Clinical analysis of 132 cases COVID-19 from Wuhan.

Numerous cases of pneumonia from a novel coronavirus (SARS-CoV-2) emerged in Wuhan, China during December 2019.We determined the correlations of patient parameters with disease severity in patients with COVID-19.A total of 132 patients from Wuhan Fourth Hospital who had COVID-19 from February 1 to February 29 in 2020 were retrospectively analyzed.Ninety patients had mild disease, 32 had severe disease, and 10 had critical disease. The severe/critical group was older (P < .05), had a higher proportion of males (P < .05), and had a greater mortality rate (0% vs 61.9%, P < .05). The main symptoms were fever (n = 112, 84.8%) and cough (n = 96, 72.7%). Patients were treated with antiviral agents (n = 94, 71.2%), antibiotics (n = 92, 69.7%), glucocorticoids (n = 46, 34.8%), intravenous immunoglobulin (n = 38, 27.3%), and/or traditional Chinese medicine (n = 40, 30.3%). Patients in the severe/critical group received mechanical ventilation (n = 22, 16.7%) or high-flow nasal can-nula oxygen therapy (n = 6, 4.5%). Chest computed tomography (CT) indicated bilateral pneumonia in all patients. Relative to the mild group, the severe/critical group had higher levels of leukocytes, C-reactive protein (CRP), procalcitonin (PCT), D-dimer, B-type natriuretic peptide (BNP), liver enzymes, and myocardial enzymes (P < .05), and decreased levels of lymphocytes and blood oxygen partial pressure (P < .05).The main clinical symptoms of patients from Wuhan who had COVID-19 were fever and cough. Patients with severe/critical disease were more likely to be male and elderly. Disease severity correlated with increased leukocytes, CRP, PCT, BNP, D-dimer, liver enzymes, and myocardial enzymes, and with decreased lymphocytes and blood oxygen partial pressure.

Early autoimmunity and outcome in virus encephalitis: a retrospective study based on tissue-based assay.

To explore the autoimmune response and outcome in the central nervous system (CNS) at the onset of viral infection and correlation between autoantibodies and viruses. METHODS: A retrospective observational study was conducted in 121 patients (2016-2021) with a CNS viral infection confirmed via cerebrospinal fluid (CSF) next-generation sequencing (cohort A). Their clinical information was analysed and CSF samples were screened for autoantibodies against monkey cerebellum by tissue-based assay. In situ hybridisation was used to detect Epstein-Barr virus (EBV) in brain tissue of 8 patients with glial fibrillar acidic protein (GFAP)-IgG and nasopharyngeal carcinoma tissue of 2 patients with GFAP-IgG as control (cohort B). RESULTS: Among cohort A (male:female=79:42; median age: 42 (14-78) years old), 61 (50.4%) participants had detectable autoantibodies in CSF. Compared with other viruses, EBV increased the odds of having GFAP-IgG (OR 18.22, 95% CI 6.54 to 50.77, p<0.001). In cohort B, EBV was found in the brain tissue from two of eight (25.0%) patients with GFAP-IgG. Autoantibody-positive patients had a higher CSF protein level (median: 1126.00 (281.00-5352.00) vs 700.00 (76.70-2899.00), p<0.001), lower CSF chloride level (mean: 119.80±6.24 vs 122.84±5.26, p=0.005), lower ratios of CSF-glucose/serum-glucose (median: 0.50[0.13-0.94] vs 0.60[0.26-1.23], p=0.003), more meningitis (26/61 (42.6%) vs 12/60 (20.0%), p=0.007) and higher follow-up modified Rankin Scale scores (1 (0-6) vs 0 (0-3), p=0.037) compared with antibody-negative patients. A Kaplan-Meier analysis revealed that autoantibody-positive patients experienced significantly worse outcomes (p=0.031). CONCLUSIONS: Autoimmune responses are found at the onset of viral encephalitis. EBV in the CNS increases the risk for autoimmunity to GFAP.

A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection.

