 Subjects -> SOCIAL SERVICES AND WELFARE (Total: 224 journals)
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- Neonatal Disease Prediction Using Machine Learning Techniques
Abstract: Neonatal diseases are among the main causes of morbidity and a significant contributor to underfive mortality in the world. There is an increase in understanding of the pathophysiology of the diseases and the implementation of different strategies to minimize their burden. However, improvements in outcomes are not adequate. Limited success is due to different factors, including the similarity of symptoms, which can lead to misdiagnosis, and the inability to detect early for timely intervention. In resource-limited countries like Ethiopia, the challenge is more severe. Low access to diagnosis and treatment due to the inadequacy of neonatal health professionals is one of the shortcomings. Due to the shortage of medical facilities, many neonatal health professionals are forced to decide the type of disease only based on interviews. They may not have a complete picture of all variables that have a contributing effect on neonatal disease from the interview. This can make the diagnosis inconclusive and may lead to a misdiagnosis. Machine learning has great potential for early prediction if relevant historical data is available. We have applied a classification stacking model for the following four main neonatal diseases: sepsis, birth asphyxia, necrotizing enter colitis (NEC), and respiratory distress syndrome. These diseases account for 75% of neonatal deaths. The dataset has been obtained from the Asella Comprehensive Hospital. It has been collected between 2018 and 2021. The developed stacking model was compared to three related machine-learning models XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). The proposed stacking model outperformed the other models, with an accuracy of 97.04%. We believe that this will contribute to the early detection and accurate diagnosis of neonatal diseases, especially for resource-limited health facilities.
PubDate: Thu, 23 Feb 2023 08:20:00 +000
- Retracted: Discipline Construction and Development of Medical Universities
in Complex Environment under Digital Technology and Structural Equation
Model
PubDate: Wed, 22 Feb 2023 12:05:00 +000
- Edge-Enabled Heart Rate Estimation from Multisensor PPG Signals
Abstract: Heart rate (HR) estimation from multisensor PPG signals suffers from the dilemma of inconsistent computation results, due to the prevalence of bio-artifacts (BAs). Furthermore, advancements in edge computing have shown promising results from capturing and processing diversified types of sensing signals using the devices of Internet of Medical Things (IoMT). In this paper, an edge-enabled method is proposed to estimate HRs accurately and with low latency from multisensor PPG signals captured by bilateral IoMT devices. First, we design a real-world edge network with several resource-constrained devices, divided into collection edge nodes and computing edge nodes. Second, a self-iteration RR interval calculation method, at the collection edge nodes, is proposed leveraging the inherent frequency spectrum feature of PPG signals and preliminarily eliminating the influence of BAs on HR estimation. Meanwhile, this part also reduces the volume of sent data from IoMT devices to compute edge nodes. Afterward, at the computing edge nodes, a heart rate pool with an unsupervised abnormal detection method is proposed to estimate the average HR. Experimental results show that the proposed method outperforms traditional approaches which rely on a single PPG signal, attaining better results in terms of the consistency and accuracy for HR estimation. Furthermore, at the designed edge network, our proposed method processes a 30 s PPG signal to obtain an HR, consuming only 4.24 s of computation time. Hence, the proposed method is of significant value for the low-latency applications in the field of IoMT healthcare and fitness management.
PubDate: Wed, 22 Feb 2023 11:05:00 +000
- Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency
in IoHT Systems
Abstract: Deep neural networks (DNNs) have been widely adopted in many fields, and they greatly promote the Internet of Health Things (IoHT) systems by mining health-related information. However, recent studies have shown the serious threat to DNN-based systems posed by adversarial attacks, which has raised widespread concerns. Attackers maliciously craft adversarial examples (AEs) and blend them into the normal examples (NEs) to fool the DNN models, which seriously affects the analysis results of the IoHT systems. Text data is a common form in such systems, such as the patients’ medical records and prescriptions, and we study the security concerns of the DNNs for textural analysis. As identifying and correcting AEs in discrete textual representations is extremely challenging, the available detection techniques are still limited in performance and generalizability, especially in IoHT systems. In this paper, we propose an efficient and structure-free adversarial detection method, which detects AEs even in attack-unknown and model-agnostic circumstances. We reveal that sensitivity inconsistency prevails between AEs and NEs, leading them to react differently when important words in the text are perturbed. This discovery motivates us to design an adversarial detector based on adversarial features, which are extracted based on sensitivity inconsistency. Since the proposed detector is structure-free, it can be directly deployed in off-the-shelf applications without modifying the target models. Compared to the state-of-the-art detection methods, our proposed method improves adversarial detection performance, with an adversarial recall of up to 99.7% and an F1-score of up to 97.8%. In addition, extensive experiments have shown that our method achieves superior generalizability as it can be generalized across different attackers, models, and tasks.
