Search Results (7,933)

Foreign matter accumulating on catheters during ascites paracentesis in cancer patients can interfere with the procedure. The paracentesis site must be visually inspected by patients or medical staff. We propose a monitoring method using sensors, as they enable real-time, automatic status detection. The proposed design integrates a sensor into the drainage tube to detect liquid flow using the Lorentz force. The sensor consists of a permanent magnet, a yoke, and a signal processing circuit. Mu-metal shields the magnet to prevent interference with surrounding circuits. Physiological saline solution is used as a substitute for bodily fluids. Sensor performance was evaluated via finite element analysis. The Lorentz force generated an average voltage of 11.07 μV when liquid was present, enabling detection of the flow status. The proposed sensor is non-invasive and features a clip design, allowing attachment and detachment from the drainage tube for reuse. Non-invasiveness ensures safety from infection, and reusability can reduce medical costs. This study proposes a sensor for monitoring peritoneal puncture status. By detecting liquid flow using the Lorentz force, the system enables real-time monitoring during the procedure. Full article

Background: Airway bacterial infection and inflammation are often present in children with bronchiectasis. Systemic inflammation has also been reported. Currently, there are no data on the association between systemic inflammatory markers with airway pathogens or neutrophilia in children with bronchiectasis. We aimed to define the bronchoalveolar lavage (BAL) pathogens (bacteria and viruses), and cytology in children with bronchiectasis and to explore any association between peripheral inflammatory markers and airway neutrophilia. Methods: Participants numbering 402, aged <18 years, with peripheral blood and BAL results within 3 months of diagnosis of bronchiectasis were included. Blood and BAL results were reviewed and analysed for possible associations. Results: Of 355 children (88.31%), cultured bacteria from BAL and Haemophilus influenzae (n = 185) were the most frequent. A virus was identified in 131 (32.59%). Adenovirus (n = 69) was most common. Children numbering 279 (69.40%) had airway neutrophilia (neutrophils > 15%) which was associated with the presence of H. influenzae (OR 2.03 95% CI 1.31–3.15, p = 0.002), S. pneumonia 2.41 (95% CI 1.36–4.29, p = 0.003), and Adenovirus (OR 2.06 95% CI 1.06–4.04, p = 0.033). Airway neutrophilia was associated with raised CRP (OR 2.26 95% CI 1.14–4.49, p = 0.019), but there were no other systemic inflammatory markers including monocyte/lymphocyte ratio, neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and platelet/mean platelet volume ratio. Conclusions: In children, there is an association between airway neutrophilia and raised CRP in bronchiectasis, but not with other peripheral inflammatory markers. There is a need to identify non-invasive inflammatory markers in children with bronchiectasis. Full article

Background/Objectives: Justice-involved (JI) youth frequently endorse a history of traumatic brain injury (TBI). TBI, even mild TBI, can have substantial implications for long-term neurocognitive and psychosocial functioning. However, reliable, noninvasive biological indicators of chronic brain changes remain elusive. Micro-ribonucleic acids (miRNAs) are small non-coding segments of RNA that regulate a host of cellular processes. miRNAs are perturbed immediately following TBI but may continue to show changes in the chronic phase of TBI recovery. Methods: We investigated miRNA expression in a group of JI youth (n = 42, ages 12–17 [M = 14.42, SD = 1.21; 57.1% male]) with (n = 22) and without reported histories of TBI. Results: After controlling multiple comparisons, independent samples t-tests revealed five miRNAs (miR-425-3p, miR-30b-5p, miR-582-5p, miR-200c-3p, and miR-150-5p) were significantly different between youth with and without a history of TBI. Among these, four (miR-425-3p, miR-30b-5p, miR-582-5p, and miR-200c-3p) showed higher expression in youth with TBI history, whereas miR-150-5 showed lower expression in youth with TBI history. Three miRNAs (miR-584-5p, miR-10b-5p, and miR-30b-5p) were significantly different between youth with and without a history of loss of consciousness (LOC). MiR-584-5p was lower in youth with LOC history, whereas miR-30b-5p and miR-10b-5p were higher in youth with a history of LOC. Many of these miRNAs have been implicated in prior studies as being involved with inflammatory processes, including neuroinflammation. Conclusions: These results, although preliminary, provide a starting point for understanding the cellular processes related to long-term TBI outcomes within adolescents. For example, they suggest that molecular pathways involved in stress and inflammation (as well as in certain types of behavioral disorders such as substance abuse) may be implicated in long-term brain changes following TBI during development. If replicated, it may suggest future targets for pharmacological intervention. Full article

