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With the development of society and economy, patients with coronary heart disease increasingly hope to receive more humanized services after surgery, and the reengineering of the nursing process is precisely based on humanized management. In order to create a nursing intervention that helps in boosting total nursing gains and lowering disasters, the primary element of the nursing business process reengineering is to reintegrate or condense the creative work flow that was fragmented by specialization into a whole job. The "patient" is positioned in the center of nursing process reengineering, with service quality as the main guiding principle. It follows the management principles of continuous regulation and innovation, and cultivates and promotes the development of the nursing field from the perspective of "sublation." In the method part, this article introduces and supplements related information on nursing process reengineering, coronary heart disease, and postoperative cognitive behavioral intervention. Algorithmically, this paper proposes 3D RPN structure, loss function, capsule network, and improved activation function algorithm for coronary heart disease region extraction. In the part of experiment and results, this article collects general data of patients and compares memory ability, grades of self-management behaviors of patients treated with coronary heart disease, scores of quality control standards before and after nursing process reengineering, scores of patients' mental state, and comparison of postoperative physical indicators. According to the test results, the test group's diastolic and systolic blood pressure declined by more than 20, their total cholesterol dropped from 6.23 to 5.28, and their triglyceride levels dropped from 1.82 to 1.39 after the stent was implanted. Triglycerides reduced from 1.82 to 1.39, and total cholesterol dropped from 6.15 to 5.98. It indicates postoperative memory in patients with coronary heart disease. Abilities, including recognition, number, touch memory, and associative memory, decline significantly, but it can be improved after receiving the nursing process reengineering. It can be seen that nursing process reengineering can play a positive role in the postoperative cognitive behavior of patients with coronary heart disease. [ABSTRACT FROM AUTHOR]

Researchers in the past discussed the psychological issue like stress, anxiety, depression, phobias on various forms, and cognitive issues (e.g., positive thinking) together with personality traits on traditional research methodologies. These psychological issues vary from one human to other human based on different personality traits. In this paper, we discussed both psychological issues together with personality traits for predicting the best human capital that is mentally healthy and strong. In this research, we replace the traditional methods of research used in the past for judging the mental health of the society, with the latest artificial intelligence techniques to predict these components for attaining the best human capital. In the past, researchers have point out major flaws in predicting psychological issue and addressing a right solution to the human resource working in organizations of the world. In order to give solution to these issues, we used five different psychological issues pertinent to human beings for accurate prediction of human resource personality that effect the overall performance of the employee. In this regard, a sample of 500 data has been collected to train and test on computer through python for selecting the best model that will outperform all the other models. We used supervised AI techniques like support vector machine linear, support vector machine radial basis function, decision tree model, logistic regression, and neural networks. Results proved that psychological issue data from employee of different organizations are better means for predicting the overall performance based on personality traits than using either of them alone. Overall, the novel traditional techniques predicted that sustainable organization is always subject to the control of psychological illness and polishing the personality traits of their human capital. [ABSTRACT FROM AUTHOR]

In this paper, Lignosus rhinocerotis (Cooke) Ryvarden (L. rhinocerotis) cultivated in rice medium (LRR) and in sawdust medium (LRS) was harvested. Then, in terms of the LRR, LRS, and wild L. rhinocerotis (LRW), the total flavonoid contents, total polyphenol contents, total polysaccharide contents, and metabolites were detected; antioxidants of their aqueous extracts and anti-inflammatory of their polysaccharides were performed. In addition, the possible mechanism of the polysaccharides of L. rhinocerotis inhibiting lung damage was elucidated. The results showed that 32 compounds were characterized in L. rhinocerotis, including flavonoids, terpenoids, lignans, and steroids and there were 20 compounds in cultivated and wild L. rhinocerotis; LRR has the highest total polyphenol and flavonoid contents, as well as ABTS and DPPH scavenging capacity. The total polysaccharide contents and the FRAP scavenging capacity of wild L. rhinocerotis were higher than those of cultivated L. rhinocerotis. The inhibition of polysaccharides of LRW (PLRW) on LPS-induced MRC-5 damage was stronger than that of the polysaccharides from cultivated L. rhinocerotis. The PLRW may alleviate lung damage by inhibiting the NLRP3 pathway and thereby suppressing the inflammatory response. In summary, both cultivated and wild L. rhinocerotis are abundant in bioactive components and have antioxidant and anti-inflammatory activities. [ABSTRACT FROM AUTHOR]

