The clinical and immunological characteristics in fatal severe fever with thrombocytopenia syndrome virus (SFTSV) infection.

OBJECTIVE: This study aimed to make a comprehensive evaluation of peripheral immune profiles for further understanding the immunopathogenesis of severe fever with thrombocytopenia syndrome (SFTS). METHODS: Forty-seven patients with SFTS virus infection were included, of which 24 were deceased. The percentages, absolute numbers, phenotype of lymphocyte subsets were detected by flow cytometry. RESULTS: In patients with SFTS, the numbers of CD3+T, CD4+T, CD8+T and NKT cells were decreased compared with healthy controls (HCs), accompanied with highly active and exhausted phenotypes for T cells, and overproliferating plasmablasts. High inflammatory status, dysregulation of coagulation and host immune response were more obvious in deceased patients than that of survivors. Higher levels of PCT, IL-6, IL-10, TNF-α, APTT, TT and the occurrence of hemophagocytic lymphohistiocytosis were poor prognostic indicators of SFTS. CONCLUSIONS: The evaluation of immunological markers in combination with laboratory tests has critical value for selecting prognostic markers and potential treatment target.

Rubber band ligation versus coagulation for the treatment of haemorrhoids: a meta-analysis of randomised controlled trials.

Non-surgical therapies have the advantage of lower postoperative pain and complication rates compared with surgical therapies. Rubber band ligation and coagulation are two kinds of non-surgical therapies. The aim of this study is to compare the clinical outcomes of rubber band ligation and coagulation. A systematic review was conducted to identify randomised clinical trials that compare rubber band ligation and coagulation treatments for haemorrhoids. PubMed and Web of Science were searched, from inception to April 30th,2022. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Fifty-nine studies were identified. Nine trials met the inclusion criteria. All trials were of moderate methodological quality. No significant difference was found between rubber band ligation and coagulation in terms of efficacy rate, postoperative prolapse rate, recurrence rate and postoperative urine retention rate after treatment. Patients undergoing rubber band ligation had higher postoperative pain rate and lower postoperative bleeding rate than patients undergoing coagulation. The subgroup analysis showed that there was no significant difference between rubber band ligation and infrared coagulation or non-infrared coagulation in terms of efficacy rate, postoperative bleeding and postoperative urine retention rate after treatment. Patients undergoing rubber band ligation had a higher postoperative pain rate than patients undergoing infrared coagulation or non-infrared coagulation. We believe that coagulation for haemorrhoids still has a good future. PROSPERO registration number CRD42022311281.

CD39 pathway inhibits Th1 cell function in tuberculosis.

The role of CD39 pathway in Th1 cell function in tuberculosis (TB) is rarely elucidated. The present study aims to investigate the modulating mechanism of CD39 pathway during Mycobacterium tuberculosis (MTB) infection. CD39 expression was examined on host immune cells among patients with TB. The relationship between CD39 expression and Th1 cell function was analysed. Patients with TB displayed dramatically higher CD39 expression on Th1 cells than healthy controls, and a significantly increased expression of surface markers, including activation, exhaustion and apoptosis markers, were noted in CD39+ Th1 cells in comparison with CD39- Th1 cells. Conversely, CD39 expression on Th1 cells was associated with diminished number of polyfunctional cells producing Th1-type cytokines, and CD39+ Th1 cells showed obviously lower proliferation potential. Notably, tetramer analysis demonstrated a predominant CD39 expression on TB-specific CD4+ cells, which was associated with higher apoptosis and lower cytokine-producing ability. Transcriptome sequencing identified 27 genes that were differentially expressed between CD39+ and CD39- Th1 cells, such as IL32, DUSP4 and RGS1. Inhibition of CD39 pathway could enhance the activation, proliferation and cytokine-producing ability of Th1 cells. Furthermore, there was a significantly negative correlation between CD39 expression on Th1 cells and nutritional status indicators such as lymphocyte count and albumin levels, and we observed a significant decline in CD39 expression on Th1 cells after anti-TB treatment. CD39 is predominantly expressed on TB-specific Th1 cells and correlated with their exhausted function, which suggests that CD39 could serve as a prominent target for TB therapy.

