Search Results (546)

Background: Breast cancer is the most common neoplasm in women. Despite effective treatments, sequelae such as decreased muscle strength, upper limb dysfunction, and tissue changes are common, highlighting the need for functional assessments during rehabilitation. This study analysed the morphofunctional profile of the upper limbs in breast cancer survivors, comparing muscle strength and ultrasound findings between groups with and without lymphoedema, as well as between ipsilateral and contralateral limbs. Methods: This cross-sectional study included female breast cancer survivors treated at an oncology physical therapy clinic. Muscle strength was measured using dynamometry (handgrip and arm flexor strength), and ultrasound assessed the thickness of the dermal–epidermal complex (DEC), subcutaneous tissue (SUB), and muscle (MT). Results: The upper limbs of 41 women were evaluated. No significant differences were observed between those with and without breast cancer-related lymphoedema (BCRL). When comparing the ipsilateral and contralateral limbs, significant reductions were observed in arm flexor strength (p < 0.001; 95% CI: −9.77 to −2.50), handgrip strength (p < 0.001; 95% CI: −4.10 to −1.22), and tissue thickness, with increased DEC thickness on the forearm (0.20 mm; p = 0.022) and arm flexors (0.25 mm; p < 0.001) of the ipsilateral limb. Conclusion: Significant differences in muscle strength and tissue structure between ipsilateral and contralateral limbs may reflect surgical and local pathophysiological effects. A trend toward reduced values for these parameters was also noted in limbs with BCRL, reinforcing the importance of future research to elucidate underlying mechanisms and guide more effective therapeutic strategies. Full article

Background/Objectives: Malnutrition is a common problem in children with cerebral palsy (CP). The aim of this study was to investigate the suitability and diagnostic performance of mid-upper arm circumference (MUAC) z-score in diagnosing wasting in children with CP, and its impact on diagnostic accuracy when evaluated concomitantly with additional clinical factors (birth weight, history of phototherapy). Methods: This single-center, cross-sectional study included 83 children with CP, aged 6 months–17 years, followed-up in our clinic. Anthropometric measurements (MUAC, Body Mass Index (BMI)) and clinical data (birth weight, history of phototherapy, Gross Motor Function Classification System (GMFCS)) were prospectively collected. Wasting was defined according to the BMI z-score ≤ −2 criteria. The diagnostic performance of MUAC z-score was evaluated by Receiver Operating Characteristic (ROC) analysis. The contribution of additional covariates was examined using logistic regression analysis and the backward elimination method. Results: MUAC z-score alone demonstrated good discrimination in diagnosing wasting with an Area Under the Curve (AUC) value between 0.805 and 0.821, but its sensitivity was limited (67.0%). No statistically significant difference was found in diagnostic performance between MUAC measurements of the right arm, left arm, and the unaffected arm (p > 0.050). In logistic regression analysis, MUAC z-score (p = 0.001), birth weight (p = 0.014), and a history of phototherapy (p = 0.046) were found to be significantly associated with wasting malnutrition. The simplified model including these variables yielded an AUC value of 0.876. Conclusions: MUAC z-score is a usable tool for wasting malnutrition screening in children with CP. Although its sensitivity is limited when used alone, its diagnostic accuracy increases when evaluated concomitantly with additional clinical factors such as birth weight and a history of phototherapy. This combined approach may offer clinicians a more robust tool for the early diagnosis and management of wasting malnutrition in children with CP. Full article

