Impact of glycemia and insulin treatment in fatal outcome of severe fever with thrombocytopenia syndrome.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne disease with a high fatality rate. How the glucose level might affect the clinical outcome remains obscure. METHODS: A multicenter study was performed in 2 hospitals from 2011 to 2021. Patients with SFTS and acute hyperglycemia (admission fasting plasma glucose [FPG] ≥7 mmol/L), postadmission hyperglycemia (admission FPG <7 mmol/L but FPG ≥7 mmol/L after admission), and euglycemia (FPG <7 mmol/L throughout hospitalization) were compared for their clinical progress and outcomes. RESULTS: A total of 3225 patients were included in this study, 37.9% of whom developed acute hyperglycemia and 7.6% postadmission hyperglycemia. The presence of acute hyperglycemia, with or without known diabetes, was associated with increased risk of death (odds ratio [OR]: 1.63; 95% confidence interval [CI]: 1.29-2.05) compared with euglycemia. This effect, however, was only determined in female patients (OR: 2.15; 95% CI: 1.54-2.93). Insulin treatment of patients with SFTS and acute hyperglycemia without previous diabetes was associated with significantly increased mortality (OR: 1.58; 95% CI: 1.16-2.16). CONCLUSION: Acute hyperglycemia can act as a strong predictor of SFTS-related death in female patients. Insulin treatment of hyperglycemia in patients with SFTS without pre-existing diabetes has adverse effects.

Impact of cancer types on COVID-19 infection and mortality risk: a protocol for systematic review and meta-analysis.

INTRODUCTION: The COVID-19 pandemic has created a huge social and economic burden, and the lifestyles of individuals have significantly changed. In addition, the diagnosis, treatment and management of patients with cancer were greatly affected. Studies have shown that patients with cancer are at a higher risk of COVID-19 infection-related complications, which require aggressive preventive measures. Different types of cancer may have different risks of COVID-19 infection and death, and different preventive care measures are needed for different types of patients with cancer. Here, we designed a protocol for systematic review and meta-analysis to explore the impact of cancer types on COVID-19 infection and mortality risk. METHODS AND ANALYSIS: A systematic search plan will be performed to filter the eligible studies in the seven databases, namely PubMed, Cochrane search strategy, EMBASE search strategy, SinoMed, China National Knowledge Infrastructure, China Science and Technology Journals Database, and Wanfang database from 2019 to 10 August 2021. Two independent reviewers will choose the eligible studies and extract the data. The risk of bias will be evaluated based on the Newcastle-Ottawa Scale recommended by the Cochrane Collaboration. Finally, a systematic review and meta-analysis will be performed using Review Manager (V.5.3) statistical software. ETHICS AND DISSEMINATION: Formal ethical approval is not required, and the findings will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42021271108.

Identification of a recombinant equine coronavirus in donkey, China.

Equine coronavirus (ECoV) was first identified in the USA and has been previously described in several countries. In order to test the presence of ECoV in China, we collected 51 small intestinal samples from donkey foals with diarrhoea from a donkey farm in Shandong Province, China between August 2020 and April 2021. Two samples tested positive for ECoV and full-length genome sequences were successfully obtained using next-generation sequencing, one of which was further confirmed by Sanger sequencing. The two strains shared 100% sequence identity at the scale of whole genome. Bioinformatics analyses further showed that the two Chinese strains represent a novel genetic variant of ECoV and shared the highest sequence identity of 97.05% with the first identified ECoV strain - NC99. In addition, it may be a recombinant, with the recombination region around the NS2 gene. To our knowledge, this is the first documented report of ECoV in China, highlighting its risk to horse/donkey breeding. In addition, its potential risk to public health also warrants further investigation.

Noninvasive fecal testing for colorectal cancer.

