Search Results (161)

Visible light communication (VLC) has emerged as a transformative optical wireless technology for sixth-generation (6G) networks, offering license-free spectrum access, inherent electromagnetic-interference immunity, high spatial confinement, and the unique ability to combine high-speed wireless connectivity with solid-state lighting infrastructure. However, the transition from conventional VLC links to practical 6G optical wireless systems requires far more than advanced modulation and signal processing. Future VLC performance will be strongly determined by the co-design of photonic front-ends, including high-speed transmitters, spectrally engineered emitters, reconfigurable optical interfaces, intelligent receivers, and energy-autonomous detection units. This article provides a comprehensive, device-centered review of photonic hardware and artificial intelligence (AI) enablers for next-generation 6G VLC systems. Particular attention is given to micro-LEDs, laser diodes, color-conversion materials, including perovskite quantum dots, advanced photodetectors, imaging receivers, wavelength-shifting fiber receivers, solar-cell-based receivers, optical reconfigurable intelligent surfaces (RISs), metasurfaces, beam-steering components, and optical wireless power transfer. This review discusses how AI can support inverse photonic design, transmitter and receiver calibration, nonlinear impairment mitigation, channel-aware beam control, and energy-aware resource management. Unlike broader VLC surveys that mainly emphasize network architecture, this article provides a device-centered perspective on AI-enabled photonic integration for 6G VLC, supported by a comprehensive survey of recent experimental demonstrations. Key challenges related to bandwidth, optical efficiency, receiver field of view, mobility, safety, standardization, and practical deployment are summarized, followed by a research roadmap for 2025–2032. Full article

Livestock production in Africa occurs across highly heterogeneous agroecological and management environments, ranging from extensive pastoral and mixed crop–livestock systems to intensive enterprises. These systems are characterized by seasonal and spatial variation in feed resources, reliance on locally available forage and agricultural by-products, climatic stress, endemic diseases, and the use of indigenous and locally adapted breeds. Such conditions create distinctive microbiome–host interactions that remain poorly represented in global livestock omics research. Although the gut microbiome is central to nutrient utilization, immune function, metabolic homeostasis, and resilience, the functional mechanisms linking microbial communities, diet, host physiology, and productivity in African livestock remain insufficiently characterized. African systems are particularly underrepresented in integrated microbiome–metabolomics datasets, longitudinal studies, and artificial intelligence (AI)-enabled predictive models, limiting the development of context-specific precision nutrition strategies. This review examines the integration of metabolomics and AI with microbiome and host data to advance precision livestock nutrition within an African and One Health context. It identifies both substantial constraints and strategic opportunities. Limited research infrastructure, high-quality regional datasets, computational capacity, and specialized expertise remain major barriers. Conversely, Africa’s diversity of livestock breeds, feed resources, agroecological conditions, and naturally occurring resilience phenotypes provides an important opportunity to identify microbiome–metabolite signatures associated with feed efficiency, disease resilience, climate adaptation, and product quality. Emerging metabolomics and computational capacity, particularly in South Africa, could support regional research networks and continental data infrastructures. Furthermore, the review proposes an Africa-specific approach that develops locally grounded, scalable, and resource-sensitive precision nutrition strategies, strengthening antimicrobial stewardship, animal health, food safety, climate resilience, sustainable livestock production, and broader One Health objectives. Full article