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.

Immunologic memory to SARS-CoV-2 in convalescent COVID-19 patients at 1 year postinfection.

BACKGROUND: Understanding the complexities of immune memory to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is key to gain insights into the durability of protective immunity against reinfection. OBJECTIVE: We sought to evaluate the immune memory to SARS-CoV-2 in convalescent patients with longer follow-up time. METHODS: SARS-CoV-2-specific humoral and cellular responses were assessed in convalescent patients with coronavirus disease 2019 (COVID-19) at 1 year postinfection. RESULTS: A total of 78 convalescent patients with COVID-19 (26 moderate, 43 severe, and 9 critical) were recruited after 1 year of recovery. The positive rates of both anti-receptor-binding domain and antinucleocapsid antibodies were 100%, whereas we did not observe a statistical difference in antibody levels among different severity groups. Accordingly, the prevalence of neutralizing antibodies (nAbs) reached 93.59% in convalescent patients. Although nAb titers displayed an increasing trend in convalescent patients with increased severity, the difference failed to achieve statistical significance. Notably, there was a significant correlation between nAb titers and anti-receptor-binding domain levels. Interestingly, SARS-CoV-2-specific T cells could be robustly maintained in convalescent patients, and their number was positively correlated with both nAb titers and anti-receptor-binding domain levels. Amplified SARS-CoV-2-specific CD4+ T cells mainly produced a single cytokine, accompanying with increased expression of exhaustion markers including PD-1, Tim-3, TIGIT, CTLA-4, and CD39, while the proportion of multifunctional cells was low. CONCLUSIONS: Robust SARS-CoV-2-specific humoral and cellular responses are maintained in convalescent patients with COVID-19 at 1 year postinfection. However, the dysfunction of SARS-CoV-2-specific CD4+ T cells supports the notion that vaccination is needed in convalescent patients for preventing reinfection.

Zhang Weishen: A Pioneer in the Development and Industrial Production of Penicillin in New China.

The research and development of penicillin started with difficulty before 1949 and achieved certain results. In 1951, after the founding of the People's Republic of China, Zhang Weishen, as the only Chinese scientist who had been trained and worked in a penicillin research and development center in the United States for many years, overcame many difficulties and returned to China. In 1953, with the efforts of Zhang Weishen and his colleagues, China realized the industrialized production of penicillin, alleviating the urgent needs of the masses. Antibiotics has also become the first discipline to achieve major scientific and technological achievements after the founding of the New China. In the mid-1950s, the technical breakthrough in the localization of lactose substitutes marked the localization of the raw materials of the penicillin-producing culture medium, which paved the way for the industrialized production of penicillin with Chinese characteristics. Antibiotics have become one of the most widely used and affordable drugs for hundreds of millions of patients in China, and China has since ended the humiliating history of the "Sick Man of East Asia".

Dynamic changes in peripheral blood lymphocyte subset counts and functions in patients with diffuse large B cell lymphoma during chemotherapy.

