Breast cancer is a leading cause of mortality and morbidity among females worldwide. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we provided an updated comprehensive assessment of the epidemiological trends, disease burden, and risk factors associated with breast cancer globally, regionally, and nationally from 1990 to 2023. Breast cancer incidence, mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) were estimated by age and sex for 204 countries and territories from 1990 to 2023. Mortality estimates were generated using GBD Cause of Death Ensemble models, leveraging data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Mortality-to-incidence ratios were calculated to derive both mortality and incidence estimates. Prevalence was calculated by combining incidence and modelled survival estimates. YLLs were established by multiplying age-specific deaths with the GBD standard life expectancy at the age of death. YLDs were estimated by applying disability weights to prevalence estimates. The sum of YLLs and YLDs equalled the number of DALYs. Breast cancer burden attributable to seven risk factors was examined through the comparative risk assessment framework. The GBD forecasting framework was used to forecast breast cancer incidence and mortality from 2024 to 2050. Age-standardised rates were calculated for each metric using the GBD 2023 world standard population. In 2023, there were an estimated 2·30 million (95% uncertainty interval [UI] 2·01 to 2·61) breast cancer incident cases, 764 000 deaths (672 000 to 854 000), and 24·1 million (21·3 to 27·5) DALYs among females globally. In the World Bank low-income group, where a low age-standardised incidence rate (ASIR) was estimated (44·2 per 100 000 person-years [31·2 to 58·4]), the age-standardised mortality rate (ASMR) was the highest (24·1 per 100 000 [16·8 to 31·9]). The highest ASIR was in the high-income group (75·7 per 100 000 [67·1 to 84·0]), and the lowest ASMR was in the upper-middle-income group (11·2 per 100 000 [10·2 to 12·3]). Between 1990 and 2023, the ASIR in the low-income group increased by 147·2% (38·1 to 271·7), compared with a 1·2% (-11·5 to 17·2) change in the high-income group. The ASMR decreased in the high-income group, changing by -29·9% (-33·6 to -25·9), but increased by 99·3% (12·5 to 202·9) in the low-income group. The increase in age-standardised DALY rates followed that of ASMRs. Risk factors such as dietary risks, tobacco use, and high fasting plasma glucose contributed to 28·3% (16·6 to 38·9) of breast cancer DALYs in 2023. The risk factors with a decrease in attributable DALYs between 1990 and 2023 were high alcohol use and tobacco. By 2050, the global incident cases of breast cancer among females were forecast to reach 3·56 million (2·29 to 4·83), with 1·37 million (0·841 to 2·02) deaths. The stable incidence and declining mortality rates of female breast cancer in high-income nations reflect success in screening, diagnosis, and treatment. In contrast, the concurrent rise in incidence and mortality in other regions signals health system deficits. Without effective interventions, many countries will fall short of the WHO Global Breast Cancer Initiative's ambitious target of achieving an annual reduction of 2·5% in age-standardised mortality rates by 2040. The mounting breast cancer burden, disproportionately affecting some of the world's most vulnerable populations, will further exacerbate health inequalities across the globe without decisive immediate action. Gates Foundation, St Jude Children's Research Hospital.
Significant genetic heterogeneity has hindered the identification of molecular targets and development of effective targeted therapies for triple negative breast cancer. Currently available targeted therapies are not curative for TNBC patients. Eukaryotic Elongation Factor-2 kinase (eEF2K) is a clinically significant proto-oncogenic therapeutic target linking this atypical alpha kinase to poor patient survival and a key driver of tumor growth and progression in TNBC, positioning it as a critical and emerging molecular target. Development of eEF2K inhibitors for clinical translation has been challenging due to the unknown three-dimensional structure and lack of potent and selective eEF2K inhibitors. Here, we employed a homology modeling, in silico physics-based molecular simulations studies to rationally design, synthesize and in vitro and in vivo identification a novel potent eEF2K inhibitor. The lead compound-2I demonstrated a potential to engage in covalent interactions with eEF2K enzyme, as suggested by in silico covalent docking and static interaction analyses, and significant in vitro inhibitory activity and suppressed primary and multidrug resistant TNBC cell proliferation at submicromolar concentrations, induced ferroptosis and apoptosis, while having no impact on normal breast epithelial cells. In vivo systemic injection of the eEF2K inhibitor encapsulated in single-lipid nanoparticles demonstrated remarkable therapeutic efficacy and suppressing tumor growth in multiple orthotopic TNBC xenograft models in mice with no sign of toxicity. eEF2K inhibition synergistically enhanced the efficacy of standard chemotherapeutics such as paclitaxel. Our results indicate that the novel eEF2K-targeted nanotherapy is safe and has a significant potential for clinical translation as a monotherapy or in combination with chemotherapy for treatment of patients with TNBC or other eEF2K-dependent solid cancers.
