bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fine-Mapping of the TMEM106B Locus Reveals Four Haplotypes That are Differentially Associated With Risk for Neurodegeneration
Authors: Salazar Alex , Tesi Niccolò , Knoop Lydian , Pijnenburg Yolande , van der Lee Sven , Wijesekera Sanduni , Krizova Jana , Hiltunen Mikko , Damme Markus , Petrucelli Leonard , Reinders Marcel , Smit August B. , Hulsman Marc ,   ADGC, Bonn, CHARGE, EADB, EADI, FinnGen, GERAD, GR@ACE/DEGESCO, PGC-ALZ, Holstege Henne 
Published: February 2025

ABSTRACTGenome-wide association studies (GWAS) linkedTMEM106Bvariants to susceptibility for neurodegenerative diseases, but the causal genetic elements remain unclear.Genotyping of 5,792 Alzheimer disease cases and controls revealed that theTMEM106Blocus consists of four major haplotypes: HA/Ha (covering the coding region), and HB/Hb (covering the upstream regulatory region). These combine into four combinations with varying population-frequencies:HAB(57%),HaB(34%),Hab(9%), andHAb(<1%).Long-read sequencing of 513 individuals showed thatHAhaplotypes (marked by 185-Threonine) carry unique methylated CpG sites and an AluYb8-retrotransposon in the 3’ UTR, while theHahaplotypes are marked by the 185-Serine allele.Hbhaplotypes carry several structural variants (SVs) in nearby distal enhancers, including a 19 Kbp rearrangement, absent in all other haplotypes.Joint association models revealed that theHABcombination (AluYb8+185-Threonine) is risk-increasing, whileHab(SVs+185-Serine) confers the protective effect.HaB(185-Serine only) is neutral, whileHAbwas too rare to assess. Indeed, relative to middle-aged non-demented controls, cognitively healthy centenarians were more enriched withHab(OR=1.49, padj=2.18×10-2) than withHaB(OR=1.23, padj=5.06×10-2). Finally, proteomic analysis of temporal cortex tissues (n=182) indicated that relative to the neutralHaBcombination, the protectiveHabis associated with 0.91-fold lower TMEM106B C-terminal peptide abundance (CI=[0.81-1.02]), while the risk-increasingHABis associated with 1.07-fold higher abundance (CI=[0.99-1.15]).Together, this presents a complex genetic structure underlying the association of theTMEM106Blocus with neurodegenerative diseases.ONE SENTENCE SUMMARYGWAS and long-read sequencing suggests that the neurodegenerative genetic etiology associated withTMEM106Bis conferred by multiple independent haplotypes.
Peri-somatic modulation of diffracted light and its variation with consciousness
Authors: Helekar Santosh A. 
Published: February 2025

AbstractExisting noninvasive neurophysiological recording methods can acquire neuronal correlates of consciousness, but they do not differentiate between conscious and unconscious states mechanistically. Here we describe a method that detects a new physical effect of apparent neural origin in the peri-somatic space that might allow such differentiation. This effect is a slow decrease in the intensity of diffracted low power laser light emitted within a photoelectronic device placed close to the body of an awake subject. It is markedly attenuated by general anesthesia in mice and unconsciousness in patients. Its direction is reversed when the device is exposed to live invertebrates or to the decapitated head of a euthanized mouse. These findings reveal a previously unrecognized biophysical phenomenon that appears to be related to consciousness.
How does the response of egocentric boundary cells depend upon the coordinate system of environmental features?
Authors: Hasselmo Michael E. , LaChance Patrick A. , Robinson Jennifer C. , Malmberg Samantha L. , Patel Mahir , Gross Elaina , Everett Danielle E. , Sankaranarayanan Shruthi , Fang Jiarui 
Published: February 2025