Many synthetic routes manufacture zirconium nanoparticles in metal oxide, nitride, and other combination forms. Coupled with other variables such as concentration, pH, and form of precursor used, the various synthetic methods support synthesizing the zirconium metal oxide nanoparticles with changed features. Various synthetic methods were studied, such as sol-gel, hydrothermal, laser ablation, and precipitation. All have different synthetic routes, different precursors and solvents were sued, and the product was characterized by SEM, TEM, photo luminance spectroscopy, UV-absorption spectroscopy, and powder X-ray diffraction. X-ray diffraction determined the crystal structure by identifying the crystal shape, arrangement of atoms, and spacing between them. SEM and TEM studied the particle size and morphology of nanoparticles. UV-visible absorption spectroscopy and PL spectroscopy were used for the determination of optical properties of nanoparticles. Zirconium oxide nanoparticles have many applications in the medical field. The review study primarily focuses on the efficient combination of zirconium dioxide with other additive materials and functionalization techniques used to improve the material's properties, assisting the use of the material in hip arthroplasty and bone tissue applications. The development of sophisticated near-infrared (NIR) absorbing small molecules for useful phototheranostic applications was discussed in this paper. [ABSTRACT FROM AUTHOR]

Objective. To model and compare the stress variation and distribution of the implant and its supporting components under two types of loading with the abutment in the axial and coronal lingual augmentation positions by means of the 3D finite element method. Method. 15 all-ceramic crowns completed by the same technician between the years 2014 and 2015 were randomly selected. A high precision laser scanner was used to scan the specimen models of all-ceramic crowns and then converted and imported into the promapping software to create 15 solid models each in the axial position of the crown and the lingual augmentation position of the crown. Results. We showed that the abutments were significantly more stressed in the bone cortex than in the bone cancellous under both loads when the abutments were in the long axis position and in the lingual ridge position of the dentition. The distribution of stresses in the bone tissue was mainly concentrated in the cortical bone. The stresses induced by oblique forces were greater than those induced by vertical forces. When comparing the abutment in the long axis position of the dentition with the lingual ridge position of the dentition, the peak stresses obtained from the stress analysis of the abutment in the lingual ridge position were all increased to different degrees under both loads, and the differences were statistically significant (p < 0.05) suggesting that the design of the abutment in the direction of the long axis of the dentition is less stressful than that of the crown in the lingual augmentation position, and the risk of alveolar ridge resorption and screw fracture is less. Conclusion. In this paper, we proposed a dental 3D scanning system, which is less stressful based on a 3D reconstruction algorithm using Fourier transform contouring that achieved a speed dental 3D scanner with Fourier transform contouring by projecting a raster pattern onto a dental impression. [ABSTRACT FROM AUTHOR]

The sudden arrival of the new crown epidemic has had a significant and long-lasting impact on the division's economic environment as well as the production and operation activities of businesses. As far as the financial management is concerned, opportunities and difficulties are faced by enterprises of all types. With reference to the available research data, enterprises have an important contribution to GDP and jobs, but they still face a series of difficulties and challenges in their development in the context of the normalization of the epidemic. By analyzing the impact of the new crown pneumonia epidemic on the financial management work of enterprises, this paper proposes an artificial neural network-based enterprise financial forecasting and early warning method to provide an effective method for enterprise financial management. For the time-series characteristics of enterprise finances, a prediction model based on long- and short-term memory networks is developed which acknowledges the necessity of combining the temporal dimension with the spatial dimension for forecasting. This model incorporates time qualities into the data to the existing forecasting model. It also considers both working and nonworking day data and thoroughly considers the factors influencing corporate finance. Then, using BP neural network for financial risk prediction, nonfinancial index factors should be added to the financial early warning model thus eliminating the limitations of the financial early warning model. At the same time, the accuracy of the prediction can be improved which is more suitable for enterprises to apply in practice. The experimental results demonstrate that the financial prediction model built by multilayer feed forward neural networks and recurrent neural networks based on error back propagation training is inferior to the prediction model built by long- and short-term memory network. Regardless of the degree of fitting or prediction accuracy, the BP neural network model outperforms the conventional model for enterprise financial warning. Under the normalization of the pandemic, the combined use of both can offer an efficient technique for enterprise management. [ABSTRACT FROM AUTHOR]

