Search

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

Objectives. To evaluate the usefulness of a novel acoustic uroflowmetry- (UFM-) based mobile application (app) voiding diary (VD) focusing on the (1) compliance and (2) correlation with a conventional paper-based VD. Materials and Methods. A total of 78 patients were included between December 2019 and June 2020, and a subsequent review of all data was performed. The analyzed data were as follows: (1) survey of convenience/satisfaction/preference comparing the two methods, (2) compliance regarding the completeness of both methods, and (3) correlation of each metric (24-hour urine volume, nocturnal urine volume, nocturnal polyuria index, total number of voids, number of daytime voids, number of nocturnal voids, and maximal bladder capacity) between the two methods. Results. The survey results of convenience, satisfaction, and preference were as follows. With regard to convenience and satisfaction area, higher scores are reported in the mobile app VD (mean ± standard deviation (SD); convenience: 7.47 ± 2.19 [app] vs. 4.20 ± 2.49 [paper]; satisfaction: 7.36 ± 2.17 [app] vs. 5.07 ± 2.65 [paper]). The median score of the overall preference for using the mobile app instead of the paper-based VD was 9 out of 10 (mean ± SD 7.82 ± 2.68). We also found a good correlation between the two methods for nocturnal urine volume (r = 0.55 , p = 0.04), nocturnal polyuria index (r = 0.66 , p = 0.23), total number of voids (r = 0.9 , p = 0.02), number of nocturnal voids (r = 0.83 , p = 0.02), and maximal bladder capacity (r = 0.89 , p = 0.04). Conclusion. The acoustic UFM-based mobile app VD demonstrated favorable findings compared to the conventional paper-based VD. [ABSTRACT FROM AUTHOR]

The paper mulberry (Broussonetia papyrifera) leaf is rich in alkaloids and flavonoids, which has high medicinal and feeding value. We aimed to analyze the effects of B. papyrifera leaf extract on growth performance, antioxidant capacity, immune functions, and fecal microflora of weaned piglets. Thirty healthy, 28-day-old piglets were randomly assigned to three groups and fed with a basal diet supplemented with 0, 150, and 300 g/t B. papyrifera leaf extract for 42 days (control group, group I, and group II) separately. The result revealed that the final weight of piglets in group II was higher than the other groups, and the diarrhea rate in this group was 62.9% lower than in the control group. The feed conversion ratio in group I was significantly lower than the other two groups. Higher blood urine nitrogen concentration was noted in group II, higher glutathione peroxidase and catalase in group II, higher superoxide dismutase in the control group, and higher immune globulins (Ig) IgG, IgA, and IgM in group II. There was no significant difference in community richness and community diversity among the three groups of fecal samples. The relative level of Roseburia was higher in groups I and II, while Lactobacillus was higher in the control group. In conclusion, supplementation with B. papyrifera leaf extract at a certain dosage can increase growth performance and antioxidant capacity of weaned piglets, reduce the occurrence of diarrhea, enhance immune functions and disease resistance, and affect the composition of fecal microflora. [ABSTRACT FROM AUTHOR]

Introduction. The wide variety of methods for recording occlusal contacts and the contradictory data on the interpretation of the obtained markings provoked us to make a comparative laboratory study between different occlusal indicators. Purpose. Evaluation of a qualitative and quantitative method for registration of occlusal contacts in static occlusion in laboratory conditions. Materials and Methods. In completion of the objective, we designed an apparatus for registration of the occlusal contacts (AROC) in static occlusion which is used, corresponding to the MIP in clinical conditions. The occlusal indicators that were included in the study were articulating paper 100 μ, articulating foil 12 μ, and T-Scan Novus system with a sensor thickness of 100 μ. The collected primary statistical information was entered and processed with the statistical package SPSS Statistics 19.0, and the graphs were prepared using Microsoft Office 2019. We performed descriptive statistical analysis in this study. Comparisons were performed using one-way analysis of variance (ANOVA), Student's t -test, and Pearson coefficient method. For a significance level, p < 0.05 was chosen. Result and Discussion. With quality occlusal indicators, it is possible only to visually determine the size, number, and intensity of the marked contacts. After the statistical processing of the obtained data on the number of registered contacts, a significant difference is found in the number of contacts of certain teeth (18, 24, 25, 28, 38, 35, 34, 33, 44, 45, and 48) registered with articulating paper and articulating foil. The maximum force that is reported during the study with the T-Scan system is 93.72% and the forces in the right half of the dentition are 51.7% and in the left 48.9%. To visualize the location of the registered occlusal contacts with the system, it is intraoral to use a quality indicator and we recommend the use of articulating foil. Conclusion. Based on findings from the current in vitro simulation, we can conclude that the type of occlusal indicator influences the registration of contacts, and therefore, we propose as a method of choice to achieve a balanced occlusion in clinical practice to combine the use of one conventional and one quantitative method. [ABSTRACT FROM AUTHOR]

Introduction. Ethiopia has made significant efforts to enhance family planning services despite variations in the community's use of modern contraception in different parts of the country. Various studies have reported the proportion and determinant factors of adolescents' contraceptive uptake in Ethiopia. These studies are not consistent in terms of size, scope, and geographic coverage, and the results need to be systematically collated to inform policies. Therefore, this review was aimed at analyzing the findings of those primary studies to obtain more representative evidence of adolescents' contraceptive uptake in Ethiopia. Methods. Five databases (MEDLINE via PubMed, Google Scholar, Scopus, ScienceDirect, and CINAHL) were searched for papers published from January 2000 up to June 2022 in English. Of thirty eligible studies, eight papers were included in this meta-analysis. Between-study heterogeneity was evaluated by the forest plot and inconsistency index ( I 2 ). A random-effects model was used to calculate the pooled estimates of adolescents' contraceptive uptake. Results. The overall pooled proportion of adolescents' contraceptive uptake was 40% ( I 2 = 99.70 , p ≤ 0.001 ; 95 % CI = 19 , 61). Adolescents' usage of contraception was influenced by a number of factors: individual-, sociocultural-, knowledge- (about contraceptive methods), and healthcare service-related factors. Individual-related factors include the educational status of adolescents, being of young age, and the income status of adolescents' families. Sociocultural-related factors comprise discussion with the family/relatives, parent disapproval and pressure from partners, and being married or having a partner. Healthcare service-related factors include the availability of youth clubs and inconvenient service hours for SRH services. Knowing contraceptive methods and SRH services was also positively associated with adolescents' contraceptive utilization. Conclusions. The proportion of adolescents who used contraception in Ethiopia was 40%. Adolescents' use of contraceptives was influenced by a variety of factors: individual-, sociocultural-, healthcare-, and knowledge-related factors. Hence, integrated interventions targeted at tackling barriers to contraceptive uptake may be helpful to improve adolescents' contraceptive utilization in Ethiopia. [ABSTRACT FROM AUTHOR]

Fourteen Ganoderma lucidum strains from different geographic regions were identified using ITS region sequencing. Based on the sequences obtained, the genomic relationship between the analyzed strains was determined. All G. lucidum strains were also genetically characterized using the AFLP technique. G. lucidum strains included in the analysis displayed an AFLP profile similarity level in the range from 9.6 to 33.9%. Biolog FF MicroPlates were applied to obtain data on utilization of 95 carbon sources and mitochondrial activity. The analysis allowed comparison of functional diversity of the fungal strains. The substrate utilization profiles for the isolates tested revealed a broad variability within the analyzed G. lucidum species and proved to be a good profiling technology for studying the diversity in fungi. Significant differences have been demonstrated in substrate richness values. Interestingly, the analysis of growth and biomass production also differentiated the strains based on the growth rate on the agar and sawdust substrate. In general, the mycelial growth on the sawdust substrate was more balanced and the fastest fungal growth was observed for GRE3 and FCL192. [ABSTRACT FROM AUTHOR]

