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AbstractObjectivesIn December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19.MethodsIn this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy.ResultsThe data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954–10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097–0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111–0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746–0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients.ConclusionsWe designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management.

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The worldwide severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak has led to the rapid spread of coronavirus disease (COVID-19). The quantitative real time PCR (qPCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, more and more infected patients are relapsing after discharge, which suggests qPCR may fail to detect the virus in some cases. In this study, we selected 74 clinical samples from 43 recovering inpatients for qPCR and Droplet Digital PCR (ddPCR) synchronous blind detection, and established a cutoff value for ddPCR diagnosis of COVID-19. The results showed that at a cutoff value of 0.04 copies/μL, the ddPCR sensitivity and specificity are 97.6% and 100%, respectively. In addition, we also analyzed 18 retained samples from 9 discharged patients who relapsed. Although qPCR showed all 18 samples to be negative, ddPCR showed 12 to be positive, and there was only one patient with two negative samples; the other eight patients had at least one positive sample. These results indicate that ddPCR could significantly improve the accuracy of COVID-19 diagnosis, especially for discharged patients with a low viral load, and help to reduce misdiagnosis during recovery.

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Background Klebsiella pneumoniae is a primary pathogen of pyogenic liver abscess (PLA). However, little data are available on combination with sepsis. In this study, we aimed to evaluate the clinical characteristics and prognostic differences of PLA patients with sepsis. Methods This retrospective cohort study was conducted to investigate 135 patients with confirmed Klebsiella pneumoniae-caused liver abscesses (KPLA) from a tertiary teaching hospital, from 2013 to 2019. The patients were divided into two groups, KPLA with sepsis and KPLA without sepsis. The demographic characteristics, clinical features as well as laboratory and microbiologic findings were analyzed. Results A total of 135 patients with KPLA were analyzed. The mean age of patients was 60.9 ± 12.7 years, and the percentage of men was 59.3%. Among them, 37/135 (27.4%) of patients had sepsis and the mortality rate was 1.5%. The most common symptom was fever (91.1%). KPLA patients with sepsis had a significantly higher proportion of frailty, diarrhea, fatty liver, chronic renal insufficiency, and hepatic dysfunction compared to KPLA patients without sepsis (p < 0.05). Antibiotic therapy and percutaneous drainage were most frequently therapeutic strategy. Furthermore, the incidences of sepsis shock and acute respiratory distress syndrome were higher in the sepsis group compared to the non-sepsis group. As for metastatic infections, the lung was the most common site. In addition, KPLA patients with sepsis showed respiratory symptoms in 11 patients, endophthalmitis in 4 patients, and meningitis in 1 patient. Conclusion Our findings emphasize that KPLA patients combined with or without sepsis have different clinical features, but KPLA patients with sepsis have higher rates of complications and metastatic infections. Taken together, further surveillance and control of septic spread is essential for KPLA patients.

In this study, we characterized a novel plasmid from a carbapenem-resistant K. michiganensis (CRKM) isolate, which harbors two metallo-β-lactamases (MBLs), IMP-4 and NDM-1, is capable of transconjugation and contains three replicons. Our results first expand the diversity of plasmids co-harboring carbapenemase genes in Enterobacterales , which exhibits epidemic importance in bacterial resistance.