Studies have shown that most patients after PCI cannot adhere to the cardiac rehabilitation program. The survey found that due to the lack of secondary prevention of cardiac rehabilitation, the phenomenon of drug reduction and withdrawal after PCI is very common after discharge, leading to recurrence of the patient's disease or worsen and repeated hospitalizations, so continuity of care is very important. In this paper, in addition to proposing a network care continuum with artificial intelligence handler in order to improve the healthcare system and provide new ideas for improving the postoperative recovery of CHD patients, we analyze the impact of PCI on the coronary heart disease patients' PCI postoperative quality of life. In the method part, this article introduces the concepts of continuation care and PCI after surgery, introduces the marker delivery algorithm in the field of artificial intelligence, and introduces the SF-36 scale for patient quality of life analysis. This article designs an experiment combining artificial intelligence processors to carry out network continuity care for patients and divides 100 eligible patients into an experimental group and a control group. In the analysis part, the two groups of patients were analyzed in terms of general data comparison, physical function, biochemical indicators, quality of life, and dependence. It can be seen from the experimental analysis that the anxiety and depression of the two groups of subjects have different degrees of decline. The HAMA value of the experimental group is 9.06 ± 0.77 , and the HAMD value is 9.18 ± 1.20 , which is significantly lower than that of the control group, P < 0.05. It can be seen that the use of network continuation care can reduce the negative emotions of patients more than general care. Through psychological counseling and postoperative follow-up, it can improve the optimism and positive emotions of the patients, reduce the negative emotions of the patients, and improve it to a certain extent. [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]

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]

Since the early 1980s when MRI imaging technology was put into clinical use, the number of MRI clinical tests has steadily increased by more than 10% every year. At the same time, exogenous MRI contrast agents have also been developed with the development of MRI technology. However, there are still challenges in the preparation of contrast agents for magnetic resonance imaging, such as how to prepare high-efficiency contrast agents with high stability and low biological toxicity. In order to study the contrast agent with simple preparation method, low cost, and good imaging effect, a magnetic resonance contrast agent was prepared by magnetic nanoparticles. By acting on magnetic resonance imaging detection method, and using polymer ligands to synthesize magnetic nanoparticles, experiments and tests of P(MA-alt-VAc) polymer ligand-modified magnetic nanoparticles were carried out. The experimental results showed that when nanoparticles containing different iron ion concentrations were incubated with DC 2.4 normal cells for 48 hours, the cell viability was still higher than 80% at concentrations up to 200 μm. It shows that the nanoparticle has high cell activity and good biological adaptability. The transverse relaxation ( r 2 ) value of the nanoparticles in aqueous solution at 37°C and 1.5 T magnetic field is 231.1 m-1 s-1, which is much higher than that of PTMP-PMAA ( r 2 = 35.1 mM-1 s-1), which is also more than five times the relaxation of SHU-555C ( r 2 = 44 mM-1 s-1). It shows that the nanoparticles prepared in this paper have good effect and can be used as a contrast agent in human brain for magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

Applying machine learning technology to automatic image analysis and auxiliary diagnosis of whole slide image (WSI) may help to improve the efficiency, objectivity, and consistency of pathological diagnosis. Due to its extremely high resolution, it is still a great challenge to directly process WSI through deep neural networks. In this paper, we propose a novel model for the task of classification of WSIs. The model is composed of two parts. The first part is a self-supervised encoding network with a UNet-like architecture. Each patch from a WSI is encoded as a compressed latent representation. These features are placed according to their corresponding patch's original location in WSI, forming a feature cube. The second part is a classification network fused by 4 famous network blocks with heterogeneous architectures, with feature cube as input. Our model effectively expresses the feature and preserves location information of each patch. The fused network integrates heterogeneous features generated by different networks which yields robust classification results. The model is evaluated on two public datasets with comparison to baseline models. The evaluation results show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

With the increasing popularity of online foreign language teaching and learning practice, learners and teachers have developed a high demand for evaluation of teaching effectiveness. When we focus discussion on international Chinese teaching, which has been developed for a relatively short time and not experienced enough, online teaching effectiveness evaluation has become an important obstacle to the development of teaching. This paper introduces a hybrid technique based on fuzzy evaluation method, for determining and suggesting possible types of errors in international Chinese online listening and speaking instruction and giving suggestions for improvement. The system can help learners to identify and determine the types of errors in Chinese listening and speaking learning in a timely manner and make a more objective and comprehensive evaluation of learning performance; at the same time, it helps teachers to trace the effectiveness of teaching design and implementation in a targeted manner and make corresponding scientific decisions. This hybrid technology combines existing language teaching evaluation models, takes advantage of data from online education, and creates corresponding criteria through machine learning fuzzy algorithms and large data sample training, combined with the theory of effective teaching evaluation, which is beneficial for all participants of online Chinese listening and speaking teaching to improve their learning effectiveness. [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]