While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection. Optimizing vaccine dosing schedules to ensure prolonged continuity in protection could thus help control the pandemic. We developed a mechanistic model of immune response to vaccines as an in silico tool for dosing schedule optimization. The model was calibrated with clinical data sets of acquired immunity to COVID-19 mRNA vaccines in healthy and immunocompromised participants and showed robust validation by accurately predicting neutralizing antibody kinetics in response to multiple doses of COVID-19 mRNA vaccines. Importantly, by estimating population vulnerability to breakthrough infections, we predicted tailored vaccination dosing schedules to minimize breakthrough infections, especially for immunocompromised individuals. We identified that the optimal vaccination schedules vary from CDC-recommended dosing, suggesting that the model is a valuable tool to optimize vaccine efficacy outcomes during future outbreaks.

CRISPR Assays for Disease Diagnosis: Progress to and Barriers Remaining for Clinical Applications.

Numerous groups have employed the special properties of CRISPR/Cas systems to develop platforms that have broad potential applications for sensitive and specific detection of nucleic acid (NA) targets. However, few of these approaches have progressed to commercial or clinical applications. This review summarizes the properties of known CRISPR/Cas systems and their applications, challenges associated with the development of such assays, and opportunities to improve their performance or address unmet assay needs using nano-/micro-technology platforms. These include rapid and efficient sample preparation, integrated single-tube, amplification-free, quantifiable, multiplex, and non-NA assays. Finally, this review discusses the current outlook for such assays, including remaining barriers for clinical or point-of-care applications and their commercial development.

[Main Infection Control Measures for Respiratory Infectious Diseases in Medical Institutions and Public Places in China].

Respiratory infectious diseases (RID) are the major public health problems threatening the people's lives and health.Infection control (IC) is one of the effective tools to contain the occurrence and spread of RID.We collected the articles and data on IC published since January 1,2018 and summarized the achievements,problems,and challenges of IC from administrative control,management control,environment and engineering control,and personal protection in the medical institutions and public places in China.The efforts for IC vary in different regions and medical institutions of different levels.There are still links to be improved for IC from administrative control,management control,environment and engineering control,and personal protection,especially in community-level medical institutions and public areas.It is urgent to strengthen the implementation of IC policies and conduct IC precisely according to local situations.We proposed the following suggestions.First,the existing IC products and tools should be applied to precisely implement the IC measures;second,modern high technology should be employed to develop efficient and convenient IC products and tools;finally,a digital or intelligent IC platform should be built for monitoring infections,so as to contain the occurrence and spread of RID.

Identification of potential targets against SARS-CoV-2 of antiviral drugs based on photoaffinity probes.

Facing the sudden outbreak of coronavirus disease 2019 (COVID-19), it is extremely urgent to develop effective antiviral drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Drug repurposing is a promising strategy for the treatment of COVID-19. To identify the precise target protein of marketed medicines, we initiate a chemical biological program to identify precise target of potential antivirus drugs. In this study, two types of recombinant human coronavirus SARS-CoV-2 RdRp protein capturing probes with various photoaffinity labeling units were designed and synthesized based on the structure of FDA-approved drugs stavudine, remdesivir, acyclovir, and aladenosine. Fortunately, it was found that one novel photoaffinity probe, RD-1, could diaplayed good affinity with SARS-CoV-2 RdRp around the residue ARG_553. In addition, RD-1 probe also exhibited potent inhibitory activity against 3CLpro protease. Taken together, our findings will elucidate the structural basis for the efficacy of marketed drugs, and explore a rapid and efficient strategy of drug repurposing based on the identification of new targets. Moreover, these results could also provide a scientific basis for the clinical application of marketed drugs.

Greater traditionalism predicts COVID-19 precautionary behaviors across 27 societies.

People vary both in their embrace of their society's traditions, and in their perception of hazards as salient and necessitating a response. Over evolutionary time, traditions have offered avenues for addressing hazards, plausibly resulting in linkages between orientations toward tradition and orientations toward danger. Emerging research documents connections between traditionalism and threat responsivity, including pathogen-avoidance motivations. Additionally, because hazard-mitigating behaviors can conflict with competing priorities, associations between traditionalism and pathogen avoidance may hinge on contextually contingent tradeoffs. The COVID-19 pandemic provides a real-world test of the posited relationship between traditionalism and hazard avoidance. Across 27 societies (N = 7844), we find that, in a majority of countries, individuals' endorsement of tradition positively correlates with their adherence to costly COVID-19-avoidance behaviors; accounting for some of the conflicts that arise between public health precautions and other objectives further strengthens this evidence that traditionalism is associated with greater attention to hazards.

