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Nanomedicine-based cancer immunotherapy integrates nanotechnology with immune modulation, representing a promising strategy to improve both the efficacy and safety of cancer treatment. Despite substantial preclinical potential, clinical translation is hindered by interconnected challenges in pharmacology, pharmacodynamics, and long-term safety. This mechanism-oriented prospective analyzes translational bottlenecks, pharmacological uncertainty from biomolecular corona, suboptimal pharmacodynamics due to tumor barriers, and metabolism/excretion affecting biosafety. Using a concept-driven framework, we link nano-bio interactions to clinical outcomes within a 'barrier-strategy' paradigm. Corresponding strategies such as mechanism-driven design, AI-assisted optimization, and advanced delivery systems are discussed, with emphasis on safety-by-design principles. Collectively, this perspective provides a forward-looking roadmap for future research, underscoring the importance of integrated technologies, advanced translational models, and scalable manufacturing to fully realize the clinical potential of nanoimmunotherapy.

The gut microbiome functions as a key regulator of tumorigenesis and progression, thereby modulating tumor development and treatment outcomes (including chemoresistance, immunotherapy efficacy, and adverse effects) through its influence on the immune microenvironment and metabolite-mediated signaling pathways. Recent advances in multiomics technologies (metagenomics, metabolomics, and transcriptomics) have generated large-scale, comprehensive, and heterogeneous datasets whose complexity exceeds the capabilities of manual analysis, thus necessitating the implementation of artificial intelligence-based approaches. This review systematically examines the crucial role of the gut microbiome in tumorigenesis, with particular emphasis on colorectal cancer (CRC), specifically addressing its utility as a diagnostic and prognostic biomarker. Furthermore, building upon existing applications of artificial intelligence (AI) in microbiome research and cancer diagnosis and treatment, this review presents an AI-driven precision intervention framework and delineates personalized treatment strategies.

Prostate cancer (PCa) is a leading cause of male cancer-related death globally. While the gut microbiota is linked to PCa, its genetic association remains unclear. We screened genetic instruments related to the gut microbiota and paired them with PCa genome-wide association study data to conduct Mendelian randomization (MR) analysis. Positive MR findings were then subjected to colocalization analysis. Subsequently, we utilized the Gene Expression Omnibus (GEO) dataset to perform differential expression analysis, aiming to identify differentially expressed associated genes (DEAGs). We determined the importance scores of these DEAGs through four machine learning models and constructed a nomogram based on these findings, and then validated it in another group of the GEO dataset. MR analysis found 16 gut bacteria causally linked to PCa (7 risk, 9 protective), with 144 related genes. PLCL1, VSNL1, ROR2, NRXN3, and TEAD1 were identified as feature genes for constructing a nomogram that provides a quantitative prediction of the risk of PCa onset. This study indicates that there are causal links between the gut microbiota and PCa. Feature genes may affect the occurrence of PCa by inhibiting the epithelial-mesenchymal transition, proliferation, migration, and invasion of cells.

Prognostic stratification in stage III colon cancer remains poor, despite treatment advances. Tumor-infiltrating lymphocytes, particularly CD3+ T cells, are potential prognostic markers, but manual assessment is labor-intensive and not robust. This study aimed to develop a deep learning model for automated analysis of CD3-stained histological slides to improve prognostic prediction. A total of 1737 patients from three international cohorts (PETACC08, PRODIGE-13, and HARMONY) were analyzed. The deep learning model (VGG19) identified tumor core (TC) and invasive margin (IM) regions on CD3-stained slides. Features from VGG19 and UNI models were used to cluster patients using hierarchical classification. Prognostic performance was evaluated using disease-free survival (DFS) across training, internal validation, and external validation sets. Deep learning classifiers identified distinct patient clusters with significantly different DFS based on TC and IM. For both IM and TC analysis, patients in the favorable group had a better DFS in all sets (IM: p < 0.001, p = 0.04, p = 0.02; TC: p = 0.002, p = 0.01, p = 0.12, respectively). Combining classifiers enhanced prognostic accuracy in all sets (p < 0.001, p = 0.01, p = 0.06, respectively). The model outperformed traditional clinical variables and CD3 enumeration, which demonstrated variability across cohorts. Automated deep learning analysis of CD3-stained slides enables robust and reproducible prognostic stratification in stage III colon cancer, independently of staining and scanning variations. This approach holds promise for guiding personalized treatment strategies.ClinicalTrials.gov Identifiers: NCT00265811, NCT00995202.

