Search Results (655)

Diabetic chronic wounds, characterized by persistent inflammation and a complex microenvironment, pose a major challenge to global healthcare. Traditional dressings act merely as passive physical barriers, lacking the ability to sense biochemical fluctuations or respond to dynamic pathological changes. Therefore, developing smart platforms for in situ, continuous, and non-invasive monitoring is crucial for early warning and precision intervention. This review systematically explores recent advances in high-fidelity wound monitoring, focusing on the deep integration of “front-end interface engineering” and “back-end data analysis”. We first analyze the specific physicochemical and biochemical abnormalities of the diabetic wound microenvironment. Next, we discuss how advanced material designs, such as active fluid management, anti-biofouling zwitterionic networks, and nanozyme-based reactive oxygen species (ROS) scavenging, ensure the long-term stability of sensing interfaces against complex microenvironmental interference. Building on this hardware foundation, we summarize in situ sensing strategies and multiparameter decoupling techniques tailored for key biomarkers, including pH, temperature, glucose, ROS, and MMP-9. Furthermore, we highlight cutting-edge developments in signal digitization, emphasizing the pivotal role of portable devices and machine learning algorithms in extracting high-dimensional features and translating complex multimodal signals into objective clinical metrics. By outlining this comprehensive technological closed-loop, this review aims to provide a systematic theoretical framework for the development and clinical translation of next-generation smart wound monitoring platforms. Full article

In recent years, liquid biopsy has emerged as a promising non-invasive strategy for the identification of tumor-derived biomarkers. Among circulating analytes, cell-free DNA (cfDNA), including both nuclear and mitochondrial fractions, has been extensively investigated in a variety of biological fluids for its potential applications in cancer diagnosis, disease monitoring, and prognostic stratification. Owing to its higher copy number per cell compared with nuclear DNA, mitochondrial DNA (mtDNA) is typically present at higher concentrations in body fluids and is therefore potentially detectable. Circulating cell-free mitochondrial DNA (cfmtDNA) is closely associated with pro-inflammatory signaling pathways and cellular damage responses, including apoptosis, necrosis, and neutrophil extracellular trap formation (NETosis). This review provides a comprehensive overview of the reported alterations of cfmtDNA in the most prevalent gynecological malignancies, namely ovarian and endometrial cancers, which are characterized by a chronic inflammatory microenvironment. We further critically assess the current evidence supporting cfmtDNA as a potential non-invasive biomarker in these malignancies, highlighting current limitations and future research directions. Full article

A circulating tumor DNA (ctDNA) assay is an emerging non-invasive diagnostic approach providing real-time insights into the heterogeneous tumor molecular landscape of advanced prostate cancer, overcoming the limitations of traditional tissue biopsies and PSA. Detection methods include droplet digital PCR, next-generation sequencing, and new epigenomic and fragmentomic strategies (investigational) designed to improve sensitivity in cases of low ctDNA shedding. While ctDNA’s role in localized prostate cancer is limited, it offers significant prognostic value in metastatic cases, where high ctDNA levels correlate with shorter survival. Additionally, longitudinal ctDNA monitoring can predict treatment response and identify emerging resistance mechanisms, including androgen receptor alterations associated with androgen receptor pathway inhibitor therapy and BRCA reversion mutations linked to PARP inhibitors. Importantly, liquid biopsy enables genomic characterization to inform treatment decision-making, particularly in clinical scenarios where tissue biopsy is challenging, such as bone-only disease. However, the widespread clinical implementation of ctDNA analysis is hindered by several analytical challenges, including low sensitivity in localized disease and low disease burden, and the risk of false positives due to clonal hematopoiesis. Furthermore, greater efforts are required to standardize pre-analytical workflows and post-analytical data interpretation and reporting across institutions. This review aims to summarize the evolving role of cfDNA technologies in localized and advanced prostate cancer, highlighting their prognostic and predictive value and their role in uncovering mechanisms of treatment resistance. Full article

