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Host dependency factors (HDF) are essential for viral replication and are promising targets for broad-spectrum antivirals. However, most work has focused on individual viruses or individual data types, limiting our understanding of shared host mechanisms across viruses. We developed a pan-viral framework that integrates multi-omics data-including genome-wide perturbation screens, single-cell transcriptomes and viral interactomes-and combines graph-based learning with classical machine-learning models to prioritize HDF for four RNA viruses (SARS-CoV-2, influenza A virus, dengue virus and Zika virus). Across viruses, the framework achieved high discrimination, with area under the receiver operating characteristic curve (ROC-AUC) greater than 0.90 on benchmark datasets, and identified a conserved signature of 118 genes shared by all four viruses and 427 genes shared by at least three. These genes converge on recurrent host programmes such as clathrin-mediated entry and endomembrane trafficking, nuclear transport, RNA processing and stress granules, and proteostasis and ubiquitin-proteasome signalling. The pan-viral signature generalizes beyond the training set, as genes shared by three or more viruses are strongly enriched among top-ranked Ebola virus candidates. We further provide a prioritized shortlist and an experimental validation roadmap to guide follow-up perturbation studies. Our integrative multi-omics and machine-learning approach outlines a prediction-based, data-driven map of pan-viral host liabilities and highlights tractable opportunities for host-directed therapy against diverse RNA viruses.

African swine fever virus (ASFV) is a large, complex DNA virus that causes a haemorrhagic disease with lethality rates approaching 100%. Mechanisms of virulence are still poorly understood, but loss of members of the multigene families (MGF) is commonly observed in naturally occurring attenuated virus strains, indicating that these proteins play an important role in disease pathogenesis. Here, a suppressor of cytokine signalling (SOCS)-box like motif was identified in proteins of the MGF505 cluster. SOCS-box motifs typically recruit the Cullin-RING-ligase (CRL) machinery, a superfamily of E3 ubiquitin ligases that target proteins for proteasomal degradation. Data presented show that MGF505-1R inhibits the host innate immune responses controlled by the activation of the transcription factors IRF3 and NFκB. MGF505‑1R expression was shown to correlate with a reduction in the protein levels of the transcriptional co‑activator p300. Targeted mutations of the SOCS box motif were shown to reverse the observed IRF3 and NFκB inhibition. The data support a role for MGF505-1R in hijacking the host ubiquitin machinery to evade the host immune responses.

The gut microbiome supports digestion, immunity, and metabolism; its imbalance (dysbiosis) drives inflammation and metabolic dysfunction, contributing to chronic diseases such as diabetes, cardiovascular disease, inflammatory bowel disease, and autoimmune disorders. Medicinal plants provide a wide range of phytochemicals (such as polyphenols, flavonoids, alkaloids, saponins), which reach the colon and undergo two-sided interactions with microbes in the gut, acting as potential microbiome modulators and substrates of biotransformation into bioactive metabolites. This structured narrative review synthesises evidence from peer-reviewed studies indexed in PubMed, Scopus, and Web of Science over the last 10 years on the role of medicinal plants in microbiome-mediated chronic disease modulation. This literature is organised into three mechanistic axes: (i) perturbations, defined here as measurable shifts in microbial diversity or taxonomic composition relative to a baseline or healthy reference state, together with beneficial taxa enrichment; (ii) alterations in microbial metabolite output, especially short-chain fatty acids (SCFAs) and other immunometabolic mediators; and (iii) downstream host metabolic and immune signalling. Rather than broad descriptive summaries, the literature is organised using an axis-based mechanistic framework, highlighting key translational constraints such as botanical heterogeneity, dose/formulation variability, and inconsistent microbiome endpoint standardisation, that must be addressed to strengthen human evidence and clinical relevance. Illustrative microbiome-mediated processes involve botanicals such as turmeric (curcumin), ginseng (ginsenosides), and green tea (catechins), though evidence strength varies by study design. Future progress requires standardised phytochemical characterisation, microbiome-stratified trials, and integration of multi-omics with artificial intelligence analytics to enhance mechanistic insight, identify responders, and enable personalised plant-based microbiome therapies.

