Background: Measurements of body-composition changes are important for assessing patients' nutritional status and the progression of diseases such as sarcopenia, cachexia, atrophy, and cancer. Ultrasound imaging is the preferred technique for direct body-composition measurements owing to its accessibility, ease of use, and non-invasiveness. Typically, ultrasound imaging techniques use echogenicity changes to identify the interface between different tissue layers, resulting in time-consuming measurements with accuracies that depend on the ultrasound image quality. Radio frequency (RF) signals obtained directly from an ultrasound system ensure the reproducibility of measurements. Purpose: This study proposes a two-dimensional ultrasound signal-processing technique for measuring body composition. Basic procedure: Backscattered RF signals were acquired from the forearms of human subjects using an ultrasound system. The interfaces of subcutaneous fat, muscle, and bone were identified using the proposed signal processing technique for RF signals and ultrasound imaging. The longitudinal-sectional areas of the subcutaneous fat and muscle measured using ultrasound signals were validated by comparison with those measured using ultrasound imaging. Main findings: The results of the proposed technique and ultrasound imaging exhibited strong linear correlation. The area correlations between the proposed technique and ultrasound imaging were 0.937 and 0.991 for subcutaneous fat and muscle, respectively. Conclusions: The proposed technique demonstrates feasibility of clinical applications by performing an in vivo human study. (c) 2026 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Context: Surgical work-related musculoskeletal disorders (WRMSDs) has mostly been investigated using qualitative observational assessments and questionnaires, as well as quantitative marker-based motion analysis. The main limitations of these methods are the subjectivity of questionnaires and the impracticality of markers in operating room. This study investigates the feasibility of predicting ergonomic level and surgical expertise level using quantitative postural metrics. Material and method: Sixty orthopedic surgeons with three expertise levels were recorded without markers using two cameras, frontal and sagittal views. Human pose was extracted from the 120 videos using OpenPose combined with a custom post-processing pipeline. The ergonomic level was assessed by two expert surgeons using a 5-point Likert scale. A large set of quantitative postural metrics, designed with expert surgeons, was computed and used to predict both ergonomic level and surgical expertise level using Random Forest and XGBoost Tree. Results: The best performance for surgical expertise prediction was achieved using Random Forest model, with a balanced accuracy of 48%. For ergonomic level prediction, the lowest error was obtained with the Random Forest model, with a RMSE of 0.77. The most informative postural metrics for predictions were primarily related to upper-limb joints.
Objective: The integration of multi-omics data to uncover the biological mechanisms of human diseases remains a significant challenge in bioinformatics. While deep learning (DL) has emerged as a powerful tool for this task, current methods often fail to model the complex correlations among features and samples, limiting both predictive performance and interpretability. Methods: To address this, we propose MOFRCDLANet (Multi-Omics Feature Reordering Correlations Deep Attention Network), a novel framework for predicting tumour recurrence and identifying biomarkers. Our model introduces a feature reordering strategy to prioritise prognostically relevant features. It then employs a self-attention module coupled with Maximum Mean Discrepancy (MMD) and contrastive regularisation to learn robust latent representations that capture cross-sample relationships and align feature distributions across omics types. Finally, an attribution-based method identifies key biomarkers, providing biological insight into the model's predictions. Results: Extensive experiments on ten TCGA cancer datasets demonstrate that MOFRCDLANet outperforms state-of-the-art methods across key metrics, including accuracy and AUC. The top genes identified by the model were biologically validated through enrichment analyses (KEGG and GO), confirming their relevance to cancer pathways and reinforcing the framework's efficacy. Conclusion: MOFRCDLANet provides a robust, interpretable solution for multi-omics integration, advancing both the predictive accuracy and mechanistic understanding of cancer progression. This work offers a valuable tool for precision oncology, enabling improved prognostic stratification and biomarker discovery. (c) 2026 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Background: Objective assessment of lower-limb