PubDate: Wed, 22 Feb 2023 10:20:00 +000
- A Lightweight Three-Party Mutual Authentication Protocol for Internet of
Health Things Systems
Abstract: In Internet of Health Things (IoHT) systems, there is a two-hop network structure between the authentication server TA, Internet of Things Connector (IotC), and wearable sensor (WS). Attackers can use the sensor layer network (the first hop) between the IotC and WS to steal patient’s health-related information and undermine the security of the system and the privacy of sensitive information. To address this threat, this study proposes a lightweight identity authentication and key agreement protocol for third-party authentication servers TA, IotC, and WS. The results of the formal security proof, BAN logic analysis, and AVISPA tool simulation show that the scheme proposed in this study has an ideal security performance and can meet the security requirements of IoHT. In terms of performance, the proposed scheme could dynamically construct a sensor layer network (the first hop) and offline networking according to the diagnostic needs of doctors. Compared with other related protocols, the proposed scheme can significantly reduce the computing resource requirements of IotC and server TA and the resource requirements of database I/O operation of server TA in the application scenario of concurrent access of multiple WS nodes.
PubDate: Wed, 22 Feb 2023 07:50:00 +000
- A Pilot Study of Plantar Mechanics Distributions and Fatigue Profiles
after Running on a Treadmill: Using a Support Vector Machine Algorithm
Abstract: The treadmill is widely used in running fatigue experiments, and the variation of plantar mechanical parameters caused by fatigue and gender, as well as the prediction of fatigue curves by a machine learning algorithm, play an important role in providing different training programs. This experiment aimed to compare changes in peak pressure (PP), peak force (PF), plantar impulse (PI), and gender differences of novice runners after they were fatigued by running. A support vector machine (SVM) was used to predict the fatigue curve according to the changes in PP, PF, and PI before and after fatigue. 15 healthy males and 15 healthy females completed two runs at a speed of 3.3 m/s ± 5% on a footscan pressure plate before and after fatigue. After fatigue, PP, PF, and PI decreased at hallux (T1) and second-fifth toes (T2–5), while heel medial (HM) and heel lateral (HL) increased. In addition, PP and PI also increased at the first metatarsal (M1). PP, PF, and PI at T1 and T2–5 were significantly higher in females than in males, and metatarsal 3–5 (M3–5) were significantly lower in females than in males. The SVM classification algorithm results showed the accuracy was above average level using the T1 PP/HL PF (train accuracy: 65%; test accuracy: 75%), T1 PF/HL PF (train accuracy: 67.5%; test accuracy: 65%), and HL PF/T1 PI (train accuracy: 67.5%; test accuracy: 70%). These values could provide information about running and gender-related injuries, such as metatarsal stress fractures and hallux valgus. Application of the SVM to the identification of plantar mechanical features before and after fatigue. The features of the plantar zones after fatigue can be identified and the learned algorithm of plantar zone combinations with above-average accuracy (T1 PP/HL PF, T1 PF/HL PF, and HL PF/T1 PI) can be used to predict running fatigue and supervise training. It provided an important idea for the detection of fatigue after running.
PubDate: Tue, 21 Feb 2023 09:35:00 +000
- Investigating the Mediating Effect of Patient Self-Efficacy on the
Relationship between Patient Safety Engagement and Patient Safety in
Healthcare Professionals
Abstract: Patient safety and involvement of the patients in their safety engagement activities are considered the most important elements in the healthcare professions due to their impact on various individual and organizational outcomes. The study used responses of 456 patients. The simple random sampling (SRS) technique was used to collect data from the respondents. The researcher used individuals as the unit of analysis in this study. The results revealed that patient safety engagement had a positive significant effect on patient safety. When the mediating variable of self-efficacy was analyzed, it showed a significant mediated effect on patient safety. Therefore, it was concluded that self-efficacy mediated the relationship between patient safety engagement and patient safety. The findings of the current study convey that engagement of the patient in the practices for patient safety is predicted through the level of self-efficacy of the patient. The study discussed various implications for theory and practice. The study also discussed potential avenues for future research.
