Respiratory Infectious Diseases Research Articles

Association between COVID-19 and outstanding academic performance at a Spanish university

BackgroundSARS-CoV-2 is the causative agent of COVID-19 identified in December 2019, an acute infectious respiratory disease that can cause persistent neurological and musculoskeletal symptoms such as headache, fatigue, myalgias difficulty concentrating, among others including acute cerebrovascular disease with a prevalence of 1–35%. The aim of this study is to evaluate the impact of COVID-19 in undergraduate students on their academic performance as an indicator of their intellectual ability and performance in a university that maintained 100% face-to-face teaching during the 2020–2021 academic year.MethodsA total of 7,039 undergraduate students were analyzed in a prospective cohort study at the University of Navarra. A questionnaire including sociodemographic and behavioral questions was sent. PCRs were performed throughout the academic year for the diagnosis of SARS-CoV-2 infection and students’ academic results were provided by the academic center, adjusted descriptive and multivariate models were performed to assess the association.ResultsA total of 658 (9.3%) participants were diagnosed with COVID-19, almost 4.0% of them achieved outstanding academic results, while uninfected students did so in 7.3%. SARS-CoV-2 infection was associated with a significant decrease in having outstanding academic results (OR = 0.57; 95% CI: 0.38–0.86).ConclusionHaving COVID-19 disease, decreased academic performance in undergraduate students. Therefore, it is necessary to prevent infection even in the youngest sections of the population.

A noval pulmonary function evaluation method based on ResNet50 + SVR model and cough

Traditionally, the clinical evaluation of respiratory diseases was pulmonary function testing, which can be used for the detection of severity and prognosis through pulmonary function parameters. However, this method is limited by the complex process, which is impossible for patients to monitor daily. In order to evaluate pulmonary function parameters conveniently with less time and location restrictions, cough sound is the substitute parameter. In this paper, 371 cough sounds segments from 150 individuals were separated into 309 and 62 as the training and test samples. Short-time Fourier transform (STFT) was applied to transform cough sound into spectrogram, and ResNet50 model was used to extract 2048-dimensional features. Through support vector regression (SVR) model with biological attributes, the data were regressed with pulmonary function parameters, FEV1, FEV1%, FEV1/FVC, FVC, FVC%, and the performance of this models was evaluated with fivefold cross-validation. Combines with deep learning and machine learning technologies, the better results in the case of small samples were achieved. Using the coefficient of determination (R2), the ResNet50 + SVR model shows best performance in five basic pulmonary function parameters evaluation as FEV1(0.94), FEV1%(0.84), FEV1/FVC(0.68), FVC(0.92), and FVC%(0.72). This ResNet50 + SVR hybrid model shows excellent evaluation of pulmonary function parameters during coughing, making it possible to realize a simple and rapid evaluation for pneumonia patients. The technology implemented in this paper is beneficial in judge the patient's condition, realize early screening of respiratory diseases, evaluate postoperative disease changes and detect respiratory infectious diseases without time and location restrictions.

Nitric oxide-assisted mucus-walking zwitterionic nanocomplexes for synergistic treatment of severe respiratory infectious disease

Among patients who suffer from lung-related disability, acute respiratory distress syndrome (ARDS) is one of the diseases with the significant mortality rate and the main cause is the interference in pulmonary drug delivery by accumulated bronchoalveolar lavage fluid (BALF) in the alveolar region. Here, a zwitterion-functionalized multi-drug nanocomplex (ZnC) capable of anti-mucociliary clearance was synthesized. Additionally, nitric oxide (NO) was functionalized with ZnC to fuel the mobility in the BALF through its role as a nanomotor as well as anti-inflammation. Subsequently, dexamethasone (Dex) was loaded and final product of anti-inflammatory mucus permeator (AIM) was introduced. By in situ tracking the AIM within the mucus-rich environment, the fueled mobility by NO and zwitterion was demonstrated. Notably, when AIM was inhaled by the in vivo ARDS model, there was an increase in the anti-inflammation effect due to the presence of ZnC as well as the synergy between NO and Dex, which recovered the pulmonary function of the model and increased survival rate.