Purpose. Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method. This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of the starting point of main wave and the dicrotic notch, and the dataset of the pulse signal period segmentation was established. Consequently, the labeled dataset was input into the LSTM for training and testing, and the results were compared with sum slope function method. Result. The remarkable result with the whole period segmentation accuracy of 92.8% was achieved for the segmentation of seven types of pulse signals. And the segmentation accuracies of the systolic phase, diastolic phase, and whole period using this method were higher than those of the sum slope function method. Conclusion. LSTM-based pulse signal segmentation method can achieve outstanding, robust, and reliable segmentation effects of the systolic phase, diastolic phase, and whole period of pulse signals. The research provides a new idea and method for the segmentation of pulse signals. [ABSTRACT FROM AUTHOR]

With various social pressures and the lack of knowledge about physical health, students have poor physical education quality and insufficient knowledge acquisition about physical health. Traditional physical health teaching is a process in which the teacher tells the theory of physical health and students passively accept it, which leads to physical health problems such as low learning efficiency of students' physical health knowledge and low interest in learning physical health knowledge. With the emphasis on physical health teaching and the development of technologies such as machine learning, machine learning is used to analyze the problems of physical health teaching and help students to learn physical health better to improve the efficiency of physical health teaching. The results of this paper show that the machine learning-based physical education and traditional physical education can reduce the injury rate of students' sports by 7.7% compared with traditional physical education, make students' interest in physical education and health learning reach 53.3%, and improve the efficiency of physical education and health learning. There is a degree of students' acquisition of physical health knowledge. The change from traditional physical health teaching ideology to machine learning-based physical education ideology can improve the teaching efficiency of physical health teaching, allow students to acquire more physical health knowledge, and effectively reduce the risk of students' injuries in sports. [ABSTRACT FROM AUTHOR]

The development of neuroimaging technology and molecular genetics has produced a large amount of imaging genetics data, which has greatly promoted the study of complex mental diseases. However, because the feature dimension of the data is too high, the correlation measure assumes that the data obey Gaussian distribution, and traditional algorithms often cannot explain these two types of data well. This article mainly studies image genetics analysis and its application based on neural network. In this paper, based on the theory and application technology of neural network, the tree structure is established by prior knowledge, that is, each SNP site is used as a leaf node of the tree, and the LD block and genome formed by the linkage imbalance of multiple SNP sites are used as intermediate nodes. Then, the hierarchical relationship of features was introduced. On this basis, a sparse learning method based on tree structure guidance is used to select features from multiple features of multiple SNPs locus regression candidate brain regions. Finally, the identification of SNPs in feature selection is used to predict quantitative traits of brain regions. The distribution of the typical vector values obtained by the algorithm in the experimental data is basically consistent with the distribution of the median of the actual data, and the correlation coefficient obtained is closest to the actual correlation coefficient in the data set. The average correlation coefficient of the algorithm reaches 82.3%, which is about 4.2% higher than the control algorithm. Experimental results show that this method can not only significantly improve the regression performance but also detect the risk gene SNPs loci with spatial clustering features and functional interpretation significance. It is practical and effective to use it in clinical trials. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Pharmaceutical excipients are compounds or substances other than API which are added to a dosage form, these excipients basically act as carriers, binders, bulk forming agents, colorants, and flavouring agents, and few excipients are even used to enhance the activity of active pharmaceutical ingredient (API) and various more properties. However, despite of these properties, there are problems with the synthetic excipients such as the possibility of causing toxicity, inflammation, autoimmune responses, lack of intrinsic bioactivity and biocompatibility, expensive procedures for synthesis, and water solubility. However, starch as an excipient can overcome all these problems in one go. It is inexpensive, there is no toxicity or immune response, and it is biocompatible in nature. It is very less used as an excipient because of its high digestibility and swelling index, high glycemic index, paste clarity, film-forming property, crystalline properties, etc. All these properties of starch can be altered by a few modification processes such as physical modification, genetic modification, and chemical modification, which can be used to reduce its digestibility and glycemic index of starch, improve its film-forming properties, and increase its paste clarity. Changes in some of the molecular bonds which improve its properties such as binding, crystalline structure, and retrogradation make starch perfect to be used as a pharmaceutical excipient. This research work provides the structural modifications of native starch which can be applicable in advanced drug delivery. The major contributions of the paper are advances in the modification of native starch molecules such as physically, chemically, enzymatically, and genetically traditional crop modification to yield a novel molecule with significant potential for use in the pharmaceutical industry for targeted drug delivery systems. [ABSTRACT FROM AUTHOR]