The mutualistic association between plants and their seed-associated microbiota has emerged as a key determinant of crop productivity, influencing plant nutrition, immunity, and tolerance to abiotic stress. Seed biopriming, the controlled application of beneficial microorganisms to seeds before sowing, exploits this interaction to enhance germination, seedling establishment, and stress resilience. Unlike conventional chemical priming, seed biopriming induces coordinated molecular reprogramming through changes in the seed metabolome, proteome, and epigenome. This review synthesizes current evidence demonstrating that seed biopriming promotes the accumulation of osmoprotectants, strengthens antioxidant defenses, enhances secondary metabolism, and generates priming-specific proteomic responses. We further examine how these changes interact with phytohormonal signaling networks and epigenetic mechanisms, including DNA methylation, histone modification, and small RNA-mediated regulation, to establish stress memory and improve plant adaptation. The review also discusses recent advances in synthetic microbial communities and nanobiotechnology for improving inoculant stability and efficacy. Despite promising progress, large-scale application remains constrained by inconsistent field performance, formulation stability, and regulatory challenges. Finally, we highlight the integration of multi-omics and artificial intelligence as promising approaches to improve mechanistic understanding, optimize microbial selection, and accelerate the development of reliable seed biopriming strategies for sustainable agriculture under climate change. Full article

Prostate adenocarcinoma is characterized by substantial inter-patient heterogeneity, limiting the clinical reliability of conventional diagnostic tools, including prostate-specific antigen testing. This limitation underscores the need for robust molecular biomarkers that may complement conventional diagnostic tools, highlighting the urgent need for biomarkers capable of enhancing diagnostic accuracy and enabling more precise risk stratification. In the present study, transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed using an integrative bioinformatics and machine learning pipeline., The proposed workflow was designed as a stepwise and reproducible biomarker prioritization framework in which differential expression analysis, functional enrichment, protein–protein interaction (PPI) based network interpretation, graph-convolutional feature selection, and hybrid ensemble machine learning were sequentially integrated. Differential gene expression analysis was combined with pathway enrichment (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome), protein–protein interaction network construction, and graph-convolutional feature selection. Multiple machine learning algorithms, including Random Forest, Gradient Boosting Machine, Support Vector Classifier, Artificial Neural Network, and AdaBoost, were systematically evaluated. A hybrid ensemble model integrating Gradient Boosting Machine and Random Forest (GBM+RF) was subsequently developed. Model performance was assessed using accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic (ROC) and externally validated using the independent GSE14206 dataset. The analysis revealed a coordinated molecular pattern characterized by dysregulated cell cycle activity and enhanced interferon-mediated immune signaling. Protein–protein interaction analysis identified STAT1 and PLK1 as highly connected network hub genes within immune-related and cell-cycle-associated modules. Among the evaluated models, the hybrid GBM+RF framework achieved the highest predictive performance on the TCGA dataset, with AUC: 0.9526; Accuracy: 97.49%. External validation using the GSE14206 dataset confirmed the robustness of this model (AUC: 0.9156; Accuracy: 91.53%). These findings support a broader multi-gene candidate signature in prostate adenocarcinoma, in which machine learning prioritized genes such as XAF1, APP, RPA3, IFIH1, UBE2D2, RSAD2, KIF2C, and PLK1, while STAT1 and PLK1 provided complementary network-level biological relevance. The proposed framework provides a robust and transferable strategy for biomarker discovery and precision oncology. Full article