muscular strength typically requires maximal-effort testing or laboratory-based equipment, limiting practicality in routine exercise and rehabilitation settings. Wearable exoskeletons provide controlled resistance and quantitative biomechanical monitoring, offering a potential platform for structured strength evaluation. This study proposes an interpretable framework for estimating lower-limb strength using joint torque and surface electromyography (sEMG) metrics obtained during exoskeleton-assisted exercise. Methods: Thirty healthy adults completed conventional strength assessments to construct a composite reference index. Participants then performed guided squat, knee-up, and lunge exercises using a hip-joint exoskeleton. Torque and sEMG signals were used to derive execution-based performance metrics. Decision-tree models were developed for three-level strength classification, and regression models were constructed for quantitative strength estimation. Results: Metrics reflecting exercise execution consistency, particularly guided exercise pace, emerged as the most influential indicators of strength. The expert rule achieved precision up to 0.95 for strength classification, and regression models showed strong association with the composite strength index (maximum r = 0.86, p < 0.001 ). Conclusion: The proposed framework enables interpretable classification and quantitative estimation of lower-limb strength using standardized wearable exercise. Integration of torque and sEMG-derived metrics supports practical and data-driven strength assessment without reliance on maximal-effort testing. (c) 2026 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Background: The metabolic cost of walking reflects the energy required to move the body over a given distance. Standard measurement methods require prolonged exertion from participants, which is not always feasible. Estimating instantaneous metabolic cost is valuable for real-time control of assistive devices such as exoskeletons or prostheses. This can be achieved by computing mechanical work and applying separate efficiencies to positive and negative work. Objective: This study aimed to compute and compare positive and negative work efficiencies using four different methods. A second objective was to evaluate how well metabolic cost, estimated from joint mechanical work weighted by these efficiencies, matched measured values. Method: Eleven participants walked on slopes of +/- 24%, +/- 12%, +/- 8% and level. The +/- 24% slopes were used to represent conditions of predominantly positive (ascent) and negative (descent) work. Mechanical work and efficiencies were calculated using four methods: potential energy (PE), combined limb (CLM), individual limb (ILM), and summed joint (& sum;joints). Metabolic costs estimated from summed joint work, adjusted by the efficiency pairs, were then compared to measured metabolic costs on intermediate slopes. Results: Results showed that efficiencies - particularly for negative work - depended on the mechanical work calculation method (negative efficiency range: -0.91 to -1.08). Despite this, all methods showed strong correlations with measured metabolic cost (r >= 0.97). Moreover, positive efficiency had a greater influence on the estimated metabolic cost than negative efficiency. Discussion: Efficiencies derived from the CLM, ILM, and & sum;joints methods more accurately captured changes between conditions than those obtained from the PE method, suggesting these methods are preferable. Future studies should assess their relevance in other contexts, such as load carriage or speed variations. (c) 2026 AGBM. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Background and Objective: Major Depressive Disorder (MDD) affects a wide range of populations and causes significant harm to individuals and society. Hence, early recognition of MDD is crucial. MDD recognition using wearable electroencephalographic (EEG) devices has gained significant attention, with reliable and effective classification algorithms central to its success. Methods: Herein, an end-to-end framework named an end-to-end shuttle neural network (ESNN), is proposed for efficient recognition of depression on multichannel EEG signals. The ESNN comprises three parts: i) a multiscale saliency-encoded spectrogram that effectively captures time-frequency information from multichannel EEG signals; ii) TSUnet, a two-stream temporal spectrogram U-Net incorporating the crossmodule attention to redistribute feature weights and enhance critical information; and iii) a crosschannel-wise block to integrate time-frequency features from the two-stream network. Results: Two public EEG datasets [the Hospital Universiti Sains Malaysia (HUSM) and MODMA)] and one private EEG dataset [Zhongda Hospital, Southeast University (ZHSU)] were used to confirm the model's performance. The