Influencing factors of negative conversion time of nucleic acid detection in 228 patients infected with Omicron variant of SARS-CoV-2 in Shanghai

Objective: To analyze and compare the effects of different clinical characteristics on the negative conversion time of nucleic acid detection after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant infection, and to provide a scientific basis for the isolation and treatment of coronavirus disease 2019 (COVID-19). Methods: The epidemiological and clinical data of 228 mild SARS-CoV-2 Omicron variant infected patients diagnosed in Shanghai were retrospectively collected from April 27, 2022 to June 8, 2022 in Wujiaochang designated Hospital, Yangpu District, Shanghai. The negative conversion time of nucleic acid detection was used as the outcome variable, and the patients were divided into A (18 days) and B (>18 days). Univariate and multivariate logistic regression analysis were used to analyze the influencing factors of the negative conversion time of nucleic acid detection. Results: The mean nucleic acid conversion time of 228 patients was (18.7+or-12.1) d, with the median time of 18 (2-46) d. Among them, 120 patients in group A had an average nucleic acid conversion time of (13.2+or-2.0) d, and 108 cases in group B had an average nucleic acid conversion time of (20.8+or-1.3) d. Univariate analysis showed that there were no statistically significant differences in the effects of hypertension, coronary heart disease, diabetes, hypokalemia, malignant tumors, neuropsychiatric diseases, chronic digestive diseases on the negative nucleic acid conversion time (P > 0.05);however, there were significant differences in the effects of combined cerebrovascular disease, leukopenia, chronic respiratory system diseases and vaccination on the negative nucleic acid conversion time (P < 0.05). Further multivariate logistic regression analysis revealed that the combination of chronic respiratory diseases and non-vaccination were significant risk factors for prolongation of negative nucleic acid conversion time (P < 0.05). Conclusions: The results of this study show that gender, age and whether hypertension, coronary heart disease, diabetes mellitus, hypokalemia, malignant tumor, neuropsychiatric disease and chronic digestive disease have no significant effect on the nucleic acid conversion time, whereas chronic respiratory disease and no vaccination are significantly correlated with the prolongation of nucleic acid conversion time in SARS-CoV-2 Omicron-infected patients.

An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people's health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper's proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then be combined using a feature fusion network and a multilayer perceptron. Last but not least, the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty, realizing the short-term and long-term prediction of COVID-19 daily confirmed cases, and verifying the effectiveness and accuracy of the suggested prediction method, as well as reducing the hysteresis of the prediction results.

Acute myocardial infarction and coronavirus infection (COVID-19).

The aim of the study is to describe a case of COVID-19 and myocardial infarction in an elderly patient. Material and methods. The analysis of medical documentation (outpatient card of the patient, medical history, postmortem report) was carried out. Studied macro- and micropreparations (staining with hematoxylin and eosin). Results. A 67-year-old patient, from 23.04.2020 to 26.04.2020, was hospitalized with a diagnosis of suspected coronavirus infection (COVID-19). On the background of the treatment, the patient's biological death occurred (26.04.2020). The sectional study revealed signs of bilateral total hemorrhagic pneumonia. The signs of acute transmural myocardial infarction of the anterior wall of the left ventricle were determined. Posthumously, SARS-CoV-2 RNA was detected in the lung tissue by nucleic acid amplification. In the described clinical case, a patient with concomitant cardiovascular diseases, such as arterial hypertension, coronary heart disease, developed complications against the background of COVID-19: hemorrhagic pneumonia and myocardial infarction with a fatal outcome.Copyright © Infectious Diseases: News, Opinions, Training.

Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models. © 2023 IEEE.

A Survey on Machine Learning Techniques for Multimodal Biomedical Signal Processing

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

The evaluation of practical learning instruction management and student satisfaction with the "photocolorimetric methodology platform for measuring egg yolk color" during the COVID-19 pandemic situation

Online teaching management has been widely used during the COVID-19 pandemic situation and it has a direct impact on practical teaching management because students do not have access to equipment, chemicals, and tools. This study's purpose is to evaluate practical learning instruction management and student satisfaction with "photocolorimetric methodology platform for measuring egg yolk color" during the COVID-19 pandemic. This study compared the student satisfaction and effectiveness of a learning instruction platform for measuring egg yolk color using a laboratory machine and an online teaching management platform using photocolorimetric methodology. The results of this experiment revealed that the two platforms evaluated yolk colors L*, a*, and b* similarly (P > 0.05). Furthermore, the students were satisfied with the learning instruction with the photocolorimetric methodology platform for measuring egg yolk color at 4.76 points or the most level.

