 Subjects -> SOCIAL SERVICES AND WELFARE (Total: 224 journals)
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- Subgroup State Prediction under Different Noise Levels Using MODWT and
XGBoost
Abstract: In medical states prediction, the observations of different individuals are generally assumed to follow an identical distribution, whereas precision medicine has a rigorous requirement for accurate subgroup analysis. In this research, an aggregated method is proposed by means of combining the results generated from different subgroup models and is compared with the original method for different denoising levels as well as the prediction gaps. The results using real data demonstrate the effectiveness of the aggregated method exhibiting superior performance such as 0.95 in AUC, 0.87 in F1, and 0.82 in sensitivity, particularly for the denoising level that is set to be 2. With respect to the variable importance, it is shown that some variables such as heart rate and lactate arterial become more important when the denoising level increases.
PubDate: Tue, 31 Jan 2023 09:50:00 +000
- Development and Evaluation of BLE-Based Room-Level Localization to Improve
Hand Hygiene Performance Estimation
Abstract: Hand hygiene is one of the most effective ways to prevent infection transmission. However, current electronic monitoring systems are not able to identify adherence to all hand hygiene (HH) guidelines. Location information can play a major role in enhancing HH monitoring resolution. This paper proposes a BLE-based solution to localize healthcare workers inside the patient room. Localization accuracy was evaluated using one to four beacons in a binary (entrance/proximal patient zone) or multiclass (entrance/sink/right side of the bed/left side of the bed) proximity-based positioning problem. Dynamic fingerprints were collected from nine different subjects performing 30 common nursing activities. Extremely randomized trees algorithm achieved the best accuracies of 81% and 71% in the binary and multiclass classifications, respectively. The proposed method can be further used as a proxy for caregiving activity recognition to improve the risk of infection transmission in healthcare settings.
PubDate: Tue, 31 Jan 2023 08:50:00 +000
- Polyetherketoneketone Mesh for Alveolar Bone Augmentation: Geometric
Parameter Design and Finite Element Analysis
Abstract: Objective. To evaluate the mechanical properties of porous polyetherketoneketone (PEKK) meshes with different thicknesses, pore sizes, and porosities through finite element analysis to provide an optimal PEKK design for alveolar bone augmentation in the posterior mandibular region. Methods. A three-dimensional evaluation model of severe alveolar bone defects in the mandibular posterior was constructed based on cone beam computerized tomography (CBCT) data. Then, PEKK meshes with different structural designs were obtained. Two key parameters were set with different values: five levels of thickness (0.2 mm, 0.3 mm, 0.4 mm, 0.5 mm, and 0.6 mm) and three levels of pore size (1 mm, 2 mm, and 3 mm) with a corresponding porosity of 19.18%–42.67%. A 100 N physiological force was simultaneously loaded by finite element analysis (FEA), and the deformation and stress data were outputted for further analysis. Results. The deformation and stress of the PEKK meshes are negatively correlated with the changes in thickness and positively correlated with the changes in pore size. The FEA results show that the maximum deformation, equivalent stress, and maximum principal stress of the PEKK meshes are 0.168 mm–0.478 mm, 49.243 MPa–124.890 MPa, and 31.549 MPa–104.200 MPa, respectively. The PEKK mesh group with a thickness of 0.2 mm, pore size of 3 mm, and porosity of 42.67% is in danger of plastic deformation or even fracture during use. Conclusion. According to the FEA results, the PEKK meshes with larger thicknesses and smaller pore sizes and porosities behave better. In consideration of reducing soft tissue stimulation and promoting bone regeneration, an ultrathin porous PEKK mesh with a pore size of no more than 3 mm, porosity of no more than 42.67%, and thickness of 0.2 mm can be used clinically to meet the mechanical performance requirements of the guided bone regeneration (GBR) structure.
PubDate: Tue, 31 Jan 2023 07:20:00 +000
- Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for
Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model
Abstract: The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.
PubDate: Tue, 31 Jan 2023 01:35:01 +000
- Retracted: Analysis of the miRNA-mRNA Regulatory Network Reveals the
Biomarker Genes in the Progression of Myocardial Ischemic Reperfusion
PubDate: Mon, 30 Jan 2023 12:50:05 +000
- Retracted: Analysis on Value of Applying Serum miR-144 and miR-221 Levels
in Diagnosing Atherosclerosis
PubDate: Mon, 30 Jan 2023 12:50:03 +000
- Retracted: LncRNA MAGI2-AS3 Suppresses the Proliferation and Invasion of
Cervical Cancer by Sponging MiR-15b
PubDate: Mon, 30 Jan 2023 12:50:00 +000
- Early Detection and Diagnosis of Chronic Kidney Disease Based on Selected
Predominant Features
Abstract: In numerous perilous cases, a quick medical decision is needed for the early detection of chronic diseases to avoid austere consequences that may be fatal. Chronic kidney disease (CKD) is a prevalent disease that presents a variety of challenges, including soaring costs for intervention, urgency, and, more importantly, difficulty in early detection of the disease. The current study carries out a prediction-based method that helps in detecting and diagnosing CKD patients which enables a fast and accurate decision-making process at the early stage. A combination of preprocessing and feature selection methods was developed; additionally, several prediction models, such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and bagging, were trained based on the processed dataset. The performance evaluation shows higher reliability of all models in terms of accuracy, precision, sensitivity, F-measure, specificity, and area under the curve (AUC) score. Specifically, KNN outperformed with an accuracy of 99.50%, sensitivity of 99.2%, precision of 100%, specificity of 98.7%, and F-measure and AUC score of 99.6%. The experimental results of KNN show the best fitted model compared to the existing state-of-the-art methods. Moreover, the reduced feature set proves that just a few clinical tests are enough to detect CKD, resulting in diagnosis cost reduction.