Background: Emergency rescuers frequently carry heavy equipment for extended periods, making musculoskeletal disorders a major occupational concern. Loading type and road slope play important roles in inducing physical fatigue; however, the assessment of physical fatigue under these conditions remains limited. Aim: This study aims to investigate physical fatigue under different loading types and road slope conditions using both electromyography (EMG) and eye movement metrics. In particular, this work focuses on eye movement metrics as a non-contact data source in comparison with EMG, which remains largely unexplored for physical fatigue assessment. Method: Prolonged load-bearing walking was simulated to replicate the physical demands experienced by emergency rescuers. Eighteen male participants completed experimental trials incorporating four loading types and three road slope conditions. Results: (1) Loading type and road slope significantly affected EMG activity, eye movement metrics, and perceptual responses. (2) Saccade time (ST), saccade speed (SS), and saccade amplitude (SA) exhibited significant differences in their rates of change across three stages defined by perceptual fatigue. ST, SS, and SA showed strong correlations with subjective fatigue throughout the entire load-bearing walking process, whereas pupil diameter demonstrated only a moderate correlation with subjective ratings. (3) Eye movement metrics were incorporated into multivariate quadratic regression models to quantify physical fatigue under different loading types and road slope conditions. Conclusions: These findings enhance the understanding of physical fatigue mechanisms by demonstrating the potential of eye movement metrics as non-invasive indicators for multidimensional fatigue monitoring in work environments involving varying loading types and road slopes. Full article

The accidental contamination of olives by mineral hydrocarbons, such as diesel, motor lubricants, and hydraulic fluids from agricultural machinery, has become a growing concern in the olive oil industry. In response, European regulatory bodies are working on establishing new standards to address this issue. This study explores the feasibility of using Metal Oxide Semiconductor (MOS) gas sensors as a non-invasive method for detecting such contaminants on freshly harvested olives across different maturity stages. By assessing the sensitivity and selectivity of MOS sensors, this research aims to identify hydrocarbons that may adhere to the olive surface during harvesting and processing. The study involves controlled laboratory contamination scenarios, with samples exposed to various hydrocarbons to evaluate the relative response of individual MOS sensors under reproducible conditions. Findings from this research may provide valuable insights into rapid and cost-effective detection systems, supporting quality control and regulatory compliance in olive oil production, and contributing to the safety and traceability of olive-derived products. As a feasibility study, the results provide a basis for future developments involving multivariate analysis, field-contaminated samples, and industrial implementation. Full article

Introduction: Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease of the central nervous system, frequently associated with visual and oculomotor disturbances. Quantitative analysis of eye movements represents a non-invasive method for assessing central nervous system dysfunction beyond conventional imaging; however, the diagnostic and predictive value of oculomotor metrics remains insufficiently defined. Objectives: The aims of this study were to compare smooth pursuit gain and reflexive saccade parameters (latency, velocity, and precision) between individuals with MS and healthy controls, and to evaluate their ability to discriminate disease status. Methods: This cross-sectional study included 46 clinically stable patients with MS (EDSS ≤ 6.5) and 46 age- and sex-matched healthy controls. Oculomotor function was assessed using videonystagmography under standardized conditions. Group differences across horizontal and vertical gaze directions were analyzed using linear mixed-effects models. Random forest models were applied to assess the discriminative performance of oculomotor parameters, with permutation-based feature importance and receiver operating characteristic (ROC) curve analysis. Results: Patients with MS showed significantly reduced smooth pursuit gain across most horizontal and vertical directions compared with controls. Saccadic latency was significantly prolonged in all tested movement directions. Saccadic velocity exhibited selective directional impairment consistent with subtle medial longitudinal fasciculus involvement, whereas saccadic precision did not differ significantly between groups. A random forest model combining pursuit and saccadic parameters demonstrated only moderate discriminative performance between MS patients and controls (AUC = 0.694), with saccadic latency contributing most strongly to classification. Conclusions: Quantitative eye-movement assessment revealed widespread oculomotor abnormalities in MS, particularly reduced smooth pursuit gain and prolonged saccadic latency. Although the overall discriminative accuracy of oculomotor parameters was limited, these findings support their potential role as complementary markers of central nervous system dysfunction. Further longitudinal and multimodal studies are required to clarify their clinical relevance and prognostic value. Full article

