Gait speed is a key clinical indicator in neurological and orthopaedic conditions, yet waveform-level adaptations in ground reaction forces (GRF) and multi-muscle electromyography (EMG) remain poorly characterised. Existing approaches often analyse discrete outcomes or individual modalities, leaving limited integration of continuous waveform inference, dimensionality reduction, explainable machine learning, and equivalence testing within a unified multimodal framework. To compare three-axis GRF and six-muscle EMG between slow (0.5 m/s) and fast (1.0 m/s) treadmill walking using statistical parametric mapping (SPM), functional principal component analysis (fPCA), explainable machine learning, and equivalence testing. Fifty-eight healthy adults were analysed (55 with complete EMG). Paired SPM with cluster-based permutation assessed waveform differences. fPCA-derived features entered a Random Forest with leave-one-subject-out cross-validation and SHAP interpretability. Two one-sided tests (TOST) assessed equivalence of the vertical GRF. No significant cluster-level SPM differences were found for any GRF component. In contrast, significant EMG clusters were detected in tibialis anterior (ten clusters), gastrocnemius medial and lateral, vastus lateralis, rectus femoris, and semitendinosus. The Random Forest achieved 87.2
Tracheal reconstruction remains a significant clinical challenge due to the difficulty of maintaining airway patency and promoting epithelial regeneration following tracheal injury or resection. Recent advances in tissue engineering have explored 3D-printed scaffolds as potential solutions; however, limitations remain regarding scaffold biocompatibility, mechanical stability, and integration with host tissue. This study evaluated the feasibility of a non-biodegradable polyurethane–polyvinyl chloride (PU-PVC) composite scaffold for partial tracheal wall reconstruction in a rabbit model. Nine New Zealand white rabbits underwent anterior tracheal wall reconstruction using a customized half-pipe PU-PVC scaffold. Scaffold mechanical properties were characterized before implantation. Animals were monitored for 30 days, after which bronchoscopic evaluation was performed to assess luminal patency. Histological analysis was conducted to evaluate epithelialization and inflammatory response. Explanted scaffolds were also assessed for structural integrity and mechanical stability. The 30-day survival rate was 88.9
Epilepsy remains a major global health concern, particularly in regions where continuous medical monitoring is difficult to implement. This study introduces a wearable system powered by edge-based artificial intelligence, designed to detect epileptic seizures in real time. The device integrates multiple sensors—accelerometers for motion tracking, photoplethysmography (PPG) for cardiovascular monitoring, and GPS for location detection—to enhance reliability through sensor fusion. Multiple machine learning models, including support vector machines (SVM), neural networks (NN), and random forest classifiers, were deployed and assessed directly on the device. Among these, the optimized random forest algorithm achieved the highest accuracy and fastest response time. The fusion of sensor data significantly improved specificity and maintained a very low false alarm rate. When a seizure is detected, the system instantly sends SMS alerts with precise location details to assigned caregivers, enabling prompt medical intervention. Simulation results demonstrate that this cost-effective and self-contained platform offers strong potential for improving patient safety and facilitating rapid response in low-resource settings where conventional monitoring tools are unavailable.
Abstract Background Seat cushion materials affect the mechanical demands of sit-to-stand (STS) movements; however, the effects of specific material properties, such as resilience and hardness, remain unclear. Understanding how these factors influence lower-limb joint moments and movement strategies during STS may contribute to the development of seat designs that assist individuals with reduced lower-limb strength. Therefore, this study aimed to clarify the fundamental mechanical effects of seat cushion resilience and hardness on STS. Methods Fifteen healthy young adults performed STS from five polyurethane foam cushions that differed in resilience (14–55%) and 40% compression hardness (66–336 N). The material ranges were determined with reference to technical documents and a published patent specification to ensure they were within the range commonly used in everyday seating products. Kinematic and kinetic data were collected using a motion capture system and two force plates. Net joint moments were calculated via inverse dynamics, and differences among seat conditions were analyzed using repeated-measures ANOVA or the Friedman test, with Bonferroni-adjusted pairwise comparisons (α = 0.05). Results Seat resilience significantly affected the peak hip and knee extensor moments (p < 0.01, η² = 0.35–0.48). High-resilience cushions delayed seat-off timing and maintained greater seat reaction force at the timings of peak hip and knee extensor moments. In contrast, seat hardness mainly influenced horizontal center-of-mass (COM) velocity and hip joint moment (p < 0.01, η² = 0.31–0.37), with softer seats producing larger values. Conclusion High-resilience cushions delayed seat-off and maintained buttock support for a longer duration, thereby reducing the peak hip and knee extensor moments. In contrast, softer seats promoted a strategy involving greater horizontal momentum generation by the upper body, which consequently required an increased hip extensor moment to decelerate this momentum. Cushions with a resilience of ≥ 53% and 40% compression hardness of ≥ 180 N effectively reduced lower-limb joint loading. The results of this study provide fundamental insights that may contribute to future research on chair design and cushion selection in clinical and caregiving environments. Clinical trial Not applicable.
