bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficacy and Safety of Habitual Consumption of a Food Supplement Containing Miraculin in Malnourished Cancer Patients: the CLINMIR Pilot Study
Authors: López-Plaza Bricia , Álvarez-Mercado Ana Isabel , Arcos-Castellanos Lucía , Plaza-Diaz Julio , Ruiz-Ojeda Francisco Javier , Brandimonte-Hernández Marco , Feliú-Batlle Jaime , Hummel Thomas , Gil Ángel , Milla Samara Palma 
Published: May 2024

AbstractTaste disorders (TDs) are common among systemically treated cancer patients and negatively impact their nutritional status and quality of life. A food supplement containing the natural taste-modifying protein miraculin (DMB®) has emerged as a possible alternative treatment for TDs. The present study aimed to evaluate the efficacy and safety of habitual DMB consumption in malnourished cancer patients undergoing active treatment. An exploratory clinical trial was carried out in which 31 cancer patients were randomized into three arms [standard dose of DMB (150 mg DMB/tablet), high dose of DMB (300 mg DMB/tablet) or placebo (300 mg freeze-dried strawberry)] for three months. Patients consumed an intervention DMB tablet or placebo before each main meal. Throughout the five main visits, electrochemical taste perception, nutritional status, dietary intake, quality of life and the fatty acid profile of erythrocytes were evaluated. Patients consuming a standard dose of DMB exhibited improved taste acuity over time (% change right/left side: ‒52.8 ± 38.5 / ‒58.7 ± 69.2%) and salty taste perception (2.29 ± 1.25vs.high dose: 2.17 ± 1.84vs.placebo: 1.57 ± 1.51 points, p < 0.05). They also had higher energy intake (p = 0.075) and covered better energy expenditure (107 ± 19%). The quality of life evaluated by symptom scales improved in patients receiving the standard dose of DMB (constipation, p = 0.048). The levels of arachidonic (13.1 ± 1.8; 14.0 ± 2.8, 12.0 ± 2.0%; p = 0.004) and do-cosahexaenoic (4.4 ± 1.7; 4.1 ± 1.0; 3.9 ± 1.6%; p = 0.014) acids in erythrocytes increased over time after DMB intake. The standard dose of DMB increased fat‒free massvs. placebo (47.4 ± 9.3 vs. 44.1 ± 4.7 kg, p = 0.007). Importantly, habitual patients with DMB did not experience any adverse events, and metabolic parameters remained stable and within normal ranges. In conclusion, habitual consumption of a standard 150 mg dose of DMB improves electrochemical food perception, nutritional status (energy intake, fat quantity and quality, fat-free mass) and quality of life in malnourished cancer patients receiving antineoplastic treatment. Additionally, DMB consumption appears to be safe, with no changes in major biochemical parameters associated with health status. The clinical trial was registered athttp://clinicaltrials.gov(NCT05486260).
Unveiling Tissue Structure and Tumor Microenvironment from Spatially Resolved Transcriptomics by Hypergraph Learning
Authors: Liao Yi , Zhang Chong , Wang Zhikang , Qi Fei , Huang Weitian , Cai Shangyan , Li Junyu , Yuan Zhiyuan , Song Jiangning , Cai Hongmin 
Published: May 2024

AbstractSpatially resolved transcriptomics (SRT) technologies acquire gene expressions and spatial information simultaneously, reshaping the perspectives of life sciences. Identifying spatial patterns is essential for exploring organ development and tumor microenvironment. Nevertheless, emerging SRT technologies have also introduced diverse spatial resolutions, posing challenges in characterizing spatial domains with finer resolutions. Here we propose a hypergraph-based method, termed HyperSTAR to precisely recognize spatial domains across varying spatial resolutions by utilizing higher-order relationships among spatially adjacent tissue programs. Specifically, a gene expression-guided hyperedge decomposition module is incorporated to refine the structure of the hypergraph to precisely delineate the boundaries of spatial domains. A hypergraph attention convolutional neural network is designed to adaptively learn the significance of each hyperedge. With the power of capturing intricate higher-order relationships within spatially neighboring multi-spots/cells, HyperSTAR demonstrates superior performance across different technologies with various resolutions compared to existing advanced graph neural network models in multiple tasks including uncovering tissue sub-structure, inferring spatiotemporal patterns, and denoising spatially resolved gene expressions. It successfully reveals spatial heterogeneity in breast cancer section and its findings are further validated through functional and survival analyses of independent clinical data. Notably, HyperSTAR performs well with diverse spatial omics data types and seamlessly extends to large-scale datasets.
The Endoplasmic Reticulum as an Active Liquid Network
Authors: Scott Zubenelgenubi C. , Steen Samuel B. , Huber Greg , Westrate Laura M. , Koslover Elena F. 
Published: May 2024