Comparative Study of VGG-13, AlexNet, MobileNet and Modified-DarkCovidNet for Chest X-Ray Classification

Convolutional neural network (CNN) is the most widely used structure-building technique for deep learning models. In order to classify chest x-ray pictures, this study examines a number of models, including VGG-13, AlexN ct, MobileNet, and Modified-DarkCovidNet, using both segmented image datasets and regular image datasets. Four types of chest X- images: normal chest image, Covid-19, pneumonia, and tuberculosis are used for classification. The experimental results demonstrate that the VGG offers the highest accuracy for segmented pictures and Modified Dark CovidN et performs best for multi class classification on segmented images. © 2023 Bharati Vidyapeeth, New Delhi.

Establishment of a duplex fluorescent quantitative PCR assay for detection of Porcine circovirus type 2 and type 3

To establish a method for simultaneous detection of porcine circovirus type 2 (PCV2) and porcine circovirus type 3 (PCV3), specific primers and TaqMan probes were designed after sequence alignment according to the specific sequences of PCV2 Cap gene and PCV3 Cap gene on GenBank. By optimizing the reaction conditions, a duplex fluorescence quantitative PCR detection method for simultaneous detection of porcine circovirus type 2 and 3 was established, and the specificity, sensitivity, and reproducibility were tested. Specificity test results showed that in addition to the positive test results for PCV2 and PCV3, tests for PRRSV, CSFV, PPV, PRV, PEDV, and TGEV were all negative with no cross-reaction, indicating its good specificity. Sensitivity test results showed that the minimum detection limit for detection of PCV2 and PCV3 can both reach 10 copies.L-1, indicating its high sensitivity. The coefficient of variation within and between groups of this method was less than 2%, indicating its good stability. A total of 181 pork and whole blood samples collected from Zhejiang Province were tested using the detection method established in this article and the standard common fluorescent PCR detection method. The results showed that the positive rate of PCV2 was 50.83% (92/181), the positive rate of PCV3 was 37.57% (68/181), and the co-infection rate of PCV2 and PCV3 was 12.15% (22/181). The above detection results of ordinary fluorescent PCR were 50.28% (91/181), 36.46% (66/181), and the co-infection rate was 11.60% (21/181). The coincidence rates of the two methods for PCV2 and PCV3 can reach 98.91% and 97.06%, and the coincidence rate for PCV2 and PCV3 mixed infection were 95.45%. In summary, the duplex fluorescence quantitative PCR detection method established in this experiment can distinguish PCV2 and PCV3 rapidly, which can be used for pathogen detection and epidemiological investigation.

Diagnosis of COVID-19 from the X-Ray images using BAT Algorithm with Deep Convolutional Neural Network

The COVID-19 widespread has posed a chief contest to the scientific community around the world. For patients with COVID-19 illness, the international community is working to uncover, implement, or invent new approaches for diagnosis and action. A opposite transcription-polymerase chain reaction is currently a reliable tactic for diagnosing infected people. This is a time- and money-consuming procedure. Consequently, the development of new methods is critical. Using X-ray images of the lungs, this research article developed three stages for detecting and diagnosing COVID-19 patients. The median filtering is used to remove the unwanted noised during pre-processing stage. Then, Otsu thresholding technique is used for segmenting the affected regions, where Spider Monkey Optimization (SMO) is used to select the optimal threshold. Finally, the optimized Deep Convolutional Neural Network (DCNN) is used for final classification. The benchmark COVID dataset and balanced COVIDcxr dataset are used to test projected model's performance in this study. Classification of the results shows that the optimized DCNN architecture outperforms the other pre-trained techniques with an accuracy of 95.69% and a specificity of 96.24% and sensitivity of 94.76%. To identify infected lung tissue in images, here SMO-Otsu thresholding technique is used during the segmentation stage and achieved 95.60% of sensitivity and 95.8% of specificity. © 2023 IEEE.

Assessing the clinical manifestations, laboratory and radiological findings of hospitalization of pediatrics patients with COVID-19 in pediatric hospital of Amirkola, Babol

Variant Omicron was discovered as a newest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first emergence of the omicron variant was detected in November 2021. In this study, we investigated the clinical manifestation, laboratory and radiological findings and responding to treatment of 70 pediatric patients with positive RT- PCR COVID-19 in Omicron peak. We described 20 criteria associated with efficacy, such as demographic data, clinical manifestation, laboratory and radiological findings. All of the patients received Remdesivir that 5.7% of patients responded to the treatment. No patients were given Intravenous Immunoglobulin (IVIG). This is the first study aimed at assessing symptoms clinical manifestation among hospitalization pediatrics patients in pediatric Hospital of Amir kola, Babol. The findings of this study can be effective in preventing and controlling disease transmission among children.

