Renal cell carcinoma (RCC) is distinguished by a highly inflamed tumor microenvironment (TME) that offers both opportunities and challenges for immunotherapy. This review synthesizes current insights into the immunological landscape of RCC, highlighting robust cluster of differentiation 8‑positiv (CD8⁺) T-cell infiltration, unconventional antigen sources such as endogenous retroviruses and frameshift neoantigens, and the heterogeneity of immune niches revealed by single-cell and spatial profiling. We then examine the clinical impact and mechanisms of immune checkpoint inhibitors —including programmed cell death protein 1 (PD‑1), programmed death‑ligand 1 (PD‑L1), and cytotoxic T‑lymphocyte‑associated protein 4 (CTLA‑4)—tumor vaccines, cellular therapies such as chimeric antigen receptor T cell (CAR‑T) therapy and tumor‑infiltrating lymphocytes (TILs) and bispecific antibody constructs, emphasizing advances in dosing, engineering, and combination regimens. Combination strategies—including dual checkpoint blockade, integration with anti-angiogenic tyrosine kinase inhibitors, radiotherapy, metabolism-targeted agents such as adenosine and poly (ADP‑ribose) polymerase (PARP) inhibitors, and hypoxia modulators—are reviewed for their capacity to overcome resistance and remodel the microenvironment. We further explore intrinsic and acquired resistance mechanisms, the immunosuppressive roles of myeloid and stromal elements, and emerging biomarker approaches spanning genomic, transcriptomic, spatial, and circulating analytes. Finally, we discuss current limitations—such as variable clinical response, toxicities, and biomarker gaps—and outline future prospects, including personalized combination regimens, next-generation engineered cell products, and artificial intelligence (AI)-driven precision monitoring. Together, these insights chart a path toward more effective, individualized immunotherapy in RCC.

The rapid advancement of artificial intelligence (AI) in healthcare has significantly enhanced diagnostic accuracy and clinical decision-making processes. This review examines four pivotal studies that highlight the integration of large language models (LLMs) and multimodal systems in medical diagnostics. BioBERT demonstrates the efficacy of domain-specific pretraining on biomedical texts, improving performance in tasks such as named entity recognition, relation extraction, and question answering. Med-PaLM, a large-scale language model tailored for clinical question answering, leverages instruction prompt tuning to enhance accuracy and reduce harmful outputs, validated through the MultiMedQA benchmark. DR.KNOWS integrates medical knowledge graphs with LLMs, enhancing diagnostic reasoning and interpretability by grounding model predictions in structured medical knowledge. Medical Multimodal Foundation Models (MMFMs) combine textual and imaging data to improve tasks like segmentation, lesion detection, and automated report generation. These studies demonstrate the importance of domain adaptation, structured knowledge integration, and multimodal data fusion in developing robust and interpretable AI-driven diagnostic tools.

Diabetes mellitus (DM), the most prevalent metabolic disorder globally, has been extensively investigated due to its significant clinical implications. Among its most common complications is diabetic peripheral neuropathy (DPN), which predominantly manifests as bilateral limb pain, numbness, and paresthesia. In advanced cases, DPN may progress to foot ulceration and potentially necessitate limb amputation. The precise etiology and pathogenesis of diabetic neuropathy remain incompletely understood; however, sustained hyperglycemia, dysregulated lipid metabolism, and impaired insulin signaling are recognized as key triggers for the cascade of pathophysiological alterations in DPN. Under these metabolic disturbances, the peripheral nervous system’s structural and functional integrity—encompassing myelinated and unmyelinated axons, neuronal somata, neurovascular units, and glial cells—is progressively undermined. This review synthesizes recent mechanistic advances in DPN research, centering on the sigma-1 receptor’s modulatory orchestration, SGC dysfunction and GalCer metabolic dysregulation in DPN pathogenesis, inhibition of neuroinflammation and apoptosis by microRNA-146a, nerve damage due to Ca²⁺ overload, and dorsal root ganglia neuronal apoptosis resulting from SFA, and summarize possible interventions based on these mechanisms. A systems-level understanding of how these mechanisms collectively drive neuropathy pathogenesis is critical for advancing both early diagnostic tools and pathway-selective therapeutics.

