Search Results (82)

Explosive ordnance (EO), including AXO (abandoned explosive ordnance), IEDs (improvised explosives devices), and UXO (unexploded ordnance), are widely recognised for their blast and fragmentation hazards, but they also represent a persistent and under-addressed source of occupational chemical exposure for explosive ordnance disposal (EOD) personnel. EOD core activities liberate mixed metals and energetic chemicals, resulting in exposures that are multi-route (inhalation of dusts and fumes, dermal loading amplified by sweat and glove occlusion, and ingestion via hand-to-mouth transfer during eating, drinking, or smoking) and multi-temporal (repeated low-dose background plus task-driven spikes), as well as chemically complex. Clinically, this can present as syndromic overlap across acute and chronic domains, with symptoms that are easily misattributed to heat stress, dehydration, infection, or fatigue. Acute effects of concern include neurotoxic presentations (headache, dizziness, confusion, tremor, and seizure), respiratory and mucosal irritation following dust or fume events, gastrointestinal symptoms, and patterns suggestive of acute hepatic or renal stress, particularly when high-intensity tasks occur in hot environments that compound physiologic strain. Chronic outcomes relevant to repeatedly exposed EOD personnel include renal function decline, neurocognitive effects that can degrade operational decision making and safety, persistent haematologic abnormalities, and endocrine disruption signals, with long-latency risks requiring cautious interpretation given sparse longitudinal data and confounding co-exposures. This review synthesises the current evidence base through an EOD lens and translates it into pragmatic clinical and programmatic actions: task-based exposure characterisation; tiered biomonitoring and medical surveillance aligned to operational tempo; incident-triggered assessment pathways after high-residue events; and prevention strategies that work under field constraints, including contamination control zones, hygiene enforcement, glove and respiratory protection optimisation, tool and vehicle decontamination, and measures to prevent secondary transfer and take-home exposure. The central takeaway is practical: EOD programs can reduce morbidity and improve readiness by treating explosive ordnance as a chemical mixture exposure problem, adopting mixture-aware clinical triage, and embedding surveillance and controls that match how EOD work is actually performed. Full article

Modified chitosan (Cs) derivatives were synthesized and evaluated as potential antibacterial and antiviral coatings of medical protective equipment (facial masks, gloves, …). Quaternized chitosan (HTCC) and chitosan–silver nanocomposites (Ag/Cs) were successfully prepared, with structural characterization confirming efficient quaternization and uniform incorporation of silver nanoparticles. Antibacterial testing revealed that HTCC exhibited concentration-dependent activity, while Ag/Cs showed strong broad-spectrum antibacterial effects and enhanced thermal stability. Antiviral assays against SARS-CoV-2 demonstrated significant viral inhibition for HTCC6 and Ag/Cs at non-cytotoxic concentrations (6 mg/mL), highlighting the role of cationic charge and nanoparticle inclusion in antiviral efficacy. These findings indicate that the developed chitosan derivatives are promising candidates for sustainable functional coatings on medical devices, offering potential applications in infection prevention. Full article

The translation of knowledge and methodologies across disciplines represents a valuable source of innovation, particularly in user-centered design approaches that have become essential in medical device development. This study explores the use of the PICO (Population, Intervention, Comparison, and Outcome) framework, a cornerstone of evidence-based medicine for formulating clinical questions, as a conceptual structure to support the alignment between clinical needs and engineering design consideration in robotic rehabilitation devices, with a focus on hand exoskeletons. Through a conceptual reinterpretation and application-oriented exploration supported by illustrative case studies involving both rigid and soft robotic glove prototypes, this study shows how each PICO component can inform engineering parameters, from defining user impairments and intervention strategies to benchmarking and outcome measurements. The analysis highlights the potential of PICO in fostering a user-centered design perspective and bridging clinical and engineering domains while also identifying its structural limitations when applied to device design contexts. This study concludes that while the PICO framework offers a valuable foundational structure, it requires customization to fully address the multifactorial requirements of effective, patient-specific robotic rehabilitation device design. Full article

