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Journal of Healthcare Engineering
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ISSN (Print) 2040-2295 - ISSN (Online) 2040-2309
Published by Hindawi Homepage  [339 journals]
  • Using the Laney p’ Control Chart for Monitoring COVID-19 Cases in

    • Abstract: In this research, we examine the use of the Laney p’ control chart and the application of test rules to assess governmental interventions throughout the COVID-19 pandemic and understand how certain activities and events that took place affected the infection rate. Data for the infection rate (IR) were collected between October 31, 2020, and March 19, 2022. The IR was calculated by dividing the number of confirmed cases by the number of PCR (polymerase chain reaction) tests performed. The IR data were subsequently plotted on the Laney p’ control charts using the Minitab software. The charts thereby allowed us to study the effects on infection rates of the government’s moves to restrict the movements and activities of the population, as well as the results of easing these restrictions. The restrictive measures proved to be effective in decreasing the infection rate, whereas relaxing these measures had the opposite effect. Typically, test signals are considered as an indication of a change in a process, although in some situations we have observed that slight changes are not accompanied by a signal. Regardless, the analysis shows cases where using test rules rapidly detected patterns and changes in IR, and allowing remedial action to be taken without delay. In this study, we use the Laney p’ control chart to monitor the COVID-19 IR and compare its performance with that of the EWMA control chart. In addition, we analyze the performance of various test rules in detecting IR changes. Comparing the Laney p’ control chart with the EWMA control chart, the data showed that in most cases, the Laney p’ control chart was able to identify the change of IR faster. Comparing the performance of different tests in detecting changes in the IR, one can see that no particular test outperformed the others in all cases. We also recommend analyzing the data points in both single-stage and multistage analyses in accordance with this new perspective rather than the traditional one used in process improvement projects. Accordingly, the single-stage analysis gives a complete picture of how the infection rate is changing overall, whereas the multistage analysis is more sensitive to small changes.
      PubDate: Mon, 19 Sep 2022 08:35:00 +000
  • Exploring Potential Biomarkers, Ferroptosis Mechanisms, and Therapeutic
           Targets Associated with Cutaneous Squamous Cell Carcinoma via Integrated
           Transcriptomic Analysis

    • Abstract: Background. Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with nonmelanoma skin cancers (NMSC). However, the unclear pathogenesis of cSCC limits the application of molecular targeted therapy. Methods. Three microarray datasets (GSE2503, GSE45164, and GSE66359) were downloaded from the Gene Expression Omnibus (GEO). After identifying the differentially expressed genes (DEGs) in tumor and nontumor tissues, five kinds of analyses, namely, functional annotation, protein-protein interaction (PPI) network, hub gene selection, TF-miRNA-mRNA regulatory network analysis, and ferroptosis mechanism, were performed. Results. A total of 146 DEGs were identified with significant differences, including 113 upregulated genes and 33 downregulated genes. The enriched functions and pathways of the DEGs included microtubule-based movement, ATP binding, cell cycle, P53 signaling pathway, oocyte meiosis, and PLK1 signaling events. Nine hub genes were identified (CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, and ASPM). Finally, RRM2, AURKA, and SAT1 were identified as significant ferroptosis-related genes in cSCC. The differential expression of these genes has been verified in two other independent datasets. Conclusions. By integrated bioinformatic analysis, the hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC and are expected to become future biomarkers or therapeutic targets.
      PubDate: Mon, 19 Sep 2022 08:05:00 +000
  • Glucose Determination by a Single 1535 nm Pulsed Photoacoustic
           Technique: A Multiple Calibration for the External Factors

    • Abstract: Photoacoustic spectroscopy has been proved to be a potential method for noninvasive blood glucose detection. We used 1535 nm pulsed laser to excite photoacoustic signal in glucose solution and then explored the influence of different glucose concentration on photoacoustic signal to analyze the sensitivity of photoacoustic signal to glucose at this wavelength. We designed a simple photoacoustic cell structure, which used a focused ultrasonic transducer to receive signals, so as to reduce signal attenuation. In terms of the results, we have found that for high-concentration glucose solutions, the results have strong linearity and discrimination, and when the concentration is close to the human body level, the signal difference is not so obvious. Therefore, we explore the external factors affecting the photoacoustic signal in detail and propose a calibration method. Through calibration, the signal generated by the low-concentration glucose solution also has a good linearity.
      PubDate: Mon, 19 Sep 2022 05:50:00 +000
  • Connecting to Nature through 360° Videos during COVID-19 Confinement: A
           Pilot Study of a Brief Psychological Intervention

