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The Internet of Medical Things (IoMT) enables sophisticated medical devices, but it also poses significant challenges in terms of data privacy, real-time processing, and energy efficiency for edge devices with limited resources. In this paper, we propose a hierarchical framework for intelligent and secure IoMT-based healthcare monitoring. At the sensor nodes, Federated Variational Mode Decomposition (VMD) is used to decompose physiological signals and locally extract high-fidelity features, ensuring data privacy. To overcome the computational limitations of microcontroller- based sensor nodes, a SparseBonsai neural network is designed for real-time classification of medical signals on the sensor nodes. A centralized orchestration layer, controlled by a Proximal Policy Optimization (PPO) reinforcement learning agent, makes dynamic decisions on whether to queue data for low-latency processing at the edge server or offload to the cloud, depending on data severity, network conditions, and battery level. To further improve energy efficiency, an advanced Sha-Dragon (Shannon-Entropy Dragonfly) optimization algorithm is proposed for resource and transmission power allocation in the IoT network. For security, a dual-layer approach is adopted: ASCON v1.2 lightweight authenticated encryption is used to secure node-to-edge communications, and a WireGuard VPN with ChaCha20-Poly1305 encryption protects data in transit to the cloud. Experimental validation on a Raspberry Pi 5 testbed with a cloud-connected laptop shows that the proposed system achieves a significant reduction in latency for critical alerts and improves the battery life of IoT nodes (8.5 days) compared to the conventional non-adaptive offloading approach. The results confirm the effectiveness of the proposed framework to facilitate energy-efficient, privacy-preserving, and real-time healthcare monitoring in IoMT.

Extended Reality (XR) technologies offer transformative potential for language education, yet current platforms largely neglect the accessibility needs of deaf and hard-of-hearing individuals. Existing solutions typically operate in single-language environments and lack integrated support for sign language and multimodal communication. There is a critical need for inclusive platforms that serve both deaf and hearing learners through cross-modal AI services embedded in immersive environments. This study presents a modular platform integrating six AI services: speech-to-text transcription (OpenAI Whisper), multilingual translation (Meta NLLB), text-to-speech synthesis (AWS Polly), sentiment analysis (RoBERTa), session summarisation (flan-t5-base-samsum), and International Sign (IS) translation via Google MediaPipe. An IS dataset of 750 gesture videos was processed to extract hand landmark coordinates mapped to 3D avatar animations within a Unity-based VR environment on Meta Quest 3 headsets. The system was validated through technical benchmarking of AI service performance, including comparative evaluation of text-to-speech services and multilingual translation models (NLLB-200 and EuroLLM 1.7B variants), load testing to assess platform. scalability, and end-to-end pipeline latency measurement for both the hearing and the deaf user pathways. The educational scenario was additionally evaluated in a companion pilot study, 50 which shares the same underlying AI services and provides complementary user-perception evidence. Technical benchmarking confirmed the platform's viability for real-time XR deployment. TTS benchmarking confirmed AWS Polly's lowest latency (50-100 ms first byte) at competitive cost. The EuroLLM 1.7B Instruct model achieved a BLEU score of 84.34, outperforming NLLB's 79.25. Load testing with 1,000 simulated concurrent users demonstrated average response times under 800 milliseconds with no critical failures. Avatar animation latency for IS sign rendering remained consistently under 300 milliseconds. End-to-end pipeline latency averaged 2.05 ± 0.31 s for the hearing pathway and 2.32 ± 0.34 s for the deaf (IS) pathway, both within accepted thresholds for conversational educational use. The companion pilot (N = 10) reported a mean overall experience rating of 4.6/5.0, 92% user satisfaction and unanimous (100%) demand for expanded language and sign-language support. 50. The results presented in this study focus on the technical feasibility of integrating cross-modal AI services within XR environments for accessible, multilingual language learning. The modular architecture enables independent scaling and adaptation to diverse contexts, laying the groundwork for equitable educational solutions aligned with EU digital accessibility objectives. Learning a new language can be challenging, and it is even more difficult for deaf individuals who rely on sign language. This study addresses the challenge by creating a virtual reality (VR) learning environment where a digital 3D character (avatar) can speak, translate, and perform sign language in real time. The system uses several artificial intelligence tools working together: one that converts speech into text, another that translates text between multiple languages, a third that converts text back into spoken language, and a fourth that translates text into International Sign Language gestures performed by the avatar. Users wear a VR headset and interact with the avatar in a virtual classroom where they can select their preferred language and receive immediate translations in both spoken and signed forms. The system’s technical performance was validated through benchmarking of AI translation models, text-to-speech services, end-to-end pipeline latency measurement, and scalability testing, confirming its suitability for real-time educational applications. Complementary user feedback gathered in a companion pilot study, which uses the same underlying AI services, is cross-referenced where relevant. 50 This research is a step toward creating virtual learning environments where language barriers and hearing limitations no longer prevent people from accessing education.