ABSTRACTNeurons in the retrosplenial (RSC) (Alexander et al., 2020a; LaChance and Hasselmo, 2024) and postrhinal cortex (POR) respond to environmental boundaries and configurations in egocentric coordinates relative to an animals current position. Neurons in these structures and adjacent structures also respond to spatial dimensions of self- motion such as running velocity (Carstensen et al., 2021; Robinson et al., 2024). Data and modeling suggest that these responses could be essential for guiding behaviors such as barrier avoidance and goal finding (Erdem and Hasselmo, 2012; 2014). However, these findings still leave the unanswered question: What are the features and what are the coordinate systems of these features that drive these egocentric neural responses? Here we present models of the potential circuit mechanisms generating egocentric responses in RSC. These can be generated based on coding of internal representations of barriers in head-centered coordinates of distance and angle that are transformed based on current running velocity for trajectory planning and obstacle avoidance. This hypothesis is compared with an alternate potentially complementary hypothesis that neurons in the same regions might respond to retinotopic position of features at the top, bottom or edges of walls as a precursor to head-centered coordinates. Alternate hypotheses include the forward scanning of trajectories (ray tracing) to test for collision with barriers, or the comparison of optic flow on different sides of the animal. These hypotheses generate complementary modeling predictions about how changes in environmental parameters could alter the neural responses of egocentric boundary cells that are presented here.
Exploring the relationship between cultural and structural workforce issues and retention of nurses in general practice (GenRet): A qualitative interview study
Authors: Anderson Helen , Brady Louise , Adamson Joy 
Published: February 2025

ABSTRACTBackgroundIncreasing shortfalls in nursing workforces are detrimental to safety critical patient care. In general practice in England up to one-in-two nursing posts are predicted to be unfilled by 2030/31, with Wales similarly threatened. This is reflected internationally. Limited attention has been paid to how cultural and structural issues affect retention of nurses in general practice. The aim of our study is to understand factors that challenge retention and support nurses to stay in general practice.MethodsWe conducted an exploratory qualitative interview study with n=41 with members of nursing teams working in, or who have worked in, general practice as well as nurse leaders associated with general practice across England and Wales. Recruitment was through professional and social media networks and snowballing techniques. Data were collected between October 2023-June 2024 and analysed following framework analysis. University of York ethics approval (Ref: HSRGC/2023/586/A) was gained. The study was funded by the General Nursing Council Trust.ResultsRecognition of value of nurses working in general practice was central to the retention of nurses at all levels of practice and was affected by structural and cultural issues and reflected in several themes: The essence of nursing in general practice; The commodification and deprofessionalisation of nursing in general practice; Opportunities for development; Employment of nurses outside of the National Health Service; Lack of voice, precarity of position and lack of recourse; Tipping points.ConclusionCultural and structural issues impacted on retention of nurses in general practice. While some supported retention, others revealed deep-seated, complex issues which require addressing at practice, local and national organisational levels. Nurses in general practice experience factors which leave them vulnerable and underserved. Policy makers, employers and professional organisations need to work to support retention and enable nurses in general practice, not only to survive, but thrive.Protocol RegistrationOpen Science Framework (https://osf.io/) Identifier: DOI 10.17605/OSF.IO/2BYXChttps://osf.io/2byxc/
Fair machine learning models for disease prediction: In-depth interviews with key health experts
Authors: Nghiem Nhung , Tiatia Ramona 
Published: February 2025

AbstractArtificial intelligence (AI) and machine learning (ML) pose enormous potential for improving quality of life. It can also generate significant social, cultural and other unintended risks. We aimed to explore fairness concepts that can be applied in ML models for disease prediction from key health experts’ perspectives in an ethnically diverse high-income country. In-depth interviews with key experts in the health sector in Aotearoa New Zealand (NZ) were implemented between July and December 2022. We invited participants who are key leaders in their ethnic communities, including Māori (Indigenous), Pasifika and Asian. The interview questionnaire comprised six sections: (1) Existing attitudes to healthcare allocation; (2) Existing attitudes to data held at the general practitioner (GP) level; (3) Acceptable data to have at the GP level for disease prediction models; (4) Trade-offs for obtaining benefits vs generating unnecessary concern in deploying these models; (5) Reducing bias in risk prediction models; and (6) Including community consensus into disease prediction models for fair outcomes. The study shows that participants were strongly united in the view that ML models should not create or exacerbate inequities in healthcare due to biased data and unfair algorithms. An exploration of fairness concepts showed that carefully selected data types must be considered for predictive modelling and that trade-offs for obtaining benefits versus generating unnecessary concern produced conflicting opinions. The participants expressed high acceptability for using ML models but expressed deep concerns about inequity issues and how these models might affect the most vulnerable communities (such as Māori in middle-ages and above and those living in deprived communities). Our results could help inform the development of ML models that consider social impacts in an ethnically diverse society.
Machine Learning Based Classification of Aggressive and Malignant Renal Tumors from Multimodal Data
Authors: Aminy Mehrnegar , Gala Tejal , Dasgupta Agnimitra , Cen Steven Y , Pawan S J , Gill Inderbir , Duddalwar Vinay , Oberai Assad A 
Published: February 2025