Objective: Visceral adipose tissue is a primary driver of insulin resistance and dysglycemia in type 2 diabetes (T2D), yet its clinical assessment remains challenging. This study aimed to validate neck circumference (NC) as a novel, practical anthropometric biomarker for estimating visceral fat area (VFA) and identifying metabolic risk in a T2D cohort, facilitating its integration into public health and primary care screening strategies. Methods: In a cross-sectional study of 1139 T2D patients, we collected data on NC, biochemical parameters (fasting plasma glucose, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides), and precisely measured VFA and subcutaneous fat area (SFA) via bioelectrical impedance analysis (Omron HDS-2000). We employed Pearson’s correlation and multivariate logistic regression to analyze the relationship between NC and metabolic indicators. Receiver operating characteristic (ROC) curve analysis was used to establish sex-specific NC cut-off values for predicting abnormal VFA. Results: The cohort comprised 687 (60.3%) males and 452 (39.7%) females. NC demonstrated strong positive correlations with VFA (p < 0.001), as did body mass index (BMI), waist–hip ratio (WHR), and SFA. In males, NC was further positively correlated with key metabolic biomarkers including fasting insulin, Insulin Resistance Index, triglycerides, and creatinine. ROC analysis identified NC > 39.5 cm for males and >35.5 cm for females as the optimal cut-off points for detecting abnormal visceral adiposity, highlighting its diagnostic utility. Conclusions: NC serves as a highly accessible and effective biomarker for visceral adiposity and associated metabolic dysfunction in patients with T2D. The established sex-specific cut-off values provide a simple, non-invasive tool for risk stratification in clinical and public health settings, enabling early intervention and improved management of metabolic disease. Full article

Background: Heart failure (HF) remains a major cause of hospitalizations in the United States (US). Respiratory syncytial virus (RSV) has been associated with HF exacerbations. We compared in-hospital outcomes and healthcare utilization among US HF hospitalizations with and without RSV. Methods: Using the Nationwide Readmissions Database (2016–2022), we propensity-matched HF hospitalizations with a secondary diagnosis of RSV (HF-RSV) 1:1 to those without RSV (HF-noRSV). Multivariable logistic and Poisson regression models were used to assess associations between RSV and outcomes. The primary outcome was in-hospital mortality; secondary outcomes included adverse events, length of stay (LOS), hospitalization costs, and 30-day readmissions. Results: Among 11,158,836 HF hospitalizations, 32,419 (0.29%) had RSV. Compared with matched HF-noRSV hospitalizations, HF-RSV was associated with higher odds of in-hospital mortality (adjusted odds ratio [aOR] 1.12; 95% CI 1.04–1.20), septic shock (aOR 1.40; 95% CI 1.29–1.52), acute respiratory failure (aOR 3.43; 95% CI 3.32–3.55), and noninvasive mechanical ventilation (aOR 2.15; 95% CI 2.04–2.26). HF-RSV had lower odds of cardiogenic shock (aOR 0.82; 95% CI 0.73–0.92), ventricular tachycardia/fibrillation (aOR 0.73; 95% CI 0.68–0.78), ischemic stroke (aOR 0.31; 95% CI 0.27–0.36), transient ischemic attack (aOR 0.33; 95% CI 0.25–0.44), and 30-day readmissions (aOR 0.54; 95% CI 0.46–0.56). HF-RSV hospitalizations had higher costs (adjusted coefficient 0.02; 95% CI 0.01–0.02) and longer LOS (adjusted coefficient 0.14; 95% CI 0.13–0.14). Conclusions: Among US HF hospitalizations, RSV was associated with higher mortality and respiratory-related complications and increased healthcare resource utilization. Full article