The determinants of student attitude toward e-learning academic achievement during the COVID-19 pandemic

Many educational institutions have adopted e-learning methods during the COVID-19 pandemic to maintain school teaching activities. However, systematic research on e-learning effectiveness in such a crisis is quite insufficient. This study aims to explore the impact of e-learning quality on students' satisfaction during the pandemic in regard to academic achievement and behavioral intention. Through a questionnaire and semester score database, the relevant data of 683 students were collected and then analyzed by PLS-SEM. The result shows that instructor teaching quality and e-learning system quality influence students' academic achievement and behavioral intention through increasing students' e-learning satisfaction. E-learning system quality moderated the impact of instructor teaching quality on students' e-learning satisfaction. The findings of this study provide valuable insights into the comprehensive implementation of e-learning during a crisis. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

The impact of epidemic information on the public's worries and attitude toward epidemic prevention measures during the COVID-19 outbreak.

Since the outbreak of 2019 coronavirus disease (COVID-19) in December 2019, the Chinese government has implemented effective epidemic prevention measures. To provide useful information for governments to manage this public health crisis, we conducted an online survey among Chinese general population from February 24 to 28, 2020. In this study, we examined the impact of epidemic information and rumors on public's worries and attitude toward prevention measures during the outbreak of COVID-19. A total of 853 valid questionnaires (641 women, 75.1%) were collected from 24 provincial regions in China. Most respondents' ages ranged from 18 to 60 (833 participants, 97.66%). A mediation model was built to analyze the influence of epidemic information and rumors on worries and attitude. The results showed that the amount of epidemic information positively predicted public's worries, which in turn predicted a supportive attitude toward the prevention measures. Worries partially mediated the relationship between the amount of epidemic information and the supportive attitude. The amount of rumors negatively predicted the supportive attitude. The results of this study implied the importance of timely and credible information providing to evoke a certain level of worry and promote public cooperation, and the necessary attention to refute and resist rumors for effective risk communication in a public health crisis.

The coverage of SARS-CoV-2 vaccination and the willingness to receive the SARS-CoV-2 variant vaccine among employees in China.

BACKGROUND: COVID-19, which is caused by SARS-CoV-2, is a major global health threat. The dominant variant of SARS-CoV-2 has changed over time due to continuous evolution. We aimed to evaluate the coverage of SARS-CoV-2 vaccination among employees in China, explore their willingness to receive the SARS-CoV-2 variant vaccine and examine the potential factors influencing vaccination coverage and willingness. METHODS: A cross-sectional epidemiological survey was conducted online from January 1, 2022, to January 30, 2022. The information collected in the survey included sociodemographic characteristics, lifestyle habits, vaccination coverage, willingness to be vaccinated against SARS-CoV-2 variants and the reasons for vaccination and willingness. Multivariable logistic regression models were used to assess the associations of potential factors with the rate of vaccination and the willingness to be vaccinated. RESULTS: Among 62,395 eligible participants, the coverage of SARS-CoV-2 vaccination was 98.9% for at least one dose and 70.1% for a booster. The great majority of vaccinated individuals (94.4%) voluntarily received the vaccine. A total of 60,694 respondents (97.7%) were willing to be vaccinated against SARS-CoV-2 variants, mainly due to confidence in the effectiveness of vaccines (92.8%). A total of 1431 respondents were unwilling to be vaccinated, mainly because of concerns about the adverse effects of vaccines (77.6%). Longer education duration was associated with a higher rate of SARS-CoV-2 vaccination and willingness to be vaccinated. General or poor health status and having no history of influenza vaccination were associated with a lower rate of SARS-CoV-2 vaccination and willingness to be vaccinated. Additionally, we observed a significant positive association of abuse experience with the willingness to be vaccinated. CONCLUSION: Although the rate of SARS-CoV-2 vaccination and the willingness to be vaccinated were relatively high in the study population, there were still some respondents with vaccine hesitancy. Relevant strategies based on significant related factors should be developed and implemented to encourage vaccination.

