Search Results (3,834)

Salivary microbiota constitutes complex microbial assemblages and acts as a source of reliable non-invasive biomarkers for evaluating growth, metabolism, and health status of farm animals. This review explores the research value of saliva and its resident microbes in livestock health monitoring. We summarize saliva composition, physiological functions, and sampling protocols for pigs, cattle, sheep, and goats. Core microbial taxa of monogastric and ruminant species are outlined, together with their roles in digestion, rumen fermentation, growth, and stress responses. We also present classic salivary diagnostic indicators and the impacts of oral bacteria on common livestock diseases. Current research is limited by undefined causal relationships, low diagnostic specificity, and heterogeneous technical standards, and thus fails to support accurate diagnosis at the individual animal level. Future studies should elucidate microbial interaction mechanisms, standardize experimental protocols, and establish multi-index evaluation models. This review advances theoretical research and promotes the practical application of salivary microbiota in precision livestock farming. Full article

Azoospermia represents the most severe manifestation of male infertility and is classified into obstructive azoospermia (OA) and non-obstructive azoospermia (NOA). NOA patients experience a lack of sperm due to testicular dysfunction, posing significant challenges in clinical diagnosis and treatment. Recent advancements in molecular biology and high-throughput technologies have led to the discovery and validation of numerous biomarkers, including proteins, non-coding RNAs, genetic polymorphisms, and imaging indicators, which have greatly enhanced the understanding of the pathophysiological mechanisms of azoospermia and facilitated non-invasive diagnostic approaches. This review aims to systematically summarize the pathogenesis of azoospermia and critically evaluate the latest advancements in diagnostic and prognostic biomarkers, including small RNAs, proteomic profiles, genetic markers, and imaging features. The overarching goal is to synthesize this knowledge toward the development of integrated, biomarker-guided strategies for precise diagnosis, prognosis prediction, and improved clinical management of azoospermia, particularly NOA. Full article

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes. Full article

Chronic diseases such as cardiovascular disorders, diabetes, neurological conditions, and kidney disease continue to rise worldwide. These conditions create a growing demand for continuous, non-invasive, and personalized health monitoring technologies. Wearable biosensors meet this need by enabling real-time physiological and biochemical measurements outside traditional clinical settings. Among wearable biosensors, those based on biofluids like sweat, tears, and saliva provide a painless alternative to blood sampling. These fluids also grant access to metabolites, electrolytes, hormones, proteins, and disease related biomarkers that reflect systemic health status. Advanced sensing technology allow us to continuously track health status by analyzing key biomarkers in these accessible biofluids. This review summarizes recent advances in non-invasive wearable biosensors and focuses on their sensing principles which includes biorecognition elements, signal transduction mechanisms, and data acquisition strategies. We also discussed key sensing modalities, including electrochemical, optical, thermal, and piezoelectric approaches, highlighting their advantages for wearable integration and performance in biofluid sensing. Finally the review also outlines recent developments and applications of these systems in biofluid sensing. In the end we highlights existing challenges, potential solutions, and future directions toward clinically deployable, AI-assisted precision healthcare systems. Full article