All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and have a wide range of cellular targets, bacteria are unlikely to be able to develop resistance to them in the short term. The underlying structure of AMPs is critical in determining the selectivity with which they target their respective targets. As far as we know, peptides have not been tested in a lab to see if they can fight bacteria, fungus, and viruses in real life. In this paper, we develop an artificial neural network (ANN) using a back propagation neural network (BPNN) that enables optimal classification of tendency of a peptide sequence that involves the activities of antifungal, antibacterial, or antiviral. The BPNN is trained on the datasets collected across different repositories and then the overfitting is avoided using particle swarm optimization (PSO) algorithm. Hence, at the time of testing, the BPNN clearly finds the predicted samples belonging to the same classes and this avoids the problem of finding the false positives. The simulation is conducted to test the efficacy of the model against various metrics that includes accuracy, precision, recall, and f1-measure. The effectiveness of the BPNN-PSO model in classifying instances at a faster rate than other techniques is demonstrated by its performance. The principle is straightforward, it is not difficult to programme, it converges more quickly, and it generally offers a superior solution. [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]

In recent years, the popularity of sports has been increasing. In the high-intensity competition, many players have been injured; most of them are myocardial ischemia. With the development of medical science and technology, myocardial ischemia can be reperfusion. However, sometimes, the function of tissues and organs cannot be restored, but the dysfunction and structural damage of tissues and organs can be aggravated. Therefore, it is necessary to actively carry out intermittent exercise to protect players from myocardial ischemia-reperfusion injury. The purpose of this paper is to explore the protective effect of intermittent exercise on the myocardial ischemia-reperfusion injury of players. 60 clean male Sprague Dawley (SD) old rats were selected as the research objects, which were divided into control group, ischemia/reperfusion model group, intermittent exercise + model group, and intermittent exercise + control group, and compared the parameters of heart function indexes of each group. The results showed that the creatine kinase (CK) content of control group was 0.577 ± 0.176 u / ml , lactate dehydrogenase (LDH) content was 7.834 ± 1.507 u / ml , model group was 1.257 ± 0.113 u / ml , lactate dehydrogenase (LDH) content was 14.441 ± 1.793 u / ml , intermittent exercise + model group was 0.987 ± 0.127 u / ml , lactate dehydrogenase (LDH) content was 11.714 ± 3.017 u / ml , intermittent exercise + control group was 1.103 ± 0.125 u / ml , and lactate dehydrogenase (LDH) content was 14.647 ± 2.575 u / ml. It can be seen that intermittent exercise has a good protective effect on myocardial ischemia-reperfusion injury. If the interval exercise method is integrated into the daily training of athletes, it can effectively improve the perfusion injury caused by myocardial ischemia. [ABSTRACT FROM AUTHOR]

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance. [ABSTRACT FROM AUTHOR]

Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset. [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]

Root canal treatment has been the treatment of choice for carious pulp exposures. In the perspective of minimally invasive dentistry and preventive endodontics, a direct pulp capping procedure with a reliable bioactive material may be considered an alternative approach provided that the pulp status is favorable. However, the treatment of pulp exposure by pulp capping is still a controversial issue with no clear literature available on this topic, leaving the concerned practitioner more confused than satisfied. Biodentine is a relatively new bioactive material explored for vital pulp therapy procedures. This article discusses its role in direct pulp capping procedures. A thorough literature search of the database was done using PubMed, Google Scholar, and Scopus using the keywords preventive endodontics, calcium silicate cement, direct pulp capping, Biodentine, and vital pulp therapy. Reference mining of the articles that were identified was used to locate other papers and enrich the findings. No limits were imposed on the year of publication, but only articles in English were considered. This paper is aimed at reviewing the current literature on Biodentine as a direct pulp capping material. The review will provide a better understanding of Biodentine's properties and can aid in the decision-making process for maintaining the vitality of exposed dental pulp with minimal intervention. [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]

Motivation. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature selection aiming at eliminating redundant signals and obtain representative features; and the third step is to build a learning model and predict the unknown data with regression or bivariate correlation analysis. This type of method requires manual extraction of feature single-nucleotide polymorphisms (SNPs), and the extraction process relies on empirical knowledge to a certain extent, such as linkage imbalance and gene function information in a group sparse model, which puts forward certain requirements for applicable scenarios and application personnel. To solve the problems of insufficient biological significance and large errors in the previous methods of association analysis and disease diagnosis, this paper presents a method of correlation analysis and disease diagnosis between SNP and region of interest (ROI) based on a deep learning model. It is a data-driven method, which has no obvious feature selection process. Results. The deep learning method adopted in this paper has no obvious feature extraction process relying on prior knowledge and model assumptions. From the results of correlation analysis between SNP and ROI, this method is complementary to other regression model methods in application scenarios. In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics. The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique. The experimental results show that the SNP-ROI joint feature describes the information of the samples from different angles, which makes the diagnosis accuracy higher. [ABSTRACT FROM AUTHOR]