Medical workers are frequently exposed to high-stress environments, highlighting the need for non-invasive stress monitoring strategies based on autonomic nervous system activity. Ammonia emitted from the human skin surface has been reported to increase under physical and psychological stress; however, its relationship with autonomic nervous system (ANS) dynamics remains unclear. In this study, we performed simultaneous, time-resolved measurements of dermal ammonia emission and heart rate variability (HRV) in 11 medical workers during 3 h of routine work. Dermal ammonia emission flux was continuously monitored using a passive flux sampler (PFS) coupled with ion chromatography, while autonomic nervous system activity was assessed by Holter electrocardiography. The temporal profiles of ammonia emission were analyzed in relation to HRV indices, including high frequency (HF) and the low-frequency-to-high-frequency ratio (LF/HF). Dermal ammonia emission increased under conditions characterized by lower HF and/or higher LF/HF, whereas elevated HF was associated with reduced ammonia emission (r = −0.47, p < 0.001). Furthermore, temporal fluctuations in ammonia emission were associated with sympathetic–parasympathetic switching. These findings suggest that dermal ammonia emission may be associated with HRV-related physiological responses under real-world working conditions and may have potential as a non-invasive indicator for stress-related physiological monitoring. Full article

The degradation of works of art constitutes a significant problem for the preservation of cultural heritage. In the case of paintings, the observed alterations can be physical, chemical, or visual, affecting both the integrity and appearance of the artworks. Degradation compromises the authenticity, aesthetic legibility, and historical value of paintings, making the early monitoring of such issues, as well as the development of appropriate conservation and restoration strategies, essential. For an effective approach, the characterisation of the materials and techniques used by the artist, as well as the degradation processes inherent in the materials used, proves to be crucial. In this context, the application of artificial intelligence (AI) emerges as a non-invasive solution capable of detecting and predicting degradation in works of art. This bibliographic review aims to explore existing studies in this field in depth, with special attention to contemporary paintings considered as case studies. The methodology involved a systematic review of peer-reviewed studies, theses, and interdisciplinary databases, using keywords related to the topic under investigation (e.g., “degradation detection,” “artificial intelligence,” “craquelure segmentation”). The results indicate that artificial intelligence enables the early detection of degradations that may not yet be visible to the naked eye while also improving objectivity and consistency in the analysis of complex and irregular patterns typical of paintings. It became evident that there is a significant gap in the literature, regarding studies addressing the potential of AI for degradation detection specific to contemporary paintings. However, these could be a valuable case study given their potential material and technical heterogeneity, as well as their differences from traditional easel paintings. Full article

Background: Fiberoptic bronchoscopy (FOB) is a procedure routinely performed in clinical practice for both diagnostic and therapeutic purposes. FOB frequently impairs respiratory function, which may exacerbate respiratory failure. Currently, conventional oxygen therapy (COT) is the most commonly used form of respiratory support; however, non-invasive ventilation (NIV) and high-flow nasal cannula (HFNC) are being used increasingly. The optimal settings and indications for NIV and HFNC in patients with respiratory acidosis undergoing FOB have not yet been determined. Methods: This is a prospective, multicenter, randomized controlled trial including two parallel study populations defined by the indication for bronchoscopy and the type of respiratory acidosis. Therapeutic FOB (Study 1): Patients with decompensated type 2 respiratory failure (pH < 7.35 and PaCO₂ > 45 mmHg) will be randomized to receive one of four methods of respiratory support during bronchoscopy: COT, NIV, HFNC, or invasive mechanical ventilation (IMV) (n = 315). Diagnostic FOB (Study 2): Patients with chronic respiratory acidosis (pH ≥ 7.35, PaCO₂ > 45 mmHg, and/or HCO₃⁻ > 27 mmol/L) will be randomized to receive COT, NIV, or HFNC during bronchoscopy (n = 210). Before FOB, patients in both groups will undergo arterial blood gas (ABG) analysis. During FOB, vital signs will be continuously monitored, including SpO₂, FiO₂, TcCO₂, ECG, and heart rate. After FOB, ABG analysis will be repeated, and study endpoints and complications, if any, will be recorded. The planned study period is from April 2026 to April 2029. Results: Based on the study results, we aim to evaluate the effectiveness and safety of different respiratory support strategies during flexible bronchoscopy, with the primary objective of comparing the rate of treatment failure among COT, HFNC, NIV, and IMV. Treatment failure is defined as the need for endotracheal intubation, premature termination of the procedure, or escalation of respiratory support. Additionally, we aim to identify the optimal NIV and HFNC settings, as well as complication rates in both study groups. Conclusions: The results of this study will help define the role of optimal respiratory support in patients with respiratory acidosis undergoing FOB, potentially leading to a shorter time from admission to diagnosis, better tolerance of the procedure, and faster recovery afterward. Full article