1AbstractPurposeThis study aimed to develop and evaluate a machine learning pipeline using multiphase contrast-enhanced CT images and clinical data to classify renal tumors as benign, malignant-indolent, or malignant-aggressive, while assessing the contribution of each data source to the classification.MethodsIn this retrospective study, 448 patients (mean age: 60.7±12.6 years, 306 male, 142 female) who underwent nephrectomy and preoperative CECT between June 2008 and July 2018 were included. Tumors were histologically categorized as benign-indolent, malignant-indolent, or malignant-aggressive. Self-supervised feature extraction converted 4-phase CECT images into 512 real-valued features, combined with clinical data and tumor size for classification. Two machine learning classifiers, random forest (RF) and multi-layer perceptron (MLP), were used to predict tumor type. Nested five-fold cross-validation was employed for hyperparameter tuning and model evaluation, and performance was assessed using area under the curve (AUC) analysis.ResultsThe best-performing models achieved an AUC of 0.90 (95% CI: 0.88–0.93) for classifying indolent versus aggressive tumors and 0.76 (95% CI: 0.71–0.81) for benign versus malignant tumors. Models incorporating tumor size significantly improved classification accuracy. RF classifiers excelled in distinguishing indolent from aggressive tumors, while MLP classifiers performed better for benign versus malignant classification.ConclusionThe machine learning pipeline demonstrated high accuracy in differentiating aggressive from indolent renal tumors, offering valuable prognostic insights for personalized treatment. Tumor size was a critical factor, complementing CECT images and clinical data. These findings highlight the potential of ML techniques in enhancing renal tumor risk stratification.
Iterative improvement of deep learning models using synthetic regulatory genomics
Authors: Ribeiro-dos-Santos André M. , Maurano Matthew T. 
Published: February 2025

ABSTRACTGenerative deep learning models can accurately reconstruct genome-wide epigenetic tracks from the reference genome sequence alone. But it is unclear what predictive power they have on sequence diverging from the reference, such as disease- and trait-associated variants or engineered sequences. Recent work has applied synthetic regulatory genomics to characterized dozens of deletions, inversions, and rearrangements of DNase I hypersensitive sites (DHSs). Here, we use the state-of-the-art model Enformer to predict DNA accessibility across these engineered sequences when delivered at their endogenous loci. At high level, we observe a good correlation between accessibility predicted by Enformer and experimentally measured values. But model performance was best for sequences that more resembled the reference, such as single deletions or combinations of multiple DHSs. Predictive power was poorer for rearrangements affecting DHS order or orientation. We use these data to fine-tune Enformer, yielding significant reduction in prediction error. We show that this fine-tuning retains strong predictive performance for other tracks. Our results show that current deep learning models perform poorly when presented with novel sequence diverging in certain critical features from their training set. Thus an iterative approach incorporating profiling of synthetic constructs can improve model generalizability, and ultimately enable functional classification of regulatory variants identified by population studies.
Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modelingin vitroneurons
Authors: Sumi Takuma , Houben Akke Mats , Yamamoto Hideaki , Kato Hideyuki , Katori Yuichi , Soriano Jordi , Hirano-Iwata Ayumi 
Published: February 2025

