Characteristics of 12-Month Readmission for Hospitalized Patients with COPD: A Propensity Score Matched Analysis of Prospective Multicenter Study.

Purpose: Hospitalization for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is considered as severe exacerbations. Readmission for severe exacerbations is a crucial event for COPD patients. However, factors associated with readmission for severe exacerbations are incomplete. The study aimed to investigate different characteristics between the severe and non-severe exacerbation groups. Patients and Methods: Patients hospitalized for severe AECOPD were included in multi-centers, and their exacerbations in next 12 months after discharge were recorded. According to exacerbations, patients were separated into the severe-exacerbation group and the non-severe exacerbation group. Propensity-score matching (PSM) and multivariable analyses were performed to compare the baseline characteristics of two groups. The Hosmer-Lemeshow test and receiver operating characteristic curve were applied to evaluate how well the model could identify clusters. Results: The cohort included 550 patients with severe AECOPD across 27 study centers in China, and 465 patients were finally analyzed. A total of 41.5% of patients underwent readmission for AECOPD within 1 year. There were no significant differences in baseline characteristics between groups after PSM. Severe exacerbations in the 12 months were related to some factors, eg, the duration of COPD (13 vs 8 years, P<0.001), the COPD Assessment Test (CAT) score (20 vs 17, P<0.001), the blood eosinophil percentage (1.5 vs 2.0, P<0.05), and their inhaler therapies. Patients readmitted with AECOPD had a longer time of diagnosis (≥9 years), more symptoms (CAT ≥10), and lower blood eosinophils (Eos <2%). A clinical model was derived to help identify patients at risk of readmission with severe exacerbations. Conclusion: These analyses confirmed the relevance of COPD at admission with future severe exacerbations. A lower blood eosinophils percentage appears to be related to readmission when combined with clinical history. Further studies are needed to evaluate whether this study can predict the risk of exacerbations.

Serosurvey for SARS-CoV-2 among blood donors in Wuhan, China from September to December 2019.

The emerging of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused COVID-19 pandemic. The first case of COVID-19 was reported at early December in 2019 in Wuhan City, China. To examine specific antibodies against SARS-CoV-2 in biological samples before December 2019 would give clues when the epidemic of SARS-CoV-2 might start to circulate in populations. We obtained all 88,517 plasmas from 76,844 blood donors in Wuhan between 1 September and 31 December 2019. We first evaluated the pan-immunoglobin (pan-Ig) against SARS-CoV-2 in 43,850 samples from 32,484 blood donors with suitable sample quality and enough volume. Two hundred and sixty-four samples from 213 donors were pan-Ig reactive, then further tested IgG and IgM, and validated by neutralizing antibodies against SARS-CoV-2. Two hundred and thirteen samples (from 175 donors) were only pan-Ig reactive, 8 (from 4 donors) were pan-Ig and IgG reactive, and 43 (from 34 donors) were pan-Ig and IgM reactive. Microneutralization assay showed all negative results. In addition, 213 screened reactive donors were analyzed and did not show obviously temporal or regional tendency, but the distribution of age showed a difference compared with all tested donors. Then we reviewed SARS-CoV-2 antibody results from these donors who donated several times from September 2019 to June 2020, partly tested in a previous published study, no one was found a significant increase in S/CO of antibodies against SARS-CoV-2. Our findings showed no SARS-CoV-2-specific antibodies existing among blood donors in Wuhan, China before 2020, indicating no evidence of transmission of COVID-19 before December 2019 in Wuhan, China.

Modeling of emergency support capacity and optimization of delivery service system for urban living materials under uncertain situations: a case study of Xi'an City during COVID-19 epidemic.

