RAPID IDENTIFICATION OF ETIOLOGICAL AGENTS OF RESPIRATORY TRACT INFECTIONS DURING THE COVID-19 PANDEMIC USING THE MULTIPLEX-PCR METHOD.

Objective. Most respiratory infections have similar symptoms, so it is clinically difficult to determine their etiology. This study aimed to show the importance of molecular diagnostics in identifying the etiological agent of respiratory infections, especially during the coronavirus disease 2019 (COVID-19) pandemic. Methods. A total of 849 samples from patients hospitalized at the University Clinical Center Kragujevac (from January 1 to August 1, 2022) were examined using automated multiplex-polymerase chain reaction (PCR) tests. The BioFire-FilmArray-Respiratory Panel 2.1 test was used for 742 nasopharyngeal swabs [identification of 19 viruses (including SARS-CoV-2) and four bacteria], while the BioFire-FilmArray-Pneumonia Panel was used [identification of 18 bacteria and nine viruses] (BioMerieux, Marcy l'Etoile, France) for 107 tracheal aspirates. The tests were performed according to the manufacturer's instructions, and the results were available within an hour. Results. In 582 (78.4%) samples, the BioFire-FilmArray-Respiratory Panel 2.1 plus test identified at least one pathogen. The rhinovirus (20.6%), SARS-CoV-2 (17.7%), influenza A (17.5%), respiratory syncytial virus (12.4%), and parainfluenza 3 (10.1%) were the most common. Other viruses were found less frequently, and Bordetella parapertussis was detected in one sample. In 85 (79.4%) samples, the BioFire-FilmArray-Pneumonia Panel test identified at least one bacterium or virus. The most prevalent bacteria were Staphylococcus aureus (42.4%), Haemophilus influenzae (41.2%), Streptococcus pneumoniae (36.5%), Moraxella catarrhalis (22.3%), and Legionella pneumophila (2.4%). Among viruses, rhinovirus (36.5%), adenovirus (23.5%), influenza A (11.8%), and the genus Coronavirus (4.7%), were detected. Conclusion. Multiplex-PCR tests improved the implementation of therapeutic and epidemiological measures, preventing the spread of the COVID-19 infection and Legionnaires' disease.Copyright © 2022, Serbian Medical Society. All rights reserved.

Role of noscomial infection informatics surveillance system in the prevention and control of multidrug-resistant organisms infections

OBJECTIVE: To analyze the role of nosocomial infection informatics surveillance system in the prevention and control of multidrug-resistant organisms(MDROs) infections. METHODS: The First Affiliated Hospital of Guangdong Pharmaceutical University was selected as the study subjects, which had adopted the nosocomial infection informatics surveillance system since Jan.2020. The period of Jan.to Dec.2020 were regarded as the study period, and Jan.to Dec.2019 were regarded as the control period. The situation of nosocomial infection and MDROs infections in the two periods were retrospectively analyzed. RESULTS: The incidence of nosocomial infections and underreporting of nosocomial infection cases in this hospital during the study period were 2.52%(1 325/52 624) and 1.74%(23/1 325), respectively, and the incidences of ventilator associated pneumonia(VAP), catheter related bloodstream infection(CRBSI), catheter related urinary tract infection(CAUTI)were 4.10(31/7 568), 2.11(14/6 634), and 2.50(25/9 993) respectively, which were lower than those during the control period(P< 0.05). The positive rate of pathogenic examination in the hospital during the study period was 77.95%(1 269/1 628), which was higher than that during the control period(P<0.05), the overall detection rate of MDROs was 15.77%(206/1 306), the detection rates of MDROs in Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Staphylococcus epidermidis, Pseudomonas aeruginosa and Staphylococcus aureus were lower than those during the control period(P<0.05). CONCLUSION: The development and application of the informatics technology-based surveillance system of nosocomial infection could effectively reduce the incidence of nosocomial infections and device related infections, decrease the under-reporting of infection cases, and also reduce the detection rate of MDROs as well as the proportion of MDROs detected in common pathogenic species.