    • Abstract: Psychological interventions have been shown to be beneficial in mitigating stress related to COVID-19 confinement. According to theories of restorative environments, exposure to natural surroundings has positive effects on well-being and stress through its restorative qualities. With 360° video-based Virtual Reality (VR), people can be exposed to nature and so better manage the consequences associated with mobility restrictions during confinement. The main aim of this pilot study was to examine whether a 360° video-based VR intervention composed of five 13-minute sessions (once a day) has positive effects on affect, well-being, and stress. The sample was made up of 10 participants (4 men and 6 women; age : M = 46.5, SD = 11.7) who were confined at home (voluntarily or not) during the COVID-19 pandemic. Participants were instructed to watch a 360° video each day (of a “beach” or “lake” environment) using their smartphone and VR glasses sent to them by mail. Participants responded with several self-reports before and/or after each session (emotions and sense of presence) and before and/or after the intervention (affect, well-being, perceived stress, perceived restorativeness of nature, and the usefulness and acceptability of the intervention). Results showed a tendency to improve positive (e.g., happiness) and negative (e.g., anxiousness) emotions and experience a high sense of presence after each session. Moreover, perceived restorative qualities of the environment and their cognitive and behavioral effects were high. A significant decrease in negative affect was found after the intervention. Usefulness and acceptability were also high. This is the first study to show that an affordable and accessible technology can be used to overcome the negative consequences of confinement and counteract its harmful psychological effects.
      PubDate: Wed, 14 Sep 2022 07:35:01 +000
  • Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in
           Chinese Pregnant Women: A Prospective Cohort Study

    • Abstract: Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner’s inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628–0.648)) and specificity (0.683 (0.669–0.698)) and lowest error rate (0.362 (0.352–0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663–0.670)) and specificity (0.725 (0.721–0.729)) and decreased the error rate (0.333 (0.330–0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.
      PubDate: Tue, 13 Sep 2022 11:50:00 +000
  • Prognostic Implication of a Cuproptosis-Related miRNA Signature in
           Hepatocellular Carcinoma

    • Abstract: Background. Hepatocellular carcinoma (HCC) is one of the most frequently diagnosed malignancies globally, accounting for the third cause of cancer mortality. Cuproptosis, a copper-induced cell death, was recently reported in Science. The purpose of this study was to evaluate the prognostic implication of cuproptosis-related miRNAs (CRMs) in HCC. Methods. Transcriptomic data and clinicopathological features of patients with HCC were extracted from the Cancer Genome Atlas (TCGA) database. Prognostic CRM signature was established by utilizing univariate Cox regression and LASSO analyses. To validate the accuracy of prediction, the Kaplan-Meier (K-M) and time-dependent receiver operating characteristic (ROC) analyses were adopted. A nomogram comprising clinical characteristics and the miRNA signature was developed to improve the prediction of patient outcomes. Finally, functional enrichment analysis and immune infiltration analysis were carried out. Results. Of CRMs, 14 were obtained to construct a prognostic miRNA signature. This CRM signature was an independent factor for predicting overall survival (OS). Kaplan-Meier curves demonstrated a noteworthy difference in survival rates between different risk subgroups (). The robust prognostic capacity of this signature was exhibited by sampling verification and stratified survival analysis. Functional analysis indicated that the high-risk group was mainly enriched in signaling pathways and different levels of immune infiltration were revealed between the two risk groups. The potential interaction of the model with the immune checkpoint activities was also detected. Conclusion. The CRM signature could act as an independent predictor to guide individual treatment strategies, which could provide fundamental insights for further studies.
      PubDate: Tue, 13 Sep 2022 07:35:00 +000
  • Construction of a Prediction Model for the Mortality of Elderly Patients
           with Diabetic Nephropathy

    • Abstract: To construct a prediction model for all-cause mortality in elderly diabetic nephropathy (DN) patients, in this cohort study, the data of 511 DN patients aged ≥65 years were collected and the participants were divided into the training set (n = 358) and the testing set (n = 153). The median survival time of all participants was 2 years. The data in the training set were grouped into the survival group (n = 203) or the death group (n = 155). Variables with P ≤ 0.1 between the two groups were selected as preliminary predictors and involved into the multivariable logistic regression model and the covariables were gradually adjusted. The receiver operator characteristic (ROC), Kolmogorov-Smirnov (KS), and calibration curves were plotted for evaluating the predictive performance of the model. Internal validation of the performance of the model was verified in the testing set. The predictive values of the model were also conducted in terms of people with different genders and ages or accompanied with chronic kidney disease (CKD) or cardiovascular diseases (CVD), respectively. In total, 216 (42.27%) elderly DN patients were dead within 2 years. The prediction model for the 2-year mortality of elderly patients with DN was established based on length of stay (LOS), temperature, heart rate, peripheral oxygen saturation (SpO2), serum creatinine (Scr), red cell distribution width (RDW), the simplified acute physiology score-II (SAPS-II), hyperlipidemia, and the Chronic Kidney Disease Epidemiology Collaboration equation for estimated glomerular filtration rate (eGFR-CKD-EPI). The AUC of the model was 0.78 (95% CI: 0.73–0.83) in the training set and 0.72 (95% CI: 0.63–0.80) in the testing set. The AUC of the model was 0.78 (95% CI: 0.65–0.91) in females and 0.78 (95%CI: 0.68–0.88) in patients ≤75 years. The AUC of the model was 0.74 (95% CI: 0.64–0.84) in patients accompanied with CKD. The model had good predictive value for the mortality of elderly patients with DN within 2 years. In addition, the model showed good predictive values for female DN patients, DN patients ≤75 years, and DN patients accompanied with CKD.
      PubDate: Mon, 12 Sep 2022 10:35:00 +000
  • Low Testosterone Level and Risk of Adverse Clinical Events among Male
           Patients with Chronic Kidney Disease: A Systematic Review and
           Meta-Analysis of Cohort Studies

