Analysis of risk characteristics for early progression and late progression in locally advanced rectal cancer patients: a large population-based and validated study.

BACKGROUND AND OBJECTIVE: The current study aimed to explore the factors influencing early progression (EP) and late progression (LP) in locally advanced rectal cancer (LARC) patients. METHODS: The patients were classified into EP and LP groups using one year as a cutoff. The random survival forest model was utilized to calculate the probability of time-to-progression. Besides, inverse probability of treatment weighting (IPTW) analysis and the Surveillance, Epidemiology, and End Results (SEER) were conducted to validate our results. RESULTS: Our study revealed that PNI, CEA level, and pathological stage were independent prognostic factors for PFS both in EP group and LP group. For EP group patients, Group 1 had the highest probability of progression at the 9th month of follow-up, while Group 2 exhibited the highest probability at the 6th month. Group 3, on the other hand, showed two peaks of progression at the 4th and 8th months of follow-up. As for LP group patients, Groups 4, 5, and 6 all exhibited peaks of progression between the 18th and 24th months of follow-up. Furthermore, our results suggested that PNI was also an independent prognostic factor affecting OS in both EP group and LP group. Finally, the analysis of IPTW and SEER database further confirmed our findings. CONCLUSIONS: Our results indicated a significant correlation between immune and nutritional status with PFS and OS in both EP and LP groups. These insights can aid healthcare professionals in effectively identifying and evaluating patients' nutritional status, enabling them to develop tailored nutrition plans and interventions.

Evaluating the diagnostic and therapeutic significance of KL-6 in patients with interstitial lung diseases.

Background: This study aimed to assess the diagnostic value of Krebs von den Lungen-6 (KL-6), Surfactant protein-A (SP-A), SP-D and molecular matrixmetalloproteinase-7 (MMP-7) in discriminating patients with interstitial lung diseases (ILDs) from disease control subjects. Methods: Serum levels of KL-6, SP-A, SP-D and MMP-7 were measured in both the ILD and non-ILD (NILD) groups. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic potential of these markers and laboratory indices. High-resolution computed tomography (HRCT) fibrosis scores were determined, and their correlation with the serum markers was analyzed. Results: Serum levels of KL-6 and MMP-7 were significantly elevated in the ILD group compared to the control group, while no significant differences were observed for SP-A and SP-D. ROC analysis of KL-6 demonstrated superior diagnostic accuracy, with a sensitivity of 76.36%, specificity of 91.07%, and an area under curve (AUC) of 0.902 (95%CI 0.866-0.945). These findings were consistent across an additional cohort. Correlation analysis revealed a link between KL-6 levels at initial diagnosis and HRCT fibrosis scores, indicating disease severity. Moreover, a negative correlation was found between KL-6 and pulmonary function indices, reflecting disease progression. Patients with increased 12-month HRCT fibrosis score showed higher lactate dehydrogenase (LDH) levels, with LDH exhibiting an AUC of 0.767 (95% CI: 0.520-0.927) as a predictor of progression. Conclusions: Serum KL-6 detection proves to be a valuable tool for accurately distinguishing ILDs from control subjects. While KL-6 shows a correlation with HRCT fibrosis scores and a negative association with pulmonary function indices, its predictive value for ILDs prognosis is limited. Trial registration: This study received retrospective approval from the Ethical Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (institutional review board ID: TJ-IRB20210331, date: 2021.03.30).

Comprehensive evaluation of immune dysregulation in secondary hemophagocytic lymphohistiocytosis.

Host immune dysfunction plays a crucial role in the onset, progression, and outcome of hemophagocytic lymphohistiocytosis (HLH). This study aimed to comprehensively evaluate the peripheral immune profiles in patients with newly diagnosed secondary hemophagocytic lymphohistiocytosis (sHLH), and explore their predictive value for patient prognosis. A total of 77 patients with sHLH were enrolled in this study, with 31 of them experiencing mortality. Flow cytometry was used to assess the percentages, absolute numbers, and phenotypes of lymphocyte subsets. Simultaneously, cytokine levels and routine laboratory indicators were also collected. In sHLH patients, lymphocyte subset absolute numbers were significantly impaired, accompanied by T cell hyperactivation, B cell hyperactivation, and increased plasmablast proliferation. Prognostic analysis revealed that lower CD8+ T cell percentages, elevated APTT, IL-6, IL-10 levels, and increased CD4+CD28null T cell proportions were associated with poor patient outcomes. The study demonstrates dysregulation in the counts and phenotypes of lymphocyte subsets in sHLH patients. Several key factors, including IL-6, IL-10, APTT, and various T cell percentages, have potential as prognostic markers and therapeutic targets in sHLH.

Prediction of liver metastasis and recommended optimal follow-up nursing in rectal cancer.

