Search Results (177)

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

Background: Airway management in otolaryngology presents unique challenges due to shared airway access, altered anatomy, and specific procedural requirements. This article examines current techniques and oxygenation strategies across various ENT procedures to provide a guide for otolaryngologists. Methods: A narrative review was performed of the contemporary literature, focusing on airway techniques in ENT surgery, including laryngeal surgery, pediatric bronchoscopy, transoral surgery, and trauma and emergency scenarios. A systematic search for difficult airway guidelines was performed using the EMBASE, Pubmed, and Cochrane databases to examine where guidelines are published. Results: The key areas for specialist airway management included laryngeal surgery in the tubeless field and adjuncts for emergency situations. High-flow nasal oxygen (HFNO), jet ventilation, video laryngoscopy, and specialized tubes emerged as key technological advances, improving safety and outcomes. A systematic search identified 947 difficult airway articles across 82 publishers. These were predominantly in anesthetic journals (n = 301), with limited representation in the otolaryngology literature (n = 8) and limited guidance concerning awake surgical tracheostomies under local anesthetic. Awake tracheal intubation and emergency front-of-neck access were identified as key techniques across multiple publications. Conclusions: Modern ENT airway management requires multidisciplinary planning, advanced equipment familiarity, and procedure-specific techniques. Despite having the expertise to perform the gold standard, the limited otolaryngology literature on difficult airways suggests that guidelines are primarily developed by the anesthetic community. Full article

The authenticity of digital video content has become a critical issue in multimedia security due to the significant rise in video editing and manipulation in recent years. The detection of interframe forgeries is essential for identifying manipulations, including frame duplication, deletion, and insertion. These are popular techniques for altering video footage without leaving visible visual evidence. This study provides a detailed review of various methods for detecting video forgery, with a primary focus on interframe forgery techniques. The article evaluates approaches by assessing key performance measures. According to a statistical overview, machine learning has traditionally been used more frequently, but deep learning techniques are gaining popularity due to their outstanding performance in handling complex tasks and robust post-processing capabilities. The study highlights the significance of interframe forgery detection for forensic analysis, surveillance, and content moderation, as demonstrated through both evaluation and case studies. It aims to summarize existing studies and identify limitations to guide future research towards more robust, scalable, and generalizable methods, such as the development of benchmark datasets that reflect real-world video manipulation diversity. This emphasizes the necessity of creating large public datasets of manipulated high-resolution videos to support reliable integrity evaluations in dealing with widespread media manipulation. Full article

We explore the feasibility of classifying perceived pain intensity—despite the stimulus being identical—using multimodal physiological signals and self-reported emotional ratings. A total of 112 healthy participants watched the same anger-inducing video, yet reported varying pain intensities (5, 6, or 7 on a 7-point scale). We recorded electrocardiogram, skin conductance (SC), respiration, photoplethysmogram results, and finger temperature, extracting 12 physiological features. Participants also rated their valence and arousal. Using a random forest model, we classified pain versus baseline and distinguished intensity levels. Compared to baseline, the painful stimulus altered heart rate variability, SC, respiration, and pulse transit time (PTT). Higher perceived pain correlated with more negative valence, higher arousal, and elevated SC, suggesting stronger sympathetic activation. The classification of baseline versus pain using SC and respiratory features reached an F1 score of 0.83. For intensity levels 6 versus 7, including PTT and skin conductance response along with valence achieved an F1 score of 0.73. These findings highlight distinct psychophysiological patterns that reflect perceived intensity under the same stimulus. SC features emerged as key biomarkers, while valence and arousal offered complementary insights, supporting the development of personalized, psychologically informed pain assessment systems. Full article

Subtle locomotor asymmetries are common in horses and may go unnoticed during routine pre-race clinical inspections, particularly when based solely on subjective evaluation. This study aimed to describe vertical head and pelvic movement asymmetries in racehorses that passed official pre-race inspections at a traditional racing event. Twenty-four horses were analysed using a markerless AI-based gait analysis system while trotting in-hand and during lungeing in both directions. Asymmetry parameters (HDmin, HDmax, PDmin, and PDmax) were extracted from video recordings, with values ≥0.5 considered clinically relevant. Vertical asymmetries were detected in 71% of horses during straight-line evaluation and in 79% during at least one lungeing direction. Some horses showed relevant asymmetries only under specific movement conditions, underscoring the complementary role of straight-line and lungeing assessments in comprehensive gait evaluation. These results suggest that objective gait analysis could enhance pre-race veterinary assessments, especially in traditional racing, where horses are subjected to significant biomechanical stress, including variable surface properties and repetitive directional loading. In such complex and dynamic environments, relying solely on visual assessment may result in the underdiagnosis of subtle locomotor alterations. The AI-based tools offer potential to improve the detection of subtle irregularities and support evidence-based decisions in performance horse management. Further investigations are warranted to validate the clinical relevance of currently adopted asymmetry thresholds, refine their diagnostic value, and support their integration into standardized pre-race evaluation protocols. Full article

