bluuebunny/crossref_metadata_2025_split huggingface dataset:
Climacteric Women’s Perspectives on Menopause and Hormone Therapy: Knowledge Gaps, Fears, and the Role of Healthcare Advice
Authors: Rodrigues Marcio Alexandre H. , Reis Zilma Silveira Nogueira , Verona Ana Paula de Andrade , Teixeira Matheus Candido , Pappa Gisele Lobo , Soares Junior Jose Maria , da Silva Filho Agnaldo Lopes , Baracat Edmund Chada , Maciel Gustavo Arantes Rosa 
Published: December 2024

AbstractObjectiveThis study aims to evaluate the knowledge, attitudes, and practices of Brazilian women regarding menopause, related symptoms, and the use of hormone therapy, including indications and contraindications.MethodsA cross-sectional study was conducted between 18/07/2022 and 01/10/2023, involving women aged 40 to 65 years from various cities in Minas Gerais, São Paulo, and other regions of Brazil. A structured KAP (Knowledge, Attitudes, and Practices) Survey was used to assess sociodemographic characteristics, the prevalence of menopausal symptoms, and the participants’ knowledge and practices concerning Menopause Hormone Therapy.ResultsThe median age of the women surveyed was 50 years; 55.4% were postmenopausal (median age 55), and 44.6% were premenopausal (median age 45). The data indicate limited knowledge about menopause among Brazilian women. Less than one-third (30.3%) expressed satisfaction with the information they had received regarding menopause and available treatment options. Furthermore, 92.97% of participants demonstrated little or no knowledge of hormone therapy. Nearly 27% were unaware of MHT, and 22% opposed its use. Only 29% reported current use of MHT, with common reasons for discontinuation including fear of side effects and contraindications advised by gynecologists.ConclusionThe findings indicate that many Brazilian women have insufficient knowledge about menopause and hormone therapy. Furthermore, a lack of information and training among healthcare providers may lead to the low utilization of menopausal hormone therapy (MHT). To effectively address menopausal symptoms and empower women to make informed choices about hormone therapy, it is crucial to improve access to accurate information and enhance the training of healthcare providers.
CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies
Authors: Di Sante Moises , Pezzotti Melissa , Zimmermann Julius , Enrico Alessandro , Deschamps Joran , Balmas Elisa , Becca Silvia , Reali Alessandro , Bertero Alessandro , Jug Florian , Pasqualini Francesco S. 
Published: December 2024

AbstractMeasuring cell structure and function along with cell cycle progression in live cell imaging has been challenging because Fluorescence Ubiquitin Cell Cycle Indicators (FUCCI) and most phenotypic sensors both utilize green (GFP) and red (RFP) fluorescence proteins. We introduce CALIPERS, a method for cell cycle-aware live-cell imaging for phenotyping experiments and regeneration studies. CALIPERS uses a custom FUCCI sensor called FUCCIplex that spectrally multiplexes with GFP and RFP-based sensors. To demonstrate the broad range of applications for CALIPERS, we validated it with epithelial and human induced pluripotent stem cell multi-color reporter lines in proliferation, migration, cardiac drug testing, and regenerative medicine studies.
Noninvasively real-time monitoring in-vivo immune cell and tumor cell interaction by NIR-II nanosensor
Authors: Huang Liwen , Ming Jiang , Wang Zhihua , Wu Jiaxin , Yun Baofeng , Liang Aibin , Fan Yong , Zhang Fan 
Published: December 2024

AbstractImmunocytotherapy holds significant promise as a novel cancer treatment, but its effectiveness is often hindered by delayed responses, requiring evaluations every two to three weeks based on current diagnostic methods. Early assessment of immune cell-tumor cell interactions could provide more timely insights into therapeutic efficacy, enabling adjustments to treatment plans. In this study, we report a noninvasive nanosensor (C8R-DSNP) for real-time monitoring of in vivo immune cell activities in the second near-infrared long-wavelength (NIR-II-L) window (1500-1900 nm), which offers deep tissue transparency. The C8R-DSNP responds rapidly to caspase-8, a key apoptotic signaling molecule generated during interactions between natural killer (NK-92) immune cells and tumor cells. Using ratiometric NIR-II-L fluorescence imaging, we captured dynamic in vivo observations of NK-92 cell engagement with tumor cells in a mouse model. These results demonstrated cancer cell apoptosis that happens as early as 4.5 hours after NK-92 cell infusion. Additionally, in vitro urine imaging confirmed the initiation of apoptosis via cleaved fluorescent small molecules, while single-cell tracking within blood vessels and tumors further elucidated immune cell dynamics. This real-time NIR-II-L monitoring approach offers valuable insights for optimizing immunocytotherapy strategies.
Gene regulatory network integration with multi-omics data enhances survival predictions in cancer
Authors: Pop Romana T. , Hsieh Ping-Han , Belova Tatiana , Mathelier Anthony , Kuijjer Marieke L. 
Published: December 2024