Healthcare-associated infections remain an ongoing concern across medical and dental practice, prompting continuous evaluation of infection prevention measures. In dental extractions, the necessity of sterile gloves is debated, as the oral cavity represents an inherently contaminated environment. This systematic review and meta-analysis evaluated whether the use of sterile gloves reduces postoperative socket infections compared with non-sterile gloves. A search of MEDLINE, Embase, Web of Science, Cochrane CENTRAL, and Google Scholar identified randomized controlled trials, clinically controlled trials, and observational trials directly comparing sterile versus non-sterile glove use during dental extractions. The primary outcome of this study was extraction socket infection at day 7 post-surgery. A meta-analysis using relative risk (RR) was performed for dichotomous data. Of the initial 7170 publications found, seven articles met inclusion criteria. Infection rates ranged from 0% to 3.9%, with an overall infection rate of 0.3% in the sterile glove group (672 patients) and 1.3% in the non-sterile glove group (758 patients). Three studies qualified for meta-analysis, resulting in an RR of 0.30 (95% CI 0.07–1.24), indicating no significant difference in postoperative infections between sterile and non-sterile glove usage. Given the limitations of small sample sizes, low event rates, incomplete reporting, and lack of subgroup data for surgical versus non-surgical extractions, no difference in postoperative infection was found between sterile and non-sterile glove use. Additional research is needed to determine whether glove sterility influences infection risk, particularly in surgical procedures. Full article

Millions of healthcare workers experience percutaneous exposure to bloodborne communicable infectious disease pathogens annually, with the risk of contracting occupationally acquired infections. In this study, we aimed to assess the status of occupational safety and outbreak preparedness in Congolese nurses and laboratory technicians in Kongo central and the Katanga area, amidst multiple ongoing public health emergencies in the Democratic Republic of the Congo (DRC). This was a multicenter analytical cross-sectional study conducted in five referral hospitals located in Kongo central province and the Katanga area between 2019 and 2020 amidst Ebola, Yellow fever, Cholera and Chikungunya outbreaks. Participants were adult A0 grade nurses, A1 nurses, A2 nurses and medical laboratory technicians (N = 493). They answered a structured, self-administered questionnaire related to hospital hygiene and standard precautions for occupational infection prevention. The majority of the respondents were females (53.6%), and 30.1% of them have never participated in a training session on hospital infection prevention during their career. The proportions of those who have been immunized against hepatitis B virus (HBV) was markedly low, at 16.5%. Of the respondents, 75.3% have been using safety-engineered medical devices (SEDs), whereas 93.5% consistently disinfected medical devices after use. Moreover, 78% of the respondents used gloves during medical procedures and 92.2% wore masks consistently. A large majority of the respondents, 82.9%, have been recapping the needles after use. Regarding participation in outbreak response, 24.5% and 12.2% of the respondents were Chikungunya and Cholera epidemic responders, respectively; 1.8% have served in Ebola outbreak sites. The proportion of the respondents who sustained at least one percutaneous injury by needlestick or sharp device, blood/body fluid splash or both in the previous 12-month period was high, 89.3% (41.8% for injury, 59.2% for BBF event), and most of them (73%) reported over 11 events. Compared to laboratory technicians, nurses had higher odds for sustaining percutaneous injury and BBF events [OR = 1.38 (0.16); p < 0.01], whereas respondents with longer working experience were less likely to sustain those events [OR = 0.47 (0.11); p < 0.001]. Findings from this study suggest that Congolese nurses and laboratory technicians experience a high frequency of injury and BBF events at work, and remain at high risk for occupationally acquired infection. There is a need for periodic capacity-building training for the healthcare workforce to improve infection prevention in health settings, the provision of sufficient and appropriate PPE and SEDs, post-exposure follow-up and keeping records of occupational injuries in hospitals in Congolese healthcare settings. Full article

