Exercise is commonly classified by external dose or metabolic demand, yet each bout also imposes spatially patterned mechanical loads on muscle, vessels, extracellular matrix, stromal niches, and mobile immune cells. This review asks whether that mechanical dimension can be organised into a testable framework linking exercise to immune biology. We conducted a critical mechanistic narrative synthesis of exercise physiology, mechanotransduction, immunometabolism, immune mechanobiology, and exercise epigenetics, with structured searches updated through 31 August 2026 and targeted citation checks through 8 September 2026; the studies underpinning the principal causal claims were appraised in duplicate, and their study-level basis is reported in a supplementary evidence table. Throughout, we label whether a mechanism has been demonstrated during exercise in vivo or only in non-exercise mechanical systems. Exercise-specific mechanosensing is well supported in skeletal muscle and endothelium and supported in stromal compartments, including Piezo1-dependent muscle stromal sensing and mechanosensitive bone-marrow niches, both currently demonstrated in mice. Immune cells also possess force-sensitive machinery, but this has been established almost entirely outside exercise, and direct human evidence for durable immune-cell epigenetic change after exercise is limited, short in duration, and does not establish a mechanical mechanism. We therefore propose the Mechanometabolic Immune Interface Model of Exercise (MIIM-E), a multiroute, falsifiable model that separates endothelium-mediated from leukocyte-intrinsic haemodynamic exposure, tissue-niche mechanotransduction, and systemic biochemical signalling, and that distinguishes tests of necessity, sufficiency, mediation, and incremental explanatory value. Persistence of an immune phenotype and its attribution to a mechanical cause are treated as separate hypotheses to be tested, not as assumed consequences of exercise. Mechanical information may contribute to immune adaptation, but its causal role depends on cell type, route, exposure, and persistence.
Background: Hand-foot syndrome (HFS) is a dose-dependent toxicity of capecitabine that affects quality of life and may lead to treatment interruption. Currently, no clinical prediction tool exists to identify patients at high risk of developing moderate to severe HFS (grades 2 to 3). This study aimed to develop a clinical prediction model to estimate the risk of grade 2 to 3 HFS in patients receiving capecitabine. Methods: A retrospective cohort study was conducted in capecitabine-treated cancer patients at a tertiary cancer center between January 2018 and December 2024. Nine prespecified predictors including capecitabine dosage, sex, age, body mass index, body surface area, combination chemotherapy, estimated glomerular filtration rate, topical diclofenac use, and serum albumin were analyzed using multinomial logistic regression to develop the prediction model. Results: Of 596 patients, 132 (22.2%) developed grade 2–3 HFS. The final model included five predictors: sex, age, body mass index, combination chemotherapy, and capecitabine dosage. The model demonstrated good discrimination, with an optimism-adjusted C-statistic of 0.72 (95% CI, 0.67–0.77), and good calibration, with a calibration slope of 1.012, calibration-in-the-large of 0.000, and an observed-to-expected ratio of 0.996. Conclusions: The model was implemented as a web-based application to estimate individual patient risk and facilitate risk stratification. This internally validated prediction model may serve as a pilot clinical tool for identifying patients at increased risk of capecitabine-induced grade 2–3 HFS and warrants external validation before broader clinical implementation.