Background/Objectives: Potassium-competitive acid blockers (P-CABs) offer rapid gastric acid inhibition and lower toxicity compared to proton pump inhibitors (PPIs). This study investigates the drug–drug interaction between P-CABs and immunosuppressants tacrolimus and mycophenolate in kidney transplant recipients (KTRs). Methods: Sixty-two KTRs were randomized to receive either 50 mg of tegoprazan or 20 mg of pantoprazole. Patients were monitored using a smart clinical trial platform incorporating remote monitoring and safety management systems, which tracked drug adherence and vital signs. General and gastrointestinal (GI) symptoms were surveyed via a self-developed app on patients’ phones. Trough levels of tacrolimus and mycophenolate were measured every 4 weeks over 12 weeks. Results: Medication adherence was 100% in both groups. A total of 13,726 biometric data points and 5031 questionnaire responses were collected, with 5704 feedback messages and 56 video visits conducted. At 12 weeks, the mean trough levels of tacrolimus and mycophenolate were similar between the tegoprazan and pantoprazole groups (5.5 ± 1.6 vs. 5.8 ± 2.0 ng/mL, p = 0.50 and 2.7 ± 1.4 vs. 2.6 ± 1.4 µg/mL, p = 0.57, respectively). The intragroup difference in trough levels from baseline to week 12 was not significant in either group. GI symptoms scores, vital signs, and allograft function remained stable and comparable between groups. Conclusions: Tegoprazan does not alter the blood trough levels of tacrolimus and mycophenolate during the 12-week follow-up in KTRs and has a similar impact on GI symptoms as pantoprazole. This study confirms the feasibility and safety of using a smart clinical trial system with remote monitoring for randomized trials. Full article

Background: Hyperbaric oxygen therapy (HBOT) is the standard treatment for moderate to severe carbon monoxide (CO) poisoning, but middle ear barotrauma (MEB) remains a common complication. This study identified risk factors associated with MEB in patients undergoing monoplace HBOT. Methods: This retrospective cohort study included patients treated for CO poisoning with monoplace HBOT at a tertiary academic hospital between May 2021 and December 2023. MEB severity was assessed before and after treatment using video otoscopy and graded according to the modified O’Neill Grading System. Results: MEB occurred predominantly at lower severity grades according to the O’Neill scale. In univariate analysis, significant risk factors for MEB included altered mental status at presentation (OR: 3.16, 95% CI: 1.35–7.40, p = 0.008), serum albumin > 4.3 g/dL (OR: 0.22, 95% CI: 0.10–0.65, p = 0.004), and magnesium levels (OR: 0.21, 95% CI: 0.05–0.98, p = 0.046). Multivariate analysis confirmed altered mental status (OR: 3.16, 95% CI: 1.05–9.52, p = 0.041), albumin > 4.3 g/dL (OR: 0.26, 95% CI: 0.10–0.65, p = 0.004), and magnesium level (OR: 0.21, 95% CI: 0.05–0.88, p = 0.033) as independent predictors of MEB. Patients with higher albumin and magnesium levels showed lower risk. Conclusions: Altered mental status, lower albumin, and lower magnesium levels predicted middle ear barotrauma in patients undergoing monoplace HBOT for CO poisoning. These findings highlight the importance of careful pre-treatment evaluation and close monitoring during therapy to reduce the incidence of MEB. Full article

The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion proteins. To address these challenges, this study investigated sensor-based detection of the pre-detachment phase in a MPWM (Microfluidic Pulse Width Modulation) extraction system. Our approach integrates a micromechatronic system with a microfluidic suction circuit, real-time CCD imaging, and computational analysis to detect and characterize the pre-detachment moment before full extraction. A precisely controlled hydrodynamic force field progressively disrupts adhesion in multiple stages, reducing mechanical stress and preserving cell integrity. Real-time video analysis enables continuous monitoring of positional dynamics and oscillatory responses. Image processing and deep learning algorithms determine object center coordinates, allowing the MPWM system to dynamically adjust suction parameters. This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. By combining microfluidics, sensor detection, and AI-driven image processing, this study established a non-invasive method for optimizing adherent cell detachment. These findings have significant implications for single-cell research, regenerative medicine, and high-throughput biotechnology, ensuring maximal viability and minimal perturbation. Full article