We aimed to analyze and investigate the clinical factors that influence the occurrence of liver metastasis in locally advanced rectal cancer patients, with an attempt to assist patients in devising the optimal imaging-based follow-up nursing. Between June 2011 and May 2021, patients with rectal cancer at our hospital were retrospectively analyzed. A random survival forest model was developed to predict the probability of liver metastasis and provide a practical risk-based approach to surveillance. The results indicated that age, perineural invasion, and tumor deposit were significant factors associated with the liver metastasis and survival. The liver metastasis risk of the low-risk group was higher at 6-21 months, with a peak occurrence time in the 15th month. The liver metastasis risk of the high-risk group was higher at 0-24 months, with a peak occurrence time in the 8th month. In general, our clinical model could predict liver metastasis in rectal cancer patients. It provides a visualization tool that can aid physicians and nurses in making clinical decisions, by detecting the probability of liver metastasis.

Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.

OBJECTIVE: Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators. METHODS: A total of 925 SARDs patients were included, categorised into SLE, Sjögren's syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model. RESULTS: Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles. CONCLUSION: This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.

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.

A large-scale study integrating nutritional indicators and clinicopathological parameters to evaluate prognosis, follow-up, and postoperative chemotherapy decisions in rectal cancer patients.

OBJECTIVE: The aim of this study was to evaluate the role of nutritional indicators and clinicopathological parameters in predicting the progression and prognosis for pathological stage II-III rectal cancer (RC) patients without neoadjuvant radiotherapy. In addition, we sought to explore the high-risk population who may require postoperative chemotherapy. METHODS: A total of 894 consecutive RC patients were enrolled in this study. Univariate and multivariate Cox analysis were performed to identify the independent risk factors for PFS and OS. The nomogram and calibration curves were conducted according to multivariable analysis result. Kaplan-Meier survival curves and log-rank tests were performed for different groups. Finally, random survival forest (RSF) model was developed to predict the probability of progression. RESULTS: Our results revealed that CEA level, pathological stage, tumor deposit, and PNI were independently associated with PFS in RC patients. Similarly, the results indicated that CEA level, pathological stage, tumor deposit, PNI, and NRI were independently associated with OS. RSF model revealed that group 1 had the highest risk of progression at the 12th month of follow-up, group 2 had the highest risk of progression at the 15th month of follow-up, while group 3 had the highest risk of progression at the 9th month of follow-up. Besides, subgroup analysis suggested that the high-risk group needs postoperative adjuvant chemotherapy, while patients in the low- and moderate-risk groups may not need postoperative adjuvant chemotherapy. Finally, we validated our results with the SEER database. CONCLUSIONS: In conclusion, we demonstrated that preoperative nutritional indicator and clinicopathological parameters could act as auxiliary prognostication tools for RC patients without neoadjuvant radiotherapy. We also established follow-up strategies for different groups of patients. Collectively, incorporating nutritional assessment into risk stratification for RC resection is crucial and should be an integral part of preoperative planning.

Kinetics and prognostic significance of laboratory markers in patients with severe fever with thrombocytopenia syndrome: insight from a comprehensive analysis.

Severe Fever with Thrombocytopenia Syndrome (SFTS) is an emerging infectious disease with significant mortality. Identifying prognostic factors that influence patient outcomes is crucial for effective clinical management. In this study, we assessed the dynamic changes of laboratory markers and their association with outcomes in 93 SFTS patients. We found that age and hypertension were significantly associated with poor outcomes in SFTS patients. The deceased group exhibited lower platelet counts, elevated liver and kidney function markers, coagulation profiles, inflammatory markers, and cytokines compared to the survival group. Kinetic analysis showed that these markers gradually normalized in the survival group, while they remained persistently abnormal in the deceased group. Furthermore, hypertension, elevated AST, PCT, and IL-10 were identified as independent risk factors for predicting poor prognosis of SFTS patients. These findings provide valuable insights into the prognostic significance of laboratory markers and highlight the importance of early identification of high-risk SFTS patients.

Detection of serum human neutrophil lipocalin is an effective biomarker for the diagnosis and monitoring of children with bacterial infection.

BACKGROUND: The study aimed to investigate the diagnostic efficiency of human neutrophil lipocalin (HNL) in bacterial infections in children. METHODS: This study included 49 pediatric patients with bacterial infections, 37 patients with viral infections, 30 patients with autoimmune diseases (AID) and 41 healthy controls (HCs). HNL, procalcitonin (PCT), C-reactive protein (CRP), white blood cell (WBC) and neutrophil counts were detected in the initial diagnosis and the following days. RESULTS: In the patients with bacterial infections, the levels of HNL, PCT, CRP, WBC and neutrophils were significantly increased than that of disease controls and HCs. The dynamic of these markers was monitored during antibiotic treatment. The level of HNL was decreased rapidly in patients with effective treatment, but maintained at high levels in deteriorated patients according to the clinical progression. CONCLUSIONS: HNL detection is an effective biomarker to identify bacterial infections from viral infections and other AIDs, and has potential value to evaluate the effect of antibiotic treatment in pediatric patients.

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.

Network Pharmacology-Based Study on the Active Ingredients and Mechanism of Pan Ji Sheng Traditional Chinese Medicine Formula in the Treatment of Inflammation.