Bone and skeletal muscle are essential components of musculoskeletal system, enabling movement, load-bearing, and systemic homeostasis. These tissues communicate through dynamic bone-muscle crosstalk mediated by cytokines, growth factors, and extracellular-matrix (ECM) proteins. The spatial organization of these mediators is critical for maintaining tissue integrity, and its disruption contributes to diseases, such as osteoporosis, sarcopenia, and metabolic syndrome. Despite this importance, spatial transcriptomics (ST) studies of bone-muscle interactions remain limited. Here, we applied 10x Genomics Visium ST with computational tools, e.g., SMART and CellChat, to deconvolute cell-type composition and characterize cell-cell communication networks and ligand-receptor (L-R) interactions in mouse femur and adjacent skeletal muscle. We identified eight major cell types (erythroid cells, endothelial cells, skeletal muscle cells, osteoblasts, myeloid cells, monocytes/macrophages, mesenchymal stem cells, and adipocytes) with distinct spatial transcriptional profiles and thirteen CellChat-inferred pathways, such as ECM-receptor related (e.g., COLLAGEN, TENASCIN, THBS) and secreted-signaling involved (e.g., VEGF) pathways. Representative L-R pairs include Col1a1/Col1a2-Sdc4, mediating osteoblast-to-muscle interactions, and Col4a1-Sdc4, facilitating muscle-to-osteoblast interactions in COLLAGEN, Tnxb-Sdc4 in TENASCIN, supporting muscle-to-osteoblast/muscle/myeloid/endothelial communication, Comp-Sdc4 in THBS, driving monocyte/macrophage-to-osteoblast/muscle signaling, and Vegfa-Vegfr1/Vegfr2 in VEGF, mediating muscle-to-endothelial/myeloid signaling. Immunostaining validated colocalization of several representative L-R pairs with their corresponding cells. Additionally, independent mouse and human bone scRNA-seq datasets reproduced most of the pathways and L-R pairs identified in ST, underscoring the robustness and cross-species relevance of our findings. Together, we present an initial spatially resolved transcriptome-wide map of bone-muscle intercellular communication, providing novel insights into molecular crosstalk and establishing groundwork for future studies in musculoskeletal disorders.
This study was aimed to investigate the blood composition and potential mechanism of Liujun Jiaoxian Tang (LJJXT) for treating sepsis. After drug intervention in rats, the main active components in LJJXT liquid and serum were identified by UPLC-QE-MS analysis. The effective components and their targets of LJJXT were further screened through the TCMSP database; the disease-related action targets were retrieved by using the Disgenet and Genecards databases. The intersection of the two sets of targets was taken to construct the "LJJXT-components-targets-diseases" network, PPI diagram, GO and KEGG enrichment analysis diagram. Subsequently, molecular docking studies were conducted on the key targets for treating diseases screened by PPI and the corresponding effective components in LJJXT. There were 2159 active ingredients in LJJXT, of which 90 were effective in blood. The 20 screened active ingredients matched 139 targets. There were a total of 2585 disease-related targets, and 76 targets shared by drugs and diseases. There were 2113 biological processes, 43 cell components, 211 molecular functions in GO analysis and 172 pathways obtained by KEGG analysis. The results showed that LJJXT may act on AKT1, TNF, PTGS2 and other targets through the active ingredients in blood such as terpenoids, flavonoids, phenols and alkaloids. It was involved in the regulation of lipid and atherosclerosis, toxoplasmosis, and other signaling pathways to play anti-inflammatory, immune enhancement, reduce oxidative stress and other effects, so as to exert drug efficacy and alleviate sepsis. Molecular docking results showed that kaempferol and vitamin A had high affinity with key therapeutic targets involved in lipid and atherosclerotic signaling pathways, and the combination of kaempferol and JUN was the best. This study revealed the effective ingredients and potential mechanisms of LJJXT for treating sepsis, providing sufficient theoretical basis for its clinical treatment of sepsis and subsequent basic research.