PubDate: Tue, 21 Feb 2023 08:50:01 +000
- Critical Device Reliability Assessment in Healthcare Services
Abstract: Medical device reliability is the ability of medical devices to endure functioning and is indispensable to ensure service delivery to patients. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) technique was employed in May 2021 to evaluate existing reporting guidelines on medical device reliability. The systematic searching is conducted in eight different databases, including Web of Science, Science Direct, Scopus, IEEE Explorer, Emerald, MEDLINE Complete, Dimensions, and Springer Link, with 36 articles shortlisted from the year 2010 to May 2021. This study aims to epitomize existing literature on medical device reliability, scrutinize existing literature outcomes, investigate parameters affecting medical device reliability, and determine the scientific research gaps. The result of the systematic review listed three main topics on medical device reliability: risk management, performance prediction using Artificial Intelligence or machine learning, and management system. The medical device reliability assessment challenges are inadequate maintenance cost data, determining significant input parameter selection, difficulties accessing healthcare facilities, and limited age in service. Medical device systems are interconnected and interoperating, which increases complexity in assessing their reliability. To the best of our knowledge, although machine learning has become popular in predicting medical device performance, the existing models are only applicable to selected devices such as infant incubators, syringe pumps, and defibrillators. Despite the importance of medical device reliability assessment, there is no explicit protocol and predictive model to anticipate the situation. The problem worsens with the unavailability of a comprehensive assessment strategy for critical medical devices. Therefore, this study reviews the current state of critical device reliability in healthcare facilities. The present knowledge can be improved by adding new scientific data emphasis on critical medical devices used in healthcare services.
PubDate: Mon, 20 Feb 2023 11:50:00 +000
- Retracted: The Application of DOMS Mechanism and Prevention in Physical
Education and Training
PubDate: Sun, 19 Feb 2023 08:50:00 +000
- Integrating Fuzzy Multiobjective Programming and System Dynamics to
Develop an Approach for Talent Retention Policy Selection: Case on
Health-Care Industry
Abstract: The demand for medical services has been increasing yearly in aging countries. Medical institutions must hire a large number of staff members to provide efficient and effective health-care services. Because of high workload and pressure, high turnover rates exist among health-care staff members, especially those in nonurban areas, which are characterized by limited resources and a predominance of elderly people. Turnover in health-care institutions is influenced by complex factors, and high turnover rates result in considerable direct and indirect costs for such institutions (Lo and Tseng 2019). Therefore, health-care institutions must adopt appropriate strategies for talent retention. Because institutions cannot determine the most effective talent retention strategy, many of them simply passively adopt a single human resource (HR) policy and make minor adjustments to the selected policy. In the present study, system dynamics modeling was combined with fuzzy multiobjective programming to develop a method for simulating HR planning systems and evaluating the suitability of different HR policies in an institution. We also considered the external insurance policy to be the parameter for the developed multiobjective decision-making model. The simulation results indicated that reducing the turnover rate of new employees in their trial period is the most effective policy for talent retention. The developed procedure is more efficient, effective, and cheaper than the traditional trial-and-error approaches for HR policy selection.
