Influenza surveillance in Ezhou city, Hubei Province, 2016-2021

ObjectiveTo analyze the influenza surveillance data in Ezhou City, Hubei Province from 2016 to 2021, determine the epidemiological characteristics and etiological trend of influenza like illness (ILI), and to provide scientific evidence for influenza prevention and control. MethodsThe ILI surveillance data were reported by Ezhou influenza sentinel hospitals and etiological examination results were collected by network laboratory. Influenza surveillance data from 2016 to 2021 were analyzed. ResultsFrom 2016 to 2021, the percentage of ILI visits (ILI%) in Ezhou city was 2.81% and increased over years. Majority (55.55%) of ILI cases were 0-4 years. A total of 7 716 ILI samples were examined from 2016 to 2021, of which 1 467 tested positive with a positive rate of 19.01%. Influenza A H1N1 was mainly concentrated in January-April, A H3N2 mainly in August-December, B Victoria mainly in April-July and December-March, and B Yamagata mainly in December-February. Influenza network laboratory isolated influenza virus from the 1 467 positive samples by using MDCK cells and SPF chicken embryos. The overall isolation rate was 32.78%, which was 26.93% by MDCK cells and 5.86% by SPF chicken embryos. From 2016 to 2021, a total of 13 ILI outbreaks were reported in Ezhou City. Temporally, the outbreaks mainly occurred in winter and spring. Spatially, they were mainly in primary schools, middle schools and kindergartens. ConclusionThe winter and spring are the key time period of influenza prevention and control in Ezhou City, as they are susceptible to influenza outbreaks. Children aged 0-14 are the key population of prevention and control. Diverse subtypes of influenza virus alternate by years, which warrants continually strengthening monitoring. Additionally, certain countermeasures against COVID-19 may be recommended in the prevention and control of influenza.

Carryover Contamination-Controlled Amplicon Sequencing Workflow for Accurate Qualitative and Quantitative Detection of Pathogens: a Case Study on SARS-CoV-2.

Carryover contamination during amplicon sequencing workflow (AMP-Seq) put the accuracy of the high-throughput detection for pathogens at risk. The purpose of this study is to develop a carryover contaminations-controlled AMP-Seq (ccAMP-Seq) workflow to enable accurate qualitative and quantitative detection for pathogens. By using the AMP-Seq workflow to detect SARS-CoV-2, Aerosols, reagents and pipettes were identified as potential sources of contaminations and ccAMP-Seq was then developed. ccAMP-Seq used filter tips and physically isolation of experimental steps to avoid cross contamination, synthetic DNA spike-ins to compete with contaminations and quantify SARS-CoV-2, dUTP/uracil DNA glycosylase system to digest the carryover contaminations, and a new data analysis procedure to remove the sequencing reads from contaminations. Compared to AMP-Seq, the contamination level of ccAMP-Seq was at least 22-folds lower and the detection limit was also about an order of magnitude lower-as low as one copy/reaction. By testing the dilution series of SARS-CoV-2 nucleic acid standard, ccAMP-Seq showed 100% sensitivity and specificity. The high sensitivity of ccAMP-Seq was further confirmed by the detection of SARS-CoV-2 from 62 clinical samples. The consistency between qPCR and ccAMP-Seq was 100% for all the 53 qPCR-positive clinical samples. Seven qPCR-negative clinical samples were found to be positive by ccAMP-Seq, which was confirmed by extra qPCR tests on subsequent samples from the same patients. This study presents a carryover contamination-controlled, accurate qualitative and quantitative amplicon sequencing workflow that addresses the critical problem of pathogen detection for infectious diseases. IMPORTANCE Accuracy, a key indicator of pathogen detection technology, is compromised by carryover contamination in the amplicon sequencing workflow. Taking the detection of SARS-CoV-2 as case, this study presents a new carryover contamination-controlled amplicon sequencing workflow. The new workflow significantly reduces the degree of contamination in the workflow, thereby significantly improving the accuracy and sensitivity of the SARS-CoV-2 detection and empowering the ability of quantitative detection. More importantly, the use of the new workflow is simple and economical. Therefore, the results of this study can be easily applied to other microorganism, which has great significance for improving the detection level of microorganism.

