Modification of carbapenemase inhibition test and comparison of its performance with NG-Test CARBA 5 for detection of carbapenemase-producing Enterobacterales.

AIMS: Adequately and accurately identifying carbapenemase-producing Enterobacterales (CPE) is vital for selecting appropriate antimicrobial therapy and implementing effective infection control measures. This study aims to optimize the phenotypic detection method of carbapenemase for routine diagnostics in clinical microbiology laboratories. METHODS AND RESULTS: Carbapenemase genes in 2665 non-duplicate CRE clinical strains collected from various regions of China were confirmed through whole-genome sequencing (WGS). The carbapenemase inhibition test (CIT) was conducted and interpreted using different methods and breakpoints, then compared with the NG-Test CARBA 5 for carbapenemase detection. The diagnostic performance of the CIT method was optimal when the carbapenemase types were determined by comparing the inhibition zone diameters of the imipenem disc with 3-aminophenylboronic acid (APB) plus ethylenediaminetetraacetic acid (EDTA) to those of the imipenem disc with either APB or EDTA alone, with a breakpoint of 4 mm. The overall sensitivities of the current CIT, the modified CIT, and NG-Test CARBA 5 were 91.4%, 94.9%, and 99.9%, respectively. For detecting isolates co-producing Klebsiella pneumoniae carbapenemase (KPC) and metallo-ß-lactamases (MBLs), the modified CIT method had higher sensitivity than the current method (70.0% vs. 53.3%), though this difference was not statistically significant (P = 0.063). The NG-Test CARBA 5 showed excellent performance for multi-carbapenemases diagnosis, with sensitivity and specificity of 97.1% and 100%, respectively. CONCLUSIONS: Optimizing and standardizing the CIT method for clinical use is necessary. It has certain advantages in diagnosing multi-carbapenemase and rare carbapenemase production. However, for identifying common carbapenemase types, the NG-Test CARBA 5 demonstrated superior performance.

Evaluation of artificial intelligence-assisted morphological analysis for platelet count estimation.

INTRODUCTION: This study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC-80 digital morphology analyzer. METHODS: Digital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT-M1) and estimate factor (PLT-M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT-I and PLT-O), digital morphology analyzer (PLT-M1 and PLT-M2), and flow cytometry (PLT-IRM) were compared. RESULTS: There was no significant deviation observed before and after verification for both PLT-M1 and PLT-M2 across the analysis range (average bias: -0.845/-0.682, 95% limit of agreement (LOA): -28.675-26.985/-29.420-28.056). When platelet alarms appeared, PLT-M1/PLT-M2 showed the strongest correlation with PLT-IRM than PLT-I with PLT-IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT-M1/PLT-M2 and PLT-IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT-M1 and PLT-M2 is related to the number of RBCs. Compared with PLT-I, PLT-M1/PLT-M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low-value PLT. CONCLUSION: The novel platelet count estimation on the MC-80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.

The immune landscape of sepsis and using immune clusters for identifying sepsis endotypes.

Background: The dysregulated immune response to sepsis still remains unclear. Stratification of sepsis patients into endotypes based on immune indicators is important for the future development of personalized therapies. We aimed to evaluate the immune landscape of sepsis and the use of immune clusters for identifying sepsis endotypes. Methods: The indicators involved in innate, cellular, and humoral immune cells, inhibitory immune cells, and cytokines were simultaneously assessed in 90 sepsis patients and 40 healthy controls. Unsupervised k-means cluster analysis of immune indicator data were used to identify patient clusters, and a random forest approach was used to build a prediction model for classifying sepsis endotypes. Results: We depicted that the impairment of innate and adaptive immunity accompanying increased inflammation was the most prominent feature in patients with sepsis. However, using immune indicators for distinguishing sepsis from bacteremia was difficult, most likely due to the considerable heterogeneity in sepsis patients. Cluster analysis of sepsis patients identified three immune clusters with different survival rates. Cluster 1 (36.7%) could be distinguished from the other clusters as being an "effector-type" cluster, whereas cluster 2 (34.4%) was a "potential-type" cluster, and cluster 3 (28.9%) was a "dysregulation-type" cluster, which showed the lowest survival rate. In addition, we established a prediction model based on immune indicator data, which accurately classified sepsis patients into three immune endotypes. Conclusion: We depicted the immune landscape of patients with sepsis and identified three distinct immune endotypes with different survival rates. Cluster membership could be predicted with a model based on immune data.

Water quality and microecosystem of water tanks in karst mountainous area, Southwest China.