BACKGROUND: This study aimed to analyze the lymphocyte subsets, their activities and their dynamic changes during immunochemotherapy in patients newly diagnosed with diffuse large B cell lymphoma (DLBCL). METHODS: Patients with DLBCL (n = 33) were included in the present study. Their peripheral lymphocyte subsets, phenotypes and functions were detected using flow cytometry. The dynamic results of lymphocyte activities were available for 18 patients. RESULTS: Compared with healthy controls (HCs), the counts of CD3+, CD4+, and CD8+ T cells as well as those NK cells decreased in patients newly diagnosed with DLBCL, mainly attributed to patients with high risk of prognosis assessed by International Prognostic Index (IPI) score. Lymphocyte counts didn't present significant difference between high risk (IPI scores 3-5) and low risk patients (IPI scores 0-2), but CD4+ T cells and CD8+ T cells expressed higher levels of CD28 and HLA-DR, respectively, in patients with IPI score ranging from 3 to 5. Patients at high risk harbored higher percentage of regulatory T cells (Tregs), and their CD4+ and CD8+ T cells produced lower levels of IFN-γ, reflecting an impaired cellular immune response. The dynamic changes of lymphocyte numbers and functions during treatment were further investigated. Total counts of CD3+, CD4+, CD8+ T and NK cells progressively decreased because of the cytotoxicity of chemotherapy and then gradually recovered after six cycles treatment (rituximab combined with cyclophosphamide, doxorubicin, vincristine and prednisone, R-CHOP). The functions of CD4+ and CD8+ T cells recovered by the end of two cycles R-CHOP treatment, although NK cell function was not significantly affected throughout treatment. These results suggest that the counts and functions of lymphocytes are significantly decreased in patients with DLBCL, particularly those of CD4+ and CD8+ T cells. CONCLUSIONS: The absolute counts and functions of CD4+, CD8+ T cells, which were significantly lower in patients with DLBCL, gradually recovered after effective treatment. Therefore, combined detection of T cell counts and functions are critically important for administering effective personalized immunotherapy as well as for identifying new prognostic markers or DLBCL.

Bacterial characteristics of carbapenem-resistant Enterobacteriaceae (CRE) colonized strains and their correlation with subsequent infection.

BACKGROUND: Searching the risk factors for carbapenem-resistant Enterobacteriaceae (CRE) infection is important in clinical practice. In the present study, we aim to investigate bacterial characteristics of colonizing strains and their correlation with subsequent CRE infection. METHODS: Between May 2018 and January 2019, patients hospitalized in the department of haematology and intensive care unit (ICU) were screened for CRE by rectal swabs and monitored for the outcome of infection. We identified the species and carbapenemase-encoding genes of colonizing strains and performed antimicrobial susceptibility tests and multilocus sequence typing (MLST). Risk factors for subsequent CRE infections were ascertained by univariate and multivariable analysis. RESULTS: We collected a total of 219 colonizing strains from 153 patients. Klebsiella pneumoniae was the most abundant species, and MLST analysis showed rich diversity. K. pneumoniae carbapenemase (KPC) was predominant in the infection group (72.4%). In the non-infection group, 35.4% of strains were non-carbapenemase-producing CRE (NCP-CRE), and New Delhi metallo-ß-lactamase (NDM) was predominant (42.2%). The rate of high-level carbapenem resistance (minimum inhibitory concentration [MIC] ≥ 64 mg/L for meropenem and ertapenem, ≥ 32 mg/L for imipenem) was remarkably higher in the infection group than in the non-infection group (P <  0.001). Univariate analysis showed that K. pneumoniae, high-level carbapenem resistance, CP-CRE and KPC-CRE were infection risk factors after CRE colonization. On multivariable analysis with different carbapenemase dichotomizations, KPC-CRE (adjusted odds ratio [aOR], 4.507; 95% confidence interval [CI], 1.339-15.171; P = 0.015) or imipenem MIC ≥ 32 mg/L (aOR, 9.515; 95% CI, 1.617-55.977; P = 0.013) were respectively identified as independent risk factors for subsequent infection. CONCLUSIONS: Patients colonized with KPC-CRE or strains with an imipenem MIC ≥ 32 mg/L were at particularly high risk of subsequent CRE infections during their hospital stay.

Combination of prealbumin and tuberculosis-specific antigen/phytohemagglutinin ratio for discriminating active tuberculosis from latent tuberculosis infection.

