspecialty categories and compared to established passing thresholds.ResultsAll models exceeded the threshold for passing, with OpenAI o1 achieving the highest accuracy (87.6%), followed by OpenEvidence (84.2%), Claude 3.5 Sonnet (83.2%), Gemini 2.0 (81.0%) and xAI Grok2 (79.0%). Performance was strongest in Other General (97.4%) and Peripheral Nerve (97.1%) categories, while Neuroradiology showed the lowest accuracy (57.4%) across all models.ConclusionsState of the art LLMs continue to improve, and all models demonstrated strong performance on neurosurgical board examination questions. Medical image analysis continues to be a limitation of current LLMs. The current level of LLM performance challenges the relevance of written board examinations in trainee evaluation and suggests that LLMs are ready for implementation in clinical medicine and medical education.
Mapping functional hemodynamic and metabolic responses to dementia: a broadband spectroscopy pilot study
Authors: Acharya Deepshikha , Butters Emilia , Caicedo Alexander , Su Li , O’Brien John , Bale Gemma 
Published: February 2025

AbstractSignificanceBroadband near-infrared spectroscopy (bNIRS) can simultaneously monitor several chromophores including the oxidative state of cytochrome c-oxidase (oxCCO), an oxygen metabolism biomarker whose activity is altered in Alzheimer’s disease. Being a portable and non-invasive neuromonitoring technique, bNIRS could thus provide accessibility to brain-specific biomarkers and aid in the dementia diagnostic pathway.AimWe use bNIRS recorded functional haemodynamic and oxCCO changes to assess their relevance in Alzheimer’s disease dementia as potential biomarkers.ApproachUsing a visual-stimulus paradigm, we recorded functional changes in oxy-, deoxy-haemoglobin and oxCCO in three similarly aged cohorts: healthy controls (n=5), individuals with mild cognitive impairment (n=7) and individuals with Alzheimer’s dementia (n=7). We then selected features from these functional responses to find the best correlation with clinical cognitive markers (cognitive and behavioural test scores and clinical diagnosis) using canonical correlation analysis.ResultsWe found individual variations in peak amplitude and time-to-peak for all the stimulus-evoked bNIRS signals across the three cohorts. Canonical correlation analysis showed a strong correlation between bNIRS features and the clinical cognitive markers (r=0.902). However, repeating the same analysis by excluding the bNIRS oxCCO features leads to a significantly lower correlation (r=0.687) with the clinical markers.ConclusionsoxCCO could be a crucial biomarker, partly explaining cognitive differences with dementia. bNIRS uniquely provides a portable and non-invasive technique to monitor several chromophores simultaneously including oxCCO with potential future applications in tracking dementia progression.
Filling the gap in the Neotropics: new firefly species provide insights into Central American biodiversity
Authors: Catalán Ana , Pérez Archila Claudia María , Echeverría Méndez Adriana María , Guitiérrez José Andrés , Monzón José , Badillo Viridiana Vega , Höhna Sebastian 
Published: February 2025

AbstractTropical ecosystems, particularly in the Neotropics, remain vastly understudied, especially regarding the biodiversity of many taxa such as fireflies. This study investigates the biodiversity and phylogenetic relationships of fireflies in Guatemala, focusing on the generaPhotinus, Photuris, andBicellonycha. Using genetic barcoding we generate time-calibrated phylogenetic trees, revealing deep divergent times between Guatemalan and North American fireflies and potentially multiple colonialization events. Using morphometric information, taxonomic keys and species delimitations models (mPTP) we identified four newPhotinusspecies:P. hunahpú, P. helodermensis, P. schusteri, andP. semetabajense. This work highlights the significant firefly biodiversity in Nuclear Central America and the potential for future species discovery. Biodiversity research is necessary for conservation efforts of key ecosystems and will broaden our understanding of general evolutionary processes.
Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke within the UK Biobank
Authors: MacCarthy Gideon , Pazoki Raha 
Published: February 2025