The review was based on analysis of 180 sources from databases PubMed, eLibrary, Cohrane Library, MEDLINE for 2015-2025 using keywords precision medicine, personalized medicine, neuro-oncology, oncology, cranio-cerebral injury, neuro-trauma, neuro-proteomics and AI. The purpose of the study was to demonstrate, on the basis of analysis of publications on precision medicine application in neurosurgery, the significance and perspectives of mentioned approach in modern neurosurgical practice. The methods of precision medicine, digital revolution and progress in multi-modal Big Data processing permit to better understand of tumor genesis, their clinical heterogeneity, functional effects and causes underlying their resistance to treatment. The precision medicine methods provide valuable information on pathophysiological mechanisms underlying neuro-trauma through analysis of complex protein interactions and changes. The future of precision medicine in neurosurgical practice is in permanent enhancement of AI and machine learning, permitting rapid and accurate decision-making based on comprehensive molecular data. The future of neurosurgery lies in harmonious integration of such interdisciplinary approaches as precision medicine and clinical neurosurgery to discover new possibilities of targeted and personalized therapy.

Janus kinase (JAK) dysregulation plays a central role in the pathogenesis of inflammatory, autoimmune, and malignant disorders, making the JAK family an essential therapeutic target across multiple disease domains. Over the past two decades, the field has progressed from the identification of early JAK2 inhibitors to the approval of several first-generation agents, including ruxolitinib, tofacitinib, baricitinib, and fedratinib, which validated the clinical feasibility of JAK blockade. However, limitations related to safety, isoform selectivity, long-term tolerability, and off-target kinase interactions continue to restrict their broader application and highlight the need for next-generation molecules. In this review, we provide a comprehensive and strategic assessment of the molecular features underpinning JAK2 and JAK3 selectivity, including signaling features directly relevant to inhibitor design, mutational landscapes, and structural determinants such as the uniquely targetable Cys909 residue in JAK3. Although the JAK family comprises four kinases, this review intentionally focuses on JAK2 and JAK3, where structural divergence, disease relevance, and emerging selectivity strategies provide the strongest opportunities for next-generation precision inhibitor design. We integrate recent advances in synthetic chemistry, including hinge-binding optimization, heterocyclic diversification, multicomponent reactions, and scaffold-hopping strategies, with computational methodologies such as molecular docking, molecular dynamics simulations, QM/MM calculations, and machine-learning-based predictive modelling. Together, these multidisciplinary approaches have accelerated hit discovery, refined selectivity, and improved the pharmacokinetic and safety profiles of emerging JAK inhibitors. By consolidating progress across medicinal chemistry, structural biology, and computational design, this review outlines key opportunities and remaining challenges in developing next-generation JAK inhibitors with enhanced precision and therapeutic value for oncology, immunology, and chronic inflammatory diseases.

Although FGFR2 is a well-validated oncogenic target, no selective FGFR2 inhibitors have been approved for clinical use. In this study, we report the discovery of 2H-pyrazolo[3,4-d]pyrimidin-4-amine derivative as novel, irreversible FGFR2 inhibitors. The optimal compound, PLW559, potently inhibited FGFR2 with an IC50 value of 13.59 nM and demonstrated exceptional selectivity over FGFR1, FGFR3, and FGFR4. Covalent binding to the target was confirmed by mass spectrometry. In cellular models, PLW559 exhibited potent and selective antiproliferative effects against FGFR2-driven cancer cells, effectively suppressed downstream FGFR2 signalling and induced cancer cell apoptosis. Notably, it showed minimal activity in non-FGFR2-dependent cells. This work presents a new class of selective FGFR2 inhibitors based on a novel scaffold, offering promising lead compounds for the development of FGFR2-target therapies.