This study showed the prevalence of human papillomavirus (HPV) variants as well as nucleotide changes within L1 gene and LCR of the HPV16, HPV31, and HPV58 found in cervical lesions of women from North-East Brazil. [ABSTRACT FROM AUTHOR]

Predicting the polyproline type II (PPII) helix structure is crucial important in many research areas, such as the protein folding mechanisms, the drug targets, and the protein functions. However, many existing PPII helix prediction algorithms encode the protein sequence information in a single way, which causes the insufficient learning of protein sequence feature information. To improve the protein sequence encoding performance, this paper proposes a BERT-based PPII helix structure prediction algorithm (BERT-PPII), which learns the protein sequence information based on the BERT model. The BERT model's CLS vector can fairly fuse sample's each amino acid residue information. Thus, we utilize the CLS vector as the global feature to represent the sample's global contextual information. As the interactions among the protein chains' local amino acid residues have an important influence on the formation of PPII helix, we utilize the CNN to extract local amino acid residues' features which can further enhance the information expression of protein sequence samples. In this paper, we fuse the CLS vectors with CNN local features to improve the performance of predicting PPII structure. Compared to the state-of-the-art PPIIPRED method, the experimental results on the unbalanced dataset show that the proposed method improves the accuracy value by 1% on the strict dataset and 2% on the less strict dataset. Correspondingly, the results on the balanced dataset show that the AUCs of the proposed method are 0.826 on the strict dataset and 0.785 on less strict datasets, respectively. For the independent test set, the proposed method has the AUC value of 0.827 on the strict dataset and 0.783 on the less strict dataset. The above experimental results have proved that the proposed BERT-PPII method can achieve a superior performance of predicting the PPII helix. [ABSTRACT FROM AUTHOR]

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods. [ABSTRACT FROM AUTHOR]

The light field is an important way to record the spatial information of the target scene. The purpose of this paper is to obtain depth information through the processing of light field information and provide a basis for intelligent medical treatment. In this paper, we first design an attention module to extract the features of light field images and connect all the features as a feature map to generate an attention image. Then, the attention map is integrated with the convolution layer in the neural network in the form of weights to enhance the weight of the subaperture viewpoint, which is more meaningful for depth estimation. Finally, the obtained initial depth results were optimized. The experimental results show that the MSE, PSNR, and SSIM of the depth map obtained by this method are increased by about 13%, 10 dB, and 4%, respectively, in some scenarios with good performance. [ABSTRACT FROM AUTHOR]

Background. Coronavirus disease (COVID-19) is an infectious illness that spreads widely over a short period of time and finally causes a pandemic. Unfortunately, the lack of radiologists, improper COVID-19 diagnosing procedures, and insufficient medical supplies have all played roles in these devastating losses of life. Deep learning (DL) could be used to detect and classify COVID-19 for potential image-based diagnosis. Materials and Methods. This paper proposes an improved deep convolutional neural network (IDConv-Net) to detect and classify COVID-19 using X-ray and computed tomography (CT) images. Before the training phase, preprocessing methods such as filtering, data normalization, classification variable encoding, and data augmentation were used in conjunction with the proposed IDConv-Net to increase the effectiveness of the detection and classification processes. To extract essential features, deep CNN is then employed. As a result, the suggested model can identify patterns and relationships crucial to the image classification task, resulting in more precise and useful diagnoses. Python and Keras (with TensorFlow as a backend) were used to carry out the experiment. Results. The proposed IDConv-Net was tested using chest X-rays and CT images collected from hospitals in Sao Paulo, Brazil, and online databases. After evaluating the model, the proposed IDConv-Net achieved an accuracy of 99.53% and 98.41% in training and testing for CT images and 97.49% and 96.99% in training and testing for X-ray images, respectively. Further, the area under the curve (AUC) value is 0.954 and 0.996 for X-ray and CT images, respectively, indicating the excellent performance of the proposed model. Conclusion. The findings of our proposed IDConv-Net model confirm that the model outperformed compared to existing COVID-19 detection and classification models. The IDConv-Net outperforms current state-of-the-art models by 2.25% for X-rays and 2.81% for CT images. Additionally, the IDConv-Net training approach is significantly quicker than the current transfer learning models. [ABSTRACT FROM AUTHOR]

Background. This is the first systematic review of the relationship between humeral shaft fractures and radial nerve palsy in children. The present comprehensive review is aimed at identifying important clinical findings between humeral diaphysis fractures and radial nerve injuries and assessing the effects of treatment. Methods. We searched electronic bibliographic databases, including PubMed, the Cochrane Library, Scopus, and Web of Knowledge, until March 2022. This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the patients, interventions, comparisons, outcomes guidelines. Results. We identified 23 original papers, of which 10 were eligible for further analysis. Cases of 32 young patients with radial nerve palsy were identified and analyzed. The prevalence of radial nerve palsy was 4.34% (eight cases out of 184 patients with humeral shaft fractures). The radial nerve was most often associated with a simple transverse fracture (12A3, 17 cases (65.4%)). Conclusions. Radial nerve injury in humeral shaft fractures in children is rare, with a frequency of 4.34%. We highly recommend early surgical nerve exploration with transverse fractures in the distal third segment combined with primary radial palsy. Furthermore, we recommend making thoughtful decisions regarding early nerve exploration in the Holstein–Lewis fractures. In addition, consideration of early surgical nerve exploration in fractures resulting from high-energy trauma and open fractures despite their morphology is recommended. [ABSTRACT FROM AUTHOR]

Immunohistochemical staining is a common technique to study tissue morphology and in situ protein expression. With the rise of immunotherapy, more and more markers in the tumor microenvironment have been discovered. In this paper, the multiplex immunofluorescence staining method was used to stain paraffin sections of 35 cases of gastric adenocarcinoma tissue and 35 cases of colorectal adenocarcinoma tissue and its adjacent tissue. The infiltration of CD8+ T cells and CD103+CD8+ T cells in the slices was detected by using the slice scanner to form the map and CaseViewer2.0 software analysis. The experimental results in this paper showed that compared with adjacent tissues, the infiltration degree of CD8+ T cells in gastric adenocarcinoma tissue was significantly lower (Z = 2.244 , P = 0.025 , P < 0.05), and the infiltration degree of cells was significantly lower (Z = 2.785 , P = 0.005 , P < 0.05). In gastric adenocarcinoma tissue, the infiltration of CD8+ T cells was correlated with the tumor diameter of gastric adenocarcinoma (P = 0.002). There was a statistically significant difference. The degree of CD103+CD8+ T cell infiltration was related to the survival of patients with gastric adenocarcinoma (P = 0.002) and the degree of tumor differentiation (P = 0.004). The infiltration of CD103+CD8+ T cells in gastric cancer tissue and colorectal cancer tissue is related to the survival of patients. CD103+CD8+ T cells are thought to play a role in the prognosis of gastric adenocarcinoma and colorectal adenocarcinoma. Experiments have shown that the more the CD103+CD8+ T cells, the better the prognosis of gastric adenocarcinoma and colorectal adenocarcinoma, so the prognosis of gastric adenocarcinoma and colorectal adenocarcinoma can be improved by increasing CD103+CD8+ T cells. [ABSTRACT FROM AUTHOR]