Optically pumped magnetometers magnetoencephalography (OPM-MEG) have demonstrated their value in the diagnosis and mapping of epilepsy, as well as their advantages in pediatric applications. We present a case of 8-year-old boy with drug-resistant epilepsy, whose epileptogenic lesion is in left Broca's region. The boy underwent language function evaluation and localization by on-scalp OPM-MEG before surgery. Dipole clusters and Dipole Density of epileptogenic signals by OPM-MEG were located in the left inferior frontal gyrus, though language verbal generation mapping of OPM-MEG signals were mainly located in left frontal orbital gyrus, indicating a localization of the language function area. Seizure freedom and no loss of language function were achieved after MRI-guided laser interstitial thermal therapy. This article underscores the feasibility of using OPM-MEG to record abnormal discharges of seizure and assess language function area in children, especially in drug-resistant epilepsy surgery involving brain functional areas.

Accurate detection of KRAS codon mutations is essential for precision oncology in colorectal cancer (CRC), yet conventional liquid biopsy methods often lack sufficient sensitivity for rare ctDNA variants, particularly in early diseases. We developed a three-dimensional (3D) plasmonic KRAS microarray integrating blocked recombinase polymerase amplification with plasmon-enhanced fluorescence. Quencher-modified blocking probes suppress wild-type DNA while selectively enabling mutant signal amplification. A single primer-probe set per codon allows comprehensive detection of all substitutions within KRAS codons 12/13, 61, and 146. The platform achieved detection down to 1 fM by direct hybridization and 100 zM after blocked amplification, exceeding conventional PCR and next-generation sequencing sensitivity. Codon-level specificity was validated in CRC cell lines, with distinct signals for each mutation. Clinical analysis of 58 patients showed 100% concordance between tissue, plasma, and urine in mutation-positive malignant cases when sufficient input was available, indicating accurate reflection of tumor profiles. In benign tumors, detection was rare despite tissue mutations, likely due to limited ctDNA release.This plasmonic microarray enables ultra-sensitive, specific, and non-invasive detection, supporting early diagnosis, minimal residual disease monitoring, and longitudinal CRC management.

In the last two decades, Neuropeptide S (NPS) has been identified as a key bioactive peptide in the mammalian brain, influencing fear, anxiety, wakefulness, reward, and learning. While some reviews have addressed its role in reward-seeking and anxiety, few have addressed its particular role in learning and memory. The neuropeptide S receptor 1 is highly expressed in key areas for learning processing, such as the hippocampus, cortex, thalamus, and amygdala. This review aims to examine evidence from human and animal studies that focused on the NPS system's role in modulating learning and memory. A special focus is given to experiments addressing the impact of NPS on associative learning leading to addiction and in fear conditioning, pointing to its potential therapeutic value in associated pathologies. An advanced search was conducted using the databases PubMed, Google Scholar, Web of Science, and Scopus, focusing on memory and Neuropeptide S. The reviewed data suggest that NPS modulation occurs at all memory phases, including acquisition, consolidation, and retrieval, and in extinction learning, whether motivated by appetitive or aversive stimuli. The summarized evidence shows that the NPS system interferes with working and short-term memory, mitigates learning impairments, enhances spatial and object memory consolidation, supports fear extinction learning and inhibitory avoidance consolidation, and reinstates drug-seeking behaviors. The NPS system closely interacts with key neuromodulators, including orexinergic, dopaminergic, and noradrenergic systems, in influencing memory. The Neuropeptide S system emerges as a critical modulator of memory processes. The NPS signaling may preferentially influence learning that involves emotionally or motivationally relevant stimuli. This highlights the NPS system's potential as a target for therapeutic interventions for particular memory impairments.

