Background and Objective Electrical stimulation (ES) is widely used to promote maturation and functionality in engineered skeletal muscle systems. However, in three-dimensional (3D) muscle-on-a-chip platforms, local electric-field (EF) exposure and mechanical response are strongly influenced by device geometry and material properties, making experimental characterization challenging. This study aimed to develop a multiphysics computational model to characterize EF distribution and passive mechanical deformation in a 3D engineered skeletal muscle-on-a-chip system under externally applied ES. Methods A 3D computational model was developed in COMSOL Multiphysics by coupling Electrostatics and Solid Mechanics. The model included culture medium, biomaterial, PDMS structures, graphite electrodes, and micropillars. Applied voltages from 1 to 25 V were evaluated to quantify EF distribution. Mechanical response was assessed using three constitutive formulations: Linear Elastic, Neo-Hookean, and St. Venant–Kirchhoff. Mesh convergence and sensitivity analyses were performed to evaluate numerical robustness. Results Simulations showed nonuniform EF distribution, with localized field amplification near electrodes, micropillars, and biomaterial–medium interfaces. At 10 V, the maximum EF magnitude reached 4423.51 mV/mm in the culture medium and 1489.33 mV/mm in the biomaterial. Mechanical simulations showed voltage-dependent pillar displacement across all constitutive models. At 25 V under unloaded conditions, pillar displacement ranged from 8.92 to 10.40 µm. Under a passive load condition of 1.35 N/m 2 , displacement increased to 11.15–12.89 µm. Among the evaluated constitutive formulations, the Neo-Hookean model provided a mechanically appropriate representation for hydrogel-like biomaterial behavior. Conclusion This study presents a multiphysics computational model for evaluating EF distribution and passive mechanical deformation in engineered skeletal muscle-on-a-chip systems. The model provides quantitative insight into how voltage, geometry, and material properties influence local electrical and mechanical conditions, supporting microdevice characterization and experimental design.
Background Classical integer-order membrane and bioelectrical circuit models are useful for idealized ion-channel behavior, but they often fail to reproduce the distributed relaxation, power-law impedance, depressed Nyquist arcs, and memory-dependent phase dispersion observed in biological membranes and tissue interfaces. Objectives This study aims to develop a fractional four-element Windkessel-constant phase framework, denoted BioMem-FWK4α, for analog simulation and frequency-domain interpretation of biological ion-channel and membrane-like impedance responses. Design A theoretical, computational, and analog-circuit modeling study was conducted using normalized fractional circuit parameters. The study is intended as a mechanistic modeling framework rather than as a direct experimental validation study. Methods The classical four-element Windkessel topology was reformulated by replacing ideal compliance with a constant-phase element of order α and by introducing fractional inertial behavior of order β and an active/passive resistance ratio γ. Caputo and Caputo-Fabrizio formulations were compared under explicit zero-initial frequency-domain assumptions. The model was analyzed through transfer functions, Bode plots, Nyquist loci, Nichols diagrams, RC-ladder approximation, sensitivity indicators, and biological interpretation tables. Results The model predicts non-integer gain slopes approaching -20α dB per decade, phase plateaus near -90α degrees, and depressed Nyquist arcs whose depression increases as α decreases. The γ parameter controls the transition from passive leakage-dominated filtering to active or mixed active-passive regimes, while β modifies delayed ionic or kinetic effects. The RC-ladder approximation supports the physical realizability of the fractional element, although biological validation requires future fitting against patch-clamp and bioimpedance datasets. Conclusion BioMem-FWK4α provides a compact and interpretable circuit framework for describing memory-rich membrane impedance and ion-channel-like dynamics. It does not replace Hodgkin-Huxley or Markov-state models for detailed nonlinear gating, but complements them by offering a reduced-order frequency-domain and analog-realizable representation of distributed membrane memory. Future work should validate the model using time-resolved patch-clamp, tissue bioimpedance, and voltage-dependent nonlinear parameter identification.
