Search Results (1,408)

Non-coding RNAs (ncRNAs) are biologically plausible biomarkers in male infertility, but no assay is ready for routine use. This narrative review organizes human evidence by intended clinical decision and defines clinical actionability as a test’s ability to inform a specified decision through analytical reliability, clinical validity, incremental value, decision-level benefit, and feasible implementation. Evidence is most developed for obstructive versus non-obstructive azoospermia (NOA) classification and sperm-retrieval prognosis. Most reports, however, use selected case–control samples, single-center development cohorts, or same-program evaluations. Mixed biospecimens, incompletely specified RNA isoforms and normalization, uncertain cohort independence, imperfect diagnostic references, and protocol-dependent retrieval outcomes limit transportability. No independent geographic validation of a locked ncRNA assay or prospective evaluation of ncRNA-guided management was identified within the retrieved sources. Current guidelines do not recommend routine ncRNA testing. Progress requires prespecified intended uses, locked assays, representative multicenter validation, same-patient comparison with contemporary care, calibrated risk estimates, decision-curve analysis, and patient-important, couple-centered outcomes. Full article

Scapholunate (SL) instability is the most common form of carpal instability, and if untreated, it may progress through dorsal intercalated segment instability to scapholunate advanced collapse. Contemporary understanding emphasizes a dual stabilizing system in which the SL interosseous ligament (SLIL) acts as the intrinsic primary restraint, reinforced by extrinsic “critical” secondary stabilizers; isolated SLIL division does not by itself produce static malalignment until these secondary restraints are also compromised. Against this background, wrist arthroscopy has become a widely used reference standard for diagnosis and grading, allowing for graded assessment through the Geissler and the more granular European Wrist Arthroscopy Society (EWAS) classifications, while dry arthroscopy has reduced fluid-related morbidity and facilitated combined arthroscopic and mini-open procedures. Treatment has correspondingly shifted from open reconstruction toward arthroscopic and arthroscopic-assisted repair, capsulodesis, and ligamentoplasty, which are intended to preserve native capsuloligamentous structures and may offer potential advantages in limiting capsular disruption and postoperative stiffness. This narrative review synthesizes the literature identified through a selective search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar (final search 13 June 2026). The evidence consists mainly of small observational series and technique reports. The review stages the injury by arthroscopic grade (EWAS/Geissler) and organizing techniques by the target ligament(s), from partial and dynamic injuries through complete, reducible, and advanced deformities. We align each technique with the corresponding pattern of intrinsic and extrinsic ligament injury to clarify indications, summarize reported outcomes, and propose a stage-based conceptual treatment framework rather than a validated, evidence-based guideline. Current evidence derives largely from small, heterogeneous case series with variable classifications and outcome measures, underscoring the need for standardized staging and comparative studies. Full article

Background: This narrative concept-generating review proposes a theoretical and heuristic framework for conceptualizing established and emerging interventions for posttraumatic stress disorder (PTSD) including three categories: top-down/conscious (e.g., cognitive processing therapy, CPT), bottom-up/non-conscious (e.g., pharmacotherapy) and hybrid/conscious and non-conscious integration (e.g., eye movement desensitization and reprocessing, EMDR). The categorization offers a preliminary novel classification of well established and emerging interventions, while highlighting gaps in current treatment approaches. Methods: Clinical guidelines and review articles from 2008 through 2026 were used to create the framework; PubMed and Google Scholar databases were searched for relevant meta-analyses, systematic reviews, narrative reviews, and randomized clinical trials. A total of 27 treatment modalities were included presenting effect size data from 43 published articles. Results: Treatments across categories present mixed results for efficacy. While guideline recommended treatments offer more robust evidence, many understudied emerging interventions exhibit preliminary efficacy. At the intersection of conscious and non-conscious approaches, hybrid modalities may present unique potential benefits. However, superiority determinations between modalities cannot be made and further empirical validation of categorization is needed. Conclusions: The proposed framework highlights the need for increased investigation of treatments that address PTSD as an interconnected whole-body disorder to potentially expand care options. Full article