    • Abstract: The phenomenon of low testosterone level is extremely common in male patients with chronic kidney diseases (CKDs). This meta-analysis aimed to evaluate whether the low circulating testosterone could independently predict adverse outcomes among male patients with chronic kidney diseases (CKDs). The data till May 2022 were systematically searched from Pubmed, Web of Science, and Embase from inception. Studies meeting the PICOS (population, intervention/exposure, control/comparison, outcomes, and study design) principles were included in this meta-analysis. Study-specific effect estimates were pooled using fixed-effects (I2 > 50%) or random-effects models (I2 
      PubDate: Sat, 10 Sep 2022 07:20:01 +000
  • Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency
           Catheter Ablation in Patients with Atrial Fibrillation

    • Abstract: Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter ablation (RFCA) should be decided after fully considering its prognosis. However, a robust prediction model reflecting the complex interactions between the features affecting prognosis remains to be developed. In this paper, we propose a deep learning model for predicting the late recurrence after RFCA in patients with AF. Aiming to predict the late recurrence (LR) of AF within 1 year after pulmonary vein isolation, we designed a multimodal model based on the multilayer perceptron architecture. For quantitative evaluation, we conducted 4-fold cross-validation on data from 177 AF patients including 47 LR patients. The proposed model (area under the receiver operating characteristic curve-AUROC, 0.766) outperformed the acute patient physiologic and laboratory evaluation (APPLE) score (AUROC, 0.605), CHA2DS2-VASc score (AUROC, 0.595), linear regression (AUROC, 0.541), logistic regression (AUROC, 0.546), extreme gradient boosting (AUROC, 0.608), and support vector machine (AUROC, 0.638). The proposed model exhibited better performance than clinical indicators (APPLE and CHA2DS2-VASc score) and machine learning techniques (linear regression, logistic regression, extreme gradient boosting, and support vector machine). The model will support clinical decision-making for selecting good responders to the RFCA intervention.
      PubDate: Sat, 10 Sep 2022 07:20:01 +000
  • A Deep Neural Network Ensemble Classifier with Focal Loss for Automatic
           Arrhythmia Classification

    • Abstract: Automated electrocardiogram classification techniques play an important role in assisting physicians in diagnosing arrhythmia. Among these, the automatic classification of single-lead heartbeats has received wider attention due to the urgent need for portable ECG monitoring devices. Although many heartbeat classification studies performed well in intrapatient assessment, they do not perform as well in interpatient assessment. In particular, for supraventricular ectopic heartbeats (S), most models do not classify them well. To solve these challenges, this article provides an automated arrhythmia classification algorithm. There are three key components of the algorithm. First, a new heartbeat segmentation method is used, which improves the algorithm’s capacity to classify S substantially. Second, to overcome the problems created by data imbalance, a combination of traditional sampling and focal loss is applied. Finally, using the interpatient evaluation paradigm, a deep convolutional neural network ensemble classifier is built to perform classification validation. The experimental results show that the overall accuracy of the method is 91.89%, the sensitivity is 85.37%, the positive productivity is 59.51%, and the specificity is 93.15%. In particular, for the supraventricular ectopic heartbeat(s), the method achieved a sensitivity of 80.23%, a positivity of 49.40%, and a specificity of 96.85%, exceeding most existing studies. Even without any manually extracted features or heartbeat preprocessing, the technique achieved high classification performance in the interpatient assessment paradigm.
      PubDate: Fri, 09 Sep 2022 07:20:00 +000
  • A Framework on Performance Analysis of Mathematical Model-Based
           Classifiers in Detection of Epileptic Seizure from EEG Signals with
           Efficient Feature Selection

    • Abstract: Epilepsy is one of the neurological conditions that are diagnosed in the vast majority of patients. Electroencephalography (EEG) readings are the primary tool that is used in the process of diagnosing and analyzing epilepsy. The epileptic EEG data display the electrical activity of the neurons and provide a significant amount of knowledge on pathology and physiology. As a result of the significant amount of time that this method requires, several automated classification methods have been developed. In this paper, three wavelets such as Haar, dB4, and Sym 8 are employed to extract the features from A–E sets of the Bonn epilepsy dataset. To select the best features of epileptic seizures, a Particle Swarm Optimization (PSO) technique is applied. The extracted features are further classified using seven classifiers like linear regression, nonlinear regression, Gaussian Mixture Modeling (GMM), K-Nearest Neighbor (KNN), Support Vector Machine (SVM-linear), SVM (polynomial), and SVM Radial Basis Function (RBF). Classifier performances are analyzed through the benchmark parameters, such as sensitivity, specificity, accuracy, F1 Score, error rate, and g-means. The SVM classifier with RBF kernel in sym 8 wavelet features with PSO feature selection method attains a higher accuracy rate of 98% with an error rate of 2%. This classifier outperforms all other classifiers.
      PubDate: Tue, 06 Sep 2022 03:20:00 +000
  • Bioinformatics-Based Analysis: Noncoding RNA-Mediated COL10A1 Is
           Associated with Poor Prognosis and Immune Cell Infiltration in Pancreatic

