Currently, there are no approved specific antiviral agents for novel coronavirus disease 2019 . In this study, 10 severe patients confirmed by real-time viral RNA test were enrolled prospectively. One dose of 200 mL of convalescent plasma (CP) derived from recently recovered donors with the neutralizing antibody titers above 1:640 was transfused to the patients as an addition to maximal supportive care and antiviral agents. The primary endpoint was the safety of CP transfusion. The second endpoints were the improvement of clinical symptoms and laboratory parameters within 3 d after CP transfusion. The median time from onset of illness to CP transfusion was 16.5 d. After CP transfusion, the level of neutralizing antibody increased rapidly up to 1:640 in five cases, while that of the other four cases maintained at a high level (1:640). The clinical symptoms were significantly improved along with increase of oxyhemoglobin saturation within 3 d. Several parameters tended to improve as compared to pretransfusion, including increased lymphocyte counts (0.65 × 10 9 /L vs. 0.76 × 10 9 /L) and decreased C-reactive protein (55.98 mg/L vs. 18.13 mg/L). Radiological examinations showed varying degrees of absorption of lung lesions within 7 d. The viral load was undetectable after transfusion in seven patients who had previous viremia. No severe adverse effects were observed. This study showed CP therapy was well tolerated and could potentially improve the clinical outcomes through neutralizing viremia in severe COVID-19 cases. The optimal dose and time point, as well as the clinical benefit of CP therapy, needs further investigation in larger well-controlled trials.

Background Klebsiella pneumoniae, as a global priority pathogen, is well known for its capability of acquiring mobile genetic elements that carry resistance and/or virulence genes. Its virulence plasmid, previously deemed nonconjugative and restricted within hypervirulent K. pneumoniae (hvKP), has disseminated into classic K. pneumoniae (cKP), particularly carbapenem-resistant K. pneumoniae (CRKP), which poses alarming challenges to public health. However, the mechanism underlying its transfer from hvKP to CRKP is unclear. Methods A total of 28 sequence type (ST) 11 bloodstream infection-causing CRKP strains were collected from Ruijin Hospital in Shanghai, China, and used as recipients in conjugation assays. Transconjugants obtained from conjugation assays were confirmed by XbaI and S1 nuclease pulsed-field gel electrophoresis, PCR detection and/or whole-genome sequencing. The plasmid stability of the transconjugants was evaluated by serial culture. Genetically modified strains and constructed mimic virulence plasmids were employed to investigate the mechanisms underlying mobilization. The level of extracellular polysaccharides was measured by mucoviscosity assays and uronic acid quantification. An in silico analysis of 2608 plasmids derived from 814 completely sequenced K. pneumoniae strains available in GenBank was performed to investigate the distribution of putative helper plasmids and mobilizable virulence plasmids. Results A nonconjugative virulence plasmid was mobilized by the conjugative plasmid belonging to incompatibility group F (IncF) from the hvKP strain into ST11 CRKP strains under low extracellular polysaccharide-producing conditions or by employing intermediate E. coli strains. The virulence plasmid was mobilized via four modes: transfer alone, cotransfer with the conjugative IncF plasmid, hybrid plasmid formation due to two rounds of single-strand exchanges at specific 28-bp fusion sites or homologous recombination. According to the in silico analysis, 31.8% (242) of the putative helper plasmids and 98.8% (84/85) of the virulence plasmids carry the 28-bp fusion site. All virulence plasmids carry the origin of the transfer site. Conclusions The nonconjugative virulence plasmid in ST11 CRKP strains is putatively mobilized from hvKP or E. coli intermediates with the help of conjugative IncF plasmids. Our findings emphasize the importance of raising public awareness of the rapid dissemination of virulence plasmids and the consistent emergence of hypervirulent carbapenem-resistant K. pneumoniae (hv-CRKP) strains.