Cyber security has become a growing concern for an increasing number of organizations, many of which are investing significant resources to address the issue. Cyber security education, based on serious games, can enhance learners’ interest, motivation and engagement. Players’ gaming experience, enjoyment and immersion can also be enhanced by the implementation of non-player characters (NPCs) into serious games. The present systematic review was based on the updated PRISMA guidelines and identified 28 papers, each one referring to a different serious game with one or more NPCs. The actions, characteristics, roles and educational benefits NPCs can offer in the context of serious games for cyber security education were examined, as well as the evaluation methods and data collection techniques researchers used. The analysis of the results revealed that NPCs supported players during the game by providing them with feedback, guidance and encouragement primarily through verbal communication and also that NPCs have the potential to increase players’ interest. The evaluation methods and data collection techniques used in serious games for cyber security education involved mostly qualitative and quantitative data collection methods with pre-, in-, and post-game activities, focusing mainly on target users and less on the comparison of the control and experimental groups. The development of a comprehensive classification of NPCs’ actions and characteristics could help fill the gap identified in the literature and could be useful for researchers and designers aiming to implement NPCs into serious games for cyber security education. Full article

Despite guideline recommendations against routine prophylactic drainage following elective colectomy, surgical drains continue to be used selectively in colorectal practice. This study aimed to identify factors associated with drain placement and describe post-operative outcomes associated with selective drain use following colectomy for colon cancer. A retrospective cohort study of 400 patients undergoing colectomy for colon cancer between 2015 and 2021 at a tertiary academic centre was performed. Baseline, pre-operative, intra-operative and post-operative variables were compared between patients who received surgical drains (n = 267) and those who did not (n = 133). Univariable and multivariable regression analyses were performed to evaluate associations between drain placement and post-operative outcomes. Patients receiving drains had higher ASA scores, neoadjuvant chemoradiotherapy, longer operations, greater blood loss, and more emergency and open procedures. After multivariable adjustment, drain insertion was associated with higher Clavien–Dindo classification or post-operative morbidity, longer post-operative stay, delayed mobilisation, post-operative ileus, and re-operation within 30 days. Patients receiving drains represented a higher-risk surgical population with greater operative complexity and experienced poorer post-operative outcomes. These findings likely reflect selective drain placement in more complex cases rather than a causal effect of drainage itself. Prospective studies are needed to define the role of selective drainage in high-risk colorectal surgery. Full article

Background: While non-specific pleural empyema in children can be successfully managed with conservative drainage and thoracentesis in 80–84% of cases, tuberculous pleural empyema (TB empyema) exhibits high resistance and frequently requires extensive thoracic surgery. The choice of surgical scope depends directly on disease stage and secondary complications. Methods: We conducted a retrospective cohort study of 85 pediatric patients (aged 4 to 17 years) with confirmed TB empyema treated between 1984 and 2022, and followed until 2025. Disease staging followed a predefined adaptation of the American Association for Thoracic Surgery (AATS) consensus classification. The study adhered strictly to STROBE guidelines, incorporating temporal stratification across three historical eras and standardized Clavien–Dindo complication reporting. This study describes a 38-year surgical experience at a tertiary pediatric phthisiosurgical center; non-operated patients successfully cured with exclusive chemotherapy were excluded. Results: Stage I empyema was identified in three patients, Stage II in seven, and Stage III in 75 patients (79 total procedures). Stage I–II patients underwent video-assisted thoracoscopic (VATS) debridement. In Stage III, advanced reconstructive interventions were required (39 VATS pleurectomies, 21 segmental resections, eight pleuropneumonectomies, six lobectomies, two thoracomyoplasties, two open resections, and one bronchus occlusion). Postoperative complications occurred in two patients (2.35% patient-level; categorized as Clavien–Dindo Grade IIIa and Grade IIIb), presenting as delayed lung re-expansion managed via secondary minor interventions. Overall 30-day postoperative mortality was 0.0%. Over a median follow-up of 48 months (database lock: 31 December 2025; 97.65% 36-month completeness), zero disease recurrences were documented (0.0%). Conclusion: In this tertiary surgical cohort, combined management incorporating stage-dependent surgical intervention achieved durable clinical cure. Early VATS debridement represents an effective option for Stage I–II patients who fail initial drainage, potentially reducing progression to a rigid fibrothorax and the subsequent need for extensive pulmonary resections. Full article