    • Abstract: Background. Collagen type X alpha 1 (COL10A1) is a structural component of the extracellular matrix that is aberrantly expressed in a variety of cancer tissues. However, its role in pancreatic cancer progression is not well understood. Methods. The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Gene Expression Profiling Interaction Analysis (GEPIA) data were employed to explore the expression of COL10A1 in normal and tumor tissues and its prognostic value in pancreatic adenocarcinoma. The clinical data of pancreatic cancer in TCGA were used to explore the relationship between COL10A1 and clinical features. Genes coexpressed with COL10A1 were explored using multiple databases and analyzed for functional enrichment. In addition, the lncRNA/miRNA/COL10A1 axis that may be involved in COL10A1 regulation in pancreatic cancer was explored by constructing a competitive endogenous RNA (ceRNA) regulatory axis. Finally, COL10A1 was analyzed for correlation with immune cell infiltration and various immune checkpoint molecules in pancreatic cancer. Results. It was found that the expression of COL10A1 was significantly increased in pancreatic cancer tissues. High expression of COL10A1 was related to the clinicopathological characteristics and the worse prognosis of pancreatic cancer patients. The TUG1/miR-144-3p/COL10A1 axis was identified as the most likely upstream noncoding RNA pathway for COL10A1 in pancreatic cancer. Besides, in pancreatic adenocarcinoma, the expression level of COL10A1 showed a significant positive correlation with tumor immune cell infiltration, biomarkers of immune cells, and expression of immune checkpoint molecules. Conclusion. COL10A1 is an early diagnostic marker, and its high expression correlates with immune infiltration in pancreatic cancer. The TUG1/miR-144-3p/COL10A1 axis was identified as the most likely upstream noncoding RNA pathway for COL10A1 in pancreatic cancer.
      PubDate: Mon, 05 Sep 2022 06:35:01 +000
  • Clinicopathological Characteristics and Prognostic Factors of Primary
           Bladder Signet Ring Cell Carcinoma

    • Abstract: Introduction. The aim of this study is to examine the treatment pattern and predictors of long-term survival of patients with primary signet ring cell carcinoma (PSRCC) of the urinary bladder based on the analysis of the SEER database. Methods. The 3-year and 5-year overall survival (OS) and cancer-specific survival (CSS) were calculated using the Kaplan–Meier method. Then, we compared the CSS curves by the log-rank test. The independent risk factors were determined using univariate and multivariate Cox regression. Results. The 3-year OS and CSS rates for PSRCC of the bladder were 25.3% and 33.3%. The 5-year OS and CSS rates for the entire cohort were 16.4% and 25.2%. The CSS rates, respectively, were 0, 25.0, 66.7, 33.2, 42.4, and 31.7% at 3 years and 0, 25.0, 34.3, 24.1, 27.2, and 31.7% at 5 years for none, transurethral resection of the bladder (TURB), partial cystectomy, radical cystectomy with reconstruction, pelvic exenteration, and other surgeries (P = 0.001). Multivariate analyses showed independent risk factors only including T stage, M stage, lymph node removal, and surgical approach. Conclusions. T stage, M stage, lymph node removal, and surgical approach are independent risk factors of PSRCC of the urinary bladder. TURB and radical cystectomy with reconstruction appear to provide a better outcome.
      PubDate: Mon, 05 Sep 2022 06:35:01 +000
  • A Study on the Association between Korotkoff Sound Signaling and Chronic
           Heart Failure (CHF) Based on Computer-Assisted Diagnoses

    • Abstract: Background. Korotkoff sound (KS) is an important indicator of hypertension when monitoring blood pressure. However, its utility in noninvasive diagnosis of Chronic heart failure (CHF) has rarely been studied. Purpose. In this study, we proposed a method for signal denoising, segmentation, and feature extraction for KS, and a Bayesian optimization-based support vector machine algorithm for KS classification. Methods. The acquired KS signal was resampled and denoised to extract 19 energy features, 12 statistical features, 2 entropy features, and 13 Mel Frequency Cepstrum Coefficient (MFCCs) features. A controlled trial based on the VALSAVA maneuver was carried out to investigate the relationship between cardiac function and KS. To classify these feature sets, the K-Nearest Neighbors (KNN), decision tree (DT), Naive Bayes (NB), ensemble (EM) classifiers, and the proposed BO-SVM were employed and evaluated using the accuracy (Acc), sensitivity (Se), specificity (Sp), Precision (Ps), and F1 score (F1). Results. The ALSAVA maneuver indicated that the KS signal could play an important role in the diagnosis of CHF. Through comparative experiments, it was shown that the best performance of the classifier was obtained by BO-SVM, with Acc (85.0%), Se (85.3%), and Sp (84.6%). Conclusions. In this study, a method for noise reduction, segmentation, and classification of KS was established. In the measured data set, our method performed well in terms of classification accuracy, sensitivity, and specificity. In light of this, we believed that the methods described in this paper can be applied to the early, noninvasive detection of heart disease as well as a supplementary monitoring technique for the prognosis of patients with CHF.
      PubDate: Thu, 01 Sep 2022 09:50:00 +000
  • Application of Image-Fusion 3D Printing Model in Total En Bloc
           Spondylectomy for Spinal Malignant Tumors