BACKGROUND: Given that there is no rapid and effective method for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI), the discrimination between these two statuses remains challenging. This study sought to investigate the value of nutritional indexes and tuberculosis-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) for distinguishing ATB from LTBI. METHODS: Participants were consecutively recruited based on positive T-SPOT.TB results between January 2018 and January 2020. ATB was diagnosed by positive mycobacterial culture and/or positive GeneXpert MTB/RIF, with clinical symptoms and radiological characteristics suggestive of ATB. Individuals with positive T-SPOT.TB but without the evidence of ATB were defined as LTBI. Patients younger than 17 years and undergoing anti-TB treatment were excluded. RESULTS: A total of 709 (312 ATB and 397 LTBI) and another 309 (120 ATB and 189 LTBI) subjects were respectively recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The level of prealbumin was significantly lower in ATB than in LTBI. With a cut-off value of 139 mg/L, the sensitivity and specificity of prealbumin in distinguishing ATB from LTBI were 50.96% (45.41%-56.51%) and 91.69% (88.97%-94.40%). Meanwhile, TBAg/PHA ratio was found statistically higher in ATB compared with LTBI. If using the threshold of 0.29, the sensitivity and specificity of TBAg/PHA ratio were 65.71% (60.44%-70.97%) and 90.93% (88.11%-93.76%), respectively. Moreover, the combination of prealbumin and TBAg/PHA ratio (obtaining by diagnostic model) yielded better specificity (90.18%, [87.25%-93.10%]) and sensitivity (87.18%, [83.47%-90.89%]), while the clinical utility index (CUI) positive and CUI negative were respectively 0.76 and 0.81. After anti-TB treatment, TBAg/PHA ratio was declined while the level of prealbumin was restored (Wilcoxon test, P < 0.001). Furthermore, the performance of diagnostic model obtained in Qiaokou cohort was confirmed in Caidian cohort. CONCLUSIONS: The diagnostic model based on combination of prealbumin and TBAg/PHA ratio is a rapid and accurate tool for discriminating ATB from LTBI.

Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection.

BACKGROUND: There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease. METHODS: A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal (n = 51) and survived (n = 688) groups according to the patient's outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously. RESULTS: The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4+ T cells, CD8+ T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-α on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4+ T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death. CONCLUSIONS: Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19.

Intestinal ultrasound scan predicts corticosteroid failure and colectomy risk in patients with ulcerative colitis.

OBJECTIVE: Intestinal ultrasound (IUS) is an emerging modality for assessing disease activity, extent, and treatment response in ulcerative colitis. This study aimed to evaluate the potential of IUS in predicting severe flares, the need for rescue therapy (corticosteroid failure), and colectomy in patients with ulcerative colitis. METHODS: We conducted a retrospective review of medical records, collecting clinical and IUS data. The Milan Ultrasound Criteria (MUC) score was used to assess ulcerative colitis severity. Group comparisons were performed to identify differences in MUC scores between mild-to-moderate and severe ulcerative colitis, between steroid responders and nonresponders, and between patients who underwent colectomy and those who did not. Receiver operating characteristic (ROC) analysis was used to predict outcomes in patients with ulcerative colitis. RESULTS: This analysis included 102 patients with ulcerative colitis categorized as mild/moderate (60) or severe (42). MUC scores were significantly higher in the severe ulcerative colitis group compared with the mild/moderate group ( P  < 0.001). Analysis (using ROC) identified a cutoff MUC score of >8.54 to indicate severe ulcerative colitis with good sensitivity (64.29%) and excellent specificity (93.33%). Similarly, a cutoff of MUC > 10.54 showed promise in predicting corticosteroid failure, with acceptable sensitivity (50%) and high specificity (90.91%). Finally, a cutoff MUC score >12.5 demonstrated potential for predicting colectomy, exhibiting moderate sensitivity (55.56%) but excellent specificity (96.97%). CONCLUSION: IUS may be useful for differentiating severe ulcerative colitis from mild-to-moderate disease, identifying early stage failure of corticosteroid therapy, and predicting the potential need for colectomy.

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.

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.

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.

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.

Clinical characteristics and long-term outcomes of hospitalised patients with ischemic colitis with different degrees of haematochezia: a retrospective study.