Chronic neuropathic pain affects 7–10% of the global population and imposes a substantial socioeconomic burden, yet existing pharmacological options remain inadequate in efficacy and burdened by systemic toxicity. Antibody-drug conjugates (ADCs) — engineered to deliver neuromodulatory payloads selectively to peripheral nociceptors — represent a compelling but unexplored therapeutic modality; no clinical-stage pain ADC currently exists. To the best of our knowledge based on a structured literature search (January 2000–March 2025) and targeted updates through 6 June 2026, this review provides a systematic and comprehensive conceptual framework mapping AI methodologies validated in oncology ADC development onto the unique challenges of chronic neuropathic pain. The framework spans five pipeline stages: multi-omics integration and graph neural networks for nociceptor-selective target prioritization (Nav1.7, TRPV1, P2X3, TrkA, CGRP receptor, ASIC3); structure prediction and protein language models for antibody engineering against transmembrane pain targets; generative AI for neuromodulatory payload design; deep learning for neural tissue pharmacokinetics and neurotoxicity prediction; and AI-driven patient stratification for precision clinical trial design. A unique three-layer optimization challenge — neural selectivity, analgesic potency, and systemic safety — distinguishes pain ADC AI from oncology ADC AI and defines the core design constraints of this review. Clinical precedent from FDA-approved anti-CGRP pathway antibodies (erenumab, fremanezumab, galcanezumab, eptinezumab) and Phase III anti-NGF antibody trials establishes that peripheral nociceptor-targeting biologics are both feasible and clinically active, providing the biological foundation for the pain-ADC concept. Realizing this framework requires curated pain-ADC datasets, interpretable AI, and closed-loop platforms incorporating dorsal root ganglion (DRG) organoids.

Background: Lipomas are the most common benign tumours, but some deep lipomas are technically difficult to remove surgically. Early diagnosis and treatment of lipomas can be facilitated by early genetic biomarkers; however, the key genes and signalling pathways that influence lipoma development are not well understood. The aim of this study was to identify hub genes and signalling pathways associated with the development of lipomas. Methods: A dataset of human lipomas (GSE141027) was first downloaded from Gene Expression Omnibus, differential genes (DEGs) for expression profiles were analysed in R software via the edgeR package, and a protein‒protein interaction network was constructed. Based on preliminary data, further modular analysis, neighbour node analysis and Hubba analysis were performed using Cystoscope to identify intersecting genes and display them in a Venn diagram to obtain key hub genes. Enrichment analysis was then carried out using the ClueGO plugin in Cytoscape (v3.9). In addition, weighted gene coexpression network analysis (WGCNA) was used to identify coexpression modules positively and negatively associated with the clinicopathological features of lipoma in the whole dataset, and enrichment analysis was performed on the module genes to obtain the signalling pathways associated with the clinicopathological features of lipoma by intersecting with the signalling pathway enrichment of DEGs. All data were then used for GSEA enrichment to further validate the signalling pathways related to the clinicopathological features of lipoma. Results: A total of 418 DEGs were identified, of which 176 were upregulated and 242 downregulated. Seventeen hub genes were identified by MCODE and hubba plug-in and collateral node analysis, including CKM, ATP2A1, MYLPF, TNNI2, MYL1, ACTN3, ACTN2, ACTG2, MYH11, NEB, MYBPC2, MYOZ1, MYH2, MYBPC1, TNNC2, ACTA1 and TCAP. TCAP. The enrichment functions and signalling pathways of the DEGs were subsequently analysed by the ClueGO plugin. A Venn diagram revealed the 15 most clinically relevant modular gene-enriched signalling pathways for lipoma (including the calcium signalling pathway and ECM-receptor interaction). In addition, 9 key signalling pathways associated with lipoma were identified using GSEA. Conclusion: This study analysed hub genes and signalling pathways of lipoma by bioinformatics to provide potential targets and signalling pathways for early diagnosis and treatment.