Background. Alzheimer's disease, a neurodegenerative memory disease, primarily results from the formation of amyloid plaques (Aβ) that gradually inhibit neuron communications. The entire mechanism of Aβ production remains unclear to date, and it is of particular interest among scientists to find out the exact mechanism that leads to amyloid precursor protein (APP) cleavage through the amyloidogenic pathway so that effective treatments can be developed. Method. 2 sets of experiments with the use of human H4-N cell lines are proposed to fully investigate the validity of the hypothesis. All of the experiments would involve immunoblotting of Aβ using an anti-Aβ antibody, and the results would be analyzed with the assistance of an image analyzer. A significant amount of Aβ would be expected to be present in the cytoplasm of cells with herpes simplex virus (HSV-1) applied, as APP endocytosis would be induced by HSV-1, which leads to higher Aβ levels inside the cell. Results. In this paper, a new hypothesis is presented on how HSV-1 infection initiates APP endocytosis and causes an increase in APP cleavage and Aβ production inside the cells. It is also hypothesized that increased Aβ peptides exit the cell via exocytosis, therefore, leading to the development of Alzheimer's disease. The findings will support the hypothesis if intracellular Aβ concentration is significantly higher after the introduction of dHSV-1 and subsequently if extracellular Aβ concentration becomes higher without TeNT exocytosis inhibition. Conclusion. The results of this study would provide valuable insights into the mechanisms underlying Alzheimer's disease and open new scopes of research for its potential treatments. Further studies on virus infection and the development of memory diseases should be conducted to investigate possible correlations. [ABSTRACT FROM AUTHOR]

With the improvement of the higher nursing education system, the overall quality and work ability of clinical nurses have also been significantly improved. Most nursing staff have a positive attitude towards evidence-based nursing medicine but lacking of the knowledge and skills about evidence-based nursing. Clinical nurses are still short of the ability to evaluate relevant research data, which is one of the cause of interference for the clinical nurses to use the latest research results reasonably and effectively at present. The purpose of this paper is to explore the application effect of evidence-based nursing in gastrointestinal surgical pipeline nursing and to clarify the impact of evaluation ability on nurses in clinical practice, investigate, and discuss the application effect of evidence-based nursing in gastrointestinal surgical pipeline nursing. This paper selects the clinical nurses of the three top three hospitals in a province as the research object, using the ways of general data questionnaire, the general individual performance sensitivity scale, the evidence-based nursing application behavior questionnaire, the evidence-based nursing application behavior willingness questionnaire, and the evaluation ability training needs questionnaire to collect data for 100 cases of gastrointestinal surgery clinical nurses. Analyze nurses' awareness level of evidence-based nursing, the willingness of evidence-based nursing behavior, and the relevance of evidence-based nursing behaviors. Use multiple regression analysis methods to explore its influencing factors, and use data statistics to analyze the role and effect of evidence-based nursing in the gastrointestinal surgery pipeline. The research results show that the evaluation ability has a significant promotion effect on clinical nursing application, and it is one of the important factors for clinical nurses to use evidence-based nursing. Clinical nurses have a higher demand for evaluation ability training, and basically here, the application effect of evidence-based nursing in the gastrointestinal surgical pipeline nursing is relatively good. [ABSTRACT FROM AUTHOR]

Uremia is a manifestation of end-stage renal failure and is a clinical syndrome shared by various advanced renal diseases. In recent years, the prevalence of uremia has been increasing. Maintenance hemodialysis therapy is used as the treatment of choice for uremia and is widely used. For uremia, the day-to-day treatment has a serious impact on the patient's symptoms, causing a lot of unnecessary burdens to life. While imposing a severe symptom burden on patients, excessive sleep deprivation repair time can cause various risk injuries and further increase the probability of patient mortality. The resulting sleep deprivation can lead to the functional damage of multiple organs and systems such as the cardiovascular system and the immune system. Therefore, improving sleep quality can increase the survival rate of patients to a certain extent. There is less domestic attention to its related research, and there are not many discussions on the pathogenesis. Based on this, this paper proposes to explore the effect of hemodialysis on the sleep quality of patients with uremia under the support of intelligent data. In this study, it was found that MHD patients had various sleep quality problems, and the incidence of sleep disturbance was 61.78%. The experimental results in this paper show that the incidence of sleep disturbance in diabetic nephropathy is 89.7%. This may be combined with a series of complications in patients with diabetes, which in turn affects the quality of sleep in patients. It may increase patient mortality and affect patient quality of life and survival. Therefore, we should pay close attention to the prevention of various complications in diabetic dialysis patients, which will help to improve sleep quality. [ABSTRACT FROM AUTHOR]

Background. Pelvic abscess surgery consists mostly of open laparotomy and laparoscopic surgery. Open surgery is regarded as a classic procedure. With the rise and promotion of laparoscopic indications in recent years, comparative studies of the two's postoperative effectiveness have been limited. Objective. To compare the clinical effects of laparoscopic exploratory surgery and open surgery in the treatment of pelvic abscess. Methods. Through computer searches of PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and Weipu databases, we found publicly available case-control research on laparoscopic surgery and open surgery for treating pelvic abscess. The papers that met the evaluation criteria were screened, and meta-analysis was used to look at 8 papers on laparoscopic surgery and open surgery for treating pelvic abscess from 2010 to 2021. Results. The results of this study showed that compared with the open laparotomy group, the incidence of laparoscopic group in the incision infection rate (RR = 0.29 , 95% CI (0.20, 0.41), and P < 0.00001), the incidence of intestinal injury (RR = 0.08 , 95% CI (0.04, 0.14), and P < 0.00001), incidence of intestinal obstruction (RR = 0.26 , 95% CI (0.08, 0.90), and P = 0.03 < 0.05), and postoperative pelvic abscess recurrence rate (RR = 0.34 , 95% CI (0.13, 0.86), and P = 0.02 < 0.05) are lower than open surgery, and the difference of these four items is statistically significant. There was no difference in the risk of urinary tract injury between laparoscopic surgery and open surgery (RR = 0.92 , 95% CI (0.27, 3.17), and P = 0.89 > 0.05). Conclusion. In terms of incision infection, intestinal damage, intestinal obstruction, and recurrence of pelvic abscess, the laparoscopic group clearly outperforms the open group, and it merits clinical promotion and use. [ABSTRACT FROM AUTHOR]