The maintenance and sharing of electronic medical records are one of the essential tasks in the medical treatment combination. Traditional cloud-based electronic medical record storage system is difficult to realize data security sharing. The tamper resistance and traceability of blockchain technology provide the possibility for the sharing of highly sensitive medical data. This paper proposes a safe sharing scheme of stroke electronic medical records based on the consortium blockchain. The scheme adopts the storage method of ciphertext of medical records stored in the cloud and index of medical records stored on the blockchain. The privacy protection mechanism proposed in this paper innovatively combines proxy reencryption and searchable encryption which supports patient pseudoidentity search. The mechanism could achieve controllable sharing of medical records and precise search. According to the organizational characteristics of the stroke medical treatment combination, this paper proposes an improved Practical Byzantine Fault Tolerance mechanism to reach a consensus between consensus nodes. Then, the proposed scheme is analyzed and evaluated from three aspects of medical record integrity, user privacy, and data security. The results show that the scheme can not only ensure the privacy of patient identity information and private key data but also resist the tampering and deletion attacks of internal and external malicious nodes on the medical record data. Therefore, the proposed scheme is conducive to the improvement of the timeliness of stroke treatment and the safe sharing of electronic medical records in stroke medical treatment combination. [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]

Exercise training is not only a necessary means to improve the level of exercise, but also an important means to improve the body's immunity. Different time, intensity, items, and forms of exercise training have different effects on the body's immune function. As a double-edged sword to improve the body's immune function, exercise training is a different reaction mechanism of different immune cells after exercise training. This paper combined with foreign scholars' studies on the immune function of the body of literature from different exercise intensity, different time, different sports, different movement forms, and different external environment such as angle of view for athletes body's immune cells and humoral immunity summarized the various indexes such as combing, in order to help academia, medicine, and sports. It provides enlightenment to the contemporary public on how to participate in sports training more healthily. [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. Nanodelivery is a modern technology involving improved delivery methods and drug formulations. The current development and initial applications of nanocarriers are pointing to new directions in the current development of nanomedicine. Researchers are increasingly applying nanodelivery to the delivery of therapeutic or diagnostic agents. This article discusses the preparation and application of nanocomplexes and nanoparticles, as well as their potential future value in clinical research. Through a review and analysis, it is hoped that this will serve as a guide for the future development of various nanodelivery technologies and help researchers learn more about these technologies. Materials and Methods. A literature search was conducted using the keywords "Nano drug delivery" or "Nanomedical materials" or "Nano". A literature search was conducted in three major databases, PubMed, Web of Science, and Google Scholar, using the keywords such as "Nano drug delivery", "Nanomedical materials", or "Nanobubble drug delivery". The initial search was screened by title and abstract. In the full-text review, the titles or abstracts were reviewed according to the selection criteria based on the inclusion criteria. The risk of bias and study quality was assessed according to the Cochrane guidelines, and possible biases such as selection bias and good selection bias were included in the review. Results. A total of 297 studies were included in this study, of which 219 were excluded based on the screening criteria, resulting in the inclusion of 78 studies, the majority of which were original studies and clinical trials, and a small number of which provided design and route of administration analysis of nanomaterial particles and effect fluorograms and were studied in more depth. This paper summarises and reviews the views and directions of the included articles. The main directions include cyclodextrin-based or grafted cyclodextrin nanomaterials, nanobubbles, and stimuli-sensitive and temperature-sensitive nanodelivery systems. Conclusion. The use of innovative, targeted drug delivery systems is effective in cancer drug delivery by summarising the previous studies. However, nanodelivery systems' risks and therapeutic effects need to be evaluated before clinical application. Future research in the field of targeted drug delivery nanosystems should focus on the development of nanocarriers with high in vivo delivery capacity, good synergy with therapeutic agents, and milder short-term and long-term toxicological effects and conduct comprehensive preclinical trials on nanodrug delivery systems with high potential for clinical application as soon as possible, to find nanodrug delivery systems suitable for clinical use and put them into the clinical application as soon as possible. [ABSTRACT FROM AUTHOR]

