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.

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.

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.

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.

The signature and predictive value of immune parameters in patients with secondary hemophagocytic lymphohistiocytosis.

BACKGROUND: Secondary hemophagocytic lymphohistiocytosis (sHLH) is a rare but fatal clinical syndrome, characterized by severe immune dysfunction and overwhelming inflammatory response. However, the host immune signature and also its role in predicting the clinical outcome are not fully described. OBJECTIVE: The present study aims to investigate the host immune status of sHLH patients in the early stage of the disease, including lymphocyte subsets, phenotypes and cytokines, and also to explore its clinical value in prognosis. METHODS: Sixty-four patients with sHLH admitted to a tertiary hospital in central China between 2018 and 2022 were enrolled, of which 21 were deceased. The subsets and phenotypes of lymphocytes, and the levels of cytokines in serum were analyzed. RESULTS: In patients with sHLH, the percentages of total T cells, CD8+ T cells, HLA-DR+ T cells, HLA-DR+CD8+ T cells, CD45RO+CD4+ T cells, and the levels of IL-1ß, IL-2R, IL-6, IL-8, IL-10 and TNF-α were significantly increased, while the percentages of CD4+ T cells, NK cells, CD45RA+CD4+ T cells, CD45RA+ regulatory T (Treg) cells, the counts of total T cells, total B cells, CD4+ T cells, CD8+ T cells, NK cells, and the ratio of CD4+ T/CD8+ T cells were significantly decreased, compared with healthy controls (HC). In addition, dysregulation of host immune response and high inflammatory status were more obvious in deceased patients than that of survivors. Kaplan-Meier survival analysis and multivariate logistic regression analysis demonstrated that lower levels of CD4+ T cells count and CD28+CD4+ T cells percentage, but higher levels of NK cells percentage and IL-1ß were poor prognostic indicators of sHLH. CONCLUSION: The evaluation of immunological markers has critical value for selecting prognostic markers and potential treatment target among adults with sHLH.

Comparing the broth enrichment-multiplex lateral flow immunochromatographic assay with real time quantitative PCR for the rapid detection of carbapenemase-producing organisms in rectal swabs.

BACKGROUND: Rapid and accurate identification of carbapenemase-producing organism (CPO) intestinal carriers is essential for infection prevention and control. Molecular diagnostic methods can produce results in as little as 1 h, but require special instrumentation and are expensive. Therefore, it is urgent to find an alternative method. The broth enrichment-multiplex lateral flow immunochromatographic assay was recently reported, but using it to directly detect CPO intestinal carriers in rectal swabs still requires the evaluation of many samples. The aim of this study was to compare the performance of these two methods, and to explore the control measures of CPO infection. METHODS: Through CPO selective culture, PCR and DNA sequencing, 100 rectal swabs confirmed to be CPO-positive and 100 rectal swabs with negative results were collected continuously. After eluting the rectal swabs with saline, three aliquots were used: one for counting, one for detection by Xpert Carba-R, and one for culture in broth for 0 h, 1 h, 2 h, 3 h and 4 h, followed by NG-Test CARBA 5 assessment. The sensitivity and specificity of the NG-Test CARBA 5 method after different incubation times were calculated. The limit of detection (LoD) of this assay after 4 h broth incubation was estimated by examining the bacterial suspensions and simulated faecal suspensions prepared with CPOs producing different types of carbapenemases. RESULTS: Xpert Carba-R demonstrated a combined sensitivity of 99.0% and specificity of 98.0%. The sensitivity and specificity were higher than 90.0% for the different enzyme types. The specificities of five common carbapenemases detected by the broth enrichment NG-Test CARBA 5 combined method after different incubation times were 100%. The sensitivities increased with increasing incubation time. At 4 h, the Klebsiella pneumoniae carbapenemase (KPC), New Delhi metallo-beta-lactamase (NDM), imipenemase (IMP), Verona integron-encoded metallo-beta-lactamase (VIM), and oxacillinase (OXA) -48 detection sensitivities were 93.0%, 96.3%, 100%, 100% and 85.7%, respectively. The LoDs were between 102 and 104 CFU/mL for all five enzymes after 4 h of incubation. CONCLUSIONS: This investigation highlighted that the broth enrichment-multiplex lateral flow immunochromatographic assay can be used as a new method for screening CPOs in rectal swabs.

Convolutional neural network based on T-SPOT.TB assay promoting the discrimination between active tuberculosis and latent tuberculosis infection.