Quantifying the role of airborne transmission in the spread of COVID-19.

There is an ongoing debate on the different transmission modes of SARS-CoV-2 and their relative contributions to the pandemic. In this paper, we employ a simple mathematical model, which incorporates both the human-to-human and environment-to-human transmission routes, to study the transmission dynamics of COVID-19. We focus our attention on the role of airborne transmission in the spread of the disease in a university campus setting. We conduct both mathematical analysis and numerical simulation, and incorporate published experimental data for the viral concentration in the air to fit model parameters. Meanwhile, we compare the outcome to that of the standard SIR model, utilizing a perturbation analysis in the presence of multiple time scales. Our data fitting and numerical simulation results show that the risk of airborne transmission for SARS-CoV-2 strongly depends on how long the virus can remain viable in the air. If the time for this viability is short, the airborne transmission route would be inconsequential in shaping the overall transmission risk and the total infection size. On the other hand, if the infectious virus can persist in aerosols beyond a few hours, then airborne transmission could play a much more significant role in the spread of COVID-19.

A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US.

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.

Mental health and academic achievement among Chinese adolescents during COVID-19 pandemic: The mediating role of self-regulation learning.

The changes that COVID-19 pandemic has brought upon the world are unprecedented. Its impact on students' learning is equally profound, making it critical to heed students' academic achievement effects that may derive from these alterations. Therefore, the present study explored an integrative model of mental health, self-regulated learning and academic achievement among adolescents during the pandemic. Participants were 1001 senior high school students (Mage = 17.00, SDage = 0.78, 48.7% female) from China. Results showed that the degree to which students were mentally healthy was not significantly related to academic achievement, whereas academic achievement and mental health were positively associated with self-regulated learning. Following structural equation modelling analysis, the effect of mental health on academic achievement was fully mediated by self-regulated learning. Taken together, the findings emphasised the necessity of developing self-regulated learning strategies during public health emergencies and have clinical and educational implications for planning psychological interventions in order to improve mental health and academic performance as well.

Effect of attribution on the emotions and behavioral intentions of third-party observers toward intergroup discriminators during the COVID-19 pandemic.

The global outbreak of novel coronavirus disease COVID-19 has caused intergroup discrimination associated with the disease to become increasingly prominent. Research demonstrates that the attitudes and behaviors of third-party observers significantly impact the progression of discrimination incidents. This study tested a parallel mediating model in which the attribution tendencies of observers influence their behavioral intentions through the mediating effect of the emotions of anger and contempt. The first two studies confirmed the proposed model with discrimination incidents reported against "returnees from Wuhan" and "returning workers from Hubei." Study 3 further manipulated the attribution tendencies of observers, providing empirical evidence for the causality from attribution tendencies to emotions, confirming the validity of the model. These findings enrich the cognitive (attribution)-emotion-action model, further enhancing our understanding of the role of third parties in intergroup conflicts, with implications for the management of people's emotions and behaviors in social crises.

Changes in speeding on Virginia roads during the beginning of the COVID-19 pandemic.

OBJECTIVE: We investigated changes in the prevalence of speeding during March-June 2020, the beginning of the COVID-19 pandemic, in Virginia. METHODS: Vehicle speed data from 506 permanent speed counter stations around the state collected during March-June 2019 and March-June 2020 were analyzed. RESULTS: Increases in the proportion of vehicles traveling at least 5 mph and 10 mph above the speed limit were greatest on urban interstates and other freeways, during early morning (6:00-8:59 a.m.) and afternoon commuting hours (3:00-5:59 p.m.) on weekdays, and during afternoons (12:00-5:59 p.m.) on weekends. Logistic regression revealed that the risk of speeding by at least 5 mph increased in 2020 by 22% and by at least 10 mph increased 51% after accounting for road type, time of day, day of week, and traffic volume, relative to 2019. DISCUSSION: Future research should continue to identify where and when speeding problems are most severe, and countermeasures should be directed to the roads and time periods with the largest speeding problems.