There is an increasing societal demand for transparency in reporting on the quality of life of farmed animals reared for meat production. High animal welfare standards are also associated with improved efficiency and the sustainability of production systems, but they also play a critical role in ensuring food quality and safety. The routine monitoring of animal welfare is crucial for tracking performance to ensure high welfare standards are met. Traditional on-farm welfare assessments conducted by human observers are subjective and prone to observer bias, time-consuming, costly, and pose risks to biosecurity. Monitoring the welfare of pigs at slaughter provides an option for animal welfare oversight across large numbers of animals to verify and complement on-farm assessments. We present a real-time computer vision system for the automated assessment of pig welfare indicators on carcasses. The system evaluates skin and tail lesions, tail length, and hernias using a modular pipeline that combines YOLOv4 detection, U-Net segmentation, and colorimetric and geometric analysis. The architecture robustness was demonstrated by processing video streams containing specific welfare conditions, yielding accuracies of 93.0% for hernias (16 pigs, 1 min), 86.3% and 90.4% for dorsal and lateral skin lesions (75 pigs, 7 min and 40 pigs, 3 min, respectively), and 86.8% for tail lesions (63 pigs, 5 min). Tail length is estimated via a custom segmentation and curve-fitting process, with a root mean squared error (RMSE) of 4.45 cm. Operating at 30.31 FPS, the framework offers a scalable and objective solution for real-time welfare monitoring in industrial settings.

Peroxynitrite (ONOO-) and cysteine (Cys) are pivotal components of reactive nitrogen and sulfur species that together shape a complex redox regulatory network in living systems. High-resolution, real-time imaging of these species is essential for unraveling their roles in mitochondrial redox dynamics. However, the development of fluorescent probes capable of simultaneously detecting ONOO- and Cys with high fidelity remains limited. In this work, we introduce a single-excitation, dual-response fluorescent probe system (probes A and B) that enables synchronous visualization of ONOO- and Cys. The probe design integrates two orthogonal sensing chemistries: ONOO- is selectively recognized via the oxidation of a naphthylimide-borate unit, while Cys is detected through nucleophilic substitution with a benzothiol-modified tetrahydro-acridine conjugate salt. Incorporation of a long-chain linker effectively suppresses undesired FRET, and the large Stokes shift of the Cys-responsive unit ensures clean spectral separation. As a result, probe B exhibits strong ONOO- emission at 550 nm and distinct Cys emission at 652 nm under a single 460 nm excitation source, with both fluorescence channels operating independently and without spectral crosstalk. Probe B demonstrates high sensitivity, excellent anti-interference capability, and precise mitochondrial localization. Dual-channel imaging in living cells confirms its ability to simultaneously monitor endogenous ONOO- and Cys. Importantly, the probe enables real-time visualization of dynamic redox fluctuations during inflammatory responses and tumor ferroptosis. Overall, this work presents a robust dual-channel fluorescent imaging platform for the simultaneous detection of ONOO- and Cys, offering a powerful tool for dissecting mitochondrial redox dynamics in health and disease.