AbstractImpaired brain function is restored following injury through dynamic processes that involve synaptic plasticity. This restoration is supported by the brain’s inherent modular organization, which promotes functional separation and redundancy. However, it remains unclear how the modular structure interacts with synaptic plasticity, most notably in the form of spike-timing-dependent plasticity (STDP), to define the damage response and recovery efficiency. In this work, we numerically modeled the response and recovery to damage of a neuronal networkin vitrobearing a modular structure. Consistent with thein vitroobservations, thein siliconumerical model effectively captured the decline and subsequent recovery of spontaneous activity following the injury. We revealed that the modular structure confers robustness to injury, minimizes the decrease in neuronal activity, and promotes recovery via STDP. Finally, using the reservoir computing framework, we show that information representation in the neuronal network improves with the recovery of synchronous activity. Our work provides an experimental-numerical platform for predicting recovery in damaged neuronal networks and may help developing effective models for brain injury.
Cholecystokinin input from the anterior cingulate cortex to the lateral periaqueductal gray mediates nocebo pain behavior in mice
Authors: Poulson Sandra J. , Skvortsova Aleksandrina , Paz Lisiê V. , Cui Wanning , Mandatori Antonietta , Burek Jacob , Zahra Fatama T. , Dinh Anton , Boorman Damien C. , Liang Lianfang , Mogil Jeffrey S. , Martin Loren J. 
Published: February 2025

AbstractThe nocebo effect, the evil twin of the better-known placebo effect in which anticipation of harm causes that harm, is increasingly thought to be responsible for poor clinical outcomes. In humans, nocebo hyperalgesia (i.e., increased sensitivity to pain) is blocked by proglumide, a cholecystokinin (CCK) receptor antagonist. Yet the neural circuitry underlying nocebo hyperalgesia remains unidentified, largely due to lack of appropriate animal models. Independently, our two laboratories developed unique animal models of CCK-dependent nocebo hyperalgesia in which expectation of pain resulted from environmental or social cues. We find that both nocebo paradigms share a neural circuit involving CCK release from neurons projecting from the anterior cingulate cortex to the lateral periaqueductal gray. This pathway, which had not been previously recognized, could represent a promising target for therapeutic interventions in pain-related disorders.One-Sentence SummaryPain expectations, whether environmentally conditioned or transmitted socially, share a neural circuit, with cholecystokinin (CCK) projections from the anterior cingulate cortex (ACC) to the lateral periaqueductal gray (PAG) playing a pivotal role.
Health facility support and its influence on weight gain for neonates under Kangaroo Mother Care in Western Kenya
Authors: Mukabi Seline , Morema Everlyne Nyanchera , Kipmerewo Mary , Shisanya Morris Senghor 
Published: February 2025

AbstractIntroductionKangaroo Mother Care (KMC) is a recommended cost-effective intervention for low birthweight neonates that promotes neonatal growth, and survival. Its effectiveness is dependent on several factors. This study examines facility factors that influence outcome of KMC in Western Kenya.MethodsThis was a mixed methods study that examined facility support factors influencing KMC outcomes in Western Kenya. A multi-stage sampling approach was used attain the sample size (275). Data collection involved structured questionnaires for caregivers and key informant interviews (KIIs) for healthcare providers. Quantitative data was analyzed using SPSS v26. Binary logistic regression used to determine influence of facility support on KMC neonates weight gain with α = 0.05. Qualitative data from 12 KIIs was thematically analyzed to provide deeper insights into the factors affecting KMC implementation.ResultsThe average facility KMC implementation score was 10.78±2.59 out of 19 (56.71%±13.65%). Only 38.18% of neonates achieved the recommended daily weight gain (≥15g/kg). Average daily weight gain was 12.96g±4.85g. The average length of stay on KMC was 36.25±10.09 days, less than the average recommended 41.21±16.02 days for optimal weight gain. Facility-related predictors of achieving ≥15g/kg daily weight gain included the availability of breast pumps (AOR:2.81, P=0.009), milk banks (AOR:3.22, P=0.004), adequate food for mothers (AOR:5.32, P<0.001), seating for mothers (AOR:3.72, P=0.001), dedicated rooms for breast milk expression (AOR:4.77, P<0.001), presence of easily understandable KMC information (AOR:2.86, P=0.007), family-centered KMC support (AOR:2.41, P=0.032), and adequate staffing for KMC (AOR:3.44, P=0.002). The qualitative data showed that nurses played key roles in clinical care, education, research, advocacy, and community engagement for KMC. While formal training improved competency, gaps remained, emphasizing the need for continuous learning..ConclusionsFacility level support is crucial for maximizing benefits of KMC especially through provision of maternal nutrition, infrastructure support, guaranteeing maternal comfort, family-centered care and adequate staffing.
A genome-wide association study identified 10 novel genomic loci associated with intrinsic capacity
Authors: Beyene Melkamu Bedimo , Visvanathan Renuka , Alemu Robel , Benyamin Beben , Bhattacharjee Rudrarup , Beyene Habtamu B. , Theou Olga , Sumi Yuka , Thiyagarajan Jotheeswaran Amuthavalli , Cesari Matteo , Beard John R. , Amare Azmeraw T. 
Published: February 2025