Ambient atmospheric PM worsens mouse lung injury induced by influenza A virus through lysosomal dysfunction

BackgroundParticulate matter (PM) air pollution poses a significant risk to respiratory health and is especially linked with various infectious respiratory diseases such as influenza. Our previous studies have shown that H5N1 virus infection could induce alveolar epithelial A549 cell death by enhancing lysosomal dysfunction. This study aims to investigate the mechanisms underlying the effects of PM on influenza virus infections, with a particular focus on lysosomal dysfunction.ResultsHere, we showed that PM nanoparticles such as silica and alumina could induce A549 cell death and lysosomal dysfunction, and degradation of lysosomal-associated membrane proteins (LAMPs), which are the most abundant lysosomal membrane proteins. The knockdown of LAMPs with siRNA facilitated cellular entry of both H1N1 and H5N1 influenza viruses. Furthermore, we demonstrated that silica and alumina synergistically increased alveolar epithelial cell death induced by H1N1 and H5N1 influenza viruses by enhancing lysosomal dysfunction via LAMP degradation and promoting viral entry. In vivo, lung injury in the H5N1 virus infection-induced model was exacerbated by pre-exposure to silica, resulting in an increase in the wet/dry ratio and histopathological score.ConclusionsOur findings reveal the mechanism underlying the synergistic effect of nanoparticles in the early stage of the influenza virus life cycle and may explain the increased number of respiratory patients during periods of air pollution.

Impact of Media Awareness and Use of Face-Masks on Infectious Respiratory Disease

Infectious respiratory diseases have been a threat to our lives and livelihoods. Communities with a higher population density ultimately are at a higher risk of a rapid spread of infectious disease once they occur. Mathematical models have extensively been used to analyse the impact of several mitigation measures to the transmission of infectious diseases. The focus of this study is to determine the impact of media awareness, use of face-masks alongside quarantine and isolation on the transmission dynamics of a respiratory infection where there is no available vaccination or treatment plan. The model is based on a system of deterministic ordinary differential equations (ODE’s). By the use of the next generation matrix (NGM), the basic reproduction is determined. The local stability analysis analysis of the disease free equilibrium (DFE) is obtained by the trace-determinant approach while it’s global stability was determined by the use of the Lyapunov-Krasovskii method. It was established that the local DFE was asymptotically stable while it was unstable globally implying that with application of this intended strategies, the transmission of the disease could be lowered but not eradicated. The importance of media awareness to any campaign towards reduction of transmission is paramount. Effort should be geared towards the public having correct information on a given disease. The impact of face mask is dependent on the efficacy of the face - mask and its compliant and consistent use, these two factors are multiplicative. Numerical simulations were done by the use of Python software and it was established that if quarantine rate and the isolation rate was to be maximised then the disease would be curtailed. The results of this study gives valuable information on the intervention strategies to be applied by the public heath officials.

Incidence Among Infants in Moscow Including the COVID-19 Statistics

The study presents the data on incidence among infants in Moscow. The author analyzed the indicators of overall incidence, the characteristics of disease distribution, and the level of follow-up care in children of the given age group. The data was obtained from the open sources of Federal State Statistics Service of the Russian Federation (Rosstat) and from the Federal Statistical Observation Form No. 12 “On the number of diseases registered in patients residing in the service area of a medical organization” from 2016 to 2022.
 The author evaluated the impact of COVID-19 pandemic on the pre-COVID incidence trends in general and by specific disease categories, as well as on the distribution of diseases common to infants. The results show a decrease in overall incidence and specifically of infectious diseases, respiratory diseases, injuries, poisonings, and other external causes in all age groups. While overall incidence was decreasing between 2020 and 2022, the number of cases requiring the follow-up care was constantly growing. The absence of herd immunity and newborn vaccination due to the age characteristics of the group has led to an explosion of COVID-19 cases in the last three years. The study also revealed the variation in indicators for certain diseases, which warrants further investigation of the dynamics in these nosological groups.