Abstract Cuffed tracheal tubes enable a secure airway during mechanical ventilation; however, improper cuff inflation can lead to complications such as inspiratory gas leaks, pulmonary ingress of orogastric secretions, or tracheal injury. This study explores a sensor-based approach to optimise cuff inflation using fibre Bragg grating (FBG) optical sensors embedded within the cuff to detect contact with the tracheal wall. Testing was conducted on multiple trachea models, including cylindrical and bio-inspired models, as well as ex vivo porcine trachea samples, to assess cuff–trachea contact. Seal performance was evaluated at both standard inflation pressures and contact-guided inflation pressures. The contact-sensing tracheal tube successfully detected cuff–trachea contact in all models and ex vivo samples tested. Seal tests showed that larger diameter tracheal models exhibited lower leakage rates at contact-guided inflation pressures compared with standard inflation, indicating improved sealing. In contrast, smaller diameter models showed higher leakage rates at contact-guided pressures due to cuff folding, which formed leakage pathways. These results demonstrate that contact sensing improved cuff performance in larger tracheal models by enhancing sealing effectiveness. The contact-sensing tracheal tube differentiated tracheal model sizes, indicating potential for personalised cuff inflation to accommodate variations in tracheal size and shape. Future work will focus on further sensor miniaturisation and integration within lower-volume cuffs to enable effective contact sensing across a wider range of tracheal geometries and support translation to in vivo use.
Processed electroencephalography (EEG) monitors such as the bispectral index (BIS) and patient state index (PSI), are used clinically to estimate anesthetic depth, yet their algorithmic design obscures how closely these indices reflect underlying neural complexity. Entropy-based analyses, grounded in information theory, provide a quantitative framework for characterizing EEG signal irregularity and have been proposed as physiologically interpretable alternatives. However, the relationship between these commercial indices and theoretical entropy measures remains unclear. This systematic review aimed to (1) synthesize existing evidence on the relationship between commercially available processed EEG metrics and entropy-based EEG analyses, and (2) identify factors influencing their comparability, including algorithmic, demographic, and anesthetic variables. A comprehensive literature search identified experimental and clinical studies comparing BIS, PSI, and related commercial indices with theoretical entropy measures (e.g., approximate entropy, sample entropy, and permutation entropy, state entropy and response entropy) across various anesthetic agents and clinical populations. Data were extracted on study design, patient demographics, EEG metrics, analytical methods, and reported correlations or prediction probabilities. Ninety-four studies were included, encompassing participants across diverse anesthetic modalities. Overall, BIS exhibited moderate-to-strong correlations with entropy-derived measures and comparable prediction probabilities for distinguishing anesthetic depth. Entropy indices demonstrated greater resistance to certain artifacts but higher susceptibility to electromyographic contamination. Age, anesthetic type, and the use of neuromuscular blocking agents significantly influenced the relationship between indices. Across studies, heterogeneity in preprocessing, entropy algorithms, and patient selection limited direct comparability. Commercially processed EEG indices and theoretical entropy measures capture overlapping but distinct dimensions of cortical dynamics during anesthesia. While both reliably track transitions in consciousness, discrepancies arise from differences in signal filtering, algorithm design, and physiological variability. Future research should prioritize transparent algorithmic frameworks and standardized entropy computation to enhance the interpretability and cross-device comparability of EEG-derived anesthesia monitors.