Prostate cancer is the most commonly diagnosed non-cutaneous malignancy among men in the United States and remains a leading cause of cancer-related mortality. Its marked biological, molecular, and histopathological heterogeneity creates a central diagnostic challenge: identifying clinically significant disease while limiting unnecessary biopsy and overdiagnosis of tumors unlikely to affect survival or quality of life. Although prostate-specific antigen (PSA) remains the foundation of early detection, its limited cancer specificity has driven the development of increasingly risk-adapted diagnostic pathways. Contemporary evaluation integrates clinical risk assessment and PSA-derived measures with selectively used blood- and urine-based biomarkers, multiparametric magnetic resonance imaging (mpMRI), image-guided biopsy, histopathological classification, genomic risk assessment, and molecular imaging. Biomarkers such as the Prostate Health Index, 4Kscore, IsoPSA, MiCheck, SelectMDx, and ExoDx may refine biopsy decisions in appropriately selected patients but should be interpreted according to the clinical setting, decision threshold, and surrounding diagnostic pathway. Prostate MRI and PI-RADS-based assessment have become central to pre-biopsy evaluation, while MRI-targeted biopsy improves detection of Grade Group ≥ 2 disease. Increasing use of the transperineal biopsy route offers comparable cancer detection with a lower infectious risk. Following diagnosis, Grade Group, adverse histological features, clinical risk models, and selected tissue-based genomic classifiers provide complementary prognostic information. PSMA PET/CT has further improved staging of selected patients with higher-risk disease and localization of biochemical recurrence. Precision diagnostics must also account for disease phenotypes that may not be adequately represented by conventional PSA- and imaging-based pathways, including intraductal carcinoma, cribriform architecture, ductal adenocarcinoma, and neuroendocrine prostate cancer. Emerging approaches, including artificial intelligence-assisted MRI interpretation, digital pathology, high-frequency micro-ultrasound, liquid biopsy, alternative molecular radiotracers, and multi-omic integration, show increasing potential but remain at different stages of validation and clinical adoption. This review critically examines contemporary prostate cancer diagnostics within United States clinical practice, distinguishing established guideline-supported approaches from selectively used adjuncts and emerging technologies. Particular emphasis is placed on diagnostic performance in context, clinical utility, external validation, healthcare equity, regulatory considerations, and the need to demonstrate that increasing diagnostic complexity translates into meaningful improvements in patient care. Full article

Multi-angle measurements provide essential data on the anisotropic reflectance properties of vegetation, enabling more robust retrievals of leaf area index (LAI), clumping index (CI), and canopy gap fraction compared to conventional single-view remote sensing. While Unmanned Aerial Vehicles (UAVs) offer unprecedented centimeter-level spatial resolution and flexible deployment, their application exposes a fundamental scale mismatch between ultra-high-resolution imagery and traditional bidirectional reflectance distribution function (BRDF) models. This review explicitly identifies that conventional 1D radiative transfer models (RTMs), which rely on the assumption of a statistically homogeneous canopy, suffer from severe scale-dependent biases, such as systematically underestimating hotspot reflectance (e.g., observed biases of 25% to 40% in 5-cm resolution UAV studies over specific vegetation canopies), and structural-optical confounding when directly applied to UAV data. At centimeter scales, macroscopic structural heterogeneity disrupts this homogeneity, necessitating the use of 3D RTMs that can explicitly simulate geometric occlusion and complex multiple scattering processes in highly heterogeneous environments. To bridge these theoretical and operational gaps, this review uniquely synthesizes UAV-specific multi-angle methodologies, systematically correlating canopy architectural types with optimal sensor configurations, flight strategies, and BRDF modeling frameworks. By evaluating recent advancements in multimodal data fusion, physics-informed machine learning, and physiological parameter retrieval, this review provides a comprehensive roadmap for decoupling structural and biochemical traits, highlighting how multi-angle directional signatures can substantially elevate classification accuracy, with specific experiments on spectrally similar crops and mixed tree species demonstrating improvements from roughly 40% to over 89%. Ultimately, it establishes practical, decision-oriented guidelines for overcoming transient illumination and co-registration errors, advancing high-fidelity quantitative monitoring and stress detection in complex forest ecosystems. Full article

