BackgroundClinical microbiology laboratories have to accurately identify clinical microbes. However, some isolates are difficult to identify by the automated biochemical text platforms, which are called “difficult-to-identify” microbes in this study. Therefore, the ability of 16S ribosomal DNA (16S rDNA) and internal transcribed spacer 2 (ITS2) sequencing to identify these “difficult-to-identify” bacteria and fungi was assessed in this study.MethodsSamples obtained from a teaching hospital over the past three years were examined. The 16S rDNA of four standard strains, 18 clinical common isolates, and 47 “difficult-to-identify” clinical bacteria were amplified by PCR and sequenced. The ITS2 of eight standard strains and 31 “difficult-to-identify” clinical fungi were also amplified by PCR and sequenced. The sequences of 16S rDNA and ITS2 were compared to reference data available in GenBank by using the BLASTN program. These microbes were identified according to the percentage of similarity to reference sequences of strains in GenBank.ResultsThe results from molecular sequencing methods correlated well with automated microbiological identification systems for common clinical isolates. Sequencing results of the standard strains were consistent with their known phenotype. Overall, 47 “difficult-to-identify” clinical bacteria were identified as 35 genera or species by sequence analysis (with 10 of these identified isolates first reported in clinical specimens in China and two first identified in the international literature). 31 “difficult-to-identify” clinical fungi tested could be identified as 15 genera or species by sequence analysis (with two of these first reported in China).ConclusionsOur results show the importance of 16S rDNA and internal ITS2 sequencing for the molecular identification of “difficult-to-identify” bacteria and fungi. The development of this method with advantages of convenience, availability, and cost-effectiveness will make it worth extending into clinical practice in developing countries.

Background The detection of serum antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is emerging as a new tool for the coronavirus disease 2019 (COVID‐19) diagnosis. Since many coronaviruses are sensitive to heat, heating inactivation of samples at 56°C prior to testing is considered a possible method to reduce the risk of transmission, but the effect of heating on the measurement of SARS‐CoV‐2 antibodies is still unclear. Methods By comparing the levels of SARS‐CoV‐2 antibodies before and after heat inactivation of serum at 56°C for 30 minutes using a quantitative fluorescence immunochromatographic assay Results We showed that heat inactivation significantly interferes with the levels of antibodies to SARS‐CoV‐2. The IgM levels of all the 34 serum samples (100%) from COVID‐19 patients decreased by an average level of 53.56%. The IgG levels were decreased in 22 of 34 samples (64.71%) by an average level of 49.54%. Similar changes can also be observed in the non–COVID‐19 disease group (n = 9). Of note, 44.12% of the detected IgM levels were dropped below the cutoff value after heating, suggesting heat inactivation can lead to false‐negative results of these samples. Conclusion Our results indicate that heat inactivation of serum at 56°C for 30 minutes interferes with the immunoanalysis of antibodies to SARS‐CoV‐2. Heat inactivation prior to immunoanalysis is not recommended, and the possibility of false‐negative results should be considered if the sample was pre‐inactivated by heating.

No abstract