AbstractBackground and objectiveStroke is one of the leading causes of mortality and long-term disability in adults over 18 years of age globally and its increasing incidence has become a global public health concern.Accurate stroke prediction is highly valuable for early intervention and treatment. Previous studies have utilized statistical and machine learning techniques to develop stroke prediction models. Only a few have included genome-wide stroke genetic liability and evaluated its predictive values. This study aimed to assess the added predictive value of genetic liability in the prediction of the risk of stroke.Materials and methodsThe study included 243,339 participants of European ancestry. Stroke genetic liability was constructed using previously identified genetic variants associated with stroke by the MEGASTROKE project through genome-wide association studies (GWAS). In our study, we built four predictive models with and without stroke genetic liability in the training set: Cox proportional hazard (Coxph), Gradient boosting model (GBM), Decision tree (DT), and Random Forest (RF) to estimate time-to-event risk for stroke. We then assessed their performances in the testing set.ResultsEach unit (standard deviation) increase in genetic liability increases the risk of incident stroke by 7% (HR = 1.07, 95% CI = 1.02, 1.12, P-value = 0.0030). The risk of stroke was greater in the higher genetic liability group, demonstrated by a 14 % increased risk (HR = 1.14, 95% CI = 1.02, 1.27, P-value = 0.02) compared with the low genetic liability group. The Coxph model including genetic liability was the best-performing model for stroke prediction achieving an AUC of 69.54 (95% CI = 67.40, 71.68), NRI of 0.202 (95% CI = 0.12, 0.28; P-value = 0.000) and IDI of 1.0×10-04(95% CI = 0.000, 3.0×10-04; P-value = 0.13) compared with the Cox model without genetic liability.ConclusionIncorporating genetic factors in the model may provide a slight incremental value for stroke prediction beyond conventional risk factors.
Investigating the impact of sex and reproductive aging on latent signatures of modifiable dementia risk factors
Authors: Mukora Alice , Costantino Manuela , Parent Olivier , Devenyi Gabriel. A. , Chakravarty M. Mallar 
Published: February 2025

AbstractTreating modifiable risk factors for dementia may serve to prevent maladaptive brain aging. These include, and are not limited to, obesity, hypertension, and other metabolic disorders. Nearly two-thirds of affected individuals are female, and emerging research has pointed to potential sex-specific factors, like reproductive aging, as potent modifiers of dementia risk. Here, we leverage neuroimaging to characterize sex differences in the neural signatures of modifiable dementia risk factors, examining their relationship with brain anatomy, and integrate female-specific factors related to menopause history in a sex-specific analysis. Using cross-sectional data from the UK Biobank, we selected a cohort of 31,711 (age 44-82, 55.2% female/44.8% male) participants without diagnosed neurological conditions and collated behavioral data previously determined as modifiable risk factors. Using Partial Least Squares Analysis (PLS), we examined latent signatures that represent linear combinations that maximize covariance between patterns of brain (mean cortical thickness from 64 regions) and risk factor variables. To examine sex differences, we performed PLS analysis using the entire sample. We performed linear models to explore age-by-sex interactions with the PLS-derived brain scores and the risk factor pattern scores. To examine sex-specific relationships, we performed separate PLS analyses for the males and females and integrated menopause-related variables into the latter analysis. Our study found sex-dependent and menopause-dependent relationships between lifestyle risk factors and cortical thickness, highlighting stronger impacts of cardiometabolic factors on males and social and obesity-related factors on females in preserving brain health. The inclusion of menopause-related variables did not change the relationships to lifestyle risk factors, and strict age-matching dampened the strength of the findings. Our findings suggest that lifestyles and the female-specific endocrine environment influence sex differences in cortical anatomy during brain aging.
Asengeprast, a Novel G-Protein Coupled Receptor 68 Antagonist, Reverses Clinically Relevant Inflammatory and Fibrotic Pathways in Chronic Kidney Disease
Authors: Eddy Sean , Papadimitriou Michelle P. , Edgley Amanda J. , Langham Robyn G. , Khong Fay L. , Kong Roy C.K. , Carbone Simona E. , Kompa Andrew R. , Zhang Yuan , Cox Alison , Mitchell Lorna , Subramanian Lalita , Martini Sebastian , Hartman John , Eichinger Felix , Godfrey Bradley , Mariani Laura H. , Kretzler Matthias , Kelly Darren J. ,   Nephrotic Syndrome Study Network (NEPTUNE)
Published: February 2025

