Adenosine has emerged as a central metabolic signal linking cellular stress to systemic physiological adaptation. Under conditions such as hypoxia, ischemia, inflammation, and tissue injury, extracellular adenosine triphosphate (eATP) released from stressed cells is sequentially metabolized by the ectonucleotidases CD39 and CD73, generating adenosine that accumulates in the extracellular microenvironment. This stress-responsive nucleoside activates four G-protein-coupled receptors (A1, A2A, A2B, and A3), triggering intracellular signaling networks including the cyclic adenosine monophosphate–protein kinase A (cAMP–PKA), mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase–protein kinase B (PI3K–Akt), and hypoxia-inducible factor-1 alpha (HIF-1α) pathways. Through these integrated mechanisms, adenosine orchestrates diverse physiological processes such as vascular regulation, metabolic adaptation, immune modulation, and cellular survival. In the cardiovascular system, adenosine promotes coronary vasodilation and ischemic preconditioning, limiting reperfusion injury. In pulmonary tissues, it mediates acute anti-inflammatory responses but may also drive chronic fibrotic remodeling. Within the central nervous system, adenosine functions as a neuromodulator regulating neuronal excitability, sleep–wake homeostasis, and neuroprotection. In the tumor microenvironment, hypoxia-driven adenosine accumulation suppresses cytotoxic T cell and natural killer activity, facilitating immune evasion and tumor progression. Collectively, adenosine signaling represents a central integrative network that links metabolic stress sensing to coordinated cellular adaptation while simultaneously emerging as a clinically actionable therapeutic target across cardiovascular, inflammatory, neurological, and oncological diseases.

Background/Objectives: Recurrent urinary tract infection (rUTI) is common after menopause, but estrogen deficiency alone does not explain variation in recurrence, symptoms, and microbial profiles. This study aimed to synthesize evidence on the interactions among estrogen deficiency, urogenital microbial ecology, diabetes, obesity, microbial reservoirs, and anatomical, neurological, and functional modifiers of postmenopausal rUTI susceptibility. Methods: PubMed and Scopus were searched for original studies published from 1 January 2021 to 15 June 2026. A structured narrative synthesis included 62 original reports comprising clinical interventions, observational cohorts, microbiome and multi-omic studies, and cellular and animal experiments. Evidence was interpreted according to study design, population relevance, and biological directness. Results: Menopause was associated with reduced Lactobacillus dominance, higher vaginal pH, and altered vaginal or urinary communities, although findings were heterogeneous. Vaginal estrogen reduced recurrence and improved the local urogenital environment, but microbiome restoration is not established as its sole mechanism. Gut, rectal, vaginal, urinary, and bladder-wall reservoirs may contribute to persistence or repeated exposure. Diabetes was linked to dysbiosis and impaired urothelial defence, whereas obesity evidence remained less direct. Pelvic organ prolapse with incomplete emptying, elevated postvoid residual urine, age-related detrusor dysfunction, stroke, immobility, functional dependence, incontinence, and catheter exposure may further modify susceptibility. Conclusions: Postmenopausal rUTI is multifactorial. The urogenital microbiome–metabolic interface is a useful integrative framework, but not a validated causal or diagnostic model. Vaginal estrogen, urine culture, metabolic and bladder-function assessment, and antimicrobial stewardship remain the clinical foundation; microbiome-directed strategies require prospective validation.

Background: Postoperative sepsis and related infectious complications remain major causes of morbidity after abdominal surgery. Artificial intelligence (AI) and machine learning (ML) models may support earlier risk stratification, but their generalizability and clinical readiness remain uncertain. Methods: We systematically searched PubMed, Scopus, and CENTRAL from inception to 10 August 2026 for studies developing, validating, or evaluating AI/ML models for postoperative sepsis, postoperative infection/surgical-site infection (SSI), anastomotic leakage, or postoperative pancreatic fistula (POPF) after major abdominal or general surgery. SSI, anastomotic leakage, and POPF were analyzed as distinct postoperative endpoints and were not treated as proxy definitions of clinical sepsis. Study selection and data extraction were performed independently by two reviewers, and risk of bias and applicability were assessed using PROBAST + AI. Owing to substantial clinical and methodological heterogeneity, results were synthesized narratively. The review was registered in PROSPERO (CRD420261492111). Results: Forty-five studies were included: 11 addressed sepsis-related outcomes, including six studies with a primary sepsis-related modeled endpoint whose definitions ranged from consensus Sepsis-3 criteria to coded or SIRS-based definitions and a qSOFA-based proxy; 19 evaluated postoperative infection/SSI; eight anastomotic leakage; five POPF; and two broader postoperative complications. Performance varied according to outcome, surgical population, prediction timing, predictor availability, and validation strategy. Fifteen of 45 studies provided strict temporal, geographic/external-center, or independent-investigator evaluation of the principal model, with variable preservation of performance outside development settings. More complex ML methods did not reliably outperform conventional regression. Explicit calibration analysis was reported in 9/45 studies, decision-curve or net-benefit analysis in 7/45, and no study prospectively evaluated the impact of model-guided care on clinical outcomes. Certainty of evidence was very low for primary postoperative sepsis/septic-shock prediction, low for postoperative infection/SSI, and very low for anastomotic leakage and POPF. Conclusions: Although several AI/ML models showed promising predictive performance, the available evidence remains insufficient to support routine use of AI/ML prediction models across major abdominal surgical populations. Future studies should prioritize clearly defined clinical use cases, multicenter external validation, calibration, prospective workflow evaluation, and demonstration of actionable clinical benefit before routine implementation.