Search Results (23,279)

Cell detection-tracking tasks are vital for biomedical image analysis with potential applications in clinical diagnosis and treatment. However, it poses challenges such as ambiguous boundaries and complex backgrounds in microscopic video sequences, leading to missed detection, false detection, and loss of tracking. Therefore, we propose an enhanced multiple object tracking algorithm IEGS-YOLO + BoT-SORT, named IEGS-BoT, to address these issues. Firstly, the IEGS-YOLO detector is developed for cell detection tasks. It uses the iEMA module, which effectively combines the global information to enhance the local information. Then, we replace the traditional convolutional network in the neck of the YOLO11n with GSConv to reduce the computational complexity while maintaining accuracy. Finally, the BoT-SORT tracker is selected to enhance the accuracy of bounding box positioning through camera motion compensation and Kalman filter. We conduct experiments on the CTMC dataset, and the results show that in the detection phase, the map50 (mean Average Precision) and map50–95 values are 73.2% and 32.6%, outperforming the YOLO11n detector by 1.1% and 0.6%, respectively. In the tracking phase, using the IEGS-BoT method, the multiple objects tracking accuracy (MOTA), higher order tracking accuracy (HOTA), and identification F1 (IDF1) reach 53.97%, 51.30%, and 67.52%, respectively. Compared with the base BoT-SORT, the proposed method achieves improvements of 1.19%, 0.23%, and 1.29% in MOTA, HOTA, and IDF1, respectively. ID switch (IDSW) decreases from 1170 to 894, which demonstrates significant mitigation of identity confusion. This approach effectively addresses the challenges posed by object loss and identity switching in cell tracking, providing a more reliable solution for medical image analysis. Full article

The heart–brain axis (HBA) is a dynamic system of reciprocal communication between the cardiovascular and central nervous system, incorporating neural, immunologic, molecular and hormonal pathways. The central autonomic network is described as a key regulator of cardiovascular activity and autonomic dysfunction as an important mechanism underlying various neurologic and cardiac disorders. Heart rate variability (HRV) is identified as the key biomarker of the axis reflecting autonomic nervous system balance. Increased understanding of its molecular mechanisms has led to the proposal of new therapeutic strategies focused on modulating heart–brain communication including β-blockers, vagus nerve stimulation, neurotrophin modulation, and nanoparticle-based approaches. The integration of wearables and artificial intelligence (AI) has allowed for real-time monitoring and innovative diagnostic and prognostic applications. The present narrative review summarizes current knowledge on the mechanisms comprising the heart–brain axis, their implication in neurologic and cardiac disorders, and their potential for developing novel therapies. It also highlights how advancements in wearable technology and AI systems are being integrated into clinical practice and transforming the landscape. Full article

Cervical cancer screening plays a crucial role in preventing invasive disease through early detection of high-grade lesions. However, traditional cytology and histology often fail to reliably differentiate between transient HPV infections and those likely to progress. This study investigates the feasibility of integrating molecular HPV testing into histopathological workflows using FFPE tissue samples to improve diagnostic precision. A retrospective analysis was conducted on 55 FFPE cervical specimens from patients undergoing colposcopy with biopsy or conization. The workflow included automated DNA extraction and real-time PCR-based HPV genotyping with the Seegene Anyplex II HPV28 assay. HPV DNA was detected in 56.4% of samples, with 21 genotypes, including multiple high-risk types. High viral loads correlated with high-grade lesions, supporting the clinical value of HPV quantification. Compared to histology, molecular analysis reduced potential overdiagnosis by confirming HPV absence in morphologically suspicious but HPV-negative lesions. Integrating viral load and genotyping improved risk stratification, optimizing colposcopy referrals and reducing unnecessary follow-ups. This study introduces a novel, fully automated molecular workflow applicable to FFPE samples, enhancing cervical cancer screening beyond traditional methods. Although based on a limited sample, the findings support the method’s potential for broader implementation and further validation in multicenter settings. Full article