cGAS-STING signaling can promote antitumor immunity, and tumor cell STING is suppressed in a variety of cancer subtypes that resist immune checkpoint blockade. Although STING agonists have failed clinical trials, precision approaches targeting restoration of tumor cell STING expression have yet to be explored. Here, we report that head and neck squamous cell cancer (HNSCC) exhibits a mechanism of STING suppression related to upregulation of protein tyrosine phosphatase non-receptor (PTPN) type 2 (PTPN2) that is also evident in other cancers. PTPN2 inhibition (PTPN2i) increases HNSCC tumor cell STING by restoring IFNγ-STAT1-mediated induction of STING mRNA. This restores sensitivity to STING agonism and natural killer cell activation, suppressing tumor growth in an immune cell-dependent manner in anti-PD-1 refractory syngeneic HNSCC mouse tumor models in female mice. Together, these findings demonstrate that PTPN2i can unleash STING agonist response, providing a rationale for the evaluation of this therapeutic combination in HNSCC and potentially other cancer types.

Visual impairment affects over 2.2 billion people worldwide and the major causes include age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy. For research in these areas, although animal models offer a more physiologically complex system than in vitro approaches, their use raises ethical considerations, and species-specific differences such as variations in protein sequences and signaling pathways. This can limit the direct translatability of the outcomes. Traditional 2-D cell cultures, in contrast, lack the multicellular organization and dynamic microenvironment necessary to replicate human retinal complexity. Retinal organoids (ROs), three-dimensional tissue constructs derived from pluripotent stem cells, have emerged as a promising model due to their human origin and complex cellular interactions that cannot be achieved in conventional 2-D/3-D co-culture models. In this review, we provide a brief overview of the evolution from 2-D to 3-D retinal models, highlight the structural and functional features of ROs including the presence of layered retinal architecture, photoreceptor outer segment formation, and light-responsive electrophysiological activity and summarize their applications in disease modeling, drug discovery, and gene and cell therapy. ROs represent a significant advancement over traditional models by enabling the recapitulation of human-specific retinal development, facilitating the study of patient-derived disease phenotypes, and providing a platform for personalized therapeutic screening. Their development has deepened understanding of pathological mechanisms in conditions such as retinitis pigmentosa and AMD, while enabling preclinical testing of targeted interventions like CRISPR-based gene editing and photoreceptor cell replacement. Nonetheless, challenges remain in fully replicating retinal vascularization, long-term functional maturation, and synaptic connectivity, underscoring the need for continued refinement and integration with complementary model systems.

Human papillomavirus (HPV) is a well-established oncogenic virus implicated in the development of several epithelial cancers, most notably cervical, anogenital, and oropharyngeal carcinomas. In contrast, neuroendocrine neoplasms (NENs)-a heterogeneous group of malignancies arising from neuroendocrine cells across various organ systems-have not traditionally been linked to HPV infection. In this study, we performed extensive genomic and transcriptomic profiling to compare HPV-positive NENs to HPV-positive non-NENs across anatomical sites, aiming to uncover biologically and clinically actionable differences. HPV16- and HPV18-positive tumors were identified from 101,343 solid tumors profiled at Caris Life Sciences (Phoenix, AZ) with DNA and RNA sequencing. Prevalence of pathogenic mutations and copy number amplifications were calculated. Fisher's exact/&#x3c7;2 tests were applied appropriately with p-values adjusted for multiple comparisons (p&#x2009;<&#x2009;0.05). HPV positivity was most frequent in cervical carcinomas (70%, 1200/1716). Importantly, 6% (96/1620) of NENs from all tissues were positive for HPV16 or HPV18. Among HPV-positive NENs, 93% were high-grade compared to 54% observed in HPV-negative NENs (p&#x2009;<&#x2009;0.001), highlighting a strong association between HPV and tumor aggressiveness in this subset. Analysis of HPV-associated sites (cervix, anorectal region, and head and neck) revealed that HPV-positive NENs possess distinct genomic and transcriptomic landscapes compared to HPV-positive non-NENs. Notably, interferon signaling was significantly suppressed in HPV-positive NENs, suggesting a unique tumor-immune microenvironment. Our findings indicate that HPV-positive NENs form a distinct subset with unique genomic features, including reduced interferon signaling, compared to HPV-positive non-NENs. Thus, future studies focused on evaluating HPV status, along with genomic and transcriptomic characteristics, may uncover biologically and clinically actionable distinctions for this rare yet clinically significant tumor subgroup. Not applicable.