Graphical abstract

Background: Chest CT had high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at early stage, giving it an advantage over nucleic acid detection in time of crisis. Deep learning was reported to discover intricate structures from clinical images and achieve expert-level performance in medical image analysis. To develop and validate an integrated deep learning framework on chest CT images for autodetection of NCP, particularly focusing on differentiating NCP from influenza pneumonia (IP). Methods: 35 confirmed NCP cases were consecutively enrolled as training set from 1138 suspected patients in three NCP designated hospitals together with 361 confirmed viral pneumonia patients from center one including 156 IP patients, from May, 2015 to February, 2020. The external validation set enrolled 57 NCP patients and 50 IP patients from eight centers.Results: 96.6% of NCP lesions were larger than 1 cm and 76.8% were with intensity below -500 Hu, indicating less consolidation than IP lesions which had nodules ranging 5-10 mm. The classification schemes accurately distinguished NCP and IP lesions with area under the receiver operating characteristic curve (AUC) above 0.93.The Trinary scheme was more device-independent and consistent with specialists than the Plain scheme, which achieved a F1 score of 0.847, higher than the Plain scheme (0.774), specialists (0.785) and residents (0.644). Conclusions:Our study potentially provides an accurate early diagnosis tool on chest CT for NCP with high transferability, and shows high efficiency in differentiating NCP and IP, helping to reduce misdiagnosis and contain the pandemic transmission.

Background: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP). Methods: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled.Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves.Results: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <−500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance.Conclusions: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Dynamic changes in metabolites may affect liver disease progression, and provide new methods for predicting liver damage. We used ultra-performance liquid chromatography-mass spectroscopy to assess serum metabolites in healthy controls (HC), and patients with acute hepatitis E (AHE) or hepatitis E virus acute liver failure (HEV-ALF). The principal component analysis, partial least squares discriminant analysis, and discriminant analysis of orthogonal projections to latent structures models illustrated significant differences in the metabolite components between AHE patients and HCs, or between HEV-ALF and AHE patients. In pathway enrichment analysis, we further identified two altered pathways, including linoleic acid metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis, when comparing AHE patients with HCs. Linoleic acid metabolism and porphyrin and chlorophyll metabolism pathways were significantly different in HEV-ALF when compared with AHE patients. The discriminative performances of differential metabolites showed that taurocholic acid, glycocholic acid, glycochenodeoxycholate-3-sulfate, and docosahexaenoic acid could be used to distinguish HEV-ALF from AHE patients. The serum levels of glycocholic acid, taurocholic acid, deoxycholic acid glycine conjugate, and docosahexaenoic acid

No abstract

Up to now, little is known about the detailed immune profiles of COVID-19 patients from admission to discharge. In this study we retrospectively reviewed the clinical and laboratory characteristics of 18 COVID-19 patients from January 30, 2020 to February 21, 2020. These patients were divided into two groups; group 1 had a severe acute respiratory syndrome coronavirus 2 nucleic acid-positive duration for more than 15 days (n = 6) and group 2 had a nucleic acid-positive duration for less than 15 days (n = 12). Group 1 patients had lower level of peripheral blood lymphocytes (0.40 vs. 0.78 ×10 9 /L, p = 0.024) and serum potassium (3.36 vs. 3.79 mmol/L, p = 0.043) on admission but longer hospitalization days (23.17 vs. 15.75 days, p < 0.001) compared to Group 2 patients. Moreover, baseline level of lymphocytes (r =-0.62, p = 0.006) was negatively correlated with the nucleic acid-positive duration. Additionally, lymphocytes (420.83 vs. 1100.56 /μL), T cells (232.50 vs. 706.78 /μL), CD4 + T cells (114.67 vs. 410.44 /μL), and CD8 + T cells (94.83 vs. 257.44 /μL) in the peripheral blood analyzed by flow cytometry were significantly different between Group 1and Group 2. Furthermore, there was also a negative correlation between lymphocytes (r =-0.54, p = 0.038) or T cells (r =-0.55, p = 0.034) at diagnosis and the nucleic acid-positive duration, separately. In conclusion, the patients with nucleic acid-positive ≥ 15 days had significantly decreased lymphocytes, T cell and its subsets compared to those who remained positive for less than 15 days.