Background Automated facial wrinkle detection is relevant to facial-ageing assessment, cosmetic analysis, dermatological screening, and personalized skincare. However, wrinkles are thin, low-contrast, and spatially irregular structures whose appearance may be affected by illumination, pose, skin texture, age, image quality, and demographic characteristics. Objective This study proposes FWD-DHRN-HSWOA, a facial wrinkle-detection framework combining attentional image enhancement, high-resolution feature representation, and metaheuristic parameter optimization. Methods Facial images were obtained from the FG-NET Aging Database and supplemented with study-specific wrinkle annotations because wrinkle labels are not native to FG-NET. Images were aligned, resized, normalized, and enhanced using Deep Attentional Guided Image Filtering. A Dynamic Lightweight High-Resolution Network was then used to preserve fine spatial information during wrinkle localization. Harbor Seal Whiskers Optimization was applied as an outer-loop procedure for tuning selected model parameters. The proposed method and implemented baselines were evaluated using the same data partitions, preprocessing conditions, and performance metrics. Results Within the evaluated FG-NET-derived annotation setting, the proposed framework produced higher observed accuracy, precision, recall, F1-score, and specificity and lower RMSE than the implemented comparison models. These results reflect performance under the reported experimental setting and should not be interpreted as evidence of equivalent performance across unrepresented demographic groups or dedicated clinical wrinkle datasets. Conclusion Combining attentional preprocessing, high-resolution feature extraction, and metaheuristic optimization shows promise for fine facial wrinkle localization. Nevertheless, external validation on dedicated wrinkle datasets with verified age, ethnicity, skin-tone, and acquisition-condition diversity is necessary before broad deployment.
Background Diagnosis of elbow instability in clinical practice commonly relies on manual stress testing by the physician with or without combination of fluoroscopy or ultrasound, which may result in variable force application and subjective interpretation. A standardized method for applying controlled mechanical loading during dynamic imaging may improve reproducibility. Objective The objective of this study was to develop and perform initial cadaveric feasibility testing of a CT-compatible diagnostic device for device-guided assessment of elbow instability with simultaneous force and positional measurement. Design Technical development study with initial cadaveric feasibility testing. Methods Building upon the initial prototype the present paper, describes the development of MELBO the follow-up prototype with adjusted kinematics and improved stability, the validation of the force sensor is addressed, and the first cadaver test results are presented. Results The MELBO diagnostic tool enabled a controlled device-guided elbow motion during 4D-CT acquisition and allowed simultaneous recording of force and positional data. The acquired dynamic CT datasets were suitable for qualitative assessment of elbow joint stability. No relevant device-related imaging artifacts were observed. Conclusion The present study demonstrates the technical feasibility of device-guided simultaneous force and positional measurement during 4D-CT using the MELBO diagnostic tool. These findings provide the basis for further cadaveric studies and future clinical investigations aimed at quantitative assessment of elbow instability under standardized loading conditions.
Objectives We developed and evaluated a lightweight, interpretable, and computationally efficient hybrid deep learning model for multiclass classification of early lung cancer in low-dose CT scans, potentially deployable in resource-limited healthcare environments. Methods A novel hybrid architecture was developed that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks, Temporal Convolutional Networks (TCNs), Efficient Channel Attention (ECA) blocks, and Local Interpretable Model-Agnostic Explanations (LIME). The model employed depthwise separable convolutions to reduce computational complexity. A multi-stream feature extraction framework was implemented to enhance interpretability and capture spatial-temporal patterns. The model was trained and validated on the IQ-OTH/NCCD dataset (version 2), containing 3,609 CT scan slices from 110 patients (1,097 original, 2,512 augmented), across three classes: normal, benign, and malignant. The dataset was split into training (60%), validation (30%), and testing (10%) subsets. Training was conducted using the AdamW optimizer for 16 epochs. Results The model achieved 98.06% accuracy, 98.15% precision, 98.06% recall, and 98.04% F1-score, with a 99.88% AUC, a model size of 3.33 MB, and 279,561 parameters. Conclusion The lightweight model achieves high diagnostic accuracy with computational efficiency, SHAP-based and LIME-based interpretability methods, enabling potential suitability for deployment in resource-constrained clinical settings.