The severe acute respiratory syndrome coronavirus 2 (COVID-19) pandemic has brought a heavy burden and severe challenges to the global economy and society, forcing different countries and regions to take various preventive and control measures ranging from normal operations to partial or complete lockdowns. Taking Xi'an city as an example, based on multisource POI data for the government's vegetable storage delivery points, logistics terminal outlets, designated medical institutions, communities, etc., this paper uses the Gaussian two-step floating catchment area method (2SFCA) and other spatial analysis methods to analyze the spatial pattern of emergency support points (ESPs) and express logistics terminals in different situations. It then discusses construction and optimization strategies for urban emergency support and delivery service systems. The conclusions are as follows. (1) The ESPs are supported by large-scale chain supermarkets and fresh supermarkets, which are positively related to the population distribution.The spatial distribution of express logistics terminals is imbalanced, dense in the middle while sparse at the edges. 90% of express terminals are located within a 500 m distance of communities, however, some terminals are shared, which restrict their ability to provide emergency support to surrounding residents. (2) In general, accessibility increases as the number of ESPs increases; under normal traffic, as the distance threshold increases, the available ESPs increase but accessibility slightly decreases; with a traffic lockdown, the travel distance of residents is limited, and as ESPs increase, accessibility and the number of POIs covered significantly increase. (3) The spatial accessibility of the ESPs has a "dumbbell-shaped" distribution, with highest accessibility in the north and south, higher around the second ring road, slightly lower in the center, and lowest near the third ring road at east and west. (4) With the goal of "opening up the logistics artery and unblocking the distribution microcirculation", based on "ESPs + couriers + express logistics terminals + residents", this paper proposes to build and optimize the urban emergency support and delivery service system to improve the comprehensive ability of the city to cope with uncertain risks.

Effects of Probiotics and Gut Microbiota on Bone Metabolism in Chickens: A Review.

Broiler leg diseases are a common abnormal bone metabolism issue that leads to poor leg health in growing poultry. Bone metabolism is a complicated regulatory process controlled by genetic, nutritional, feeding management, environmental, or other influencing factors. The gut microbiota constitutes the largest micro-ecosystem in animals and is closely related to many metabolic disorders, including bone disease, by affecting the absorption of nutrients and the barrier function of the gastrointestinal tract and regulating the immune system and even the brain-gut-bone axis. Recently, probiotic-based dietary supplementation has emerged as an emerging strategy to improve bone health in chickens by regulating bone metabolism based on the gut-bone axis. This review aims to summarize the regulatory mechanisms of probiotics in the gut microbiota on bone metabolism and to provide new insights for the prevention and treatment of bone diseases in broiler chickens.

Extracorporeal membrane oxygenation for paediatric refractory hypoxic respiratory failure caused by adenovirus in Shanghai: a case series.

BACKGROUND: To assess the outcome of extracorporeal membrane oxygenation (ECMO) for severe adenovirus (Adv) pneumonia with refractory hypoxic respiratory failure (RHRF) in paediatric patients. METHODS: A retrospective observational study was performed in a tertiary paediatric intensive care unit (PICU) in China. Patients with RHRF caused by Adv pneumonia who received ECMO support after mechanical ventilation failed to achieve adequate oxygenation between 2017 and 2020 were included. The outcome variables were the in-hospital survival rate and the effects of ECMO on the survival rate. RESULTS: In total, 18 children with RHRF received ECMO. The median age was 19 (9.5, 39.8) months, and the median ECMO duration was 196 (152, 309) h. The in-hospital survival rate was 72.2% (13/18). Thirteen patients (72.2%) required continuous renal replacement therapy (CRRT) due to fluid imbalance or acute kidney injury (AKI). At ECMO initiation, compared with survivors, nonsurvivors had a lower PaO2/FiO2 ratio [49 (34.5, 62) vs. 63 (56, 71); p = 0.04], higher oxygen index (OI) [41 (34.5, 62) vs. 30 (26.5, 35); p = 0.03], higher vasoactive inotropic score (VIS) [30 (16.3, 80) vs. 100 (60, 142.5); p = 0.04], longer duration from mechanical ventilation to ECMO support [8 (4, 14) vs. 4 (3, 5.5) h, p=0.02], and longer time from confirmed RHRF to ECMO initiation [9 (4.8, 13) vs. 5 (1.3, 5.5) h; p = 0.004]. Patients with PaO2/FiO2 <61 mmHg or an OI >43 and hypoxic respiratory failure for more than 9 days before the initiation of ECMO had worse outcomes. CONCLUSIONS: ECMO seemed to be effective, as severe paediatric Adv pneumonia patients with RHRF had a cumulative survival rate of 72.2% in our study. Our study provides insight into ECMO rescue in children with severe Adv pneumonia.

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile.

Background and Aims: The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. Methods: Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. Results: Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. Conclusions: XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

A Class of Shark-Derived Single-Domain Antibodies can Broadly Neutralize SARS-Related Coronaviruses and the Structural Basis of Neutralization and Omicron Escape.