Online or In-Person Course? Meaningful Discussion of Learning Effectiveness and Satisfaction of Microwave Filter Design Course in Graduate Students

Contribution: A research on applying blended teaching in microwave filter design in graduate students. Background: The Covid-19 epidemic has caused many universities worldwide to switch to online courses. Taiwan did not have a large-scale local infection in 2020, so the school has implemented a blended teaching plan, combining online and in-person courses. Intended Outcomes: Discuss the effectiveness and satisfaction of the Microwave Filter Design Course in Graduate Students for two classes, Online or In-person course. Application Design: This study uses a quasi-experiment to teach microwave filter courses in the two classes. The teacher integrated into the Flipped Classroom and Interactive Response System (IRS). Students must use the APP to complete the preclass preview and prepare materials. Class A [Formula Omitted] uses in-person classrooms for the whole course;Class B uses blended teaching. The first eight weeks are synchronized online, then mid-term exams, and in-person courses are used for the next ten weeks. Students in two classes in the last week filled out the course satisfaction questionnaire. Findings: Class B achieved better results in the eighth midterm exam week, showing better learning results. Although students in both classes are highly satisfied with the course, Class A is more satisfied than Class B. For graduate students participating in the microwave filter design course, in-person classrooms and blended teaching can achieve good learning results and satisfaction. However, teachers must pay attention to students' reception and understanding of flipped classrooms when using online teaching. And timely and in-depth guidance on the accuracy of APP use.

A Long Short Term Memory Machine Learning Technique for Covid-19 Patient Count Prediction

The Corona-virus H19 pandemic is quickly spreading throughout the globe. Every three to four times, waves occur and have a major effect on people's lives. Other illnesses including covid disorders are misdiagnosed in this setting. There is no reliable statistics on the total number of covid patients in the nation, and no system exists to track them. This prevents the patients from receiving the necessary care and treatment. The number of patients in a given dataset may be determined with more precision using AI methods. In this article, we show how to forecast how many patients will be included in the Covid-19 database by using an adaptive method. Python spyder is used to run the simulation. . © 2023 IEEE.

Development and application of a multiplex fluorescent quantitative RT-PCR for detection of Porcine epidemic diarrhea virus, porcine coronavirus and porcine acute diarrhea syndrome coronavirus

To develop a multiplex fluorescent quantitative RT-PCR for the detection of porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV) and swine acute diarrhea syndrome coronavirus (SADS-CoV), in this study, specific primers/probes were designed based on the conserved regions of M, M and N gene sequences of PEDV, PDCoV and SADS-CoV, respectively. After optimization of the reaction conditions, a multiplex fluorescent quantitative RT-PCR for PEDV, PDCoV and SADS-CoV was established. The results of specificity assay showed that the method was positive for detection of PEDV, PDCoV and SADS-CoV, and negative for detection of porcine transmissible gastroenteritis virus, porcine rotavirus, porcine reproductive and respiratory syndrome virus, porcine pseudorabies virus, porcine circovirus type 2, porcine parvovirus, classical swine fever virus and foot-and-mouth disease virus. The results of sensitivity assay showed that the detection limit of this method for PEDV, PDCoV, and SADS-CoV plasmids standard was 1.0x101 copies/L, and had a good linear relationship with their Ct values in the range of 101 copies/L to 106 copies/L. The results of repeatability assay showed that the coefficients of variation (CVs) of intra- and inter-assay reproducibility ranged from 0.33% to 2.53%, indicating good repeatability and stability. To evaluate the effects of the developed method, 100 clinical samples collected from different parts of Henan province were used for detection of these three viruses and compared with those of single RT-PCR and standard methods. The results of multiplex fluorescent quantitative RT-PCR showed that the positive rates of PEDV, PDCoV and SADS-CoV were 38% (38/100), 14% (14/100) and 5% (5/100), respectively. There was no mixed infection. The coincidence rate with the standard detection methods of PEDV and PDCoV was 100%, and the sensitivity was higher than that of single RT-PCR. In this study, a specific, sensitive and rapid multiplex fluorescent quantitative RTPCR method was established for the first time, which could be used for the differential detection of PEDV, PDCoV and SADS-CoV, and laid a foundation for the differential diagnosis and control of porcine diarrheal diseases.

FaST-Net: Face Style Self-Transfer Network for Masked Face Inpainting

During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods. © 2023 SPIE.

Detecting COVID-19 spreading phases with time series analysis

Piecewise linear regression (PLR) method is applied to study cumulative cases of COVID-19 evolving everyday in England up to 6th February 2022 just before travel restrictions are removed and people started not to get tested anymore in the UK and factors e.g. the lockdowns behind the spread COVID-19 are also investigated. It is clear that different periods exhibit distinct patterns depending on variants and government-imposed restriction. Therefore, the effectiveness of lockdown measures is evaluated by comparing the rate of increase after a certain period (delay effect of measures) and that of time before as well as how new variants take over as a dominant variant. In addition, autoregression function is studied to show strong effect of cases in the past on today's cases since the disease is highly infectious. Most of work is carried out thorough python built-in libraries such as pandas for preprocessing data and matplotlib which allows us to gain more insight and better visualization into the real scenario. Visualization is conducted by Geoda showing the regional level of infections. © 2023 SPIE.