Background/Objectives: Patients with chronic kidney disease (CKD) have an increased risk of ischemic heart disease (IHD). Some discrepancies exist between cardiological and nephrological guidelines regarding the extent of diagnostic procedures in CKD patients who are candidates for kidney transplantation. The aim of this study was to assess the cardiac status of these patients after cardiological checkup. Methods: The present study included all kidney transplant candidates referred to the Regional Qualification Center between January 2021 and February 2024. We characterized the group of patients in whom IHD was diagnosed during the cardiological checkup. Results: Among 346 patients, IHD was newly identified in 44 (12.7%) subjects. These patients were significantly older [median 62.9 (51.9–65.4) vs. 47.2 (36.8–57.9) years; p < 0.001], had longer dialysis vintage [median 20 (12.5–42) vs. 14 (6–31) months; p < 0.05] and were more frequently diabetic (29.6 vs. 16.9%, p < 0.05) than the rest of the study cohort. Of note, they were also characterized by significantly more frequent manifestation of atherosclerosis lesions visualized using routine imaging methods (i.e., chest X-ray and abdominal aorta and iliac artery visualization). The stepwise logistic regression analysis revealed that age [OR 1.05 (1.02–1.09); p <0.01] and the ad hoc atherosclerotic score [OR 1.88 (1.27–2.77); p < 0.001] independently predicted the diagnosis of IHD during the cardiological qualification of potential kidney transplant candidates. Conclusions: During the cardiological examination, IHD was diagnosed in a substantial number of kidney transplant candidates. The presence of atherosclerotic lesions detected by routine noninvasive vascular system imaging methods may suggest the need for extending IHD diagnostics even in relatively young patients without clinical symptoms. Full article

With the rapid aging of the global population, constipation has become a major gastrointestinal concern among elderly individuals. Bowel sounds provide a non-invasive acoustic signal for assessing gastrointestinal function, but their automatic analysis remains challenging due to sparsity and non-stationarity. This study proposes a two-stage bowel sound analysis framework based on continuous abdominal recordings. First, a Convolutional Neural Network-MAMBA (CNN-MAMBA) model was used for salient bowel sound detection. Second, a patient-level constipation classification model was developed using multi-view spectral representations and a Convolutional Neural Network-Conformer-Multiple Instance Learning (CNN-Conformer-MIL) architecture. On a held-out test set, the detection model achieved an accuracy of 0.87, an F1-score of 0.78, and a ROC-AUC of 0.93. For patient-level classification under binary Bristol Stool Form Scale (BSFS) grouping, five-fold cross-validation yielded a mean accuracy of 0.665 and an F1-score of 0.755. All BSFS labels were annotated by clinical physicians and temporally aligned with bowel sound recording. Given the modest improvement and cross-validation variability, the patient-level results should be interpreted as preliminary feasibility evidence. These findings suggest that bowel sound analysis may serve as an auxiliary screening or longitudinal monitoring tool rather than a stand-alone diagnostic system. Full article

Background: Endometriosis is a chronic estrogen-dependent inflammatory disease estimated to affect up to 190 million women of reproductive age worldwide based on clinical and population-based estimates, although only 22.3 million prevalent cases were formally documented—a gap that itself reflects substantial under-diagnosis. Despite an exponential increase in systematic reviews (SRs) and meta-analyses (MAs), the evidence base remains fragmented across clinical domains. An umbrella review provides the methodologically highest level of evidence synthesis and allows critical appraisal and hierarchical classification of published SRs and MAs. Objective: The aim of this study was to conduct a comprehensive critical synthesis of published SRs and MAs on the epidemiology, pathogenesis, diagnosis, treatment, and long-term consequences of endometriosis and to assess their methodological quality using AMSTAR-2. Methods: Systematic searches were conducted in PubMed, Embase, Cochrane Library, and Scopus (2016–2026). Eligibility: SRs with or without MA covering any clinical aspect of endometriosis in women were considered eligible. Quality was assessed using AMSTAR-2. Association strength was classified as convincing (Class I), highly suggestive (Class II), suggestive (Class III), weak (Class IV), or non-significant (NS). Results: Fifty-two SRs and MAs were included (total sample > 6,000,000 participants). AMSTAR-2 quality: high 25% (n = 13), moderate 40% (n = 21), low 29% (n = 15), critically low 6% (n = 3). Class I evidence: short menstrual cycle (<27 days) associated with endometriosis risk (OR 1.68; 95% CI 1.48–1.89). Class II: post-operative dienogest reduces recurrence by 70% (OR 0.30; 95% CI 0.18–0.53); the risks of anxiety (RR 2.82; 95% CI 1.69–4.68) and depression (RR 2.78; 95% CI 1.63–5.25) are markedly elevated. Diagnostic delay persists at 4–12 years globally. Multi-biomarker platforms and AI-assisted imaging (e.g., PromarkerEndo and IMAGENDO) have shown promising preliminary diagnostic performance (reported AUCs of 0.997 and 0.906, respectively) in initial validation studies, although external validation in larger and more diverse cohorts is required before clinical implementation can be recommended. Conclusions: Endometriosis is a systemic, chronically under-diagnosed disease requiring a multidisciplinary approach. The available evidence supports dienogest as one of the preferred options for post-operative maintenance therapy, identifies multi-biomarker platforms as a promising—though not yet clinically validated—avenue for non-invasive diagnosis, and underscores the importance of incorporating psychological assessment into multidisciplinary management. Full article