INTRODUCTION: Colorectal cancer (CRC) is the third most common malignancy worldwide, with the second highest mortality rate among all malignancies. In this review, we describe the current utility of stool diagnostic biomarkers for CRC. METHODS: We reviewed stool-related tests and biomarker candidates for the diagnosis of CRC. The guaiac-based fecal occult blood test (gFOBT), fecal immunochemical test (FIT), and multitarget stool DNA test (MT-sDNA) have been used as clinical CRC screening tools. Although microRNAs, protein biomarkers, and microbiota have not yet been used in clinical CRC screening, there is growing evidence that they have the potential to function as CRC screening tools. RESULTS: According to the literature, the sensitivity of MT-sDNA for detecting CRC was 87.0-100%, 32.7-82.0% for advanced adenomas, and the specificity was 86.1-95.2%. The sensitivity of individual biomarkers of fecal microRNAs for detecting CRC was 34.2-88.2%, 73.0% for advanced adenomas, and the specificity was 68-100%. The sensitivity of fecal protein markers for detecting CRC was 63.6-93.0%, 47.7-69.4% for advanced adenomas, and the specificity was 38.3-97.5%. The sensitivity of fecal microbiota for detecting CRC was 54.0-100.0%, 32.0-48.3% for advanced adenomas, and the specificity was 61.3-90.0%. CONCLUSION: MT-sDNA is the most sensitive CRC screening test, and its sensitivity is the highest for advanced adenomas; however, its detection cost is high. MT-sDNA was more sensitive to CRC and advanced precancerous lesions than FIT, but compared to three years of MT-sDNA, annual FIT as the first non-invasive screening test for CRC seemed to be more effective.

Establishment of a Clinical Nomogram Model to Predict the Progression of COVID-19 to Severe Disease.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a worldwide public health pandemic with a high mortality rate, among severe cases. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is important to ensure early detection of the virus to curb disease progression to severe COVID-19. This study aims to establish a clinical-nomogram model to predict the progression to severe COVID-19 in a timely and efficient manner. METHODS: This retrospective study included 202 patients with COVID-19 who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University and Shiyan Taihe Hospital from January 17 to April 30, 2020. The patients were randomly assigned to the training dataset (n = 163, with 43 progressing to severe COVID-19) or the validation dataset (n = 39, with 10 progressing to severe COVID-19) at a ratio of 8:2. The optimal subset algorithm was applied to filter for the clinical factors most relevant to the disease progression. Based on these factors, the logistic regression model was fit to distinguish severe (including severe and critical cases) from non-severe (including mild and moderate cases) COVID-19. Sensitivity, specificity, and area under the curve (AUC) were calculated using the R software package to evaluate prediction performance. A clinical nomogram was established and performance assessed using the discrimination curve. RESULTS: Risk factors, including demographic data, symptoms, laboratory and image findings, were recorded for the 202 patients. Eight of the 53 variables that were entered into the selection process were selected via the best subset algorithm to establish the predictive model; they included gender, age, BMI, CRP, D-dimer, TP, ALB, and involved-lobe. AUC, sensitivity, and specificity were 0.91, 0.84 and 0.86 for the training dataset, and 0.87, 0.66, and 0.80 for the validation dataset. CONCLUSION: We established an efficient and reliable clinical nomogram model which showed that gender, age, and initial indexes including BMI, CRP, D-dimer, involved-lobe, TP, and ALB could predict the risk of progression to severe COVID-19.

Clinical Characteristics and Immune Responses of 137 Deceased Patients With COVID-19: A Retrospective Study.