The identification of a novel class of shark-derived single domain antibodies, named vnarbodies that show picomolar affinities binding to the receptor binding domain (RBD) of Wuhan and Alpha, Beta, Kappa, Delta, Delta-plus, and Lambda variants, is reported. Vnarbody 20G6 and 17F6 have broad neutralizing activities against all these SARS-CoV-2 viruses as well as other sarbecoviruses, including Pangolin coronavirus and Bat coronavirus. Intranasal administration of 20G6 effectively protects mice from the challenges of SARS-CoV-2 Wuhan and Beta variants. 20G6 and 17F6 contain a unique "WXGY" motif in the complementary determining region 3 that binds to a hidden epitope on RBD, which is highly conserved in sarbecoviruses through a novel ß-sheet interaction. It is found that the S375F mutation on Omicron RBD disrupts the structure of ß-strand, thus impair the binding with 20G6. The study demonstrates that shark-derived vnarbodies offer a prophylactic and therapeutic option against most SARS-CoV-2 variants and provide insights into antibody evasion by the Omicron variant.

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

Background and Aims The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. Methods Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. Results Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. Conclusions XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

Spatio-temporal evolution and influencing factors of COVID-19 epidemic in Zhejiang Province

The global outbreak of novel Coronavirus Disease (COVID-19) epidemic has seriously endangered people's health and hindered rapid economic development. Geographic analysis of spatial and temporal transmission patterns in key regions can help prevent and control the epidemic. This paper takes Zhejiang province as the research area. With the help of POI data, the methods such as textual analysis, mathematical statistics, and spatial regression analysis are used to analyze the socio-demographic characteristics of confirmed cases and the spatio-temporal evolution of the epidemic, and then analyze its influencing factors. The results show that: (1) The age distribution of confirmed cases spanned a wide range, showing normal distribution of "large in the middle and small at both ends." (2) The epidemic period is divided into five stages: the initial period, the outbreak period, the steady decline period, the internal stable period, and the oversea input period. The interval between the onset time and announcing a confirmed case was mostly 0-6 d, and the time interval of non-local cases is longer than that of local cases, and the onset of most of the non-local cases occur on the day the patients leave their original place. There was no significant gender difference in the proportion of daily incidence, and the proportion of age had stage features. (3) The spatial distribution aligned in the direction of "Southeast-Northwest", the evolution trend developed from "single place distribution" to "multi-area cluster cases" and then to "key input" evolution, with "high-high" "high-low" clustering characteristics;The migration path of confirmed cases presented an obvious core-edge structure, and the first significant flow was from the center of Wuhan. (4) By analyzing the factors affecting the distribution of the epidemic,it is found that the ratio of the elderly population, per capita GDP, the proportion of the tertiary industry, the number of industries above the scale, and the distance from Wuhan were the dominant factors. Finally, several suggestions on targeted prevention and control measures are made, and the weaknesses of the study and future directions of efforts are pointed out.

Spatiotemporal evolution, pattern of diffusion, and influencing factors of the COVID-19 epidemic in Hainan Province, China.

Within 1 month after the first case occurred in Hainan Province, China, the number of confirmed cases rose to 168, and there was no increase in almost 3 months. As the southernmost province and a famous tourist destination in China, its regular economic exchanges and high-intensity population movements may affect the spread of the epidemic. It is of great theoretical and practical significance to investigate the spatiotemporal evolution, the pattern of diffusion, and factors influencing the coronavirus disease 2019 (COVID-19) epidemic in Hainan Province. Basic and geographic information of confirmed COVID-19 cases was obtained from government websites and other official media. We examined the groups of infection and calculated the diffusion ratio to demonstrate the trend of the epidemic. Map drawing, spatial analysis, and partial least squares regression were used to express the spatiotemporal evolution, the pattern of diffusion, and factors affecting the epidemic. Furthermore, we have made recommendations on the formulation and adaptation of possible future preventive steps. Results show that the COVID-19 epidemic in Hainan Province has substantial spatial heterogeneity but minimal distribution. The tourist city and central city have formed a dual-core pattern for the spread of the epidemic, which could extend to other similar regions. Population density, mobility, and level of urban development have been the major factors of epidemic distribution in the study area.

COVID-19 and Tuberculosis Coinfection: An Overview of Case Reports/Case Series and Meta-Analysis.