Beginning with a general understanding of catastrophic obstetric collapse (COC), it has been established that a catastrophic obstetric collapse is typically the result of sudden massive bleeding requiring emergency peripartum hysterectomy (EPH); this is different from historical views of what constitutes a catastrophic obstetric collapse. Current studies have found evidence that a catastrophic obstetric collapse can be the result of a longer-duration process involving gradual maternal physiological destabilization, the culmination of which creates a “maternal point of no return” for the mother. As a result of disrupting the maternal–fetal interface in placenta accreta spectrum disorders (PASDs), there are many abnormalities present in the decidua, such as: defective decidualization, fragmentation of the extracellular matrix, aberrant angiogenesis, continued hypoxic signals, and the persistence of invasive trophoblastic phenotypes. These structurally fragile vascular interfaces will eventually undergo endothelial dysfunction, oscillatory shear stress, glycocalyx injury, oxidative damage and progressive depletion of the maternal vascular adaptive reserve. Chronic inflammation will also continue to amplify immune thrombosis, alter complement function, facilitate NETosis, and cause widespread instability in diffuse microvasculature, leading to a reduced ability of the maternal system to tolerate physiological stress while maintaining macrocirculatory stability. Additionally, invasive placentation may lead to mitochondrial dysfunction, decreased oxidative phosphorylation, disrupted intracellular calcium homeostasis, ferroptotic lipid peroxidation, and redox-mediated endothelial injury, leading to a progressive limitation in the mother’s bioenergetic adaptability to hemorrhage. Ultimately, these events seem to culminate in a threshold condition where endothelial disorganization exists along with capillary transit time heterogeneity, impaired oxygen diffusion, metabolic instability, and progressive desynchrony of vascular, inflammatory, coagulative and mitochondrial networks before eventual hemodynamic collapse. Therefore, based on these findings, we propose the concept of the “Maternal Point of No Return” as a transitional state in which physiological adaptations begin to fail and irreversibly destabilize at a systems level. Lastly, we review potential applications of current technological advancements, including artificial intelligence (AI), radiomic-based placental phenotyping, exosomal biology, physiological variability analysis, spatial multi-omics, and digital twin physiology, to enable future precision-obstetrics strategies to identify a decline in maternal resilience prior to irreversible decompensation. Full article

MicroRNAs (miRNAs) are central regulators of gene expression and play pivotal roles in plant antiviral defense. In Cucurbitaceae, a globally important crop family including cucumber, melon, and watermelon, viral pathogens such as CGMMV, CMV, and ZYMV represent major constraints on productivity. However, the regulatory complexity of miRNA-mediated antiviral responses in these species remains incompletely understood. This review provides an integrated overview of recent advances in miRNA-guided antiviral immunity in Cucurbitaceae, highlighting the dynamic reprogramming of small RNA pathways upon viral infection. Conserved miRNA families act as key regulatory hubs, controlling development, hormone signaling, and defense responses, while viral suppressors interfere with RNA silencing machinery, reshaping host regulatory networks. Emerging evidence further reveals multilayered interactions between miRNAs and other non-coding RNAs, including lncRNAs and circRNAs, indicating complex cross-talk that fine-tunes antiviral responses in a species- and virus-specific manner. Importantly, miRNAs exhibit a dual role by contributing both to antiviral defense and to symptom development. Advances in artificial miRNAs and RNA-based technologies underscore their potential for engineering durable virus resistance. Overall, miRNA-centered regulatory networks represent a promising target for next-generation crop protection strategies in Cucurbitaceae. Full article

Chronic wounds remain a major global health challenge despite substantial advances in biomaterials, regenerative medicine, and wound-care technologies. Current therapeutic strategies are largely based on the assumption that chronic wounds represent impaired or incomplete healing responses and therefore require augmentation of regenerative processes. This paradigm has driven the development of increasingly sophisticated wound dressings incorporating extracellular matrix analogs, growth factors, stem cells, extracellular vesicles, biosensors, and bioelectronic components. However, the clinical impact of these innovations has often fallen short of expectations. In this review, we propose a conceptual framework intended to generate experimentally testable hypotheses rather than provide a definitive mechanistic model. Persistent alterations in immune, stromal, vascular, extracellular matrix, metabolic, mechanical, and microbial networks create interconnected feedback systems that resist transition toward regeneration. From this perspective, successful therapy requires not only stimulation of repair mechanisms but also disruption of the processes that stabilize chronicity. We discuss how advances in systems biology, immunomodulatory biomaterials, bioelectronics, artificial intelligence, and precision medicine support the emergence of adaptive therapeutic interfaces capable of sensing, interpreting, and reprogramming pathological tissue behavior. Unlike previous reviews that primarily summarize emerging wound dressings or regenerative biomaterials, this Review proposes a systems-level conceptual framework in which chronic wounds are interpreted as stable pathological tissue states maintained by multiscale biological memory. This perspective integrates biomaterials, systems biology, artificial intelligence, and tissue-state dynamics into a unified translational model that has not previously been presented in the wound-healing literature. Previous reviews have predominantly focused on the design, biological activity, or clinical performance of individual biomaterials. In contrast, the present Review proposes a systems-level framework that integrates wound biology, biological memory, tissue-state dynamics, artificial intelligence, and adaptive biomaterials into a unified conceptual model for precision wound medicine. This state-based model reframes advanced wound dressings as tools for biological state engineering and provides a translational framework for the future of chronic wound management. Full article