Objective. Artificial intelligence-powered screening systems of coronavirus disease 2019 (COVID-19) are urgently demanding since the ongoing outbreak of SARS-CoV-2 worldwide. Chest CT or X-ray is not sufficient to support the large-scale screening of COVID-19 because mildly-infected patients do not have imaging features on these images. Therefore, it is imperative to exploit supplementary medical imaging strategies. Traditional Chinese medicine has played an essential role in the fight against COVID-19. Methods. In this paper, we conduct two kinds of verification experiments based on a newly-collected multi-modality dataset, which consists of three types of modalities: tongue images, chest CT scans, and X-ray images. First, we study a binary classification experiment on tongue images to verify the discriminative ability between COVID-19 and non-COVID-19. Second, we design extensive multimodality experiments to validate whether introducing tongue image can improve the screening accuracy of COVID-19 based on chest CT or X-ray images. Results. Tongue image screening of COVID-19 showed that the accuracy (ACC), sensitivity (SEN), specificity (SPEC), and Matthew correlation coefficient (MCC) of the improved AlexNet and Googlenet both reached 98.39%, 98.97%, 96.67%, and 99.11%. The fusion of chest CT and tongue images used a tandem multimodal classifier fusion strategy to achieve optimal classification, and the results and screening accuracy of COVID-19 reached 98.98%, resulting in a significant improvement of 4.75% the highest accuracy in 375 years compared with the single-modality model. The fusion of chest x-rays and tongue images also had good classification accuracy. Conclusions. Both experimental results demonstrate that tongue image not only has an excellent discriminative ability for screening COVID-19 but also can improve the screening accuracy based on chest CT or X-rays. To the best of our knowledge, it is the first work that verifies the effectiveness of tongue image on screening COVID-19. This paper provides a new perspective and a novel solution that contributes to large-scale screening toward fast stopping the pandemic of COVID-19. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) which emerged in Wuhan, China, in December 2019, is widely controlled now in China. However, the global epidemic is still severe. To study and comment on Hubei's approaches for responding to the disease, the paper considered some factors such as suspected cases (part of them are influenza patients or common pneumonia patients, etc.), quarantine, patient classification (three types), clinically diagnosed cases, and lockdown of Wuhan and Hubei. After that, the paper established an S E L I H R model based on the surveillance data of Hubei published by the Hubei Health Commission from 10 January 2020 to 30 April 2020 and used the fminsearch optimization method to estimate the optimal parameters of the model. We obtained the basic reproduction number R 0 = 3.1571 from 10 to 22 January. R 0 was calculated as 2.0471 from 23 to 27 January. From 28 January to 30 April, R 0 = 1.5014. Through analysis, it is not hard to find that the patients without classification during the period of confirmed cases will result in the cumulative number of cases in Hubei to increase. In addition, regarding the lockdown measures implemented by Hubei during the epidemic, our simulations also show that if the lockdown time of either Hubei or Wuhan is advanced, it will effectively curb the spread of the epidemic. If the lockdown measures are not taken, the total cumulative number of cases will increase substantially. From the results of the study, it can be concluded that the lockdown, patient classification, and the large-scale case screening are essential to slow the spread of COVID-19, which can provide references for other countries or regions. [ABSTRACT FROM AUTHOR]

This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes were annotated to construct the training set. Then, an end-to-end joint learning model was established to extract the entity relations. A joint model leveraging a multihead mechanism was proposed to deal with the problem of relation overlapping. A pretrained transformer encoder was adopted to capture context information. Compared with the entity extraction pipeline, the constructed joint learning model was superior in recall, precision, and F1 measures, at 0.822, 0.825, and 0.818, respectively, 14% higher than the baseline model. The joint learning model could automatically extract features without any extra natural language processing tools. This is efficient in the disassembling of mixture symptom mentions. Furthermore, this superior performance at identifying overlapping relations could benefit the reassembling of separated symptom entities downstream. [ABSTRACT FROM AUTHOR]

This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO). Primary data were gathered from previous studies and included 244 data of biodiesel oils. The findings showed a coefficient determination ( R 2 ) of 0.937 and RMSE of 2.1053 for the GA-ANFIS model, and a coefficient determination ( R 2 ) of 0.968 and RMSE of 1.4443 for PSO-ANFIS. This study indicates the capability of the PSO-ANFIS algorithm in the estimation of the bio-oil yield. According to the performed analysis, this model shows a higher ability than the previously presented models in predicting the target values and can be a suitable alternative to time-consuming and difficult experimental tests. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an inflammatory disease damaging the myelin sheath in the central and peripheral nervous system in the brain and spinal cord. Optic Neuritis (ON) is one of the most prevalent ocular demonstrations of MS. The current diagnosis protocol for MS is MRI, but newer modalities like Optical Coherence Tomography (OCT) are already of interest in early detection and progression analysis. OCT reveals the symptoms of MS in the Central Nervous System (CNS) through cross-sectional images from neural retinal layers. Previous works on OCT were mostly focused on the thickness of retinal layers; however, texture features seem also to have information in this regard. In this research, we introduce a new pipeline that constructs layer-stacked (LS) images containing data from each specific layer. A variety of texture features are then extracted from LS images to differentiate between healthy controls and ON/None-ON MS cases. Furthermore, the definition of texture extraction methods is tailored for this application. After performing a vast survey on available texture analysis methods, a treasury of powerful features is collected in this paper. As a primary work, this paper shows the ability of such features in the diagnosis of HC and MS (ON and None-ON) cases. Our findings show that the texture features are powerful to diagnose MS cases. Furthermore, adding information of conventional thickness values to texture features improves considerably the discrimination between most of the target groups including HC vs. MS, HC vs. MS-None-ON, and HC vs. MS-ON. [ABSTRACT FROM AUTHOR]

The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences. The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required. Accurate cancer-type prediction is essential for cancer diagnosis and treatment. Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes. The Convolution Neural Network (CNN) and neural pattern recognition (NPR) approaches are used in this study paper to detect and predict the type of cancer. Different Convolution Neural Networks (CNNs) have been proposed by various researchers up to this point. Each model concentrated on a certain set of parameters to simulate the expression of genes. We have developed a novel CNN-NPR architecture that predicts cancer type while accounting for the tissue of origin using high-dimensional gene expression inputs. The 5000-person sample of the 1-D CNN integrated with NPR is trained and tested on the gene profile, mapping with various cancer kinds. The proposed model's accuracy of 94% suggests that the suggested combination may be useful for long-term cancer diagnosis and detection. Fewer parameters are required for the suggested model to be efficiently trained before prediction. [ABSTRACT FROM AUTHOR]

Under the background of 18F-FDG-PET/CT multimodal whole-body imaging for lung tumor diagnosis, for the problems of network degradation and high dimension features during convolutional neural network (CNN) training, beginning with the perspective of dividing sample space, an E-ResNet-NRC (ensemble ResNet nonnegative representation classifier) model is proposed in this paper. The model includes the following steps: (1) Parameters of a pretrained ResNet model are initialized using transfer learning. (2) Samples are divided into three different sample spaces (CT, PET, and PET/CT) based on the differences in multimodal medical images PET/CT, and ROI of the lesion was extracted. (3) The ResNet neural network was used to extract ROI features and obtain feature vectors. (4) Individual classifier ResNet-NRC was constructed with nonnegative representation NRC at a fully connected layer. (5) Ensemble classifier E-ResNet-NRC was constructed using the "relative majority voting method." Finally, two network models, AlexNet and ResNet-50, and three classification algorithms, nearest neighbor classification algorithm (NNC), softmax, and nonnegative representation classification algorithm (NRC), were combined to compare with the E-ResNet-NRC model in this paper. The experimental results show that the overall classification performance of the Ensemble E-ResNet-NRC model is better than the individual ResNet-NRC, and specificity and sensitivity are more higher; the E-ResNet-NRC has better robustness and generalization ability. [ABSTRACT FROM AUTHOR]

Background. Coronary artery disease (CAD) is one of the most common causes of sudden death with high morbidity in recent years. This paper is aimed at exploring the early peripheral blood biomarkers of sudden death and providing a new perspective for clinical diagnosis and forensic pathology identification by integrated bioinformatics analysis. Methods. Two microarray expression profiling datasets (GSE113079 and GSE31568) were downloaded from the Gene Expression Omnibus (GEO) database, and we identified differentially expressed lncRNAs, miRNAs, and mRNAs in CAD. Gene Ontology (GO) and KEGG pathway analyses of DEmRNAs were executed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified. Finally, we constructed a competitive endogenous RNA (ceRNA) regulation network among lncRNAs, miRNAs, and mRNAs. Also, the 5 miRNAs of the ceRNA network were verified by RT-PCR. Results. In total, 86 DElncRNAs, 148 DEmiRNAs, and 294 DEmRNAs were dysregulated in CAD. We received 12 GO terms and 5 pathways of DEmRNAs. 31 nodes and 78 edges were revealed in the PPI network. The top 10 genes calculated by degree method were identified as hub genes. Moreover, there were a total of 26 DElncRNAs, 5 DEmiRNAs, and 13 DEmRNAs in the ceRNA regulation network. We validated the 5 miRNAs of the ceRNA network by RT-PCR, which were consistent with the results of the microarray. Conclusions. In this paper, a CAD-specific ceRNA network was successfully constructed, contributing to the understanding of the relationship among lncRNAs, miRNAs, and mRNAs. We identified potential peripheral blood biomarkers in CAD and provided novel insights into the development and progress of CAD. [ABSTRACT FROM AUTHOR]