The effective suppression of inflammation using disease-modifying therapies is essential in the treatment of multiple sclerosis (MS). Anti-CD20 monoclonal antibodies are commonly used long-term as maintenance therapies, largely due to the lack of reliable biomarkers to guide dosing and evaluate treatment response. However, prolonged use increases the risk of infections and other immune-mediated side effects. The unique ability of brain-derived blood extracellular vesicles (EVs) to cross the blood–brain barrier and reflect the central nervous system (CNS) immune status has sparked interest in their potential as biomarkers. This study aimed to assess whether blood-derived L1CAM⁺ EVs could serve as biomarkers of treatment response to rituximab (RTX) in patients with relapsing-remitting MS (RRMS). Serum samples (n = 25) from the baseline (month 0) and after 6 months were analyzed from the RTX arm of the ongoing randomized clinical trial OVERLORD-MS (comparing anti-CD20 therapies in RRMS patients) and were compared with serum samples from healthy controls (n = 15). Baseline cerebrospinal fluid (CSF) samples from the same study cohort were also included. EVs from both serum and CSF samples were characterized, considering morphology, size, and concentration, using transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). The immunophenotyping of EV surface receptors was performed using flow cytometry with the MACSPlex exosome kit, while label-free quantitative proteomics of EV protein cargo was conducted using a proximity extension assay (PEA). TEM confirmed the presence of EVs with the expected round morphology with a diameter of 50–150 nm. NTA showed significantly higher concentrations of L1CAM⁺ EVs (p < 0.0001) in serum total EVs and EBNA1⁺ EVs (p < 0.01) in serum L1CAM⁺ EVs at baseline (untreated) compared to in healthy controls. After six months of RTX therapy, there was a significant reduction in L1CAM⁺ EV concentration (p < 0.0001) and the downregulation of TNFRSF13B (p = 0.0004; FC = −0.49) in serum total EVs. Additionally, non-significant changes were observed in CD79B and CCL2 levels in serum L1CAM⁺ EVs at baseline compared to in controls and after six months of RTX therapy. In conclusion, L1CAM⁺ EVs in serum showed distinct immunological profiles before and after rituximab treatment, underscoring their potential as dynamic biomarkers for individualized anti-CD20 therapy in MS. Full article

This article aims to utilize a microelectromechanical system (MEMS) to modulate coupling behavior of silicon nitride (Si₃N₄) waveguides to perform an optical switch based on a directional coupling (DC) mechanism. There are two states of the switch. First state, a Si₃N₄ wire is initially positioned up suspended in the air. In the second state, this wire will be moved down to be placed between two arms of the DC waveguides, changing the coupling behavior to achieve bar and cross states of the optical switch function. In the future, the MEMS will be used to move this wire down. In this work, we present simulations of the two static states to optimize the DC structure parameters. Based on the simulated results, the device size is 8.8 μm × 55 μm. The insertion loss is calculated to be approximately 0.24 dB and 0.33 dB, the extinction ratio is approximately 24.70 dB and 25.46 dB, and the crosstalk is approximately −24.60 dB and −25.56 dB, respectively. In the C band of optical communication, the insertion loss ranges from 0.18 dB to 0.47 dB. As such, this device will exhibit excellent optical switch performance and provide advantages in many integrated optics-related optical systems applications. Furthermore, it can be used in optical communications, data centers, LiDAR, and so on, enhancing important reference value for such applications. Full article

Background and Objectives: Investigating the adherence to antihypertensive medication and identifying patients with low adherence allows targeted interventions to improve therapeutic outcomes. Artificial intelligence (AI) offers advanced tools for analyzing medication adherence data. This study aimed to develop and validate several predictive models for non-adherence, using patient-reported data collected via a structured questionnaire. Materials and Methods: A cross-sectional, multi-center study was conducted on 3095 hypertensive patients from community pharmacies. A structured questionnaire was administered, collecting data on sociodemographic factors, medical history, self-monitoring behaviors, and informational exposure, alongside medication adherence measured using the Romanian-translated and validated ARMS (Adherence to Refills and Medications Scale). Five machine learning models were developed to predict non-adherence, defined by ARMS quartile-based thresholds. The models included Logistic Regression, Random Forest, and boosting algorithms (CatBoost, LightGBM, and XGBoost). Models were evaluated based on their ability to stratify patients according to adherence risk. Results: A total of 79.13% of respondents had an ARMS Score ≥ 15, indicating a high prevalence of suboptimal adherence. Better adherence was statistically associated (adjusted for age and sex) with more frequent blood pressure self-monitoring, a reduced salt intake, fewer daily supplements, more frequent reading of medication leaflets, and the receipt of specific information from pharmacists. Among the ML models, CatBoost achieved the highest ROC AUC Scores across the non-adherence classifications, although none exceeded 0.75. Conclusions: Several machine learning models were developed and validated to estimate levels of medication non-adherence. While the performance was moderate, the results demonstrate the potential of AI in identifying and stratifying patients by adherence profiles. Notably, to our knowledge, this study represents the first application of permutation and SHapley Additive exPlanations feature importance in combination with probability-based adherence stratification, offering a novel framework for predictive adherence modelling. Full article

