Through our research, it is proved that the distributed representation can improve the accuracy of the deep learning model and better solve the miRNA target site prediction problem.
This analysis was aimed at providing evidence-based medicine basis for systematic evaluation of chondroitin combined with glucosamine in the treatment of knee osteoarthritis.
The randomized controlled trials (RCTs) of chondroitin combined with glucosamine in the treatment of knee osteoarthritis (KOA) were searched in PubMed, EMBASE, ScienceDirect, Cochrane Library, China Knowledge Network Database (CNKI), China VIP Database, Wanfang Database, and China Biomedical Literature Database (CBM) online database. The retrieval time ranges from the database creation to the present. Two investigators gathered the information individually. The risk of bias was assessed using the criteria of the Cochrane back review group. RevMan5.4 statistical software analyzed the selected data.
A total of 6 RCT articles were obtained. Overall, 764 samples were evaluated by meta-analysis. The clinical efficacy of chondroitin combined with glucosamine was significantly better than that of routine treatment by meta-analysis. The coondroitin or glucosamine alone in the treatment of KOA and deserves clinical promotion. However, this conclusion still needs to be supported by multicenter, high-quality, double-blind, large-sample randomized controlled clinical trials due to the limitations of the six trials included.Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People's mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human's various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area ant sight of patients' facts which will cause a problem in sharing information over a network. So, this research paper's approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.
To explore the clinical advantages of grid body surface locator combined with preemptive analgesia in the treatment of osteoporotic lumbar fractures in daytime vertebroplasty.
A retrospective study was conducted on 120 patients who underwent lumbar vertebroplasty in the Department of Orthopedics of General Hospital of Northern Theater Command from January 2017 to January 2020. According to the preoperative planning and analgesic mode of treatment, they were divided into the daily operation experimental group and the traditional mode control group. Prone positioning of a patient under anesthetic is safe of ensuring optimum surgical access for many procedures, providing that the risks are fully understood. The general baseline data, intraoperative fluoroscopy times and operation time, bone cement injection volume, bone cement permeability, VAS score before operation, 1 day, and 3 months after operation, and the recovery of anterior vertebral height before and after operation were analyzed.
There was no stent through preoperative planning of puncture path and key puncture points, combined with advanced labor pain, but there is no significant difference in long-term pain relief.Good health is the most important and very necessary characteristic for stress-free, skillful, and hardworking people with a cooperative environment to create a sustainable society. Validating two algorithms, namely, sequential minimal optimization for regression (SMOreg) using vector machine and linear regression (LR) and using their predicted cancer patients' cases, this study presents a patient's stress estimation model (PSEM) to forecast their families' stress for patients' sustainable health and better care with early management by under-study cancer hospitals. learn more The year-wise predictions (1998-2010) by LR and SMOreg are verified by comparing with observed values. The statistical difference between the predictions (2021-2030) by these models is analyzed using a statistical t-test. From the data of 217067 patients, patients' stress-impacting factors are extracted to be used in the proposed PSEM. By considering the total population of under-study areas and getting the predicted population (2021-2030) of each area, the proposed PSEM forecasts overall stress for expected cancer patients (2021-2030). Root mean square error (RMSE) (1076.15.46) for LR is less than RSME for SMOreg (1223.75); hence, LR remains better than SMOreg in forecasting (2011-2020). There is no significant statistical difference between values (2021-2030) predicted by LR and SMOreg (p value = 0.767 > 0.05). The average stress for a family member of a cancer patient is 72.71%. It is concluded that under-study areas face a minimum of 2.18% stress, on average 30.98% stress, and a maximum of 94.81% overall stress because of 179561 expected cancer patients of all major types from 2021 to 2030.
A predictive model was established based on logistic regression and XGBoost algorithm to investigate the factors related to postoperative hypocalcemia in patients with secondary hyperparathyroidism (SHPT).
A total of 60 SHPT patients who underwent parathyroidectomy (PTX) in our hospital were retrospectively enrolled. All patients were randomly divided into a training set (
= 42) and a test set (
= 18). The clinical data of the patients were analyzed, including gender, age, dialysis time, body mass, and several preoperative biochemical indicators. The multivariate logistic regression and XGBoost algorithm models were used to analyze the independent risk factors for severe postoperative hypocalcemia (SH). The forecasting efficiency of the two prediction models is analyzed.