Real-time electrochemical measurements of ionic fluxes and bioelectric signals are set to refine cancer diagnosis and longitudinal follow-ups with the goal of targeting electrophysiological features of clinically useful matrices. Nanoengineered electrodes translate biomolecules and ions into robust electrochemical signals for both laboratory workflows and point-of-care testing. The translational aim is to integrate electrochemical sensing into coherent, physiology-grounded readouts that resolve tumor ion channel dysfunction, membrane depolarization, pericellular acidification, and redox imbalance. Realizing this vision now depends on practical engineering and clinical integration. To advance this ongoing development, researchers are building stable, disposable, portable, and miniaturized electrochemical platforms that couple detection with microfluidics to enrich tumor cells, vesicles, and bio ionic markers for multiplexed cancer measurements. The integration of wearable and implantable systems with machine learning and patient-specific digital twins will enable real-time maps of tumor electrophysiology and model-driven forecasts of treatment response. This perspective outlines a translational roadmap for electrochemical detection of bioelectric biomarkers in cancer, wherein biomarker classes are systematically mapped to corresponding transduction strategies and mechanistic fidelity is reconciled with practical assay design constraints to identify key challenges and opportunities for advancing these technologies from foundational research to validated clinical implementation in precision oncology.

Routine collection of electronic patient-reported outcome measures (ePROMs) enhances symptom monitoring and improves outcomes in cancer care. We evaluated published reports of real-world ePROM implementation in oncology, focusing on approaches to implementation, types of ePROMs used, completion rates, factors associated with ePROM completion, and impacts on patient and health care process/system-level outcomes. We also aimed to propose a classification of ePROM programmes. We systematically searched Medline, Embase, CINAHL Complete, and Scopus for articles describing routine ePROM implementation and evaluation. Data charting captured characteristics of the study, study population, characteristics of routinely collected ePROMs, and evaluation of their routine use. We also present a new classification of ePROM programmes into three levels: asynchronous, clinician-supervised, and real-time clinician-supervised, based on clinical pathway integration. Of 10 384 identified manuscripts, 50 met the inclusion criteria, reporting on 39 real-world ePROM programmes. Nearly all ePROM programmes (97.4%) collected data on symptom burden. The median overall completion rate was 61% (interquartile range 47%-76%), with substantial variability over time and across hospitals. Completion was associated with sociodemographic, clinical, and treatment-related factors. Most ePROM programmes (61.5%) were classified as asynchronous models, with five (12.8%) clinician-supervised models and three (7.7%) real-time clinician-supervised models, while seven (17.9%) lacked sufficient detail. The most frequently reported outcomes were fewer hospitalisations or emergency department visits (10.3%) at the patient level and improved symptom identification (17.9%) at the process/system level. ePROMs are increasingly embedded in routine oncology care, demonstrating benefits at both patient and system levels. However, most current programmes adapt asynchronous models with limited rapid clinician interaction.

The production of bacterial-based vaccines depends fundamentally on advanced bioreactor technologies, which are continuously evolving to meet rising demands for efficiency, quality, and scalability. In this review, several key challenges that constrain the translational impact of bioreactor technologies for bacterial vaccines were identified. First, there is notable variability in bioreactor performance across different bacterial strains and vaccine targets, which undermines the generalizability of process designs. Second, the understanding of how scale-up affects yield, product quality, immunogenicity, and regulatory readiness remains incomplete. It limits confidence in translating lab-scale results to manufacturing environments. Third, real-time analytics and decision-support tools are not sufficiently integrated into control strategies. This leads to fragmented data-to-decision workflows. The review provides concrete, actionable directions to address these challenges. Emerging trends such as real-time monitoring, simulation tools, data-driven process intensification, and modular, single-use platforms are discussed for their potential to address these limitations. The optimized bioreactor design, precise operational control, and sustainable practices are emphasized. It is also illustrated how multidisciplinary collaborations can tailor bioreactor configurations to specific bacterial strains and vaccine targets that deliver reliable scalability.