Purpose. The aim of the study was to evaluate general physical activity (PA) level on the basis of leisure time and transportation physical activity (LTPA and TPA), assess the percentage of persons not meeting PA recommendations by the World Health Organisation (WHO), and evaluate the relationship between selected sociodemographic factors and physical inactivity. Methods. The paper is based on data (n = 7,347) retrieved from five large-scale surveys (2014-2018) used to collect information on the PA of Polish society. In order to meet the aim of the paper, we selected a sample of 2,023 Poles aged ≥ 60 years old. In each wave, the Polish long version of the International Physical Activity Questionnaire was used. Mann–Whitney U and Kruskal-Wallis tests were used to investigate the differences between the types and volume of PA and sociodemographic variables. Relationships between physical inactivity and analysed variables were evaluated using log-linear analysis. To capture relationships between physical inactivity and a set of explanatory variables, a predictive model was built. Results. The total average energy expenditure amounted to 1879.5 ± 2352.5 MET-min/week, including LTPA (938.5 ± 1491.9 MET-min/week) and TPA (944.8 ± 1322.4 MET-min/week). Over the course of the last two years of the study, the average value of MET-min/week increased significantly (p < 0.05); however, prohealth WHO norms are not met by nearly 40% of Poles. Sex determines the volume of LTPA and TPA (p < 0.05) but does not determine the inactivity of seniors. Place of residence and education differentiate participation in LTPA and TPA. The lower the education level and the smaller the place of residence, the greater the inactivity. Conclusions. The target for future interventions should be people aged 60+ living in villages and small towns (especially those with primary education). It is necessary to undertake educational and motivational programmes promoting PA. It is essential to develop detailed recommendations and to create a friendly and supportive environment. [ABSTRACT FROM AUTHOR]

Background. A large amount of publications had reported the association between incidence of esophageal cancer (EC) and type 2 diabetes mellitus (T2DM) in the past decade. However, those papers' results are inconsistent on relationships between T2DM the incidence of EC. Therefore, the objective of this meta-analysis was to determine the relationship between T2DM and the risk of EC (including 2 histological types, esophageal adenocarcinoma [EADC] and esophageal squamous cell carcinoma [ESCC]). Method. We finally extracted 19 articles though Pubmed, Embased, and Cochrane library. Those identify extraction date including 14,312 cases and 24,959,067 control records and then mixed the relative risks (RRs) and corresponding 95% confidence intervals (95%CIs) through STATA. Results. We observed that there are significantly positive correlation between T2DM and EC risk (R R = 1.28 , 95% CI: 1.05-1.57, P = 0.015).Also, our study showed positive correlation between T2DM and EADC (esophageal adenocarcinoma) risk (R R = 1.28 , 95% CI: 1.05-1.57, P < 0.001). What's more, subgroup analysis based on ethnicity represented the Caucasian is more susceptible to EC (R R = 1.28 ,95% CI: 1.10-1.49, P = 0.001). Conclusion. Those results offer a recent epidemiological and integrated evidence to ascertain the correlations between T2DM and incidence of EC. Those results take public health implications on preventing T2DM and then depress the occurrence of EC. Our study also provides referenced information for the prevention. However, some data is still insufficient, and more research should be carried out. [ABSTRACT FROM AUTHOR]

At present, percutaneous nephrolithotomy has become an option for hospitals increasingly to treat calculus. However, due to the certain risk of percutaneous nephrolithotomy, it may lead to postoperative complications. Analyzing the preoperative factors of septic shock after percutaneous nephrolithotomy can provide guidance for reducing the incidence of septic shock after PCNL. This article establishes a logistic regression model based on preoperative factors, uses statistical methods, and uses the professional statistical software SPSS to create a database and analyze the data. The method of classification data analysis was used to determine various influencing factors including patient factors, stone factor, and preoperative factors and combined B-ultrasound guidance and percutaneous nephrolithotomy to reduce the risk of surgery. The experimental results found that the percutaneous nephroscope laser lithotripsy guided by the smart Internet of Things can effectively perform lithotripsy; in this paper, the preoperative data of different complications are obtained, and the risk of surgery is closely related to the operation time. The operation time exceeds 90 minutes and causes the risk of severe bleeding after PCNL. It is twice the operation time of less than 90 minutes and 5 times the operation time of less than 60 minutes. [ABSTRACT FROM AUTHOR]

First aid is to give immediate first aid to patients who have suffered accidental injuries or sudden diseases before the emergency medical personnel arrive at the scene or are sent to the hospital for treatment; myocardial infarction is a severe and emergency of coronary heart disease. It is often because of coronary atherosclerosis, plaque rupture, bleeding, or thrombosis, which leads to the acute and complete occlusion of the coronary arteries and acute necrosis of the myocardium. This article aims to investigate the effectiveness of a stratified emergency care team assisted by multidisciplinary first aid knowledge in acute myocardial infarction first aid, hoping to reduce the probability of acute myocardial infarction through first aid and nursing care. Business process reengineering is a management idea that reached its heyday in the 1990s and is usually defined as achieving workflow and productivity through the reorganization and optimization of corporate strategies, value-added operational processes, and the systems, policies, organizations, and structures that support them. This article first outlines the concepts and steps of medical image registration, analyzes the characteristics of current medical image registration methods, and uses the two most commonly used medical registration methods today; in this study, the BPR theory was used to construct the AMI emergency care process in the hospital, which effectively reduced the emergency delay time of AMI patients, improved the patient's emergency response, and increased the efficiency of emergency nurses' rescue work. The experimental results in this paper show that the sensitivities of the first four groups of ST ↑ III > II , ST ↓ aVL > I , STV 3 ↓ / STIII ↑ ≤ 1.2 , STI ↓ ≥ 0.05 mV are 82.1%, 80%, 75.3%, and 60.3%, respectively. Their sensitivities are relatively close, both are greater than 50%; among them, ST ↑ III > II has the highest sensitivity, which is 80%. In terms of specificity, ST ↑ III > II , ST ↓ aVL > I , STV 3 ↓ / STIII ↑ ≤ 1.2 , and STI ↓ ≥ 0.05 mV were 82.1%, 89.2%, 82.7%, and 65.2%, respectively. ST ↓ ≥ 0.05 mV has the lowest specificity, and ST ↓ aVL > I has the highest specificity. [ABSTRACT FROM AUTHOR]