OBJECTIVES: The study aims to investigate the potential of convolutional neural network (CNN) based on spot image of T-SPOT assay for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI). METHODS: CNN was applied to recognize and classify T-SPOT spot image. Logistic regression was used to establish prediction model based on CNN. RESULTS: Areas under the receiver operating characteristic curve (AUCs) of early secreted antigenic target 6 (ESAT-6) CNN, culture filtrate protein 10 (CFP-10) CNN, and phytohemagglutinin (PHA) CNN were more than 0.7 in differentiating ATB from LTBI, while the performance of these indicators was significantly better than that of spot number. Furthermore, prediction model based on the combination of CNNs yielded an AUC of 0.898. The model presented a sensitivity of 85.76% and a specificity of 90.23%. CONCLUSIONS: The current study identified CNN based on T-SPOT spot image with the potential to serve as a tool for TB diagnostics.

Emergence of Antifungal Resistant Subclades in the Global Predominant Phylogenetic Population of Candida albicans.

Candida albicans remains the most common species causing invasive candidiasis. In this study, we present the population structure of 551 global C. albicans strains. Of these, the antifungal susceptibilities of 370 strains were tested. Specifically, 66.6% of the azole-nonsusceptible (NS)/non-wild-type (NWT) strains that were tested belonged to Clade 1. A phylogenetic analysis, a principal components analysis, the population structure, and a loss of heterozygosity events revealed two nested subclades in Clade 1, namely, Clade 1-R and Clade 1-R-α, that exhibited higher azole-NS/NWT rates (75.0% and 100%, respectively). In contrast, 6.4% (21/326) of the non-Clade 1-R isolates were NS/NWT to at least 1 of 4 azoles. Notably, all of the Clade 1-R-α isolates were pan-azole-NS/NWT that carried unique A114S and Y257H double substitutions in Erg11p and had the overexpression of ABC-type efflux pumps introduced by the substitution A736V in transcript factor Tac1p. It is worth noting that the Clade 1-R and Clade 1-R-α isolates were from different cities that are distributed over a large geographic span. Our study demonstrated the presence of specific phylogenetic subclades that are associated with antifungal resistance among C. albicans Clade 1, which calls for public attention on the monitoring of the future spread of these clones. IMPORTANCE Invasive candidiasis is the most common human fungal disease among hospitalized patients, and Candida albicans is the predominant pathogen. Considering the large number of infected cases and the limited alternative therapies, the azole-resistance of C. albicans brings a huge clinical threat. Here, our study suggested that antifungal resistance in C. albicans could also be associated with phylogenetic lineages. Specifically, it was revealed that more than half of the azole-resistant C. albicans strains belonged to the same clade. Furthermore, two nested subclades of the clade exhibited extremely high azole-resistance. It is worth noting that the isolates of two subclades were from different cities that are distributed over a large geographic span in China. This indicates that the azole-resistant C. albicans subclades may develop into serious public health concerns.

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.

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection.

BACKGROUND: The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. METHODS: T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. RESULTS: A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively. CONCLUSIONS: Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

Heavy metal habitat: A novel framework for mapping heavy metal contamination over large-scale catchment with a species distribution model.

Heavy metal(loid)s (HMs) have been consistently entering the food chain, imposing great harm on environment and public health. However, previous studies on the spatial dynamics and transport mechanism of HMs have been profoundly limited by the field sampling issues, such as the uneven observations of individual carriers and their spatial mismatch, especially over large-scale catchments with complex environment. In this study, a novel methodological framework for mapping HMs at catchment scale was proposed and applied, combining a species distribution model (SDM) with physical environment and human variables. Based on the field observations, we ecologicalized HMs in different carriers as different species. This enabled the proposed framework to model the 'enrichment area' of individual HMs in the geographic space (termed as the HM 'habitat') and identify their 'hotspots' (peak value points) within the catchment. Results showed the output maps of HM habitats from secondary carriers (soil, sediment, and wet deposition) well agreed with the influence of industry contaminants, hydraulic sorting, and precipitation washout process respectively, indicating the potential of SDM in modeling the spatial distributions of the HM. The derived maps of HMs from secondary carriers, along with the human and environmental variables were then input as explanatory variables in SDM to predict the spatial patterns of the final HM accumulation in river water, which was observed to have largely improved the prediction quality. These results confirmed the value of our framework to leverage SDMs from ecology perspective to study HM contamination transport at catchment scale, offering new insights not only to map the spatial HM habitats but also help locate the HM transport chains among different carriers.

Review and Updates on the Diagnosis of Tuberculosis.