    • Abstract: Purpose. To examine the effects of 3D printing model in total en bloc spondylectomy (TES). Methods. We performed a retrospective chart review of 41 cases of spinal tumors at our institution between 2017 and 2020, in which TES was applied. There were 19 cases with 3D printing model and 22 cases without 3D printing model. Operation time, intraoperative blood loss, excision range, complications, VAS, and ASIA grades were recorded. Statistical methods were used to analyze the data. KaplanMeier survival curve was made to evaluate the survival. Result. There were significant differences in intraoperative blood loss between the two groups. The rate of R0 resection and tumor envelope preservation were higher in 3D group than that in non-3D group. In 3D group, the complications included surgical site infection (5.2%) and cerebrospinal fluid leak (15.7%). In non-3D group, the complications included cerebrospinal fluid leak (27.3%) and nerve root injury (13.6%). The pain and neurological dysfunction were significantly relieved before and after surgery in 3D group. However, the neurological relief in non-3D group patients was not complete. The VAS scores of non-3D group at 6 months after surgery were much higher than that of 3D group. Conclusion. The application of 3D printing model not only helps surgeons observe the morphology, invasion range, and anatomic relationship of the tumor preoperatively, but also assists surgeons to judge, locate, and separate the tumor intraoperatively. For spinal malignancies, the 3D printing model is worth promoting.
      PubDate: Wed, 31 Aug 2022 07:20:01 +000
  • The Identification of Chinese Herbal Medicine Combination Association Rule
           Analysis Based on an Improved Apriori Algorithm in Treating Patients with
           COVID-19 Disease

    • Abstract: In this work, an improved Apriori algorithm is proposed. The main goal is to improve the processing efficiency of the algorithm, and the idea and process of the Apriori algorithm are optimized. The proposed method is compared with the classical association rule algorithm to verify its effectiveness. Traditional Chinese medicine plays a certain role in the prevention and treatment of COVID-19. In order to deeply mine the association rules between Chinese herbal medicines for the prevention and treatment of COVID-19, this improved Apriori algorithm is applied from the retrieved published scientific literature and the guidelines for the prevention and treatment of COVID-19 published all over China. Based on the representation of traditional Chinese medicine data in binary form, the potential core traditional Chinese medicine combinations in the treatment of COVID-19 are identified. The results of association rules of Chinese herbal medicine data obtained from the real database provide an important reference for the analysis of COVID-19 combined treatment of Chinese herbal medicine.
      PubDate: Wed, 31 Aug 2022 07:20:00 +000
  • TCM Constitution Analysis Method Based on Parallel FP-Growth Algorithm in
           Hadoop Framework

    • Abstract: This work is devoted to establishing a comparatively accurate classification model between symptoms, constitutions, and regimens for traditional Chinese medicine (TCM) constitution analysis to provide preliminary screening and decision support for clinical diagnosis. However, for the analysis of massive distributed medical data in a cloud platform, the traditional data mining methods have the problems of low mining efficiency and large memory consumption, and long tuning time, an association rules method for TCM constitution analysis (ARA-TCM) is proposed that based on FP-growth algorithm and the open-source distributed file system in Hadoop framework (HDFS) to make full use of its powerful parallel processing capability. Firstly, the proposed method was used to explore the association rules between the 9 kinds of TCM constitutions and symptoms, as well as the regimen treatment plans, so as to discover the rules of typical clinical symptoms and treatment rules of different constitutions and to conduct an evidence-based medical evaluation of TCM effects in constitution-related chronic disease health management. Secondly, experiments were applied on a self-built TCM clinical records database with a total of 30,071 entries and it is found that the top three constitutions are mid constitution (42.3%), hot and humid constitution (31.3%), and inherited special constitution (26.2%), respectively. What is more, there are obvious promotions in the precision and recall rate compared with the Apriori algorithm, which indicates that the proposed method is suitable for the classification of TCM constitutions. This work is mainly focused on uncovering the rules of “disease symptoms constitution regimen” in TCM medical records, but tongue image and pulse signal are also very important to TCM constitution analysis. Therefore, this additional information should be considered into further studies to be more in line with the actual clinical needs.
      PubDate: Tue, 30 Aug 2022 10:35:00 +000
  • Sparse Dictionary-Based Magnetic Resonance Superresolution Imaging with
           Joint Loss Function Learning

    • Abstract: Magnetic resonance image has important application value in disease diagnosis. Due to the particularity of its imaging mechanism, the resolution of hardware imaging needs to be improved by increasing radiation intensity and radiation time. Excess radiation can cause the body to overheat and, in severe cases, inactivate the protein. This problem is expected to be solved by the image superresolution method based on joint dictionary learning, which has good superresolution performance. In the process of dictionary learning, the loss function will directly affect the dictionary performance. The general method only uses the cascade error as the optimization function in dictionary training, and the method does not consider the individual reconstruction error of high- and low-resolution image dictionary. In order to solve the above problem, In this paper, the loss function of dictionary learning is optimized. While ensuring that the coefficients are sufficiently sparse, the high- and low-resolution dictionaries are trained separately to reduce the error generated by the joint high- and low-resolution dictionary block pair and increase the high-resolution reconstruction error. Experiments on neck and ankle MR images show that the proposed algorithm has better superresolution reconstruction performance on ×2 and ×4 compared with bicubic interpolation, nearest neighbor, and original dictionary learning algorithms.
      PubDate: Mon, 29 Aug 2022 15:35:01 +000
  • Study on the Mechanism of Huanglian Jiedu Decoction in Treating
           Dyslipidemia Based on Network Pharmacology