Genome sequencing reveals molecular epidemiological characteristics and new recombinations of adenovirus in Beijing, China, 2014-2019.

To investigate the molecular epidemiological characteristics and genetic variations of human adenovirus (HAdV) in acute respiratory tract infections in Beijing. Whole-genome sequencing and phylogenetic analyses were performed for 83 strains of HAdV with different types in Beijing from 2014 to 2019. The clinical characteristics of HAdV infection were analyzed statistically. HAdV-B was divided into four genotypes, including B3 (n = 11), B7 (n = 13), B14 (n = 4), and B55 (n = 2). HAdV-C was divided into three genotypes, including C1 (n = 14), C2 (n = 13), and C5 (n = 10). In HAdV-C, nine recombinant adenovirus strains were identified in type 1, and seven recombinant strains were found in type 2. In type 1, we found three newly emerged intraspecific recombinant strains (A47, A48, and A52) collected in 2017, 2018, and 2019, respectively. In addition, the previously reported recombinant strains of HAdV-C1 showed more severe disease than other strains of HAdV-C, causing severe community-acquired pneumonia in both the elderly and children. Continuous population-wide molecular epidemiological surveillance of HAdV is essential for the prevention and control of respiratory infectious diseases.

Geographical characteristics and influencing factors of the influenza epidemic in Hubei, China, from 2009 to 2019.

Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.

Study on brain damage patterns of COVID-19 patients based on EEG signals

ObjectiveThe coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by the SARA-CoV-2, characterized by high infectivity and incidence. Clinical data indicates that COVID-19 significantly damages patients’ perception, motor function, and cognitive function. However, the electrophysiological mechanism by which the disease affects the patient’s nervous system is not yet clear. Our aim is to investigate the abnormal levels of brain activity and changes in brain functional connectivity network in patients with COVID-19.MethodsWe compared and analyzed electroencephalography signal sample entropy, energy spectrum, and brain network characteristic parameters in the delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands of 15 patients with COVID-19 and 15 healthy controls at rest.ResultsAt rest, energy values of the four frequency bands in the frontal and temporal lobes of COVID-19 patients were significantly reduced. At the same time, the sample entropy value of the delta band in COVID-19 patients was significantly increased, while the value of the beta band was significantly decreased. However, the average value of the directed transfer function of patients did not show any abnormalities under the four frequency bands. Furthermore, node degree in the temporal lobe of patients was significantly increased, while the input degree of the frontal and temporal lobes was significantly decreased, and the output degree of the frontal and occipital lobes was significantly increased.ConclusionThe level of brain activity in COVID-19 patients at rest is reduced, and the brain functional network undergoes a rearrangement. These results preliminarily demonstrate that COVID-19 patients exhibit certain brain abnormalities during rest, it is feasible to explore the neurophysiological mechanism of COVID-19’s impact on the nervous system by using EEG signals, which can provide a certain technical basis for the subsequent diagnosis and evaluation of COVID-19 using artificial intelligence and the prevention of brain nervous system diseases after COVID-19 infection.

The risk of aerosol transmission of respiratory infectious diseases in the isolation ward

The risk of aerosol transmission of respiratory infectious diseases in the isolation ward

Risk evaluation of venue types and human behaviors of COVID-19 outbreaks in public indoor environments: A systematic review and meta-analysis