PubDate: Sat, 18 Feb 2023 08:20:00 +000
- Retracted: Study on Toll-Like Receptor 2-Mediated Inflammation-Induced
Familial Hypertension Combined with Hyperlipemia and Its Mechanism
PubDate: Sat, 18 Feb 2023 06:35:03 +000
- Retracted: Application of Deep Neural Network Model Combined with Factor
Analysis in Clinical Nursing Effect Analysis of Blood Glucose Level in
Elderly Type 2 Diabetic Patients
PubDate: Sat, 18 Feb 2023 06:35:00 +000
- Development and Validation of Deep Learning Models for the
Multiclassification of Reflux Esophagitis Based on the Los Angeles
Classification
Abstract: This study is to evaluate the feasibility of deep learning (DL) models in the multiclassification of reflux esophagitis (RE) endoscopic images, according to the Los Angeles (LA) classification for the first time. The images were divided into three groups, namely, normal, LA classification A + B, and LA C + D. The images from the HyperKvasir dataset and Suzhou hospital were divided into the training and validation datasets as a ratio of 4 : 1, while the images from Jintan hospital were the independent test set. The CNNs- or Transformer-architectures models (MobileNet, ResNet, Xception, EfficientNet, ViT, and ConvMixer) were transfer learning via Keras. The visualization of the models was proposed using Gradient-weighted Class Activation Mapping (Grad-CAM). Both in the validation set and the test set, the EfficientNet model showed the best performance as follows: accuracy (0.962 and 0.957), recall for LA A + B (0.970 and 0.925) and LA C + D (0.922 and 0.930), Marco-recall (0.946 and 0.928), Matthew’s correlation coefficient (0.936 and 0.884), and Cohen’s kappa (0.910 and 0.850), which was better than the other models and the endoscopists. According to the EfficientNet model, the Grad-CAM was plotted and highlighted the target lesions on the original images. This study developed a series of DL-based computer vision models with the interpretable Grad-CAM to evaluate the feasibility in the multiclassification of RE endoscopic images. It firstly suggests that DL-based classifiers show promise in the endoscopic diagnosis of esophagitis.
PubDate: Sat, 18 Feb 2023 06:05:00 +000
- Segmentation of Brain Tissues from MRI Images Using Multitask Fuzzy
Clustering Algorithm
Abstract: In recent years, brain magnetic resonance imaging (MRI) image segmentation has drawn considerable attention. MRI image segmentation result provides a basis for medical diagnosis. The segmentation result influences the clinical treatment directly. Nevertheless, MRI images have shortcomings such as noise and the inhomogeneity of grayscale. The performance of traditional segmentation algorithms still needs further improvement. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. First, we introduce multitask learning strategy into FCM to extract public information among different segmentation tasks. It combines the advantages of the two algorithms. The algorithm enables to utilize both public information among different tasks and individual information within tasks. Then, we design an adaptive task weight learning mechanism, and a weighted multitask fuzzy C-means (WMT-FCM) clustering algorithm is proposed. Under the adaptive task weight learning mechanism, each task obtains the optimal weight and achieves better clustering performance. Simulated MRI images from McConnell BrainWeb have been used to evaluate the proposed algorithm. Experimental results demonstrate that the proposed method provides more accurate and stable segmentation results than its competitors on the MRI images with various noise and intensity inhomogeneity.
PubDate: Fri, 17 Feb 2023 08:05:00 +000
- Healthcare Facilities Redesign Using Multicriteria Decision-Making: Fuzzy
TOPSIS and Graph Heuristic Theories
Abstract: Background. Healthcare facilities are crucial assets that are necessary to be updated and evaluated regularly. One of the most pressing issues today is the renovation of healthcare facilities to match international standards. In large projects involving nations renovating healthcare facilities, it is necessary to rank the evaluated hospitals and medical centers in making optimal decisions for the redesign process. Objective. This study presents the process of renovating old healthcare facilities to meet international standards, applying proposed algorithms for measuring compliance for redesign, and deciding whether or not the redesign process is beneficial. Methods. The evaluated hospitals were ranked using a fuzzy technique for order of preference by similarity to ideal solution algorithm and a reallocation algorithm that calculates the layout score before and after applying the proposed algorithm for the redesign process using bubble plan and graph heuristics techniques. Results and Conclusion. The results of the methodologies applied to 10 evaluated hospitals as selected hospitals in Egypt showed that the hospital with the abbreviation (D) had the most required general hospital criteria, and the hospital with the abbreviation (I) had no cardiac catheterization laboratory and lacked the most international standard criteria. After applying the reallocation algorithm, one hospital’s operating theater layout score improved by 32.5%. Proposed algorithms support decision-making by helping organizations redesign healthcare facilities.
