Technology and Health Care最新文献

The effect of continuous medical service intervention on health management for people who have suffered from Acute Ischemic Stroke (AIS) is an important issue in health care tracking. To pick out core aspects related to health, a relapse prediction model, evaluate the efficiency of continuous care and boost post-discharge results, a structured study is designed. After investigation and scientific verification, important signs and symptoms were chosen to set up a Significant Factors Neural Network Relapse Prediction Model (SFNNR) which aims to predict possible relapses based on previous patterns in medical data. The continuous care group was compared with the control group, and it turned out that participants in continuous care had significantly better results with fewer chances of having relapses and controlling chronic risks while displaying less psychological stress compared to the control group; furthermore, the continuous medical service showed great value on long-term management of AIS patients. The study points out that the integrated care approach should be taken more seriously as it can help healthcare staff predict the risk of relapse accurately so as to come up with personalized plans to control the relapse probability of the patients.

BackgroundForward head posture (FHP) is a common disorder worsened by prolonged use of electronic devices, causing increased neck load and musculoskeletal issues. While McKenzie neck exercises (MNE) are widely used to address FHP, the potential benefits of hip extensor exercises (HEE) remain underexplored.ObjectiveThis study aims to compare the effects of MNE and HEE on neck disability index (NDI), craniovertebral angle (CVA), cranial rotation angle (CRA), and the thickness of the LC muscle and carotid artery (CA) in individuals with FHP.MethodsTwenty participants with FHP were randomly assigned to either the MNE or HEE group, undergoing their respective exercises for 20 min per session, three times a week for two weeks. Pre- and post-intervention assessments included NDI questionnaire, CVA, CRA measurements, and ultrasonographic evaluation of LC muscle and CA thickness.ResultsBoth MNE and HEE groups showed significant improvements in NDI, CVA, CRA, and LC muscle thickness post-intervention (P < 0.05), with no significant group differences (P > 0.05). CA thickness increased in both groups, though not significantly.ConclusionsBoth MNE and HEE effectively improved symptoms and alignment associated with forward head posture. These findings suggest that hip extensor exercises may be a beneficial approach to mitigating FHP, similar to MNE.

BackgroundIntravenous infusion often faces difficulties in patients with obesity, aging, or dark skin. Low-cost vein detection using near-infrared (NIR) light is gaining attention to improve vascular access. Previous studies focused mainly on high-end devices or single algorithm performance.ObjectiveThis study aimed to develop a low-cost vein detection system using 850 nm NIR LEDs and Raspberry Pi 4. It also sought to evaluate and compare multiple image enhancement algorithms. Performance was assessed using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM) metrics.MethodsThe device consisted of an NIR LED module, IR-sensitive camera, and Raspberry Pi 4. Algorithms used were Contrast Limited Adaptive Histogram Equalization (CLAHE), Unsharp Masking, Median Filter, and Fuzzy Adaptive Gamma. Images from 13 subjects were enhanced and evaluated using three quantitative metrics.ResultsUnsharp Masking achieved the lowest MSE (36.17) and highest PSNR (32.98), showing strong contrast enhancement. Median Filtering produced the highest SSIM (0.926), effectively preserving structural consistency. Combining CLAHE + Unsharp Masking + Median Filter yielded the best overall performance. However, this combination led to a slight SSIM decrease due to over-enhancement and edge distortion. Hardware limitations (low resolution and processing speed of Raspberry Pi 4) also impacted image quality and SSIM.ConclusionThe proposed low-cost vein detection system effectively enhanced vascular images using selected algorithms. Unsharp Masking and Median Filtering were particularly effective in improving contrast and maintaining structure. Future work should focus on real-time optimization and hardware upgrades to improve clinical applicability.