The progressive skeletal muscle degeneration observed in Duchenne Muscular Dystrophy (DMD) patients requires multiple cycles of satellite cells (SCs) activation to promote tissue regeneration. Dystrophic SCs present intrinsic defects, and the disrupting fibrotic niche hinders appropriate muscle recovery. Traditional 2D culture systems face challenges in modeling the DMD muscle niche and SCs behavior. Our aim was to validate a 3D culture of skeletal muscle spheroids (iSMS) for DMD modeling, as compared to the traditional 2D culture, while investigating the pathophysiological mechanisms of dystrophin deficiency in vitro. To compare iSMS with traditional 2D myogenic differentiation, we differentiated wild-type (WT), dystrophic (DMD) isogenic induced pluripotent stem cells (iPSCs), as well as iPSCs derived from DMD patients, characterized myogenic markers levels and assessed differences in proliferation and differentiation using RT-qPCR, immunofluorescence, and flow cytometry. Our data showed that iSMS improved PAX7 expression in vitro, while MYOD1, MYOG, MYF5, and MYH3 expression were significantly reduced. These findings suggest that, at three weeks of myogenic differentiation, iSMS cultures retained satellite-like cells in a less activated, progenitor-like state. Accordingly, we identified higher expression of canonical Notch signaling genes such as JAG1 and NOTCH1 in iSMS compared to 2D. We also characterized the response of 2D and iSMS to terminal differentiation medium, providing a valuable comparison with muscle fibers derived from human adult myoblasts. Additionally, we showed that DMD iSMS-derived progenitors proliferated at reduced levels compared with WT, a characteristic not observed in progenitors derived from 2D cultures. Finally, we performed iSMS and 2D myogenic differentiation of iPSC lines from three patients with DMD. Our results highlight important advantages of using the iSMS differentiation platform over 2D for DMD in vitro modeling. Exploring these 3D systems may help to gain a deeper understanding of SCs behavior to advance in novel treatments for DMD, which might be applicable to other forms of muscular disorders.

Expanders in organic Rankine cycle systems serve as critical energy-conversion components in low-grade waste heat recovery installations, yet their reliable operation is threatened by faults such as bearing defects, rotor imbalance, and blade cracking. Conventional diagnostic methods often struggle with non-stationary vibration characteristics, class imbalance, and low signal-to-noise ratios inherent to these working environments. This paper proposes an improved deep residual network, referred to as multi-scale convolutional block attention module residual network, that integrates a multi-scale parallel feature extraction module with convolutional block attention mechanisms for intelligent fault diagnosis. The multi-scale module employs three parallel convolutional branches with different kernel sizes to simultaneously capture transient impulses, periodic modulation, and low-frequency envelope features across multiple temporal scales. Attention-enhanced residual blocks sequentially recalibrate channel and spatial responses to emphasize fault-sensitive features while suppressing noise interference. A training optimization scheme combining Focal Loss, cosine annealing, and targeted data augmentation is further introduced to address the small-sample imbalanced-data challenge. Five-fold cross-validation experiments conducted on a 10&#xa0;kW single-screw expander test rig demonstrate that the proposed model achieves 98.11&#x2009;&#xb1;&#x2009;0.34% diagnostic accuracy across four health states, surpassing the standard deep residual network baseline by 6.57 percentage points, with only 3.27% relative accuracy degradation at 10&#xa0;dB signal-to-noise ratio. Ablation studies confirm a multiplicative synergy between the multi-scale and attention modules, statistical significance tests validate the robustness of the observed improvements, and comparative evaluations against six benchmark methods demonstrate the superiority and generalizability of the proposed approach.