Background/objectives: Prostate cancer (PCa) is the most common cancer among males. Approximately 20–40% of patients with clinically localized PCa will present with a biochemical recurrence after a radical prostatectomy (RP), while some will present with recurrent metastasis. Monitoring the disease post-treatment is crucial for detecting a potential cancer recurrence early. Urinary volatile organic compounds (VOCs) have shown potential to detect PCa. However, their application in disease monitoring remains unexplored. Methods: A total of 165 urine samples were collected from male adults with biopsy-designated PCa-positive results before (n = 55) and after a RP (n = 55), and with biopsy-designated PCa-negative diagnosis (n = 55). The post-RP cohort was subdivided into three groups based on their health status after surgery as recovered healthy, biochemical recurrence, and recurrent metastasis. VOCs in the urine samples were extracted by stir bar sorptive extraction and analyzed using gas chromatography and mass spectrometry. We explored the use of metabolomics and a machine learning algorithm tool to investigate the potential of using VOCs for differentiating PCa diagnoses before and after the RP procedure with different outcomes. Results: Over 100 potential VOCs were identified to differentiate PCa patients before and after a RP, and those with biochemical recurrence and recurrent metastasis. Conclusions: Urinary VOCs are promising biomarkers that could be used to differentiate PCa patients pre- and post-RP. The findings from this research provide preliminary insights and could aid future investigations in developing tools for PCa patients after treatment. The absence of a validation cohort limits the reproducibility and translational impact of these findings; therefore, the results should be considered exploratory and require confirmation in larger, independent cohorts. Full article

Background/Objectives: Pediatric episodic vestibular syndrome (EVS) is increasingly recognized, with recurrent vertigo of childhood (RVC) and vestibular migraine of childhood (VMC) being the most prevalent disorders. In 2021, the Bárány Society and the International Headache Society proposed new diagnostic criteria for RVC, VMC, and probable VMC (pVMC), replacing the older term benign paroxysmal vertigo (BPV). This study aimed to evaluate the clinical applicability of these new criteria. Methods: We conducted a cross-sectional study at a pediatric neurotology clinic within a tertiary hospital, including patients under 18 years with episodic vestibular symptoms evaluated between 2018 and 2025. All patients underwent a standardized neuro-otological assessment. Diagnoses were assigned using both the 2018 ICHD-3 and the 2021 Bárány criteria. Patients who did not fulfill any of the three new diagnostic categories, nor met criteria for any other specific vestibular disorder, were grouped into an undetermined category referred to as episodic vestibular syndrome without hearing loss (EVSw/oHL). Demographic and clinical variables were compared across diagnostic groups using non-parametric and chi-squared tests. Results: Among the 202 children evaluated, 109 met the inclusion criteria and were classified as RVC (n = 55), VMC (n = 23), pVMC (n = 13), or EVSw/oHL (n = 18). All patients previously diagnosed with BPV met the new criteria for RVC. Application of the Bárány criteria significantly reduced the proportion of unclassified EVS cases (from 35.78% to 16.51%). Significant clinical differences were observed among the groups in terms of episode duration, presence of vomiting, migraine and headache, and family history of migraine. Conclusions: The new Bárány criteria provide a more inclusive and clinically meaningful framework for classifying pediatric EVS. They improve diagnostic clarity, reduce the proportion of unclassifiable cases, and support earlier and more tailored management strategies. Full article

Background/Objectives: The Internet of Things (IoT), integrated with application software, has increasingly been used to support health management through monitoring indicators like physical activity, sleep, and heart rate, in pregnant and postpartum women. However, limited evidence exists regarding its effectiveness in improving health outcomes for pregnant and postpartum women. The objective of this systematic review and meta-analysis was to evaluate and synthesize the role of IoT in enhancing the health outcomes of pregnant and postpartum women. Methods: A systematic search was conducted on 13 February 2023, across CENTRAL, CINAHL, ClinicalTrials.gov, Embase, MEDLINE, PsycINFO, PubMed, and WHO ICTRP to identify all randomized controlled trials. Studies were included if they involved pregnant or postpartum women in high-income countries and used sensor-based data collection via smartphones or wearable devices. Two reviewers independently selected the studies, extracted data, and assessed the risk of bias using the Cochrane Collaboration’s risk of bias assessment tool 2.0. We performed a pairwise meta-analysis using a random effects model. The findings were reported according to PRISMA guidelines. Results: Seven studies with 1638 pregnant and postpartum women were included in this review. Of the seven included studies, half targeted women with gestational diabetes and the other half targeted obese women. A meta-analysis revealed that IoT interventions may reduce gestational weight gain in women with obesity with a mean difference of −3.35 kg (95% confidence interval (CI): −5.23 to−1.46; I² = 36%; two studies; 242 women; moderate certainty of evidence). Conclusions: This review suggested that IoT interventions may limit gestational weight gain in pregnant women with obesity. Future studies should evaluate the long-term effects of IoT-based interventions on maternal and neonatal health outcomes. Full article