The pathological stiffening of the extracellular matrix (ECM) in solid tumors drives immunosuppression and therapeutic resistance. However, non-specific ECM degradation has limited clinical benefit and risks promoting metastasis. To address this dilemma, this review proposes a paradigm shift from indiscriminate physical degradation toward spatiotemporally controlled reconstruction of mechanical homeostasis using smart nanomedicine, an approach that lies at the intersection of materials science, cancer biology, and immunotherapy. We first concisely outline how aberrant ECM stiffness drives a vicious cycle of physical immune exclusion, mechanotransduction-mediated immune reprogramming, and reciprocal fibrotic activation. We then critically analyze how advanced nanomedicine platforms can. achieve precise ECM modulation without off-target toxicity. Central to this approach is the "mechano-therapeutic window", an optimal stiffness range that maximizes immune infiltration and therapy sensitization without provoking metastatic risk. Furthermore, we highlight the "priming" strategy, wherein nanomedicine-mediated ECM softening serves as an upstream step to synergistically enhance subsequent immunotherapy, chemotherapy, and radiotherapy, as well as other emerging therapies. Finally, we outline future directions, including the development of adaptive delivery systems that sense and adapt to stiffness dynamics in real-time. By shifting focus from indiscriminate degradation to intelligent mechanical homeostasis, this framework aims to improve cancer immunotherapy outcomes in desmoplastic solid tumors.

This study aimed to investigate the expression patterns and prognostic significance of insulin-like growth factor-binding protein 3 (IGFBP3) in hepatocellular carcinoma (HCC) and other cancer types. IGFBP3 expression was analyzed using The Cancer Genome Atlas (TCGA)-LIHC, International Cancer Genome Consortium (ICGC-LIRI-JP), Gene Expression Omnibus (GEO; GSE102079 and GSE112790), and Genotype-Tissue Expression (GTEx) databases. Experimental validation included immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR) on 10 paired HCC tissues. Survival was assessed via Kaplan-Meier analysis in TCGA, ICGC, and 47-patient cohorts stratified by IGFBP3 immunoreactive score (IRS). Univariate and multivariate Cox regression identified prognostic factors. Immune infiltration correlations were evaluated using ESTIMATE and TIMER algorithms, with pan-cancer analysis conducted via TCGA database. IGFBP3 was significantly downregulated in HCC across all cohorts and validated in our cohort (p&#x2009;<&#x2009;0.01). Elevated intratumoral IGFBP3 correlated with advanced tumor stage, high grade, and elevated AFP. High IGFBP3 predicted poorer overall survival in TCGA (HR&#x2009;=&#x2009;1.666, p&#x2009;=&#x2009;0.004) and ICGC (HR&#x2009;=&#x2009;4.631, p&#x2009;=&#x2009;0.0009), and was associated with shorter survival in our cohort (p&#x2009;=&#x2009;0.0002). Multivariate analysis confirmed IGFBP3 as an independent prognostic factor (HR&#x2009;=&#x2009;2.95, p&#x2009;=&#x2009;0.014). IGFBP3 expression positively correlated with immunosuppressive cell infiltration (M2 macrophages, regulatory T cells) and enriched pathways including TGF-&#x3b2; and JAK-STAT signaling. Pan-cancer analysis revealed cancer-specific expression patterns, with HCC showing both overall downregulation and high-risk prognostic value in high-expression tumors. IGFBP3 was low expression in HCC, yet high expression indicates poor prognosis. This suggests that IGFBP3 is a potential novel prognostic biomarker and therapeutic target in HCC.

Bedside teaching is essential to instilling clinical and communication skills, and its benefits to both learners and patients are particularly notable in psychiatry. Bedside rounding has declined in teaching hospitals, however, a trend that may reflect constraints on teaching and clinical time, perceived inefficiency, and awkwardness for trainees. Creating safe and nonpunitive learning environments, maximizing efficiency, and formatting rounds to support learners are strategies that may reduce these barriers. In this column, the authors describe using dialogic practice, a method of communication derived from Finland's Open Dialogue approach, to integrate bedside teaching into psychiatry inpatient rounds. This is the first description of dialogic practice as a teaching tool rather than a clinical approach. In this model, teaching is integrated directly into the patient interview. The attending psychiatrist may step in to make an intervention or teaching point, then hand the interview back to the trainee. The authors demonstrate this method using example cases. The use of dialogic practice, which incorporates multiple voices in each encounter, allows for integrating feedback and teaching into clinical care. Patients benefit by feeling fully included in discussions about their care, while learners receive valuable real-time feedback that they can immediately integrate into the ongoing interview. Dialogic teaching is highly efficient while modeling valuable skills and respecting both the patient and the learner. It must, however, be implemented in a psychologically safe learning environment.