AbstractThe emergence of high-throughput omics technologies has resulted in their wide application to cancer studies, greatly increasing our understanding of the disruptions occurring at different molecular levels. The role of gene regulation as a core driver of biological processes and its role in the development and progression of cancer has been well established. Transcriptional regulation, a crucial aspect of gene regulation, can be represented as a network of interactions between regulators (such as transcription factors) and their target genes. These networks are known as gene regulatory networks (GRNs). Several joint dimensionality reduction (JDR) methods and tools for integrating multi-modal data have been developed in recent years. This study presents a new approach to integrate multi-modal data of different dimensions to consider sample-specific GRNs with multi-omics data in ten cancer datasets from The Cancer Genome Atlas (TCGA). We compare JDR models that include GRNs with those that do not, assessing their association with patient survival. We find that including GRNs can improve associations with patient survival across several cancer types. Focusing on liver cancer, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression.
Prediction of Cell States and Key Transcription Factors of the Human Cornea through Integrated Single-Cell Omics Analyses
Authors: Arts Julian A. , Fallo Sofia , Florencio Melanie S. , Smits Jos G.A. , Cunha Dulce Lima , Roubroeks Janou A.Y. , Dickman Mor , LaPointe Vanessa L.S. , Yu Rosemary , Zhou Huiqing 
Published: December 2024

AbstractThe cornea, a transparent tissue composed of multiple layers, allows light to enter the eye. Several single-cell RNA-seq analyses have been performed to explore the cell states and to understand the cellular composition of the human cornea. However, the inconsistences in cell state annotations between these studies complicate the application of these findings in corneal studies. To address this, we integrated single-cell RNA-seq data from four published studies and created a human corneal cell state meta-atlas. This meta-atlas was subsequently evaluated in two applications. First, we developed a machine learning pipeline cPredictor, using the human corneal cell state meta-atlas as input, to annotate corneal cell states. We demonstrated the accuracy of cPredictor and its ability to identify novel marker genes and rare cell states in the human cornea. Furthermore, cPredictor revealed the differences of the cell states between pluripotent stem cell-derived corneal organoids and the human cornea. Second, we integrated the single-cell RNA-seq based cell state meta-atlas with chromatin accessibility data, conducting motif-focused and gene regulatory network analyses. These approaches identified distinct transcription factors driving cell states of the human cornea. The novel marker genes and transcription factors were validated by immunohistochemistry. Overall, this study offers a reliable and accessible reference for profiling corneal cell states, which facilitates future research in cornea development, disease and regeneration.Significance statementThis study creates a human corneal cell state meta-atlas that provides a common nomenclature of cells in the human cornea, through integrating multiple single-cell RNA-seq analyses. Using this meta-atlas, we developed a machine learning pipeline, cPredictor, to accurately annotate cell states in corneal studies using single-cell RNA-seq. Additionally, we identified distinct transcription factors driving cell states through integrating the atlas with chromatin accessibility data. This meta-atlas and the computational tool cPredictor enable future research in cornea development, disease, and regeneration.
Breaking Boundaries: New locality records and a reassessment of the conservation status of the Khasi Hill Toad (Bufoides meghalayanus) endemic to Meghalaya, Northeast India
Authors: Sengupta Samrat , Chachei Esau Asakho , Boruah Bitupan , Nath Anukul , Das Abhijit 
Published: December 2024

AbstractBufoides meghalayanusis a Critically Endangered toad, endemic to East Khasi Hills, Meghalaya, Northeast India. This endemic toad was presumed to be restricted to just three locations, all within 1-2 km from one another around the type locality in Meghalaya. This article gives the first account of three previously unknown populations, extending the known geographic range of the species significantly. Additionally, we include the newly obtained molecular sequences ofB. meghalayanusin a comprehensive molecular phylogeny with a note on its genetic and morphometric variations within these new populations and its congeneric members.
OmiGA: A Toolkit for Ultra-efficient Molecular Trait Analysis in Complex Populations
Authors: Teng Jinyan , Zhang Wenjing , Gong Wentao , Chen Jiajian , Gao Yahui , Fang Lingzhao , Zhang Zhe 
Published: December 2024