leave-one-subject-out validation experiment was conducted to ensure subject independence. Our proposed ESNN achieved accuracies of 98.70% and 86.36% on HUSM and MODMA datasets, respectively. On ZHSU dataset, the framework remarkably performed with 83.85% accuracy. Conclusion: The results verified that different scale features could be adequately captured by branch processing and fusion of time-frequency information. Ablation experiments also suggested that the proposed crossmodule attention and channel-wise block effectively focused significant information, suggesting that this model could potentially recognize depression in a real-world scenario. Our model exhibits the potential for application as a clinical decision support tool. By assisting physicians in diagnosis, it contributes to the conservation of healthcare resources. The code is provided in: https:// github.com/zf703/ESNN. (c) 2026 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Background and objective: Sprinting-induced fatigue significantly compromises neuromuscular performance and elevates the risk of injury. Accurate monitoring of muscle fatigue is essential for designing personalized training and rehabilitation strategies. This study presents a novel method that integrates hybrid feature extraction with interpretable machine learning to assess fatigue during dynamic contraction. Methods: In this work, time-frequency methods, namely Stockwell Transform (S-transform), B-Distribution (BD), and Extended Modified B-Distribution (EMBD), were applied to distinguish dynamic muscle fatigue states. Surface electromyography (sEMG) signals were recorded from the lower limb muscles of 14 healthy collegiate athletes during sprinting. The non-fatigue, fatigue progression, and fatigue segments of the signals were preprocessed and analyzed using these methods. From each method, thirteen features were extracted, and prominent features were selected using Genetic Algorithm (GA) and Principal Component Analysis (PCA). Classification of fatigue states was performed using four machine learning algorithms: Decision Tree, Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN). Furthermore, spectral features such as mean frequency and median frequency were analyzed to compare fatigue across different muscles. Results: The results demonstrate that fatigue is characterized by a progressive decline in median frequency (MDF) and mean frequency (MNF) over time. The gastrocnemius lateral head exhibited the steepest decrease in both MDF and MNF, indicating a higher susceptibility to fatigue during dynamic contractions, followed by the gastrocnemius medialis head. Comparative analysis of spectral features across sEMG segments revealed that the transition from fatigue progression to established fatigue occurred more rapidly than the shift from non-fatigue to fatigue progression. Classifier performance evaluation showed that the Random Forest model achieved the highest accuracy of 96.62% using features selected by the Genetic Algorithm (GA), outperforming models trained on Principal Component Analysis (PCA)-selected features (90.79%) and all features combined (92.83%). In contrast, the Support Vector Machine (SVM) classifier recorded the lowest accuracy at 66%. Conclusions: The proposed method effectively detects dynamic muscle fatigue and shows strong potential for integration into real-time fatigue monitoring for wearable systems. (c) 2026 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Objectives: Breast cancer remains one of the most prevalent and life-threatening malignancies worldwide, wherein accurate and early diagnosis plays a pivotal role in improving patient outcomes. The screening is commonly done by traditional means, which tend to be time-consuming and labor-intensive. The detection relies on expert review, sometimes leading to issues of subjectivity, delayed diagnosis, and treatment. Methods: This study presents a novel Deep Learning (DL) approach, designed to improve texture-structure understanding for breast cancer classification. In the proposed approach, Res-MorphNet synergistically combines ResNet with a Morphological Texture Encoding (MTE) module to extract intricate morphologic textures, while Swin-CPSANet leverages the Swin Transformer and a Cross-Patch Spatial Aggregation (CPSA) block to enhance global context understanding and feature interaction. To the best of our knowledge, this is the first attempt to integrate morphological texture encoding with cross-patch attention within a unified CNN-Transformer fusion framework for histopathological breast cancer classification. Results: Evaluation on the BACH dataset indicates that the proposed model achieves an accuracy of 95%, a precision of 95.49%, a recall of 95%, and an F1 score of 95.05%. These results outperform traditional CNN-and transformer-based baseline models, demonstrating the effectiveness of combining morphological texture encoding with cross-patch attention in a unified framework. Conclusion: The findings indicate that the proposed architecture achieved efficient and well-balanced classification performance across multiple breast tissue classes, underscoring its potential as a valuable tool to assist in clinical diagnosis. (c) 2026 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