Anemia remains a critical global health burden, often driven by impaired erythropoietin (EPO) signaling, which reduces red blood cell production. While recombinant EPO therapy is effective, its high cost and associated safety concerns limit its accessibility. This study explores microbial metabolites as affordable and safe alternatives that act as EPO mimetics that can bind and activate the erythropoietin receptor (EPOR). A computational screening of 90 microbial bioactive compounds was conducted, and from those, 16 were selected for detailed analysis. The extracellular domain of EPOR (PDB: 1EBP) was used as the target protein. Molecular docking was performed using AutoDock, followed by ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling with SwissADME and ProTox-III. Protein-protein interaction (PPI) networks were also analyzed in Cytoscape, and the stability of the top complexes was validated via 100 ns molecular dynamics (MD) simulations. Docking results identified Abyssomicin W, Abyssomicin C, and Camptothecin as the top candidates with strong binding affinities (-7.60 kcal/mol) to EPOR. ADMET predictions confirmed their favorable drug-likeness and safety profiles, with Abyssomicin W exhibiting the most promising characteristics, including high gastrointestinal absorption, and no predicted hepatotoxicity or carcinogenicity. PPI network analysis underscored the functional relevance of EPOR in erythropoietic pathways, while molecular dynamics (MD) simulations revealed that Abyssomicin W and Camptothecin formed highly stable complexes with the receptor, whereas the Abyssomicin C complex was unstable. The integrated computational pipeline successfully identified Abyssomicin W as the most stable and promising EPO mimetic candidate. In conclusion, this study identifies Abyssomicin W as a potential and stable EPO mimetic candidate, highlighting the potential of microbial metabolites as cost-effective therapeutics for anemia. Further experimental validation, including direct binding and functional cell-based assays is recommended to confirm its efficacy and safety in biological systems.
In biomedical and engineering research, entropy metrics have become well-established tools for assessing the complexity of dynamic and physiological systems. This scoping review examines the relationship between information theory quantifiers (ITQs), bioelectrical signals, and time series, and the limited diagnostic value of these measures in functional dyspepsia (FD). Three main variables were defined in this study: entropy, health experiments, and FD. Eighty-five academic documents were analyzed using the PRISMA methodology, following 4 phases: (i) heuristic; (ii) classification and systematic review; (iii) hermeneutic analysis; and (iv) presentation of results. ITQs are currently applied in the study of neurodegenerative diseases, cardiological conditions, and, to a lesser extent, gastric disorders, thereby opening new avenues for diagnosis and comprehensive clinical management. The review of the documents shows that, despite the methodological robustness, statistical testing, classification approaches, and the breadth of entropy measures employed, significant challenges remain when integrating these techniques due to the intrinsic complexity and heterogeneity of bioelectrical signals in FD. Furthermore, knowledge gaps persist, particularly in digestive disorders such as FD, underscoring the need to deepen and diversify analytical methodologies.
Ras proteins are prominent oncogenes, with KRas mutations found in approximately 80% of cancer cells harboring Ras mutations. The mechanism by which Ras mutations cause cancer remains unclear. Human Son of Sevenless (SOS) promotes the GDP-to-GTP exchange in the inactive GDP-bound Ras (RasGDP) by interacting with RasGDP conformation, thereby leading to the development of human cancer. Elucidating the Ras-SOS interaction mechanism can guide the drug design for Ras and SOS proteins. Based on our previously sampled special structure KRasGDP·Mg2+S1.2, this study constructs a functional ternary complex (KRasGDP·Mg2+)·SOS1·(KRasGTP·Mg2+). Furthermore, the KRas-SOS1 interactions regulated by the KRas G12D mutation and the SOS1 inhibitor BI-3406 that reportedly exhibits synergistic effects with G12D-mutant Ras inhibitors, are investigated through molecular dynamics (MD) simulations. The findings reveal that the G12D mutation and BI-3406 both affect the KRas-SOS1 interaction via the Switch-II (SW2) region of KRas. The negatively charged Asp12 has a repulsive effect on KRas, particularly on SW2, altering the interfacial electrostatic landscapes and diminishing the binding affinities by approximately 25 kcal/mol for both KRasGDP·Mg2+ and KRasGTP·Mg2+. BI-3406 forms a hydrogen-bond bridge between SW2 and SOS1 in wild type (WT) KRas, interrupting the interactions among the N-terminal residues of SW2 and SOS1. Moreover, BI-3406 was found here to attenuate the binding affinity of both WT and G12D-mutant KRasGDP·Mg2+ to SOS1. Interestingly, BI-3406 hardly affects the binding affinity of WT KRasGTP·Mg2+, while enhances the binding affinity of G12D-mutant KRasGTP·Mg2+. The change of binding affinity makes the catalytic pocket of SOS1 prefer to KRasGTP·Mg2+ and inhibits the growth of G12D-mutant KRas-driven tumors. These mechanistic insights provide valuable information for designing SOS1-co-targeting inhibitors to potentiate antitumor efficacy against G12D-mutated KRas.
