Fungal pleural infection due to Microascus gracilis with pulmonary aspergillosis after COVID-19 pneumonia (preprint)

Background: Scopulariopsis/Microascus is a rare but devastating pathogen due to its intrinsic resistance to nearly all available antifungal agents. Microascus gracilis, an ascomycetous mould in the order Microascales, family Microascaceae, has recently emerged as a significant invasive pathogen causing opportunistic infections. Objectives and Methods: We present a case of pleural infection caused by M. gracilis with pulmonary aspergillosis in an immunocompromised man after COVID-19 pneumonia. To further understand the characteristics of the pathogen isolated from the patient, we identified the strain through mycological characteristics, matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MALDI-TOF MS) and internal transcribed spacer (ITS)-based sequencing, and performed in vitro drug susceptibility testing against common antifungal agents. Moreover, we assessed lymphocyte subsets and programmed cell death protein 1 (PD-1) expression in peripheral blood and pleural effusion to monitor the efficacy of therapy with thymosin-α-1 and intravenous immunoglobulin. Results: Filamentous fungi isolated from pleural fluid were identified as M. gracilis based on classical morphology, mass spectrometry and molecular biology methods. The susceptibility results in vitro revealed that multiple antifungal agents were inactive against the strain. Adjuvant immunomodulatory treatment successfully increased the levels of CD3+ T and CD4+ T cells while decreasing the levels of CD3+PD-1+ and CD4+PD-1+ T cells in both peripheral blood and pleural effusion. Conclusions: The immunocompromised host with opportunistic M. gracilis infection, rapid and accurate recognition through direct microscopic testing with calcofluor white and MOLDI-TOF MS, is the key to achieving a definite diagnosis, and a combination of antifungal therapy with immunomodulatory therapy is vital for improving survival.

How was a sedentary lifestyle associated with the prevalence of thyroid nodules among government staff? (preprint)

Purpose A sedentary lifestyle has been an increasing public health issue especially since the outbreak of COVID-19. Limited evidence was reported on the association between sedentary lifestyle and prevalence of thyroid nodules, a benign but prevalent health concern in the modern society.Methods This study used the annual examination data of Guangzhou public officials in 2020. Thyroid nodules were diagnosed following standard criteria. Sedentary lifestyle was self-reported as spending < 2 h, 2 ~ 4 h, 4 ~ 6 h, or > 6 h per day staying sedentary. Multivariate logistic regressions were used to evaluate the association between sedentary lifestyle and thyroid nodules. Results were also stratified by covariates.Results Among 18,023 participants, sedentary lifestyle was positively associated with the prevalence of thyroid nodules, with ORs (95%CIs) of 1.23 (1.05–1.43), 1.69 (1.45–1.96), and 1.74 (1.49–2.03) for those with daily sedentary time of 2 ~ 4h, 4 ~ 6h, >6h compared with < 2h group (P-trend < 0.01). The three estimates (i.e., 1.29, 1.92, and 1.96 respectively) were stronger among the participants with thyroid nodules of TI-RADS-2 stage compared with other groups. Stratified analysis also indicated a modification effect of age, with the estimates for participants aged ≥ 50 being 1.21, 1.79 and 2.00, compared with 1.04, 1.29 and 1.31 among those aged 35 ~ 49, and 1.13, 1.37 and 1.34 among those under 34 years old (P-interaction = 0.012).Conclusions A sedentary lifestyle may be associated with increased prevalence of thyroid nodules among government staff, with the elderly being the most vulnerable. The sedentary lifestyle should be modified to promote their well-being.

Influence of the Government Department on the Production Capacity Reserve of Emergency Enterprises Based on Multi-Scenario Evolutionary Game

With the frequent occurrence of world emergency events, the emergency management of government departments in various countries is facing great challenges. In the context of the COVID-19 pandemic, the shortage of various emergency resources is prominent, and the emergency supplies reserve combined by governments and enterprises is an important guarantee for dealing with emergency management problems. This paper mainly studies the impact of a government sustainability-oriented innovation policy on the production capacity reserve of security emergency enterprises (SEEs), and constructs an evolutionary game model between the local government and SEEs. Then, it focuses on the impact of a reputation mechanism on the government enterprise cooperative reserve. According to the condition of a country, the central government's punishment mechanism is introduced, and the evolutionary and stable strategies of the behavior choices of the local government and SEEs are compared and analyzed. Combined with the evolution degree of emergency events, the numerical simulation analysis is used to deduce and simulate the multi scenario of the example model. The results show that the reputation mechanism can effectively promote the evolution of government and enterprises to the ideal state under various scenarios. When the punishment intensity of the central government is higher than the total benefit of the implementation of the mere formality policy by the local government, it can effectively prevent the omission of the local government. This study provides a new idea for the government to formulate reasonable policies to promote SEEs to reserve production capacity, which is more conducive to government enterprise cooperation to deal with emergency events.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19: Challenges of Emerging Variants (preprint)