1) Objectives: Early and, accurate detection of lung cancer from histopathological images is time-consuming and often requires additional staining, leading to delayed diagnosis. This study aims to develop an automated and optimized framework for reliable lung cancer detection from H&E-stained histopathological images using advanced segmentation, feature optimization, and classification techniques. 2) Materials and Methods: The proposed framework begins with image preprocessing using an adaptive median filter to suppliess noise while preserving structural details. A novel Hybrid Simple Linear Iterative Clustering K-Means Fuzzy C Means (SLIC-KM-FCM) based segmentation approach is employed to extract diagnostically relevant regions. Feature dimensionality reduction is performed using the following nature-inspired optimization algorithms, namely Whale Optimization Algorithm (WOA) and Harmony Search Optimization Algorithm (HSOA). The most discriminative features are selected using Monkey Search Algorithm (MSA) and T-statistics (T-Stat). These selected features are provided as input to multiple classifiers, including SVM, KNN, RF, DT, SDC, MLP, and BLDC. Classifier performance is evaluated using standard metrics, both with and without hyper-parameter tuning using Grid Search (GS) and Stochastic Gradient Descent (SGD). 3) Results: Without hyper-parameter tuning, the SVM classifier combined with WOA-based feature extraction and MSA-baked feature selection achieved an accuracy of 87.50%. The application of hyper-parameter optimization significantly improved classification performance. The highest accuracy of 93.75% was obtained using the BLDC classifier with HSOA-based feature extraction, T-Stat feature selection, and Grid Search optimization. 4) Conclusion: The proposed hybrid optimization-driven framework effectively improves lung cancer classification from histopathological images. The integration of advanced segmentation, nature-inspired feature optimization, and hyper-parameter tuning demonstrates strong potential for developing robust computer-aided diagnostic systems in digital pathology. 2026 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining. Al training, and similar technologies.
Background: Inertial motion capture systems like Xsens Awinda are increasingly used for assessing movement in clinical populations. However, it remains unclear how stroke-related motor impairments affect the quality of motion capture calibration, which is critical for obtaining reliable data. Research question: Does motor impairment severity in stroke patients influence calibration quality when using the Xsens Awinda system? Methods: Forty-eight individuals with a primary stroke (median age 66 years; 21 female; FAC >= 3) performed a total of 117 motion capture assessments using the Xsens Awinda system. Calibration quality was rated using Xsens' internal quality metrics. Kruskal-Wallis tests were conducted to compare functional motor abilities, assessed using the BOOMER test and 10-meter walk time, across calibration quality levels. Results: Of the 109 valid trials, 55% achieved good, 29% acceptable, and 16% poor calibration quality. Neither the BOOMER scores nor the 10-meter walk time was significantly associated with calibration quality levels. Significance: These findings suggest that the Xsens Awinda system can be used across a range of motor impairment levels without introducing bias due to changes in calibration quality. Nonetheless, calibration may be challenging for severely affected individuals, a limitation that warrants further practical refinement of current procedures. (c) 2026 AGBM. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
The meniscus plays a vital role in knee biomechanics, contributing to shock absorption, joint stability, proprioception, and lubrication. Anterior cruciate ligament reconstruction (ACLR) aims to restore knee stability after anterior cruciate ligament (ACL) injury; however, 30-60% of patients experience concurrent or subsequent meniscal damage. Despite this, the influence of ACLR surgical parameters on meniscal biomechanics remains largely unexplored. This study investigates how four key ACLR surgical parameters-graft type, graft size, tunnel location, and pre-tensioning-affect meniscal contact forces and stress using a coupled neuromusculoskeletal-finite element (NMSKFE) modeling approach during simulated walking. NMSK-FE simulations were conducted in six participants to assess