The application of chaos theory has positive results in different fields of science. Its nonlinear modeling properties and its vision of dynamic systems have enabled it to capture complex relationships in fields such as physics, financial econometrics, social systems and mathematical demography. This paper reviews the implication of chaos theory in the medical sciences. We carried out a systematic literature review under Cochrane’s international standards. A search strategy was executed with indexed terms (MeSH, DeCS and Emtree) that varied according to each database (Embase, MEDLINE, SciELO, LILACS). The PROSPERO registration number was CRD42023491407. In total, 2598 articles were retrieved, of which 20 were included. Algorithmic applications of chaotic systems were diverse. The medical fields with the largest studies were cardiology, neurology and oncology. The most used software was Matlab, however, in all cases, except one, we did not find open-source codes related to the studies. We found a wide heterogeneity in the studies reviewed, and this was reflected in the scope of research results. While some papers focus on proving the existence of chaotic behavior or understanding the nature of the phenomena being studied, others propose practical implications, such as in prescribing medicines and organizing health units. Not applicable.
Continuous glucose monitoring (CGM) in children with type 1 diabetes is faced with major challenges due to prohibitive price, invasiveness and lack of compliance with insulin of the currently available interstitial devices, which is particularly severe in low- and middle-income countries (LMICs). To investigate emerging solutions, we have undertaken a systematic review (2016–2025) of non-invasive, sweat-based glucose sensors and used a structured biomedical engineering model to determine the technological maturity and pediatric and global health usage of the sensors. The review of 25 peer-reviewed articles indicates a significant shift to non-enzymatic sensing schemes based on strong approaches and incorporating highly sensitive nanomaterials like MXene and scalable libraries of fabrications. An analysis of qualitative cost accuracy trade-off, formalized by new, specially developed LMIC Scalability Score, proves that whereas complex and expensive systems would reach the lowest mean absolute relative difference (MARD), simpler and highly scalable devices would reach the same performance with a MARD value under 10.5
Abstract Background Spinal cord injury (SCI) causes long-term neurological deficits resulting in functional disabilities. While longitudinal recovery patterns of sensorimotor outcomes after SCI have been studied, few analyses have applied machine learning to systematically model the relationship between neurological impairments and functional independence at different post-injury phases. Methods This study compared ordinal and nominal classification models predicting functional independence from sensorimotor status cross-sectionally. Inputs included motor and sensory scores from the International Standards for Neurological Classification of SCI, age, sex, and time since injury collected in the European Multicenter Study about SCI. Models were evaluated on a task from each domain of the Spinal Cord Independence Measure, namely grooming (self-care), bladder management (respiration and sphincter management), and indoor mobility (mobility). Analyses were stratified into early (≤ 40 days), intermediate (70–100 days), and late (> 182 days) post-injury phases. Models were ranked based on five evaluation metrics, and interpretability explored using Shapley Additive Explanations (SHAP). Results Model accuracy improved over time (early phase: 46–71%, late phase: 50–85%), indicating that functional independence is more reliably determined from sensorimotor scores in later post-injury phases. Across all scenarios, random forest achieved the best overall performance (0.93 ± 0.03, averaged across different metrics). Ordinal models yielded fewer severe misclassifications compared to nominal models. Motor scores were stronger predictors than sensory scores, with lower limb function (L2–L4) strongly associated with mobility, voluntary anal contraction with bladder control, and upper limb function (C6, C8) with grooming ability, highlighting that models utilise known relationships. Conclusion We show that both multiclass and ordinal models can accurately classify SCIM-based functional independence outcomes after SCI from neurological assessments at different time points post-injury. Ordinal approaches provide particular clinical value by minimizing severe misclassifications, a crucial advantage when distinguishing between functional independence classes that require fundamentally different care approaches. Interpretability analysis showed that the predictions are grounded in clinical knowledge. The developed models provide the basis for a modular prognostic framework, in which predicted ISNCSCI scores can be used to derive the most likely functional independence class, enabling a modular, computationally efficient and scalable approach to prediction in SCI care across a range of neurological and functional outcomes.