The pituitary and gonads serve as central regulatory hubs and functional organs for gametogenesis and maturation. In this study, we performed single-cell RNA sequencing (scRNA-seq) of the pituitary and ovary in pre-spawning Pelteobagrus fulvidraco to elucidate the cellular landscape and regulatory pathways governing gonadal development and oocyte maturation. Pituitaries from four female fish (weight: 43.8 ± 3.2 g; length: 14.1 ± 0.7 cm) and four male fish (weight: 78.2 ± 11.2 g; length: 18.9 ± 1.1 cm) were subjected to single-cell transcriptomic analysis. A total of 17 distinct cell types were identified in the female pituitary, whereas 15 cell types were detected in the male pituitary. Both male and female pituitaries comprised multiple hormone-secreting endocrine populations, indicating a largely conserved cellular composition. However, sex-specific differences were observed in thyrotrope subtypes, suggesting potential sexual dimorphism in pituitary endocrine regulation. Examination of receptor gene expression revealed cell-type-specific regulatory capacities, highlighting gonadotropin, steroid, and neuropeptide responsiveness across pituitary populations. In the ovary, 10 cell types were identified, with granulosa cells (~22.9%) and theca cells (~8.6%) showing distinct transcriptional profiles. Follicle-stimulating hormone receptor (fshr) was highly expressed in granulosa cells, whereas luteinizing hormone receptor (lhcgr) and steroidogenic genes (hsd3b1, pgr) were predominantly localized in theca cells, indicating functional compartmentalization of gonadotropin and steroid signaling. Prostaglandin (PG) and melatonin (MT) pathways were implicated in paracrine regulation: the prostaglandin synthase ptgs2a was expressed in theca, germ, and immune cells, while ptger2a was expressed in granulosa cells; melatonin synthesis genes (aanat1, aanat2, asmtl) were confined to granulosa cells, with receptors (mtnr1ab) in germ cells. These findings suggest that ovarian paracrine signaling complements systemic endocrine control to modulate oocyte maturation and ovulation. This single-cell atlas provides a high-resolution framework of reproductive cell types and signaling networks in P. fulvidraco, offering insights for improving artificial breeding and reproductive management in aquaculture. Full article

Sepsis is a life-threatening syndrome characterized by a dysregulated host response to infection and progressive organ dysfunction. Although early antimicrobial therapy, source control, hemodynamic resuscitation, and organ support remain the foundations of care, these approaches do not directly reverse the cellular mechanisms that connect systemic inflammation to multi-organ failure. Mitochondrial dysfunction has emerged as a central mechanism linking impaired oxygen utilization, oxidative and nitrosative stress, immune-cell metabolic reprogramming, inflammatory amplification, and organ injury. During sepsis, inflammatory mediators, nitric oxide, microcirculatory abnormalities, calcium dysregulation, and metabolic stress converge on mitochondria, impairing oxidative phosphorylation and promoting mitochondrial reactive oxygen species/reactive nitrogen species (ROS/RNS) generation. When mitochondrial quality-control programs, including fission, fusion, mitophagy, and mitochondrial biogenesis, fail to restore network integrity, damaged mitochondria accumulate and become persistent sources of oxidative stress and danger signals. Mitochondrial damage-associated molecular patterns, particularly mitochondrial DNA, oxidized mitochondrial DNA, cardiolipin, ATP, and N-formyl peptides, activate innate immune pathways such as TLR9-MyD88-NF-kappaB, the NLRP3 inflammasome, and cGAS-STING signaling. In parallel, mitochondrial metabolism shapes macrophage activation, neutrophil function, T-cell competence, pyruvate-lactate handling through the pyruvate dehydrogenase complex, and the transition between hyperinflammation and immunosuppression. Clinical translation remains challenging because sepsis is biologically heterogeneous and mitochondrial dysfunction is dynamic, tissue-specific, and influenced by disease stage. This review synthesizes current knowledge on mitochondrial dysfunction in sepsis, emphasizing oxidative and nitrosative stress, mitochondrial quality control, mitochondrial damage-associated molecular pattern (DAMP) signaling, immunometabolism, organ-specific injury, candidate biomarkers, clinical translational strategies for mitochondria-targeted therapy, and future approaches based on multi-omics and artificial intelligence-assisted patient stratification. We argue that future therapeutic development should move beyond nonspecific antioxidant supplementation toward time-sensitive, phenotype-informed, and biomarker-guided mitochondrial medicine. Full article