BackgroundGestational weight gain (GWG) is a critical factor affecting maternal and fetal health. Excessive GWG increases the risk of complications and contributes to the prevalence of overweight and obesity among women of reproductive age. Despite existing guidelines, many pregnant individuals struggle to manage GWG effectively. Therefore, theory-based and evidence-informed interventions that provide continuous support are urgently needed. Mobile health (mHealth) applications have emerged as promising, cost-effective, and accessible tools for promoting healthy behaviors during pregnancy. This study describes the development of a theory-based mHealth application guided by Social Cognitive Theory (SCT) and the Information-Motivation-Behavioral Skills (IMB) model.ObjectiveThis study aims to present the design and development process of "Gebelikte Kilo Yönetimi" (Gestational Weight Management), a user-centered, evidence-based mHealth application intended to promote healthy nutrition, physical activity, and GWG in line with the Institute of Medicine (IOM) recommendations.MethodsA two-phase, parallel-group, single-blind randomized controlled trial was designed. In Phase 1, the mobile application was developed to support healthy GWG. In Phase 2, its effectiveness in improving adherence to IOM guidelines, promoting healthy eating, and increasing physical activity among pregnant women will be evaluated. The study is registered on ClinicalTrials.gov (NCT06542679).ConclusionsThis mHealth application may offer a scalable, accessible alternative to traditional face-to-face counseling, particularly in settings with limited healthcare access or during public health crises. It holds potential to improve GWG outcomes and support maternal health through digital innovation.

BackgroundThe prevalence of dementia is increasing among the aging global population. Innovative exercise interventions, such as virtual reality-based walking-in-place exercise (VR-WIPE) and seated cycling, are emerging for this population.ObjectiveThis study aimed to evaluate and compare the effects of these two exercise methods on physical function.MethodsThe study included 20 adult women (mean age: 78.9 ± 4.61 years) diagnosed with dementia and registered at a daycare center. Participants were randomly assigned to one of two groups according to intervention: experimental (n = 10); or control (n = 10). The experimental group received VR-WIPE, whereas the control group performed seated cycling. The primary outcome was the 5xSTS test, assessing functional mobility. Secondary outcomes included grip strength and lower limb strength.ResultsGrip strength increased significantly only in the seated cycling group (p < 0.05), with a small effect size (Cohen's d = 0.23). Both the cycling and VR-WIPE groups showed significant improvement in 5xSTS and lower limb strength (p < 0.05). Between-group comparisons revealed that the seated cycling group demonstrated significantly greater improvements in hip flexion and knee extension strength (Cohen's d = 1.36, 1.09, respectively), while ankle plantar flexion strength was significantly higher in the VR-WIPE group (p < 0.05, Cohen's d = 1.66).ConclusionsBoth seated cycling and VR-WIPE effectively improved lower limb strength and 5xSTS performance in older adult women with dementia. Seated cycling yielded greater improvements in hip and knee strength, whereas VR-WIPE was more effective in enhancing ankle plantar flexion strength.

BackgroundAccurate identification and localization of prostate zones in magnetic resonance (MR) images are essential for clinical diagnosis and treatment planning. However, convolutional object detection models like YOLO often struggle to capture the complex geometric features of the prostate.ObjectiveTo enhance the detection and segmentation performance of prostate MR images by addressing limitations in spatial feature extraction and static focusing mechanisms present in conventional YOLO models.MethodsWe propose YOLO-D, an enhanced YOLOv8-based model integrating a Deformable Convolution (DConv) module to better capture fine-grained image details and improve geometric adaptability. Additionally, the Wise-IoU loss function is employed to introduce a dynamic and non-monotonic focusing mechanism, effectively reducing inter-class interference and enhancing localization accuracy.ResultsYOLO-D was evaluated on the publicly available ProstateX dataset using precision, recall, average precision (AP), and F1 score as evaluation metrics. For detection, it achieved 93.4% precision, 91.2% recall, 94.7% AP, and an F1 score of 0.922. For segmentation, YOLO-D achieved 90.7% precision, 88.6% recall, 91.1% AP, and an F1 score of 0.897-consistently outperforming the baseline YOLOv8.ConclusionsBy incorporating DConv and Wise-IoU, YOLO-D offers a robust and efficient solution for automatic prostate zone analysis, with promising potential in real-time clinical imaging applications.