Background: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are two major infectious diseases posing significant public health threats, and their coinfection (aptly abbreviated COVID-TB) makes the situation worse. This study aimed to investigate the clinical features and prognosis of COVID-TB cases. Methods: The PubMed, Embase, Cochrane, CNKI, and Wanfang databases were searched for relevant studies published through December 18, 2020. An overview of COVID-TB case reports/case series was prepared that described their clinical characteristics and differences between survivors and deceased patients. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) for death or severe COVID-19 were calculated. The quality of outcomes was assessed using GRADEpro. Results: Thirty-six studies were included. Of 89 COVID-TB patients, 19 (23.46%) died, and 72 (80.90%) were male. The median age of non-survivors (53.95 ± 19.78 years) was greater than that of survivors (37.76 ± 15.54 years) (p < 0.001). Non-survivors were more likely to have hypertension (47.06 vs. 17.95%) or symptoms of dyspnea (72.73% vs. 30%) or bilateral lesions (73.68 vs. 47.14%), infiltrates (57.89 vs. 24.29%), tree in bud (10.53% vs. 0%), or a higher leucocyte count (12.9 [10.5-16.73] vs. 8.015 [4.8-8.97] × 109/L) than survivors (p < 0.05). In terms of treatment, 88.52% received anti-TB therapy, 50.82% received antibiotics, 22.95% received antiviral therapy, 26.23% received hydroxychloroquine, and 11.48% received corticosteroids. The pooled ORs of death or severe disease in the COVID-TB group and the non-TB group were 2.21 (95% CI: 1.80, 2.70) and 2.77 (95% CI: 1.33, 5.74) (P < 0.01), respectively. Conclusion: In summary, there appear to be some predictors of worse prognosis among COVID-TB cases. A moderate level of evidence suggests that COVID-TB patients are more likely to suffer severe disease or death than COVID-19 patients. Finally, routine screening for TB may be recommended among suspected or confirmed cases of COVID-19 in countries with high TB burden.

Quantitative CT for detecting COVID­19 pneumonia in suspected cases.

BACKGROUND: Corona Virus Disease 2019 (COVID-19) is currently a worldwide pandemic and has a huge impact on public health and socio-economic development. The purpose of this study is to explore the diagnostic value of the quantitative computed tomography (CT) method by using different threshold segmentation techniques to distinguish between patients with or without COVID-19 pneumonia. METHODS: A total of 47 patients with suspected COVID-19 were retrospectively analyzed, including nine patients with positive real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR) test (confirmed case group) and 38 patients with negative RT-PCR test (excluded case group). An improved 3D convolutional neural network (VB-Net) was used to automatically extract lung lesions. Eight different threshold segmentation methods were used to define the ground glass opacity (GGO) and consolidation. The receiver operating characteristic (ROC) curves were used to compare the performance of various parameters with different thresholds for diagnosing COVID-19 pneumonia. RESULTS: The volume of GGO (VOGGO) and GGO percentage in the whole lung (GGOPITWL) were the most effective values for diagnosing COVID-19 at a threshold of - 300 HU, with areas under the curve (AUCs) of 0.769 and 0.769, sensitivity of 66.67 and 66.67%, specificity of 94.74 and 86.84%. Compared with VOGGO or GGOPITWL at a threshold of - 300 Hounsfield units (HU), the consolidation percentage in the whole lung (CPITWL) with thresholds at - 400 HU, - 350 HU, and - 250 HU were statistically different. There were statistical differences in the infection volume and percentage of the whole lung, right lung, and lobes between the two groups. VOGGO, GGOPITWL, and volume of consolidation (VOC) were also statistically different at the threshold of - 300 HU. CONCLUSIONS: Quantitative CT provides an image quantification method for the auxiliary diagnosis of COVID-19 and is expected to assist in confirming patients with COVID-19 pneumonia in suspected cases.

Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China.