Understanding molecular protein interactions offers promising long-term health benefits for volleyball players by serving as biomarkers that enable predictions of metabolic status, inflammation, injury risk, and recovery potential. This review aims to clarify the molecular protein interactions that serve as useful biomarkers to enhance volleyball performance. For that end, a non-systematic review of the existing literature was conducted across multiple databases, including PubMed/Medline, Web of Science, Scopus, and Google Scholar. This review addressed the key molecular pathways as biomarkers that are affected during volleyball performance, such as BDNF, IGF-1, AMPK, PGC-1&#x3b1;, IL-6, and mTORC1, as well as the involvement of small non-coding RNAs, including miR-22, miR-17, miR-125b, miR-24, miR-26a, miR-93, miR-223, miR-320a, and miR-486. From the literature, we observed that a single 60-minute volleyball session elevates growth hormone levels and reduces IL-6, thereby supporting an anabolic state in volleyball players. Moreover, even 2 weeks of volleyball training increases BDNF and IGF-1 expression, partly driven by increases in specific miRNAs, such as miR-223, miR-320a, and miR-486. Notably, the magnitude of BDNF elevation varies across populations, reflecting genetic polymorphisms in the BDNF gene. While measuring these molecular markers provides valuable theoretical insight into training adaptations and stress resilience in volleyball athletes, the extreme heterogeneity of current study protocols and the lack of standardized reference values for these biomarkers make it too early to use them as biomarkers for performance improvement and training adaptation. Consequently, these biomarkers currently serve as basic candidates for future research and require extensive validation before they can be reliably used for real-time, personalized training monitoring in volleyball. This narrative review aims to identify molecular proteins and their downstream targets as biomarkers for assessing injury status in athletes, as well as enhancing performance and decision-making skills. Primarily, when these proteins spike, they can indicate that athletes&#x2019; muscles are under stress and that an injury, such as a hamstring tear, may be developing. This can help coaches detect pain early before it worsens. Additionally, some of these molecular proteins decrease below their baseline levels, which can signal that athletes should stop training. Therefore, tracking these biomarkers through blood samples can shift the focus from treating injuries after they occur to proactive performance optimization.

Children undergoing corrective surgery for congenital heart disease (CHD) are at risk of perioperative hemodynamic instability and low cardiac output syndrome (LCOS). Conventional monitoring relies on static parameters that may inadequately reflect real-time cardiac performance. Electrical cardiometry (EC) provides continuous, noninvasive assessment of cardiac output and related indices. This study evaluated whether EC-guided perioperative management improves early postoperative outcomes compared with conventional monitoring in pediatric CHD surgery. In this prospective randomized controlled trial, 60 children (0-15 years) undergoing elective corrective CHD surgery were randomized to EC-guided management (Group 1, n = 30) or conventional monitoring (Group 2, n = 30). In Group 1, perioperative fluid and vasoactive therapy were guided by EC-derived parameters, including cardiac index (CI), stroke volume, thoracic fluid content, and stroke volume variation. Group 2 was managed using standard clinical and invasive parameters. The primary outcome was the incidence of LCOS within 48 h postoperatively. The secondary outcomes included vasoactive inotropic score (VIS), major adverse cardiac events, and in-hospital mortality. Baseline characteristics were comparable. Group 1 demonstrated significantly higher mean arterial pressure (MAP) at 8, 16, and 24 h postoperatively, faster lactate clearance, and lower VIS during the early postoperative period. CI correlated positively with MAP and urine output and inversely with lactate levels. No mortality occurred. EC-guided perioperative management aids in achieving early postoperative hemodynamic stability and metabolic recovery in children undergoing corrective CHD surgery and represents a valuable noninvasive adjunct to conventional monitoring.