PubDate: Thu, 16 Feb 2023 13:05:00 +000
- Tidal Volume Level Estimation Using Respiratory Sounds
Abstract: Respiratory sounds have been used as a noninvasive and convenient method to estimate respiratory flow and tidal volume. However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels during sleep qualitatively. Respiratory sounds are filtered and segmented into one-minute clips, all clips are clustered into three categories: normal breathing/snoring/uncertain with agglomerative hierarchical clustering (AHC). Formant parameters are extracted to classify snoring clips into simple snoring and obstructive snoring with the K-means algorithm. For simple snoring clips, the tidal volume level is calculated based on snoring last time. For obstructive snoring clips, the tidal volume level is calculated by the maximum breathing pause interval. The performance of the proposed method is evaluated on an open dataset, PSG-Audio, in which full-night polysomnography (PSG) and tracheal sound were recorded simultaneously. The calculated tidal volume levels are compared with the corresponding lowest nocturnal oxygen saturation (LoO2) data. Experiments show that the proposed method calculates tidal volume levels with high accuracy and robustness.
PubDate: Thu, 16 Feb 2023 13:05:00 +000
- Detection of COVID-19 Case from Chest CT Images Using Deformable Deep
Convolutional Neural Network
Abstract: The infectious coronavirus disease (COVID-19) has become a great threat to global human health. Timely and rapid detection of COVID-19 cases is very crucial to control its spreading through isolation measures as well as for proper treatment. Though the real-time reverse transcription-polymerase chain reaction (RT-PCR) test is a widely used technique for COVID-19 infection, recent researches suggest chest computed tomography (CT)-based screening as an effective substitute in cases of time and availability limitations of RT-PCR. In consequence, deep learning-based COVID-19 detection from chest CT images is gaining momentum. Furthermore, visual analysis of data has enhanced the opportunities of maximizing the prediction performance in this big data and deep learning realm. In this article, we have proposed two separate deformable deep networks converting from the conventional convolutional neural network (CNN) and the state-of-the-art ResNet-50, to detect COVID-19 cases from chest CT images. The impact of the deformable concept has been observed through performance comparative analysis among the designed deformable and normal models, and it is found that the deformable models show better prediction results than their normal form. Furthermore, the proposed deformable ResNet-50 model shows better performance than the proposed deformable CNN model. The gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions’ localization effort at the final convolutional layer and has been found excellent. Total 2481 chest CT images have been used to evaluate the performance of the proposed models with a train-valid-test data splitting ratio of 80 : 10 : 10 in random fashion. The proposed deformable ResNet-50 model achieved training accuracy of 99.5% and test accuracy of 97.6% with specificity of 98.5% and sensitivity of 96.5% which are satisfactory compared with related works. The comprehensive discussion demonstrates that the proposed deformable ResNet-50 model-based COVID-19 detection technique can be useful for clinical applications.
PubDate: Thu, 16 Feb 2023 09:35:01 +000
- Retracted: A Study on the Effects of Chinese Massage on Physical and
Mental Health in Participants Based Smart Healthcare
PubDate: Thu, 16 Feb 2023 09:05:01 +000
- Clinical Observation of Anatomical and Visual Outcomes of Macular Hole
after Inverted Internal Limiting Membrane Flap in Patients with Idiopathic
Macular
Abstract: Objective. To investigate anatomical and visual outcomes of macular hole (MH) after inverted internal limiting membrane (ILM) flap technique for idiopathic macular hole (IMH). Methods. A total of 13 IMH cases diagnosed in Shanxi Eye Hospital between January 2015 and June 2016 were included in the study. All patients underwent vitrectomy combined with indocyanine green-assisted inverted ILM flap technique. The MH closure rate, best-corrected visual acuity (BCVA), changes of ellipsoid zone (EZ), and external limiting membrane (ELM) were examined before operation and one, three, and six months after operation. Furthermore, 488 nm fundus autofluorescence (FAF) and spectral domain optical coherence tomography (SD-OCT) were used to observe the dynamic changes in function of macular area after surgery. Results. One month after the surgery, the MH closure rate was 100% and the visual acuity (VA) was stable, with no recurrence. Additionally, the average logMAR BCVA before operation was 1.208 ± 0.158, and this value became 0.877 ± 0.105 one month after the operation, showing a significant decrease. Three months after surgery, the average logMAR BCVA was 0.792 ± 0.103, which was significantly lower than the level one month after the surgery but much higher than that six months after surgery (0.708 ± 0.131). Besides, the diameter of the EZ defect of the postoperative one month, three months, and six months was (1377.46 ± 198.65) μm, (964.62 ± 336.26) μm, and (817.08 ± 442.99) μm, respectively. In postoperative one month, three months, and six months, the diameter of the ELM defect diameter was (969.62 ± 189.92) μm, (649.92 ± 413.15) μm, and (557.62 ± 412.50) μm, respectively. The diameter of both EZ and ELM defects was significantly reduced with the passage of time after surgery. Conclusion. Inverted ILM flap technique can reconstruct macular anatomical structure and improve VA. This technique is effective for the treatment of IMH with large MH minimum diameter and base diameter.