    • Abstract: Objective. This study aimed to determine the active ingredients of Huanglian Jiedu decoction (HLJDD) and the targets for treating dyslipidemia through network pharmacology to facilitate further application of HJJDD in the treatment of dyslipidemia. Methods. Potential drug targets for dyslipidemia were identified with a protein-protein interaction network. Gene ontology (GO) enrichment analysis and KEGG pathway analysis were performed to elucidate the biological function and major pathways involved in the HLJDD-mediated treatment of dyslipidemia. Results. This approach revealed 22 components, 234 targets of HLJDD, and 221 targets of dyslipidemia. There were 14 components and 31 common targets between HLJDD and dyslipidemia treatment. GO enrichment analysis showed that these targets were mainly associated with the response to DNA-binding transcription factor activity, lipid localization and storage, reactive oxygen species metabolic process, and inflammatory response. The results of KEGG analysis indicated that the AGE-RAGE, NF-κB, HIF-1, IL-17, TNF, FoxO, and PPAR signalling pathways were enriched in the antidyslipidemic action of HLJDD. Conclusion. This study expounded the pharmacological actions and molecular mechanisms of HLJDD in treating dyslipidemia from a holistic perspective, which may provide a scientific basis for the clinical application of HLJDD.
      PubDate: Wed, 24 Aug 2022 18:20:00 +000
  • Automated Detection and Characterization of Colon Cancer with Deep
           Convolutional Neural Networks

    • Abstract: Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.
      PubDate: Wed, 24 Aug 2022 15:50:00 +000
  • Effect of Unilateral Knee Extension Restriction on the Lumbar Region
           during Gait

    • Abstract: Unilateral knee extension restriction might change trunk alignment and increase mechanical load on the lumbar region during walking. We aimed to clarify lumbar region mechanical load during walking with restricted knee extension using a musculoskeletal model simulation. Seventeen healthy adult males were enrolled in this study. Participants walked 10 m at a comfortable velocity with and without restricted right knee extension of 15° and 30° using a knee brace. L4–5 joint moment, joint reaction force, and muscle forces around the lumbar region during walking were calculated for each condition. Peaks of kinetic data were compared among three gait conditions during 0%–30% and 50%–80% of the right gait cycle. Lumbar extension moment at early stance of the bilateral lower limbs was significantly increased in the 30° restricted condition (). Muscle force of the multifidus showed peaks at stance phase of the contralateral side during walking, and the erector spinae showed force peaks at early stance of the bilateral lower limb. Muscle force of the multifidus and erector spinae increased with increasing degree of knee flexion (), with a large effect size (η2 = 0.273–0.486). The joint force acting on L4–5 showed two peaks at early stance of the bilateral lower limbs during the walking cycle. The anterior and vertical joint force on L4–5 increased by 14.2%–36.5% and 10.0%–23.0% in walking with restricted knee extension, respectively (), with a large effect size (η2 = 0.149–0.425). Restricted knee joint extension changed trunk alignment and increased the muscle force and the vertical and anterior joint force on the L4–5 joint during walking; this tendency became more obvious with increased restriction angle. Our results provide important information for therapists engaged in the rehabilitation of patients with knee contracture.
      PubDate: Mon, 22 Aug 2022 17:50:01 +000
  • Effects of Ultrasound-Guided Stellate Ganglion Block on Postoperative
           Quality of Recovery in Patients Undergoing Breast Cancer Surgery: A
           Randomized Controlled Clinical Trial

    • Abstract: Surgery has been the primary treatment for breast cancer. However, instant postoperative complications, such as sleep disorder and pain, dramatically impair early postoperative quality of recovery, resulting in more extended hospital stays and higher costs. Recent clinical trials indicated that stellate ganglion block (SGB) could prolong sleep time and improve sleep quality in breast cancer survivors. Moreover, during the perioperative period, SGB enhanced the recovery of gastrointestinal functions in patients with laparoscopic colorectal cancer surgery and thoracolumbar spinal surgery. Furthermore, perioperative SGB decreased intraoperative requirements for anesthetics and analgesics in patients with complex regional pain syndrome. However, information is scarce regarding the effects of SGB on postoperative quality recovery in patients with breast cancer surgery. Therefore, we investigated the effects of SGB on the postoperative quality of recovery of patients undergoing breast cancer surgery. Sixty patients who underwent an elective unilateral modified radical mastectomy were randomized into two 30-patient groups that received either an ultrasound-guided right-sided SGB with 6 ml 0.25% ropivacaine (SGB group) or no block (control group). The primary outcome was the quality of postoperative recovery 24 hours after surgery, assessed with a Chinese version of the 40-item Quality of Recovery (QoR-40) questionnaire. Secondary outcomes were intraoperative requirements of propofol and opioids, rest pain at two, four, eight, and 24 hours after surgery, patient satisfaction score, and the incidence of postoperative abdominal distension. At 24 hours after surgery, global QoR-40 scores were higher in the SGB group than in the control group. Besides, in the SGB group, patients needed less propofol, had a lower incidence of postoperative abdominal bloating, and had higher satisfaction scores. Ultrasound-guided SGB could improve the quality of postoperative recovery in patients undergoing breast cancer surgery by less intraoperatively need for propofol and better postoperative recovery of sleep and gastrointestinal function.
      PubDate: Mon, 22 Aug 2022 17:35:00 +000
  • Nomogram Models Based on the Gene Expression in Prediction of Breast
           Cancer Bone Metastasis