Childhood obesity is a substantial health problem worldwide. The origin of obesity (increased adiposity) can be partly traced back to intrauterine life. However, the determinants of fetal fat deposition remain unclear. This study investigated the association between cord blood adipocytokines related to lipid metabolism (leptin, adiponectin, and insulin-like growth factor-1 [IGF-1]) and fetal adiposity during gestation. A prospective study was conducted in a cohort of 94 singleton pregnancies. Fetal ultrasonography was performed at 24, 30, and 36 weeks of gestation. Estimated fetal adiposity (EFA) was calculated by integrating measurements of cross-sectional arm and thigh fat area percentages and anterior abdominal wall thickness. Plasma cytokine levels and C-peptide immunoreactivity (as a proxy for fetal insulin resistance) were evaluated in cord blood samples obtained at delivery. The associations of cord blood leptin, adiponectin and IGF-1 levels with EFA at 24, 30, and 36 weeks were determined by multiple linear regression, adjusted for potential covariates. The multivariate analyses indicated that leptin was significantly correlated with EFA at 30 and 36 weeks. Leptin was also positively correlated with C-peptide immunoreactivity in the umbilical cord. Cord adiponectin levels were not associated with EFA across gestation. Cord IGF-1 levels were significantly correlated with EFA and estimated fetal body weight (EFW) at 36 weeks. In conclusion, cord leptin was associated with EFA at 30 and 36 weeks, and IGF-1 was associated with EFA at 36 and EFW at 36 weeks. In Conclusion, cord leptin was associated with EFA at 30 and 36 weeks, and IGF-1 was associated with EFA and EFW at 36 weeks. Considering the effects of leptin and IGF-1 on fetal insulin resistance and lipid metabolism, increased levels of leptin and IGF-1 are potential plasma biomarkers of increased fetal adiposity, which may predispose to infant obesity and metabolic dysfunction in later life. Full article

Body composition is associated with multiple factors. The main objective of this study is to determine the association between diet and sociodemographic factors on the body structure and composition of university students at a public university in Ecuador. This cross-sectional study allowed for the collection of detailed body composition and dietary data from 204 students (41.7% men and 58.3% women, with an average age of 23.3 ± 4.4 years). The study was conducted using validated questionnaires and bioimpedance techniques. Statistical analysis included ANOVA tests, complemented by a PCA-Biplot, to examine the relationships between study variables. Statistical analysis revealed that men’s birthplace had a significant impact on several body measurements, such as hip circumference and weight, but no significant differences were observed in body structure and composition based on nutrient intake. Furthermore, larger upper-arm circumference in women was correlated with higher fat intake. The results of the multivariate analysis indicated a differential influence of dietary components on body composition. The study highlights the need for nutritional intervention strategies and educational programs that consider the diversity of students’ backgrounds to promote healthy habits and mitigate the negative effects of eating habits and irregular physical activity patterns on their health and body composition. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

Background: Sarcopenia is a progressive muscle disease that compromises mobility and quality of life in older adults. Although dual-energy X-ray absorptiometry (DXA) is the standard for assessing Appendicular Lean Mass Index (ALMI), it is costly and often inaccessible. This study aims to develop machine learning models using anthropometric measurements to predict low ALMI for the diagnosis of sarcopenia. Methods: A cross-sectional study was conducted on 183 Mexican adults (67.2% women and 32.8% men, ≥60 years old). ALMI was measured using DXA, and anthropometric data were collected following the International Society for the Advancement of Kinanthropometry (ISAK) protocols. Predictive models were developed using Logistic Regression (LR), Decision Trees (DTs), Random Forests (RFs), Artificial Neural Networks (ANNs), and LASSO regression. The dataset was split into training (70%) and testing (30%) sets. Model performance was evaluated using classification performance metrics and the area under the ROC curve (AUC). Results: ALMI indicated strong correlations with BMI, corrected calf girth, and arm relaxed girth. Among models, DT achieved the best performance in females (AUC = 0.84), and ANN indicated the highest AUC in males (0.92). Regarding the prediction of low ALMI, specificity values were highest in DT for females (100%), while RF performed best in males (92%). The key predictive variables varied depending on sex, with BMI and calf girth being the most relevant for females and arm girth for males. Conclusions: Anthropometry combined with machine learning provides an accurate, low-cost approach for identifying low ALMI in older adults. This method could facilitate sarcopenia screening in clinical settings with limited access to advanced diagnostic tools. Full article