Importance Breath analysis has been explored as a noninvasive means to detect COVID19. However, the impact of the emerging variants such as Omicron on the exhaled breath profile and hence the accuracy of breath analysis is unknown. Objective To evaluate the diagnostic accuracies of breath analysis on detecting COVID19 patients in periods where Delta and Omicron were most prevalent. Design, Setting, and Participants A convenience cohort of patients testing positive and negative for COVID19 using reverse transcriptase polymerase chain reaction (RTPCR) were studied and included 167 COVID and nonCOVID patients recruited between April 2021 and May 2022, which covers the period when Delta (and other variants prior to Delta) was the dominant variant (April to December 2021) and when Omicron was the dominant variant (January to May 2022). The breath from those patients were collected and analyzed for volatile organic compounds (VOCs) with a newly developed portable gas chromatography based breath analyzer. Diagnostic patterns and algorithms were developed. Results A total of 205 breath samples were analyzed from 167 COVID and nonCOVID patients. The RTPCR was conducted within 18 hours of the breath analysis to confirm the COVID status of the patients. Among 94 COVID positive samples, 41 samples were collected from the patients in 2021 who were assumed to be infected by the Delta variant (or other variants occurring in 2021) and 53 samples from the patients in 2022 who were assumed to be infected by the Omicron variant (BA.1 and BA.2). Breath analysis using a set of 4 VOC biomarkers was able to distinguish between COVID (Delta and other variants in 2021) and nonCOVID with an overall accuracy of 94.7%. However, the accuracy dropped significantly to 82.1% when the same set of biomarkers were applied to the Omicron variant with and 21 out of 53 COVID positive being misidentified. A new set of 4 VOC biomarkers were found to distinguish the Omicron variant and non-COVID, which yielded an overall accuracy of 90.9%. Breath analysis was also found to be able to distinguish between COVID (for all the variants occurring between April 2021 and May 2022) and nonCOVID with an overall accuracy of 90.2%, and between the Omicron variant and the earlier variants (Delta and other variants occurring in 2021) with an overall accuracy of 91.5%. Conclusions and Relevance Breath analysis of VOCs using point of care gas chromatography may be a promising diagnostic modality for detection of COVID and similar diseases that result in VOC production. However, similar to other diagnostic modalities such as rapid antigen testing, challenges are posed by the dynamic emergence of viral variants. The results of this study warrant additional investment and evaluation on how to overcome these challenges and to exploit breath analysis to improve the diagnosis and care of patients.

Re-emergence of Severe Acute Diarrhea Syndrome Coronavirus (SADS-CoV) in Guangxi, China, 2021 (preprint)

Severe acute diarrhea syndrome coronavirus (SADS-CoV) has had a major impact on the swine industry in China, but has not been detected since 2019. Using real-time qPCR and metagenomic surveillance we identified SADS-CoV in a pig farm experiencing diarrheal disease. Genomic analysis supported the undetected circulation of SADS-CoV since 2019.

SARS-CoV-2 protein ORF3a is pathogenic in Drosophila and causes phenotypes associated with COVID-19 post-viral syndrome (preprint)

The Coronavirus Disease 2019 (COVID-19) pandemic has caused millions of deaths and will continue to exact incalculable tolls worldwide. While great strides have been made toward understanding and combating the mechanisms of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection, relatively little is known about the individual SARS-CoV-2 proteins that contribute to pathogenicity during infection and that cause neurological sequela after viral clearance. We used Drosophila to develop an in vivo model that characterizes mechanisms of SARS-CoV-2 pathogenicity, and found ORF3a adversely affects longevity and motor function by inducing apoptosis and inflammation in the nervous system. Chloroquine alleviated ORF3a induced phenotypes in the CNS, arguing our Drosophila model is amenable to high throughput drug screening. Our work provides novel insights into the pathogenic nature of SARS-CoV-2 in the nervous system that can be used to develop new treatment strategies for post-viral syndrome.

Trend of respiratory pathogens during the COVID-19 epidemic: comparison between 2020 and the last 5 years (preprint)

In China, the first SARS-CoV-2 infection was diagnosed in Wuhan on December 8. Spreads in other regions have occurred since the end of January, happens to be the start of Lunar New Year holiday. In this study, we analyzed the prevalence of common respiratory pathogens in children with respiratory infections during the SARS-CoV-2 pandemic and compared them with the time trends from 2016 to 2019. Overall, results obtained indicate that the time trend of other respiratory infections were significantly different from previous years, especially the pattern of influenza and Mycoplasma pneumonia. Therefore, in the current scenario of COVID-19 pandemic, other common pathogens testing should not be excluded. The natural home isolation period in new year holiday may weaken the transmission of common respiratory viruses.