BACKGROUND: Based on the literature, haematochezia is associated with a benign clinical course of ischaemic colitis. However, most cases in the literature presented mild haematochezia associated with ischaemic colitis. Therefore, we aimed to investigate the impact of different degrees of haematochezia on the clinical outcomes of ischaemic colitis. METHODS: Patients were divided into nonhaematochezia, mild-haematochezia, and severe-haematochezia cohorts stratified by the degree of haematochezia. The clinical characteristics and prognoses were retrospectively reviewed. RESULTS: Haematochezia cohort (n = 89) was associated with a lower rate of severe illness (25% vs. 52%, P = 0.001), lower rate of isolated right colon ischaemia (7% vs. 28%, P = 0.001), lower surgery rates (13% vs. 36%, P = 0.001), and shorter hospital stay (12 vs. 17 days, P < 0.001) compared with nonhaematochezia cohort (n = 50). Severe-haematochezia cohort (n = 11) had a higher frequency of severe illness (73% vs. 18%, P < 0.001), higher surgical intervention rate (55% vs. 6%, P < 0.001), higher nonsurgical complication rate, higher in-hospital mortality (45% vs. 0%, P < 0.001), and longer hospital stay (28 vs. 10 days, P = 0.001), compared with mild-haematochezia cohort (n = 78). Additionally, in-hospital mortality (45% vs. 6%, P = 0.003) and nonsurgical complication rate were higher in the severe-haematochezia than in the nonhaematochezia cohort. However, the three cohorts had comparable prognoses for long-term survival and recurrence. CONCLUSIONS: Mild haematochezia was related to a benign clinical course of ischaemic colitis, while lack of haematochezia or severe haematochezia was associated with worse hospitalisation outcomes.

Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection.

BACKGROUND: Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI. METHODS: Participants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning. RESULTS: A total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively. CONCLUSIONS: We successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.

The Dynamic Immunological Parameter Landscape in Coronavirus Disease 2019 Patients With Different Outcomes.

Objectives: The longitudinal and systematic evaluation of immunity in coronavirus disease 2019 (COVID-19) patients is rarely reported. Methods: Parameters involved in innate, adaptive, and humoral immunity were continuously monitored in COVID-19 patients from onset of illness until 45 days after symptom onset. Results: This study enrolled 27 mild, 47 severe, and 46 deceased COVID-19 patients. Generally, deceased patients demonstrated a gradual increase of neutrophils and IL-6 but a decrease of lymphocytes and platelets after the onset of illness. Specifically, sustained low numbers of CD8+ T cells, NK cells, and dendritic cells were noted in deceased patients, while these cells gradually restored in mild and severe patients. Furthermore, deceased patients displayed a rapid increase of HLA-DR expression on CD4+ T cells in the early phase, but with a low level of overall CD45RO and HLA-DR expressions on CD4+ and CD8+ T cells, respectively. Notably, in the early phase, deceased patients showed a lower level of plasma cells and antigen-specific IgG, but higher expansion of CD16+CD14+ proinflammatory monocytes and HLA-DR-CD14+ monocytic-myeloid-derived suppressor cells (M-MDSCs) than mild or severe patients. Among these immunological parameters, M-MDSCs showed the best performance in predicting COVID-19 mortality, when using a cutoff value of ≥10%. Cluster analysis found a typical immunological pattern in deceased patients on day 9 after onset, which was characterized as the increase of inflammatory markers (M-MDSCs, neutrophils, CD16+CD14+ monocytes, and IL-6) but a decrease of host immunity markers. Conclusions: This study systemically characterizes the kinetics of immunity of COVID-19, highlighting the importance of immunity in patient prognosis.

Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis.

Background: The differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM. Methods: Patients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model. Results: A total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840-0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%-77.77%) and a specificity of 92.86% (95% CI, 85.98%-96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921-0.978), with 81.58% (95% CI, 71.42%-88.70%) sensitivity and 91.84% (95% CI, 84.71%-95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867-0.980) with 79.49% (95% CI, 64.47%-89.22%) sensitivity and 90.91% (95% CI, 81.55%-95.77%) specificity. Conclusions: The diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.