Autonomous Oropharyngeal-Swab Robot System for COVID-19 Pandemic

The outbreak of COVID-19 has led to the shortage of medical personnel and the increasing need for nucleic acid testing. Manual oropharyngeal sampling is susceptible to inconsistency caused by fatigue and close contact could also cause healthcare personnel exposure and cross infection. The innate deficiency calls for a safer and more consistent way to collect the oropharyngeal samples. Therefore a fully autonomous oropharyngeal-swab robot system is proposed in this paper. The system is installed in a negative pressure chamber and carrying out a standardized sampling process to minimize individual sampling differences. A hierarchical throat detection algorithm is presented and multiple modality sensory information are fused to safely and accurately localize the optimum sampling location. Also, a force/position hybrid control method is adopted to ensure both accurate sampling and subject comfort. The robot system described in this paper can safely and efficiently collect the oropharyngeal sample, providing a scalable solution for large-scale Polymerase Chain Reaction (PCR) Molecular sample collection for various respiratory diseases. Note to Practitioners-During the COVID-19 pandemic, pre-diagnostic is essential for both prevention and treatment. Existing approaches, including nasal swab and oropharyngeal-swab, require extensive medical worker training and increase the chance of cross-infection. The robot system introduced in this paper can take oropharyngeal-swab samples from subjects with minimum human intervention, reducing medical worker exposure, alleviating the work pressure of medical staff, and speed up large quantity of sampling plan. The robot will first guide the subject into position with vocal commands, and automatically detect the optimum sampling location with a real-time machine learning algorithm. A dedicated control strategy aiming at minimizing discomfort and uniforming sample quantity is then applied to safely collect nucleic samples from the throat. Eventually, while the swab is being stored in the culture medium, a disinfection process is carried out simultaneously to prepare the robot for the next subject. Preliminary clinical trials show that our robot system can safely and accurately collect samples from subjects.

Smart Bracelet System for Temperature Monitoring and Movement Tracking Analysis.

In several epidemic diseases, one of the main symptoms exhibited by people is abnormal body temperature. Therefore, monitoring body temperature is crucial for preventing the spread of infectious diseases and facilitating timely responses. This study presents a wearable bracelet that can be used as a temperature monitoring and trajectory analysis system. The temperature sensor in the bracelet can effectively monitor the body temperature of the wearer in the target scene, and the data transmission between the bracelet and the data collector can effectively detect the movement range and trajectory of the wearer. Through these, the whole set can also detect the direct and indirect contact of the wearer in any period of time, which is very helpful for the prevention and control of infectious diseases and the isolation of potentially infected persons.

Severe 2019 novel coronavirus pneumonia may cause lung bullae and bronchiectasis: A case report and concise literature review

Most of the critically ill novel coronavirus 2019 pneumonia (NCP) patients progress promptly, and soon match the ARDS diagnostic criteria. When mechanical ventilation and prone position cannot reverse the fatal hypoxia—extra-corporeal-membrane-oxygenation (ECMO) will be applied as a salvage treatment if available. Here, we report a novel coronavirus 2019 pneumonia (NCP) patient, a male, 67 years old, who was treated with ECMO for 30 days. In the midst, bronchoscopy was utilized to comprehend the airway lesions and clear secretions. And computed tomography (CT) scans were performed before and after the treatment of ECMO. In the recovering phase of his disease, the patient experienced multiple times pneumothorax on both sides. Some newly developed lung bullae in the subpleural area and modest bronchiectasis were found by the CT scan. The newly developed lung bullae was the probable cause of pneumothorax. Notably, in the whole process of his illness, the serum IL-6 only had a slight elevation in the early period, there is no typical cytokine storm as that was seen in non-COVID-19 ARDS. After 3-months meticulous treatment, the patient made a full recovery and now is discharged from our hospital. Though COVID-19 may not cause typical cytokine storm, the inflammation in lung may inflict severe damage to lung. Severe NCP may cause lung bullae and bronchiectasis, making the patients hard to be weaned from mechanical ventilation or ECMO. [ABSTRACT FROM AUTHOR] Copyright of European Journal of Inflammation (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Cough Recognition Based on Mel-Spectrogram and Convolutional Neural Network.