Gesture recognition enables intuitive human–robot communication, where surface electromyography (sEMG) provides a minimally invasive interface for detecting motor activity. However, the lack of systematic evaluation of processing pipelines represents a critical barrier to reliable subject-independent deployment. This work presents a systematic evaluation of sEMG processing pipelines for three-gesture recognition (Neutral, Ask, Take) using a Bagged Trees classifier under Leave-One-Subject-Out (LOSO) cross-validation across ten participants. A mixed-effects ANOVA over 1872 experimental configurations revealed that the preprocessing pipeline is the dominant factor affecting classification accuracy, followed by inter-subject variability, while window size and overlap exhibit smaller but statistically significant effects. A consistency-based elbow analysis identified a compact subset of five features that reduced input dimensionality by 94.79% while improving accuracy from 68.10% to 69.86% and reducing training time by 51.62%. Bayesian hyperparameter optimization was also tested, but it did not yield statistically significant improvements over the five-feature baseline; given the limited cohort (n = 10), this indicates the absence of a detectable difference and points to inter-subject variability as a leading factor limiting performance rather than model configuration. These findings provide empirically grounded guidelines for designing computationally efficient sEMG-based gesture recognition systems for collaborative robotics. Full article

Earthquakes remain among the most destructive natural hazards, necessitating reliable monitoring and early warning systems for effective risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved seismic signal analysis, enabling more accurate event detection, phase picking, classification, and magnitude estimation. This study presents a systematic review of ML- and DL-based approaches for earthquake monitoring, with particular emphasis on Distributed Acoustic Sensing (DAS) as an emerging technology for high-resolution, real-time seismic observation. Following the PRISMA 2020 guidelines, a systematic literature search was conducted across Scopus, Web of Science, IEEE Xplore, and Google Scholar, yielding 252,223 initial records. After applying the predefined publication period, removing duplicate records, conducting relevance screening, and performing eligibility assessment, 138 peer-reviewed studies published between 2021 and 2025 were retained for detailed analysis and synthesis. The review reveals a significant transition from conventional signal-processing techniques to advanced artificial intelligence-based approaches, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Bidirectional Long Short-Term Memory (BiLSTM) networks, Transformer-based architectures, hybrid models, and Bayesian learning methods for uncertainty quantification. The findings further demonstrate that Distributed Acoustic Sensing (DAS) has emerged as a transformative sensing technology because of its dense spatial coverage, high spatial resolution, and continuous monitoring capability. However, several challenges remain, including the lack of standardized datasets, limited model generalization across diverse geological settings, insufficient model interpretability, high computational complexity, and the limited integration of uncertainty-aware approaches for real-time seismic monitoring. This review identifies these critical research gaps and highlights promising future research directions, including multimodal data fusion, interpretable artificial intelligence, physics-informed learning, self-supervised learning, and robust uncertainty quantification for next-generation intelligent seismic monitoring systems. Unlike previous review studies that primarily focus on individual machine learning techniques or conventional seismic monitoring, this review provides a comprehensive and systematic synthesis of recent advances in machine learning, deep learning, and Distributed Acoustic Sensing (DAS), identifies current research gaps, and offers practical recommendations to guide future research on intelligent earthquake monitoring systems. Full article