Endometriosis is a chronic, debilitating condition affecting approximately 10–15% of reproductive-aged women and it is often associated with significant diagnostic delays due to its heterogeneity and unreliable non-invasive tests. Artificial intelligence (AI) offers innovative methods for improving endometriosis diagnosis, prognosis and research via advanced pattern recognition and data analysis capabilities. The integration of AI in diagnostic workflow has the potential to improve efficiency, accuracy, and patient outcomes. This review summarises current developments of AI—including machine learning, deep learning, and natural language processing—in the diagnostic workflow of endometriosis. It analyses different fields of diagnostics ranging from AI-assisted imaging in detection of pouch of Douglas to multi-omics biomarkers assisting the clinical decision process. AI can enhance accuracy, reducing diagnostic delays and supporting personalised treatment planning. However, there are multiple limitations, such as small datasets, overfitting, and lack of external validation and variability. Further research and evaluation are required before it can be implemented into healthcare systems. AI holds promise as a non-invasive, scalable adjunct to current diagnostics, potentially reducing the economic and personal burden endometriosis carries. Full article

Background: Coronary bifurcation lesions represent one of the most technically demanding scenarios in coronary artery disease (CAD), associated with higher procedural complexity, restenosis, and periprocedural complications. Recent advances in coronary computed tomography angiography (CCTA) have markedly improved its ability to visualize complex coronary anatomy, assess plaque morphology, and guide revascularization. Objectives: This review summarizes (1) technological advances in CCTA over the last decade, (2) its role in evaluating bifurcation stenosis, (3) assessment of plaque morphology and distribution, (4) quantification of bifurcation geometry, and (5) emerging evidence supporting its application in revascularization planning and guidance. Findings: Modern wide-detector and dual-source CT systems, iterative and deep-learning reconstruction algorithms, and photon-counting CT (PCCT) have significantly improved temporal and spatial resolution, reduced blooming artifacts, and lowered radiation dose. CCTA now reliably quantifies bifurcation stenosis and plaque distribution, characterizes high-risk plaque features, and accurately measures bifurcation angles. The integration of CT-derived fractional flow reserve (FFR-CT) and artificial intelligence (AI)-based plaque quantification further strengthens its diagnostic and prognostic performance. CCTA-derived bifurcation scores and 3D modelling support procedural strategy selection, stent sizing, and side-branch (SB) protection. Conclusions: CCTA has evolved into a comprehensive tool for non-invasive diagnosis, physiological assessment, and pre-procedural planning of bifurcation disease. With the advent of PCCT and AI-enhanced quantitative tools, CCTA is poised to become a central component of revascularization decision-making in complex coronary bifurcations. Full article