AbstractIn 2015, the World Health Organization introduced the concept of intrinsic capacity (IC), a composite of all the individual-level attributes that contribute to healthy aging. To investigate the genetic basis of IC, we used data from the UK Biobank (UKB; N=44,631) and the Canadian longitudinal study on aging (CLSA; N=13,085). We estimated SNP-based heritability (h2SNP) at 25.2% in UKB and 19.5% in CLSA. A Genome-Wide Association Study (GWAS) identified 38 independent SNPs for IC across 10 genomic loci and 4,289 candidate SNPs mapped to 197 genes. Post-GWAS analysis revealed the role of these genes on cellular processes such as cell proliferation, immune function, metabolism, and neurodegeneration, with high expressions in muscle, heart, brain, adipose, and tibial nerve tissues. Of the 52 traits tested, 23 showed significant genetic correlations with IC, and a higher genetic loading for IC was associated with higher IC scores. This study is the first to identify genetic variants and pathways associated with IC, providing a foundation for future research on healthy aging.
Vascular amyloidβ load in the meningeal arterial network correlates with loss of cerebral blood flow and pial collateral vessel enlargement in the J20 murine model of Alzheimer’s disease
Authors: Kaloss Alexandra M. , Browning Jack L. , Li Jiangtao , Pan Yuhang , Watsen Sachi , Sontheimer Harald , Theus Michelle H. , Olsen Michelle L. 
Published: February 2025

Structured AbstractINTRODUCTIONGlobal reduction in cerebral blood flow (CBF) is an early pathology in Alzheimer’s disease, preceding significant plaque accumulation and neurological decline. Chronic reduced CBF and subsequent reduction in tissue oxygenation and glucose may drive neurodegeneration, yet the underlying cause of globally reduced CBF remains unclear.METHODSUsing premortem delivery of Methoxy-XO4 to label Aβ, and arterial vascular labeling, we assessed Aβ burden on the pial artery/arteriole network and cerebral blood flow in aged male and female WT and J20 AD mice.RESULTSThe pial artery/arteriole vascular network selectively displayed extensive vascular Aβ burden. Pial collateral arteriole vessels, the by-pass system that reroutes blood flow during occlusion, displayed significant enlargement in J20 mice. Despite this, CBF was decreased by approximately 15% in 12-month J20 mice when compared to WT littermates.DISCUSSIONSignificant Aβ burden on the meningeal arterial network may contribute to the restriction of CBF. Redistribution of CBF through enlarged pial collateral vessels may serve as a compensatory mechanism to alter CBF during disease progression in cases of CAA.
Group personality, rather than acoustic noise, causes variation in group decision-making in guppy shoals
Authors: Clark Molly A. , Waples Ella , Radford Andrew N. , Simpson Stephen D. , Ioannou Christos C. 
Published: February 2025

AbstractGroup living has essential fitness benefits for many species. While numerous studies have explored how environmental conditions impact collective movement, their impact on decisions made in a social context—a central component of group-living—is poorly documented. In this study, we assess how acoustic noise impacts group decision-making, cohesion and activity in fish shoals, using Trinidadian guppies (Poecilia reticulata) as a model species. Movements within a radially symmetric five-armed maze were measured using high-resolution trajectory data from video-tracking software. The behaviour of groups with and without continuous acoustic white noise were measured over a four-day testing period in a repeated-measures design. We found no significant change in swimming speed or group cohesion with additional acoustic noise. However, there was evidence for fewer following events (moves into already occupied arms) in the noise treatment compared to the control, but this additional noise had no effect on leadership attempts (moves into empty arms). We found strong evidence for consistent, repeatable differences between groups in all behavioural parameters indicating strong personality variation at the group level. Rather than environmental factors, these results provide evidence that consistent group-level differences dominate variation in collective behaviour, including group decision-making, in fish shoals.