For adolescents and young adults (AYAs) with cancer, fertility preservation is recommended before starting gonadotoxic treatments. This is an important aspect of psychological support in the treatment of the disease. However, the enormous psychological impact of this procedure on adolescents and young adults with cancer needs to be addressed by professionals. The traumatic nature of cancer diagnosis disrupts the psychosocial development of AYAs. A young adolescent’s perception of reproduction, and in particular of sperm freezing, is greatly altered by the disease. For a teenager, the success of sperm banking results from a positive balance between facilitators and barriers, which are mentioned here. Moreover, this article proposes a symbolic interpretation of sperm banking, referring to landmarks integrated during childhood, especially in fairytales. Furthermore, it offers an original video documentary that can be used as an information support to help AYAs adhere to the process of preserving their fertility through sperm freezing. Full article

Inflammatory bowel diseases (IBDs), comprising Crohn’s disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy—and potentially also in histology and imaging in the context of IBD—is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs. Full article

Different beddings result in alterations in barn environments and are responsible for changes in heifer behaviors and welfare. In this experiment, twenty-four Holstein heifers were randomly divided into four pens with various beddings, including sawdust (SD), fermented manure and sawdust (FSD), fermented manure and straw (FST), and a fermented manure mixture (FMM) for 31 days. The water content and surface layer temperature of the beddings were measured. We studied the activity budget, diurnal rhythms, and locations in lying and elimination of these heifers by videoing them for three consecutive days in the early and late stages of the experiment. Compared with the three fermented beddings (FSD, FST, and FMM), the SD bedding had a higher surface layer temperature and lower water content (p < 0.05). The SD bedding resulted in significantly less time ruminating (p < 0.01) and more time walking (p < 0.01) for heifers. The heifers in the SD and FMM bedding spent less time lying (p < 0.01) and more time standing (p < 0.01). Heifers in SD and FSD beddings presented lower scores in surface cleanliness rating (p < 0.01). Heifer activities, including eating, drinking, elimination, and lying, showed clear circadian patterns in four groups. Elimination behavior had a significant positive correlation with drinking/eating (p < 0.01). Moreover, similar location distributions in lying and elimination of heifers in various bedding pens were observed. Heifers preferred to eliminate the near eating area and drinking area and keep lying out of the eating region (p < 0.01). These findings could provide a reference for the selection of bedding materials and bedding management in dairy farming. Full article

Background/Objectives: The most frequent neurologic cause of recurrent vertigo is vestibular migraine (VM). However, its diagnosis relies primarily on patients’ histories, as specific diagnostic tests for VM are currently lacking. We aimed to examine and compare clinical features, vestibulo-ocular reflexes (VORs), and subjective visual vertical (SVV) between the ictal (IC) and inter-ictal (II) phases in VM patients. Methods: A repeated-measures study involved 31 patients with definite VM. Vestibular function was assessed using a video head impulse test (vHIT) to evaluate VOR results, and SVV testing to determine verticality perception. Otoneurological examination, including migraine-related disability, was noted. Analyses of repeated measures for numerical traits (SVV deviations, VOR, and clinical outcomes) were conducted using a linear mixed model (LMM), with phase, age, and sex as fixed effects and individual-specific random intercepts. Differences between the IC and II phases for dichotomous variables were analyzed using the χ2 or Fisher’s exact test. Results: The LMM analysis revealed that SVV deviations were significantly higher ictally (IC-ly) (β = 0.678, p = 1.51 × 10⁻⁶) than interictally (II-ly). VOR results remained normal across phases (p > 0.05), and refixation saccades did not differ significantly based on vHIT results (p > 0.05). Nausea (100% vs. 38.71%, p = 6.591 × 10⁻⁸), photophobia (100% vs. 35.48%, p = 1.839 × 10⁻⁸), and phonophobia (90.32% vs. 6.45%, p = 9.336 × 10⁻¹²) were significantly more frequent IC-ly than II-ly. Conclusions: Our findings highlight phase-dependent alterations in spatial orientation, with increased SVV deviations IC-ly despite stable VOR. The significant differences in migraine-associated symptoms reinforce the dynamic nature of VM. These results emphasize the importance of timing in vestibular assessments and suggest that SVV testing during IC VM episodes may enhance diagnostic accuracy. Full article