Smartphone-assisted point-of-care testing of nucleic acids based on hybridization chain reaction, magnetic beads, and gold nanorods etching

Colorimetric biosensors are simple but effective tools that are gaining popularity due to their ability to provide low-cost, rapid, and accurate detection for viruses like the Novel coronavirus, Influenza A, and Dengue virus, especially in point-of-care testing (POCT) and visual detection. In this study, a smartphone-assisted nucleic acid POCT was built using hybridization chain reaction (HCR), magnetic beads (MBs), and oxidized 3,3′,5,5′-tetramethylbenzidine (TMB2+)-mediated etching of gold nanorods (GNRs). The application of HCR without enzyme isothermal characteristics and MBs with easy separation, can quickly amplify nucleic acid signal and remove other reaction components. The blue shift of longitudinal localized surface plasmon resonance (LSPR) based on GNRs showed significant differences in etching color for different concentrations of target nucleic acid, which convert the signal into a visually semi-quantitative colorimetric result, achieving quantitative analysis with the color recognition software built into smartphones. This strategy, which only takes 40 min to detect and is two-thirds less time than the PCR, was successfully applied for the detection of the Dengue target sequence with a detection limit of 1.25 nM and exhibited excellent specificity for distinguishing single-base mutations, indicating broad application prospects in clinical laboratory diagnosis and enriching the research of nucleic acid POCT.

PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available. © 2023 Association for Computational Linguistics.

A Mask Detection Algorithm Based on Improved Yolov5s

Aiming at the severe form of new coronavirus epidemic prevention and control, a target detection algorithm is proposed to detect whether masks are worn in public places. The Ghostnet and SElayer modules with fewer design parameters replace the BottleneckCSP part in the original Yolov5s network, which reduces the computational complexity of the model and improves the detection accuracy. The bounding box regression loss function DIOU is optimized, the DGIOU loss function is used for bounding box regression, and the center coordinate distance between the two bounding boxes is considered to achieve a better convergence effect. In the feature pyramid, the depthwise separable convolution DW is used to replace the ordinary convolution, which further reduces the amount of parameters and reduces the loss of feature information caused by multiple convolutions. The experimental results show that compared with the yolov5s algorithm, the proposed method improves the mAP by 4.6% and the detection rate by 10.7 frame/s in the mask wearing detection. Compared with other mainstream algorithms, the improved yolov5s algorithm has better generalization ability and practicability. © 2022 ACM.

A Comprehensive Review of Sound-Based Modalities for Automatic COVID-19 Detection using Deep Learning-Based Techniques

The World Health Organization has labeled the novel coronavirus illness (COVID-19) a pandemic since March 2020. It's a new viral infection with a respiratory tropism that could lead to atypical pneumonia. Thus, according to experts, early detection of the positive cases with people infected by the COVID-19 virus is highly needed. In this manner, patients will be segregated from other individuals, and the infection will not spread. As a result, developing early detection and diagnosis procedures to enable a speedy treatment process and stop the transmission of the virus has become a focus of research. Alternative early-screening approaches have become necessary due to the time-consuming nature of the current testing methodology such as Reverse transcription polymerase chain reaction (RT-PCR) test. The methods for detecting COVID-19 using deep learning (DL) algorithms using sound modality, which have become an active research area in recent years, have been thoroughly reviewed in this work. Although the majority of the newly proposed methods are based on medical images (i.e. X-ray and CT scans), we show in this comprehensive survey that the sound modality can be a good alternative to these methods, providing faster and easiest way to create a database with a high performance. We also present the most popular sound databases proposed for COVID-19 detection. © 2022 IEEE.

Evaluation of a Video Generation Method Linking Dance and Scenes

Owing to the impact of COVID-19, the venues for dancers to perform have shifted from the stage to the media. In this study, we focus on the creation of dance videos that allow audiences to feel a sense of excitement without disturbing their awareness of the dance subject and propose a video generation method that links the dance and the scene by utilizing a sound detection method and an object detection algorithm. The generated video was evaluated using the Semantic Differential method, and it was confirmed that the proposed method could transform the original video into an uplifting video without any sense of discomfort. © 2023 SPIE.