Background/Objectives: Nutritional depletion and systemic inflammation are common in patients living with obesity undergoing metabolic bariatric surgery (MBS) and may influence the risk of early postoperative complications. Composite indices derived from routine blood tests—combining markers of protein/immune reserve and inflammatory burden—offer an inexpensive means of preoperative risk stratification, but their comparative performance in MBS has not been well defined. The primary objective is to compare the prognostic performance of seven preoperative indices for early postoperative complications after MBS; the secondary objective is to estimate and rank the incremental prognostic value of each index over baseline clinical risk factors. Methods: This protocol describes a single-centre retrospective cohort study of approximately 700 consecutive adults who underwent sleeve gastrectomy, one-anastomosis gastric bypass, Roux-en-Y gastric bypass, single-anastomosis duodeno-ileal bypass with sleeve gastrectomy or single-anastomosis sleeve-ileal bypass, including revisional procedures, between 1 January 2023 and 28 June 2026; data extraction will begin only after submission of this protocol. The Prognostic Nutritional Index (PNI), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), C-reactive protein-to-Albumin Ratio (CAR) and Glucose-to-Lymphocyte Ratio (GLR) will be calculated from complete blood count, albumin, C-reactive protein and fasting glucose values. The last preoperative sample is the exposure; early postoperative values (postoperative days 1–3) will be examined in a separate exploratory landmark analysis. The primary outcome is any complication of Clavien–Dindo grade II or higher within 30 days of surgery; readmission within 30 days of surgery and prolonged hospital stay will be analysed as secondary healthcare-utilisation outcomes. The indices will be compared by their optimism-corrected incremental discrimination over a baseline clinical model, using penalised logistic regression with bootstrap internal validation and calibration assessment. Conclusions: This protocol outlines a pragmatic approach to comparing seven low-cost, routinely available nutritional–inflammatory and inflammatory indices for risk stratification before MBS. Indices showing promising performance will require external, ideally multicentre, validation before they can be considered for clinical decision making.
Cardiac amyloidosis was long considered rare and untreatable, but both assumptions have given way, and the disease is now recognized as an underdiagnosed and increasingly treatable cause of heart failure. The available therapies work by slowing amyloid deposition, so their benefit depends on early diagnosis, yet the condition is still identified late and usually only after referral for specialized imaging. Artificial intelligence has started to shorten this path. Models built on the electrocardiogram and the echocardiogram detect amyloid infiltration with high accuracy and have reached external validation, while artificial-intelligence-based analysis of heart sound, the cheapest and most widely available cardiac signal, has not yet been directly evaluated in this disease. The physiological rationale for studying it is strong, because a ventricle stiffened by amyloid is expected to alter the low-frequency diastolic sounds that reflect ventricular compliance, although whether these changes are specific to amyloidosis remains unknown. This review brings together the acoustic manifestations of cardiac amyloidosis, the signal-processing and machine learning methods relevant to heart sounds, the clinical evidence across imaging and acoustic modalities, and the practical and equity questions that deployment raises. Heart sound analysis remains the most scalable and least explored opportunity in the field. We outline the mechanistic rationale, the available methods, the lessons from the more mature electrocardiographic and echocardiographic literature, and a set of minimum design requirements for a first disease-specific study.
Background: Sudden cardiac death (SCD) is a major contributor to cardiovascular mortality, frequently stemming from undiagnosed genetic predispositions. In clinical practice, underlying genetic risks are often masked by prevalent conditions such as ischemic heart disease and hypertension, necessitating precise molecular characterization of inherited cardiovascular conditions. Objectives: This study aims to evaluate the diagnostic yield of a targeted next-generation sequencing (NGS) panel in a cohort of patients with diverse cardiovascular pathologies and to refine variant interpretation using a population-specific genetic database. Methods: We conducted a prospective, observational cohort study involving targeted NGS on 270 DNA samples from patients with documented cardiovascular disease. The cohort presented with a broad spectrum of phenotypes, including ischemic heart disease, hypertension, conduction disorders, arrhythmias, cardiomyopathies, and dyslipidemias. Genetic variants were classified according to ACMG/AMP guidelines. To minimize false-positive findings, we implemented a bioinformatic pipeline leveraging the Russian Population Genetic Variant Frequency Database (GDBPop) to filter out benign variants common in the local population but rare in global repositories like gnomAD. Results: Seventeen pathogenic or likely pathogenic (P/LP) genetic variants spanning 13 different genes across 15 distinct molecular loci were identified in 15 subjects out of 270 subjects (5.56%), with two individuals carrying concurrent alterations (Patient 6326 with two variants in LDLR, and Patient 6284 with variants in FHOD3 and KCNH2). The identified genetic spectrum was predominantly driven by alterations within the KCNH2, LDLR, and LMNA genes. Conclusions: A targeted NGS panel combined with regional genetic databases provides a robust strategy for identifying potential genetic predispositions to cardiovascular disorders. This observational screening approach effectively captures clinically relevant variants across a broad phenotypic spectrum, supporting personalized variant interpretation in the Russian population.