Introduction: Goal setting is an effective strategy in altering fitness and dietary behaviours. The goal attainment scale (GAS) is a patient-reported outcome measure that can be used to quantify goal achievement. The GAS has not been extensively assessed in lifestyle intervention trials. This study aimed to describe the goal setting process and assess the impact of a digital exercise and diet service and self-efficacy on goal attainment in people with chronic disease and at increased cardiometabolic risk. Methods: This study presents a single-centre, 26-week, randomised controlled trial (RCT) comparing standard care to digital health technologies (text messages, nutrition/exercise app, video consultations with dietitian and/or exercise physiologist). The comparator group was offered dietitian review (per standard care), and both groups received a wearable activity monitor. Individualised goal setting was facilitated prior to randomisation. Goal importance, performance measures, and self-efficacy were determined by participants. Goal outcome and ‘Change in GAS’ scores, reflecting the difference between baseline and follow-up performance, were calculated using validated formulae. Results: Goal setting was completed and reviewed by 66 participants, with a median age of 51 years and 56% being male. The most common goals related to weight loss (46%), fitness (29%), and diet (15%). Most participants (62%) reported improvements in their goals, with most improvements in dietary (71%), fitness (52%), and weight loss (39%) goals. There was no significant difference in goal outcomes between intervention and comparator groups (p = 0.99). There was, however, a significant correlation between nutrition self-efficacy and dietary goal achievement (p = 0.04). Conclusions: The novelty and feasibility of goal setting and attainment were demonstrated in this RCT of lifestyle interventions in people with chronic disease. Though the intervention did not significantly improve goal attainment, most participants reported improvements in their lifestyle goals. There were greater improvements in dietary goals than in fitness or weight loss goals. Participant-led goal setting with GAS and participant self-efficacy has potentially important applications in future lifestyle modification research and clinical implementation endeavours. Full article

Recent advancements in image processing technology have positively impacted some fields, such as image, document, and video production. However, the negative implications of these advancements have also increased, with document image manipulation being a prominent issue. Document image manipulation involves the forgery or alteration of documents like receipts, invoices, various certificates, and confirmations. The use of such manipulated documents can cause significant economic and social disruption. To prevent these issues, various methods for the detection of forged document images are being researched, with recent proposals focused on deep learning techniques. An essential aspect of using deep learning to detect manipulated documents is to enhance or augment the characteristics of document images before inputting them into a model. Enhancing the distinctive features of manipulated documents before inputting them into a deep learning model is crucial to achieve high accuracy. One crucial characteristic of document images is their inherent symmetrical patterns, such as consistent text alignment, structural balance, and uniform pixel distribution. This study investigates document forgery detection through a symmetry-aware approach. By focusing on the symmetric structures found in document layouts and pixel distribution, the proposed LTHE technique enhances feature extraction in deep learning-based models. Therefore, this study proposes a new image enhancement technique based on the results of three general-purpose CNN models to enhance the characteristics of document images and achieve high accuracy in deep learning-based forgery detection. The proposed LTHE (Log-Transform Histogram Equalization) technique increases low pixel values through log transformation and increases image contrast by performing histogram equalization to make the features of the image more prominent. Experimental results show that the proposed LTHE technique achieves higher accuracy when compared to other enhancement methods, indicating its potential to aid the development of deep learning-based forgery detection algorithms in the future. Full article

Background: Exposure to violent content through video games can shape perceptions of aggression as normative or acceptable, potentially desensitizing adolescents to violence and increasing the likelihood that they will engage in aggressive behavior in their real-world interactions. This narrative review examines the relationship between violent video games (VVGs) and youth aggression, addressing (i) VVG exposure through the lens of Social Learning Theory and the General Aggression Model; (ii) the biological determinants of aggressive behavior, including neurophysiological correlates and the potential modulatory role of the gut microbiome; (iii) and current evidence on the influence of VVG exposure on adolescent aggression. Results: VVG exposure may promote moral disengagement and reinforce aggressive behavior through rewarding violent actions, potentially leading to real-life aggression. Exposure to VVGs increases aggression by elevating cortisol levels, activating the sympathetic nervous system, stimulating the brain’s reward system, and disrupting neurotransmitter balance. Alterations in gut microbiome composition could occur as a result of engaging in VVGs, potentially mirroring the physiological responses observed in violent real-world activities. The relationship between VVGs and aggression among adolescents has been widely explored, with studies generally indicating that VVG exposure can lead to desensitization to violence, reduced empathy, and diminished prosocial behavior. VVGs have also been linked to both bullying perpetration and victimization dynamics among adolescents, contributing to mental health issues, which in turn exacerbate problematic gaming behaviors. Conclusions: While there is evidence suggesting that exposure to VVGs is associated with increased adolescent aggression, discrepancies in the literature highlight the need for further research aimed at improving our understanding of this phenomenon. The studies on this topic reveal significant limitations, including a predominance of cross-sectional designs, methodological shortcomings, small sample sizes, and challenges in generalizing results. Factors such as the developmental stages of the participants, the exclusion of females, and unmeasured variables during long follow-up periods further complicate interpretations. Moreover, parental responsibility and the effectiveness of age ratings require closer examination. Full article