This paper presents a circularly polarized (CP) antenna based on crossed dipoles with bandpass-type filtering radiation response. The antenna employs a pair of crossed dipole arms as radiators, which are printed on the upper and lower planes of the substrate. To achieve bandpass filtering effects, radiation nulls are introduced on both sides of the passband. By vertically extending the ends of the four dipole arms, a ring-shaped current is formed between adjacent dipoles, generating the upper-band radiation null. Additionally, four parasitic patches are introduced parallel to the ends of the crossed dipole arms, creating another upper-band radiation null, further enhancing the frequency selectivity at the band edges and broadening the axial ratio (AR) bandwidth. Moreover, a square-ring slot is etched on the ground plane to introduce a lower-band radiation null, ultimately achieving a good bandpass filtering response. The proposed wideband CP filtering dipole antenna is implemented and tested. The antenna has a compact size of 0.49${\lambda }_0\times$ 0.49${\lambda }_0\times$ 0.16${\lambda }_0$ (where ${\lambda }_0$ denotes the wavelength corresponding to the lowest operating frequency). The measured results show that the proposed antenna has an impedance bandwidth of 75% (1.65–3.66 GHz) and an overlapping AR bandwidth of 46.9% (2.25–3.63 GHz). Without additional filtering circuits, the antenna exhibits a stable gain of approximately 7 dB and three radiation nulls, with suppression levels of 20 dB in both the lower and upper stopbands, achieving good bandpass filtering performance. Full article

The aim of this study was to evaluate the efficacy of manual therapy based on neurodynamic techniques and electrophysical modalities in the conservative treatment of cubital tunnel syndrome (CuTS). A total of 128 upper limbs affected by CuTS were initially enrolled in this study, with 82 completing the full treatment protocol. The participants were divided into the following two intervention arms: the first arm (MT) (42 arms) received therapy based on sliding and tensioning neurodynamic techniques, while the second arm (EM) (40 arms) underwent physiotherapy based on electrophysical modalities, specifically low-level laser therapy (LLLT) and ultrasound therapy (US). Chi² and Student’s t-test were used to compare the intervention arms, and no statistically significant differences were found. The evaluated outcomes included nerve conduction testing, ultrasound assessments (measuring cross-sectional area and shear modulus), pain levels, two-point discrimination, thresholds for cutaneous sensory perception, symptom severity, functional ability in specific tasks, and overall post-treatment improvement. Baseline comparisons indicated no statistically significant differences in any measured variables between the intervention groups (p > 0.05). Following treatment, each group exhibited significant improvements in their respective parameters (p < 0.01). Comparisons between groups post-intervention revealed statistically significant differences in nerve conduction results, ultrasound measurements (cross-sectional area and shear modulus), two-point discrimination, and sensory perception thresholds. These parameters improved more in the MT intervention arm. The use of neurodynamic techniques, ultrasound, and low-level laser therapy in the conservative treatment of mild to moderate forms of CuTS has a beneficial therapeutic effect. Full article

Electromyography (EMG) signals have diverse applications, ranging from prosthetic hands and assistive suits to rehabilitation devices. Nonetheless, their performance suffers from cross-subject generalization issues, electrode shifts, and daily variability. In a previous study, while transfer learning narrowed the classification performance gap to −1% in an eight-class scenario under electrode shift, they imposed the burden of additional data collection and re-training. To address this issue in real-time prediction, we investigated a sliding-window normalization (SWN) technique that merges z-score normalization with sliding-window processing to align the EMG amplitude across channels and mitigate the performance degradation caused by electrode displacement. We validated SWN using experimental data from a right-arm trajectory-tracking task involving three motion classes (rest, flexion, and extension of the elbow). Offline analysis revealed that SWN mitigated accuracy degradation to −1.0% without additional data for re-training or multi-condition training, a 6.6% improvement compared with the −7.6% baseline without normalization. The advantage of SWN is that it operates with data from a single electrode position for training, which eliminates both the collection of multi-position training data and the calibration of deep learning models before practical use in EMG applications. Moreover, combining SWN with multi-position training exceeded the classification accuracy of the no-shift condition by 2.4%. Full article