Background/Objective: The use of immersive virtual reality (VR) for cognitive training in older adults has shown promising results in recent years. However, the number of well-designed studies remains limited, and variability in methodologies makes it difficult to draw generalizable conclusions. This systematic review aims to examine the effects of VR-based cognitive training in older adults, describe the technological characteristics of these interventions, identify current gaps in the literature, and suggest future research directions. Methods: Following PRISMA guidelines, a search was conducted across major databases (PubMed, PsycINFO, Scopus, ProQuest, ACM, and Web of Science) from 2018 to 2025. The database search identified 156 studies, of which 12 met the inclusion criteria after screening and eligibility assessment. Across these studies, a total of 3202 older adult participants (aged 60 years or older) were included. Interventions varied in duration from 4 to 36 sessions, targeting domains such as memory, executive function, attention, and global cognition. Most interventions were based on cognitive training, with a few employing cognitive stimulation or cognitive rehabilitation approaches. Quality was assessed using the Effective Public Health Practice Project tool. Results: Most studies reported positive effects of VR interventions on cognitive domains such as attention, executive functions, and global cognition. Fewer studies showed improvements in memory. The majority used head-mounted displays connected to computers and custom-built software, often without public access. Sample sizes were generally small, and blinding procedures were often unclear. The average methodological quality was moderate. Conclusions: Immersive VR has potential as an effective tool for cognitive training in older adults. Future research should include larger randomized controlled trials, long-term follow-up, standardized intervention protocols, and the development of accessible software to enable replication and broader application in clinical and community settings. Full article

Background/Objectives: High dose rate brachytherapy (HDR-BT) is resource-intensive. Workflow efficiency and inter-user variability remain prevalent issues in HDR-BT. To improve workflow efficiency and reduce inter-user variability, we introduced artificial intelligence (AI)-based organ contouring (AC) and applicator modeling (AM) into our clinical workflow. Here, we present results on the impact of these tools on workflow efficiency, inter-user variability, and plan quality for vaginal cuff HDR-BT cases. Methods: 260 treated fractions were included in the analysis, half of which were treated before implementing AC and AM. Five different medical physicists performed the treatment planning. Using built-in timestamps, contouring, dose planning, and total treatment planning times were recorded for each fraction. Dosimetric data, including the dose to the highest 2cc (D2cc) of bladder and rectum, and percentage volumes of the target covered by 90–200% isodose lines (V90-200) were recorded. Analysis of variance with post-hoc Tukey tests were used to determine statistical differences between treatment planners before and after implementing AC and AM. Results: Implementing AC and AM resulted in an overall 13.7 ± 1.7-min reduction in planning time. Inter-user variability in organ and target dose metrics was reduced for most structures. Most notably, statistically significant differences in rectum D2cc and target V95 were observed between planners before, but not after, AC and AM implementation. Conclusions: AC and AM significantly improve workflow efficiency while maintaining plan quality. Reductions in inter-user variability and standardization of workflows may facilitate user training and robust outcome assessment. Full article