Foundation models excel on general benchmarks but often underperform in clinical settings due to domain shift between internet-scale pretraining data and medical data. Multimodal deep learning, which jointly leverages medical images and clinical text, is promising for diagnosis, yet it remains unclear whether domain adaptation is better achieved by fine-tuning large vision–language models or by training lighter, task-specific architectures. We address this question by introducing PairDx, a balanced dataset of 22,665 image–caption pairs spanning six medical document classes, curated to reduce class imbalance and support fair, reproducible comparisons. Using PairDx, we develop and evaluate two approaches: (i) PairDxCLIP, a fine-tuned CLIP (ViT-B/32), and (ii) PairDxFusion, a custom hybrid model that combines ResNet-18 visual features and GloVe text embeddings with attention-based fusion. Both adapted models substantially outperform a zero-shot CLIP baseline (61.18% accuracy) and a specialized model, BiomedCLIP, which serves as an additional baseline and achieves 66.3% accuracy. Our fine-tuned CLIP (PairDxCLIP) attains 93% accuracy and our custom fusion model (PairDxFusion) reaches 94% accuracy on a held-out test set. Notably, PairDxFusion achieves this high accuracy with 17 min, 55 s of training time, nearly four times faster than PairDxCLIP (65 min, 52 s), highlighting a practical efficiency–performance trade-off for clinical deployment. The testing time also outperforms the specialized model—BiomedCLIP (0.387 s/image). Our results demonstrate that carefully constructed domain-specific datasets and lightweight multimodal fusion can close the domain gap while reducing computational cost in healthcare decision support. Full article

People with Parkinson’s disease often show changes in their movement abilities during the day, especially around the time they take medication. Being able to record these variations in an objective way can help doctors adapt treatment and follow disease changes more closely. A methodology for quantitative motor assessment is proposed in this work. It employs data from a custom SmartGlove equipped with inertial sensors. A multi-method feature selection scheme is developed, integrating statistical significance, model-based importance, and variance contribution. The most significant features were retained, and higher-level artificial features were generated using Grammatical Evolution (GE). The framework combines multi-criteria feature selection with evolutionary feature construction, providing a compact and interpretable representation of motor behavior. Additionally, the framework highlights nonlinear and composite features as potential digital biomarkers for Parkinson’s monitoring. The method was validated on recordings collected from Parkinson’s patients before and after medication intake. The recordings have been retrieved during four standardized hand motor tasks targeting tremor, bradykinesia, rigidity, and general movement anomalies. The proposed method was compared with five existing machine learning models based on artificial neural networks. GE-based features reduced classification errors to 10–19%, outperforming baseline models. Furthermore, the proposed methodology performs prediction and recall 80–88%. Full article

Monitoring physiological activity and sleep states in real time is challenging, particularly for continuous assessment in daily life settings using wearable IoMT devices. We developed a 24 h wearable system that integrates electrocardiogram (ECG) electrodes for heart rate measurement and a glove-mounted flex sensor for motor responses, connected through an Internet of Medical Things (IoMT) platform. Flex signals were combined using principal component analysis (PCA) to generate a single kinematic channel, then standardized with heart rate. Time-series windows were embedded using TS2Vec and clustered with DBSCAN, while t-SNE was applied only for visualization. The framework identified four physiologically coherent states: (i) nocturnal sleep with the lowest heart rate and minimal motion, (ii) evening pre-sleep with low movement and moderately higher heart rate, (iii) daytime activity with variable motion and mid-range heart rate, and (iv) late-day high-intensity activity with the highest heart rate and increased motor responses. A few outliers were observed during transient body movements or sensor readjustments, which were identified and excluded during preprocessing to ensure stable clustering results. Across 24 h, heart rate ranged from 52 to 96 bpm (mean 77.4), while flexion spanned 0 to 165° (mean 52.5°), showing alignment between movement intensity and cardiac response. This integrated sensing and analytics pipeline provides an interpretable, subject-specific state map that enables continuous remote monitoring of physiological activity and sleep patterns. Full article