changes in meniscal-tibial contact forces and stress distributions under various ACLR configurations. While most surgical conditions restored meniscal mechanics to near-intact levels (normalized root mean square error (nRMSE) < 10%), substantial deviations were observed in certain cases, particularly in anteroposterior and mediolateral contact forces and maximum principal stress (nRMSE > 10%). Notably, posterior graft placement with zero pre-tensioning increased medial meniscus stress, potentially elevating the risk of degeneration or injury. These findings highlight the individualized nature of ACLR outcomes, influenced not only by surgical parameters but also by patient-specific factors such as knee morphology and gait patterns. This study underscores the need for pre-surgical assessments that integrate patientspecific biomechanics to optimize ACLR strategies, enhance meniscal preservation, and improve long-term knee health. By incorporating meniscal mechanics and dynamic gait analysis, this research advances personalized ACLR approaches, addressing a critical gap in the field. (c) 2026 AGBM. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
BACKGROUNDS: Vascular calcification (VC) is an actively regulated dynamic process characterizing by abnormal deposition of calcium phosphate mineral in the extracellular matrix and in cells of the arterial wall. Significant advances have been made in comprehending the ferroptosis linked to VC, yet the precise molecular mechanism is still not fully understood. Interpretability and explainability of machine learning models are crucial for incorporating them into decision-making processes. We used the Shapley additive explanation (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) methods in this study to interpret and explain a random forest model in order to discover the significant attributes. METHODS: This paper employed the GEO tools to get a VC dataset. The DEGs were discovered using the EdgeR package in R to identify potential ferroptosis-associated hub genes that could be used for VC diagnosis. We used qRT-PCR and western blotting techniques to confirm the DEGs associated with ferroptosis that were discovered in the microarray data. Finally, we suggest two innovative strategies, using SHAP and LIME, to enhance interpretation. We evaluated the explanatory outcomes of the SHAP scheme with other approaches using GEO datasets. RESULTS: We uncovered 49 ferroptosis DEGs in VC, including 31 upregulated and 18 downregulated genes. The outputs obtained from the GSEA and the study of the KEGG using WebGestalt revealed that the differentially expressed genes (DEGs) related to ferroptosis are found to be involved in six paths, one of which was the Ferroptosis signaling pathway. SHAP and LIME interpretation aligned well with the interpretations provided by the current methodologies. We demonstrated the significance of TP63 and GPX2 as crucial predictive factors for VC using of suggested methodologies. Lastly, we examined the three genes identified by two machine learning models in vitro and observed that the mRNA and protein profile levels of FTH1 exhibited an elevated level and the levels of SLC3A2 and SLC7A11 exhibited a reduced level in the j3-GP-treated class in comparison to the normal class. The nomogram and 5 potential hub genes exhibited excellent predictive performance, with AUC values ranging from 0.724 to 0.969. CONCLUSIONS: Our investigation found three ferroptosis-associated potential hub genes by comprehensive exploration (FTH1, SLC3A2, and SLC7A11). In addition, we created a nomogram for VC diagnosis utilising bioinformatics and machine learning approaches (SHAP and LIME). Our methods are effective for analyzing machine learning models and may reveal the fundamental connections among variables and outputs. (c) 2025 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Context: The imminent performance of multimodal and heterogeneous modalities to forecast the advance of Alzheimer's disease (AD) precisely is one of the key problems. Current models are usually not interpretable, time-consistent, and multimodal, making them less useful in clinical forecasting. Objective: The objective of the study is to develop a hybrid generative approach to simulate the individualized AD progression process, which can generate future anatomical and clinical states, model latent over-time dynamics, and measure the uncertainty. Methods: The proposed study suggests using a multimodal paradigm that enables a combination of Conditional Latent Diffusion Models (cLDM) and Neural Ordinary Differential Equations (ODEs). The model permits the generation of plausible future MRI, cognitive scoring, and biomarker trajectories for a patient at baseline. The ADNI dataset was evaluated with structural similarity (SSIM), clinical prediction error, and classification accuracy. Key Findings: The