Orthodontic tooth movement (OTM) is accompanied by sterile inflammation, a necessary biological process that facilitates tooth displacement but also contributes to adverse effects, including hyalinization and orthodontically induced external apical root resorption (OEARR). Despite advancements in orthodontic therapies, the inflammatory response—regulated by dynamic interactions between tissue-specific cells and their molecular mediators—remains a critical factor influencing treatment outcomes. This review summarizes the current understanding of the cellular and molecular mechanisms underlying OTM, with a focus on how these insights can support the development of targeted therapeutic strategies. These include cell- and molecule-based therapies, biomaterial-mediated delivery systems, and applications of artificial intelligence (AI). Notably, AI offers promising opportunities for modeling and simulating biological responses, enabling the optimization of individualized treatment planning. We further discuss current clinical practices and highlight emerging experimental findings, with an emphasis on unresolved research questions pivotal to improving therapeutic efficacy and reducing complications such as OEARR. This comprehensive overview aims to inform future directions in orthodontics by integrating mechanistic knowledge with technological innovation. Full article

(1) Background: Upper limb (UL) function plays a central role in daily life, enabling essential tasks such as reaching, grasping, and eating. While numerous tools exist to evaluate UL kinematics, their application in pediatric populations is often limited by a lack of age-specific validation. This study presents a novel motion analysis protocol featuring a customized marker set, aimed at assessing UL movements in the three anatomical planes across different age groups, with a focus on pediatric applicability. (2) Materials and Methods: A SmartDX motion capture system was used, with 30 markers positioned on the upper body, referencing the trunk as the root of the kinematic chain. Ten healthy participants (mean age: 18.69 ± 12.45 years; range: 8.0–41.4) without UL impairments were recruited. The broad age range was intentionally selected to assess the protocol’s transversal applicability. (3) Results: Results showed excellent intra-operator reliability for shoulder and wrist kinematics (ICC > 0.906) and good reliability for elbow movements (ICC > 0.755). Inter-operator reliability was good to excellent (shoulder ICC > 0.958; elbow ICC > 0.762; wrist ICC > 0.826) Usability, measured via the System Usability Scale, was rated as good (83.25). (4) Conclusions: The proposed protocol demonstrated strong reliability and practical usability, supporting its adoption in clinical and research settings. Its design allows for adaptability across motion capture platforms, promoting wider implementation in pediatric UL functional assessment. Full article

Background: Inborn errors of immunity (IEI) are mainly genetically driven disorders that affect immune function and present with highly heterogeneous clinical manifestations, ranging from severe combined immunodeficiency (SCID) to adult-onset immune dysregulatory diseases. This clinical heterogeneity, coupled with limited awareness and the absence of a universal diagnostic test, makes early and accurate diagnosis challenging. Although genetic testing methods such as whole-exome and genome sequencing have improved detection, they are often expensive, complex, and require functional validation. Recently, artificial intelligence (AI) tools have emerged as promising for enhancing diagnostic accuracy and clinical decision-making for IEI. Methods: We conducted a systematic review of four major databases (PubMed, Scopus, Web of Science, and Embase) to identify peer-reviewed English-published studies focusing on the application of AI techniques in the diagnosis and treatment of IEI across pediatric and adult populations. Twenty-three retrospective/prospective studies and clinical trials were included. Results: AI methodologies demonstrated high diagnostic accuracy, improved detection of pathogenic mutations, and enhanced prediction of clinical outcomes. AI tools effectively integrated and analyzed electronic health records (EHRs), clinical, immunological, and genetic data, thereby accelerating the diagnostic process and supporting personalized treatment strategies. Conclusions: AI technologies show significant promise in the early detection and management of IEI by reducing diagnostic delays and healthcare costs. While offering substantial benefits, limitations such as data bias and methodological inconsistencies among studies must be addressed to ensure broader clinical applicability. Full article

The present study offers a systematic review of the current state of research on lattice structures manufactured by additive technologies for biomedical applications, with the aim of identifying common patterns, such as the use of triply periodic minimal surfaces (TPMS) for bone scaffolds, as well as technological gaps and future research opportunities. Employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, the review process ensures methodological rigor and replicability across the identification, screening, eligibility, and inclusion phases. Additionally, PRISMA was tailored by prioritizing technical databases and engineering-specific inclusion criteria, thereby aligning the methodology with the scope of this field. In recent years, a substantial surge in interdisciplinary research has underscored the promise of architected porous structures in enhancing mechanical compatibility, fostering osseointegration, and facilitating personalized medicine. A growing body of literature has emerged that explores the optimization of geometric features to replicate the behavior of biological tissues, particularly bone. Additive manufacturing (AM) has played a pivotal role in enabling the fabrication of complex geometries that are otherwise unachievable by conventional methods. The applications of lattice structures range from permanent load-bearing implants, commonly manufactured through selective laser melting (SLM), to temporary scaffolds for tissue regeneration, often produced with extrusion-based processes such as fused filament fabrication (FFF) or direct ink writing (DIW). Notwithstanding these advances, challenges persist in areas such as long-term in vivo validation, standardization of mechanical and biological testing, such as ISO standards for fatigue testing, and integration into clinical workflows. Full article