Multivariate logistic regression analysis showed that body mass (OR = 1.203,
= 0.032), age (OR = 1.214,
= 0.035), preoperative PTH (OR = 1.026,
= 0.043), preoperative Ca (OR = 1.062,
= 0.025), and preoperative ALP (OR = 1.031,
= 0.027) were positively correlated with postoperative SH. The top three important features of XGBoost algorithm prediction model were preoperative Ca, preoperative PTH, and preoperative ALP. The area under the curve of the logistic regression and XGBoost algorithm model in the test set was 0.734 (95% CI 0.595~0.872) and 0.827 (95%
0.722~0.932), respectively.
The predictive models based on the logistic regression and XGBoost algorithm model can predict the occurrence of postoperative SH.
The predictive models based on the logistic regression and XGBoost algorithm model can predict the occurrence of postoperative SH.Cardiovascular diseases seriously endanger human physical and mental health and life safety, to investigate correlation between miR-let-7b and miR-29b and coronary artery calcification of various patients. At present, real-time fluorescence quantitative PCR (qRT-PCR) was used to detect the expression levels of plasma miR-let-7b and miR-29b in patients with coronary artery calcification and noncoronary artery calcification and to analyze whether the expression levels of miR-let-7b and miR-29b were different between the two groups. It was shown that there was no significant difference in the expression of miR-let-7d-3p between the two groups. But the expression of miR-29b in the observation group was significantly lower than that in the control group. Taken together, miR-29b might be a risk factor for coronary artery calcification and may be a marker for early diagnosis of coronary artery calcification.
The convolutional neural network (CNN) was used to improve the accuracy of digital subtraction angiography (DSA) in diagnosing moyamoya disease (MMD), providing a new method for clinical diagnosis of MMD.
A total of 40 diagnosed with MMD by DSA in the neurosurgery department of our hospital were included. At the same time, 40 age-matched and sex-matched patients were selected as the control group. The 80 included patients were divided into training set (
= 56) and validation set (
= 24). The DSA image was preprocessed, and the CNN was used to extract features from the preprocessed image. The precision and accuracy of the preprocessed image results were evaluated.
There was no significant difference in baseline data between the training set and validation set (
0.05). The precision and accuracy of the images before processing were 79.68% and 81.45%, respectively. After image processing, the precision and accuracy of the model are 96.38% and 97.59%, respectively. The area under the curve of the CNN algorithm model was 0.813 (95% CI 0.718-0.826).
This diagnostic method based on CNN performs well in MMD detection.
This diagnostic method based on CNN performs well in MMD detection.
Colon cancer (CRC), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis. So it is urgent to employ a reasonable manner to assess the prognosis of patients. We developed and validated a gene model for predicting CRC risk.
The Gene Expression Omnibus (GEO) database was used to extract the gene expression profiles of CRC patients (
= 181) from GEO to identify genes that were differentially expressed between CRC patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms. Cluster analysis using the longest distance method was drawn out, and Kaplan-Meier (KM) survival analysis was used to compare the clusters. Meanwhile, the risk score was evaluated in three independent datasets including the GEO and Illumina HiSeq sequencing platforms. The corresponding risk index was calculated, and samples were clustered into high- and low-risng-term treatment.
This study firstly developed a stable and effective 10-gene model by using novel combined methods, and CRC patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.
This study retrospectively analyzed the clinical diagnosis, treatment process, and laboratory test data of patients with pulmonary cryptococcosis to improve the understanding and diagnosis and treatment ability of the disease.
Patients with pulmonary cryptococcosis diagnosed in the First Affiliated Hospital of Dalian Medical University from October 2003 to July 2021 were selected, and their medical records were consulted. The general data, clinical manifestations, laboratory examinations, imaging characteristics, diagnosis, and treatment methods were studied. The software SPSS 22 was used for statistical analysis.
A total of 50 patients with pulmonary cryptococcosis were included in the study. The ratio of male to female was 1 1. The average age was 53.56 ± 11.99 years with a range of 27-82 years. Grouping the patients by age, with 10 years as an age group, we found that 40-60 years was the high-incidence age group. Two patients (4%) had a history of bird contact, and 18 patients (36%) had at least one underlying conditions.learn more
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