Non-invasive blood glucose estimation from exhaled breath has been proposed as a painless alternative to repeated capillary measurements; however, performance evaluation remains challenging in small-sample settings. This study investigates the estimation of blood glucose from human breath using volatile organic compound (VOC) signals acquired with an electronic nose. Responses from three metal-oxide sensor channels sensitive to CO, alcohol, and acetone were collected from 58 individuals, with one measurement per subject, and analyzed using strictly patient-level five-fold cross-validation, in which test folds comprised only real subjects. Two experimental factors were examined. First, model performance was evaluated with and without an additional interpretable alcohol–acetone log-ratio capturing relative variation between compounds. Second, model training was performed using either real data only or fold-wise tabular synthetic augmentation generated via a Gaussian copula fitted exclusively on training subjects, while evaluation remained strictly real-only. Under real-only training, classical machine learning models achieved the lowest prediction errors (approximately 6–7 mg/dL), whereas under synthetic augmentation FTTransformer was the best-performing deep learning model. This findings should be understood as a constrained proof-of-concept analysis rather than as evidence of diagnostic capability or clinical readiness. Full article

Hepatocellular carcinoma (HCC) remains a major global health burden, characterized by late diagnosis, limited therapeutic options, and high mortality rates. Conventional diagnostic tools such as serum α-fetoprotein testing and imaging lack sufficient sensitivity for early detection. In recent years, liquid biopsy has emerged as a minimally invasive approach that enables real-time molecular profiling of tumors through the analysis of circulating biomarkers such as nucleic acids, proteins, and extracellular vesicles. Recent advances have underscored exosomes—nano-sized extracellular vesicles (EVs) secreted by nearly all cell types—as pivotal mediators of intercellular communication and dynamic carriers of tumor-derived molecular information, offering exciting prospects for early cancer detection and personalized therapy. In HCC, EV microRNAs (miRNAs) participate in multiple oncogenic processes, including proliferation, angiogenesis, epithelial–mesenchymal transition, and immune modulation. Specific EV-associated miRNAs, such as miR-21, miR-122, miR-224, and miR-221, show distinctive expression profiles in HCC and correlate with tumor stage, metastasis, and patient prognosis. Moreover, panels of circulating EV-associated miRNAs demonstrate superior diagnostic accuracy compared with traditional biomarkers, underscoring their potential as non-invasive tools for early detection and disease monitoring. Their inherent stability in biofluids and resistance to enzymatic degradation further support their application in liquid biopsy approaches. Despite promising results, continued research is essential to validate EV-associated miRNA signatures and to integrate these “silent messengers” into routine clinical practice for precision management of hepatocellular carcinoma. Full article

Photonic imaging technologies have profoundly transformed neuro-ophthalmic diagnostics by enabling non-invasive visualization of neurodegenerative processes at the retinal level. This review examines how advanced light-based modalities provide unprecedented insights into the structural, physiologic, and biologic relationships between the eye and brain in conditions such as optic neuritis, multiple sclerosis, and glaucoma. Optical coherence tomography has emerged as an essential tool for quantifying thinning of the retinal nerve fiber layer and ganglion cell layer, serving as reliable biomarkers of axonal loss and disease progression across multiple sclerosis subtypes and optic neuropathies. Detection of apoptosing retinal cells imaging enables real-time visualization of retinal ganglion cell apoptosis preceding irreversible structural damage, offering a critical window for early intervention in various neurodegenerative conditions, in particular, glaucoma. Two-photon microscopy with adaptive optics enables subcellular-resolution imaging of retinal neurons, microvascular dynamics, and inflammatory processes in vivo, facilitating the characterization of neurodegenerative mechanisms at unprecedented spatial scales and redefining neuro-ophthalmology by positioning the retina as an accessible extension of the central nervous system. This review critically examines how established and investigational photonic imaging modalities may support earlier disease detection, longitudinal monitoring, and biomarker development in neuro-ophthalmic and neurodegenerative disorders, with potential implications for more timely and targeted management strategies. Full article