Objective: This study aimed to evaluate the factors associated with death in patients with coronavirus disease 2019 by clarifying the clinical characteristics and immune responses. Methods: The clinical characteristics and laboratory findings, including cytokine and lymphocyte subsets, were obtained from the electronic medical records of patients in Wuhan Tongji Hospital. Results: This study included 836 patients with confirmed COVID-19. In total, 699 (83.6%) were cured and discharged, and 137 (16.4%) died. Our analysis revealed that age ≥ 65 years, male sex, malignancy, chronic obstructive pulmonary disease, dyspnea, dizziness, respiratory rate > 20 bpm, heart rate > 100 bpm, systolic blood pressure < 90 mmHg, neutrophils > 6.3×109/L, lymphopenia, thrombocytopenia, D-dimer ≥ 0.5 mg/L, lactate dehydrogenase > 250 U/L, aspartate aminotransferase > 40 U/L, total bilirubin > 26 µmol/L, albumin < 35 g/L, blood urea nitrogen > 9.5 mmol/L, estimated glomerular filtration rate < 90 ml/min/1.73, elevated cardiac troponin I, N-terminal pro-brain natriuretic peptide ≥ 900 pg/ml, C-reactive protein ≥ 25 mg/L, procalcitonin ≥ 0.05 ng/ml and ferritin > 400 µg/L were associated with death in patients with COVID-19. The multivariate logistic regression analysis revealed that an estimated glomerular filtration rate < 90 ml/min/1.73, elevated cardiac troponin I, C-reactive protein ≥ 25 mg/L and procalcitonin ≥ 0.05 ng/ml were predictive of mortality. Regarding immune responses, IL-2R, IL-6, IL-8, IL-10, and TNFα were remarkably higher in the deceased group at admission, and the levels of IL-2R, IL-6, IL-8, IL-10, and TNFα in the deceased group showed a rapid increase; the dynamics of these cytokines were highly consistent with disease deterioration. Lymphocyte subset analysis revealed that the deceased patients showed significant decreases in lymphocyte counts, especially helper T cells, suppressor T cells and NK cells. Conclusions: This study identified that an estimated glomerular filtration rate < 90 ml/min/1.73, elevated cardiac troponin I, C-reactive protein ≥ 25 mg/L and procalcitonin ≥ 0.05 ng/ml were predictors of mortality in COVID-19 patients. Elevated cytokine levels and a continued increasing trend, including in IL-2R, IL-6, IL-8, IL-10 and TNFα, and a decrease in lymphocyte subsets, especially helper T cells, suppressor T cells and NK cells, were associated with a poor prognosis.

Preliminary CT findings of coronavirus disease 2019 (COVID-19).

OBJECTIVES: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This paper aims to examine the CT imaging characteristics of COVID-19. METHODS: We evaluated CT images obtained between 10 January 2019 and 16 February 2020 at Taihe Hospital. Scans were conducted 2-6 times per patient and the re-testing interval was 2-7 days. Ninety-five patients with positive SARS-CoV-2 nucleic acid test results were included in this study and we retrospectively analysed their CT imaging characteristics. RESULTS: Ninety-five patients underwent 2-3 SARS-CoV-2 nucleic acid tests and received a definitive diagnosis of COVID-19. Fifty-three were male and 42 were female, and their mean age was 42 ± 12 years (range: 10 months to 81 years). Sixty-nine patients (72.6%) experienced fever, fatigue, and dry cough, while 15 (15.8%) had poor appetite and fatigue, and 11 (11.6%) had a dry cough and no fever. On CT imaging, early stage patients (n = 53, 55.8%) showed peripheral subpleural ground-glass opacities; these were mainly local patches (22/53, 41.5%), while some lesions were accompanied by interlobular septal thickening. Thirty-four (35.8%) patients were classified in the 'progression stage' based on CT imaging; these patients typically showed lesions in multiple lung segments and lobes (21/34，61.8%), and an uneven increase in ground-glass opacity density accompanied by consolidation and grid-like or cord-like shadows(30.5%). Two patients (2.1%) showed a severe presentation on CT. These showed diffuse bilateral lung lesions, mixed ground-glass opacities and consolidation with cord-like interstitial thickening and air bronchograms, entire lung involvement with a "white lung" presentation, and mild pleural effusion. Six patients in remission (6.3%), visible lesion absorption, fibrotic lesions. Based on clinical signs, 71 (74.7%), 22 (23.2%), and 2 (2.1%) patients had mild or moderate, severe, and critical disease, respectively. Within the follow-up period, 93 patients recovered and were discharged, including the 53 early stage patients and 34 progression stage patients. The length of hospitalisation was 7-28 days (mean: 10 ± 3.5 days). On discharge, lesions were significantly reduced in area and had in many cases completely disappeared, while slight pulmonary fibrosis was present in some patients. One severe stage patient was still hospitalised at the end of the follow-up period and the other severe stage patient died. The overall mortality rate was 1.05%. CONCLUSIONS: Understanding the CT imaging characteristics of COVID-19 is important for early lesion detection, determining the nature of lesions, and assessing disease severity.