Alzheimer’s disease (AD) is the leading cause of dementia and a major cause of death worldwide, making early detection a critical clinical priority. Because pathological changes may begin 15–20 years before symptom onset, artificial intelligence (AI) has emerged as a promising tool for identifying and characterizing AD. In particular, multi-modal approaches that integrate cognitive, biological, and sensor-based data have attracted growing interest. This systematic review compares single- and multi-modal AI strategies for AD detection, covering machine learning and deep learning methods, feature representations, validation strategies, and classification tasks. Searches of major databases identified 568 studies published between 2016 and early 2026; 278 met the inclusion criteria according to PRISMA guidelines. Multi-modal approaches generally achieved higher performance than single-modal strategies, particularly for challenging tasks such as predicting progression between closely related disease stages, although direct comparisons under identical conditions remain scarce. Critically, only about 4% of studies evaluated their models on a genuinely independent external cohort, raising substantial concerns about model generalizability. Overall, current AI systems remain highly dependent on existing datasets and heterogeneous evaluation protocols, which limit generalizability and clinical applicability. Future research should prioritize representative multimodal datasets, rigorous external validation, and clinically interpretable AI systems. Full article

Hyperspectral (HS) sensors offer high spectral discrimination but generally limited spatial coverage, whereas multispectral (MS) sensors provide broad coverage with lower spectral detail. HS-MS complementary mapping aims to reduce coverage gaps in HS sensors by training classifiers within HS-MS overlap regions and applying them to surrounding MS-only areas. However, comprehensive comparisons across classifiers remain scarce in this complementary mapping setting. Furthermore, lightweight pixel-wise classifiers often produce spatially inconsistent predictions, whereas spatial–spectral deep learning models demand substantial computational resources. In this study, we propose a flexible HS-MS complementary mapping framework by systematically evaluating 13 classifiers across mineral and land-use/land-cover (LULC) mapping tasks and introducing class-adaptive uncertainty revocation (CAUR), a lightweight, classifier-independent post-processing module. Performance was evaluated via spatial holdout cross-validation within the HS-MS overlap area. When computational resources are sufficient, 3D convolutional neural networks (3D-CNN) achieve the highest accuracy. Conversely, lightweight models such as random forest (RF) and k-nearest neighbors (kNN) provide computationally efficient and robust baselines. Applying CAUR consistently improves spatial consistency and overall classification accuracy without model retraining, with the largest improvements observed for coarser-resolution HS reference data. These findings provide practical design guidelines for constructing efficient HS-MS complementary mapping pipelines tailored to application demands and sensor characteristics. Full article

Background/Objectives: Bone metastasis is one of the most common manifestations of advanced malignancy and a major cause of morbidity, particularly when involving the spine. Magnetic resonance imaging (MRI) plays a central role in its detection and characterisation due to its high sensitivity for bone marrow infiltration. However, bone metastases may be missed on MRI, whilst interpretation can be time-consuming and challenging. The purpose of this study is to review and summarise the present evidence for artificial intelligence (AI) applications in the detection and classification of bone metastasis on MRI. Methods: A systematic, detailed search of the main electronic medical databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov, last accessed on 1 January 2026) was undertaken in concordance with the PRISMA guidelines. Results: A total of 34 studies were included. AI applications were identified across several domains, including lesion detection, segmentation, disease classification, and predictive modelling. Deep learning approaches demonstrated strong performance for automated detection and segmentation, while radiomics-based models were frequently used for lesion differentiation and prediction tasks. Reported performance metrics were generally high, with area under the curve values commonly ranging from approximately 0.72–0.94, with most studies reporting AUCs exceeding 0.80 in internal validation, although substantial heterogeneity in study design, datasets, and validation strategies was observed. External validation and prospective evaluation were limited across most studies. Conclusions: Within the domain of bone metastasis, AI-based approaches have demonstrated encouraging performance and hold substantial potential to support clinical decision-making, including prognostication and prediction of treatment response. Nevertheless, further research is required to validate their clinical utility and to facilitate successful integration into routine clinical practice. Full article