AbstractMolecular quantitative trait loci (molQTL) mapping is one of the most popular approaches to systematically characterize functional impacts of genomic variants, leading to advanced understanding of the regulatory mechanisms underpinning complex traits and diseases. However, when applied to high-throughput molecular phenotypes, the existing molQTL mapping tools often implement simple linear models, overlooking complex inter-individual relatedness, leading to false positives and insufficient statistical power. Here, we introduce theOmicsGeneticAnalysis toolkit (OmiGA), an ultra-efficient linear mixed model (LMM) based toolkit, for molQTL mapping in populations with complex relatedness. Both computational simulations and real data analyses demonstrated that OmiGA outperformed the existing popular tools regarding molQTL discovery power, fine mapping of causal variants, colocalization of molQTL and trait associations, and computational efficiency. In summary, we recommend OmiGA for molQTL mapping in populations with complex relatedness, for example, those in the Farm animal Genotype-Tissue Expression (FarmGTEx) project and family-based molQTL studies in humans.
Detecting mtDNA effects with an Extended Pedigree Model: An Analysis of Statistical Power and Estimation Bias
Authors: Lyu Xuanyu , Burt S. Alexandra , Hunter Michael D. , Good Rachel , Carroll Sarah L. , Garrison S. Mason 
Published: December 2024

AbstractMitochondrial DNA (mtDNA) plays a crucial role in numerous cellular processes, yet its impact on human behavior remains underexplored. The current paper proposes a novel covariance structure model with seven parameters to specifically isolate and quantify mtDNA effects on human behavior. This approach uses extended pedigrees to obtain estimates of mtDNA variance while controlling for other genetic and environmental influences. Our Monte-Carlo simulations indicate that a sample size of approximately 5,000 individuals is sufficient to detect medium mtDNA effects (mt2= 5%), while a more substantial cohort of around 30,000 is required for small effects (mt2= 1%). We show that deeper pedigrees increase power to detect the mtDNA effect while wider pedigrees decrease power, given the equal total sample size. We evaluated how missing kinship records and mtDNA mutations impact bias. Both lead to underestimation of mtDNA variance, and an overestimation of the interaction between nuclear DNA and mtDNA. In addition, the false positive rate of mtDNA effect estimation is low when fitting the model with data generated without mtDNA effects. Collectively, we demonstrate that using extended pedigrees to quantify the influence of mtDNA on human behavior is robust and powerful.
T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction With Uncertainty-Aware Self-Learning for Protein-Specific Alignment
Authors: Kyro Gregory W. , Smaldone Anthony M. , Shee Yu , Xu Chuzhi , Batista Victor S. 
Published: December 2024

AbstractThere is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the rapid identification of candidate inhibitors and facilitates the optimization of on-target potency. In this work, we present T-ALPHA, a novel deep learning model that enhances protein-ligand binding affinity prediction by integrating multimodal feature representations within a hierarchical transformer framework to capture information critical to accurately predicting binding affinity. T-ALPHA outperforms all existing models reported in the literature on multiple benchmarks designed to evaluate protein-ligand binding affinity scoring functions. Remarkably, T-ALPHA maintains state-of-the-art performance when utilizing predicted structures rather than crystal structures, a powerful capability in real-world drug discovery applications where experimentally determined structures are often unavailable or incomplete. Additionally, we present an uncertainty-aware self-learning method for protein-specific alignment that does not require additional experimental data, and demonstrate that it improves T-ALPHA’s ability to rank compounds by binding affinity to biologically significant targets such as the SARS-CoV-2 main protease and the epidermal growth factor receptor. To facilitate implementation of T-ALPHA and reproducibility of all results presented in this paper, we have made all of our software available athttps://github.com/gregory-kyro/T-ALPHA.
Methods for estimating maternal, newborn, and child health and nutrition effective coverage cascades from household and health facility surveys
Authors: Munos Melinda K. , Sheffel Ashley , Carter Emily , Perin Jamie ,   IMPROVE Coverage Group
Published: December 2024

AbstractBackgroundEffective coverage cascades have been proposed to understand to what extent populations are able to benefit from interventions to address their health needs. Theoretical effective coverage cascades have been developed for reproductive, maternal, newborn, child, and adolescent health and nutrition (RMNCAH&N), but there is no consensus regarding the methods to estimate effective coverage cascades. We operationalized the proposed effective coverage cascades for selected RMNCAH&N services; this paper presents the overall methods, challenges, and lessons learned.MethodsWe used data from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, Service Provision Assessments, and the Service Availability and Readiness Assessment to estimate effective coverage cascades in seven low- and middle- income countries for the following service areas: antenatal care, care for small and/or sick newborns, postnatal care, sick child care, and maternal and child nutrition. We developed operational definitions for each of the seven steps of the effective coverage cascade and developed readiness, and, where data allowed, process quality indices for each service area. Readiness- and process quality-adjusted coverage were estimated using ecological linking by stratum. We propose approaches for dealing with multiple observations per facility; multiple care-seeking episodes; and empty strata, as well as a jackknife approach to estimate the standard errors for readiness- and process quality-adjusted coverage.ResultsWe were able to estimate effective coverage cascades through intervention coverage (step 4) for postnatal care and through process quality-adjusted coverage (step 5) for antenatal care, sick child care, and maternal and child nutrition. For small and/or sick newborn care, we did not have an appropriate denominator or measure of service contact coverage and had to modify the cascade significantly. Data gaps were the largest barrier to the estimation of effective coverage cascades for RMNCAH&N. Other challenges included accounting for community- and home-based interventions, determining whether the cascade should be nested, and interpreting the cascade.ConclusionsTo make effective coverage cascades feasible for routine use, clear guidance is needed on cascade methods and definitions, accounting for the full spectrum of RMNCAH&N interventions, and developing our understanding of how coverage cascades can be used by stakeholders to improve health systems and programs.
Protocol For a Scoping Review of Patient-Preferred Outcomes in Depressive Disorders
Authors: Boreham Megan A , Bogdanova Mariia , Bloomfield Madeleine , Cleare Anthony J , Strawbridge Rebecca 
Published: December 2024