The sensory glove (also known as data or instrumented glove) plays a key role in measuring and tracking hand dexterity. It has been adopted in a variety of different domains, including medical, robotics, virtual reality, and human–computer interaction, to assess hand motor skills and to improve control accuracy. However, no particular technology has been established as the most suitable for all domains, so that different sensory gloves have been developed, adopting different sensors mainly based on optic, electric, magnetic, or mechanical properties. This work investigates the performances of the MANUS Quantum sensory glove that sources an electromagnetic field and measures its changing value at the fingertips during fingers’ flexion. Its performance is determined in terms of measurement repeatability, reproducibility, and reliability during both quasi-static and dynamic hand motor tests. Full article

Hand motion monitoring plays a vital role in medical rehabilitation, sports training, and human–computer interaction. High-sensitivity wearable biosensors are essential for accurate gesture recognition and precise motion analysis. In this work, we propose a high-sensitivity wearable glove based on a push–pull optical fiber sensor, designed to enhance the sensitivity and accuracy of hand motion biosensing. The sensor employs diagonal core reflectors fabricated at the tip of a four-core fiber, which interconnect symmetric fiber channels to form a push–pull sensing mechanism. This mechanism induces opposite wavelength shifts in fiber Bragg gratings positioned symmetrically under bending, effectively decoupling temperature and strain effects while significantly enhancing bending sensitivity. Experimental results demonstrate superior bending-sensing performance, establishing a solid foundation for high-precision gesture recognition. The integrated wearable glove offers a compact, flexible structure and straightforward fabrication process, with promising applications in precision medicine, intelligent human–machine interaction, virtual reality, and continuous health monitoring. Full article

Worldwide, the healthcare industry produces massive quantities of medical waste (MW), most of which is incinerated, releasing large quantities of dioxins, mercury, and other pollutants. Despite this, only a limited number of studies have explored the incorporation of MW into construction materials, with a special focus on cement-based construction materials (CB-CMs). However, to the best of the authors’ knowledge, no existing review formally structures, summarizes, correlates, and discusses the findings of previous studies on MW in CB-CMs to encourage further research and applications of this promising alternative. Therefore, the added value of this study lies in providing an innovative and critical analysis of existing research on the use of MW in CB-CMs, consolidating and evaluating dispersed findings through a systematic literature review, enhancing understanding of the topic, and identifying knowledge gaps to guide future research. A robust systematic literature review was conducted, encompassing 40 peer-reviewed research articles, retrieved from the Web of Science Core Collection database. The methodology involved a three-stage process: a descriptive analysis of the included articles, the identification and synthesis of key thematic areas, and a critical evaluation of the data to ensure a rigorous and systematic report. The selection criteria prioritized peer-reviewed research articles in English with full text availability published in the last 7 years, explicitly excluding conference papers, book chapters, short reports, and articles not meeting the language or accessibility requirements. The results indicate that the influence of MW in CB-CM varies significantly. For example, while the incorporation of face masks as fiber reinforcement in concrete generally enhances its mechanical and durability properties, the use of gloves is less effective and not always recommended. Finally, it was found that further research is needed in this field due to its novelty. Full article

In the field of rehabilitation exoskeletons, addressing the needs of users and healthcare professionals is essential for the development of effective medical technologies. This study presents a user-centered evaluation of the ARTH-aid ExoGlove, a proposed rehabilitation device for muscle atrophy caused by rheumatoid arthritis. This study assesses usability and user experience through therapist and patient feedback, focusing on ergonomic and functional aspects. Ensuring compliance with usability and adaptability criteria is crucial before implementation to enhance effectiveness in clinical settings. While this study includes user feedback, further technical validation and refinement of the system are needed. The findings provide insights into patients’ perceptions of usability and impact on mobility, as well as therapists’ perspectives on its potential integration into rehabilitation protocols. These results highlight the importance of iterative validation and reinforce the role of rehabilitation technologies in complementing traditional therapeutic approaches and advancing patient-centered innovation in biomedical design. Full article