Despite increasing vaccination rates, the incidence of breakthrough infections with COVID-19 has increased due to the continued emergence of new variants of the SARS-CoV-2 coronavirus. Therefore, Non-pharmaceutical interventions remain the most effective measures for coping with the ever-changing pandemic. The lifting of compulsory interventions has made individuals primary responsibility for their own health, which highlights the importance of increasing awareness of the infection risk from the environment in which they live and their individual behaviors. We systematically searched PubMed, Web of Science, ScienceDirect, and Scopus on April 17, 2023, for all studies reporting COVID-19 outbreaks in public indoor venues. The study outcome was the attack rate. A total of 42 studies, which included cross-sectional studies, cohort studies, and case studies, reporting data on 1951 confirmed cases in 64 COVID-19 outbreaks satisfied the meta-analysis and were included in the review. A random-effect model was used in the meta-analysis, and subgroup analyses were conducted to investigate factors affecting attack rates. We found a strong level of evidence (p < 0.01) supporting a higher pooled attack rate in recreation-related venues (0.44, 95% CI: 0.30 to 0.60) than in work-related venues (0.21, 95% CI: 0.16 to 0.27). Compared to those outbreaks without that, outbreaks with high-intensity exercise, vocalization, contact behavior, or close body proximity had a higher attack rate of 0.51, 0.55, 0.33, and 0.39, respectively. Further studies suggest that different attack rates across different types of settings may be the result of heterogeneity in exposed people's behaviors. There were significant heterogeneities that may limit the interpretation of connections between influencing factors and outbreak outcomes. The identification of key behaviors that may contribute to transmission risk, and their correlation with venue type, has important implications for the development of future public health interventions and individual prevention strategies for respiratory infectious diseases such as COVID-19.

Predicting the transmission trend of respiratory viruses in new regions via geospatial similarity learning

The outbreak and spread of COVID-19 remind us again of the devastating attack that human-to-human transmitted respiratory infectious diseases (H-HRIDs) bring to global economics and public health. Predicting the trend of H-HRIDs yields important suggestions for making response strategies. Existing methods predict the future trend of H-HRIDs based on the prophase epidemiological data. However, it is also crucial to predict the potential outbreak trend of H-HRIDs in regions without prophase epidemiological data based on those with epidemiological data available. This prediction can provide constructive pharmaceutical strategies and non-pharmaceutical interventions such as pre-allocating the limited preventions to regions with different potential trends, especially at the early stage of an H-HRID. In this paper, transform the problem of predicting the early-stage trend of an H-HRID in new regions without prophase epidemiological data into the problem of learning regions’ geospatial features via a Contrastive learning-based Hierarchical Graph Convolutional Neural Network (CHGCN). Specifically, CHGCN first generates training data from regions with epidemiological data available via a Relaxed Graph Edit Distance (RGED) algorithm. CHGCN then uses a constructive learning schema to learn region embeddings, where regions with similar geospatial features and H-HRID trends exhibit similar embeddings. Once trained, CHGCN searches for the region with epidemiological data available that has the highest embedding similarity to the given query region without prophase epidemiological data, so as to achieve early-stage H-HRID trend prediction for the query region. Experimental results on COVID-19 demonstrate that the proposed CHGCN achieves state-of-the-art performance in predicting the early-stage trend of H-HRIDs, compared with the baselines.

Epidemiologic investigation and genetic characterization of canine respiratory coronavirus in the Southeastern United States.

Canine respiratory coronavirus (CRCoV) is one of the main causative agents of canine infectious respiratory disease (CIRD), an illness whose epidemiology is poorly understood. We assessed the prevalence, risk factors, and genetic characterization of CRCoV in privately owned dogs in the Southeastern United States. We PCR-screened 189 nasal swabs from dogs with and without CIRD clinical signs for 9 CIRD-related pathogens, including CRCoV; 14% of dogs, all diagnosed with CIRD, were positive for CRCoV, with a significantly higher rate of cases in younger dogs and during warmer weather. Notably, the presence of CRCoV, alone or in coinfection with other CIRD pathogens, was statistically associated with a worse prognosis. We estimated a CRCoV seroprevalence of 23.7% retrospectively from 540 serum samples, with no statistical association to dog age, sex, or season, but with a significantly higher presence in urban counties. Additionally, the genomes of 6 CRCoVs were obtained from positive samples using an in-house developed targeted amplicon-based approach specific to CRCoV. Subsequent phylogeny clustered their genomes in 2 distinct genomic groups, with most isolates sharing a higher similarity with CRCoVs from Sweden and only 1 more closely related to CRCoVs from Asia. We provide new insights into CIRD and CRCoV epidemiology in the Southeastern United States and further support the association of CRCoV with more severe cases of CIRD. Additionally, we developed and successfully tested a new amplicon-based approach for whole-genome sequencing of CRCoV that can be used to further investigate the genetic diversity within CRCoVs.