    • Abstract: Objective. The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods. Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC nonbone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan–Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. The prognostic value of the GESBN models was investigated in the GSE124647 dataset, which was validated in GSE16446 and GSE45255 datasets. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the expression and prognostic value of hub genes in BC were explored. Results. A total of 1858 DEGs were obtained. The WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival (OS). While GJA1, IGFBP6, MDFI, TGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival (PFS). Kaplan–Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Cox regression analysis further revealed that GESBN models were independent prognostic predictors for OS and PFS in BC patients. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion. Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.
      PubDate: Mon, 22 Aug 2022 17:35:00 +000
  • Numerical Analysis of Nasal Flow Characteristics with Microparticles

    • Abstract: This study was to investigate the airflow characteristics in nasal cavity under different conditions and analyze the effects of different respiratory intensity, particle diameter, and particle density on the deposition of particles carried by airflow in the nasal cavity, respectively. The three-dimensional geometric model of the nasal cavity was established based on typical medical images. The SST k-ω turbulence model in the computational fluid dynamics (CFD) was used to simulate the airflow in the nasal cavity, and the deposition of particles in the airflow was analyzed with the Lagrange discrete phase model. The results showed that the air in the nasal cavity flows in the left and right nasal passages through the perforation in front of the nasal septum and forms a vortex structure at the perforation site, and the particle deposition efficiencies (DE) under perforation nasal cavity are higher than that under normal nasal cavity. Different parameters had different effects on the particle DE. The results showed that the DE of particles with smaller size (≤2.5 μm) is lower; the higher the respiration intensity, the greater the influence on the DE of the larger particle size; and the larger particle density (>1550 kg·m−3) has little effect on the DE of larger particle size (DP = 10 μm). The results agree well with the corresponding research data.
      PubDate: Mon, 22 Aug 2022 11:35:01 +000
  • Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1

    • Abstract: Objective. Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods. In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results. The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r2 in curve regressions (). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion. We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.
      PubDate: Fri, 19 Aug 2022 07:20:00 +000
  • Mechanism of Zhinao Capsule in Treating Alzheimer’s Disease Based on
           Network Pharmacology Analysis and Molecular Docking Validation

    • Abstract: Objective. This study aimed to determine the active components of Zhinao capsule (ZNC) and the targets in treating Alzheimer’s disease (AD) so as to investigate and explore the mechanism of ZNC for AD. Methods. The active components and targets of ZNC were determined from the traditional Chinese medicine systems pharmacology database (TCMSP). The target genes of AD were searched for in GeneCards. Cytoscape was used to construct an herb-component-target-disease network. A protein-protein interaction (PPI) network was constructed by STRING. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the OmicShare. UCSF Chimera and SwissDock were used for molecular docking verification. Finally, four key target genes were validated by Western blotting. Results. In total, 55 active components, 287 targets of active components, 1197 disease genes, and 134 common genes were screened, which were significantly enriched in 3975 terms of biological processes (BP), 284 terms of cellular components (CC), 433 terms of molecular functions (MF), and 245 signaling pathways. Caspase-3 (CASP3) and beta-sitosterol, tumor necrosis factor-alpha (TNF-α) and quercetin, vascular endothelial growth factor A (VEGFA) and baicalein, and mitogen-activated protein kinase 1 (MAPK1) and quercetin showed good-to-better docking. Moreover, ZNC not only downregulated CASP3 and TNF-α protein expression but also upregulated the protein expression of VEGFA and MAPK1. Conclusions. The active components of ZNC, such as beta-sitosterol, quercetin, and baicalein may act on multiple targets like CASP3, VEGFA, MAPK1, and TNF-α to affect T cell receptor (TCR), TNF, and MAPK signaling pathway, thereby achieving the treatment of AD. This study provides a scientific basis for further exploring the potential mechanism of ZNC in the treatment of AD and a reference for its clinical application.
      PubDate: Thu, 18 Aug 2022 10:35:00 +000
  • Early Warning of Infectious Diseases in Hospitals Based on
           Multi-Self-Regression Deep Neural Network

    • Abstract: Objective. Infectious diseases usually spread rapidly. This study aims to develop a model that can provide fine-grained early warnings of infectious diseases using real hospital data combined with disease transmission characteristics, weather, and other multi-source data. Methods. Based on daily data reported for infectious diseases collected from several large general hospitals in China between 2012 and 2020, seven common infectious diseases in medical institutions were screened and a multi self-regression deep (MSRD) neural network was constructed. Using a recurrent neural network as the basic structure, the model can effectively model the epidemiological trend of infectious diseases by considering the current influencing conditions while taking into account the historical development characteristics in time-series data. The fitting and prediction accuracy of the model were evaluated using mean absolute error (MAE) and root mean squared error. Results. The proposed approach is significantly better than the existing infectious disease dynamics model, susceptible-exposed-infected-removed (SEIR), as it addresses the concerns of difficult-to-obtain quantitative data such as latent population, overfitting of long time series, and considering only a single series of the number of sick people without considering the epidemiological characteristics of infectious diseases. We also compare certain machine learning methods in this study. Experimental results demonstrate that the proposed approach achieves an MAE of 0.6928 and 1.3782 for hand, foot, and mouth disease and influenza, respectively. Conclusion. The MRSD-based infectious disease prediction model proposed in this paper can provide daily and instantaneous updates and accurate predictions for epidemic trends.
      PubDate: Thu, 18 Aug 2022 09:20:01 +000
  • Network Meta-Analysis of the Antihypertensive Effect of Traditional
           Chinese Exercises on Patients with Essential Hypertension