Medical palpation is a task that traditionally requires a skilled practitioner to assess and diagnose a patient through direct touch and manipulation of their body. In regions with a shortage of such professionals, robotic hands or sensorized gloves could potentially capture the necessary haptic information during palpation exams and relay it to medical doctors for diagnosis. From an engineering perspective, a comprehensive understanding of the relevant motions and forces is essential for designing haptic technologies capable of fully capturing this information. This study focuses on thyroid examination palpation, aiming to analyze the hand motions and forces applied to the patient’s skin during the procedure. We identified key palpation techniques through video recordings and interviews and measured the force characteristics during palpation performed by both non-medical participants and medical professionals. Our findings revealed five primary palpation hand motions and characterized the multi-dimensional interaction forces involved in these motions. These insights provide critical design guidelines for developing haptic sensing and display technologies optimized for remote thyroid nodule palpation and diagnosis. Full article

Personal protective equipment (PPE) is crucial for infection prevention and is effective only when worn correctly and consistently. Health organizations often use education or inspections to mitigate non-compliance, but these are costly and have limited success. This study developed a novel on-device, AI-based computer vision system to monitor healthcare worker PPE adherence in real time. Using a custom-built image dataset of 7142 images of 11 participants wearing various combinations of PPE (mask, gloves, gown), we trained a series of binary classifiers for each PPE item. By utilizing a lightweight MobileNetV3 model, we optimized the system for edge computing on a Raspberry Pi 5 single-board computer, enabling rapid image processing without the need for external servers. Our models achieved high accuracy in identifying individual PPE items (93–97%), with an overall accuracy of 85.58 ± 0.82% when all items were correctly classified. Real-time evaluation with 11 unseen medical staff in a cardiac intensive care unit demonstrated the practical viability of our system, maintaining a high per-item accuracy of 87–89%. This study highlights the potential for AI-driven solutions to significantly improve PPE compliance in healthcare settings, offering a cost-effective, efficient, and reliable tool for enhancing patient safety and mitigating infection risks. Full article

The study investigated the determinants of safe pesticide handling and application among farmers in rural communities of Oyo State, ssouthwestern Nigeria. A cross-sectional design utilizing 2-stage cluster sampling techniques was used to select Ido and Ibarapa central Local Government Areas and to interview 383 farmers via a structured questionnaire. Data were analyzed using descriptive statistics and logistic regression at p = 0.05. Results showed that 41.8% of the farmers had been working with pesticides on farms for at least 5 years, 33.0% attended training on pesticide application, 73.5% had good safety and health knowledge, and 72.3% had safe pesticide handling and application practices. About half (50.2%) stated that they wear coveralls, gloves, and masks to protect their body, face, and hands when applying pesticides, 9.8% use empty pesticide containers for other purposes in the house/farm, while 11.5% blow the nozzle with their mouth to unclog it if it becomes blocked. The three major health symptoms reported by the participants were skin irritation (65.0%), itchy eyes (51.3%), and excessive sweating (32.5%). Having attended training on pesticide application and use enhanced (OR = 2.821; C.I = 1.513–5.261) practicing safe pesticide handling and application. Farmers with good knowledge (OR = 5.494; C.I = 3.385–8.919) were more likely to practice safe pesticide handling and application than those with poor knowledge about pesticide use. It is essential to develop and deliver mandatory comprehensive training programs for farmers on impacts of pesticides on health and environment, along with sustainable safe handling, application, and disposal of pesticides using proper waste management techniques and recognizing early signs and seeking medical assistance. The urgent need to strengthen policy to regulate pesticide use and limit farmers’ access to banned products is also key. Full article