Gait stability in exercise is an inevitable and vexing problem in mechanics, artificial intelligence, sports, and rehabilitation medicine research. With the rapid development and popularization of science and technology, it becomes a reality for researchers to obtain large-scale human motion data sets in real time with higher efficiency. However, at present, the analysis of gait stability of moving joints is still based on image recognition technology, which is ten times less accurate and inefficient. In this paper, Vicon 3D motion capture system, dynamometer, and surface electromyography system were used to obtain the parameters of the lower limbs of the subjects. Using Anywhere modeling and simulation system, simulation experiments were carried out, and the reaction force data of lower limb joints under two environments were obtained. The gait characteristics of human gait were analyzed from the angle of internal and external adjustment mechanism. Combining one-way ANOVA and incremental occupancy rate, the adjustment process of gait stability is described comprehensively. The findings of this study can provide a theoretical basis for the research of lower limb con-assistive devices and can guide the design and development of bipedal anthropomorphic robots. [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]

Antler is the secondary sexual characteristic of deer, which develops on the forehead at puberty. It is the only organ that can be regenerated entirely in mammals. Therefore, it is often used as a research model in the field of organ regeneration and wound repair. Many growth factors and proteins play an active role throughout the developmental process of antler regeneration. With the rapid development of sequencing technology, more and more noncoding RNAs (ncRNAs) have been discovered, and the relationship between ncRNA and antler regeneration has gradually become clear. This paper focuses on the research progress of several ncRNAs (including miRNA and lncRNA) in deer antler tissues, which are helpful to reveal the molecular mechanism of deer antler regeneration at the molecular level. [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]

With the development of ceramic technology, prosthodontic ceramics are becoming a useful option for improving esthetic outcomes in dentistry. In this paper, various ceramic materials were reviewed and evaluated, and their advantages and disadvantages and indications in oral prosthodontics were analyzed objectively. The properties of resin-based ceramics, polycrystalline ceramics, and silicate ceramics were compared and analyzed. Resin-based ceramics may replace other ceramic materials in the CAD/CAM field. [ABSTRACT FROM AUTHOR]

Automatic tissue segmentation in whole-slide images (WSIs) is a critical task in hematoxylin and eosin- (H&E-) stained histopathological images for accurate diagnosis and risk stratification of lung cancer. Patch classification and stitching the classification results can fast conduct tissue segmentation of WSIs. However, due to the tumour heterogeneity, large intraclass variability and small interclass variability make the classification task challenging. In this paper, we propose a novel bilinear convolutional neural network- (Bilinear-CNN-) based model with a bilinear convolutional module and a soft attention module to tackle this problem. This method investigates the intraclass semantic correspondence and focuses on the more distinguishable features that make feature output variations relatively large between interclass. The performance of the Bilinear-CNN-based model is compared with other state-of-the-art methods on the histopathological classification dataset, which consists of 107.7 k patches of lung cancer. We further evaluate our proposed algorithm on an additional dataset from colorectal cancer. Extensive experiments show that the performance of our proposed method is superior to that of previous state-of-the-art ones and the interpretability of our proposed method is demonstrated by Grad-CAM. [ABSTRACT FROM AUTHOR]

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5–15 years to 1–2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools. [ABSTRACT FROM AUTHOR]

Aim. This paper investigates the risk factors preventing the reversal and nonreversal of Hartmann's procedure, as a surgical technique that has been performed in our clinic for ten years. Methods. The study involved a ten-year Hartmann's procedure followed up at our center. The patients were divided into Hartmann reversal and nonreversal groups. Groups were examined in terms of age, gender, diagnosis, stage of malignancy, ASA score, comorbidity, perioperative morbidity-mortality, and the length of the operation. Results. Age (p < 0.001), ASA score (p < 0.001), stage in case of malignancy (p = 0.002), and comorbidities (p < 0.001) were significant risk factors. The ratio of patients without any comorbidities to those with one or more comorbidities was 2.63 (95% CI 1.12–6.20). Among the malignant patients, the ratio of early-stage patients to advanced-stage patients in the group with reversal of Hartmann's colostomy was 2.82 (95% CI 1.30–6.10). In addition, the ratio of older patients to younger patients in group 2 was 0.95 (95% CI 0.92–0.98). A univariate analysis revealed that younger patients, those with lower ASA scores, those without comorbidities, and those with early-stage malignancy had a greater chance of closure of the stoma. Conclusion. Although Hartmann's procedure is performed in emergency surgery, the nonreversal of the colostomy is a problem in itself. It should be kept in mind that patients who have high risks are likely to have a permanent stoma. [ABSTRACT FROM AUTHOR]