PubDate: Wed, 15 Feb 2023 07:35:00 +000
- Optimization of Tree-Based Machine Learning Models to Predict the Length
of Hospital Stay Using Genetic Algorithm
Abstract: The length of hospital stay (LOS) is a significant indicator of the quality of patient care, hospital efficiency, and operational resilience. Considering the importance of LOS in hospital resource management, this research aims to improve the accuracy of LOS prediction using hyperparameter optimization (HPO). Expert physicians and related studies were reviewed to determine the variables affecting LOS. The electronic medical records of 200 patients in the department of internal medicine of a hospital in Iran were collected randomly. As the performance of machine learning (ML) models can vary based on the characteristics of the features, several models were applied and evaluated in this study. In particular, k-nearest neighbors (KNN), multivariate regression, decision tree (DT), random forest (RF), artificial neural network (ANN), and XGBoost have been evaluated and improved. The genetic algorithm (GA) was applied to optimize the tree-based models. In addition, the dummy coding technique, sometimes called the One-Hot encoding, was used to encode categorical features to increase prediction accuracy. Compared with other algorithms, the XGBoost model optimized by GA (XGB_GA) achieved higher accuracy and better prediction performance. The mean and median of absolute errors in the test dataset for this model were 1.54 and 1.14 days, respectively. In other words, the XGB_GA model reduced the mean absolute error by 37%, which is beneficial in the reliable design of a clinical decision support system.
PubDate: Tue, 14 Feb 2023 09:50:00 +000
- Retracted: Dynamic Enhanced Magnetic Resonance Imaging versus Ultrasonic
Diffused Optical Tomography in Early Diagnosis of Breast Cancer
PubDate: Tue, 14 Feb 2023 09:20:00 +000
- Predictors of Physical Activity Behavior Transitions in Children and
Adolescents: A Systematic Review Based on a Transtheoretical Model
Abstract: Background. The transtheoretical model (TTM) views individual behavioral change as a nonlinear, dynamic process, which is consistent with the complex nature of physical activity (PA) in children and adolescents. However, within this theoretical framework, the elements that facilitate the behavioral change in PA in children and adolescents need to be further explored. Objective. A systematic review of research related to TTM-based exploration of the elements of behavioral change in PA in children and adolescents, an analysis of the strengths and weaknesses in practice, and an outlook for future research. Materials and Methods. After computer searches of the CNKI, Wan-Fang, VIP, WOS, PubMed, and EBSCO databases, two researchers independently screened articles, extracted information, and evaluated the quality of the articles. Results. A total of 25 articles (26 studies) of medium- to high-quality were included in the systematic review. The meta-analysis included 30,106 children and adolescents aged 11.24 to 17.7 years. The counter-conditioning and self-liberation of the process of change, self-efficacy and decisional balance are key elements that facilitate the transition of the PA stage in children and adolescents. Extramodel psychological variables such as exercise motivation play a moderate to large role in the PA stage transition. In addition, VPA is an important discriminator of PA stage transition in children and adolescents. Conclusion. It is recommended that interventions be designed according to the key elements of behavioral change in order to better facilitate the PA stage transition of children and adolescents.