Quantitative detection of Salmonella typhimurium is of vital importance for promoting food safety monitoring and control. This study successfully developed a 3D-printed microchannel device that integrates cleaning and detection functions, enabling sensitive and semi-automatization colorimetric detection of Salmonella typhimurium. Firstly, the magnetic beads modified monoclonal antibody (MBs-Anti), Salmonella typhimurium and PtRu@ZrFe-MOFs@Apt nanozymes were successively added to the centrifuge tube to facilitate the formation of the MBs-Anti-Salmonella typhimurium-PtRu@ZrFe-MOFs@Apt sandwich complex. Then, the above incubated mixture was injected into the reaction tank of the microchannel device and washed with PBS containing hydrogen peroxide to separate the sandwich complex from the impurities in the sample solution. Subsequently, the TMB substrate solution was added to facilitate the catalytic oxidation of the sandwich complex, thereby forming the blue oxidized TMB product. The RGB image of the blue product was captured using a portable smartphone device, and the colorimetric signal of the image was analyzed to determine the concentration of Salmonella typhimurium. The microchannel device can detect Salmonella typhimurium within a concentration range 101 to 106 CFU/mL within 75&#xa0;min, with a detection limit of 3.3 CFU/mL. It is worth noting that the 3D-printed microchannel device constructed has good universality. By replacing the corresponding antibodies, aptamers and other biological recognition elements, it can be extended to the detection of other pathogenic bacteria.

Accurate detection and segmentation of moving objects constitute a fundamental challenge in computer vision, particularly for intelligent video surveillance systems operating under variable illumination, dynamic backgrounds, and environmental noise. This paper presents a fully unsupervised dual-phase motion analysis framework that effectively combines statistical independence modeling and geometric contour evolution to achieve high-precision motion detection and segmentation. In the first phase, an enhanced Fast Independent Component Analysis (Fast-ICA) algorithm is employed to perform statistical decomposition of video sequences, exploiting temporal independence to distinguish moving foregrounds from static backgrounds. This process generates an initial motion mask with strong robustness to illumination variation and noise artifacts. In the second phase, a hybrid level set segmentation model integrating the global Chan-Vese formulation and a locally adaptive Yezzi-based energy function refines object boundaries through an adaptive energy minimization process. A stabilization term and a self-regulating convergence criterion are further incorporated to ensure contour smoothness, numerical stability, and resilience to topological changes. Comprehensive experiments conducted on the CDNet-2014 benchmark dataset demonstrate that the proposed method achieves an average recall of 0.9613, precision of 0.9089, and F-measure of 0.9310, outperforming several state-of-the-art supervised, semi-supervised and unsupervised background subtraction algorithms. The proposed Fast-ICA-Level Set fusion framework thus provides a robust, adaptive, and computationally efficient solution for real-world intelligent surveillance and autonomous visual monitoring applications.

Seizure forecasting and affective state analysis using EEG-ECG data play a pivotal role in advancing neurological and mental health monitoring. However, existing methods such as Fed-Transformer, Res-1D CNN, and Fed-ESD suffer from privacy risks, inefficient feature extraction, and high computational overhead, limiting their effectiveness in real-world applications. To overcome these challenges, this study proposes NeuroFedSense, a novel Federated Learning-enabled Privacy-Preserving Framework that integrates a Temporal Convolutional Network (TCN) with an Attention Mechanism for accurate seizure forecasting and affective state analysis using EEG-ECG data, ensuring enhanced feature selection, interpretability and efficient decentralized training. The model leverages adaptive attention-based optimization and weighted feature selection to improve classification performance while ensuring data privacy. Implemented using TensorFlow, NeuroFedSense achieves 99.54% accuracy, 99.62% precision, 99.34% recall, and a 99.46% F1-score, outperforming Fed-Transformer (97.10% accuracy), Res-1D CNN (81.62% accuracy), and FML (99.10% accuracy). The ROC-AUC score of 0.99 further establishes its superiority over competing models. Additionally, the federated approach reduces energy consumption per node by 30% and optimizes communication efficiency by minimizing data transmission by 15% over 100 rounds. By ensuring high accuracy, improved privacy, reduced computational overhead, and enhanced energy efficiency, NeuroFedSense sets a new benchmark for decentralized, real-time seizure prediction and affective state monitoring. These findings underscore its potential for deployment in intelligent, privacy-preserving healthcare applications, addressing critical challenges in remote neurological monitoring.