Computer searches of the PubMed, Cochrane Library, and Embase databases for randomized controlled studies on the effects of intensive nutrition on clinical outcomes in patients with severe craniocerebral injury were conducted from the time of database creation to June 11, 2022, along with manual searches of the relevant literature. Two investigators independently screened the literature, extracted data, and evaluated the risk of bias of the included studies before the effect sizes were combined using RevMan 5.3 statistical software provided by the Cochrane Collaboration Network, and publication bias was detected using Stata 12.0 software. Meta-analysis showed that total protein levels were higher in the intensive nutrition group than in the regular nutrition group (WMD = 4.96 g/L (1.57-8.34), P < 0.001); IgA levels were significantly higher in the intensive nutrition group than in the regular nutrition group (SMD = 0.79 (0.51-1.07), P < 0.001 ; SMD = 0.98 (0.58-1.38), P < 0.001); IgG levels were significantly higher in the fortified group than in the regular group (SMD = 0.98 (0.58-1.38), P < 0.001); CD4/CD8 was significantly higher in the fortified patients than in the regular patients with a combined effect size of WMD = 0.33 (0.18-0.48) (P < RR = 0.45 (0.27-0.75), P = 0.002). The results show that effective support of early enteral nutrition can reduce the occurrence of gastrointestinal complications in patients, give them a better adaptation process to the gastrointestinal tract, and ensure the degree of tolerance of their gastric mucosa, thus absorbing more nutrition. Fortification significantly reduced the incidence of gastric retention in patients with craniocerebral injury (RR = 0.19 (0.07-0.49), P < 0.001). In the subgroup analysis of the three groups, it was shown that, depending on the starting time, the total protein level and IgG level were better in the early nutrition at 24 h than in the late nutrition above 24h and that, depending on the starting dose, the total protein level, IgA, IgG, and CD4/CD8 were better in the intervention at doses above 30 mL/h, using the starting dose of 30 mL/h as the cut-off point. In the subgroup analysis based on different nutrition methods (enteral and parenteral nutrition), IgA levels and the incidence of bloating and diarrhea were better than those of parenteral nutrition in the indicators of enteral nutrition. [ABSTRACT FROM AUTHOR]

Often more than one category of lesions in patients' gastrointestinal tracts need to be found in the endoscopic examination. Therefore, there is a need to establish an efficient synchronous real-time computer-aided detection (CADe) system for multicategory lesion detection. This paper proposes to build a system with a multiclass detection model based on the YOLOv5 to detect multicategory lesions synchronously in real-time. Two joint detection CADe systems using multiple single-class detection models with the same structure in parallel or series are established for comparison. A retrospective dataset containing 31117 images from 3747 patients is used in this study. To train the model, various online data augmentation methods and multiple loss functions are used. The proposed CADe system can synchronously detect cancers, gastrointestinal stromal tumours, polyps, and ulcers from different quality input images with 98% precision, 89% recall, and 90.2% mAP. The detection speed is 47 frames per second with a 0.04 s latency on a PC workstation. Compared to the two joint detection CADe systems, the proposed system is more accurate with faster speed and lower latency. Two extra experiments indicated that the lesion detection model based on YOLOv5x could provide better performance than other common YOLO structures and that different accuracy metrics and lesion categories have different requirements for the number of training images. The proposed synchronous real-time CADe system with the multiclass detection model can detect multicategory lesions with high accuracy and speed and low latency on limited hardware. It expands the clinical application of CADe in endoscopy and uses expensive labelled medical images more efficiently than multiple single-category lesion models for joint detection. [ABSTRACT FROM AUTHOR]

Helical CT plain scan has high spatial and area resolution, which is beneficial to the extraction of CT features of pulmonary nodules, and is of great significance for the diagnosis and differential diagnosis of pulmonary diseases. In order to deeply study the role of visual sensor image algorithm in CT image, this paper adopts clinical simulation method, data fusion method, and image acquisition method to collect images, analyze CT image features, and simplify the algorithm and create a CT model that can better diagnose secondary tuberculosis and lung cancer. We selected 45 patients with lung disease in this group, with an average age of 38 years. At the same time, the consistency analysis results of the diameter and plain CT value data of the five groups of cases measured by two observers are between 0.82 and 0.88, which has a good consistency. We could find that the nodule diameters of the five groups of cases were different (F =16.99, P < 0.01), and the difference was statistically significant (P < 0.06), indicating that our data are not only accurate but also very reliable. ROC was used to analyze the precise value of CT values in the pulmonary tuberculosis group and lung cancer group, intrapulmonary lymph node group, and pulmonary hamartoma group to determine the cutoff value. The results showed that the AUC values of the pulmonary tuberculosis group and the lung cancer group were 0.788, and the middle was the largest, indicating that the values were guaranteed. The basic realization starts with visual sensor technology and designs a clinical model that can more accurately identify CT images and differential diagnosis. [ABSTRACT FROM AUTHOR]

With the continuous improvement of human living standards, people's demand for health has become an important international research hotspot. In recent years, 41.3% of the total incidence of multiple organ failure (MOF) caused by dysfunction of the intestinal screen was found every year. The mortality rate is 62%, which is more than twice that of developed countries. This paper is aimed at observing the microscopic effects of probiotics derived from dairy products using Caco-2 cells on intestinal barrier function. Based on the above background, the purpose of this study was to construct a Caco-2 cell model under microscope to study the effect of probiotics on intestinal barrier function. This study first describes the background knowledge of the integration of modern microscope technology and medical field and the correlation between them. The results showed that the relative adhesion rates of Lactobacillus bulgaricus, Lactobacillus acidophilus, and Streptococcus thermophilus were 4.67 ± 0.07 % , 11.53 ± 0.06 % , and 18.31 ± 0.08 % , respectively, which were lower than those in the normal group. The production of antibacterial substances can inhibit intestinal pathogens and adjust the balance of intestinal flora. [ABSTRACT FROM AUTHOR]

The incidence of cancer is increasing today, particularly lung and chest cancer. Employing novel methods to detect cancer in its earliest stages and discover painless, noninvasive treatments are urgently needed. The goal of the proposed study is to investigate the value of automated breast volume scanning (ABUS) in conjunction with contrast-enhanced ultrasonography (CEUS) in properly diagnosing breast cancer in its early stages and the effectiveness of neoadjuvant chemotherapy (NAC) in treating the disease. For the research study, information on 98 patients who had NAC and surgery in the breast surgery department of the Shaanxi Provincial Cancer Hospital has been gathered. All patients have received four cycles of NAC and underwent conventional ultrasound (HUSS), CEUS, ABUS, and pathological examination. At the same time, receiver operating characteristic (ROC) curve analysis, single factor, multiple linear regression, and other methods have also been used to analyze the diagnostic efficacy of breast cancer and NAC efficacy evaluation results. The study of this paper is totally based on the data collected from Shaanxi Provincial Cancer Hospital. The statistical and computational analyses are performed on the data collected for drawing inferences. When the findings are compared to the results of the pathological examination, HUSS has demonstrated a significant distinction between benign and malignant diagnoses with a statistical value of P < 0.05.ABUS combined with CEUS has shown no considerable differences in correlation study. Except for negative likelihood ratio, the diagnostic performance indexes of CEUS+ ABUS are substantially higher than HHUS with P < 0.05. ROC curve analysis is also performed which shows that CEUS and ABUS combination has higher precision in the analysis of breast cancer. ABUS pooled with CEUS shows great application value in the judgment of breast cancer as per the results obtained from the statistical analysis on data of 98 patients. [ABSTRACT FROM AUTHOR]