PubDate: Tue, 14 Feb 2023 08:35:01 +000
- Changes in Nutritional Status of Cancer Patients Undergoing Proton
Radiation Therapy Based on Real-World Data
Abstract: Background and Aim. Patients with cancer are at high risk of malnutrition. Radiation is critical for tumor control but may also exacerbate malnutrition. Proton radiotherapy is a technological advanced radiotherapy which has advantage over conventional radiotherapy in the reduction of toxicity and the improvement of clinical outcomes. In this study, we aimed to investigate the effect of proton radiotherapy on the nutritional status of cancer patients. Methods. Observational study on 47 adult hospitalized cancer patients including 27 males and 20 females who received proton beam radiotherapy during December 2021 and August 2022. Nutritional assessments, 24 h dietary survey, handgrip strength (HGS) test, anthropometrical measurements, and hematological parameters were conducted or collected at the beginning and the completion of treatment. Results. The rate of nutritional risk and malnutrition among the total of 47 enrolled patients was 4.3% and 12.8% at the onset of proton radiation and raised up to 6.4% and 27.7% at the end of the treatment. 42.6% of patients experienced weight loss during the proton radiotherapy, and 1 of them had weight loss over 5%, and in general, the average body weight was stable over radiotherapy. The changes in patients’ 24 h dietary intakes, HGS, and anthropometrical parameters, including triceps skinfold thickness (TSF), midupper arm circumference (MUAC), and midupper arm muscle circumference (MAMC), were statistically insignificant over the treatment (all values > 0.05). The changes in patients’ hematological parameters, including total protein (TP) and serum albumin (ALB), were not statistically significant over the treatment (all values >0.05), and the level of hemoglobin (HGB) at the end of treatment was higher than that at the onset ().Conclusion. The results of this study demonstrated that proton radiotherapy might have a lighter effect on the nutritional status of cancer patients.
PubDate: Tue, 14 Feb 2023 07:50:01 +000
- Retracted: Effects of Equine-Assistant Activity on Gross Motor
Coordination in Children Aged 8 to 10 Years
PubDate: Mon, 13 Feb 2023 14:35:00 +000
- An IoMT-Based Approach for Real-Time Monitoring Using Wearable
Neuro-Sensors
Abstract: The Internet of Things (IoT) has demonstrated over the past few decades to be a powerful tool for connecting various medical equipment with in-built sensors and healthcare professionals to deliver superior health services that also reach remote areas. In addition to reducing healthcare costs, increasing access to clinical services, and enhancing operational effectiveness in the healthcare industry, it has also enhanced patient health safety. Recent research has focused on using EEG to assist and comprehend brain changes in rehabilitation facilities. These technologies can spot fluctuations in EEG constraints during treatment, which could result in more effective therapy and better functional outcomes. As a result, we have tried to use an IoT-based system for real-time monitoring of the constraints. Another unknown patient who is suffering from acute ischemic stroke may experience stroke-in-evolution or an early worsening of neurological symptoms, which is frequently associated with poor clinical outcomes. Because of this, managing an acute stroke requires early detection of these indications. The present investigation work will act as a standard reference for academic researchers, medical professionals, and everyone else involved in the use of IoMT. This study aims to anticipate strokes sooner and prevent their consequences by early intervention using an Internet of Things (IoT)-based system. Also, this study proposes usage of wearable equipment that can monitor and analyze brain signals for improved treatment and the prevention of stroke-related complications.
PubDate: Mon, 13 Feb 2023 11:05:00 +000
- Provably Secure and Lightweight Patient Monitoring Protocol for Wireless
Body Area Network in IoHT
Abstract: As one of the important applications of Internet of Health Things (IoHT) technology in the field of healthcare, wireless body area network (WBAN) has been widely used in medical therapy, and it can not only monitor and record physiological information but also transmit the data collected by sensor devices to the server in time. However, due to the unreliability and vulnerability of wireless network communication, as well as the limited storage and computing resources of sensor nodes in WBAN, a lot of authentication protocols for WBAN have been devised. In 2021, Alzahrani et al. designed an anonymous medical monitoring protocol, which uses lightweight cryptographic primitives for WBAN. However, we find that their protocol is defenseless to off-line identity guessing attacks, known-key attacks, and stolen-verifier attacks and has no perfect forward secrecy. Therefore, a patient monitoring protocol for WBAN in IoHT is proposed. We use security proof under the random oracle model (ROM) and automatic verification tool ProVerif to demonstrate that our protocol is secure. According to comparisons with related protocols, our protocol can achieve both high computational efficiency and security.