Life thrives in Earth's most inhospitable environments, from boiling hydrothermal vents to hypersaline lakes and frozen polar deserts, thanks to the remarkable adaptations of extremophilic microorganisms. The study of these organisms has rapidly evolved from early cultivation-based discoveries to a data-rich discipline powered by advanced omics technologies. This review comprehensively outlines the current landscape and future directions in extremophile research, emphasizing the pivotal role of bioinformatics, machine learning (ML), and data-driven approaches. We begin by charting the evolution of methodologies, from innovative in situ cultivation techniques and robust biomolecule extraction protocols to modern multi-omics workflows (metagenomics, transcriptomics, proteomics, and metabolomics) that decode the genetic and functional basis of extremophiles. We then catalogue essential bioinformatics resources and specialized databases critical for annotating extremophile genomes and uncovering their unique adaptive strategies, including protein stabilization and syntrophic metabolic relationships. Finally, we explore the transformative potential of artificial intelligence (AI) and ML in overcoming fundamental challenges in the field. These include predicting the functions of uncharacterized "hypothetical" proteins, identifying novel extremozymes, modeling complex genotype-phenotype relationships, and guiding the targeted engineering of industrially relevant strains. By synthesizing insights across these domains, this review highlights how integrating computational biology and AI is poised to unlock the full biotechnological potential of extremophiles and redefine the boundaries of life itself.
Identifying repurposable therapeutic targets for Alzheimer's disease (AD) remains challenging due to various clinical and biological factors. This study aimed to identify candidate genes for AD therapy. We hypothesize that gene and disease-specific network properties-learnable from these large-scale biomedical knowledge graphs-can inform implicit gene-AD connections and prioritize repurposable AD drug targets. To evaluate the hypothesis, we focused on druggable genes curated from Drug-Gene Interaction Database and Alzheimer's Knowledge Base (AlzKB). We applied scalable random walk methods to Hetionet to learn unbiased gene and disease embeddings, representative of their topological and semantic network properties. The embeddings were then used to compute gene-AD similarity and derive network-based scores for each gene. To validate the scores, using Alzheimer's Disease Sequencing Project (ADSP) data, we constructed AD classifier models with Tree-based pipeline optimizer 2 (TPOT2), an automated machine learning framework. Models were optimized for performance, model complexity, and high aggregate network-based scores. Network-based scores successfully prioritized diverse feature sets-many not previously associated with AD-that are enriched in biologically meaningful body parts such as brain, and pathways including neuronal signaling, potassium channels, and creatine metabolism. The results suggested that knowledge graphs and network-informed embeddings can capture both known and novel insights into AD mechanisms. Additionally, integrating networkbased scores with feature-set-guided TPOT2 offers a scalable and biologically interpretable framework for AD drug repurposing and discovery.
Precision neurotherapeutics represents a transformative paradigm shift from standardized "one-size-fits-all" treatments of neurological, neurodegenerative, and/or psychiatric disorders toward individualized interventions that leverage patient-specific biological, behavioral, and physiological characteristics. Traditional neurotherapeutic approaches achieve modest response rates of 30-60% for first-line treatments, necessitating personalized strategies that account for individual differences in genetics, brain structure and function, and treatment response profiles. This review examines advances across three core domains: pharmaceutical approaches utilizing fragment-based drug discovery, pharmacokinetic modeling, and quantitative systems pharmacology; neuromodulation technologies evolving from open-loop to adaptive closed-loop systems with real-time biomarker feedback; and biomarker development spanning neuroimaging, pharmacogenomics, and digital health applications. Critical challenges include developing robust methodological frameworks for single-subject parameter estimation, addressing signal-to-noise ratio limitations in neuroimaging, and navigating complex regulatory landscapes. The convergence of artificial intelligence, computational modeling, and US Food and Drug Administration policy shifts toward in silico approaches creates unprecedented opportunities for mechanistically informed biomarkers that can guide truly personalized mental health care.