Karst mountainous areas in Southwest China, the world's largest bare karst area, are faced with growing water shortages. Rainwater harvesting plays an important role in alleviating water shortage. However, there remains a substantial gap in the research regarding the water quality of tanks. Water samples were seasonally collected from ten tanks to investigate the physicochemical properties, microbial communities, and their key influencing factors. The result showed that pH, turbidity, chroma, DOC, and CODMn exceeded drinking water guidelines. The alkaline pH value and the deterioration of sensory properties was the main feature of tank water, from which the over-standard rate of the uncleaned water tanks was higher. Moreover, principal component analyses suggested that tank water quality was influenced by human activities, catchment areas, and material cycling processes within the tanks, of which in-tank microbial activities were the most important driving factors in water quality variation. Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, and Verrucomicrobia were the predominant bacterial phyla in water tanks. Acinetobacter, Cyanobium-PCC-6307, CL500-29-marine-group, Candidatus-Aquiluna, and Exiguobacterium were the most abundant genera. The bacterial communities were significantly affected by the management practices. Higher relative abundance of Cyanobacteria and lower relative abundance of Proteobacteria was detected in the uncleaned tanks, which was a sign of tank water quality deterioration. The microbial community structure was closely related to the environmental factors. There was evidence that the water quality was affected by the existence of a microecosystem dominated by photosynthetic microorganisms in the water tanks. In addition, Acinetobacter, Enterobacter, Pseudomonas, and Legionella identified as the potential opportunistic pathogenic genera were frequently detected but the relative abundances except Acinetobacter were low in the tanks. Overall, our findings indicated that management style influences water quality and bacterial communities of tank water.

Performance of the digital cell morphology analyzer MC-100i in a multicenter study in tertiary hospitals in China.

BACKGROUND: This study investigated the performance of the MC-100i, a pre-commercial digital morphology analyzer utilizing a convolutional neural network algorithm, in a multicentric setting involving up to 11 tertiary hospitals in China. METHODS: Blood smears were analyzed by MC-100i, verified by morphologists, and manually differentiated. The classification performance on WBCs and RBCs was evaluated by comparing the classification results using different methods. The PLT and PLT clump counting performance was also assessed. The total assay time including hands-on time was evaluated. RESULTS: The agreements between pre- and post-classification were high for normal WBCs (κ > 0.96) and lower for overall abnormal WBCs (κ = 0.90). The post-classification results correlated well with manual differentials for both normal and abnormal WBCs (r > 0.93), except for basophils (r = 0.8480) and atypical lymphocytes (r = 0.8211). The clinical sensitivity and specificity of each RBC abnormality after verification were above 90 % using microscopy reviews as the reference. The PLTs counted by the MC-100i before and after verification correlated well with those measured by the PLT-O mode (r = 0.98). Moreover, PLT clumps were successfully classified by the analyzer in EDTA-dependent pseudothrombocytopenia blood samples. CONCLUSIONS: The MC-100i is an accurate and reliable digital cell morphology analyzer, offering another intelligent option for hematology laboratories.

Using immune clusters for classifying Mycobacterium tuberculosis infection.

BACKGROUND: The differential diagnosis between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) is still a challenge worldwide. METHODS: Immune indicators involved in innate, humoral, and cellular immune cells, as well as antigen-specific cells were simultaneously assessed in patients with ATB and LTBI. RESULTS: Of 54 immune indicators, no indicator could distinguish ATB from LTBI, likely due to an obvious heterogeneity of immune indicators noticed in ATB patients. Cluster analysis of ATB patients identified three immune clusters with different severity. Cluster 1 (42.1 %) was a ''Treg/Th1/Tfh unbalance type" cluster, whereas cluster 2 (42.1 %) was an "effector type'' cluster, and cluster 3 was a ''inhibition type'' cluster (15.8 %) which showed the highest severity. A prediction model based on immune indicators was established and showed potential in classifying Mycobacterium tuberculosis infection. CONCLUSIONS: We depicted the immune landscape of patients with ATB and LTBI. Three immune subtypes were identified in ATB patients with different severity.

Tandem gene duplications contributed to high-level azole resistance in a rapidly expanding Candida tropicalis population.