Preclinical multimodal imaging is widely applied in small animal models for longitudinal studies of human diseases. Beyond murine systems, cost-effective and ethically sustainable models such as the chicken embryo and its chorioallantoic membrane are gaining increasing interest in accordance with the 3Rs principles. This study evaluated the feasibility of using both high-frequency ultrasound and magnetic resonance imaging for the non-invasive longitudinal monitoring of chicken embryo development in ovo. Fifty fertilized eggs were incubated under controlled conditions and examined up to embryonic day 14. High-frequency ultrasound (15–71 MHz) enabled real-time imaging and quantitative assessment of superficial structures, including cranial biometry and limb growth, while magnetic resonance imaging (7T) provided high-resolution three-dimensional visualization of internal organs and extraembryonic compartments. Together, these modalities allowed the progressive identification of key anatomical structures from ED5 onward, with HFUS enabling earlier linear measurements and MRI facilitating detailed anatomical and volumetric evaluation. The integration of these techniques allowed the generation of a developmental imaging timeline and quantitative reference dataset of normal embryogenesis. This multimodal approach represents a promising strategy for in vivo developmental studies, offering a robust baseline to characterize structural alterations induced by experimental conditions. Moreover, the use of the chicken embryo model provides significant ethical and economic advantages, supporting its application in preclinical research and imaging-based studies. Full article

Seagrasses are vital ecosystem engineers and habitat architects in coastal environments, with Posidonia oceanica in the Mediterranean playing a crucial role as an indicator of ecological health. As an endemic and vulnerable species, P. oceanica meadows are highly susceptible to environmental degradation, underscoring the importance of non-destructive monitoring techniques. Traditional SCUBA-based surveys are accurate but resource-intensive and difficult to scale, especially for estimating shoot density and leaf length. This study applies a conservative acoustic-based approach to assess Posidonia oceanica biometrics, habitat characteristics, and ecological status along the Turkish Levant coast. The method offers a non-destructive alternative to SCUBA surveys and addresses a regional knowledge gap in Mediterranean seagrass monitoring. Acoustic data collected during winter and summer 2019 along the Turkish Levant coast were analyzed to estimate seagrass biometrics and derive ecological indicators, with validation via SCUBA observations. Results show that acoustic methods can reliably estimate shoot density, leaf area index, and canopy height. They provide broad-scale coverage and efficiency, though further refinement is required to improve calibration across depths and substrates. While acoustic methods provide broad, non-invasive coverage, they are affected by spatial and temporal variability that SCUBA surveys capture more reliably. Calibration of the POSIBIOM (vers 1.1) algorithm was based on specimens collected at 15 m depth on rocky substrates. While this provided consistent regression relationships, it may limit accuracy when extrapolated to habitats such as sand, mud, or matte. This study represents the first high-resolution, spatiotemporal mapping of P. oceanica meadows and benthic habitats along a significant portion of the Turkish Levant coast using acoustics alone. Overall, the study highlights the potential of acoustics as a scalable, non-invasive tool for seagrass monitoring. This approach contributes to ecosystem-based management and conservation strategies in the Mediterranean. Future work will focus on refining models to address bottom type- and depth-dependent acoustic responses and improve biometric accuracy. Full article