The role of real-time data, artificial intelligence, and computational modeling is discussed in this review analytics Human Digital Twins (HDTs) creation- virtual persons of personalities patients which advocate predictive simulation to forecast of physiological behavior, treatment responses, and disease tracks. A synthesis of existing knowledge is done up to the technologies is a foundation to HDTs, clinical application and implementation issues of interest to precision medicine. The conceptual basis of engineering of the digital twins is analyzed and production principles, and technologies, which allow to produce HDTs-machine. Are physiological modeling, learning and distributed cloud-based computing infrastructure identified and evaluated. Cards: cardiology, oncology, genomics and immunology are critically appraised. It is based on the comparative analysis of 35 peer-reviewed documents and technical as it was reported, HDTs have great potential in enhancing personalized prediction of side effects, optimization of clinical trial design using virtual, and scheduling of treatment cohort simulation. But, model standards, an important component of model validation, are not present interoperability, ethical governing mechanisms and regulatory avenues to clinical deployment. The main priority research directions are determined, such as the development of common-validation techniques; implementation of federated learning frameworks to support sharing of data with data privacy limitations; incorporation of multi-omics data into physiological models; and introducing open ethical review procedures. This review provides substantive evidence basis to researchers, clinicians and policy makers to market the. Knowledge about HDTs technology to population health and health care provision revolutionizes.

Once rabies virus (RABV) gains access to the central nervous system, infection almost inevitably results in fatal outcomes, and our incomplete understanding of viral pathogenesis remains a major barrier to effective therapeutic intervention. Here, we identify CAMKV as an interferon-stimulated gene (ISG) that drives the macroautophagic/autophagic degradation of RABV phosphoprotein (P), thereby potently suppressing viral replication in vitro. Notably, in vivo overexpression of CAMKV significantly delays disease progression in mice challenged with a street strain of RABV. Mechanistically, CAMKV interacts with both RABV P and SQSTM1, promoting SQSTM1-mediated selective autophagic clearance of P and thereby restricting RABV transcription and replication. Collectively, our findings establish CAMKV as a critical host antiviral effector that functions through selective autophagy, highlighting CAMKV as a promising molecular target for the development of novel therapeutics against lethal RABV infection. Abbreviation: 3-MA: 3-methyladenine; ABLV: Australian bat lyssavirus; ATG: autophagy related; AKT: AKT serine/threonine kinase; Baf-A1: bafilomycin A1; CAMKV: CaM kinase like vesicle associated; CAMK2: calcium/calmodulin dependent protein kinase II; co-IP: co-immunoprecipitation; CQ: chloroquine; DUVV: Duvenhage virus; DMSO: dimethyl sulfoxide; EBLV-1: European bat lyssavirus 1; ISG: interferon stimulated gene; LAMP1: lysosome associated membrane protein 1; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; Mdivi-1: mitochondrial division inhibitor-1; MLD&#x2085;&#x2080;: 50% mouse lethal dose; MOI: multiplicity of infection; MTOR: mechanistic target of rapamycin kinase; qPCR: quantitative real-time polymerase chain reaction; RABV: rabies virus; SQSTM1/p62: sequestosome 1; WT: wild type.