The novel coronavirus pneumonia (COVID-19) outbreak that emerged in late 2019 has posed a severe threat to human health and social and economic development, and thus has become a major public health crisis affecting the world. The spread of COVID-19 in population and regions is a typical geographical process, which is worth discussing from the geographical perspective. This paper focuses on Shandong province, which has a high incidence, though the first Chinese confirmed case was reported from Hubei province. Based on the data of reported confirmed cases and the detailed information of cases collected manually, we used text analysis, mathematical statistics and spatial analysis to reveal the demographic characteristics of confirmed cases and the spatio-temporal evolution process of the epidemic, and to explore the comprehensive mechanism of epidemic evolution and prevention and control. The results show that: (1) the incidence rate of COVID-19 in Shandong is 0.76/100,000. The majority of confirmed cases are old and middle-aged people who are infected by the intra-province diffusion, followed by young and middle-aged people who are infected outside the province. (2) Up to February 5, the number of daily confirmed cases shows a trend of "rapid increase before slowing down", among which, the changes of age and gender are closely related to population migration, epidemic characteristics and intervention measures. (3) Affected by the regional economy and population, the spatial distribution of the confirmed cases is obviously unbalanced, with the cluster pattern of "high-low" and "low-high". (4) The evolution of the migration pattern, affected by the geographical location of Wuhan and Chinese traditional culture, is dominated by "cross-provincial" and "intra-provincial" direct flow, and generally shows the trend of "southwest → northeast". Finally, combined with the targeted countermeasures of "source-flow-sink", the comprehensive mechanism of COVID-19 epidemic evolution and prevention and control in Shandong is revealed. External and internal prevention and control measures are also figured out.

The prevalence of antibodies to SARS-CoV-2 among blood donors in China.

In this study, we investigate the seroprevalence of SARS-CoV-2 antibodies among blood donors in the cities of Wuhan, Shenzhen, and Shijiazhuang in China. From January to April 2020, 38,144 healthy blood donors in the three cities were tested for total antibody against SARS-CoV-2 followed by pseudotype SARS-CoV-2 neutralization tests, IgG, and IgM antibody testing. Finally, a total of 398 donors were confirmed positive. The age- and sex-standardized SARS-CoV-2 seroprevalence among 18-60 year-old adults (18-65 year-old in Shenzhen) was 2.66% (95% CI: 2.24%-3.07%) in Wuhan, 0.033% (95% CI: 0.0029%-0.267%) in Shenzhen, and 0.0028% (95% CI: 0.0001%-0.158%) in Shijiazhuang, respectively. Female sex and older-age were identified to be independent risk factors for SARS-CoV-2 seropositivity among blood donors in Wuhan. As most of the population of China remained uninfected during the early wave of the COVID-19 pandemic, effective public health measures are still certainly required to block viral spread before a vaccine is widely available.

COVID-19 Epidemic and Public Health Measures in China.

OBJECTIVES: The study aims to analyze the status quo of public health emergency measures taken in China in dealing with the spread of new coronavirus pneumonia (COVID-19), and to put forward policy suggestions for system construction and improvement. METHODS: According to the official data released by the National Health Commission, the epidemic data of infected people from 0:00 on January 24, 2020 to 24:00 on February 23, 2020 were quantitatively analyzed through statistical analysis. We used EXCEL software to draw the overall epidemic trend chart and Statistical Product and Service Solutions (SPSS) to carry out descriptive statistical analysis of mortality and cure rate. We made qualitative analysis on the emergency measures implemented by national administrative departments and provincial governments to work on controlling and monitoring COVID-19 nationwide spread. RESULTS: The number of patients diagnosed showed a linear increasing trend, with the slope increasing first and decreasing later. Suspected and new cases showed an inverted V pattern, with the peak occurring on February 8 and 12, respectively. There was a linear increase in the number of deaths and an exponential increase in the number of cures. Over the 31-day study period, the mortality rate fluctuated between 2.0% and 3.4%. The mean cure rate was 10.03%, the minimum value was 1.33%, and the maximum value was 32.05%. The quantitative and qualitative analysis shows that the public health emergency response system constructed in China plays a significant role in controlling the epidemic in a certain period of time. DISCUSSION: The four-tier emergency management system and the joint prevention mechanism established in China have provided various resources to control the epidemic, but there are still weakness in dealing with the spread of COVID-19. It is suggested to improve and strengthen the emergency management system, public health service system, health legal system, citizen health education, and international exchange and cooperation.

An open-label, randomized trial of the combination of IFN-κ plus TFF2 with standard care in the treatment of patients with moderate COVID-19.