Cell-based immunotherapies require noninvasive tools that can quantify the migration, biodistribution, and persistence of administered immune cells. This review focuses primarily on oncologic immune cell therapy, while also considering selected inflammatory disease models in which immune-cell trafficking is biologically relevant. We critically compare direct radionuclide labeling, sodium iodide symporter (NIS)-based reporter gene imaging, radionuclide-integrated nanoplatforms, and Cerenkov-based hybrid optical conversion strategies. Direct labeling with agents such as [⁸⁹Zr]Zr-oxine, [¹¹¹In]In-oxine, and [⁹⁹ᵐTc]Tc-HMPAO enables early positron emission tomography (PET)/single-photon emission computed tomography (SPECT) biodistribution assessment, usually within hours to several days after cell administration. NIS reporter imaging with [¹²⁴I]NaI, [¹²³I]NaI, [⁹⁹ᵐTc]TcO₄⁻, or [¹⁸F]TFB supports repeated viability-dependent imaging, because signal generation depends on active transporter expression in living engineered cells. Radionuclide-integrated gold nanoplatforms can improve intracellular retention and offer theranostic potential through combined imaging, photothermal, radiotherapeutic, or immunomodulatory functions. We further discuss PET/SPECT balance, radiopharmaceutical nomenclature, nanoparticle stabilization, ethical aspects of genetic modification, tumor-on-a-chip systems for preclinical testing, and limitations of narrative evidence synthesis. Together, these platforms provide complementary strategies for image-guided immune cell therapy, with translational relevance for patient selection, treatment optimization, safety monitoring, and oncology practice. In conclusion, NIS-centered nuclear imaging and radionuclide-integrated nanoplatforms represent complementary, clinically actionable tools for quantitative immune-cell tracking, therapeutic optimization, and safety monitoring in translational oncology and inflammatory disease research. Full article

Background/Objectives: Telemedicine and remote patient monitoring have emerged as promising strategies to improve outcomes in heart failure (HF), but prior meta-analyses reported conflicting results, partly due to insufficient differentiation between intervention modalities. This systematic review and meta-analysis evaluated the impact of distinct telemedicine strategies on clinically relevant outcomes in HF. Methods: Conducted according to PRISMA 2020 and a prospectively registered PROSPERO protocol (CRD420261355507), this analysis included randomized controlled trials (RCTs) comparing telemedicine-based strategies—non-invasive telemonitoring, structured remote patient management (RPM), or haemodynamic-guided monitoring—against standard care, identified through searches of PubMed/MEDLINE, Embase, and CENTRAL (inception to 15 March 2026). Random-effects meta-analyses (DerSimonian–Laird) were performed, with predefined subgroup, sensitivity, and publication bias analyses. Results: Sixteen RCTs (n = 8618) were included. Telemedicine significantly reduced all-cause mortality (RR 0.82, 95% CI 0.73–0.92; I² = 34%; GRADE: moderate), all-cause hospitalization (RR 0.79, 95% CI 0.71–0.88; GRADE: moderate), HF-related hospitalization (RR 0.68, 95% CI 0.59–0.78; GRADE: high), and composite outcomes (RR 0.75, 95% CI 0.67–0.84; GRADE: moderate). A prespecified subgroup analysis revealed a significant mechanistic gradient (p for interaction = 0.008): haemodynamic-guided monitoring conferred the largest mortality reduction (RR 0.71), followed by structured RPM (RR 0.79), whereas non-invasive telemonitoring alone did not reach statistical significance (RR 0.93; p = 0.14). Conclusions: Telemedicine-based strategies yield clinically meaningful reductions in mortality and hospitalization in HF, but benefit is contingent upon intervention intensity and physiological specificity. Haemodynamic-guided monitoring and structured RPM provide robust outcome reductions, whereas passive telemonitoring alone is insufficient. These findings support consideration of structured remote patient management and haemodynamic-guided monitoring in appropriately selected patients and settings, while implementation and comparative effectiveness research remains necessary. Full article