Invasive diseases caused by the globally distributed commensal yeast Candida tropicalis are associated with mortality rates of greater than 50%. Notable increases of azole resistance have been observed in this species, particularly within Asia-Pacific regions. Here, we carried out a genetic population study on 1571 global C. tropicalis isolates using multilocus sequence typing (MLST). In addition, whole-genome sequencing (WGS) analysis was conducted on 629 of these strains, comprising 448 clinical invasive strains obtained in this study and 181 genomes sourced from public databases. We found that MLST clade 4 is the predominant azole-resistant clone. WGS analyses demonstrated that dramatically increasing rates of azole resistance are associated with a rapid expansion of cluster AZR, a sublineage of clade 4. Cluster AZR isolates exhibited a distinct high-level azole resistance, which was induced by tandem duplications of the ERG11A395T gene allele. Ty3/gypsy-like retrotransposons were found to be highly enriched in this population. The alarming expansion of C. tropicalis cluster AZR population underscores the urgent need for strategies against growing threats of antifungal resistance.

The pathogenic and clinical characteristics of severe fever with thrombocytopenia syndrome patients with co-infections.

Objective: The study aimed to comprehensively describe and evaluate the pathogenic and clinical characteristics of severe fever with thrombocytopenia syndrome (SFTS) patients with co-infections. Methods: We retrospectively collected clinical data and laboratory indicators of the SFTS patients at Tongji Hospital from October 2021 to July 2023. Results: A total of 157 patients with SFTS virus (SFTSV) infection were involved in the analysis, including 43 co-infection and 114 non-co-infection patients. The pathogens responsible for co-infection were primarily isolated from respiratory specimens. Fungal infections, primarily Aspergillus fumigatus, were observed in 22 cases. Bacterial infections, with Klebsiella pneumoniae and carbapenem-resistant Acinetobacter baumannii as the main pathogens, were identified in 20 cases. SFTS patients with co-infection exhibited higher mortality (P=0.011) compared to non-co-infection patients. Among SFTS patients co-infected with both bacteria and fungi (8 cases) or specific drug-resistant strains (11 cases), the mortality rate was as high as 70% (14/19). In comparison with the non-co-infection group, SFTS patients with co-infection displayed significant alteration in inflammatory markers, coagulation function, and liver function indicators. Conclusion: The mortality rate of SFTS patients with co-infection is relatively high, underscoring the need for enhanced monitoring and timely, appropriate treatment to minimize the mortality rate.

Salmonella Typhimurium with Eight Tandem Copies of blaNDM-1 on a HI2 Plasmid.

Carbapenem-resistant Salmonella has recently aroused increasing attention. In this study, a total of four sequence type 36 Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) isolates were consecutively isolated from an 11-month-old female patient with a gastrointestinal infection, of which one was sensitive to carbapenems and three were resistant to carbapenems. Via antibiotic susceptibility testing, a carbapenemases screening test, plasmid conjugation experiments, Illumina short-reads, and PacBio HiFi sequencing, we found that all four S. Typhimurium isolates contained a blaCTX-M-14-positive IncI1 plasmid. One carbapenem-sensitive S. Typhimurium isolate then obtained an IncHI2 plasmid carrying blaNDM-1 and an IncP plasmid without any resistance genes during the disease progression. The blaNDM-1 gene was located on a new 30 kb multiple drug resistance region, which is flanked by IS26 and TnAs2, respectively. In addition, the ST_F0903R isolate contained eight tandem copies of the ISCR1 unit (ISCR1-dsbD-trpF-ble-blaNDM-1-ISAba125Δ1), but an increase in MICs to carbapenems was not observed. Our work further provided evidence of the rapid spread and amplification of blaNDM-1 through plasmid. Prompting the recognition of carbapenem-resistant Enterobacterales and the initiation of appropriate infection control measures are essential to avoid the spread of these organisms.

The first report of the vanC1 gene in Enterococcus faecium isolated from a human clinical specimen

The vanC1 gene, which is chromosomally located, confers resistance to vancomycin and serves as a species marker for Enterococcus gallinarum. Enterococcus faecium TJ4031 was isolated from a blood culture and harbours the vanC1gene. Polymerase chain reaction (PCR) assays were performed to detect vanXYc and vanTc genes. Only the vanXYc gene was found in the E. faecium TJ4031 isolate. The minimum inhibitory concentrations of vancomycin and teicoplanin were 2 µg/mL and 1 µg/mL, respectively. Real-time reverse transcription-PCR results revealed that the vanC1and vanXYc genes were not expressed. Pulsed-field gel electrophoresis and southern hybridisation results showed that the vanC1 gene was encoded in the chromosome. E. faecalis isolated from animals has been reported to harbour vanC1gene. However, this study is the first to report the presence of the vanC1gene in E. faecium of human origin. Additionally, our research showed the vanC1gene cannot serve as a species-specific gene of E. gallinarum and that it is able to be transferred between bacteria. Although the resistance marker is not expressed in the strain, our results showed that E. faecium could acquire the vanC1gene from different species.