Background: Heritable thoracic aortic disease (HTAD) accounts for approximately 20–25% of thoracic aortic aneurysm and dissection (TAAD) cases and is a major cause of premature death in young adults. Methods: This is a narrative, non-systematic review. We performed a selective synthesis of clinical practice guidelines (ACC/AHA 2022, EACTS/STS 2024), the revised Ghent nosology, large multicenter cohort studies (Montalcino Aortic Consortium), randomized pharmacotherapy trials, and molecular mechanism data published between 2010 and 2025; quantitative figures are reported as published in individual primary sources and were not pooled or re-analyzed. Results: The advent of next-generation sequencing (NGS) has driven a paradigm shift in HTAD management, from risk assessment based purely on phenotype (aortic diameter) to risk stratification based on genotype (molecular mutation). The 2022 ACC/AHA guideline identifies eleven genes with confirmed high-penetrance risk for HTAD; these, together with the established Loeys–Dietz gene TGFB3 (recognized through gene–disease validity assessment rather than the ACC/AHA list), can be grouped into three pathogenic mechanisms: extracellular matrix dysregulation, TGF-β signaling dysregulation, and vascular smooth muscle contractile dysfunction. Gene–disease association should be distinguished from guideline-defined classification and regarded as evolving, since additional candidate genes such as LTBP3 are already emerging in gene-negative families. Multigene panel testing identifies a pathogenic or likely pathogenic variant in roughly 8% of patients referred for suspected HTAD, a yield that rises substantially when applied to syndromic or strongly familial presentations. Prophylactic surgical thresholds are individualized by gene and are generally lower (around 4.0 cm) for high-risk TGFBR1/TGFBR2 and PRKG1 variants and higher (around 5.0 cm) for FBN1 and TGFB3, in contrast with the uniform 5.5 cm threshold historically applied to all patients. Randomized trials over the past decade—including the AIMS irbesartan trial, the Marfan Treatment Trialists’ individual patient data meta-analysis, and the celiprolol and irbesartan trials in vascular Ehlers–Danlos syndrome—now provide direct evidence that angiotensin receptor blockade slows the rate of aortic root dilation in Marfan syndrome, with more limited evidence in vascular Ehlers–Danlos syndrome and uncertain effects on dissection or mortality, while valve-sparing aortic root replacement provides durable long-term outcomes in reported single-center experience. These thresholds and pharmacotherapy recommendations rest predominantly on observational cohort, registry, and randomized trial data of varying maturity and should be interpreted as graded, evolving recommendations rather than fixed cut-points. Conclusions: This review synthesizes the molecular pathogenesis, diagnostic nosology, gene-specific epidemiologic and prognostic data, genetic testing yield, pharmacotherapy evidence, surgical outcomes, and updated prophylactic intervention algorithms per the ACC/AHA (2022) and EACTS/STS (2024) guidelines, providing a practical reference framework for individualizing surveillance and surgical decision-making in patients with HTAD. Full article

This study aims to systematically review the current applications and development trends of virtual reality (VR) technology in blue space research. It clarifies the main research methods, technical pathways, application scenarios, and existing knowledge gaps, and integrates the advantages of VR technology with the characteristics of blue spaces to provide references for future urban renewal and urban management research. Following the PRISMA-ScR guidelines, this study searched literature published up to January 2026 in the Web of Science and Scopus databases. A total of 41 original studies were included. A scoping review approach was adopted, employing descriptive statistics, cross-analysis, and classification methods. Existing studies mainly focus on blue space types such as rivers and lakes, with immersive head-mounted displays as the primary method. Most experiments adopt single session, short term exposure designs, suggesting potential positive effects of virtual blue spaces in reducing stress, improving mood, and restoring attention. Other studies demonstrate the application of VR technology in environmental education, design assessment, and urban management within blue spaces. As an indirect exposure and supplementary intervention tool, VR shows significant potential in blue space research, particularly in supporting vulnerable populations, large-scale urban planning and design assessment, and risk management. However, current research still faces several limitations, including insufficient multisensory integration, limited capability in simulating dynamic water environments, lack of methodological standardization, uneven sample distribution, absence of longitudinal studies, and weak interactive and social dimensions. The findings highlight the need for further research on these specific aspects. Full article