Evaluation of two commercial multiplex PCR tests for the diagnosis of acute respiratory infections in hospitalized children (preprint)

Background Acute respiratory tract infections (ARTI), including the common cold, pharyngitis, sinusitis, otitis media, tonsillitis, bronchiolitis and pneumonia are the most common diagnoses among pediatric patients and account for the majority of antibiotic prescriptions. A clear and rapid diagnosis is the key to preventing antibiotic abuse. Recently, based on different detection principles, many multi-target molecular analyses that can simultaneously detect dozens of pathogens have been developed, thereby greatly improving sensitivity and shortening turnaround time. In this work, we conducted a head-to-head comparative study between melting curve analysis (MCA) and capillary electrophoresis assay (CE) in the detection of nine respiratory pathogens in sputum samples collected from hospitalized ARTI childre.Methods Through MCA and CE analysis, nine common respiratory pathogens were tested on hospitalized children under the age of 13 who met the ARTI criteria.Results A total of 237 children with sputum specimens were tested. For all the targets combined, the positive detection rate of XYRes-MCA was significantly higher than ResP-CE (72.2% vs. 63.7%, p = .002). Some pathogens were detected more often with MCA, such as parainfluenza virus, influenza B and coronavirus, and some pathogens do the opposite, such as adenovirus and influenza A (all p < .01). Very good kappa values for most of pathogens were observed, except for Influenza B and coronavirus (both κ = .39).Conclusions Multiplex melting curve and capillary electrophoresis assays performed similarly for the detection of common respiratory pathogens in hospitalized children, except for Influenza B and coronavirus. Higher sensitivity was observed in the melting curve assay. By using this sensitive and rapid test, it may improv patient prognosis and antimicrobial management.

Clinical characteristics of COVID-19 and establishment of a disease risk prediction model (preprint)

Background: COVID-19 is spreading worldwide. No specific medicine has been used for the treatment of coronavirus infections. The aim of this study is to establish a new risk predictive model to screen potential critical patients for early intervention.Methods: In this study, Clinical characteristics were collected and analyzed from 317 confirmed cases of COVID-19. A total of 175 of the 317 cases with detailed examination results were included to establish models for predicting the risk of disease progression. Major independent risk factors were incorporated into MuLBSTA model to establish new models for predicting critical risk. We further tracked 25 mild or moderate patients with COVID-19 to research dynamic changes of the major independent risk factors in COVID-19 progression.Results: The average age of all of the 317 patients was 47.76 (SD 17.22). A total of 48 (15.14%) were diagnosed with mild disease with a median age of 34(39.29±13.04), 116(36.59%) were diagnosed with moderate disease with a median age of 34(38.78±12.32), 38(11.99%) were diagnosed as severe with a median age of 56(58.24±15.12), and 115(36.28) were diagnosed as critical with a median age of 59(56.89+17.09). The most common symptom at onset of illness were fever(211[66.56%] patients). Age>50, CK>64, CD4≤461, and CD8≤241 were predicted to be major independent risk factors that could promote COVID-19 progression. Compared with the MuLBSTA model, the predictive ability of the CD4-CD8-MuLBSTA model and the CD4-MuLBSTA model were improved by 11.87% and 11.79%, respectively. In the prospective study, CK value began to show significant differences from day13. The average CD4 in Severe Group began to decline significantly on the fourth day, and the CD8 maintained a relatively low level in the Severe Group after day13.Conclusions: Severe COVID-19 patients were significantly older than non-severe patients. Immune systems of severe COVID-19 patients were significantly suppressed, and advanced age(>50 years), low levels of CD4(≤461) or CD8(≤241) was important clinical manifestations of rapid deterioration. CK values in severe COVID-19 patients were significantly higher than in no severe patients. CD4 and CD8 were incorporated into the MuLBSTA to establish a new model, which is an ideal risk prediction model for COVID-19 patients.

Evaluation of two commercial multiplex PCR tests for the diagnosis of acute respiratory infections in hospitalized children (preprint)

Background Acute respiratory tract infections (ARTI), including the common cold, pharyngitis, sinusitis, otitis media, tonsillitis, bronchiolitis and pneumonia are the most common diagnoses in pediatric patients, and account for most antibiotic prescriptions. A confirmed and rapid ARTI diagnosis is key to preventing antibiotic abuse. Recently, based on different detection principles, many multi-target molecular analyses that can detect dozens of pathogens at the same time have been developed, greatly improving sensitivity and shortening turnaround time. In this work, we performed a head-to-head comparative study between melting curve analysis (MCA) and capillary electrophoresis assay (CE) in the detection of nine respiratory pathogens in sputum samples collected from hospitalized children with ARTI. Methods By MCA and CE analysis, nine common respiratory pathogens were tested in hospitalized children< 13 years of age who met the ARTI criteria respectively. Results A total of 237 children with sputum specimens were tested. For all the targets combined, the positive detection rate of XYRes-MCA was significantly higher than that of ResP-CE (72.2% vs. 63.7%, p=.002). Some pathogens were detected more often with MCA, such as parainfluenza virus, influenza B and coronavirus, and some pathogens do the opposite, such as adenovirus and influenza A (all p<.01). Very good kappa values for most of pathogens were observed, except for Influenza B and coronavirus (both κ=.39). Conclusions Multiplex melting curve and capillary electrophoresis assays performed similarly for the detection of common respiratory pathogens in hospitalized children, except for Influenza B and coronavirus. A higher sensitivity was observed in the melting curve assay. By using this sensitive and rapid test, it may be possible to achieve improved patient prognosis and antimicrobial management.