Predominance of Canine Parainfluenza Virus and Mycoplasma in Canine Infectious Respiratory Disease Complex in Dogs.

Canine infectious respiratory disease complex (CIRDC) is caused by different viruses and bacteria. Viruses associated with CIRDC include canine adenovirus type 2 (CAV-2), canine distemper virus (CDV), canine influenza virus (CIV), canine herpesvirus type 1 (CHV-1), canine respiratory coronavirus (CRCoV), and canine parainfluenza virus (CPIV). Bacteria associated with CIRDC include Bordetella bronchiseptica, Streptococcus equi subspecies zooepidemicus (S. zooepidemicus), and Mycoplasma spp. The present study examined the prevalence of CIRDC pathogens in specimens received by a Veterinary Diagnostic Laboratory in Georgia, USA., from 2018 to 2022. Out of 459 cases, viral agents were detected in 34% of cases and bacterial agents were detected in 58% of cases. A single pathogen was detected in 31% of cases, while two or more pathogens were identified in 24% of cases. The percentages of viral agents identified were CAV-2 (4%), CDV (3%), CPIV (16%), CRCoV (7%), and CIV (2%). The percentages of bacterial agents were B. bronchiseptica (10%), Mycoplasma canis (24%), Mycoplasma cynos (21%), and S. zooepidemicus (2%). Over the five-year period, the positive cases ranged from 2-4% for CAV-2, 1-7% for CDV, 1-4% for CHV-1, 9-22% for CPIV, 4-13% for CRCoV, and 1-4% for CIV. Overall, the most prevalent pathogens associated with CIRDC were CPIV, M. canis, and M. cynos.

Genomic surveillance of SARS-CoV-2 in Weihai, China, march 2022 to march 2023.

COVID-19 is an acute respiratory infectious disease caused by SARS-CoV-2. It was first reported in Wuhan, China in December 2019 and rapidly spread globally in early 2020, triggering a global pandemic. In December 2022, China adjusted the dynamic COVID-zero strategy that lasted for three years. The number of positive cases in China increased rapidly in the short term. Weihai was also affected during this period. We conducted genomic surveillance of SARS-CoV-2 variants in Weihai during this period, hoping to understand the changes in the genomic characteristics of SARS-CoV-2 before and after the adjustment of the epidemic policy. In this study,we collected SARS-CoV-2 positive samples from March 2022 to March 2023 in Weihai and performed SARS-CoV-2 whole genome sequencing on these samples using next-generation sequencing technology. we obtained a total of 704 SARS-CoV-2 whole genome sequences, and selected 581 high-quality sequences for further analysis. The analysis results showed that from March 2022 to November 2022, before the adjustment of epidemic policy, the COVID-19 cases in Weihai were mainly from four local clusters,which were caused by four variants, including BA.2,BA.1.1,P.1.15 and BA.5.2.1. Phylogenetic analysis showed that: In the same cluster,the sequences between each other were highly homologous, and the whole genome sequence were almost identical. After December 2022, the epidemic policy was adjusted, BF.7 and BA.5.2 became the dominant variants in Weihai, consistent with the main domestic strains in China during the same period. Phylodynamic analysis showed that BF.7 and BA.5.2 had a large amount of genetic diversities in December, and the effective population size of BF.7 and BA.5.2 also showed explosive growth in December. In conclusion, we reported the composition and dynamic trend of SARS-CoV-2 variants in Weihai from March 2022 to March 2023. We found that there have been significant changes in the variants and expansion patterns of SARS-CoV-2 before and after the adjustment of epidemic policies. But the dominant variants in Weihai were the same as the SARS-CoV-2 variants circulating globally at the same time and we found no persistently dominant variants or new lineages during this period.

