Artificial intelligence (AI) translation in genitourinary (GU) pathology has progressed unevenly across organs and tasks. This review addresses a central clinical question: which GU pathology AI applications are deployment-ready, which require further validation, and what frameworks can guide safe implementation? We synthesize evidence across GU organs and introduce pragmatic translation frameworks to guide deployment and prioritize translational research.

Narrative review integrating foundational literature with targeted 2023–2025 publications, emphasizing regulatory milestones, external validation, and prospective studies. Literature was identified through PubMed, Embase, and conference proceedings using structured search terms for AI, digital pathology, and GU organ-specific queries. For each organ/task, we mapped evidence strength, regulatory maturity, generalizability, workflow integration, safety, and feasibility to a Translational Readiness Index (TRI) rubric (0–30 scale).

Prostate biopsy AI demonstrates the strongest maturity (TRI 26/30), supported by U.S. Food and Drug Administration-cleared systems, multi-site validation, and prospective implementations showing efficiency gains and reduced ancillary testing. Bladder cytology shows moderate readiness (TRI 19/30), with commercial offerings supporting pilotable prescreening workflows aligned with the Paris System when paired with uncertainty-aware deferral. Bladder histology, renal neoplasia, and low-prevalence domains (testis, penis) remain emerging (TRI 6–15/30), constrained by label variability, rare subtype underrepresentation, and limited external validation.

The TRI rubric, SURE-Path safety bundle, and VALIDATED/ORCHESTRATE implementation pathway provide a practical template for evidence-based deployment in GU pathology. Clinically defensible translation requires matching intended use to validation evidence, with explicit safeguards for emerging applications.

Accurate identification of invasive fungal pathogens is crucial for appropriate antifungal therapy. The Department of Clinical Laboratory at Indus Hospital & Health Network, Karachi, Pakistan, reported two cases of invasive fungal infections between 1st January and 31st March 2024 in which conventional identification methods and automated systems produced discordant results, highlighting critical diagnostic challenges.

Two invasive yeast isolates initially showing budding yeast cells without pseudohyphae on Gram stain were subjected to conventional identification using cornmeal-Tween 80 agar, chrome agar, and BiGGY agar, followed by automated identification using the VITEK 2 ID-YST system and confirmatory API 20C AUX testing. Both isolates demonstrated typical soft, wrinkled, cream-colored colonies on Sabouraud dextrose agar, which on chrome agar appeared as dry, blue colonies and on BiGGY agar as dry, brown colonies. Characteristic arthroconidia and blastoconidia formation on cornmeal-Tween 80 agar were observed, consistent with Trichosporon species. However, the VITEK 2 ID-YST system identified both isolates as Cryptococcus laurentii with good confidence levels. India ink staining was negative for both isolates. Confirmatory API 20C AUX testing correctly identified both isolates as Trichosporon asahii (identification profile 3740734).

This discordance between automated and conventional methods underscores the continued importance of conventional identification techniques and highlights potential limitations of automated systems for certain uncommon yeasts. Laboratories should maintain proficiency in conventional methods and consider confirmatory testing when automated results conflict with morphological findings. The clinical implications of misidentification include inappropriate antifungal selection, given the different susceptibility patterns between these species.

Chaperone-like proteins are involved in the pathogenesis of coronavirus infection through regulation of the viral life cycle, immune response, and antigen presentation. A recently discovered class of chaperones, called heat-resistant obscure proteins (Hero proteins), performs functions similar to other molecular chaperones. This study aimed to investigate the association between the gene encoding the Hero protein SERF2 (Hero7) and the risk of severe COVID-19.

This case-control study was conducted according to the STROBE protocol. A total of 1,373 unrelated Russians (178 patients with severe COVID-19 and 1,195 controls) were recruited. Genotyping of rs4644832 in the SERF2 gene was performed using a probe-based polymerase chain reaction approach. The effects of the single nucleotide polymorphisms (SNPs) were analyzed using bioinformatics tools, including GTExPortal, eQTLGen, HaploReg, atSNP, Gene Ontology, Lung Disease and Common Metabolic Diseases Knowledge Portals, and the STRING database.

SNP rs4644832 in the SERF2 gene (effect allele G) was associated with a decreased risk of severe COVID-19 in the total sample (odds ratio (OR) = 0.56, 95% confidence interval (CI) 0.39–0.81, P = 0.001), females (OR = 0.51, 95% CI 0.31–0.87, P = 0.006), non-smokers (OR = 0.46, 95% CI 0.29–0.74, P = 0.0004), individuals with body mass index ≥ 25 (OR = 0.42, 95% CI 0.25–0.7, P = 0.0004), individuals with low fruit and vegetable intake (OR = 0.38, 95% CI 0.22–0.67, P = 0.0004), and individuals with low physical activity (OR = 0.41, 95% CI 0.23–0.75, P = 0.002).

The G allele of rs4644832 in the SERF2 gene appears to have a protective effect against severe COVID-19. Functional annotation of rs4644832 suggests that it may influence COVID-19 pathogenesis through regulation of proteostasis, ubiquitination, inflammation-induced protein aggregation, the viral life cycle, and cytoskeletal functions.

Insulin resistance is a common extrahepatic manifestation of hepatitis C virus (HCV) infection (HCVi), but its mechanism is poorly understood. While systemic insulin resistance is documented, portal insulin dynamics, a key regulator of hepatic metabolism, remain unexplored. This study aimed to investigate the relationship between insulin, the gut-liver axis, and immunometabolic changes in patients with HCV.