Background: Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease. Intestinal barrier impairment represents a core pathogenic mechanism and a key therapeutic target for achieving mucosal healing and sustained remission. Methods: This narrative review summarizes intestinal barrier structure, disruption mechanisms, barrier-targeted therapies, and non-invasive monitoring approaches. A reproducible literature search was conducted in PubMed, Web of Science, and ClinicalTrials.gov from 2015 to 2026. Results: Barrier disruption in UC involves genetic susceptibility, proinflammatory cytokines, zonulin-mediated tight junction injury, gut microbiota dysbiosis, decreased short-chain fatty acids and secondary bile acids, impaired autophagy, and an abnormal mucin 2 (MUC2)-dependent mucus layer. Validated non-invasive monitoring tools include fecal calprotectin/lactoferrin, intestinal ultrasound, diffusion-weighted magnetic resonance imaging (MRI), and intravoxel incoherent motion (IVIM). Emerging therapies focus on tight junction stabilization, epithelial regeneration, autophagy regulation, MUC2 restoration, and microbiota modulation. Conclusions: Intestinal barrier dysfunction drives the initiation and progression of UC. Barrier-based monitoring and targeted repair strategies improve UC management. Future studies should develop personalized therapies, precise microbiota engineering, and multi-dimensional digital evaluation systems. Full article

Chronic kidney disease (CKD) is a progressive and irreversible disorder affecting over 850 million individuals globally and is associated with significant morbidity, mortality, and healthcare burden. Conventional diagnostic approaches rely on intermittent laboratory measurements, including serum creatinine, estimated glomerular filtration rate (eGFR), and urinary albumin, which provide limited temporal resolution and fail to capture dynamic physiological changes. Recent advances in wearable biosensing technologies offer new opportunities for continuous, non-invasive monitoring of biochemical and physiological markers relevant to renal function. This review provides a comprehensive analysis of wearable biosensors for CKD monitoring, focusing on sensing mechanisms (electrochemical, optical, and field-effect transistor), biofluid interfaces (sweat, interstitial fluid, and saliva), and materials engineering strategies enabling flexible, high-performance devices. Emphasis is placed on biofluid transport dynamics, analytical performance across sampling matrices, and system-level integration with wireless communication and digital health platforms. Key challenges limiting clinical translation, including biofouling, enzymatic instability, and variability in biofluid composition, are examined—alongside emerging solutions such as antifouling interfaces, synthetic recognition elements, and multimodal sensing architectures. Finally, regulatory pathways and the role of artificial intelligence in digital nephrology are discussed. This review highlights the potential of wearable biosensors to transform CKD management through continuous monitoring, early detection, and personalized therapeutic intervention. Full article

Prostate cancer (PCa) progression and treatment resistance are driven by tumor-intrinsic mechanisms and adaptive remodeling of the tumor microenvironment, in which cancer-associated fibroblasts (CAFs) play a crucial role. Although CAF biology is increasingly recognized, a major translational gap remains: CAFs are highly heterogeneous, and comprise distinct functional states with divergent effects on disease progression, immune regulation, and therapeutic resistance. To bridge this gap, we synthesize evidence from single-cell and spatial transcriptomic studies, tissue-based pathology, liquid biopsy assays, and molecular imaging to construct an evidence-tiered, decision-oriented translational framework that connects stromal mechanisms, translational measurement strategies, and therapeutic interventions in PCa. Single-cell and spatial transcriptomic analyses have consistently identified multiple CAF programs, including matrix-remodeling, inflammatory, immunoregulatory, antigen-presenting, and therapy-imprinted states, each with distinct functional outputs and clinical correlates. Tissue-based readouts, including reactive stromal grade (RSG) and fibroblast activation protein (FAP) immunohistochemistry, provide practical proxies for stromal activation and correlate with disease-specific mortality and imaging phenotypes. Circulating CAFs (cCAFs) represent an emerging liquid biopsy modality for longitudinal stromal monitoring, although technical standardization is required before clinical implementation. FAP-targeted PET imaging and emerging dual prostate-specific membrane antigen (PSMA)/FAP-targeted theranostic strategies provide noninvasive tools for patient selection and response assessment, particularly in PSMA-discordant or tracer-heterogeneous disease. Androgen receptor (AR)-targeted therapy can reprogram stromal states toward resistance-promoting circuits, highlighting the dynamic and plastic nature of the CAF compartment. A state-based CAF framework organizes stromal biology into testable translational hypotheses rather than immediate clinical standards. RSG and FAP-based tissue or imaging readouts are practical markers of stromal activation, whereas spatial CAF-immune signatures and cCAF assays remain investigational and require assay harmonization and prospective validation. Future trials should pre-specify stromal biomarkers as enrichment or pharmacodynamic variables when matched to the intervention and should avoid treating CAFs as a uniform therapeutic target. Full article