BACKGROUND: Epidemic outbreaks caused by SARS-CoV-2 are worsening around the world, and there are no target drugs to treat COVID-19. IFN-κ inhibits the replication of SARS-CoV-2; and TFF2 is a small secreted polypeptide that promotes the repair of mucosal injury and reduces the inflammatory responses. We used the synergistic effect of both proteins to treat COVID-19. METHODS: We conducted an open-label, randomized, clinical trial involving patients with moderate COVID-19. Patients were assigned in a 1:1 ratio to receive either aerosol inhalation treatment with IFN-κ and TFF2 every 24 h for six consecutive dosages in addition to standard care (experimental group) or standard care alone (control group). The primary endpoint was the time until a viral RNA negative conversion for SARS-CoV-2 in all clinical samples. The secondary clinical endpoint was the time of CT imaging improvement. Data analysis was performed per protocol. This study was registered with chictr.org.cn, ChiCTR2000030262. FINDINGS: Between March 23 and May 23 of 2020, 86 COVID-19 patients with symptoms of moderate illness were recruited, and 6 patients were excluded due to not matching the inclusion criteria (patients with pneumonia through chest radiography). Among the remaining 80 patients, 40 patients were assigned to experimental group, and the others were assigned to control group to only receive standard care. Efficacy and safety were evaluated for both groups. The time of viral RNA negative conversion in experimental group (Mean, 3·80 days, 95% CI 2·07-5·53), was significantly shorter than that in control group (7·40 days, 95% CI 4·57 to 10·23) (p = 0.031), and difference between means was 3·60 days. The percentage of patients in experimental group with reversion to negative viral RNA was significantly increased compared with control group on all sampling days (every day during the 12-day observation period) (p = 0·037). For the secondary endpoint, the experimental group had a significantly shorter time until improvement was seen by CT (Mean 6·21 days, N = 38/40, 95% CI 5·11-7·31) than that in control group (8·76 days, N = 34/40, 95% CI 7·57-9·96) (p = 0.002), and difference between means was 2·55 days. No discomfort or complications during aerosol inhalation were reported to the nurses by any experimental patients. INTERPRETATION: In conclusion, we found that aerosol inhalation of IFN-κ plus TFF2 in combination with standard care is safe and superior to standard care alone in shortening the time up to viral RNA negative conversion in all clinical samples. In addition, the patients in experimental group had a significantly shortened CT imaging improvement time than those in control group. This study suggested that this combination treatment is able to facilitate clinical improvement (negative for virus, improvement by CT, reduced hospitalization stay) and thereby result in an early release from the hospital. These data support the need for exploration with a large-scale trial of IFN-κ plus TFF2 to treat COVID-19. FUNDING: Funding was provided by the National Natural Science Foundation of China, National Major Project for Control and Prevention of Infectious Disease in China, Shanghai Science and Technology Commission, Shanghai Municipal Health Commission.

Early prediction of level-of-care requirements in patients with COVID-19.

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.

The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients' vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available.

Public health initiatives from hospitalized patients with COVID-19, China.

BACKGROUND: Since December 2019, when it first occurred in Wuhan, China, coronavirus disease 2019 (COVID-19) has spread rapidly worldwide via human-to-human transmission. We aimed to describe the epidemiological and demographic features of COVID-19 outside Wuhan. METHODS: A single-center case series of 136 consecutive (from January 16 to February 17, 2020) patients with confirmed COVID-19 hospitalized in The First People's Hospital of Jingzhou, China, was retrospectively analyzed. Outcomes were followed up until February 19, 2020. RESULTS: Of the 136 patients (median age, 49 years; interquartile range [IQR], 33-63 years; range, 0.3-83 years), 91 (67%) had been to Wuhan or contacted persons from Wuhan. Forty-five (33.1%) were familial clusters. The median incubation period was 6 days (IQR: 4-11 days). All children had an exact exposure history, family members with COVID-19, and "Mild/Moderate" symptoms at admission. Among the 64 elderly patients, 14 (21.9%) had no exposure history, and 43 (67.2%) had a chronic illness. All 11 (8.1%) "Severe/very severe" illness at onset cases and 5 (3.7%) fatal cases were elderly patients. The duration from symptom onset to admission was positively correlated with the duration from symptom onset to endpoint. Overall, patients with a longer incubation period had more severe outcomes. CONCLUSION: As high-risk susceptible groups, strong protection should be implemented for children and the elderly. Universal screening should be performed for people with a clear exposure history, even lacking apparent symptoms. Given the rapid progression of COVID-19, people should be admitted quickly following symptom onset.