Influenza viruses continue to pose a significant threat to both animal and public health due to their rapid evolution and the frequent emergence of antiviral resistance. Host-directed antiviral (HDA) strategies, which target host factors essential for viral replication, may represent an alternative to conventional virus-targeting approaches. However, the identification of reliable and therapeutically actionable host targets remains a major challenge, primarily due to the complexity and context dependency of host–virus interactions. Recent advancements in multi-omics technologies, including functional genomics, transcriptomics, and proteomics, have facilitated the systematic characterization of host factors involved in influenza virus infection. These methodologies have unveiled intricate regulatory networks that govern viral replication and host immune responses. Nonetheless, translating large-scale datasets into biologically meaningful targets necessitates robust integrative frameworks. In this context, artificial intelligence (AI) and machine learning methods offer powerful tools for data integration, target prioritization, and predictive modeling. In this Review, we summarize current insights into host factors that regulate influenza virus infection and discuss how multi-omics and AI-driven approaches are expediting host target discovery. Furthermore, we highlight the potential of these strategies to enhance antiviral development while addressing key challenges related to specificity, safety, and translational application. Collectively, these advancements lay a foundation that may support the rational design of next-generation host-directed antivirals. Full article

Aspartame is a widely used artificial sweetener, but its possible relationship with rheumatoid arthritis (RA) remains insufficiently understood. This study aimed to explore, rather than prove, potential molecular links between aspartame-related targets and RA-associated gene networks. Three public RA transcriptomic datasets (GSE55235, GSE55457, and GSE77298) from the Gene Expression Omnibus (GEO) database were integrated as discovery/training data. Because these datasets included different tissue origins, batch correction was used to reduce dataset-level technical variation, whereas tissue-origin-related biological variation was not assumed to be fully removable. After differential expression analysis, RA-associated differentially expressed genes (DEGs) were identified. The single-cell dataset GSE200815 was used for cell annotation and cellular expression visualization; because its comparator group consists of psoriatic arthritis (PsA) samples rather than healthy controls, single-cell results were interpreted as RA-vs-PsA observations and were not treated as disease-versus-healthy-control evidence. Potential targets of aspartame were retrieved from ChEMBL, SwissTargetPrediction, and the Similarity Ensemble Approach (SEA), and were intersected with RA-related DEGs to construct an aspartame-gene-RA regulatory network. Diagnostic models were developed using 113 machine-learning algorithm combinations to determine an optimal multigene model and its core genes. HLA-DRB1 was selected for exploratory scTenifoldKnk-based virtual knockout mainly because it was included in the optimal model and has a well-established role in RA immunogenetics; the single-cell analysis was used only to describe cellular distribution in the RA/PsA dataset. Molecular docking was then used to evaluate the possible interaction between aspartame and HLA-DRB1. Forty-four intersected genes linked the predicted aspartame targets with RA DEGs. The random forest plus partial least-squares generalized linear model (RF + plsRglm) identified 16 core genes. Network-level interpretation indicated that these genes were distributed across immune/antigen-processing, inflammatory-signaling, protease/extracellular-matrix-remodeling, adhesion, metabolic, and proliferation-related modules; therefore, HLA-DRB1 was treated as a prioritized immune-module candidate rather than as the sole driver of the network. Following virtual knockout of HLA-DRB1, affected genes were enriched in extracellular matrix organization, extracellular structure organization, extracellular matrix, collagen trimer, extracellular matrix structural constituent, and collagen binding. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included integrin signaling, focal adhesion, proteoglycans in cancer, cytoskeleton in muscle, and phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) signaling. Molecular docking showed a minimum binding energy of −6.7 kcal/mol, which was more negative than the preset stability criterion of −5.0 kcal/mol, and the docking pose suggested contacts around ARG-146. This integrative analysis suggests a hypothesis-generating association between aspartame-related predicted targets and RA-relevant molecular networks involving HLA-DRB1 and other core genes. The findings do not establish causality and require experimental, epidemiological, biophysical, and tissue-stratified validation before any causal or clinical inference can be made. Full article