ABSTRACTDepressive disorders remain a leading cause of health-related burden globally. Patients often suffer with symptoms throughout their lifetime, and rates of remission have been estimated to be as high as 66%. Clinicians typically favour the use of scales monitoring quantity and severity of depressive symptoms to evaluate response to treatment and determine whether patients have entered remission. However, given the complexity of the disorders and the subjective nature of mental health, research indicates that patients value outcomes beyond this narrow definition, identifying outcomes related to functioning and quality of life as important in addition to control of depressive symptoms. These studies are typically limited by relatively small sample sizes and associated lack of generalisability.Here, we outline the protocol for a scoping review that aims to comprehensively collect and review research into patient preferred outcomes in treatment and management of depressive disorders. Our review was designed in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR) statement. Embase, Medline, PsycINFO, and CINAHL will be searched for relevant records. Qualitative and mixed-methods studies will be included, and all data will be synthesised and presented in narrative form. We anticipate that our results will provide useful insight for clinicians when caring for patients with depressive disorders and highlight potential misalignment between patient and clinician treatment goals.
Impact of wildfire smoke and diesel exhaust on inflammatory response in aging human microglia
Authors: Cuní-López Carla , Ng Mei Fong , Stewart Romal , Milton Laura A. , Etebar Fazeleh , Sun Yifan , Vivian Emily , Nguyen Tam Hong , Asare Patrick F. , Lupton Michelle K. , Roberts Tara L. , Ristovski Zoran , Hodge Sandra , Reynolds Paul N. , White Anthony R. , Quek Hazel 
Published: December 2024

AbstractBackgroundAir pollution, particularly from Diesel Exhaust Particles (DEP) and Wildfire Smoke (WFS), is increasingly recognised as a significant driver of neuroinflammation linked to brain diseases. However, the role of microglia in mediating these neuroinflammatory responses remains poorly understood. This study aimed to investigate the effects of air pollution on monocyte-derived microglia-like cells (MDMi) from both young (< 40 years of age), and (older > 60 years of age) healthy individuals, focusing on immune response, cytokine secretion, nitrosative stress, and phagocytic activity.ResultsOur study demonstrated that DEP and WFS extract (WFSE) significantly upregulated expression of the oxidative stress marker, heme-oxygenase-1 (HO-1) in MDMi after 24 hr, with levels normalising by 96 hr, indicating a transient oxidative stress response. Both DEP and WFSE elicited distinct inflammatory cytokine profiles. DEP induced a rapid response, increasing TNF-α, IL-6, IL-23, and IL-33 within 2 hr in young MDMi and 24 hr in aged MDMi. In contrast, WFSE triggered a delayed but sustained inflammatory response, with TNF-α, IFN-γ, IL-23, and IL-33 levels persisting at 96 hr in aged MDMi, highlighting an age-related vulnerability to air pollutant-induced inflammation.Both pollutants activated p38, ERK, and NF-κB pathways, with p38 activity resolving by 96 hours and ERK activation persisting, reflecting their distinct roles in cellular stress and inflammation. NF-κB p65 nuclear translocation, observed at 24 hours, highlighted its critical role in cytokine release and inflammation following exposure to DEP and WFSE. This is the first report of NF-κB activation in human microglia exposed to air pollutants.ConclusionsThese results highlight the distinct and potentially harmful effects of DEP and WFSE on immune and inflammatory responses in MDMi, particularly in ageing populations, with significant implications for brain health. DEP triggers acute oxidative stress and inflammatory responses, while WFSE induces more prolonged effects, especially in aged microglia. Both pollutants activate the MAPK and NF-κB pathways and exhibit unique cytokine profiles, underscoring their overlapping yet distinct mechanisms of action. These findings advance our understanding of air pollutant-induced neuroinflammation and its contribution to neurodegeneration, providing a foundation for developing targeted interventions to mitigate the neurotoxic effects of air pollution.