Technology and Health Care最新文献

Background: After stroke, gait training is a key component of rehabilitation, and most individuals use a variety of walking aids depending on their physical condition and environment.

Objective: This study aimed to investigate the potential effect of a one-arm motorized gait device for gait assist of chronic hemiplegic stroke survivors through comparison with traditional gait devices (parallel bar and hemi-walker).

Methods: This study was conducted on 14 chronic hemiplegic stroke survivors. The participants were asked to walk under three conditions using different gait devices, and their gait parameters during walking were collected and analyzed. The first condition involved walking on parallel bars; second condition, walking using hemi-walkers; and third condition, walking using one-arm motorized gait devices. With the use of a gait analysis system, the spatio-temporal gait parameters in each condition were collected, such as gait velocity, cadence, step length, stride length, single support time, and double support time.

Results: In the results by repeated-measures ANOVA or the Friedman test, a significant difference was found in the gait parameters among all three conditions (p< 0.05). The post-hoc test showed a significant change in the spatio-temporal gait parameters (especially, velocity, cadence and affected side single and double support time) when one-arm motorized gait device were used compared with parallel bars and hemi-walkers (p< 0.05).

Conclusion: The results of this study suggest that one-arm motorized gait devices developed for hemiplegic stroke survivors may be more effective potentially than parallel bars and hemi-walkers in gait assistance of chronic hemiplegic stroke survivors.

Background: In wireless communication standard 4G and 5G, the body centric network plays an important role for the wireless communication between various devices.

Objective: This research relates to a wide-band conformal co-planar waveguide (CPW) antenna for wearable applications.

Methods: The proposed CPW antenna is printed on 0.1 mm thick bio-compatible polymide substrate whose dielectric constant and permittivity are 3.5 and 0.02 respectively. The total area of the antenna is around 17.5 × 15 mm2 which is significantly smaller than the wearable antennas proposed in literature. The proposed antenna is designed to operate in new ISM band 5.8 GHz with the bandwidth of 5.3-6.3 GHz with 2:1 Voltage Standing Wave Ratio (VSWR). The antenna is printed on the flexible substrate and hence robustness of device is evaluated by bending analysis. It reveals the superior performance of the designed CPW antenna over the desired spectrum of operation.

Results: Specific Absorption Rate (SAR) is calculated after placing the antenna at various places of human phantom model and showed that SAR values are below 1.6 W/Kg which is the maximum margin recommended by Federal Communication Commission (FCC), i.e when tested with 1 g and 10 g of human tissue of phantom model, for the test frequency range of 5.5-6.1 GHz, SAR value falls between 0.9987 and 0.921 W/Kg respectively. The antenna also shows the radiation efficiency around 92% with overall realized gain 5.2 dBi which are substantial values for wearable applications.

Conclusion: The outcomes of this research revealed the feasibility of the recommended antenna becoming a major contender of future Internet of Things (IoT) applications.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common pathological type of renal cell carcinoma (RCC), and effective biomarkers will improve diagnosis and treatment.

Objective: This study investigated NPEPL1 expression in ccRCC through public databases and clinical samples and assessed its correlation with clinicopathological features and patient prognosis.

Method: Data from The Cancer Genome Atlas and clinical specimens were gathered, NPEPL1 expression levels were analyzed; a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of NPEPL1; and clinicopathological data was used to study the correlations between expression and clinical parameters. NPEPL1's prognostic value was appraised using a Kaplan-Meier (K-M) survival curve, Cox regression analysis, and a nomogram model; Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differently expressed genes between tissues with high and low NPEPL1 expression were used to estimate the underlying mechanisms involved.

Results: NPEPL1 was significantly higher-expressed in ccRCC tissue. ROC analysis showed that NPEPL1 had noteworthy diagnostic efficacy. NPEPL1 expression was closely related to clinicopathological parameters, such as T and M stage. K-M analysis showed that overall survival was significantly shortened with high NPEPL1 expression. Cox regression analysis showed that NPEPL1 expression was an independent risk factor predicting overall survival. The nomogram showed a significantly high clinical value in predicting the 1-, 3-, and 5-year survival probabilities in ccRCC. GO and KEGG enrichment analysis suggested that NPEPL1 may promote the occurrence and development of ccRCC via the Ras signaling and other pathways.

Conclusion: NPEPL1 expression in ccRCC was higher than that in normal kidney tissues and was significantly associated with advanced clinical stage and poor prognosis. Therefore, NPEPL1 is a promising prognostic biomarker.

Background: Hypertension is a well-recognized risk factor for cardiovascular, which is also a critical factor in causing myocardial fibrosis (MF).

Objective: The study aimed to explore the effect of Atractylenolide II (ATL-II) on MF and oxidative stress in spontaneous hypertension rats (SHR).

Methods: The body weight of rats after injection of ATL-II was quantitatively analyzed. The left ventricular function of SHR was evaluated by Echocardiographic. HE staining, Masson trichrome staining, left ventricular mass index (LVMI) and immunofluorescence was applied to investigate the effects of ATL-II on MF. RT qPCR was used to detect the Collagen I, α-SMA, Fibronectin, and Vimentin mRNA expression levels in myocardial slices. The effect ATL-II on cardiomyocyte apoptosis was detected by TUNEL staining and western blot. An immunohistochemistry assay was conducted to detect α-SMA protein and TGF-β1 protein. The contents of H2O2, GSH-PX, SOD, and MDA were measured by colorimetry.