The superficial white matter (SWM), immediately beneath the cortical mantle, is thought to play a major role in cortico-cortical connectivity as well as large-scale brain function. Yet, this compartment remains rarely studied due to its complex organization. Our objectives were to develop and disseminate a robust computational framework to study SWM organization based on 3D histology and high-field 7T MRI. Using data from the BigBrain and Ahead 3D histology initiatives, we first interrogated variations in cell staining intensities across different cortical regions and different SWM depths. These findings were then translated to in vivo 7T quantitative myelin-sensitive MRI, including T1 relaxometry (T1 map) and magnetization transfer saturation (MTsat). As indicated by the statistical moments of the SWM intensity profiles, the first 2 mm below the cortico-subcortical boundary were characterized by high structural complexity. We quantified SWM microstructural variation using a nonlinear dimensionality reduction method and examined the relationship of the resulting microstructural gradients with indices of cortical geometry, as well as structural and functional connectivity. Our results showed correlations between SWM microstructural gradients, as well as curvature and cortico-cortical functional connectivity. Our study provides novel insights into the organization of SWM in the human brain and underscores the potential of SWM mapping to advance fundamental and applied neuroscience research.
Tendons and ligaments are crucial connective tissues linking bones and muscles, yet achieving full functional recovery after injury remains challenging. We investigated the characteristics of tendon stem/progenitor cells (TSPCs) by focusing on the declining tendon repair capacity with growth. Using single-cell RNA sequencing on Achilles tendon cells from 2- and 6-week-old mice, we identified Cd55 and Cd248 as novel surface antigen markers for TSPCs. Combining single-cell RNA sequencing with single-nucleus RNA and ATAC sequencing analyses revealed that Cd55- and Cd248-positive fractions in tendon tissue represent TSPCs, as confirmed by their expression of established TSPC markers, with this population decreasing at 6 weeks. We also identified candidate upstream transcription factors regulating these fractions. Functional analyses of isolated CD55/CD248-positive cells demonstrated high clonogenic potential and tendon differentiation capacity, forming functional tendon-like tissue in vitro. This study establishes CD55 and CD248 as novel TSPC surface antigens, potentially advancing tendon regenerative medicine and contributing to the development of new treatment strategies for tendon and ligament injuries.
University students face various stresses, including academic and career anxieties and a lack of interpersonal relationships. These stresses can elevate psychological burdens, negatively affecting their studies and daily lives. This pilot study aims to quantitatively evaluate the effects of mindful breathing exercises using tablet devices on autonomic nervous system activity in university students by analysis of finger plethysmogram (pulse wave amplitude values) and chaos analysis (Lyapunov exponent and fractal dimension). In this parallel-group randomized controlled trial, 18 nursing students (Mindful Breathing Group [Mi group], n = 9; control group [nMi group], n = 9) were randomly assigned. On the first day, the Mi group performed mindful breathing, the nMi group performed cross fixation, and finger plethysmograms were measured. For the next 9 days, the Mi group performed mindful breathing at home before bedtime, while the nMi group performed cross gazing, and finger plethysmograms were measured on days 1 and 9. Data were analyzed using one-way analysis of variance and t-tests. The Mi group showed a significant increase in pulse wave amplitude values over time (P = .001), whereas the nMi group showed a decrease (P = .001). Chaos analysis revealed no statistically significant differences between groups in the fractal dimension or Lyapunov exponent. Although descriptive differences were observed, these did not reach statistical significance. Both groups demonstrated positive Lyapunov exponents, suggesting nonlinear characteristics of the pulse wave signals. Mindful breathing using tablet devices may be associated with changes in pulse wave amplitude in university students, which could reflect alterations in peripheral autonomic activity under the present experimental conditions. However, no statistically significant differences were observed in chaos analysis indices. Further research with larger samples and additional physiological measures is required to clarify the relationship between mindful breathing and nonlinear autonomic dynamics. UMIN Clinical Trials Registry (UMIN000056166; Registered November 15, 2024).