B cells are key contributors to the pathogenesis of many autoimmune diseases (AID), including multiple sclerosis (MS), and appear to evade the peripheral tolerance checkpoints that normally maintain immune homeostasis. The fate of B cells at these checkpoints is believed to be regulated by intracellular Ca2+ signaling cascades triggered through engagement of B cell receptors (BCR), and by the suppressive effects of regulatory T cells (Tregs). However, most of the current knowledge about Treg-B cell interaction comes from animal studies, while data from human studies, particularly in the context of AID, are sparse. In contrast, impaired Treg-mediated inhibition of conventional T cells (Tcons) has already been described for several AID, including MS. To assess the ability of Tregs to suppress activated B cells in healthy individuals and patients with MS. B and T cell populations were isolated from 40 MS patients and 98 age- and sex-matched healthy donors (HD). Single-cell live Ca&#xb2;&#x207a; imaging was used to assess early activation signals in B cells. In vitro proliferation assays and coculture experiments were employed to evaluate downstream responses, including proliferation, transcription factor activation (NFATc1, NF-&#x138;B), interleukin 6 (IL-6) release, and surface expression levels of antigen-presenting capacity (APC) markers both in anti-IgM/anti-CD40-stimulated B cells alone, and in the presence of Tregs. We demonstrate that Tregs exert a robust suppressive effect on B cell proliferation, IL-6 secretion and NFATc1 which is [1] independent of Ca2+ signaling [2], dependent on direct cell contact, and [3] impaired in MS. In contrast, early Ca2+ responses and downstream effects of anti-IgM/anti-CD40 stimulation, including activation of NFATc1 and NF-&#x3ba;B, as well as proliferation, did not differ between MS- and HD-derived B cells. This study provides new data on Treg-mediated suppression of B cells in humans, including at single-cell level. Our findings show that the Treg dysfunction in MS previously described in the context of Tcon regulation extends to B cell regulation. Given the critical role of B cells in MS pathogenesis, this impaired Treg-B cell interaction may represent a previously underappreciated disease mechanism with potentially important therapeutic implications.

Extracellular vesicles (EVs) are cell-secreted phospholipid bilayer vesicles that play a key role in intercellular communication by transporting molecular cargo and engaging in surface-level signaling. Due to their intrinsic biological features, EVs not only reflect the functional attributes of their originating cells but also hold promise as both therapeutic agent and natural carriers for targeted delivery. In recent years, plant-derived nanovesicles (PDNVs) containing bioactive molecules have attracted the attention of researchers because of their better biocompatibility, low immunogenicity, wide range of sources, and ability to act as natural therapeutic agents for diseases. PDNVs play an increasingly important role in human-plant interactions, as they are able to enter the human system and deliver effector molecules to cells, which in turn modulate cellular signaling pathways. PDNVs play a critical role in human health and disease. This review provides a comprehensive overview of PDNVs, encompassing their biogenesis, methods of isolation and purification, physicochemical characterization, stability, and storage strategies. It further explores their routes of administration, internalization, and biodistribution as therapeutic agents, highlighting their potential in the treatment of conditions such as inflammation, cancer, tissue regeneration, viral infections, liver and brain disorders, and osteoporosis. Lastly, the review examines current clinical applications of PDNVs and the key challenges hindering their broader implementation. We look forward to further exploration of the functions of PDNVs to facilitate their clinical translation and increase their benefits in humans.