AbstractLimiting progressive fibrosis in chronic kidney disease (CKD) is an ongoing therapeutic challenge that requires effective and safe inhibition of a broad inflammatory cell milieu that leads to irreversible organ damage. Asengeprast, an anti-fibrotic and anti-inflammatory small molecule, has shown promising efficacy in animal models of kidney disease, however its target and mechanism of action was unknown. Using in vitro assays, we showed that asengeprast modulates inflammatory and fibrotic responses through selective inhibition of G protein-coupled receptor 68 (GPR68), a proton sensor, expressed in tissue-resident and immune-infiltrating cells of the kidney. Transcriptomic analysis of kidney tissue from animal models of diabetic kidney disease (DKD) and CKD demonstrated that fibrotic and inflammatory pathways dysregulated in disease were reversed by asengeprast treatment. Differential expression analysis of upstream regulators showed that the major, distinct signaling networks reversed were centered on a key driver of fibroblast activation, transforming growth factor β1, and associated signaling molecules. An asengeprast response gene signature derived from the CKD animal model when mapped onto gene expression profiles obtained from human kidney biopsies confirmed that the molecular pathways modulated by asengeprast were also dysregulated in human DKD and CKD. Further, this asengeprast response signature correlated with clinical markers of disease progression and tissue pathology. Overall, these findings provide evidence for targeted inhibition of GPR68 by asengeprast as a promising therapeutic strategy for treatment of CKD and potentially other fibrotic and inflammatory conditions.Translational StatementExisting therapeutic strategies for chronic kidney disease (CKD) do not directly target both inflammatory and fibrotic processes needed to slow or halt the progression of disease.Asengeprast is a Phase II candidate drug for CKD that blocks G protein-coupled receptor 68 in animal models to reverse inflammatory and fibrotic pathways, thereby improving kidney function. These same pathways were shown to be dysregulated in human CKD, providing strong evidence that the therapeutic effects observed in pre-clinical models will translate to the clinic. Using a novel mechanism of action, asengeprast has the potential to significantly improve the lives of patients with CKD.
Assessing the Drivers of Wasting among Children Under 5 and Their Mothers in The Bay and Hiran Regions of Somalia
Authors: Grounds Samantha , Walton Shelley , Alier Kemish Kenneth , Garretson Sydney , Mohamoud Said Aden , Abdikadir Sadiq , Khattak Qundeel , Nur Mohamud Ali , Mohamoud Abdullahi Muse , Omer Meftuh , Mahat Mohamed Billow , Mohamed Adam Abdulkadir , Mohamed Abdifatah , Tripaldi Marina , Akseer Nadia 
Published: February 2025

AbstractBackgroundTo address Somalia’s high burden of wasting, it is imperative to understand the country’s context-specific drivers of wasting. This study assessed the drivers of wasting among children under 5 (CU5) and mothers in Somalia’s Bay and Hiran regions to inform strategies to address wasting.MethodsThe data comes from the midline (September 2023) and endline (December 2023) data collection of a randomized controlled trial. Child and maternal outcomes (weight-for-height z-scores (WHZ) and mid-upper arm circumferences (MUAC), respectively) were explored continuously for children via linear regression and as binary outcomes via Poisson regression for children and mothers. A hierarchical model building approach was used, mapping variables into the basic, intervention, underlying, and immediate levels. Separate midline and endline models were analyzed cross-sectionally, comparing drivers by seasonality, and CU5 models were further stratified by region and age.ResultsThe burden of CU5 wasting was 12.9% at midline and 14.4% at endline. The following variables were drivers of low WHZ across different models: child illness, open defecation, low maternal MUAC, no maternal education, having a male-headed household, and living in a household without joint decision-making. Egg/flesh food consumption and higher maternal MUAC were protective of WHZ. Wasting among mothers was 8% at midline and 12% at endline. Household food insecurity, open defecation, and poor waste disposal practices were drivers of mothers’ wasting, whereas maternal decision-making was protective.ConclusionThis study highlights variation in the key drivers of wasting by region, season, and child age and contributes to an expanding body of evidence on the multifactorial drivers of wasting, encouraging context-specific approaches that address the immediate, underlying, and basic causes of malnutrition. The findings emphasize the importance of maternal nutrition for child nutrition outcomes and the need for interventions considering household food security, sanitation, and gender dynamics in this humanitarian setting.Registration:The cluster-RCT is registered at ClinicalTrials.gov, ID:NCT06642012.
Risk Behavior, Backward Bifurcation, and Vaccine Effectiveness in Disease Dynamics
Authors: Pedroza-Meza Irasema , Acuña-Zegarra M. Adrian , Velasco-Hernández Jorge X. 
Published: February 2025

AbstractMany studies have shown that vaccines are not completely effective, meaning that a vaccinated population includes both people who develop immunity from the vaccine and those who, despite being vaccinated, do not. This can be problematic, as some vaccinated individuals may mistakenly believe they are fully protected and cannot acquire the disease. This perception can significantly influence behavior, leading some vaccinated people to be less diligent in following preventive or mitigation measures.Motivated by the above, we examine how behavioral changes in vaccinated people who do not develop immunity influence the dynamics of a directly transmitted disease and key indices such as the basic reproductive number and vaccine effectiveness. We propose a model that considers a vaccine with three facets of failure: “take”, “degree”, and “duration”. Additionally, the behavioral change of non-immune vaccinated individuals is modeled through a parameter that adjusts their contact rate based on compliance with mitigation measures.Our results allow us to visualize the role of behavioral change in various factors influencing disease transmission dynamics. First, we demonstrate the existence of a backward bifurcation common in models for not fully effective vaccines. Second, we define a behavioral index threshold, which serves as a key indicator for determining whether the disease persists due to behavioral effects. Finally, our results highlight that both the behavioral index and the initial value of the infected population can play a decisive role in determining whether vaccine effectiveness reaches negative values.