Background/Objectives: Electrical threshold testing (ETT) offers a promising method for assessing somatosensory function. Despite its growing use, fundamental aspects such as the physiological recovery time required between repeated threshold measurements remain poorly understood. This gap is critical when evaluating sensory, motor, or pain thresholds (EST, EMT, EPT) in pre–post designs or rapid intra-session protocols. The aim is to investigate the short-term recovery dynamics of electrical thresholds following electrical threshold testing, and to determine the minimum interval required for values to return to a stable baseline. Methods: In this pilot, repeated-measures study, 10 healthy adults (20 upper limbs) underwent three progressive stimulation trials (sensory, motor, and pain). Electrical thresholds were assessed at fixed recovery intervals (0–120 s), with duplicate measurements at each time point. Stability was defined as the absence of significant differences between repeated measures. Results: EST stabilized rapidly after sensory or motor stimulation, showing no significant differences beyond 0 and 15 s, respectively. Within pain stimulation, EST recovered at 60 s. EMT showed immediate recovery with motor stimulation and required longer recovery with pain stimulation, with stabilization observed at 90 s. EPT exhibited the highest variability, with the smallest time-dependent differences observed immediately after the first assessment. Conclusion: Recovery time after electrical stimulation varies by threshold type and intensity of the stimuli. EST and EMT can be reliably reassessed immediately after sensory and motor stimulation, respectively. However, when stimulation reaches EPT level, EST requires 60 s to recover and EMT needs 90 s. EPT demonstrates higher variability, indicating the need for further investigation. These findings support the implementation of standardized recovery intervals in ETT and underscore the importance of interpreting EPT results with caution during rapid assessments.

Background/Objectives: Percutaneous electrolysis is a physiotherapeutic technique based on the application of galvanic current to induce structural and biochemical changes in musculoskeletal tissues. Although widely used in tendinopathies, its application in muscle tissue, particularly regarding optimal dosage, remains poorly understood. This study aimed to evaluate the dose-dependent effects of galvanic current on cadaveric muscle tissue (medial gastrocnemius) using quantitative ultrasound analysis, and to identify objective biomarkers to guide dosage. Methods: An experimental model was employed, applying galvanic current at varying intensities (0–10.0 mA) to 29 samples of cadaveric medial gastrocnemius. Quantitative ultrasound parameters were measured, including geometric and textural features. Correlation analyses and simple and multiple linear regressions were performed to assess the relationship between current intensity and ultrasound variables. Additionally, dose segmentation into three groups (low: 0–1.0 mA, medium: 1.0–4.0 mA, high: >4.0 mA) allowed for comparative statistical analysis using Kruskal–Wallis and post hoc Mann–Whitney U tests. Results: Significant dose–response relationships were observed in key ultrasound parameters, including A_Number, A_Area, A_Perimeter, and A_Contrast (p < 0.001). Regression analysis revealed that a combination of A_Area, A_Number, and A_Perimeter accounted for 66.7% of the variance in applied dose (R² = 0.667, p < 0.001), leading to the creation of the predictive variable Muscle_Electrolysis_Dose. Comparative analysis confirmed significant differences between low-, medium-, and high-dose groups, particularly between lower and higher doses. Conclusions: Quantitative ultrasound effectively detects structural changes in muscle tissue following percutaneous electrolysis. The results support the development of objective, image-based criteria for optimizing galvanic current dosage, enhancing the precision and personalization of treatment.