Results: ATL-II could dose-dependently improve the BW of SHRs (P< 0.05) and enhance myocardial function. Moreover, ATL-II effectively reduced cardiomyocyte apoptosis in SHRs. Alternatively, ATL-II could inhibit the Collagen I, α-SMA, Fibronectin, and Vimentin mRNA and protein expression levels in SHRs. ATL-II could ameliorate oxidative stress by improving the activities of SOD and GSH-PX and lowering the contents of H2O2 and MDA in ATL-II-treated SHRs, which reach about 80%.

Conclusion: ATL-II could exert an inhibiting effect on MF and oxidative stress in SHRs. Hence, ATL-II may hold promise for the treatment of MF and oxidative stress in Spontaneous Hypertension.

Background: According to previous studies, the relationship between lumbar lordosis and thoracic kyphosis or that between pelvic parameters and thoracic kyphosis have been inconsistent.

Objective: The purpose of this study was to investigate spinal sagittal alignment and its relationship to global and regional lumbar and thoracic angles, pelvic and sway angles, and C7-S1 distance measurements, followed by a detailed subgroup analysis using an inertial measurement unit system.

Methods: A total of 51 asymptomatic volunteers stood in a comfortable posture with inertial measurement units attached to the T1, T7, T12, L3, and S2 vertebrae. T1, T7, T12, L3, and S2 sagittal angles were acquired during standing posture using the Eulerian angle coordinate system. All angles are reported as the mean of three 5-s measurements. Following the measurement of lumbar lordosis angles (T12 relative S2), participants were divided into the flat lumbar and normal lordosis groups.

Results: There were different correlation patterns between groups because of spinal sagittal imbalance, which was greater in the flat lumbar group than in the normal lordosis group. In addition, sacral inclination proved the ideal parameter to evaluate reciprocal balance in lumbar lordosis, showing a stronger correlation with lower than with upper lumbar lordosis. T1 was the key element in assessing thoracic kyphosis, which showed a stronger correlation with upper than with lower thoracic kyphosis.

Conclusion: We suggest that when assessing posture, it is necessary to identify the global and regional angles and it is useful to classify spinal sagittal alignment into subgroups according to lumbar lordosis and evaluate the groups separately.

Background: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose.

Objective: To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles.

Method: Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison.

Results: The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method.

Conclusion: The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates.

Background: With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients.

Objective: The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage.

Methods: An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tešanj General Hospital, Bosnia and Herzegovina.

Results: The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections.

Conclusion: The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.

Background: Improved access to media and medical knowledge has elicited stronger public health awareness.

Objective: This study developed a smart drug interaction reminder system for patients to increase knowledge and reduce nurse workload.

Methods: This study used a single-group pre-test/post-test design and applied mining techniques to analyze the weight and probability of interaction among various medicines. Data were collected from 258 participants at a teaching hospital in northern Taiwan using convenience sampling. An app was used to give patients real-time feedback to obtain access to information and remind them of their health issues. In addition to guiding the patients on medications, this app measured the nurses' work satisfaction and patients' knowledge of drug interaction.

Results: The results indicate that using information technology products to assist the app's real-time feedback system promoted nurses' work satisfaction, improved their health education skills, and helped patients to better understand drug interactions.

Conclusion: Using information technology to provide patients with real-time inquiring functions has a significant effect on nurses' load reduction. Thus, smart drug interaction reminder system apps can be considered suitable nursing health education tools and the SDINRS app can be integrated into quantitative structure-activity relationship intelligence in the future.

Background: Day surgery is a new surgical model in which patients complete the admission, surgery, and discharge on the same day.

Objective: The present study aimed to explore the effect of pre-admission "quasi-collective" health education for patients with ophthalmic day surgery.

Methods: For this study, a total of 200 patients undergoing ophthalmic day surgery from February 2019 to December 2019 were enrolled as the research subjects. The patients were divided randomly into the observation group and the control group, with 100 cases in each group. For the control group, conventional health education was conducted after admission. On the day of admission, the admission education and peri-operative health education were performed. For the observation group, pre-admission health education was provided to the patients, and detailed education on the admission instructions, pre-operative precautions, and simulation of the intra-operative process were given by the medical staff. On the day of admission, the understanding of the education was evaluated, and any weaknesses in the health education were addressed. The anxiety status, method of handwashing, method of administering the drug to the eye, preoperative preparations, intra-operative training, preoperative medication, diet guidance, and postoperative care were compared between the two groups of patients.

Results: Before discharge, there were significant differences in the anxiety scores, impact, and satisfaction of health education between the two groups of patients, all of which were statistically significant (P< 0.05).

Conclusion: The pre-admission "quasi-collective" health education for patients undergoing day surgery in ophthalmology was better than conventional health education.

Background: In view of the lack of attention and predictability in postpartum breastfeeding in primiparas, health education on breastfeeding during pregnancy should be carried out to publicize the benefits of breastfeeding.

Objective: To investigate how well the primiparas during pregnancy are known of breastfeeding knowledge, and to provide a basis for developing health education measures for them.

Methods: With the adoption of the objective sampling method and the principle of saturation, 10 primiparas in the obstetrics outpatient department of the Hunan Provincial People's Hospital were selected as the study participants. Semi-structured in-depth interviews combined with the observation method were used for data collection. The interview data were analyzed and the theme was refined by Colaizzi's seven-step method.

Results: The results of the four themes of the cognition of breastfeeding knowledge among primiparas were as follows: Lack of knowledge and curiosity about breastfeeding in some women, lack of access to correct breastfeeding knowledge, insufficient support from family members for postpartum breastfeeding, and lack of approach to solve problems during breastfeeding among primiparas.

Conclusion: Due to the current problems of the cognition of breastfeeding knowledge in primiparas, it was imperative to build a health education model suitable for primiparas to improve this knowledge.