Recent increases in overweight and obesity have established Metabolic–Bariatric Surgery (MBS) as a principal intervention for durable weight reduction and metabolic improvement. Given the elevated perioperative complication risk among patients with obesity, there is a growing imperative to enhance the precision of preoperative management. This review synthesizes evidence from 2020–2025 on the application of artificial intelligence (AI) to bariatric surgery preoperative assessment, focusing on machine learning (ML), deep learning (DL), and large language models (LLMs). We summarize AI applications across three domains: preoperative risk prediction and individual assessment, patient stratification and procedure selection, and preoperative education and surgical training. The evidence suggests that integrating AI into routine preoperative workflows enables individualized, quantitative estimation of high-risk complications and weight-loss prognosis, thereby optimizing risk management; it can also support patient stratification and procedure matching to facilitate patient-centered shared decision-making; and NLP- and vision-based tools promote standardization and visualization of knowledge for patients and surgeons. Overall, AI is driving preoperative assessment toward greater individualization, interpretability, and multidisciplinary coordination, but its clinical generalizability requires validation in multicenter, prospective studies.

In recent years, advancements in gene structure prediction have been significantly driven by the integration of deep learning technologies into bioinformatics. Transitioning from traditional thermodynamics and comparative genomics methods to modern deep learning-based models such as CDSBERT, DNABERT, RNA-FM, and PlantRNA-FM prediction accuracy and generalization have seen remarkable improvements. These models, leveraging genome sequence data along with secondary and tertiary structure information, have facilitated diverse applications in studying gene functions across animals, plants, and humans. They also hold substantial potential for multi-application in early disease diagnosis, personalized treatment, and genomic evolution research. This review combines traditional gene structure prediction methods with advancements in deep learning, showcasing applications in functional region annotation, protein-RNA interactions, and cross-species genome analysis. It highlights their contributions to animal, plant, and human disease research while exploring future opportunities in cancer mutation prediction, RNA vaccine design, and CRISPR gene editing optimization. The review also emphasizes future directions, such as model refinement, multimodal integration, and global collaboration. By offering a concise overview and forward-looking insights, this article aims to provide a foundational resource and practical guidance for advancing nucleic acid structure prediction research.

Xenotransplantation, leveraging genetically engineered porcine donors, represents a promising solution to the global organ shortage crisis. Recent breakthroughs in genome editing have enabled the creation of pigs with multiple modifications, including knockout of key xenoantigens (GGTA1, CMAH, B4GALNT2) and insertion of human transgenes that regulate complement, coagulation, and innate immunity (e.g., hCD46, hCD55, hTBM, hCD47). Building on this genetic foundation, landmark clinical achievements have recently emerged. However, while the first porcine liver xenotransplants into human recipients demonstrated initial function, significant hurdles such as profound thrombocytopenia and coagulopathy—driven by factors like porcine vWF-human GPIb interactions and immune cell-mediated clearance—persist. Similarly, cardiac and renal xenotransplantation have seen milestones like the first pig-to-human heart transplants and extended kidney graft survival (up to 130 days) in a living recipient, yet delayed rejection and thrombotic microangiopathy remain critical challenges. Advanced strategies, including potent immunosuppression centered on anti-CD40 blockade, improved coagulation management (e.g., via hTFPI, hEPCR, hCD39), and emerging tolerance protocols, are actively being developed to overcome these barriers. This review synthesizes these pivotal 'bench-to-bedside' advancements, critically evaluating the current immunological and genetic innovations driving the progress of cardiac, renal, and hepatic xenotransplantation, and outlining the future directions necessary for successful clinical translation.