A 4-dimensional spatial gene expression atlas of Hordeum vulgare (barley) grain development and germination was generated using spatial transcriptomic analysis of serial sections to reconstruct transcript abundance in 3 physical dimensions and with temporal kinetics. We investigated the subtissue localizations of specific biological activities, using energy biology as an example, including genes encoding proteins involved in starch synthesis and degradation, sugar transport, and mitochondrial and chloroplast activity. This atlas revealed different patterns in gene expression across tissues and developmental stages. Heterogeneity in gene expression was observed between clusters, within domains of the individual clusters, across 2-dimensional (xy, 55-μm resolution) and 3-dimensional (xyz, 8-μm resolution in z-plane) axes. Yet, other genes, including typical housekeeping genes such as Actin, Tubulin, and others, display homogeneous expression patterns. Expression of several genes matched previous gene-specific studies in different barley varieties verifying the robustness of the approach and indicating that patterns of gene expression are conserved at least for some categories of genes between varieties. Trajectory analysis of aleurone tissue spanning from early development to the completion of germination, provided a comprehensive roadmap of tissue development in terms of processes and identified transcription factors with spatial specificity that play roles in seed development and germination. A public visualization browser is available to view 2- and 3-dimensional transcription abundance profiles at https://barley-4d.latrobe.edu.au/or https://barley-4d-gene-atlas.hutton.ac.uk/.
Glycerol kinase (GK) is a key part of glycerol metabolism. It connects the metabolic pathways for lipids and carbohydrates by phosphorylating glycerol to glycerol-3-phosphate in an ATP-dependent reaction. This is essential for maintaining carbohydrate homeostasis, plasma glycerol withdrawal, and the utilization of glycerol by different tissues. Together, these processes impact glucose uptake and lipid metabolism. This review discusses the structure of GK, highlights the implications of mutations in the primary sequence, and provides insights on the roles of the various functional domains in the GK-catalyzed reaction. It also discussed the roles of GK in glycerol metabolism, energy production, and its connections with various cellular pathways and disease conditions. The proper regulation of GK activity is crucial, reflecting its critical role in various important cellular processes. Therefore, its regulation has been analyzed from the gene level to posttranslational modification and has implications for GK-linked disease. Separately, the critical role of this enzyme in some disease-causing organisms made it a promising target for inhibitor development. We here explore the current state of GK inhibitor research and discuss strategies for their development. Challenges in GK inhibitor research are identified, and approaches such as high-throughput screening, structure-based drug design, and computational modelling for discovering novel inhibitors are reviewed. Finally, the review highlights critical areas for further research, including the role of GK in synthetic biology and tumour development, among others.
Cellular senescence is a key mechanism of skeletal aging in both physiological and accelerated conditions, such as radiotherapy. This study aimed to identify common differentially regulated microRNAs (miRs) across these contexts. We performed miR sequencing on three models: femurs from young (5-month-old) versus old (24-month-old) mice; focally radiated versus non-radiated femurs; and osteocytes from young versus old mice. Osteocytes were included in the comparison, as they have the longest lifespan in the mineralized bone matrix and they form 90-95% of all mesenchymal bone cell types. Among the three groups, miR-135a-5p and miR-671-5p were the common (i.e., shared) miRs that were downregulated, and miR-183-5p, a miR that regulates the WNT pathway, was the only shared upregulated miR, while miR-155-5p, a miR that regulates the Senescence-Associated Secretory Phenotype (SASP), was elevated in two conditions. The WNT-pathway has been positively associated with bone health and Sclerostin, a WNT-pathway inhibitor produced and secreted by osteocytes, has been implicated in accelerated skeletal deterioration following radiation. Thus, we used a neutralizing antibody to Sclerostin (Scl-Ab), to assess genes related to the WNT pathway and senescence, which are regulated by miR-183-5p and miR-155-5p, respectively. We further performed miR sequencing in radiated bones from mice treated with Scl-Ab and identified miR-133a-3p, a key miR that inhibits bone metabolism and function, which is upregulated in accelerated skeletal aging (i.e., focal radiation) downregulated by Scl-Ab. Overall, our study identifies potential regulatory gene pathways that modulate skeletal aging in the presence and absence of a WNT activator, Scl-Ab.