FMS-like tyrosine kinase 3 (FLT3/CD135) regulates haematopoiesis and is frequently mutated as FLT3-internal tandem duplication (FLT3-ITD) in acute myeloid leukaemia (AML), associated with poor prognosis. Although FLT3 inhibitors show clinical benefits, resistance remains a challenge. This study hypothesises that antibody-drug conjugate (ADC) efficacy depends on distinct FLT3 trafficking mechanisms in FLT3-wt and FLT3-ITD cells. Confocal imaging showed that in THP-1 (FLT3-wt) cells, FLT3 mAb trafficked to lysosomes, while in MV4-11 (FLT3-ITD) cells, it accumulated in the Golgi. To evaluate the impact of this trafficking difference, we synthesised an anti-FLT3 mAb-MMAE, linked via a Val-Cit-PAB linker at the Fc N-glycan, which exhibited lower cytotoxicity in MV4-11 than THP-1 cells, indicating that the impaired lysosomal trafficking of FLT3-ITD limits drug release and reduces ADC potency. These findings highlight that effective lysosomal targeting is essential for ADC activity and suggest that optimising linker design or restoring lysosome trafficking may enhance FLT3-targeted ADC in AML.

Kidney transplantation (KT) is the most effective treatment for end-stage renal disease. Hypothermic machine perfusion (HMP) can improve renal energy metabolism and reduce ischemia-reperfusion injury compared with static cold storage. This study aimed to evaluate the association between HMP parameters and graft function in deceased donor kidney transplantation (DDKT) and to develop a predictive model for early risk stratification. A retrospective analysis was conducted on 2,041 DDKT recipients from 1 January 2015 to 30 June 2023. The primary outcome, delayed graft function (DGF), was defined as the need for at least one dialysis session within the first week after transplantation. Consensus clustering (CC) and restricted cubic spline (RCS) analysis were used to evaluate the associations between clinical data, HMP parameters, and graft function. Feature selection was performed using Lasso-penalized logistic regression (LR), and multivariable LR was used to construct the predictive model. The model's performance was assessed using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Among the DDKT recipients, 12.9% developed DGF. HMP parameters varied significantly between the two groups, with DGF recipients showing distinct patterns in perfusion resistance, flux, and pressure. CC identified two recipient clusters with distinct DGF risk profiles, graft function, and donor characteristics. Non-linear relationships were identified between HMP parameters and DGF risk, with thresholds for initial resistance, terminal resistance, and terminal flux. The predictive model integrating six variables achieved an AUC of 0.78 (95% CI: 0.76-0.82) in the test set. Calibration and DCA confirmed good reliability and net clinical benefit. Non-linear relationships between HMP parameters and DGF underscore graft perfusion complexity. The proposed model demonstrated robust internal performance and may support early post-transplant risk stratification. External validation in independent cohorts is warranted to confirm generalizability and clinical applicability.

A series of oxadiazole-based dual inhibitors targeting GSK3 and HDAC6 were rationally designed by integrating key pharmacophores into a single molecule. Among these derivatives, 4-(((5-(benzo[d][1, 3]dioxol-5-yl)-1,3,4-oxadiazol-2-yl)thio)methyl)-N-hydroxybenzamide (15i) was identified as the most potent compound with IC50 of 5.50, 69 nM and 88 nM against HDAC6, GSK3α and GSK3β, respectively. 15i also exhibited potent cytotoxicity against the AGS cancer cell line, with IC50 values in the submicromolar range. Molecular docking simulation confirmed that 15i fitted well into the active sites of both HDAC6 and GSK3β. These findings establish compound 15i as a promising candidate for further evaluation.