Background/Objectives: The cerebellum plays a central role in sensorimotor integration and temporal processing, yet its direct electrophysiological investigation in humans remains challenging, and cerebellar contributions to somatosensory responses remain poorly defined. This study aimed to determine whether cerebellar responses to peripheral nerve stimulation can be detected using scalp EEG and whether time–frequency analysis provides advantages over time-domain approaches. Methods: Scalp EEG was recorded during electrical stimulation of the median nerve and tibial nerve in 16 healthy participants. Electrode montages included posterior fossa placements targeting cerebellar activity, together with standard cortical and subcortical derivations. Data were analyzed in the time domain using evoked potentials and channel comparisons, including bipolar cerebellar derivations, and in the time–frequency domain using spectral power analysis. Results: Time-domain analyses revealed early and intermediate latency components following both upper- and lower-limb stimulation; however, these responses showed limited spatial specificity and were strongly influenced by reference effects and subcortical contamination. In contrast, time–frequency analysis consistently revealed sustained increases in oscillatory power in cerebellar channels. Power increases emerged approximately 50 ms after stimulation and persisted beyond 300 ms, peaking around ~20 Hz for upper-limb stimulation and ~10 Hz for lower-limb stimulation, with evidence of side specificity. Conclusions: Non-invasive EEG can detect cerebellar responses to peripheral nerve stimulation, particularly in the time–frequency domain. Oscillatory dynamics provide a more robust marker of cerebellar involvement than time-locked responses and may complement conventional somatosensory evoked potentials in studies of cerebellar physiology and spinocerebellar pathway integrity. Full article

Breast cancer continues to be the most frequently diagnosed cancer among women worldwide and remains a leading cause of cancer-related mortality. Despite advances in imaging and biopsy-based approaches, current diagnostic methods are invasive, costly, and often insufficient to capture the molecular heterogeneity of tumors. Extracellular vesicles (EVs) have emerged as promising non-invasive biomarkers owing to their role in intercellular communication and their enrichment with tumor-specific cargo. This study conducted a systematic review and meta-analysis of published literature to investigate proteomic alterations in EVs derived from breast cancer samples. From an initial 1097 records screened, four eligible studies were identified, reporting 628 differentially expressed proteins, of which 38 were consistently observed across multiple datasets. Functional enrichment analyses revealed predominant localization of these proteins to vesicle-associated compartments and significant involvement in biological processes related to cell growth, immune regulation, and tumor progression. Pathway analysis further highlighted integrin-mediated interactions, platelet activation, and hemostasis pathways as key molecular mechanisms represented within breast cancer EVs. Overall, the findings reveal a distinct EV proteomic signature in breast cancer that could support early detection and patient monitoring through minimally invasive testing. Future large-scale and standardized studies are needed to validate these candidate proteins and advance EV proteomics toward clinical application in breast cancer management. Full article

Core body temperature is a critical physiological indicator for assessing and diagnosing animal health status. In bovines, continuously monitoring this metric enables accurate evaluation of their physiological condition; however, traditional rectal measurements are labor-intensive and cause stress in animals. To achieve intelligent, contactless temperature monitoring in cattle, we proposed a non-invasive method based on thermal imaging combined with environmental data fusion. First, thermal infrared images of the cows’ faces were collected, and the You Only Look Once (YOLO) object detection model was used to locate the head region. Then, the YOLO segmentation network was enhanced with the Online Convolutional Re-parameterization (OREPA) and High-level Screening-feature Fusion Pyramid Network (HS-FPN) modules to perform instance segmentation of the eye socket area. Finally, environmental variables—ambient temperature, humidity, wind speed, and light intensity—were integrated to compensate for eye socket temperature, and a random forest algorithm was used to construct a predictive model of rectal temperature. The experiments were conducted using a thermal infrared image dataset comprising 33,450 frontal-view images of dairy cows with a resolution of 384 × 288 pixels, along with 1471 paired samples combining thermal and environmental data for model development. The proposed method achieved a segmentation accuracy (mean average precision, mAP_50–95) of 86.59% for the eye socket region, ensuring reliable temperature extraction. The rectal temperature prediction model demonstrated a strong correlation with the reference rectal temperature (R² = 0.852), confirming its robustness and predictive reliability for practical applications. These results demonstrate that the proposed method is practical for non-contact temperature monitoring of cattle in large-scale farms, particularly those operating under confined or semi-confined housing conditions. Full article