PubDate: Mon, 13 Feb 2023 09:50:00 +000
- Development of Health Digital GIS Map for Tuberculosis Disease
Distribution Analysis in Sudan
Abstract: Health digital GIS map provides a great solution for medical geographical distribution to efficiently explore diseases and health services. In Sudan, tuberculosis disease is expanding in different areas, which requires a digital GIS map to collect information about the patients and support medical institutions by geographical distribution based on health services, drug supply, and consumption. This paper developed a health digital GIS map to provide a fair geographical distribution of tuberculosis health centers and control the drug supply according to medical reports. The proposed approach extracts the unfair distribution of medicine, as some centers receive medicine but do not receive patients, while others receive a large number of patients but limited amounts of medicine. The analysis results show that there is a defect in some states representing the distribution of tuberculosis centers. In the Northern State, there are 15 tuberculosis centers distributed over all localities, serving about 84 tuberculosis-infected patients only.
PubDate: Mon, 13 Feb 2023 09:05:00 +000
- Pyroptosis-Related Genes as Markers for Identifying Prognosis and
Microenvironment in Low-Grade Glioma
Abstract: Low-grade glioma (LGG) is one of the most common brain tumors and often develops into the worst glioblastoma (GBM). Pyroptosis is related to inflammation and immunization. It has been demonstrated to influence the progression of a variety of cancers. However, the value of pyrosis-related genes (PRGs) in LGG remains unclear. Public TCGA-LGG data are used to analyze the differential expression and genetic variation of PRGs in LGG. Subsequently, this paper identifies pyroptosis-related subtypes and constructs prognostic models. This paper analyzes the expression and function of selected CASP5 in LGG and constructs a ceRNA regulatory network. Final CASP5-related immune infiltration analysis and methylation analysis are performed. Most PRGs are differentially expressed and altered in LGG. Subtypes and prognostic models based on PRGs not only have good functions but also have a great connection with immune infiltration. Enrichment analysis of PRGs with prognostic value of LGG also shows functions correlated mainly with immunity and inflammation. CASP5 is significantly differentially expressed in different grades of gliomas and different prognoses. Despite fewer mutations, CASP5 has a clear correlation for both immune cells and immune checkpoint molecules in the LGG microenvironment. Its methylation may also have a role in the prognosis of LGG. This paper shows the association of pyrosis-related subtypes, prognostic models, and genes, with immune infiltration.
PubDate: Sat, 11 Feb 2023 06:50:00 +000
- An Optimization Method Combining RSSI and PDR Data to Estimate Distance
between Smart Devices for COVID-19 Contact Tracing
Abstract: Distance estimation methods arise in many applications, such as indoor positioning and COVID-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large-ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide high accuracy of walking distance and direction. Moreover, the parameters of the path loss model are optimized to dynamically fit the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor environments and compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with an improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, compared with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.
PubDate: Thu, 09 Feb 2023 11:05:00 +000
- Self-Healing Supramolecular Hydrogels with Antibacterial Abilities for
Wound Healing
Abstract: Wound healing due to skin defects is a growing clinical concern. Especially when infection occurs, it not only leads to impair healing of the wound but even leads to the occurrence of death. In this study, a self-healing supramolecular hydrogel with antibacterial abilities was developed for wound healing. The supramolecular hydrogels inherited excellent self-healing and mechanical properties are produced by the polymerization of N-acryloyl glycinamide monomers which carries a lot of amides. In addition, excellent antibacterial properties are obtained by integrating silver nanoparticles (Ag NPs) into the hydrogels. The resultant hydrogel has a demonstrated ability in superior mechanical properties, including stretchability and self-healing. Also, the good biocompatibility and antibacterial ability have been proven in hydrogels. Besides, the prepared hydrogels were employed as wound dressings to treat skin wounds of animals. It was found that the hydrogels could significantly promote wound repair, including relieving inflammation, promoting collagen deposition, and enhancing angiogenesis. Therefore, such self-healing supramolecular hydrogels with composite functional nanomaterials are expected to be used as new wound dressings in the field of healthcare.
PubDate: Thu, 09 Feb 2023 11:05:00 +000
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Open Access journal
