Prognosis and antibody profiles in survivors of critical illness from COVID-19: a prospective multicentre cohort study.

BACKGROUND: There is a need to assess the long-term outcomes of survivors of critical illness from COVID-19. METHODS: Ninety-two survivors of critical illness from COVID-19 from four hospitals in Hubei Province, China participated in this prospective cohort study. Multiple characteristics, including lung function (lung volumes, diffusing capacity for carbon monoxide, chest computed tomography scores, and walking capacity); immune status (SARS-CoV-2-neutralising antibody and all subtypes of immunoglobulin (Ig) G against SARS-CoV-2, immune cells in response to ex vivo antigen peptide stimuli, and lymphocyte count and its subtypes); liver, coagulation, and kidney functions; quality of life; cognitive function; and mental status, were assessed after 3, 6, and 12 months of follow-up. RESULTS: Amongst the 92 enrolled survivors, 72 (78%) patients required mechanical ventilation. At 12 months, the predicted percentage diffusing capacity of lung for carbon monoxide was 82% (inter-quartile range [IQR]: 76-97%) with a residual volume of 77 (64-88)%. Other lung function parameters and the 6-min walk test improved gradually over time and were almost back to normal by 12 months. The titres of IgG and neutralising antibody to COVID-19 remained high at 12 months compared with those of controls who were not infected with COVID-19, although IgG titres decreased significantly from 34.0 (IQR: 23.8-74.3) to 15.0 (5.8-24.3) AU ml-1 (P<0.001), whereas neutralising antibodies decreased from 29.99 (IQR: 19.43-53.93) AU ml-1 at 6 months to 19.75 (13.1-29.8) AU ml-1 (P<0.001) at 12 months. In general, liver, kidney, physical, and mental functions also improved over time. CONCLUSIONS: Survivors of critical illness from COVID-19 show some persistent long-term impairments in lung function. However, a majority of these tests were normal by 12 months. These patients still had detectable levels of neutralising antibodies against SARS-CoV-2 and all types of IgG at 12 months, but the levels had declined over this time period. CLINICAL TRIAL REGISTRATION: None.

Development of a method based on surface enhanced laser desorption and ionization time of flight mass spectrometry for rapid identification of Klebsiella pneumoniae.

A method based on surface enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF MS) was developed for the rapid identification of Klebsiella pneumoniae by directly applying bacterial colonies without further protein extraction. A total of 40 K. pneumoniae and 114 other related microorganisms isolated clinically were analyzed by SELDI-TOF MS. An identification model for K. pneumoniae was established by artificial neural networks (ANNs) with classification accuracy of 100%. The model was blindly tested with 43 K. pneumoniae and 53 control bacteria again. The results showed that the model was successful with accuracy of 96.9%, sensitivity of 100% and specificity of 943%. This strategy is potential for rapid identification of K. pneumoniae.

Rapid identification of Staphylococcus aureus by surface enhanced laser desorption and ionization time of flight mass spectrometry.

Staphylococcus aureus (S. aureus), a vital nosocomial pathogen, is responsible for several diseases. With the increasing isolation rate in clinical specimens, rapid identification of this bacterial species is required. But present identification via conventional methods is time-consuming and lacks accuracy. The purpose of the current study was to evaluate the use of surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) for rapid identification of S. aureus. A total of 120 clinical isolates of S. aureus and 153 non-S. aureus species were identified by conventional methods, and the species nature of all staphylococci was further confirmed by 16S rDNA sequencing. All strains observed were analyzed by SELDI-TOF MS. An identification model for S. aureus was developed and validated by an artificial neural network. The model based on 6 protein peaks exhibited a sensitivity of 98.4% and specificity of 98.6%. This strategy has the potential for rapid identification of S. aureus.