Background/Objectives: Accurate preoperative TN staging is essential for guiding surgical and adjuvant treatment decisions in colorectal cancer (CRC), yet conventional imaging still faces limitations in reliably distinguishing early from advanced disease. This study aimed to evaluate whether CT-based radiomics combined with machine learning can noninvasively predict both tumor (T) and nodal (N) stages of CRC, and to identify which feature groups most contribute to each task. Methods: Fifty-three patients (55 tumors) with histologically confirmed CRC who underwent preoperative contrast-enhanced CT were retrospectively analyzed. A total of 107 radiomic features were extracted using PyRadiomics version 3.1.0, including shape, first-order, and texture features. Multiple preprocessing strategies—z-score normalization, PCA, and SMOTE—were tested across 11 machine learning classifiers. Results: For T staging, logistic regression using shape-based features achieved a mean sensitivity of 0.721, a specificity of 0.68, a balanced accuracy of 0.70, and an AUC of 0.751. For N staging, the AdaBoost model using texture-based features achieved a sensitivity of 0.742, a specificity of 0.622, a balanced accuracy of 0.682, and an AUC of 0.750. Shape features predominantly contributed to T prediction, while texture matrices drove N prediction, reflecting morphological and microstructural correlates of invasiveness and lymphatic dissemination. Conclusions: CT-based radiomics can quantitatively capture both morphological and textural patterns of tumor behavior, providing a noninvasive framework for preoperative TN staging in CRC. Full article

The operational reliability of Insulated Gate Bipolar Transistor (IGBT) modules in demanding transportation applications, such as traction systems, is critically challenged by solder layer and bond wire failures under cyclic thermal stress. To address this, this paper proposes a novel health monitoring framework that innovatively synergizes micro-scale spatial thermal analysis with microsecond electrical dynamics inversion. The method requires only non-invasive temperature measurements on the module baseplate and utilizes standard electrical signals (load current, duty cycle, switching frequency, DC-link voltage) readily available from the converter’s controller, enabling simultaneous diagnosis without dedicated voltage or high-bandwidth current sensors. First, a non-invasive assessment of solder layer fatigue is achieved by correlating the normalized thermal gradient ($\nabla T_{\mathrm{P}}$) on the baseplate with the underlying thermal impedance ($Z_{\mathrm{JC}}$). Second, for bond wire aging, a cost-effective inversion algorithm estimates the on-state voltage ($V_{\mathrm{ce},\mathrm{on}}$) by calculating the total power loss from temperature, isolating the conduction loss ($P_{\mathrm{cond}}$) with the aid of a Foster-model-based junction temperature ($T_{\mathrm{J}}$) estimate, and finally computing $V_{\mathrm{ce},\mathrm{on}}$ at a unique current inflection point ($I_{\mathrm{C},\inf }$) to nullify $T_{\mathrm{J}}$ dependency. Third, the health states from both failure modes are fused for comprehensive condition evaluation. Experimental validation confirms the method’s accuracy in tracking both degradation modes. This work provides a practical and economical solution for online IGBT condition monitoring, enhancing the predictive maintenance and operational safety of transportation electrification systems. Full article

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

Iberian ham is a Spanish product that is highly prized for its unique sensory characteristics. The possibility of predicting these characteristics using non-invasive methods is therefore of great interest. In this context, this study compares the performance of two benchtop and four portable devices in addition to the influence of recording factors (lean meat, fat, and whole slices) for the prediction of sensory parameters. A total of 28 descriptors were determined by a trained panel for 60 samples (100% Iberian and Iberian), and the sensory profile was predicted by using a feedforward multilayer perceptron artificial neural network. A significant effect of the breed on the sensory profile and the spectral characteristics was observed, especially in the case of fat. On the other hand, the NIRFlex N-500 (desktop) and MicroPhazir (portable) devices gave the best performances, with 27 parameters predicted for whole slices and fat, respectively (R² > 0.5). In addition, it was possible to predict 25 parameters by using the MicroNIR portable device at all recording sites, which demonstrated that portable devices are suitable for this analysis. The results indicate that the number and type of sensory parameters predicted depend largely on the recording site and that measurements taken in different areas provide complementary information. Full article