Background: Hormonal fluctuations during the menstrual cycle (MC) influence pain perception, potentially affecting exercise performance and rehabilitation in women. This effect may be more pronounced in individuals with primary dysmenorrhea (PD), requiring tailored physiotherapeutic and exercise interventions. Objective: To analyze the influence of MC phases on sensory electrical threshold (SET) and pain electrical threshold (PET) in eumenorrheic women with and without PD, considering the potential implications for physical activity and rehabilitation. Methods: An observational longitudinal study was conducted with 34 physically active women, divided into a control group (CG) and a PD group. SET and PET were measured using transcutaneous electrical nerve stimulation (TENS) at the forearm (peripheral site) and lower abdomen (pain-referred site) across five MC phases. Pain intensity was assessed using a Visual Analog Scale (VAS). Results: SET and PET were significantly lower in the premenstrual phase (p < 0.001), suggesting increased pain sensitivity. VAS scores were higher in the PD group during all phases, except for the follicular phase (p < 0.033), with the highest pain levels recorded in the menstrual and premenstrual phases. While no significant differences in SET and PET were found between groups across most phases, the PD group exhibited a significantly higher SET in the forearm during the premenstrual phase (p = 0.005), potentially indicating altered central pain modulation. Conclusions: MC-related hormonal fluctuations affect pain sensitivity, particularly in women with PD. These findings underscore the need for phase-specific exercise adaptations and rehabilitation strategies to improve performance, pain management, and recovery in physically active women.

Ultrasound evaluation of the median nerve in carpal tunnel syndrome (CTS) is inherently operator-dependent. Automating CTS severity grading from ultrasound using deep learning may improve objectivity, reproducibility, and scalability in clinical practice. Background/Objectives: To develop convolutional neural network (CNN) models for automated CTS severity classification using B-mode ultrasound and electrodiagnostic results as the reference standard, and to compare performance between ultrasound scans. Methods: Fifty participants with suspected CTS provided 94 wrists, each classified into four severity levels (normal, mild, moderate, severe) through standardized nerve conduction studies. Ultrasound videos in transverse and longitudinal planes were acquired under a uniform protocol. From 11,895 frames, expert quality control produced a dataset of 2,518 valid images. Two CNN classifiers (transverse and longitudinal) were trained on nerve-centred crops using a 75/25 training–validation split, Adam optimization, and categorical cross-entropy loss. Performance was assessed through accuracy, class-wise precision, and confusion matrices. Results: Both models showed stable convergence. The transverse classifier achieved 0.92 validation accuracy, with precision values of 0.88 (normal), 0.94 (mild), 0.93 (moderate), and 0.97 (severe). The longitudinal classifier achieved 0.94 accuracy, with precision values of 0.98 (normal), 0.94 (mild), 0.96 (moderate), and 0.85 (severe). Misclassifications occurred mainly between adjacent categories, and reduced performance for severe longitudinal cases reflected limited sample representation. Conclusions: CNN-based classifiers can automatically predict CTS severity from B-mode ultrasound with high agreement to electrodiagnostic labels. These operator-independent models may support standardized diagnostic pathways and ultrasound-based screening. Future work should expand and balance datasets, incorporate multi-center data, and conduct external validation.

Quantitative assessment of peripheral sensorimotor function via surface electrical stimulation provides essential insights into neural responses; however, standardized stimulation parameters remain poorly defined. This study aimed to characterize the strength-duration (S-D) relationship of electrical sensory, motor, and pain thresholds (EST, EMT, and EPT) across a wide range of pulse durations to identify physiological stabilization plateaus and determine the pulse durations that maximize the discrimination between threshold types. Thirty healthy volunteers underwent electrical threshold testing (ETT) on the forearm using a symmetrical biphasic current (100 Hz). Eleven randomized pulse durations (0.02–0.65 ms) were evaluated. S‑D was analysed following Weiss’s model, and discriminative capacity was assessed using effect-size analysis. All thresholds exhibited the characteristic hyperbolic decay of the S‑D curve, although stabilization dynamics could not be determined within the pulse duration range evaluated. Thresholds were well fitted by Weiss’s linear model (R ² > 0.902), and the estimated rheobase resulted ∼0.7 mA for EST, ∼2 mA for EMT, and ∼3.7 mA for EPT. The corresponding chronaxie values were 1.09 ms for EST, 0.57 ms for EMT, and 0.41 ms for EPT. Extreme pulse durations (0.02 and 0.65 ms) demonstrated the highest discriminative capacity between thresholds (η ² _p = 0.936 and 0.921), although all pulse durations obtained large effect sizes (η ² _p > 0.869). These findings demonstrate that electrical thresholds follow a predictable neurophysiological pattern across pulse durations and suggest that pulse duration selection may be important for achieving target threshold responses, providing a reference framework for standardizing ETT protocols in both research and clinical diagnosis.