In the past, in the study of special sports quality of heavy antagonistic sports events, the study of strength quality training was emphasized. The transformation of athletes' strength quality to special strength was highlighted, and the special exercises which tended to be consistent with the characteristics of wrestling events were added. However, in competition and training, athletes' spine bears a heavy load. Long-term static contraction of lumbar muscles can lead to excessive local load and injury of lumbar muscles. In this paper, the test results of athletes' joints are analyzed, and the waist protection scheme for athletes' strength training is obtained. First of all, solve the problem of athletes' action mode and improve athletes' muscle endurance. All special sports quality evaluation can reach a good level. Then, enhance athletes' explosive power. Thus, all special sports quality evaluations can reach an excellent level. It is concluded that the waist and back are the key parts to support their participation in various sports, maintain body balance, and realize power transmission. Through the study, it is found that there are significant differences in the muscle strength indexes of the maximum strength of the waist and back of athletes of different levels, which fully proves the characteristics of the maximum strength of the waist and back. Through the test, we can understand the flexion and extension strength, range, speed, force, and flexion and extension ratio of athletes' joints and take timely optimization measures according to the test results to avoid sports injuries in training. It has a key guiding significance for the measurement, analysis, and evaluation of athletes' joint muscle strength, as well as rehabilitation training after injury and prevention of reinjury. [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]

Objective. To evaluate Peruvian scientific publications in dentistry according to sex disparity (2011-2020). Methods. This was a retrospective bibliometric study. The unit of analysis was made up of Peruvian dentistry publications indexed in the Scopus database during the last 10 years. Records with metadata (410) corresponding to the period 2011-2020 were downloaded and standardized and refined by analyzing the metadata. The search strategy was developed based on the individual profiles of each Peruvian institution that has a dental school or college. It was evaluated according to the AF-ID of each institution in the Scopus database. In addition, the information provided by the Scopus SciVal tool was used. Finally, publications, impact, and collaboration indicators were used, such as total number per document, per author, average of citations, h -index, collaboration rate, number of institutions, the Source Normalized Impact per Paper indicator, the CiteScore, and the Scopus Field-Weighted Citation Impact. Results. The greatest increase was evident in 2018, with 2019 and 2020 being the maximum peak of scientific publication growth. However, sustained growth has not been evidenced in relation to the female sex. The analysis of coauthorship by the authors revealed four large clusters, of which the first three were represented by male researchers, such as Arriola-Guillen L., Mayta-Tovalino F., and Mendoza-Azpur G., and one by a female, Guerrero María E. Evaluating the national scientific publication in dentistry according to the CiteScore, it was found that most of the publications (145) from Peru were published in Q4 journals, although 90 manuscripts were published in Q1 journals. Conclusions. The Peruvian national dental publication in the last 10 years was mainly supported by male dentists, which invites us to reflect on the need to equalize opportunities so that female researchers can also reduce these gaps. [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]

Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so they are difficult to extract accurately. Moreover, the number of spiculation samples is limited, so it is difficult to build a stable network structure. Thus, a novel pulmonary nodule spiculation recognition algorithm is proposed. MCA (morphological component analysis) model is built to segment pulmonary nodules in accordance with the composition of pulmonary CT images. Subsequently, the maximum density projection mechanism is introduced to characterize the boundary features of pulmonary nodules to the maximum extent. Inspired by time series dynamic programming, this paper proposes DTW (dynamic time warping) distance to measure data similarity. Lastly, a semisupervised generative adversarial network is built to solve the problem of insufficient positive samples, and it is capable of recognizing pulmonary nodule spiculation. As revealed by the experimental result, the proposed algorithm exhibited strong robustness. [ABSTRACT FROM AUTHOR]

Overweight and obesity are health conditions tightly related to a number of metabolic complications collectively called "metabolic syndrome" (MetS). Clinical diagnosis of MetS includes the presence of the increased waist circumference or so-called abdominal obesity, reduced high density lipoprotein level, elevated blood pressure, and increased blood glucose and triacylglyceride levels. Different approaches, including diet-induced and genetically induced animal models, have been developed to study MetS pathogenesis and underlying mechanisms. Studies of metabolic disturbances in the fruit fly Drosophila and mammalian models along with humans have demonstrated that fruit flies and small mammalian models like rats and mice have many similarities with humans in basic metabolic functions and share many molecular mechanisms which regulate these metabolic processes. In this paper, we describe diet-induced, chemically and genetically induced animal models of the MetS. The advantages and limitations of rodent and Drosophila models of MetS and obesity are also analyzed. [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]

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC). [ABSTRACT FROM AUTHOR]