Detecting new or worsening hypomobility in acute ischemic cerebrovascular patients is challenging, especially when asleep or unattended. This study used a wearable movement acceleration monitoring system to identify changes in these patients, aiming to improve early detection. Continuous bilateral upper limb acceleration data, clinical characteristics, specific treatments, stroke etiology, and in-hospital outcomes were collected from patients. The primary outcome was newly emerging or worsening hypomobility during monitoring. An XGBoost model, trained with synthetic minority oversampling to address class imbalance and validated via 5-fold cross-validation, analyzed movement acceleration features to diagnose hypomobility timing. Model performance was evaluated through AUC and feature importance metrics. From April 2023 to February 2025, 85 patients with acute ischemic cerebrovascular events were enrolled; three were excluded due to data errors. A total of 82 patients were included in the analysis, comprising 76 (92.7
This feasibility study aimed to examine whether an insole-type active assist device designed to dynamically adjust ankle alignment at heel contact can be safely delivered and evaluated during an on-the-spot stepping task in patients with medial knee osteoarthritis (OA). The study specifically assessed the feasibility of intervention delivery, testing procedures, and motion-capture-based outcome measurement. Six ambulatory patients with medial knee OA (Kellgren–Lawrence grade II–III) performed repeated on-the-spot stepping trials under two conditions: Active (device control enabled) and Inactive (device control disabled). The assist device tilts the heel toward eversion in response to detected ankle inversion at heel contact. Feasibility outcomes included participant recruitment and completion, safe execution of the stepping task, device activation during trials, successful acquisition and analysis of motion capture data, and occurrence of adverse events. Lateral knee thrust was quantified descriptively using a three-dimensional motion capture system to characterize measurement variability and inform future study design. All participants provided informed consent and completed the stepping protocol (6/6, 100
To compare virtual unenhanced images (VUE) from a prototype deep silicon photon-counting detector computed tomography (PCD CT) system with true unenhanced images (TUE). Five subjects including various anatomic structures were scanned on a prototype deep silicon PCD CT at 120 kV across different phases with mA adjusted for anatomy and subject size. Five thin and three thick slices were selected from each image set at two locations to provide a variety of anatomy, including vessels, soft tissue, and adipose. Regions of interest were drawn over the anatomy on both TUE and VUE images. VUE error was defined as VUE CT number in Hounsfield units (HU) minus the TUE CT number. For all tested structures, 82
The mechanical properties of tumor tissue differ from those of healthy tissue. Therefore, surgeons palpate accessible surgical sites to determine tumor boundaries prior to resection. However, palpation is not possible during minimally invasive surgery, so instrumented palpation is required instead. This study investigates the suitability of an engineering method that combines mechanical object scanning and indentation to determine Young’s modulus of soft, tissue-like materials. To establish a defined reference, we tested our concept on silicone phantoms containing stiff tumor-like inclusions. We used a sensor consisting of a load cell connected to a rigid probe with a spherical indenter tip. Young’s modulus was calculated by measured force, indentation depth, and indenter geometry. These results were compared with those of a palpation experiment on the same specimens, conducted with surgeons. Validation results reflect the accuracy of the method. Error in estimation of Young’s modulus is: soft material 6.7
Continuous cardiac output (CCO) monitoring using pulmonary artery (PA) thermodilution and newly introduced beat-to-beat cardiac output (CO) monitoring technologies exhibits different response time delays. These differences can hinder accurate comparisons of their trending abilities. To address this, we applied moving average processing to the beat-to-beat CO monitor data to evaluate its effect on trending assessment accuracy. This study aimed to confirm the effectiveness of moving average processing for such comparisons. This was a single-center, retrospective, observational study conducted at a 916-bed university hospital. A total of 20 patients undergoing kidney transplantation were included. We analyzed the trending ability of arterial pressure cardiac index (APCI) and estimated continuous cardiac index (esCCI) relative to continuous cardiac index (CCI) derived from PA thermodilution. Trending ability was assessed using a Polar plot and Bland-Altman analyses. A wide range of moving average windows (0–60 min) was applied to APCI and esCCI. The polar concordance rate at 30° exceeded 92