Epidemiological characteristics of seven notifiable respiratory infectious diseases in the mainland of China: an analysis of national surveillance data from 2017 to 2021

BackgroundRespiratory infectious diseases (RIDs) remain a pressing public health concern, posing a significant threat to the well-being and lives of individuals. This study delves into the incidence of seven primary RIDs during the period 2017–2021, aiming to gain deeper insights into their epidemiological characteristics for the purpose of enhancing control and prevention strategies.MethodsData pertaining to seven notifiable RIDs, namely, seasonal influenza, pulmonary tuberculosis (PTB), mumps, scarlet fever, pertussis, rubella and measles, in the mainland of China between 2017 and 2021 were obtained from the National Notifiable Disease Reporting System (NNDRS). Joinpoint regression software was utilized to analyze temporal trends, while SaTScan software with a Poisson probability model was used to assess seasonal and spatial patterns.ResultsA total of 11,963,886 cases of the seven RIDs were reported during 2017–2021, and yielding a five-year average incidence rate of 170.73 per 100,000 individuals. Among these RIDs, seasonal influenza exhibited the highest average incidence rate (94.14 per 100,000), followed by PTB (55.52 per 100,000), mumps (15.16 per 100,000), scarlet fever (4.02 per 100,000), pertussis (1.10 per 100,000), rubella (0.59 per 100,000), and measles (0.21 per 100,000). Males experienced higher incidence rates across all seven RIDs. PTB incidence was notably elevated among farmers and individuals aged over 65, whereas the other RIDs primarily affected children and students under 15 years of age. The incidences of PTB and measles exhibited a declining trend from 2017 to 2021 (APC = −7.53%, P = 0.009; APC = −40.87%, P = 0.02), while the other five RIDs peaked in 2019. Concerning seasonal and spatial distribution, the seven RIDs displayed distinct characteristics, with variations observed for the same RIDs across different regions. The proportion of laboratory-confirmed cases fluctuated among the seven RIDs from 2017 to 2021, with measles and rubella exhibiting higher proportions and mumps and scarlet fever showing lower proportions.ConclusionsThe incidence of PTB and measles demonstrated a decrease in the mainland of China between 2017 and 2021, while the remaining five RIDs reached a peak in 2019. Overall, RIDs continue to pose a significant public health challenge. Urgent action is required to bolster capacity-building efforts and enhance control and prevention strategies for RIDs, taking into account regional disparities and epidemiological nuances. With the rapid advancement of high-tech solutions, the development and effective implementation of a digital/intelligent RIDs control and prevention system are imperative to facilitate precise surveillance, early warnings, and swift responses.Graphical

Chinese expert consensus on standardized inhaler device application in stable chronic airway diseases (2023)

Chinese expert consensus on standardized inhaler device application in stable chronic airway diseases (2023)

Validation of a real-time PCR panel for detection and quantification of nine pathogens commonly associated with canine infectious respiratory disease

Canine infectious respiratory disease (CIRD) is a complicated respiratory syndrome in dogs [1–3]. A panel PCR was developed [4] to detect nine pathogens commonly associated with CIRD: Mycoplasma cynos, Mycoplasma canis, Bordetella bronchiseptica; canine adenovirus type 2, canine herpesvirus 1, canine parainfluenza virus, canine distemper virus, canine influenza virus and canine respiratory coronavirus [5–16]. To evaluate diagnostic performance of the assay, 740 nasal swab and lung tissue samples were collected and tested with the new assay, and compared to an older version of the assay detecting the same pathogens except that it does not differentiate the two Mycoplasma species. Results indicated that the new assay had the same level of specificity, but with higher diagnostic sensitivity and had identified additional samples with potential co-infections. To confirm the new assay is detecting the correct pathogens, samples with discrepant results between the two assays were sequence-confirmed. Spiking a high concertation target to samples carrying lower concentrations of other targets was carried out and the results demonstrated that there was no apparent interference among targets in the same PCR reaction. Another spike-in experiment was used to determine detection sensitivity between nasal swab and lung tissue samples, and similar results were obtained.•A nine-pathogen CIRD PCR panel assay had identified 139 positives from 740 clinical samples with 60 co-infections;•High-concentration target does not have apparent effect on detecting low-concentration targets;•Detection sensitivity were similar between nasal swab and lung tissue samples.