The axis linking the gut to the brain to the immune system connects all tissues involved—bacteria, immune cells, metabolism and the CNS—through a multidirectional communication network. Several studies have confirmed that when this axis is disrupted, it can be responsible for Alzheimer’s disease, Parkinson’s disease, obesity, type 2 diabetes, and NAFLD, and the main consequences come from increased systemic inflammation, altered regulation of immune cells, the production of microbial metabolites that alter signals to the immune cells and nervous system, increase in oxidative stress, breakdown of the gut barrier, and more. In recent years, advanced multi-omics technologies, such as metagenomics, transcriptomics, metabolomics, proteomics, and single-cell sequencing, have provided significant advancement in our understanding of all of the interacting nodes involved in the gut–brain–immune axis. These advanced sequencing technologies can characterize the microbial communities, host immune cells, metabolic profiles, and the degree of cell heterogeneity during a specific disease. Combining multi-omics information can reveal a few shared pathways between neurodegenerative and metabolic disorders, such as NF-κB, NLRP3 inflammasome activation, mitochondrial dysfunction, changes in SCFA metabolism, and the alteration of microbial populations in Alzheimer’s and Parkinson’s disease; metabolic dysbiosis and increased risk for Parkinson’s disease; or changes in gut-to-brain-to-immune signaling contributing to diabetes complications and NAFLD. Artificial intelligence (AI) and machine learning are becoming promising tools for detecting biomarkers from these datasets, extracting knowledge, interpreting systems biology, and helping with developing precision medicine. In this review, we summarize current evidence that supports the role of the gut–brain–immune axis in neurodegenerative and metabolic diseases, highlighting results gained with the utilization of multi-omics approaches. We will describe the key microbial, immune, and metabolic pathways involved in pathogenesis and therapeutic approaches including psychobiotics, tailored nutrition, modulation of the microbiome, and metabolite interventions, discussing future perspectives of the translation of the gut–brain–immune axis knowledge into clinical practice. Full article

Post-COVID-19 syndrome (PCS), also referred to as post-acute sequelae of SARS-CoV-2 infection (PASC), represents a heterogeneous set of persistent clinical manifestations developing after acute infection. These conditions are associated with immune dysregulation, autonomic imbalance, impaired thymic function, and possible viral persistence. Objective: This study aims to systematically synthesise current evidence on the immunopathogenesis of PCS and to critically evaluate the application of artificial intelligence (AI) and machine learning (ML) approaches for its prediction and clinical stratification. Methods: A PRISMA 2020–informed systematic review was conducted using PubMed/MEDLINE, Scopus, Web of Science, elibrary.ru and Embase databases (January 2020–December 2025). Studies addressing immunopathological mechanisms and AI/ML applications in PCS were selected based on predefined eligibility criteria. Risk of bias in prediction studies was assessed using the PROBAST tool. Due to heterogeneity, a structured qualitative synthesis was performed. Current evidence indicates that PCS may result from sustained systemic inflammation, cytokine dysregulation, autoimmunity, and delayed restoration of T-cell homeostasis, including reduced thymic output of naïve T lymphocytes. Persistent thymic dysfunction may contribute to prolonged immune imbalance, increased susceptibility to secondary infections, and reactivation of latent viruses. AI/ML approaches—including gradient boosting, ensemble learning, deep neural networks, and natural language processing—have demonstrated promising performance across multimodal datasets. However, significant limitations were identified, including small sample sizes, overfitting, lack of external validation, and heterogeneity in outcome definitions. Conclusions: The integration of immunopathological insights with data-driven modelling highlights the potential of combined approaches for improving PCS risk stratification. However, current AI models remain insufficiently validated for clinical implementation. Future research should prioritise methodological standardisation, external validation, and incorporation of mechanistically informed biomarkers. Full article