Peroxybenzoic acid and hydroxybenzoic acid are phenolic compounds commonly used in cosmetics and pharmaceuticals that have shown potential as anti-inflammatory agents. We compared their effects on allergic airway inflammation and Th2 cytokine responses in a murine model of ovalbumin-induced allergic asthma in Balb/c mice. Ten Balb/c mice were randomly assigned to four groups: control, asthma, asthma treated with peroxybenzoic acid, and asthma treated with hydroxybenzoic acid. Asthma was induced through intraperitoneal sensitization with ovalbumin. Then, the mice received intranasal instillations of either peroxybenzoic acid or hydroxybenzoic acid. Then, lung tissues were harvested for histological staining (H E, PAS) to assess peribronchial and perivascular inflammatory infiltrates as well as goblet cell hyperplasia. Serum levels of IgE, IL-4, IL-5, and IL-13 were measured using ELISA. Data were analyzed using one-way ANOVA with Tukey’s post-hoc test. Mice treated with hydroxybenzoic acid (the asthma + hydroxybenzoic acid group) showed a significant reduction in both peribronchial and perivascular inflammation. The histopathological scores for this group were markedly lower than those of the untreated asthma group (p < 0.01). Additionally, this group demonstrated a significant decrease in IL-5 levels (p < 0.05), while serum concentrations of IgE, IL-4, and IL-13 remained unchanged (p > 0.05). In contrast, mice in the asthma + peroxybenzoic acid group did not show significant differences in airway inflammation or levels of the measured cytokines and IgE when compared to the untreated asthma group (p > 0.05). In this murine model, hydroxybenzoic acid selectively suppresses eosinophil-driven inflammation and IL-5 production, setting it apart from peroxybenzoic acid and many broad-spectrum anti-inflammatory compounds. These findings support the need for further preclinical and early-phase clinical studies to evaluate hydroxybenzoic acid’s potential as a targeted therapy for allergic asthma.
Stiff-knee gait is a common movement disorder in individuals with stroke; however, standardized criteria for its identification remain lacking. This study aimed to examine suitable criteria for identifying stiff-knee stroke survivors to facilitate comparisons across studies. Twenty-four stroke survivors (45.2±13.7 years old) and 24 age- and sex-matched controls (45.5±13.5 years old) with no known gait impairment participated in this study. Participants walked along a 10-m walkway at a self-selected comfortable speed. A motion capture system recorded the trajectories of retroreflective markers placed on specific body landmarks. The following knee flexion parameters during gait cycle were analyzed: (1) peak knee flexion during the swing period, (2) total range of motion (RoM cycle), calculated as the difference between maximum and minimum knee excursion during gait cycle, (3) RoM from toe-off to peak knee flexion (“RoM swing”), and (4) timing of peak flexion. Comparisons were made among control, paretic, and non-paretic limbs. Among the 21 stroke survivors identified with stiff-knee gait, the paretic limb showed reduced peak swing, RoM swing, and RoM cycle compared to both the control and non-paretic limbs, as well as earlier timing compared to the non-paretic limb only. Among the four examined criteria to identify stiff-knee gait in stroke survivors, the most suitable are peak knee flexion during the swing period of less than 40°, and knee range of motion from toe-off to peak knee flexion of less than 12°.
Healthcare practitioners in low and middle-income countries encounter numerous challenges, including insufficient staffing, an unreliable electrical infrastructure, and constrained resources. Unstable availability of electricity constitutes a significant impediment to the efficacy of public health initiatives that depend on technology requiring electrical power. More than 95
Glaucoma is a leading cause of irreversible blindness, necessitating early and accurate diagnosis to prevent vision loss. Traditional diagnostic methods often suffer from subjectivity and variability, emphasizing the need for more reliable approaches. This study evaluates the application of machine learning (ML) techniques in glaucoma diagnosis, analyzing their effectiveness and identifying the most promising methods and datasets. A systematic review of five major databases was conducted, selecting 35 studies based on predefined criteria. The findings reveal that structured data, including optical coherence tomography (OCT), visual field (VF) tests, and demographic factors, significantly enhance diagnostic accuracy. ML models such as support vector machine (SVM), deep learning (DL), random forest, and ensemble methods demonstrated accuracy ranging from 76 to 98.3%, with AUC values between 52.5% and 99%. Despite these advancements, challenges such as data imbalance and limited sample sizes impact model generalizability. The results highlight the potential of ML to improve glaucoma detection, though further research is needed to enhance data quality and model validation for broader clinical applicability.