The peripheral endoplasmic reticulum (ER) forms a dense, interconnected, and constantly evolving network of membrane-bound tubules in eukaryotic cells. While individual structural elements and the morphogens that stabilize them have been described, a quantitative understanding of the dynamic large-scale network topology remains elusive. We develop a physical model of the ER as an active liquid network, governed by a balance of tension-driven shrinking and new tubule growth. This minimalist model gives rise to steady-state network structures with density and rearrangement timescales predicted from the junction mobility and tubule spawning rate. Several parameter-independent geometric features of the liquid network model are shown to be representative of ER architecture in live mammalian cells. The liquid network model connects the time-scales of distinct dynamic features such as ring closure and new tubule growth in the ER. Furthermore, it demonstrates how the steady-state network morphology on a cellular scale arises from the balance of microscopic dynamic rearrangements.SIGNIFICANCEThe peripheral endoplasmic reticulum (ER) forms a continuous, dynamic network of tubules that plays an important role in protein sorting, export, and quality control, as well as cellular signaling and stress response. Elucidating how the unique morphology of the ER arises and supports its function is critical to developing a mechanistic understanding of the many neurological diseases associated with ER structural perturbations. We develop a physical model of the ER as an active liquid network to understand how its cellular-scale structure emerges from small-scale dynamic rearrangements. The model demon-strates how key features of ER architecture can arise from a balance of tubule growth and tension-driven sliding. This work provides insight into the fundamental physical mechanisms underlying the emergent morphology of the ER.
Functional brain networks predicting sustained attention are not specific to perceptual modality
Authors: Corriveau Anna , Ke Jin , Terashima Hiroki , Kondo Hirohito M. , Rosenberg Monica D. 
Published: May 2024

AbstractSustained attention is essential for daily life and can be directed to information from different perceptual modalities including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults’visualsustained attention performance generalizes to predictauditorysustained attention performance in three independent datasets (N1=29, N2=60, N3=17). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality-general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention.
Scale-sensitive Mouse Facial Expression Pipeline using a Surrogate Calibration Task
Authors: Telfer Andre , van Kaick Oliver , Abizaid Alfonso 
Published: May 2024

AbstractEmotions are complex neuro-physiological states that influence behavior. While emotions have been instrumental to our survival, they are also closely associated with prevalent disorders such as depression and anxiety. The development of treatments for these disorders has relied on animal models, in particular, mice are often used in pre-clinical testing. To compare effects between treatment groups, researchers have increasingly used machine learning to help quantify behaviors associated with emotionality. Previous work has shown that computer vision can be used to detect facial expressions in mice. In this work, we create a novel dataset for depressive-like mouse facial expressions using varying LypoPolySaccharide (LPS) dosages and demonstrate that a machine learning model trained on this dataset was able to detect differences in magnitude via dosage amount.
iTreg mediated TGF-Β1 therapy improves functional engraftment of cell therapy in rd1 Retinitis Pigmentosa mouse model
Authors: Mohan K Varsha , Mishra Alaknanda , Sinha Prakriti , Muniyasamy Abaranjitha , Nagarajan Perumal , Chaudhary Kiran , Upadhyay Pramod 
Published: May 2024