Background: Shirakiopsis indica (Willd.) (Family: Euphorbiaceae), a mangrove species found in the Asian region, is a popular folkloric plant. Locally, the plant is traditionally used to treat various types of ailments, especially for pain relief. Therefore, the current study investigates the neuropharmacological, antipyretic, thrombolytic, and anthelmintic properties of the S. indica fruit methanolic extract (SIF-ME). Methods: The neuropharmacological activity was evaluated using several bioactive assays, and the antipyretic effect was investigated using the yeast-induced pyrexia method, both in Swiss albino mice models. Human blood clot lysis was employed to assess thrombolytic activity, while in vitro anthelmintic characteristics were tested on Tubifex tubifex. Insights into phytochemicals from SIF-ME have also been reported from a literature review, which were further subjected to molecular docking, pass prediction, and ADME/T analysis and validated the wet-lab outcomes. Results: In the elevated plus maze test, SIF-ME at 400 mg/kg demonstrated significant anxiolytic effects (200.16 ± 1.76 s in the open arms, p < 0.001). SIF-ME-treated mice exhibited increased head dipping behavior and spent a longer time in the light box, confirming strong anxiolytic activity in the hole board and light–dark box tests, respectively. It (400 mg/kg) also significantly reduced depressive behavior during forced swimming and tail suspension tests (98.2 ± 3.83 s and 126.33 ± 1.20 s, respectively). The extract induced strong locomotor activity, causing mice’s mobility to gradually decrease over time in the open field and hole cross tests. The antipyretic effect of SIF-ME (400 mg/kg) was minimal using the yeast-induced pyrexia method, while it (100 μg/mL) killed T. tubifex in 69.33 ± 2.51 min, indicating a substantial anthelmintic action. SIF-ME significantly reduced blood clots by 67.74% (p < 0.001), compared to the control group’s 5.56%. The above findings have also been predicted by in silico molecular docking studies. According to the molecular docking studies, the extract’s constituents have binding affinities ranging from 0 to −10.2 kcal/mol for a variety of human target receptors, indicating possible pharmacological activity. Conclusions: These findings indicate that SIF-ME could serve as a promising natural source of compounds with neuropharmacological, anthelmintic, thrombolytic, and antipyretic properties. Full article

Background/Objectives: The aim of this secondary analysis is to determine the baseline characteristics that are associated with a higher likelihood of weight-loss success in a personalized nutrition intervention. Methods: Data were analyzed in adults with abnormal glucose metabolism and obesity from a 6-month behavioral counseling randomized clinical trial. Participants were randomized to two calorie-restricted diets: a low-fat diet (Standardized) or a personalized nutrition diet leveraging a machine learning algorithm (Personalized). The gradient boosting machine method was used to determine the baseline variables (i.e., age, weight-loss self-efficacy) that predicted successful weight loss (≥5%) at 6 months in each study arm separately, using repeated five-fold cross-validation with 100 repetitions. Results: A total of 155 participants (Personalized: n = 84 vs. Standardized: n = 71) contributed data (mean [standard deviation]: age, 59 [10] y; 66.5% female; 56.1% White; body mass index (BMI), 33.4 [4.6] kg/m²). In both arms, higher baseline self-efficacy for weight loss was a predictor of weight-loss success. Participants with a higher BMI (p < 0.0001) in the Standardized arm and those who were older (p < 0.0001) in the Personalized arm were more likely to achieve successful weight loss. Conclusions: Future weight-loss interventions may consider providing tailored behavioral support for individuals based on weight-loss self-efficacy, BMI, and age. Full article

Background: Arterial variations in the upper limb, although infrequent, carry critical clinical implications. The presence of superficial ulnar and radial arteries, especially when originating from high levels, increases the risk of iatrogenic injury, misdiagnosis, and surgical complications. To confirm and describe, through ultrasound and anatomical dissection, the presence of a high-origin superficial ulnar artery and a superficial radial artery in a cadaver, highlighting their anatomical trajectory and clinical relevance. Methods: A cross-sectional ultrasound and anatomical study was conducted on 150 upper limbs from fresh-frozen cadavers. High-frequency ultrasound was used to scan the vasculature from the axilla to the wrist. Subsequently, dissection was performed to confirm sonographic findings. Results: One case (0.66%) of concurrent superficial ulnar artery and superficial radial artery was identified in the left arm of a 79-year-old male cadaver. The superficial ulnar artery originated from the axillary artery and coursed superficially along the forearm, anterior to the flexor muscles. The superficial radial artery emerged from the brachial artery and ran subcutaneously in the distal forearm. These arteries remained in close relation to key neural and venous structures, increasing their vulnerability to clinical error. Conclusions: The identification of high-origin superficial arteries is essential for clinical practice. Ultrasound serves as a reliable, non-invasive method for detecting such variations preoperatively. Awareness of these anomalies can prevent inadvertent vascular injuries, improve diagnostic accuracy, and inform safer surgical and anesthetic approaches in upper limb interventions. Full article