Gait kinetics explains dynamics of gait deviations, which inform surgical and non-surgical clinical-decision-making to enhance walking performance of children with cerebral palsy. Kinetic gait profile of children with lesser crouch angle is known; however lower-extremity gait kinetics of ambulatory children at a further continuum of the spectrum with greater crouch angle is unclear. Therefore, present cross-sectional study evaluated influence of varying crouch angle on gait kinetics and walk distance. Following ethical approval and signed informed consent of parents, 3-D gait of 33 ambulatory children with CP(10.4 year) and 31 age-matched typically-developing children was studied to compute the magnitude and timing of lower-extremity external net joint moments and power during stance phase. An average of 3gait trials walked bare-feet at self-selected pace was considered for analyses. Walk distance was measured with 2-min walk test. Typically developing children were classified as Group I, children with mild crouch-angle (mean knee flexion angle during stance) ≥ 16.80and ≤ 250 were classified as Group II(n = 17), whereas children with severe crouch-angle i.e. ≤ 250 throughout stance phase were classified as Group III(n = 16). Three groups were compared with one-way-ANOVA(p ≤ 0.05). Bonferroni adjustment was made for post-hoc analyses (p ≤ 0.01). Gait speed, cadence and 2-minute walk distance decreased from Group I to II to III(p ≤ 0.01). Hip flexion, extension and adduction; knee flexion and ankle dorsiflexion moments were significantly different between three groups(p ≤ 0.01)). Rise in crouch-angle was associated with an increase in peak hip flexion moment and increase in power generated at hip and decrease in power generated at knee and ankle (p ≤ 0.01). The timing of peak hip and knee moments during stance phase also differed across the 3 groups (p ≤ 0.01) indicating a delay in the occurrence of peak hip flexion-extension; abduction-adduction and knee flexion moment with a rise in crouch angle. Present findings inform lower-extremity joint kinetics during gait across the spectrum of mild to severe crouch angle with reference to typically-developing children. Precise knowledge of magnitude and pattern of net joint moments and power along with the timing of moments and decline in walking distance in children with severe crouch, can guide therapeutic interventions to restore the optimum dynamic lever arm function for improved walking performance. CTRI registration no. CTRI/22/12/048524/27/12/2022. Trial registry: CTRI/22/12. Trial registration number: 048524. Trial registration date: 27th December 2022.
Electrocardiography (ECG) is a non-invasive tool used to identify abnormalities in heart rhythm. It is used to evaluate dysfunctions in the electrical system of the heart. It offers a mechanism that does not cause any harm to patients. Being affordable makes it accessible. It provides a comprehensive assessment of the condition of the heart. Although it provides a successful analysis opportunity for arrhythmia detection, it is time-consuming and depends on the clinician's experience. In addition, since the ECG patterns in pediatric patients are different from the ECG patterns in adults, physicians consider it a difficult and complex task. For this reason, a custom dataset of pediatric patients was created in this study. This dataset consists of 1318 abnormal beats and 1403 normal beats. MobileNetv2 transfer learning architecture was used to classify this balanced dataset. However, the stability of the results is a valuable. Therefore, the optimization algorithm that minimizes the loss function and the regularization method that controls the complexity of the model are proposed. In this direction, Proposed Optimization Algorithm V5 and Proposed Regularization Method V5 approaches have been integrated into the MobileNetv2 transfer learning model. The accuracy rates produced in the training and test datasets are 0.9801 and 0.9509, respectively. These results have acceptable improvement and stability compared to the accuracies of 0.9633 and 0.9399 produced by the original MobileNetv2 architecture on the training and test dataset, respectively. However, performance values provide limited information about the generalizability of the model. Therefore, the same processes were repeated on a more complex dataset with 6 categories. As a result of the classification, the accuracy rates for the training and test data sets were obtained as 0.9200% and 0.8975%, respectively. Training was performed under the same conditions as the training performed on 2-category datasets. Therefore, it is normal for the test dataset to experience a decrease of approximately 5%. The results obtained show that generalizations can be made for comprehensive, highly diverse and rich datasets.