Diagnosis of tuberculosis, and especially the diagnosis of extrapulmonary tuberculosis, still faces challenges in clinical practice. There are several reasons for this. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of Mtb-specific immune responses cannot always differentiate active disease from latent infection, and some of the serological markers of infection with Mtb are insufficiently specific to differentiate tuberculosis from other inflammatory diseases. New tools based on technologies such as flow cytometry, mass spectrometry, high-throughput sequencing, and artificial intelligence have the potential to solve this dilemma. The aim of this review was to provide an updated overview of current efforts to optimize classical diagnostic methods, as well as new molecular and other methodologies, for accurate diagnosis of patients with Mtb infection.

Age- and Severity-Associated Humoral Immunity Response in COVID-19 Patients: A Cohort Study from Wuhan, China.

Age has been found to be the single most significant factor in COVID-19 severity and outcome. However, the age-related severity factors of COVID-19 have not been definitively established. In this study, we detected SARS-CoV-2-specific antibody responses and infectious disease-related blood indicators in 2360 sera from 783 COVID-19 patients, with an age range of 1−92 years. In addition, we recorded the individual information and clinical symptoms of the patients. We found that the IgG responses for S1, N, and ORF3a and the IgM for NSP7 were associated with severe COVID-19 at different ages. The IgM responses for the S-protein peptides S1-113 (aa 673−684) and S2-97 (aa 1262−1273) were associated with severe COVID-19 in patients aged <60. Furthermore, we found that the IgM for S1-113 and NSP7 may play a protective role in patients aged <60 and >80, respectively. Regarding clinical parameters, we analyzed the diagnostic ability of five clinical parameters for severe COVID-19 in six age groups and identified three-target panel, glucose, IL-6, myoglobin, IL-6, and NT proBNP as the appropriate diagnostic markers for severe COVID-19 in patients aged <41, 41−50, 51−60, 61−70, 71−80, and >80, respectively. The age-associated severity factors revealed here will facilitate our understanding of COVID-19 immunity and diagnosis, and eventually provide meaningful information for combating the pandemic.

[Study on the value of prothrombin time for predicting the severity and prognosis of septic patients].

OBJECTIVE: To explore the predictive efficacy of prothrombin time (PT) with regarding for the severity and prognosis of septic patients, along with comparing with other routine coagulation parameters. METHODS: A retrospective analysis was conducted. The clinical data of 302 septic patients who were admitted to the intensive care unit (ICU) of Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology from January 1 to December 31 in 2019 were enrolled. Demographic and basic clinical data were collected. Laboratory data, including PT, activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (FIB), D-dimer, fibrin (fibrinogen) degradation product (FDP), antithrombin (AT), platelet count (PLT) at ICU admission were recorded, and sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation II (APACHE II) score within 24 hours of admission to ICU were also collected. What's more, some major clinical events, such as septic shock, disseminated intravascular coagulation (DIC), etc. during ICU stay were also monitored. A follow-up 28 days observation of prognosis was performed. The patients were divided into the septic shock group and the non-septic shock group according to the occurrence of septic shock, and they were divided into the survival group and the non-survival group according to the 28-day prognosis. The differences in terms of above parameters between each two groups were compared. Spearman correlation method was used to analyze the correlation between routine coagulation parameters and SOFA score or APACHE II score. Receiver operator characteristic curve (ROC curve) was plotted to determine the predictive efficacy of each routine coagulation parameter with regarding to predict septic shock and 28-day mortality. Based on the cut-off value of PT, the septic patients were divided into two risk stratifications, and then the major clinical and end point outcome were compared. Kaplan-Meier survival curve analysis was applied to investigate the difference of the 28-day cumulated survival rate based on the different risk stratifications of PT level. Finally, multivariate Logistic regression analysis was used to explore whether prolonged PT level was an independent risk factor for septic shock and 28-day mortality. RESULTS: The 302 patients were all enrolled, including 120 patients with septic shock and 182 patients without. Seventy-five patients died within 28 days, while 227 survived. Comparing with the non-septic shock group or the survival group, the septic shock group or the non-survival group patients both had longer PT, APTT and TT, higher D-dimer, FDP and lower PLT, FIB and AT. Correlation analysis revealed that PT and PLT were better correlated with SOFA score (r values were 0.503 and -0.524, both P < 0.01), and PT was better correlated with APACHE II score (r = 0.407, P < 0.01). ROC curve analysis showed that PT had the most powerful predictive efficacy for septic shock and 28-day mortality. The area under the ROC curve (AUC) and 95% confidence interval (95%CI) were 0.831 (0.783-0.879) and 0.739 (0.674-0.805), respectively. The cut-off value were 16.8 s and 16.3 s, respectively, with the sensitivity of 64.2%, 72.0% and the specificity of 89.0%, 70.9%, respectively. Risk stratification based on PT level revealed that the patients with PT > 16.5 s (n = 103) had higher rate of 28-day mortality, incidence of septic shock and DIC, and score of SOFA and APACHE II comparing to those with PT ≤ 16.5 s (n = 199). Kaplan-Meier survival curve analysis showed that the 28-day cumulative survival rate was significantly lower in the patients with PT > 16.5 s than those with PT ≤ 16.5 s (52.43% vs. 86.93%; Log-Rank test: χ2 = 49.428, P < 0.001). Multivariate Logistic regression analysis revealed that PT > 16.5 s was an independent risk factor both for septic shock and 28-day mortality [model 1 (enrolled SOFA score): odds ratio (OR) and 95%CI were 6.003 (3.040-11.855), 4.842 (2.114-11.089); model 2 (enrolled APACHE II score): OR and 95%CI were 7.675 (4.007-14.702), 5.160 (2.258-11.793)]. CONCLUSIONS: Compared with other routine coagulation parameters, PT has the potential best predictive value for evaluating the severity of sepsis and the prognosis. When a patient is diagnosed with sepsis and has a result of PT longer than 16.5 s at ICU admission, the patient may have a higher risk of progression to septic shock and short-term death.