AbstractPurposeRetinitis Pigmentosa (RP) is a progressive and hereditary disease that primarily affects the retina, leading to partial or complete vision loss. In addition to the direct impact on vision, the degeneration of the retina in RP also leads to inflammation in the eye, which can further damage the retina and make it difficult to treat the condition with cell therapy. This inflammation led to oxidative stress and cell death, creating an unfavourable environment for the introduction of new cells via cell therapy.MethodsThe potential of Transforming Growth Factor-Beta1 (TGF-B1) as an anti-inflammatory agent to treat ocular inflammation was investigated done by administering TGF-B1 intravitreally to the eyes of rd1 mice. However, due to the transient effect of TGF-B1 injection, the in-vitro-induced Treg (iTregs) cells that secrete TGF-B1, were generated and transplanted into the conjunctiva of 4 weeks old rd1 mice to achieve a sustained release of TGF-B1. After administering iTregs, Retinal Neuron-Like Cells (RNLCs) were transplanted into the rd1 mouse retina as a form of cell therapy to improve vision perception.Flow cytometry was used to estimate the number of Qtracker labelled RNLCs post 30 days of transplantation. The potential of iTregs as an adjunct transplantation with RNLCs to improve cell therapy survival and vision rescue was investigated by conducting Electroretinography and behavioural studies.ResultsThe study found that ocular inflammation can be reduced by treating with TGF-B1. After 30 days, mice transplanted with iTregs showed a significant increase in the number of transplanted RNLCs that survived compared to the mice who only received RNLCs. In the total fluid of the eye (aqueous plus vitreous), there was a significant increase in the levels of anti-inflammatory cytokines TGF-B1 and IL-10, and some decrease in the levels of pro-inflammatory cytokines Monocyte Chemoattractant Protein-1 (MCP1). The adjunct therapy of iTregs transplantation resulted in improvement in ERG wave functions and vision preservation compared to the group without adjunct iTregs.ConclusionsThe administration of TGF-B1-secreting iTregs to the affected eye reduced the inflammatory environment, which enabled transplanted RNLCs to stay longer compared to without TGF-B1. The iTregs mediated sustained anti-inflammatory adjunct therapy can improve the outcome of cell therapy for RP.
Application of observational research methods to real-world studies for rare disease drugs: a scoping review protocol
Authors: Patel Yuti P , Ghaddar Lea , Lin Yuqi , Karim Nuzat , Chan Kelvin , Dupuis Lee , Tadrous Mina 
Published: May 2024

AbstractObjectiveThe primary objective is to identify which observational research methods have been used in the last 5 years in rare disease drug evaluation and how they are applied to generate adequate evidence regarding the real-world effectiveness or safety of rare disease drugs.BackgroundRare disease is an umbrella term for a condition which affects <200,000 people each year and despite the rarity of these conditions, collectively they encompass approximately 7000 different conditions. With the striking number of rare conditions, many pharmaceutical manufacturers are introducing an increased number of drugs to treat them. However, due to small patient populations, heterogeneity and other factors related to rare diseases, there are feasibility concerns regarding the generation of adequate efficacy and safety evidence using conventional randomized controlled trials (RCTs). Recently, real-world evidence generated through observational (or real-world) studies has been proposed to address some of the feasibility concerns with RCTs by measuring drug effectiveness or safety in the real-world setting. However, there remain methodological concerns due to a lack of randomization/masking. This proposed scoping review aims to identify which observational research methods in the last 5 years are used in rare disease drug evaluation to address methodological concerns and how they’re applied to generate evidence on drug effectiveness or safety.Inclusion criteriaArticles must be primary observational or real-world studies reporting rare disease drug effectiveness or safety published between 2018-2023. Literature reviews, meta-analyses, randomized control trials, case series, case reports, opinion pieces, conference abstracts, and studies with unavailable full-text articles will be excluded.MethodsThe search strategy will combine the following key search concepts: rare disease, drugs for rare disease and observational/real-world studies. The search will be conducted in MEDLINE and EMBASE.Review registration numberOpen Science Framework,https://osf.io/f3wpv
Intraindividual Variability Differentiated Older Adults with Physical Frailty and the Role of Education in the Maintenance of Cognitive Intraindividual Variability
Authors: Wu Jingyi , Chen Jinyu , Wu Juncen , Hsu Chun Liang 
Published: May 2024