A Predictive Model for Assessing Satisfaction with Online Learning for Higher Education Students During and After COVID-19 Using Data Mining and Machine Learning Techniques: A Case of Jordanian Institutions

Higher education institutions confronted an escalating unexpected pressure to rapidly transform throughout and after the COVID-19 pandemic, by replacing most of the traditional teaching practices with online-based education. Such transformation required institutions to frequently strive for qualities that meet conceptual requirements of traditional education due to its agility and flexibility. The challenge of such electronic learning styles remains in their potential of bringing out many challenges, along with the advantages it has brought to the educational systems and students alike. This research came to shed the light on several factors presented as a predictive model and proposed to contribute to the success or failure in terms of students' satisfaction with online learning. The study took the kingdom of Jordan as a case example country experiencing online education while and after the covid -19 intensive implementation. The study used a dataset collected from a sample of over "300” students using online questionnaires. The questionnaire included "25” attributes mined into the Knime analytics platform. The data was rigorously learned and evaluated by both the "Decision Tree” and "Naive Bayes” algorithms. Subsequently, results revealed that the decision tree classifier outperformed the naïve bayes in the prediction of student satisfaction, additionally, the existence of the sense of community while learning electronically among other reasons had the most contribution to the satisfaction. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Digitalisation and modern imaging technologies in dermatology

Teledermatology is one of the most important developments of digitalisation in dermatology. It has helped to ensure continuity of care during the COVID-19 pandemic. The combination of teledermatology with artificial intelligence can significantly improve medical decision-making. Among imaging modalities, dermoscopy is the most widely used, and its effectiveness can be significantly enhanced when combined with artificial intelligence. Novel techniques that have emerged in recent years include high-frequency ultrasound, optical coherence tomography or multispectral imaging. These are currently used in dermatological research but are expected to gradually become part of daily patient care. The knowledge of digital technologies and new imaging techniques is essential for the modern dermatologist. In the future, it is expected to be an essential part of modern and optimised patient care.

Medical Image Diagnosis Using Deep Learning Classifiers for COVID-19

COVID-19 and other diseases must be precisely and swiftly classified to minimize disease spread and avoid overburdening the healthcare system. The main purpose of this study is to develop deep-learning classifiers for normal, viral pneumonia, and COVID-19 disorders using CXR pictures. Deep learning image classification algorithms are used to recognize and categorise image data to detect the presence of illnesses. The raw image must be pre-processed since deep neural networks perform the most important aspect of medical image identification, which includes translating the raw image into an intelligible format. The dataset includes three classifications, including normal and viral pneumonia and COVID-19. To aid in quick diagnosis and the proposed models leverage the performance validation of several models, which are summarised in the form of a recall, Fl-score, precision, accuracy, and AUC, to distinguish COVID-19 from other types of pneumonia. When all the deep learning classifiers and performance parameters were analyzed, the ResNetl0lV2 achieved the highest accuracy of COVID-19 classifications is 97.S2%, ResNetl0lV2 had the greatest accuracy of the normal categorization is 92.04% and the Densenet201 had the greatest accuracy of the pneumonia classification is 99.92%. The suggested deep learning system is an excellent choice for clinical use to aid in the COVID-19, normal, and pneumonia processes for diagnosing infections using CXR scans. Furthermore, the suggested approaches provided a realistic technique to implement in real-world practice, assisting medical professionals in diagnosing illnesses from CXR images. © 2023 IEEE.

Night-time Human Mobility during Pandemic in the Philippines as Observed by VIIRS Satellite

Recent research has analyzed and studied the growing literature on human mobility during quarantine periods using various methodology and techniques. There are several ways to use light pollution to assess mobility. The data from the VIIRS satellite can be used to quantify light pollution and human mobility in the Philippines during quarantine. The data utilized in this study came from NASA's EOSDIS Worldview website. The number of cases and pixels count increases from early April 2020 to late August 2020. However, the cases increased from February to April 2021. This could be attributed to the active human mobility seen between December 2020 and January 2021. Human interactions have been intense since August 2020, causing an increase in COVID cases that peaked between March and April 2021, before dropping in May 2021. Following the conclusion of this study, light pollution VIIRS satellite pictures can be used to identify possible COVID- 19 cases. There are many more factors and variables to consider when writing a comprehensive paper. With the relaxed quarantine time has been achieved beyond June 2021, additional dates may be explored since there may be a direct relationship between light pollution and COVID-19 instances. © 2022 IEEE.