BackgroundElectroencephalography (EEG), a noninvasive technique for recording the brain's electrical activity, has been widely utilized to investigate neurological disorders.ObjectiveEEG recordings can estimate scalp connectivity and select representative channels, which reveal network connectivity and associated brain regions. These details are considered essential for understanding the characteristics of neurological disorders.MethodsThis work proposes an explainable Reassigned Smoothed Pseudo Wigner-Ville Distribution (RSPWVD) based EEG microstate sequence approach to achieve scalp connectivity estimation and channel selection. Epilepsy, one of the most frequently studied neurological disorders using EEG, has been selected for method validation. Receiver Operating Characteristic (ROC) curve analysis and consistency analysis with conventional techniques are performed to specify key parameters such as connection thresholds and time durations, ensuring the reliability of the outcomes.ResultsThe experimental results of the clinical Karunya dataset indicate that the proposed microstate sequence compressed from the EEG contains sufficient information to estimate scalp connectivity and select representative channels. The scalp connectivity results reveal differences between focal and generalized seizures, where focal seizures exhibit more localized connectivity and generalized seizures display a widespread distribution. Moreover, statistical results demonstrate that the F4, C4, T4, and P4 channels present a higher rate of being representative channels in this dataset.ConclusionsThe proposed approach offers valuable characteristics, indicating brain networks that assist in epilepsy analysis by focusing on the most informative scalp locations and reducing computational complexity. It lays the groundwork for investigating various neurological disorders through scalp behaviors from EEG, guiding personalized diagnostics and therapeutic strategies.

BackgroundSocial anxiety disorder (SAD) significantly impairs social functioning. Virtual reality exposure therapy (VRET) offers a promising treatment by providing a controlled, customizable environment. This study aimed to develop and evaluate the efficacy and user experience of a VRET program.MethodsThe study was conducted in two phases: Phase I with the general population and Phase II with individuals diagnosed with SAD at a university hospital. Social anxiety, depression, anxiety, and stress were measured using the Social Interaction Anxiety Scale and the Depression Anxiety and Stress Scale at three time points: before, immediately after, and two weeks post-VRET. The Virtual Reality Neuroscience Questionnaire assessed user experience, game mechanics, in-game assistance, and any VR-induced symptoms. Our VRET program utilized graded exposure techniques within culturally relevant social scenarios.ResultsBoth groups exhibited significant reductions in social anxiety levels following VRET sessions (ps < 0.01) compared to pre-VRET levels. However, individuals with SAD reported increased social anxiety at the two-week follow-up, while the general population maintained their improvement. The VR software received satisfactory ratings for usability, safety, and acceptability.ConclusionThis program demonstrates potential for reducing social anxiety and provides a satisfactory VR experience, supporting its feasibility for individuals with SAD in a developing country. Given the pilot nature and limited sample size, these findings should be interpreted cautiously. Future research with larger samples and repeated sessions is needed to enhance efficacy and ensure long-term benefits. Comprehensive treatment protocols, including tutorials, relaxation techniques, and stress monitoring, are recommended for optimal outcomes.

BackgroundThe invasion and metastasis of hepatocellular carcinoma (HCC) are closely associated with angiogenesis, positioning anti-angiogenic strategies as a promising approach for cancer treatment. This study aims to investigate the role of collagen triple helix repeat containing 1 (CTHRC1) in regulating angiogenesis in HCC.MethodsRelevant bioinformatics analysis was conducted by retrieving publicly available datasets of HCC patients to identify genes exhibiting significant expression patterns linked to vascular invasion. In vitro assays were performed using human liver cancer cell lines (Hep3B, HepG2) and human umbilical vein endothelial cells (HUVECs) to evaluate the effects of CTHRC1 on vascular endothelial growth factor (VEGF) levels and cellular behaviors, including proliferation, migration, and tube formation.ResultsElevated CTHRC1 expression was significantly associated with poor prognosis in HCC patients. Furthermore, CTHRC1 exhibited a positive correlation with VEGF-A, VEGF-B, and VEGF-C levels. Manipulating CTHRC1 expression directly impacted VEGF production and influenced the growth, migration, and tube formation capabilities of HUVECs, as well as the invasion potential of HCC cells.ConclusionCTHRC1 modulates HUVEC proliferation, motility, and tube formation by regulating VEGF expression,thereby influencing HCC progression.