A Multiscale Model of COVID-19 Dynamics.

COVID-19, caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a global pandemic and created unprecedented public health challenges throughout the world. Despite significant progresses in understanding the disease pathogenesis and progression, the epidemiological triad of pathogen, host, and environment remains unclear. In this paper, we develop a multiscale model to study the coupled within-host and between-host dynamics of COVID-19. The model includes multiple transmission routes (both human-to-human and environment-to-human) and connects multiple scales (both the population and individual levels). A detailed analysis on the local and global dynamics of the fast system, slow system and full system shows that rich dynamics, including both forward and backward bifurcations, emerge with the coupling of viral infection and epidemiological models. Model fitting to both virological and epidemiological data facilitates the evaluation of the influence of a few infection characteristics and antiviral treatment on the spread of the disease. Our work underlines the potential role that the environment can play in the transmission of COVID-19. Antiviral treatment of infected individuals can delay but cannot prevent the emergence of disease outbreaks. These results highlight the implementation of comprehensive intervention measures such as social distancing and wearing masks that aim to stop airborne transmission, combined with surface disinfection and hand hygiene that can prevent environmental transmission. The model also provides a multiscale modeling framework to study other infectious diseases when the environment can serve as a reservoir of pathogens.

Sleep quality and influencing factors and correlation with T-lymphocyte subpopulation counts in patients with pulmonary tuberculosis: a cross-sectional study.

BACKGROUND: Patients diagnosed with pulmonary tuberculosis (TB) have poor sleep quality due to multiple factors. We aimed to assess the sleep status and related factors of TB patients in Shenzhen, China. METHODS: A questionnaire survey was conducted on 461 TB patients hospitalized at Shenzhen Third People's Hospital from March 2021 to January 2022, and sleep quality was assessed using the Pittsburgh sleep quality index (PSQI). RESULTS: A total of 459 valid questionnaires were collected, and 238 of the 459 TB patients had general or poor sleep quality (PSQI > 5). Patients' gender, marriage, nutritional screening score, family atmosphere, fear of discrimination, fear of interactions, and the impact of the disease on their work life had significant effects on sleep quality (P < 0.05); PSQI scores of TB patients were negatively correlated with lymphocyte counts (r = - 0.296, P < 0.01), T-lymphocyte counts (r = - 0.293, P < 0.01), helper T lymphocyte counts (r = - 0.283, P < 0.01), killer T lymphocyte counts (r = - 0.182, P < 0.05), and were positively correlated with depression scores (r = 0.424, P < 0.01). Multivariable logistic regression analysis showed that male (OR = 1.64,95% CI 1.11-2.42, P < 0.05), unmarried (OR = 1.57, 95% CI 1.02-2.42, P < 0.05), NRS score grade 3(OR = 5.35, 95% CI 2.08-15.73, P < 0.01), general family atmosphere (OR = 2.23, 95% CI 1.07-4.93, P < 0.05), and the disease affecting work (OR = 1.66, 95% CI 1.11-2.50, P < 0.05) were factors influencing poor sleep quality. CONCLUSION: Most TB patients had varying degrees of sleep disturbance, which may be affected by their gender, marriage, family atmosphere, nutritional status, the effect of the disease on work life, and, depression, as well as lower absolute T-lymphocyte subpopulation counts. Appropriate interventions should be implemented to improve their sleep quality, when treating or caring for such patients.

Application of ultrasonography in neonatal lung disease: An updated review.

Lung disease is often life-threatening for both preterm and term newborns. Therefore, an accurate and rapid diagnosis of lung diseases in newborns is crucial, as management strategies differ with different etiologies. To reduce the risk of radiation exposure derived from the conventionally used chest x-ray as well as computed tomography scans, lung ultrasonography (LUS) has been introduced in clinical practice to identify and differentiate neonatal lung diseases because of its radiation-free characteristic, convenience, high accuracy, and low cost. In recent years, it has been proved that LUS exhibits high sensitivity and specificity for identifying various neonatal lung diseases. Here, we offer an updated review of the applications of LUS in neonatal lung diseases based on the reports published in recent years (2017 to present).

Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case

It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process.