Identification and characterization of a novel cell binding and cross-reactive region on spike protein of SARS-CoV-2.

Given that COVID-19 continues to wreak havoc around the world, it is imperative to search for a conserved region involved in viral infection so that effective vaccines can be developed to prevent the virus from rapid mutations. We have established a twelve-fragment library of recombinant proteins covering the entire region of spike protein of both SARS-CoV-2 and SARS-CoV from Escherichia coli. IgGs from murine antisera specifically against 6 spike protein fragments of SARS-CoV-2 were produced, purified, and characterized. We found that one specific IgG against the fusion process region, named COVID19-SF5, serologically cross-reacted with all twelve S-protein fragments. COVID19-SF5, with amino acid sequences from 880 to 1084, specifically bound to VERO-E6 and BEAS-2B cells, with Kd values of 449.1 ± 21.41 and 381.9 ± 31.53 nM, and IC50 values of 761.2 ± 28.2 nM and 862.4 ± 32.1 nM, respectively. In addition, COVID19-SF5 greatly enhanced binding of the full-length CHO cell-derived spike protein to the host cells in a concentration-dependent manner. Furthermore, COVID19-SF5 and its IgGs inhibited the infection of the host cells by pseudovirus. The combined data from our studies reveal that COVID19-SF5, a novel cell-binding fragment, may contain a common region(s) for mediating viral binding during infection. Our studies also provide valuable insights into how virus variants may evade host immune recognition. Significantly, the observation that the IgGs against COVID19-SF5 possesses cross reactivity to all other fragments of S protein, suggesting that it is possible to develop universal neutralizing monoclonal antibodies to curb rapid mutations of COVID-19.

Comparison of the Performance of 24 Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Assays in the Diagnosis of Coronavirus Disease 2019 Patients.

Background: The accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the key to control Coronavirus Disease-2019 (COVID-19). The performance of different antibody detection methods for diagnosis of COVID-19 is inconclusive. Methods: Between 16 February and 28 February 2020, 384 confirmed COVID-19 patients and 142 healthy controls were recruited. 24 different serological tests, including 4 enzyme-linked immunosorbent assays (EIAs), 10 chemiluminescent immunoassays (CLIAs), and 10 lateral flow immunoassays (LFIAs), were simultaneously performed. Results: The sensitivities of anti-SARS-CoV-2 IgG and IgM antibodies with different reagents ranged from 75 to 95.83% and 46.09 to 92.45%, respectively. The specificities of both anti-SARS-CoV-2 IgG and IgM were relatively high and comparable among different reagents, ranged from 88.03 to 100%. The area under the curves (AUCs) of different tests ranged from 0.733 to 0.984, and the AUCs of EIAs or CLIAs were significantly higher than those of LFIAs. The sensitivities of both IgG and IgM gradually increased with increase of onset time. After 3-4 weeks, the sensitivities of anti-SARS-CoV-2 IgG were maintained at a certain level but the sensitivities of IgM were gradually decreased. Six COVID-19 patients who displayed negative anti-SARS-CoV-2 results were associated with the factors such as older age, having underlying diseases, and using immunosuppressant. Conclusion: Besides the purpose of assessing the impact of the SARS-CoV-2 pandemic in the population, SARS-CoV-2 antibody assays may have an adjunct role in the diagnosis and exclusion of COVID-19, especially by using high-throughput technologies (EIAs or CLIAs).