AbstractObjectivesPhysical frailty is associated with increased risk of cognitive impairment. However, its impact on sustained cognitive processing as evaluated by intraindividual variability (IIV), and factors beneficial to IIV in physically frail older adults remain unexplored. This study aimed to quantify differences in IIV between older adults with and without physical frailty, and examine whether education facilitated maintenance of IIV.MethodsThis cross-sectional study included 121 community-dwelling older adults 65-90 years with/without physical frailty (PF and non-PF; n=41 and n=80 respectively). Physical frailty was determined via Short Physical Performance Battery. Dispersion across the seven components of the Montreal Cognitive Assessment (MoCA) was computed to ascertain IIV. Multivariate analysis of covariance was used to determine group differences in total score and IIV. Four moderation models were constructed to test the effects of education on age-total score and age-IIV relationships in PF and non-PF.ResultsCompared with non-PF, PF showed greater IIV (p= .022; partial η² = 0.044). Among PF, education moderated age-total score (R-sq = 0.084, F = 5.840,p< 0.021) and age-IIV (R-sq = 0.101, F = 7.454,p= 0.010) relationships. IIV increased with age for those with five years (β = 0.313,p= 0.006) or no formal education (β = 0.610,p= 0.001). Greater than seven years of education (β = 0.217,p= 0.050) may be required to maintain IIV at older age.ConclusionIIV may be a sensitive method to differentiate physically frail older adults. Additionally, perceived cognitive benefits of education may be dependent on physical functioning.
Typologies of activity-related behaviours from childhood to adolescence and their transitions: A longitudinal analysis of the ELSPAC cohort
Authors: Janda David , Gába Aleš , Ayala Ana Maria Contardo , Timperio Anna , Andrýsková Lenka , Piler Pavel , Arundell Lauren 
Published: May 2024

AbstractObjectivesTo identify typologies of activity-related behaviours in childhood and adolescence and to explore transitions between the identified typologies. Additionally, we aimed to identify demographic indicators associated with the transitions and typology membership.DesignProspective cohort studySettingCzech RepublicParticipantsIndividuals involved in the Czech part of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC-CZ) study, aged 11 to 18 years. The study involved more than 563 individuals of which 380 provided complete data the analysis.Primary outcome measuresTime spent outdoors, participation in organised physical activity (PA) and sport activities, time watching television and using personal computer, and total sleep duration. Latent variables defined by the Latent Transition Analysis.ResultsFour typologies of activity-related behaviours were identified and labelled to reflect their behavioural profiles: 1)Actives(high outdoor time and organised PA and sport participation, low screen time, optimal sleep duration), 2)Active screeners(median outdoor time, high organised PA and sport participation, high screen time, optimal sleep duration), 3)Poor sleepers(average outdoor time and organised PA and sport participation, low screen time, not meeting sleep guidelines), and 4)Averages(average duration of all behaviours and optimal sleep duration). A major shift in typology membership from 11 to 18 years was observed with a decreasing proportion of individuals in typologies characterised by a high proportion of outdoor time and participation in organised PA and sport activities (i.e.Actives;Active screeners). A high proportion of individuals also transitioned to the typology with poor sleeping habits (i.e.Poor sleepers). Sex and maternal education were associated with the typology membership and transition probabilities (p< 0.05).ConclusionsOur findings highlight the importance of implementing lifestyle interventions in childhood to prevent possible sleep disorders and low physical activity later in life.Strengths and limitations of this studyThe longitudinal design allows an investigation of unique activity-related behaviour patterns across the transition from childhood to adolescents.Utilising various activity-related behaviours (e.g. outdoor time or screen time) provide a comprehensive understanding of the lifestyle of children and adolescents.Data were collected from a specific geographic region from the Central Europe, which may limit the generalisability to broader population.
PRS-GRID: A Cross and Within Ancestry Polygenic Risk Prediction Method Based on Individual Genetic Distance
Authors: Tang Liwei , You Cong , Kong Xue-jun , Napolioni Valerio , Huang Jie 
Published: May 2024