A Detection System for Comfortable Locations Based on Facial Expression Analysis While Riding Bicycles

In recent years, the use of bicycle as a healthy and economical means of transportation has been promoted worldwide. In addition, with the increase in bicycle commuting due to the COVID-19, the use of bicycles are attracting attention as a last-mile means of transportation in Mobility as a Service(MaaS). To help ensure a safe and comfortable ride using a smartphone mounted on a bicycle, this study focuses on analyzing facial expressions while riding to determine potential comfort along the route with the surrounding environment and to provide a map that users can explicitly feedback(FB) after riding. Combining the emotions of facial expressions while riding and FB, we annotate comfort to different locations. Afterwards, we verify the relationship between locations with high level of comfort based on the acquired data and the surrounding environment of those locations using Google Street View(GSV). © 2023 Owner/Author.

Exploring Social Media for Early Detection of Depression in COVID-19 Patients

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.

Recent progresses in rapid detection of novel coronavirus SARS-CoV-2

The global pandemic coronavirus pneumonia (COVID-19), the disease infected by the new coronavirus (SARS-CoV-2), is extremely contagious. It is mainly spread among people through respiratory droplets, aerosols, direct or indirect contact, fecal-oral transmission, and cold chain transportation. Especially, patients who are in the incubation period or have no obvious symptoms already have the ability to infect others. SARS-C0V-2 is a positive-sense single-stranded RNA virus, with a single linear RNA segment. Each SARS-CoV-2 virion is 60-140 mm in diameter. Like other coronaviruses, SARS-CoV-2 has four structural proteins, known as the spike (S), envelope(E), membrane (M), and nucleocapsid (N) proteins. To date, a variety of detection methods for the SARS-CoV-2 have been developed based on the virus structural basis and 'etiological characteristics, which would provide an effective guarantee for the diagnosis of COVID-19 patients and the control of the epidemic. In order to help for the early diagnosis and prevention of COVID-19, the pathogenic characteristics and recent progresses of detection base on nucleic acid, immunology and biosensors of the SARS-CoV-2 are reviewed in this paper.

Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

How Trust in Authority and Trust in Networks Relates to Verification and Sharing Behaviours: COVID-19 Health Information Sharing on WhatsApp

Messaging platforms like WhatsApp are some of the largest contributors to the spread of Covid-19 health misinformation but they also play a critical role in disseminating credible information and reaching populations at scale. This study explores the relationships between verification behaviours and intention to share information to users that report high trust in their personal network and users that report high trust in authoritative sources. The study was conducted as a survey delivered through WhatsApp to users of the WHO HealthAlert chatbot service. An adapted theoretical model from news verification behaviours was used to determine the correlation between the constructs. Due to an excellent response, 5477 usable responses were obtained, so the adapted research model could be tested by means of a Structural Equation Model (SEM) using the partial least squares algorithm on SmartPLS4. The findings suggest significant correlations between the constructs and suggest that participants that have reported high levels of trust in authoritative sources are less likely to share information due to their increased behaviours to verify information. © 2023 IEEE.

Automatic Detection of Lung Ultrasound Artifacts using a Deep Neural Networks approach

The COVID-19 pandemic has challenged many of the healthcare systems around the world. Many patients who have been hospitalized due to this disease develop lung damage. In low and middle-income countries, people living in rural and remote areas have very limited access to adequate health care. Ultrasound is a safe, portable and accessible alternative;however, it has limitations such as being operator-dependent and requiring a trained professional. The use of lung ultrasound volume sweep imaging is a potential solution for this lack of physicians. In order to support this protocol, image processing together with machine learning is a potential methodology for an automatic lung damage screening system. In this paper we present an automatic detection of lung ultrasound artifacts using a Deep Neural Network, identifying clinical relevant artifacts such as pleural and A-lines contained in the ultrasound examination taken as part of the clinical screening in patients with suspected lung damage. The model achieved encouraging preliminary results such as sensitivity of 94%, specificity of 81%, and accuracy of 89% to identify the presence of A-lines. Finally, the present study could result in an alternative solution for an operator-independent lung damage screening in rural areas, leading to the integration of AI-based technology as a complementary tool for healthcare professionals. © 2023 SPIE.

Efficient Centroid Based Distance Monitoring System Using Deep Learning

COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection. © 2023 IEEE.

Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19

Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public's response is through Twitter's social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation- based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately © 2023 The Authors. Published by Universitas Airlangga.

Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Metodo LAMP como alternativa diagnostica para la deteccion del virus SARS-CoV-2

Summary Introduction: The SARS-CoV-2 coronavirus, that causes the COVID-19 disease, has become a global public health problem that requires the implementation of rapid and sensitive diagnostic tests. Aim(s): To evaluate and compare the sensitivity of LAMP assay to a standard method and use RT-LAMP for the diagnosis of SARS-CoV-2 in clinical samples from Colombian patients. Method(s): A descriptive and cross-sectional study was conducted. A total of 25 nasopharyngeal swab samples including negative and positive samples for SARS-CoV-2 were analyzed, through the RT-LAMP method compared to the RT-qPCR assay. Result(s): LAMP method detected ~18 copies of the N gene, in 30 min, evidenced a detection limit similar to the standard method, in a shorter time and a concordance in RT-LAMP of 100% with the results. Conclusion(s): RT-LAMP is a sensitive, specific, and rapid method that can be used as a diagnostic aid of COVID-19 disease.Copyright © 2021. All Rights Reserved.

Improved mask detection based on YOLOv5s

COVID-19 epidemic is not over. The correct wearing of masks can effectively prevent the spread of the virus. Aiming at a series of problems of existing mask-wearing detection algorithms, such as only detecting whether to wear or not, being unable to detect whether to wear correctly, difficulty in detecting small targets in dense scenes, and low detection accuracy, It is suggested to use a better algorithm based on YOLOv5s. It improves the generalization and transmission performance of the model by changing the ACON activation function. Then Bifpn is used to replace PAN to effectively integrate the target features of different sizes extracted by the network. Finally, To enable the network to pay attention to a wide area, CA is introduced to the backbone. This embeds the location information into the channel attention. © 2023 SPIE.

Fake News Detection Using Transformer and Ensemble Learning Models

On social media, misinformation can spread quickly, posing serious problems. Understanding the content and sensitive nature of fake news and misinformation is critical to prevent the damage caused by them. To this end, the characteristics of information must first be discerned. In this paper, we propose a transformer-based hybrid ensemble model to detect misinformation on the Internet. First, false and true news on Covid-19 were analyzed, and various text classification tasks were performed to understand their content. The results were utilized in the proposed hybrid ensemble learning model. Our analysis revealed promising results, establishing the capability of the proposed system to detect misinformation on social media. The final model exhibited an excellent F1 score (0.98) and accuracy (0.97). The AUC (Area Under The Curve) score was also high at 0.98, and the ROC (Receiver Operating Characteristics) curve revealed that the true-positive rate of the data was close to one in this model. Thus, the proposed hybrid model was demonstrated to be successful in recognizing false information online. © 2022 IEEE.

Single-step multiplex reverse transcription-polymerase chain reaction for the detection and differentiation of QX-like infectious bronchitis virus from the Thai variant and vaccine strains H120, Ma5, and 4/91

Background and Aim: QX-like infectious bronchitis virus (IBV) is a highly infectious avian coronavirus that causes respiratory and kidney disease. It is linked to increased mortality and loss of performance in infected chickens worldwide, including Thailand. Thus, a simple and rapid diagnostic method for the diagnosis of QX-like IBV is needed. This study aimed to develop a single-step multiplex reverse transcription-polymerase chain reaction (mRT-PCR) assay to detect and differentiate QX-like IBV from Thai IBV and vaccine strains used in the poultry industry (H120, Ma5, and 4/91). Materials and Methods: Primer sets specific for QX-like and Thai IBV were designed to target the S1 gene. The specificity of the technique was verified using nine isolates of QX-like IBV, four isolates of Thai IBV, and other avian viral respiratory pathogens. The detection limit was evaluated using a serial ten-fold dilution of QX-like and Thai IBV. Results: The results showed that single-step mRT-PCR could detect QX-like IBV and differentiate it from Thai IBV and the vaccine strains H120, Ma5, and 4/91. The limit of detection of the developed assay was 102.2 embryo infectious dose (EID)50/mL for QX-like IBV and 101.8 EID50/mL for Thai IBV. Interestingly, the developed assay could identify mixed infection by both IBVs in a single sample. Conclusion: The single-step mRT-PCR assay developed in this study can potentially discriminate QX-like IBV from Thai IBV and the vaccine strains H120, Ma5, and 4/91 in a single reaction. It is also suitable for use in all laboratories with access to conventional PCR equipment. [ FROM AUTHOR] Copyright of Veterinary World is the property of Veterinary World and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)