In daily life, there are a variety of complex sound sources. It is important to effectively detect certain sounds in some situations. With the outbreak of COVID-19, it is necessary to distinguish the sound of coughing, to estimate suspected patients in the population. In this paper, we propose a method for cough recognition based on a Mel-spectrogram and a Convolutional Neural Network called the Cough Recognition Network (CRN), which can effectively distinguish cough sounds.

Abnormal upregulation of cardiovascular disease biomarker PLA2G7 induced by proinflammatory macrophages in COVID-19 patients.

High rate of cardiovascular disease (CVD) has been reported among patients with coronavirus disease 2019 (COVID-19). Importantly, CVD, as one of the comorbidities, could also increase the risks of the severity of COVID-19. Here we identified phospholipase A2 group VII (PLA2G7), a well-studied CVD biomarker, as a hub gene in COVID-19 though an integrated hypothesis-free genomic analysis on nasal swabs (n = 486) from patients with COVID-19. PLA2G7 was further found to be predominantly expressed by proinflammatory macrophages in lungs emerging with progression of COVID-19. In the validation stage, RNA level of PLA2G7 was identified in nasal swabs from both COVID-19 and pneumonia patients, other than health individuals. The positive rate of PLA2G7 were correlated with not only viral loads but also severity of pneumonia in non-COVID-19 patients. Serum protein levels of PLA2G7 were found to be elevated and beyond the normal limit in COVID-19 patients, especially among those re-positive patients. We identified and validated PLA2G7, a biomarker for CVD, was abnormally enhanced in COVID-19 at both nucleotide and protein aspects. These findings provided indications into the prevalence of cardiovascular involvements seen in patients with COVID-19. PLA2G7 could be a potential prognostic and therapeutic target in COVID-19.

Successful treatment of a critically ill patient with acute respiratory distress syndrome from COVID-19 using mechanical ventilation strategy with low levels of positive end-expiratory pressure: A CARE-compliant case report.

INTRODUCTION: Acute respiratory distress syndrome (ARDS) secondary to COVID-19 is different from the ARDS caused by other infections. Conventional mechanical ventilation strategies using high levels of PEEP may not be beneficial and can even be harmful to patient with ARDS from COVID-19. So the ventilation strategies should be adjusted in order to improve the pulmonary ventilation function and oxygenation status, and outcomes of the patient. PATIENT CONCERNS: Herein, we present a 76-year-old male patient with ARDS secondary to COVID-19. We describe our experience with mechanical ventilation strategy and the changes in respiratory mechanics in the patient during treatment. DIAGNOSIS: The patient had tested positive for coronavirus (COVID-19) in nucleic acid test. Chest CT showed multiple ground glass shadows in both lungs. INTERVENTIONS: The patient received mechanical ventilation with low tidal volume and low PEEP. OUTCOMES: After treatment, the patients condition, as well as oxygenation status was improved, and he tested negative for the coronavirus several times. CONCLUSION: This case demonstrated that the low tidal volume with low levels of PEEP ventilation strategy may be more suitable for ARDS from COVID-19.

Respiratory bacterial pathogen spectrum among COVID-19 infected and non-COVID-19 virus infected pneumonia patients.

COVID-19 positive (194) and negative (212) pneumonia patients were selected to analyze bacterial pathogens coinfection. Results showed that 50% of COVID-19 patients were coinfected or carried bacterial pathogens. Bordetella pertussis infection rate was significantly higher in positive patients. Consequently, preventions should be taken to control bacterial pathogens coinfection in COVID-19 patients.