Optical calcium imaging is a powerful tool for recording neural activity across a wide range of spatial scales, from dendrites and spines to whole-brain imaging through two-photon and widefield microscopy. Traditional methods for analyzing functional calcium imaging data rely heavily on spatial features, such as the compact shapes of somas, to extract regions of interest and their associated temporal traces. This spatial dependency can introduce biases in time trace estimation and limit the applicability of these methods across different neuronal morphologies and imaging scales. To address these limitations, the Graph Filtered Temporal Dictionary Learning (GraFT) uses a graph-based approach to identify neural components based on shared temporal activity rather than spatial proximity, enhancing generalizability across diverse datasets. Here we present significant advancements to the GraFT algorithm, including the integration of a more efficient solver for the L1 least absolute shrinkage and selection operator (LASSO) problem and the application of compressive sensing techniques to reduce computational complexity. By employing random projections to reduce data dimensionality, we achieve substantial speedups while maintaining analytical accuracy. These advancements significantly accelerate the GraFT algorithm, making it more scalable for larger and more complex datasets. Moreover, to increase accessibility, we developed a graphical user interface to facilitate running and analyzing the outputs of GraFT. Finally, we demonstrate the utility of GraFT to imaging data beyond meso-scale imaging, including vascular and axonal imaging.
Artificial Intelligence (AI) technologies continue to expand their role in clinical medicine, with large language models (LLMs) and multimodal systems now applied to communication, imaging, and predictive analytics. Advances in generative and retrieval-augmented methods have improved the accuracy and contextual grounding of clinical summaries, patient messaging, and decision support. At the same time, new benchmarks in imaging, vision, and spontaneous speech have underscored both progress and the persistence of unsolved challenges. Predictive modeling efforts highlight causality, longitudinal trajectories, and informative clinical events, while methodological contributions emphasize uncertainty management, abstention, and interpretable causal structures. Finally, frameworks for evaluation and governance address the crucial gap between laboratory performance and real-world deployment.
Surface adhesion is critical to the survival of pathogenic bacteria both in natural niches and during infections, often via forming matrix-embedded communities called biofilms. Vibrio cholerae, the causal agent of pandemic cholera, is capable of forming biofilms adhering to both biotic and abiotic surfaces and the biofilm lifestyle has been implicated in promoting the survival of V. cholerae both in the natural reservoir and during host colonization. Previously, a 57-amino acid loop in the biofilm-specific adhesin Bap1 (Bap1-57aa) has been identified as a key contributor to the adhesion of V. cholerae biofilms to various surfaces including lipid membranes. However, the mechanism underlying its interaction with lipids, as well as its secondary structures, remain unresolved. Here, we combined biophysical, computational, and genetic approaches to elucidate the molecular mechanism of how this adhesive peptide interacts with lipids and lipid-coated surfaces. We found that a central aromatic-rich motif anchors the peptide to lipid bilayers while peripheral pseudo repeats enhance binding through avidity. Surprisingly, the core motif undergoes a lipid-induced conformational transition into a β-hairpin, enabling robust membrane insertion. We confirmed these findings both in vitro and in the biofilm context. Moreover, we demonstrated that the adhesive peptide can adhere to model host surfaces and is sensitive to membrane curvature. Finally, we show that the biofilm-derived peptide is found in several other Vibrio species, and its sequence is well-conserved. Our results provide molecular insight into biofilm adhesion and may lead to new strategies for targeted biofilm removal, as well as the design of bioinspired underwater adhesives.
Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for early and accurate diagnosis to support effective prevention and treatment strategies. This study presents a machine-learning-based approach for predicting heart disease using clinical and demographic data from a publicly available dataset. Four widely used classification algorithms-Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and Decision Trees-were evaluated to identify the most effective predictive model. The dataset underwent comprehensive preprocessing, including handling missing values, categorical encoding, and feature normalization, to enhance data quality and model robustness. Model performance was assessed using accuracy, precision, recall, and AUC-ROC metrics. Findings show that hyperparameter-optimized models, particularly Random Forest and KNN, demonstrated strong predictive performance. Explainability techniques, specifically SHapley Additive exPlanations (SHAP), were incorporated to improve interpretability, transparency, and clinical trust. SHAP values were used to analyze feature importance and provide explanations for individual predictions. The results underscore the potential of interpretable machine-learning models as valuable tools for early diagnosis, risk stratification, and clinical decision support. Future research should employ larger datasets and investigate real-time predictive applications further to enhance the generalizability and clinical utility of these models.