Objectıve. Ankaferd hemostat (ABS; Ankaferd Blood Stopper®) contains standardized plant extracts comprising Alpinia officinarum, Glycyrrhiza glabra, Thymus vulgaris, Urtica dioica, and Vitis vinifera. ABS especially was recognized for its hemostatic effect; however, antineoplastic role of ABS was identified during the last decade. The aim of this paper is to review the molecular basis and associated clinical implications of the ABS as a topical antineoplastic agent. Materials and Methods. Up to June 2022, literature searches were performed using the internet search engines Medline, Google Scholar, and Embase: Ankaferd. PRISMA flow diagram described the Ankaferd search. Results. ABS have important effects in several cellular processes, like control of the cell cycle, apoptosis, angiogenesis, signal transduction, inflammation, immunologic, and metabolic mechanisms. The molecular basis of antineoplastic roles of ABS depends on its proteomics, metabolomics, and transcriptomics features. ABS has antineoplastic effects on solid tumors like colon, bladder, breast, and osteosarcoma cancer cells. Also, ABS effects renal tubular apoptosis and has antitumoral roles on malign melanoma cells. ABS inhibits hematological tumors like myeloma and lymphoid cells. ABS induces apoptosis in retinal cells and has inhibitory effects on mesenchymal stem cells. It has an antiproliferative role on gastrointestinal tumors like hepatocellular carcinoma cells. Moreover, ABS has a treatment supportive role in cancer since it can prevent oxidative DNA damage and decrease the intestinal damage in necrotizing enterocolitis. Furthermore, it has chemopreventive and hepatoprotective features and can be used for prophylaxis and treatment of oral mucositis. Conclusion. ABS alters cell metabolism and cell cycle. ABS has antineoplastic role on cancer cells. The expanding context of ABS compromises anti-infective, antineoplastic, and wound healing features. ABS may also be used for the palliative, adjuvant, neoadjuvant, or supportive use by interventional radiology procedures for the treatment of solid tumors. Future controlled studies are necessary to clarify the pleiotropic role of ABS like antineoplastic, antithrombotic, anti-inflammatory, anti-infective, antifungal, and antioxidative effects. [ABSTRACT FROM AUTHOR]

Dynamic balance is particularly important for the maintenance, transformation, and restabilization of various postures of the human body. Combined with the characteristics of unbalanced sports events, although there is no physical contact in sports events, it has intense antagonism due to the constant change of body movement. Athletes need to have the ability of fast movement and continuous high-intensity multibeat confrontation. They need to adjust their body posture any time according to the situation, constantly move their body from one side to the other side in fast movement, and constantly change between being imbalanced and being stable to keep the center of gravity stable and adjust their body posture. Combining with the characteristics of unbalanced sports, this paper is aimed at the problem that young athletes have poor dynamic balance ability and cannot maintain the stability of their body's center of gravity in the process of fast multidirectional movement and braking. The supporting leg needs ankle dorsiflexion, knee flexion, and hip flexion during SEBT testing. Therefore, the lower limb needs sufficient range of motion, strength, proprioception, and neuromuscular control ability to achieve the optimal test results. A randomized controlled experiment was used to test whether there were significant differences in the test indicators between the experimental group and the control group before and after the experiment by using a paired test. The test indicators between the experimental group and the control group before and after the intervention were tested by independent sample test to explore the influence of core balance training on the dynamic balance ability of teenagers in unbalanced sports. Through analysis, it is proved that strengthening balance exercise can significantly improve the functional movements and physical fitness of adolescents, which is of great significance to the development of basic flexibility and stability of joints and the strengthening of weak chain muscles. [ABSTRACT FROM AUTHOR]

Objective. Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods. Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results. From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion. The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come. [ABSTRACT FROM AUTHOR]

The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme. [ABSTRACT FROM AUTHOR]

Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are an important aspect of cardiac diagnostics. An electrocardiogram (ECG), a recording collecting the heart's pumping activity, is regarded the guideline for catching these abnormal episodes. Nevertheless, because the ECG contains so much data, extracting the crucial data from imagery evaluation becomes extremely difficult. As a result, it is vital to create an effective system for analyzing ECG's massive amount of data. The ECG image from ECG signal is processed by some image processing techniques. To optimize the identification and categorization process, this research presents a hybrid deep learning-based technique. This paper contributes in two ways. Automating noise reduction and extraction of features, 1D ECG data are first converted into 2D pictures. Then, based on experimental evidence, a hybrid model called CNNLSTM is presented, which combines CNN and LSTM models. We conducted a comprehensive research using the broadly used MIT_BIH arrhythmia dataset to assess the efficacy of the proposed CNN-LSTM technique. The results reveal that the proposed method has a 99.10 percent accuracy rate. Furthermore, the proposed model has an average sensitivity of 98.35 percent and a specificity of 98.38 percent. These outcomes are superior to those produced using other procedures, and they will significantly reduce the amount of involvement necessary by physicians. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method to analyze the risk factors of coronary heart disease (CHD) and their correlation with inflammatory factors in patients with type 2 diabetes mellitus (T2DM). To verify and implement this idea, we have selected a total of 165 patients with T2DM treated in our hospital from March 2019 to October 2021 that were divided into CHD group (n = 72) and non-CHD group (n = 93) according to the results of coronary angiography. Patients with CHD were divided into three groups according to SYNTAX score: low-risk group, medium-risk group, and high-risk group. The clinical data of all patients were collected. Univariate and multivariate analyses were used to screen the risk factors of CHD in patients with T2DM. The related inflammatory factors such as C-reactive protein (CRP), interleukin-6 (IL-6), and water-soluble CD40 ligand (sCD40L) were detected in all patients. Pearson's linear correlation analysis was used to analyze the correlation between the expression levels of CRP, IL-6, and sCD40L and CHD in patients with T2DM. The receiver working curve (ROC) was used to evaluate the efficacy of IL-6, CRP, and sCD40L in predicting high risk of CHD in patients with T2DM. Multivariate analysis showed that age and course of T2DM, FFA, UA, and Hcy were risk factors for CHD in patients with T2DM. The serum levels of IL-6, CRP, and sCD40L in patients with CHD were significantly higher than those in patients without CHD. According to SYNTAX score, 72 patients with CHD were divided into low-risk group (n = 36), medium-risk group (n = 26), and high-risk group (n = 10). Compared with the low-risk group, the expression levels of serum IL-6 CRP and sCD40L in the middle-risk group and high-risk group were significantly higher than those in the low-risk group. The expression levels of IL-6 CRP and sCD40L in the high-risk group were also significantly higher than those in the medium-risk group. There is a positive correlation between syntactic score and IL-6 expression in patients with T2DM complicated with coronary heart disease (r = 0.778 , P < 0.001), with the expression of CRP (r = 0.756 , P < 0.001) and with the expression of sCD40L (r = 0.748 , P < 0.001). Advanced age, long course of T2DM, elevated levels of FFA, UA, and Hcy are all risk factors of CHD in patients with type 2 diabetes. T2DM patients with the above risk factors should be vigilant and pay attention to monitoring the related indexes of coronary heart disease to avoid the occurrence of serious cardiovascular disease. CRP, IL-6, and sCD40L are involved in the progression of CHD in patients with T2DM. The more severe CHD is, the higher the expression of IL-6, CRP, and sCD40L in serum. [ABSTRACT FROM AUTHOR]

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods. [ABSTRACT FROM AUTHOR]

The heart is one of the most important organs of the human body. The role of the heart is to promote blood flow and provide sufficient blood flow to organs and tissues. The research on the heart has important theoretical and clinical significance. Because of the noninvasive and intuitive display of ultrasound image, it can dynamically obtain the heart state and has become the main means to detect the heart dynamics. We analyze the characteristics of cardiac ultrasound image from the medical point of view and signal processing. The heart movement is periodic and rhythmic. The image signal can be decomposed. Firstly, the image is decomposed into high- and low-frequency signals to highlight different dimensional information. Then, the attention model was introduced, focusing on the heart region. Finally, the multidimensional network carrying model was established to achieve cardiac segmentation. The experimental results show that the AOM of the algorithm proposed in this paper reaches 92%, which has a certain degree of advancement and can assist doctors to make accurate diagnosis. [ABSTRACT FROM AUTHOR]