Background/Objectives: Active surveillance (AS) is recommended for men with low-risk prostate cancer to minimize overtreatment while monitoring for disease progression. However, current surveillance strategies rely heavily on repeat biopsies, which are invasive and associated with morbidity. MyProstateScore 2.0—Active Surveillance (MPS2-AS) is a urine-based biomarker test developed to predict progression to Grade Group ≥ 2 (GG ≥ 2) and Grade Group ≥ 3 (GG ≥ 3) prostate cancers in men on AS. The objective of this study was to analytically validate the reproducibility and robustness of MPS2-AS analyte detection and risk score calculation across key laboratory variables. Methods: Analytical precision was evaluated using pooled urine specimens processed using the MPS2-AS laboratory workflow. Eight pooled urine samples were tested in a within-laboratory design across five days, with two runs per day, and two replicates per run. Additional reproducibility studies assessed variability across three QuantStudio™ 12K Flex Real-Time PCR Systems and three OpenArray™ chip lots. Ten RNA biomarkers were quantified by RT-PCR and used to calculate the MPS2-AS GG1-2 and GG1-3 risk scores. Variance components were estimated using hierarchical ANOVA. Results: The MPS2-AS analyte measurements demonstrated high precision across within-laboratory testing, with standard deviations ranging from 0.00 to 0.60 and coefficients of variation (%CV) from 0.00 to 4.01%. The reproducibility across qPCR instruments and OpenArray chip lots showed similar robustness, with analyte %CVs of ≤4.57% and ≤4.10%, respectively. These stable analyte measurements translated to reproducible model outputs, with %CV ≤ 10.69% for the GG1-2 risk score and ≤7.20% for the GG1-3 risk score across all tested conditions. No systematic bias was observed between runs, days, instruments, or reagent lots. Conclusions: MPS2-AS demonstrates strong analytical precision and reproducibility for quantifying urinary biomarkers and generating GG1-2 and GG1-3 risk scores. These results support the reliability of MPS2-AS for clinical laboratory implementation and its use as a non-invasive tool to inform biopsy decisions in men with Grade Group 1 prostate cancer undergoing active surveillance. Full article

Accurate, non-destructive estimation of crop biomass is essential for automated high-frequency monitoring and optimization in controlled-environment agriculture, yet standardized approaches remain limited for short-cycle hydroponic systems. This study introduces a reproducible and fully automated method for estimating the biomass of hydroponically grown wheat sprouts (HWSs) using high-frequency RGB imaging. The workflow integrates image preprocessing, tray segmentation, and canopy feature extraction with synchronized load-cell measurements to enable continuous, non-invasive growth tracking. To account for irrigation events and associated weight fluctuations, raw mass signals were processed using a second-order low-pass Bessel filter, preserving underlying biomass trends while removing short-term oscillations. Across 3024 paired image–mass observations collected under commercial cultivation conditions, several canopy coverage, color-based indices (AGI, Proxy NDVI), and texture features exhibited strong predictive relationships with biomass. Features reflecting greenness, canopy density, and color uniformity were positively associated with plant mass, whereas brightness- and red-channel features showed consistent negative relationships. Feature selection using an elastic-net approach identified a compact subset of informative predictors, improving model stability and interpretability. Under a nested cross-validation framework based on contiguous interval splits within sprout-growth cohorts, support vector regression (SVR) achieved the best predictive performance, with an sMAPE of 3.64% and an RMSE of 0.16 kg. Additional experiments under altered illumination conditions showed that including light intensity as an explicit covariate improved model robustness across lighting regimes. These results demonstrate that combining elastic-net feature selection with environmental covariates provides a robust and transferable framework for visual biomass estimation in hydroponic HWS. More broadly, the proposed pipeline enables non-destructive crop monitoring and supports the development of intelligent, feedback-driven control strategies for hydroponic production systems. Full article