Background: Magnetic resonance imaging (MRI) in children may require sedation to minimize motion artifacts and obtain diagnostic-quality images. Intranasal dexmedetomidine (IN DEX) is increasingly used as a non-invasive sedation option; however, evidence regarding its reported effectiveness and safety as a sole agent in routine clinical practice remains limited. This systematic review and meta-analysis aimed to evaluate the reported efficacy and safety of IN DEX monotherapy for pediatric MRI. Methods: A systematic search of PubMed, MEDLINE, and Embase via Ovid, Scopus, Web of Science, and the Cochrane Library was conducted for studies published from 17 December 1999 to 10 January 2026. Observational studies involving pediatric patients undergoing MRI with IN DEX as the sole sedative agent were included. Outcomes included sedation success, rescue sedation use, bradycardia, hypotension, sedation onset time, and MRI duration. Random-effects meta-analyses were performed, and methodological quality was assessed using the National Institutes of Health quality assessment tool. Results: Twelve observational studies comprising 1828 children were included. The pooled reported sedation success rate was 84% (95% CI: 73–95%), and rescue sedation was required in 19% (95% CI: 8–29%) of cases. The pooled incidences of bradycardia and hypotension were 3% (95% CI: 0–6%) and 1% (95% CI: 0–3%), respectively; no clinically significant events requiring intervention were reported. The pooled mean sedation onset time was 18.4 min, and the pooled mean MRI duration was 38.9 min. Substantial heterogeneity was observed across the efficacy outcomes. Conclusion: Intranasal dexmedetomidine appears to be a feasible and well-tolerated option for pediatric MRI sedation. Although pooled observational data suggest high reported sedation success and low adverse-event rates, findings should be interpreted cautiously because of substantial heterogeneity across studies. Full article

Background/Objectives: Ultrasound elastography (UE) is a non-invasive imaging technique that evaluates tissue stiffness and may complement conventional ultrasound in the assessment of uterine diseases. This systematic review aimed to summarize the current evidence on the role of strain elastography (SE) and shear wave elastography (SWE) in the diagnosis and management of benign and malignant uterine pathologies. Methods: A systematic literature search of MEDLINE (PubMed) and Embase was performed to identify studies published between January 2018 and February 2026. Original studies evaluating UE in adenomyosis, uterine fibroids, cervical lesions, and endometrial pathologies were included. Data were qualitatively synthesized according to pathology type and elastographic technique. Results: Twenty studies met the inclusion criteria. In benign myometrial disorders, adenomyosis and uterine fibroids generally showed higher stiffness than normal myometrium, although differentiation between these entities was not always consistent across studies. In cervical disease, malignant and high-grade lesions typically demonstrated increased stiffness compared with benign or low-grade lesions. In endometrial pathology, endometrial carcinoma was generally associated with higher stiffness values than benign lesions and elastography also showed potential in assessing myometrial invasion. Across studies, UE demonstrated promising diagnostic performance, but substantial heterogeneity was observed in acquisition methods, parameters, and reported thresholds. Conclusions: UE appears to be a promising adjunct to conventional ultrasound for the evaluation of uterine pathologies. However, further standardized, large-scale studies are needed to define reproducible protocols and clinically applicable diagnostic thresholds. Full article

The emergence of organic–inorganic hybrid composites integrating magnetic nanoparticles (MNPs) with polymers has been an important advancement in modern biological research. Among these systems, magnetic sodium alginate (SA)-based hydrogels (MSABHs), produced by embedding MNPs within an SA framework, exhibit remarkable potential for biomedical applications owing to their high biocompatibility, rapid magnetic response, controllable spatiotemporal behavior, and remote, non-invasive operation. Under the influence of an alternating magnetic field (AMF), MSABHs can exhibit various responses, including deformation, motion, and thermal generation, which are highly valuable for diagnostic and therapeutic medical applications. This review first outlines the key studies on SA and MNPs, along with the various synthesis routes used to fabricate MSABHs. Subsequently, the discussion primarily focuses on their versatile biomedical applications, including tissue engineering, targeted drug delivery, thermotherapy, imaging, and micro-robotics, followed by an evaluation of current challenges and prospects for future improvement. Through this comprehensive examination and synthesis, the review aims to further reveal the full potential of MSABHs and broaden their applications in the biological domain. Full article