    • Abstract: Background. In recent years, traditional Chinese exercises (TCEs) have been gradually used to reduce the blood pressure levels of patients with essential hypertension. However, there are several types of TCEs, and there is no comparative study on the antihypertensive effects of various TCEs in patients with essential hypertension. Objective. The objective is to compare the therapeutic effects of Taijiquan (TJQ), Baduanjin (BDJ), Wuqinxi (WQX), and Yijinjing (YJJ) on essential hypertension and provide a reference for clinical treatment and scheme optimization. Methods. The China National Knowledge Infrastructure (CNKI), Wanfang, China Scientific Journal Database, China Biology Medicine database (CBM), PubMed, Embase, Cochrane Library, and Web of Science databases were searched to collect all randomized controlled trials (RCTs) of TCEs in the treatment of essential hypertension. The search time was from the establishment of each database to November 2021. After data extraction and quality evaluation, the network meta-analysis was performed with Stata 16.0 and ADDIS 1.16.8. Results. Finally, 45 RCTs involving 3864 patients were included. Network meta-analysis showed that YJJ had the best effect in reducing systolic blood pressure, and the difference was statistically significant [MD = −14.27, 95% CI = (−20.53∼−8.08), ]. The best probability ranking was YJJ () > TJQ () > WQX () > BDJ (). In terms of reducing diastolic blood pressure, the treatment effect of YJJ was the best, and the difference was statistically significant [MD = −7.77, 95% CI (−12.19∼−3.33), ]. The best probability ranking was YJJ () > TJQ () > WQX () > BDJ ().Conclusion. The results showed that TCEs significantly reduced systolic and diastolic blood pressure compared with the control group, and YJJ might be the best choice. However, a larger sample, multicenter, double-blinded, high-quality RCTs are needed to make clear conclusions.
      PubDate: Wed, 17 Aug 2022 10:50:00 +000
  • Development and Evaluation of a Pillow to Prevent Snoring Using the
           Cervical Spine Recurve Method

    • Abstract: Snoring lowers the quality of sleep, causing many secondary diseases. In response, various types of preventive devices were manufactured, but most of them were far from actual snoring prevention by only sensing snoring and giving feedback. In this study, we proposed a new method to prevent snoring by adjusting the posture during sleep by widening the oropharynx space. An increase in the oropharynx area was confirmed through the expansion of the cervical spine, and a dedicated pillow that can extend through an angle of up to 20° was manufactured. Through this developed method, it was possible to easily extend the cervical spine angle in a supine position to the user, and the frequency of snoring was then tested. As a result, it was confirmed that by using the pillow with an expansion angle of 20° or more, snoring did not occur. Furthermore, looking at the evaluation results of the subjective levels of satisfaction, sleep-related items received an average of 5.9 or higher, and function-related items received high scores with an average of 5.7. We can confirm that the reliability of performance evaluation will be dramatically improved if the scope of the subject group is expanded to include various body types, ages, and genders and conduct performance evaluations for each group.
      PubDate: Wed, 17 Aug 2022 10:50:00 +000
  • Predictive Modeling of Short-Term Poor Prognosis of Successful Reperfusion
           after Endovascular Treatment in Patients with Anterior Circulation Acute
           Ischemic Stroke

    • Abstract: This study aimed to propose and internally validate a prediction model of short-term poor prognosis in patients with acute ischemic stroke (AIS). In the retrospective study, 356 eligible AIS patients receiving endovascular treatment (EVT) were included and divided into the good prognosis group and the poor prognosis group. Data from 70% of patients were collected as training set and the 30% as validation set. Univariate analysis and multivariate logistic regression were used for identifying independent predictors. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and the paired Chi-square test was used for internal validation. A model for the prediction of short-term poor prognosis in atherosclerotic AIS patients who successfully underwent endovascular reperfusion was developed: log (Pr/1 − Pr) = 3.500 + Blood glucose  0.174 + Infarct volume  0.128 + the National Institutes of Health Stroke Scale score × Onset-to-reperfusion time (NIHSS-ORT)  0.014 + Intraoperative hypotension (Yes)  1.037 + Mean arterial pressure (MAP) decrease from baseline (>40%) 2.061 (Pr represented the probability of short-term poor prognosis). The area under the curve (AUC) was 0.806 (0.748 − 0.864) in the training set and 0.850 (0.779 − 0.920) in the testing set, which suggested the good performance of the model. We proposed and validated a combined prediction model to predict the short-term poor prognosis of AIS patients after EVT, which could provide reference for clinicians to identify AIS patients with a higher risk of poor outcomes and thus improving the prognosis of EVT.
      PubDate: Fri, 12 Aug 2022 12:20:00 +000
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