PubDate: Mon, 30 Jan 2023 11:05:00 +000
- A New Approach to Noninvasive-Prolonged Fatigue Identification Based on
Surface EMG Time-Frequency and Wavelet Features
Abstract: In sports, fatigue management is vital as adequate rest builds strength and enhances performance, whereas inadequate rest exposes the body to prolonged fatigue (PF) or also known as overtraining. This paper presents PF identification and classification based on surface electromyography (EMG) signals. An experiment was performed on twenty participants to investigate the behaviour of surface EMG during the inception of PF. PF symptoms were induced in accord with a five-day Bruce Protocol treadmill test on four lower extremity muscles: the biceps femoris (BF), rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL). The results demonstrate that the experiment successfully induces soreness, unexplained lethargy, and performance decrement and also indicate that the progression of PF can be observed based on changes in frequency features (ΔFmed and ΔFmean) and time features (ΔRMS and ΔMAV) of surface EMG. This study also demonstrates the ability of wavelet index features in PF identification. Using a naïve Bayes (NB) classifier exhibits the highest accuracy based on time and frequency features with 98% in distinguishing PF on RF, 94% on BF, 9% on VL, and 97% on VM. Thus, this study has positively indicated that surface EMG can be used in identifying the inception of PF. The implication of the findings is significant in sports to prevent a greater risk of PF.
PubDate: Mon, 30 Jan 2023 10:20:00 +000
- Retracted: Correlation of ICU Nurses’ Cognitive Level with Their
Attitude and Behavior toward the Prevention of Ventilator-Associated
Pneumonia
PubDate: Sun, 29 Jan 2023 12:35:05 +000
- Retracted: MicroRNA-517c Functions as a Tumor Suppressor in Hepatocellular
Carcinoma via Downregulation of KPNA2 and Inhibition of PI3K/AKT Pathway
PubDate: Sun, 29 Jan 2023 12:35:03 +000
- Comprehensive Bioinformatics Analysis Reveals PTPN1 (PTP1B) Is a Promising
Immunotherapy Target Associated with T Cell Function for Liver Cancer
Abstract: Recently, PTP1B was identified as a novel immune checkpoint whose removal can unleash T cell responses. However, research on the influence of PTP1B as an immune regulator on liver cancer is limited. This study aimed to investigate the immunological correlation and function of PTP1B in liver cancer. The expression profiles and corresponding clinical information of liver cancer patients were obtained from the TCGA and ICGC databases. GSE146115 and GSE98638 retrieved from the GEO database were used for the single-cell RNA-seq analysis. The mRNA expression of PTP1B (PTPN1) was increased in patients with most malignancies (all ), including liver cancer (). Furthermore, up-regulated PTPN1 was connected to advanced tumor stage () and worse prognosis () in liver cancer. Through Cox regression analysis, PTPN1 was considered as an independent prognosis factor of overall survival () and acted as a high-risk factor (hazard ratio > 1). Gene function and pathway analysis suggested PTPN1 was involved in T cell-related immune responses. Moreover, a close relationship was also found between PTPN1 expression and immune checkpoints as well as immune cells, especially with T cell-related checkpoints (all ) and T cells (all ). Single-cell RNA-seq analysis further illustrated that the enrichment of PTPN1 in the T cell population may be linked to its exhaustion in the liver cancer microenvironment. Overall, PTPN1 (PTP1B) closely related to T cell may function as an immunotherapy target for liver cancer.