model provided an SSIM equal to 0.86 on synthesizing future MRI, and the MAE of MMSE prediction was equal to 1.5. It exceeded baselines in all the imaging, cognitive, and biomarker settings. The conversion of AD resulted in an accuracy of the classification of 88% with stable multimodal generalization at calibrated output of probability. Conclusion: The proposed model offers a feasible and explainable approach to the forecast of an AD trajectory, allowing realistic simulations of a digital twin and projecting its progress within a multiyear perspective. It also supports early detection, custom intervention, and uncertainty-conscious clinical decision-making. (c) 2025 AGBM. Published by Elsevier Masson SAS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction: Visual impairment can significantly affect psychological and physiological well-being, potentially due to autonomic imbalance, and while deep breathing has been shown to improve autonomic modulation as measured by heart rate variability (HRV), its impact on individuals with visual impairment remains underexplored, prompting this study to investigate its immediate and long-term effects on HRV in this population compared to normally sighted individuals. Materials and methods: A total of 98 participants with visually impaired (VI) individuals (n = 49) and normally sighted (NS) individuals (n = 49) were recruited. The HRV, including standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences, normalized low frequency (nLF), normalized high frequency (nHF), and low frequency to high-frequency ratio (LF/HF), was measured at baseline (BL), immediate post intervention (IPI) and post-intervention (POST) after 2 weeks daily audio-guided deep breathing. Results: Kruskal-Wallis tests revealed significant phase effects for nLF (p = 0.002), nHF (p = 0.002), and LF/HF (p = 0.024) in the VI group, with post hoc analyses indicating significantly higher nLF (p = 0.004), LF/HF (p = 0.007), and lower nHF (p = 0.004) at IPI compared to BL. While the NS group showed no significant changes across phases. Between-group comparisons revealed significantly higher nLF (p = 0.034), LF/HF (p = 0.007), and lower nHF (p = 0.034) at IPI in the VI group compared to the NS group. Conclusion: Deep breathing led to immediate increases in nLF and LF/HF, and a decrease in nHF, in individuals with visual impairment compared to sighted individuals, suggesting baroreflex resonance at 0.1 Hz. However, the absence of significant SDNN changes limits conclusions about parasympathetic modulation. Further research is needed to assess the potential long-term benefits. (c) 2025 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Background: Proximal femoral osteotomy (PFO) is a surgical correction of proximal femoral deformity. Surgical choices, notably the postoperative neck-shaft angle (NSA), can affect postoperative stability and healing. While NSA's role in femoral mechanics is recognized, its impact on bone healing remains unclear. Objective: To determine the influence of postoperative NSA on bone healing; to investigate the interaction of healing-related parameters and mechanical safety. Methods: Medical imaging, gait data, and surgical information from nine patients (10 femurs) were used to build personalized finite element models of PFO-implanted femurs. Three postoperative neck-shaft angles (128 degrees, 135 degrees, 143 degrees) were tested. During simulated walking, interfragmentary movement, deviatoric strain, mechanical stimulus, bone-implant micromotion, and peak von Mises stress (PVMS) were evaluated. Healing mode (primary vs. secondary) was classified based on interfragmentary movement thresholds. Results: Mono-modal healing (primary in four and secondary in three) was observed in seven femurs, independent of postoperative NSA. In three femurs, a transition from primary to secondary healing occurred with increased NSAs. The PVMS for the implant and the bone exceeded critical values across all NSAs for two femurs, and micromotion was deemed critical only at 128 degrees in two femurs. Conclusion: This study highlights the value of integrating patient-specific modelling into preoperative planning. Bone healing modes were sensitive to postoperative NSA in 30% of cases, while 70% exhibited a single healing mode across the tested angles. Overall, findings suggest the need to simultaneously consider the complex interaction between NSA and subject-specific factors on mechanical safety and healing outcomes following PFO. (c) 2025 AGBM. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Introduction Acute appendicitis is the commonest cause of surgical abdominal pain, yet diagnosis in children remains challenging; delays increase the risk of perforation, peritonitis and sepsis. We sought to develop a rapid, inexpensive and interpretable clinical-decision support system (CDSS) that leverages routine blood tests (RBT) to assist early paediatric triage. Materials and Methods In this retrospective single-centre study (January 2020-December 2024) we analysed 275 emergency-department encounters for abdominal pain (75 histology-confirmed appendicitis, 200 controls). The six-stage pipeline comprised (1) cohort selection; (2) exploratory logistic-regression screening of RBT variables; (3) training of Random Forest, Gradient Boosting and LightGBM ensembles (with/without SMOTE) under 10 x 10 stratified cross-validation; (4) SHAP-based feature interpretation; (5) exhaustive generation of every two- and three-parameter arithmetic biomarker from seven RBT features; and (6) derivation of probability-threshold curves and a three-zone rule tree for the top biomarker. Performance was reported with accuracy (ACC), Matthews correlation coefficient (MCC), AUC-ROC, sensitivity, specificity, F1-Score PPV and NPV. Results Logistic regression and SHAP confirmed CRP, WBC and neutrophil count as strong positive predictors, whereas MPV and PDW were protective; PLT remained non-informative. All three ensemble classifiers surpassed 97% accuracy, 98% AUC-ROC and 0.93 MCC, with no gain from SMOTE. An extensive formula search, the best two-parameter marker was Neutrophil divided by PDW (MCC = 0.73, specificity 95%). Its ensemble curve crosses P = 0.5 five times; practical cut-offs of < 0.633 (strongly indicate healthy) and > 0.794 (strongly indicate appendicitis) retain high NPV (similar to 91%) and PPV (similar to 86%). Among triple formulas that do not rely on PLT, the leading biomarker was CRP+WBC+Neutrophil (MCC = 0.85, PPV 92%, NPV 95%). The ensemble curve intersects at P = 0.5 at three points; values >27 strongly predict appendicitis, <23 indicates a healthy state, and values 23-27 leave a small uncertain band. A rule-based CDSS built on these two biomarkers correctly classified all controls (specificity 100%), sensitivity 95%, achieved 91% overall accuracy, and offers interpretable, electronic health records (EHRs)-ready cut-offs for paediatric appendicitis triage. Conclusion Routine haematology-biochemistry data, interpreted through ensemble learning and engineered biomarkers, can deliver fast, transparent and highly accurate support for paediatric appendicitis triage. Given its zero false-positive rate, the proposed CDSS is best suited to in-hospital monitoring, where minimising false negatives is critical. Prospective multi-centre validation is warranted. (c) 2025 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Background: Palpation is the most widely used approach to empirically assess the mechanical properties of superficial tissues. While elastography is used for volume measurements, it remains difficult to assess skin properties with non-invasive methods. This study aimed to compare the performances of an impact-based analysis method (IBAM) consisting in studying the dynamic response of a punch in contact with the tissue with other approaches available on the market. Materials and methods: IBAM consists in analyzing the time dependent force signal induced when a hammer instrumented with a force sensor impacts a cylindrical punch placed in contact with soft tissue. Sensitivities to stiffness changes and to spatial variations were compared between IBAM and four other mechanical surface characterization techniques: IndentoPro (R) (macroindentation), Cutometer (R) (suction), MyotonPro (R) (damped oscillation) and Shore Durometer (durometry) using soft tissue phantoms based on polyurethane gel. Results: For stiffness discrimination in homogeneous phantoms, IBAM was slightly better than IndentoPro and MyotonPro (by 20% and 35% respectively), and outperformed the Shore Durometer and Cutometer by a factor of 2 to 4. Furthermore, for stiffness and thickness variations in bilayer phantoms, the axial sensitivity of IBAM was between 2.5 and 4.5 times better than that of MyotonPro and IndentoPro. In addition, the Cutometer appeared to be severely limited by its measurement depth. Conclusion: IBAM seems to be a promising technique for characterizing the mechanical properties of soft tissue phantoms at relatively low depth. These results will need to be confirmed in future in vivo measurements on biological tissues. This work could pave the way to the development of a decision support system in the field of dermatology and cosmetics. However, its clinical applicability remains to be demonstrated ex vivo and in vivo. (c) 2026 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Objectives: Currently, the response to chemotherapeutic treatment for most solid tumors is assessed using the surgical specimen obtained after the tumor is surgically removed, following a whole chemotherapy cycle (e.g. 8 weeks). Therefore, early detection of tumor response is of paramount importance. Parameters derived from the backscatter coefficient (BSC) and envelope statistics provide information on tissue microstructure and may therefore be of interest for monitoring therapies that induce morphological changes in the tumor. In this study, our objective was to detect ex vivo early response to chemotherapy in ex vivo murine osteosarcoma model. Material an Methods: BSC-derived parameters using Lizzi-Feleppa approach and the Gaussian model and envelop statistics parameters using Nakagami and Homodyned-K distributions were extracted on control and treated tumors. Tumors received either 2, 4, or 5 doses of chemotherapy. To investigate the underlying causes of changes in ultrasound parameters, histological and molecular analyses (RNA sequencing) were conducted. Results: Although the tumor models show resistance to chemotherapy as evidenced by continued tumor growth at the therapeutic dose used, significant differences between treated and control tumors were observed in several BSC-derived and envelope statistics parameters depending on the number of treatments received. Conclusion: These differences might reflect early molecular changes occurring before the establishment of chemoresistance mechanisms. They might be attributed to morphological changes linked to the underexpression of genes involved in chromatin condensation and/or collagen within the extracellular matrix. These initial findings require further investigation in a larger cohort. (c) 2025 AGBM. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
This study aims to evaluate stress distributions in the medial gastrocnemius muscle (GM) of patients with spastic myopathy after stroke. Shear wave elastography was employed to measure the shear modulus in three specific regions (upper, middle, and lower) of the muscle in six participants (three healthy and three post-stroke). Shear modulus measurements served as inputs for a finite element model to estimate stress distributions during uniform muscle stretching. The skeletal muscle was modeled as a hyperelastic, incompressible, and inhomogeneous material. The results showed that the stress distribution tends to increase in the post-stroke group, particularly in the middle (+60%) and lower regions (+13%). These results demonstrate the feasibility of estimating stress distributions using SWE data in post-stroke conditions, highlighting potential for further optimization of both experimental protocols and numerical models. These advancements could ultimately provide valuable insights into the clinical challenges associated with understanding spastic myopathy pathologies. (c) 2025 AGBM. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Background & Aim: As falls are common during gait in older adults, investigating the factors contributing to stable gait has gained growing interest. In this context, the contribution of visual, somatosensory, and vestibular systems (i.e., sensory integration) to gait has been studied for years, albeit primarily as individual systems. Although an earlier attempt was made to develop a test to comprehensively assess the sensory integration during gait, this effort encountered certain limitations that impacted its overall effectiveness. Thus, this study aims to develop a new test to evaluate sensory integration during gait, called the "Sensory Integration in Walking (SensIWalk) Test" and assess its validity and reliability in both young and older adults. Methods: This study is planned as an observational study. Younger (n=24, 18-35 years old) and older adults (n=24, >= 65 years old) will be invited to participate, and all measurements will be performed at the Computer Assisted Rehabilitation Environment (CAREN, Motek Medical BV, Amsterdam, The Netherlands). SensIWalk, adapted from the framework of the Clinical Test of Sensory Interaction and Balance (CTSIB) with the same six conditions, will be modified to accommodate locomotion. The conditions of SensIWalk will be as follows: 1) Walking at preferred speed on a firm surface (i.e., on the treadmill) with eyes open, 2) Walking at preferred speed on a firm surface in the dark (1.3 lux), 3) Walking at preferred speed on a firm surface with sways of the virtual reality (VR) environment (i.e., visual conflict), 4) Walking at preferred speed with foam insoles (2 cm thick) with eyes open, 5) Walking at preferred speed with foam insoles in the dark, 6) Walking at preferred speed with foam insoles with sways of the VR environment. Discussion: This study will allow delving into the underlying sensory mechanisms explaining suboptimal balance during walking by assessing the effects of sensory strategies on movement patterns. This may provide a deeper insight into the underlying mechanisms of falls in older adults, which could foster novel training or rehabilitation paradigms to decrease the risk of falls in older adults. (c) 2025 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