Excessive exposure to ultraviolet radiation poses serious risks to human health, necessitating the development of reliable personal UV monitoring devices. For practical use, such devices must be versatile, UV selective, and capable of providing detailed feedback on cumulative dosage. In this work, thermally evaporated CuBr thin films are employed as UV dosimeters. Owing to charge trapping at CuBr grain boundaries and surface oxide formation, the devices exhibit sustained current increase under prolonged UV exposure. The device response is markedly stronger under UV-A illumination compared to UV-B or UV-C exposure. Wearable UV dosimeters are then constructed by integrating the CuBr films with a custom-designed battery-powered circuit, enabling real-time quantification of ambient solar dosage. Additionally, with increasing solar dose, CuBr films deposited on white ceramic substrates exhibit a distinct color transition from soft amber to dark brown, facilitating dual-mode UV dosimetry. Wavelength-tunable responses are also achieved: first, by incorporating a top TiO2 layer on CuBr, which filters UV-B radiation and further enhances the UV-A selectivity; and second, by introducing mixed compositions of SnBr2-CuBr, which significantly improve UV-B sensitivity with increasing SnBr2 content. This strategy paves the way toward scalable, low-cost, and wavelength-selective smart UV dosimeters for applications in personalized and environmental monitoring.

Gastric cancer (GC) is characterized by a high metastatic propensity, which constitutes the primary cause of poor patient prognosis. Receptor-like tyrosine kinase (RYK) is associated with GC metastasis, but its regulatory mechanism remains unclear. Primary and metastatic GC tissues, together with their matched adjacent non-tumor samples, were collected. GC cell models were established to systematically evaluate the driver role of RYK in GC metastasis. We further integrated clinical specimens, GC cell models, and a mouse model of GC liver metastasis, and combined immunohistochemistry, real-time quantitative PCR, Western blot, flow cytometry, immunofluorescence, wound-healing assays, 3D sphere invasion assays, and co-immunoprecipitation to elucidate the biological functions and molecular mechanisms of RYK in GC metastasis. RYK expression was higher in both primary and metastatic tumor tissues than in adjacent normal tissues. Overexpression of RYK markedly enhanced the invasion and migration capacities of GC cells and induced epithelial-to-mesenchymal transition; blocking its membrane localization promptly attenuated this pro-metastatic effect. Mechanistically, &#x3b2;-1,3-N-acetylglucosaminyltransferase 2 (B3GNT2) bound to RYK and catalyzed its N-glycosylation, thereby promoting RYK trafficking to the plasma membrane. In GC cell models and a liver-metastasis mouse model, GC cells overexpressing B3GNT2 displayed significantly increased metastatic ability, whereas treatment with an N-glycosylation inhibitor effectively suppressed this phenomenon. B3GNT2 promotes the trafficking of RYK to the plasma membrane of GC cells by mediating its N-glycosylation, thereby driving GC metastasis.

Rotator cuff injuries are one of the more common injuries that occur in the shoulder. Four muscles and tendons make up the rotator cuff. These include the supraspinatus, infraspinatus, subscapularis, and the teres minor. The teres minor is the smallest of the rotator cuff muscles. It originates from the middle third of the lateral border of the scapula, just below the insertion of the teres major. It inserts onto the inferior facet of the greater tubercle of the humerus. The teres minor is innervated by the axillary nerve, and its muscular action is that of external rotation, and it can be a weak abductor of the shoulder. It is part of a larger force couple between the rotator cuff group and the deltoid, which stabilizes the humeral head during humeral elevation. Although the teres minor can be torn in massive rotator cuff tears, it is also commonly overused during repetitive activities requiring overhead lifting or external rotation. An accurate diagnosis of teres minor overuse, partial tears, or ruptures is essential for appropriate treatment planning and optimizing patient outcomes. Diagnostic musculoskeletal ultrasound (MSKUS) offers a portable, real-time, and cost-effective alternative that is gaining traction in rehabilitation and sports medicine settings. MSKUS has emerged as a valuable, non-invasive imaging modality for evaluating rotator cuff injuries, including tendinopathy, muscle strains (partial tears), and ruptures. MSKUS is excellent at detecting changes in tendon and muscle composition and continuity. This manuscript will review the utility of MSKUS in evaluating teres minor tendon and muscle injuries, including anatomy, common injury mechanisms, sonographic techniques, and clinical implications for those in the rehabilitation profession. By integrating MSKUS into clinical practice, providers can improve diagnostic accuracy, enhance diagnostic confidence, monitor healing progression, and guide rehabilitation strategies to achieve optimal patient outcomes for those with teres minor injuries.