HCV patients were evaluated before (HCVi; n = 29) and after sustained virologic response (SVR) achieved with sofosbuvir/velpatasvir treatment (SVR, n = 23) (NCT02400216). Liver biopsies, portal blood, and peripheral blood were collected at both phases. Statistical analyses were conducted using Wilcoxon rank-sum tests, Mann-Whitney tests, and Pearson’s correlation coefficients to assess differences and associations across insulin, glucose, cytokines, metabolites, immune cells, and hepatic liver transcriptomics to elucidate impaired insulin homeostasis in HCVi.

HCV patients had significantly reduced portal insulin compared to SVR (p = 0.02), while peripheral insulin, portal glucose, and peripheral glucose remained unchanged. Portal insulin correlated positively with proinflammatory cytokines and vascular injury markers and negatively with CD8/CD62L/CD45RA/CD3 cells (naive cytotoxic T-cells) and non-standard nucleotides. Hepatic transcriptomic analysis revealed portal insulin correlated positively with immune and negatively with amino acid pathways, reflecting insulin’s role in the perturbations of immunometabolism during HCVi.

Lower portal insulin during HCVi is associated with changes consistent with altered pancreatic insulin secretion and decreased hepatic insulin extraction. The observed correlations support a potential relationship between the immune response and insulin dynamics, indicating an interplay between the immune system, metabolism, and insulin in HCVi, with clinical implications for the management of dysglycemia.

Despite rapid advances in computational biology and regulatory reforms encouraging the reduction of animal use, a clear synthesis of how artificial intelligence (AI)-driven polypharmacology can function as a scientific and ethical bridge between traditional in vivo pharmacology and human-relevant drug development remains lacking. The shift from cage-based experimentation to code-based predictive modeling presents both opportunities and unresolved challenges in biological interpretation, regulatory acceptance, and pharmacology education. Therefore, this review aims to critically examine the transition toward AI-enabled, human-centric drug discovery within the framework of the 3R principles (Replacement, Reduction, and Refinement). Specifically, it explores (i) the global regulatory and ethical drivers accelerating non-animal methodologies, (ii) the scientific and educational gaps emerging from reduced dependence on animal models, and (iii) the role of AI and deep learning in reconstructing biological complexity through multi-omics integration and predictive toxicity modeling. By analyzing emerging AI platforms and computational strategies, this review highlights how AI-driven polypharmacology may offer a scalable, ethical, and precision-oriented framework for future pharmacological research.

Liver transplant rejection significantly affects patient prognosis. Myeloid-derived suppressor cells (MDSCs), known for their potent immunoregulatory functions, represent a promising target for managing liver transplant rejection. This study aimed to systematically characterize the diversity of MDSC subsets and their context-dependent functions, particularly within the context of transplant tolerance.

We analyzed clinical and murine liver transplants using single-cell RNA sequencing, bulk RNA sequencing, flow cytometry, multiplex immunohistochemistry, and co-culture assays to phenotype MDSC subsets.

Single-cell RNA sequencing analysis of human and murine samples revealed MDSC involvement in transplant rejection. In mice, MDSC scores followed a normal distribution during the first week post-transplant and correlated with clinical flow cytometry data at one month. A distinct LDLR+ monocytic MDSC (M-MDSC) subset was identified and confirmed through spatial mapping by multiplex immunohistochemistry. Flow cytometry demonstrated dynamic changes in LDLR+ M-MDSCs across tissues (liver, spleen, peripheral blood, bone marrow, and lymph nodes), with a peak during acute rejection. Co-culture experiments showed that LDLR−/− M-MDSCs exhibited reduced Arg-1/iNOS expression and an impaired capacity to induce inhibitory receptors (TIGIT, PD1, CTLA-4) or suppress effector molecules (GZMB, IFN-γ, IL-2) in CD8+ T cells.

These findings highlight the critical role of MDSCs in liver transplant rejection. LDLR+ M-MDSCs exhibited enhanced immunosuppressive properties, underscoring their potential clinical relevance in mitigating rejection and promoting immune tolerance.

Mediastinal germ cell tumors (GCTs) are rare malignant neoplasms that occasionally develop somatic-type malignancies (SMs), such as sarcomas, carcinomas, and hematologic malignancies.

We report a unique case of a 16-year-old male patient with a mediastinal GCT that simultaneously developed two different SMs: well-differentiated angiosarcoma and acute megakaryoblastic leukemia (AML). The patient initially presented with left shoulder pain and intermittent shortness of breath. The imaging study demonstrated a 12.5 × 9.0 × 8.5 cm heterogeneous mass in the left anterior mediastinum. The mediastinal mass was resected and showed a cystic mature teratoma with somatic transformation into well-differentiated angiosarcoma and AML. A subsequent bone marrow biopsy confirmed the diagnosis of AML, and next-generation sequencing demonstrated the presence of PTEN and TP53 gene mutations in the AML. Despite aggressive chemotherapy and allogeneic stem cell transplantation, the patient died 10 months after diagnosis.

Our report demonstrates the unique capability of mediastinal GCTs to simultaneously develop two different SMs. The presence of two different SMs in mediastinal GCTs is associated with extremely aggressive behavior and a poor prognosis.