AbstractBackgroundTwo decades of genome-wide association studies (GWAS) have led to the fast-growing application of polygenic risk prediction (PRS). However, due to population structure and evolutionary path differences, the PRS substrate derived mostly from studies of European ancestry does not work equally well for other ancestries. There is an association between prediction accuracy decay and individual genetic distance (GD) to the genetic centers (GC) of various populations.ObjectivesTo develop a new PRS method and software that utilizes individual GD to improve PRS risk prediction accuracy, especially for non-European populations.MethodWe hypothesize that adding a GD-based weight into PRS methods would enhance its risk prediction performance, particularly for minority groups. We explore the GD first by principal components (PC) and then by phylogenetic tree structures. Building on top of an emerging software (PRS-CSx) that achieves high prediction accuracy across multiple-ancestries, we present PGS-GRID, where “GRID” stands for “GeneticReference based onIndividualDistance”.ResultsWe developed a preliminary version of PRS-GRID and pilot tested its prediction performance for a classic quantitative trait (e.g., height) and a disease trait (e.g., type-2 diabetes). We found slight but noticeable improvement of risk prediction in minority populations. We further explored a random forest approach so that the performance of PRS-GRID could be clearly explained, which is a key step for PRS to be used in clinical and public health practice.ConclusionsThe PRS-GRID philosophy and method represent an innovative and significant advancement in the field of polygenic risk prediction. Our work provides a foundation for future research and clinical applications aimed at reducing health disparities and improving population health through personalized medicine.
Tuberculosis healthcare service disruptions during the COVID-19 pandemic in Brazil, India and South Africa: A model-based analysis of country-level data
Authors: de Villiers Abigail K. , Osman Muhammad , Struchiner Claudio J. , Trajman Anete , Tumu Dheeraj , Shah Vaibhav V. , Werneck Guilherme L. , Alves Layana C. , Choudhary Megha , Verma Sunita , Mattoo Sanjay K. , Meehan Sue-Ann , Singh Urvashi B. , Hesseling Anneke C. , Marx Florian M. 
Published: May 2024

AbstractTuberculosis (TB) is the leading infectious disease cause of death worldwide. In recent years, stringent measures to contain the spread of SARS-CoV-2 have led to considerable disruptions of healthcare services for TB in many countries. The extent to which these measures have affected TB testing, treatment initiation and outcomes has not been comprehensively assessed. We aimed to estimate TB healthcare service disruptions occurring during the COVID-19 pandemic in Brazil, India, and South Africa. We obtained country-level TB programme and laboratory data and used autoregressive integrated moving average (ARIMA) time-series models to estimate healthcare service disruptions with respect to TB testing, treatment initiation, and treatment outcomes. We quantified disruptions as the percentage difference between TB indicator data observed during the COVID-19 pandemic compared with values for a hypothetical no-COVID scenario, predicted through forecasting of trends during a three-year pre-pandemic period. Annual estimates for 2020-2022 were derived from aggregated monthly data. We estimated that in 2020, the number of bacteriological tests conducted for TB diagnosis was 24.3% (95% uncertainty interval: 8.4%;36.6%) lower in Brazil, 27.8% (19.8;3 4.8%) lower in India, and 32.0% (28.9%;34.9%) lower in South Africa compared with values predicted for the no-COVID scenario. TB treatment initiations were 17.4% (13.9%;20.6%) lower than predicted in Brazil, 43.3% (39.8%;46.4%) in India, and 27.0% (15.2%;36.3%) in South Africa. Reductions in 2021 were less severe compared with 2020. The percentage deaths during TB treatment were 13.7% (8.1%; 19.7%) higher than predicted in Brazil, 1.7% (−8.9%;14.0%) in India and 21.8% (7.4%;39.2%) in South Africa. Our analysis suggests considerable disruptions of TB healthcare services occurred during the early phase of the COVID-19 pandemic in Brazil, India, and South Africa, with at least partial recovery in the following years. Sustained efforts to mitigate the detrimental impact of COVID-19 on TB healthcare services are needed.