Assessing airborne transmission risks in COVID-19 hospitals by systematically monitoring SARS-CoV-2 in the air.

The continuous emergence of highly infectious SARS-CoV-2 variants with the capability of immune evasion demands more effective risk assessment and management approaches for infection prevention and control. Controlling nosocomial transmission is critical in response to the COVID-19 pandemic upon the resumption of day-to-day activities. Here, we systematically analyzed the presence of airborne SARS-CoV-2 in the isolation ward and within the contaminant, emergency, and clean zones of a designated COVID-19 hospital. A total of 359 air samples collected in the clean and emergency zones tested negative for SARS-CoV-2, demonstrating the interruption of airborne transmission in a designated COVID-19 hospital by combining the closed-loop management, unidirectional airflow, and negative pressure of isolation wards. In contrast, some air samples in the corridor outside the isolation wards tested positive for SARS-CoV-2. The leakage of virus-containing air from patient rooms to adjoining areas during rounds and meal delivery sheds light on the need for intensive and widespread virus monitoring in designated COVID-19 hospitals. In conclusion, by systematically monitoring viruses in the air, the study provides a starting point for the standardization of transmission risk management in designated hospitals for the future pandemic of a newly emerged respiratory infectious disease.IMPORTANCERisk management and control of airborne transmission in hospitals is crucial in response to a respiratory virus pandemic. However, the formulation of these infection control measures is often based on epidemiological investigations, which are an indirect way of analyzing the transmission route of viruses. This can lead to careless omissions in infection prevention and control or excessively restrictive measures that increase the burden on healthcare workers. The study provides a starting point for standardizing transmission risk management in designated hospitals by systemically monitoring viruses in the air of typical spaces in COVID-19 hospitals. The negative results of 359 air samples in the clean and emergency zones demonstrated the existing measures to interrupt airborne transmission in a designated COVID-19 hospital. Some positive cases in the corridor of the contaminant zone during rounds and meal delivery highlighted the importance of monitoring airborne viruses for interrupting nosocomial infection.

Therapeutic Effect and Safety Evaluation of Naringin on Klebsiella pneumoniae in Mice

Critically ill patients with Corona Virus Disease 2019 (COVID-19) often develop secondary bacterial infections that pose a significant threat to patient life safety, making the development of drugs to prevent bacterial infections in the lungs critical to clinical care. Naringin (NAR) is one of the significant natural flavonoids rich in Pummelo Peel (Hua Ju Hong), with anti-inflammatory, antimicrobial, and antioxidant activities, and is commonly used in treating respiratory tract infectious diseases. In this study, the in vitro and in vivo findings revealed that, after Klebsiella pneumoniae (Kpn) infection, NAR inhibited overactivation of the nuclear factor kappa-B(NF-κB) signaling pathway in alveolar macrophages of mice, reduced neutrophil (NEs) recruitment, and lowered the induced production of proinflammatory markers, such as Interleukin-6(IL-6) and tumor necrosis factor α(TNF-α). Thus, it suppressed excessive immune responses in the lungs, as well as attenuated the induced pulmonary fibrosis and inflammatory infiltrates. These results suggest that NAR has a preventive effect against Kpn in mice. In addition, the study evaluated NAR’s potential toxicity, demonstrating that NAR is safe at effective doses. These results suggested that NAR effectively reduces excessive inflammatory damage in the lungs induced by Kpn and enhances the body’s ability to clear bacteria. Therefore, NAR may be an effective and safe healthcare drug for preventing and caring for bacterial pneumonia.