There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists' mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity. [ABSTRACT FROM AUTHOR]

The paper is written to investigate the levels and significance of tumor markers [carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), and carbohydrate antigen 19-9 (CA19-9)] and cytokines [interleukin-6 (IL-6), IL-4, and IL-2] in serum and peritoneal lavage fluid of patients with peritoneal metastasis of gastric cancer. For this research, 145 patients with gastric cancer treated in our hospital were divided into peritoneal metastasis group (n = 25), other metastasis group (n = 32), and nonmetastasis group (n = 88) according to the occurrence of metastasis. At the same time, the levels of serum tumor markers and cytokines and tumor markers and cytokines in intraoperative peritoneal lavage fluid were compared among the three groups. The results showed that the proportion of TNM stage III in peritoneal metastasis group and other metastasis group was 68.00% and 62.50%, respectively, and the proportion of tumor >5 cm was 64.00% and 59.38%, respectively, which was significantly higher than that in the control group. The 1-year survival rate of peritoneal metastasis group and other metastasis group was 44.00% and 40.63%, respectively, which was significantly lower than that of nonmetastasis group (P < 0.05).The serum levels of CEA, CA125, CA19-9, IL-6, IL-4, and IL-2 in peritoneal metastasis group and other metastasis group were higher than those in nonmetastasis group. The intraoperative peritoneal lavage fluid CEA, CA125, and IL-6 were 13.41 ± 3.72 ng/ml, 8.97 ± 1.33 U/ml, and 1.85 ± 0.44 pg/ml, respectively, which were higher than those in other metastasis groups and nonmetastasis groups (P < 0.05). There was no significant difference in the levels of CA19-9, IL-4, and IL-2 in peritoneal lavage fluid among peritoneal metastasis group, other metastasis groups, and nonmetastasis groups (P > 0.05); the areas under the ROC curve of intraoperative peritoneal lavage fluid CEA, CA125, and IL-6 in predicting peritoneal metastasis were 0.850, 0.902, and 0.806, respectively, P < 0.05. Thus, the conclusion is that peritoneal lavage fluid CEA, CA125, and IL-6 have certain application value in predicting and diagnosing peritoneal metastasis of gastric cancer, while the other indexes have no application value. [ABSTRACT FROM AUTHOR]

To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a prediction model for enteral nutrition support in neurosurgical intensive care patients which is primarily based on an integrated learning algorithm. Additionally, we have compared the prediction performance of each model. The patients were divided into control and combined treatment groups according to the random number table method, and 175 patients in each group were treated with a parenteral method and early enteral nutrition support, respectively. A reentry ICU prediction model based on the integrated learning method random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) was developed, and the prediction performance of integrated learning and logistic regression was compared. The average sensitivity, positive predictive value, negative predictive value, false-positive rate, false-negative rate, area under the receiver operating characteristic curve (AUROC), and Brier score after fivefold cross-validation were used to evaluate model effects, and the best performance model based on the top 10 predictor variables in order of importance was given. Among all models, GBDT (AUROC = 0.858) was better than random forest (AUROC = 0.827) and slightly better than AdaBoost (AUROC = 0.851). The GBDT algorithm gave a higher ranking of importance for variables such as mean arterial pressure, systolic blood pressure, diastolic blood pressure, heart rate, urine volume, and blood creatinine and relatively poorer cardiovascular and renal function in neurosurgical intensive care patients. [ABSTRACT FROM AUTHOR]

Due to the complexity of clinical manifestations and the lack of standardized diagnostic criteria, it is still difficult to distinguish the etiological types of congenital edentulousness corresponding to genetic defects. This paper studies the application of deep learning image processing and digital image processing in medical images in detail and analyzes the functions of congenital edentulous hotspot genes. The cases in the control group and the study group were collected, and the gene mutations of direct sequence MSX1, PAX9, AXIN2, and BMP were analyzed, and new pathogens were found. The experimental results suggest that PAX9 and MSX1 genes may have a synergistic effect in nonsyndromic congenital edentulous patients. In severely missing teeth, the role of PAX9 may be greater than that of MSX1. The experimental results will help us lay the foundation for further understanding of the disease in the future. [ABSTRACT FROM AUTHOR]

Malignant melanoma is one of the most threatening cancers to human health. Only 14% of patients with malignant melanoma have a remaining life span of 5 years. At present, there have been some studies looking for potential prognostic indicators of esophageal cancer from the level of genes and infiltrating immune cells, but there are still some problems that need to be resolved urgently. This paper proposes IGHG3 as the immune infiltration of malignant melanoma, which takes into account the computational mathematics. It aims to deduce the characteristics of immune cell infiltration in malignant melanoma and study the relationship between different immune cell infiltration characteristics and prognosis. The method in this article is to establish a computational mathematical model for the immunotherapy of melanoma, then study the method of identification of the affinity of the IGHG3 reagent, and finally obtain the gene expression of immune infiltration. The functions of these methods are, respectively, to predict the dynamic behavior of T cells with two different specificities through mathematical models and to test the matching degree of different concentrations of IGHG3 reagent with the human body. Then use the ssGSEA algorithm to obtain immune infiltration related data and calculate the difference between the weighted empirical cumulative distribution function of all genes in the effect of IGHG3 on melanoma that was carried out. The experimental results showed the computational mathematical method genome and all the remaining genes. In this study, a computational mathematical method to detect the IGHG3 gene expression had a significant inhibitory effect on A375 cells in the experimental group, and the knockdown efficiency reached 85.6%. [ABSTRACT FROM AUTHOR]

Objective. The aim of this paper was to investigate whether two freeze-thaw cycles before embryo transfer may affect perinatal and neonatal outcomes. Materials and Methods. A total of 8,028 frozen-thawed embryo transfer patients who became pregnant between March 2013 and September 2019 were included. The patients were divided into two groups: the oocyte cryopreservation (OC) group (N = 96) and the control group (N = 7932). Propensity score matching (PSM) was used to adjust the baseline characteristics of the two groups at a proportion of 1 : 4. There were 96 patients in the OC group and 369 patients in the control group after PSM. The pregnancy-related complications and neonatal conditions after delivery of the two groups were compared. Results. The OC group had a higher stillbirth rate (3.1% vs. 0.3%, P = 0.029) than the control group after PSM. Moreover, a slightly higher pregnancy defect rate was found in the OC group. There was no significant difference in the rates of diabetes mellitus, hypertension during pregnancy, cesarean section, multiple births, low birth weight (LBW), or premature birth defects between the two groups. Conclusions. Our findings demonstrate that performing frozen-thawed embryo transfer (FET) with cryopreserved oocytes was associated with a higher rate of stillbirth than FET with fresh oocytes. The incidences of diabetes, gestational hypertension, cesarean section, multiple births, LBW, premature birth, and birth defects of the two groups were not significantly different. [ABSTRACT FROM AUTHOR]

This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The good performing capability of the proposed model is achieved by using hybrid features of ResNet50. The brain tumor tissue images were extracted by the suggested hybrid deep feature-based modified ResNet50 model and the EWS-based modified ResNet50 model with a high classification accuracy of 92% and 90%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods. [ABSTRACT FROM AUTHOR]