Objective: Dysregulation of Toll-like receptor signaling and increased proportions of Th17 and other T helper cells can facilitate esophageal squamous cell carcinoma (ESCC) progression. Methods: By integrating WGCNA, Limma, and artificial intelligence (AI, including LASSO-Cox regression and SOM) frameworks, we first identified a Toll-like receptor signaling and Th17 and T helper cell (ThpT)-related prognostic model and Thp molecular subgroups for ESCC patients in bulk transcriptomic profiles. Next, Thp-associated hub genes were identified, followed by evaluation of corresponding molecular and immune features. Indeed, the heterogeneity of ESCC was estimated using a single-cell transcriptomic dataset acquired from the GEO database. Furthermore, we also evaluated Thp-associated hub gene molecular and biological functions in spatial and temporal manners on targeted cells via pseudotime trajectory and AI-driven targeted gene knockout (KO). ESCC therapeutic agents targeting Thp-associated hub genes were enriched via a drug–gene network and then examined by ridge regression-driven drug sensitivity estimation and molecular docking. To enhance the robustness of our study, we performed in vitro studies to quantify the relationship of the targeted gene with Th17 and ESCC progression. Results: Based on Thp, we successfully identified a prognostic model and molecular subgroups of ESCC patients. DDX39A and PBK should be considered ThpT-related hub genes involved in ESCC progression and decreased infiltration of Th17 cells. Based on drug sensitivity estimation and molecular docking, bleomycin and talazoparib may be potential drugs for treating esophageal squamous cell carcinoma. Conclusions: ThpT can guide personalized and precision medicine for ESCC patients. Our study provides a novel clinical translation strategy for combating ESCC. Full article

Efficient electricity distribution in rural areas is strongly affected by seasonal agricultural energy demand, particularly in irrigation-intensive regions where electricity consumption increases substantially during summer periods. Conventional transformer operation strategies in such rural grids often fail to adapt to seasonal load variability, leading to unnecessary idle operation, increased technical losses, and reduced infrastructure efficiency. Existing approaches generally rely on static assumptions or simulated data, limiting their ability to represent real irrigation-driven seasonal load asymmetry. To address this issue, this study proposes a data-driven multi-objective seasonal transformer scheduling framework using a bio-inspired Artificial Immune System (AIS) algorithm. The model was developed using two years of empirical hourly SCADA load data and transformer operation records obtained from a real 380/154 kV TEİAŞ transmission substation in Central Anatolia, Türkiye. Hourly SCADA measurements were used for seasonal load characterization and objective-function evaluation, while transformer scheduling decisions were defined at the seasonal operational level. The proposed AIS-based scheduling strategy reduced annual technical energy losses by approximately 5.4 GWh, decreased operational costs by 10.81 million TL (≈360,000 USD), and lowered carbon emissions by about 2270 metric tons of CO₂ compared with conventional static transformer operation. The study presents a proof-of-concept framework integrating empirical SCADA measurements with AIS-assisted seasonal transformer scheduling for practical utility-scale operational planning in irrigation-dominated rural electricity networks. Full article