Strongyloidosis is a parasitic disease caused by Strongyloides stercoralis, a nematode with a complex life cycle that facilitates long-term persistence within the host. The infection affects millions of people in tropical and subtropical regions and poses a particular challenge in immunocompromised individuals. Although conventional treatments, such as ivermectin and albendazole, are generally effective, emerging concerns regarding drug resistance and adverse effects have prompted the search for alternative therapeutic options. In this context, natural products—including plant extracts, bioactive phytochemicals, and nanoparticle-based formulations derived from natural sources—are emerging as promising anti-Strongyloides potential. This review summarizes recent studies on natural products with anthelmintic activity against strongyloidiasis, with emphasis on their mechanisms of action, efficacy, and future perspectives. A systematic search of the literature was conducted using terms related to Strongyloides, plant species, extracts, and bioactive compounds with nematocidal activity. Eligible studies included those reporting the activity of plants, plant extracts, and their purified metabolites against Strongyloides spp. Data were compiled into a comprehensive table including year of publication, author, plant species, active principle, application conditions, and target nematode species. The pharmacological treatment of this parasite varies according to its life cycle stage. Various biomolecules, phytoactive compounds, and novel plant-based formulations have demonstrated promising activity and may be considered both for treatment and for inclusion in control programs for strongyloidiasis. This review highlights medicinal plants and phytochemicals with ethnopharmacological background and experimentally validated activity against Strongyloides spp., integrating evidence from in vitro, in vivo, and experimental models, as well as clinical trials. Full article

Chronic pain is a dynamic, brain-wide condition that eludes effective management by conventional, static treatment approaches. Transcutaneous Electrical Nerve Stimulation (TENS), traditionally perceived as a simple and generic modality, is on the verge of a significant transformation. Guided by advances in brain-state decoding and adaptive algorithms, TENS can evolve into a precision neuromodulation system tailored to individual needs. By integrating multimodal neuroimaging—including the spatial resolution of functional magnetic resonance imaging (fMRI), the temporal sensitivity of an Electroencephalogram (EEG), and the ecological validity of functional near-infrared spectroscopy (fNIRS)—with real-time machine learning, we envision a paradigm shift from fixed stimulation protocols to personalized, closed-loop modulation. This comprehensive review outlines a translational framework to reengineer TENS from an open-loop device into a responsive, intelligent therapeutic platform. We examine the underlying neurophysiological mechanisms, artificial intelligence (AI)-driven infrastructures, and ethical considerations essential for implementing this vision in clinical practice—not only for chronic pain management but also for broader neuroadaptive healthcare applications. Full article

Monoclonal antibodies (mAbs), as potent therapeutic agents, have been widely applied in the treatment of various major diseases, including infectious diseases, autoimmune disorders, cancers, and neurodegenerative diseases. However, early-generation mAbs were limited by high immunogenicity, short half-life, and insufficient affinity, which compromised their therapeutic efficacy. With technological advancements, novel approaches such as high-throughput screening and glycosyl modification have been introduced to improve the performance of mAbs. Furthermore, computer-aided design techniques—including molecular docking, molecular dynamics simulations, and artificial intelligence -based methods—are increasingly being employed to accelerate the optimization process. This review summarizes recent progress in the optimization of therapeutic mAbs, with a focus on technological breakthroughs and applications in affinity enhancement, development of broad-spectrum mAbs, specificity modulation, immunogenicity reduction, and stability improvement. Additionally, it discusses current challenges and future directions in antibody optimization. This review aims to provide insights and references for the development and optimization of next-generation antibody drugs, ultimately promoting the clinical application of safer and more effective mAb-based therapies. Full article