PubDate: Fri, 27 Jan 2023 10:05:00 +000
- Retracted: Investigation on the Effect of Graded Emergency Nursing Group
under the Assistance of Multidisciplinary First Aid Knowledge
Internet-Based Approach on the First Aid of Acute Myocardial Infarction
PubDate: Thu, 26 Jan 2023 11:20:02 +000
- Retracted: Clinical Effect of Bushen Huoxue Method Combined with
Platelet-Rich Plasma in the Treatment of Knee Osteoarthritis and Its
Effect on IL-1, IL-6, VEGF, and PGE-2
PubDate: Thu, 26 Jan 2023 11:20:00 +000
- Retracted: Effect of Respiration Training-Assisted Western Medicine
Therapy on Activity Tolerance, Pulmonary Function, and Quality of Life of
Chronic Obstructive Pulmonary Disease Patients in the Stable Phase
PubDate: Thu, 26 Jan 2023 11:05:00 +000
- Retracted: Exploration of the Curative Effect of Early Enteral Nutrition
Nursing on Patients with Severe Acute Pancreatitis and the Improvement of
Patients’ Mental Health and Inflammation Level
PubDate: Thu, 26 Jan 2023 10:20:02 +000
- Retracted: Effects of the Training of Aerobic Function on Clinical
Symptoms and Quality of Life in Patients with Medium and Advanced Lung
Cancer
PubDate: Thu, 26 Jan 2023 10:20:00 +000
- Retracted: MiR-139-5p Inhibits the Development of Gastric Cancer through
Targeting TPD52
PubDate: Thu, 26 Jan 2023 10:05:00 +000
- Retracted: Application of Machine Learning in Rheumatic Immune Diseases
PubDate: Thu, 26 Jan 2023 09:50:00 +000
- Retracted: Synergistic Effect of Stereotactic Radiotherapy Combined with
Karelizumab on Patients with Advanced NSCLC
PubDate: Wed, 25 Jan 2023 13:35:03 +000
- Retracted: Effect of Resveratrol Combined with Donepezil Hydrochloride on
Inflammatory Factor Level and Cognitive Function Level of Patients with
Alzheimer’s Disease
PubDate: Wed, 25 Jan 2023 13:35:01 +000
- Retracted: A Study on Risk Factors Associated with Reflux Esophagitis in
Patients Undergoing Esophageal Cancer Surgery
PubDate: Wed, 25 Jan 2023 13:35:00 +000
- Retracted: Evidence-Based Care Can Improve Treatment Compliance and
Quality of Life of Patients with Acute Pancreatitis
PubDate: Wed, 25 Jan 2023 12:35:01 +000
- Retracted: Effectiveness and Prognosis: Drainage Skin-Bridge Sparing
Surgery Combined with Fistulotomy versus Fistulotomy Only in the Treatment
of Anal Fistula
PubDate: Wed, 25 Jan 2023 12:35:00 +000
- Retracted: The Exosomes Containing LINC00461 Originated from Multiple
Myeloma Inhibit the Osteoblast Differentiation of Bone Mesenchymal Stem
Cells via Sponging miR-324-3p
PubDate: Wed, 25 Jan 2023 11:50:00 +000
- Retracted: Treatment Effect of Mometasone Furoate Cream on Lichen
Sclerosus et Atrophicus of External Genitalia in Boys and Its Correlation
with Toll-Like Receptor 4 and Myeloid Differentiation Factor 88
PubDate: Wed, 25 Jan 2023 11:35:01 +000
- An End-to-End Data-Adaptive Pancreas Segmentation System with an Image
Quality Control Toolbox
Abstract: With the development of radiology and computer technology, diagnosis by medical imaging is heading toward precision and automation. Due to complex anatomy around the pancreatic tissue and high demands for clinical experience, the assisted pancreas segmentation system will greatly promote clinical efficiency. However, the existing segmentation model suffers from poor generalization among images from multiple hospitals. In this paper, we propose an end-to-end data-adaptive pancreas segmentation system to tackle the problems of lack of annotations and model generalizability. The system employs adversarial learning to transfer features from labeled domains to unlabeled domains, seeking a dynamic balance between domain discrimination and unsupervised segmentation. The image quality control toolbox is embedded in the system, which standardizes image quality in terms of intensity, field of view, and so on, to decrease heterogeneity among image domains. In addition, the system implements a data-adaptive process end-to-end without complex operations by doctors. The experiments are conducted on an annotated public dataset and an unannotated in-hospital dataset. The results indicate that after data adaptation, the segmentation performance measured by the dice similarity coefficient on unlabeled images improves from 58.79% to 75.43%, with a gain of 16.64%. Furthermore, the system preserves quantitatively structured information such as the pancreas’ size and volume, as well as objective and accurate visualized images, which assists clinicians in diagnosing and formulating treatment plans in a timely and accurate manner.
PubDate: Tue, 24 Jan 2023 12:05:00 +000
- Retracted: Discussion on Health Service System of Mobile Medical
Institutions Based on Internet of Things and Cloud Computing
PubDate: Tue, 24 Jan 2023 06:20:05 +000
- Retracted: The Potential Mechanism of Exercise Combined with Natural
Extracts to Prevent and Treat Postmenopausal Osteoporosis
PubDate: Mon, 23 Jan 2023 12:35:01 +000
- Retracted: The Role of Three-Dimensional Reconstruction of Medical Images
and Virtual Reality in Nursing Experimental Teaching
PubDate: Mon, 23 Jan 2023 12:35:00 +000
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