Bone remodelling is essential for maintaining skeletal integrity by preserving the balance between bone formation and resorption, with excessive osteoclast activity contributing to osteoporosis. Osteocytes act as central regulators of osteoclastogenesis through mechanically sensitive paracrine signals, yet the influence of osteoblasts and their mesenchymal precursors remains less defined. Extracellular vesicles (EVs) have recently emerged as mediators of bone cell communication, although their role in osteoclast regulation are still underexplored. This study demonstrates that mesenchymal-derived bone cells inhibit osteoclastogenesis through an EV-dependent mechanism shaped by their differentiation stage and mechanical environment. Mechanically stimulated osteocyte-derived EVs showed the strongest anti-catabolic response. Notably, we identify miR-150-5p as a mechano-responsive miRNA enriched within osteocyte EVs, capable of inducing a dose-dependent reduction in osteoclastogenesis. Transcriptomic analyses reveal that EV treatment and miR-150-5p delivery induce substantial transcriptional changes in osteoclast precursors, including downregulation of shared target genes linked to bone remodelling. Overall, we highlight mechanically activated osteocytes as key regulators of osteoclastogenesis through an EV-mediated mechanism, in which miR-150-5p represents a promising candidate contributor within the broader EV cargo landscape, highlighting their potential for future cell-free therapeutic strategies.

Cancer-associated cachexia (CAC) is a multifactorial wasting syndrome characterized by progressive loss of fat and lean mass, systemic inflammation, and poor therapeutic responsiveness. While brown adipose tissue (BAT) is traditionally considered a protective, energy-dissipating organ, its qualitative remodeling in CAC remains poorly characterized.Here, we demonstrate that CAC induces a senescent conversion of BAT, marked by thermogenic failure, fibrosis, inflammation, and acquisition of a senescence-associated secretory phenotype (SASP). Through integrative transcriptomic, proteomic, and secretomic analyses in a murine model of lung cancer-induced cachexia, we identify S100A9 as a key factor selectively upregulated and secreted by brown adipocytes. Functional assays reveal that the BAT secretome exerts deleterious paracrine effects on white adipocytes and skeletal myotubes, promoting lipolysis and atrophy, while also impairing brown adipocyte identity in an autocrine manner. Co-culture and gain-of-function experiments with S100A9 recapitulate these phenotypes in vitro in mouse and human brown adipocytes, whereas pharmacological blockade of S100A9 signaling partially restores thermogenic and metabolic features. Collectively, our findings reveal that BAT undergoes functional reprogramming into a senescent and secretory tissue in cancer cachexia, with adipocyte-derived S100A9 acting as a novel pro-cachectic mediator. This work redefines the role of BAT in CAC and identifies S100A9 as a potential therapeutic target within the adipose-muscle crosstalk.

TkMYC2 mediates jasmonate-induced drought resistance and rubber biosynthesis simultaneously in Taraxacum kok-saghyz. Taraxacum kok-saghyz (T. kok-saghyz) is an important natural rubber-producing plant, yet its cultivation is often limited by drought stress, and the regulatory mechanisms underlying rubber biosynthesis and laticifer development remain incompletely understood. This study focused on TkMYC2, a core transcription factor in the jasmonate (JA) signaling pathway. Through homologous and heterologous genetic transformation, we systematically elucidated its dual functions in conferring drought tolerance and driving rubber biosynthesis. TkMYC2 expression was induced by both drought and methyl jasmonate (MeJA). Overexpression of TkMYC2 significantly enhanced the tolerance of transgenic plants to osmotic and drought stress by activating the antioxidant system (SOD, POD, CAT), maintaining ROS homeostasis, and reducing membrane lipid peroxidation. Using yeast two-hybrid and bimolecular fluorescence complementation assays, we demonstrated a direct physical interaction between TkMYC2 and TkJAZ11, a key repressor in the JA pathway. Phenotypic analyses showed that TkMYC2 overexpression promoted root thickening, laticifer development, and natural rubber accumulation, functionally supporting the hypothesis that rubber biosynthesis drives laticifer development. In summary, TkMYC2 acts as a critical molecular hub concurrently regulating drought stress response and rubber biosynthesis, providing new insights into jasmonate-mediated coordination of stress resilience and secondary metabolism, and offering a genetic resource for molecular breeding of T. kok-saghyz with enhanced yield and stress tolerance.