Background: Pan Ji Sheng Formula is a Chinese medicine formula that enables heat-free detoxification as well as anti-inflammatory and immune-boosting properties. This formula contains eight herbs. Its underlying mechanism is unknown. The bioactive ingredients were screened in our work, and the mechanism of this formula was investigated. Methods: Using traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), ingredients in Pan Ji Sheng Chinese medicine formula were screened, and we selected the main bioactive ingredients for web-based research. The targets of bioactive ingredients are primarily obtained from the SwissTargetPrediction and TCMSP databases, and the text mining method is used. STRING and Cytoscape were then used to examine the protein-protein interaction (PPI) networks. To explore the biological function and related pathways, functional annotation and pathway analysis were performed. Results: This research discovered 96 bioactive ingredients. Then, 215 potential targets of bioactive ingredients were screened. Through the analysis of the PPI network, we discovered 25 key target genes, which can be described as hub target genes regulated by bioactive ingredients. Bioactive ingredients primarily regulate CASP3, AKT1, JUN, and other proteins. The formula works synergistically to enhance immune response and antiinfection by regulating immune-related pathways, TNF signaling pathways, and apoptosis. Conclusions: A variety of bioactive ingredients in the formula could play roles in regulating CASP3, AKT1, and other genes in immune, infection, apoptosis, and tumor-related signaling pathways. Our data point the way forward for future studies on the mechanism of action of this formula.

ACR TI-RADS classification combined with number of nodules, halo features optimizes diagnosis and prediction of follicular thyroid cancer.

OBJECTIVES: To investigate the application value of The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) category combined with other ultrasound features of nodules in distinguishing follicular thyroid carcinoma (FTC) from thyroid follicular adenoma (FTA). METHODS: We collected and retrospectively analyzed clinical and ultrasound data for 118 and 459 patients with FTCs and FTAs, respectively, at our hospital. Next, we used ACR TI-RADS classification combined with other ultrasound features of nodules to distinguish FTC from FTA. Multivariate Logistic regression was used to screen independent risk factors for FTC, which were subsequently used to construct a nomogram for predicting FTC. RESULTS: ACR TI-RADS categories 4 and 5, unilateral multiple nodules, and halo thickness≥2 mm were independent risk factors for FTC. ACR TI-RADS category combined with number of nodules, halo features of the nodule was a significantly better prediction model for FTC diagnosis (AUC = 0.869) than that of ACR TI-RADS classification alone (AUC = 0.756). CONCLUTIONS: Clinicians need to pay attention to the halo of nodules when distinguishing FTA from FTC. Notably, ACR TI-RADS combined with other nodule ultrasound features has superior predictive performance in diagnosis of FTC compared to ACR TI-RADS classification alone, thus can provide an important reference value for preoperative diagnosis of FTC.

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).

Using TBAg/PHA Ratio for Monitoring TB Treatment: A Prospective Multicenter Study.

The way to monitor tuberculosis (TB) treatment is extremely lacking in clinical practice. The aim of the study is to assess the role of the TBAg/PHA ratio in the treatment monitoring of TB. TB patients were followed up for 6 months and serial T-SPOT.TB (T-SPOT) assays were performed. In patients with successful treatment outcomes, the ESAT-6 sfc, CFP-10 sfc, and TBAg/PHA ratio all showed a decreased trend after the initiation of treatment. Conversely, PHA sfc showed an increased trend after 2 months of treatment. However, these indicators had moderate performance in distinguishing between before and after 6 months of treatment, and the AUC ranged from 0.702 to 0.839. Notably, the TBAg/PHA ratio in patients without risk factors was of important value in differentiation between before and after treatment. The optimal AUC of TBAg/PHA ratio reached up to 0.890. Patients with unsuccessful treatment outcomes showed persistently high levels of TBAg/PHA ratio. The TBAg/PHA ratio in patients after 6 months of treatment showed a certain potential in distinguishing between patients with successful and unsuccessful treatment outcomes. A further calculation of the TBAg/PHA ratio in T-SPOT assay has potential value in the treatment monitoring of TB, but further confirmation is needed.

MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer.

Background: Overtreatment of axillary lymph node dissection (ALND) may occur in patients with axillary positive sentinel lymph node (SLN) but negative non-SLN (NSLN). Developing a magnetic resonance imaging (MRI)-based radiomics nomogram to predict axillary NSLN metastasis in patients with SLN-positive breast cancer could effectively decrease the probability of overtreatment and optimize a personalized axillary surgical strategy. Methods: This retrospective study included 285 patients with positive SLN breast cancer. Fifty five of them had metastatic NSLNs and 230 had non-metastatic NSLNs. MRI-based radiomic features of primary tumors were extracted and MRI morphologic findings of the primary tumor and axillary lymph nodes were assessed. Four models, namely, a radiomics signature, an MRI-clinical nomogram, and two MRI-clinical-radiomics nomograms were established based on MRI morphologic findings, clinicopathologic characteristics, and MRI-based radiomic features to predict the NSLN status. The optimal predictors in each model were selected using the 5-fold cross-validation (CV) method. Their predictive performances were determined by the receiver operating characteristic (ROC) curves analysis. The area under the curves (AUCs) of different models was compared by the Delong test. Their discrimination capability, calibration curve, and clinical usefulness were also assessed. Results: The 5-fold CV analysis showed that the AUCs ranged from 0.770 to 0.847 for the radiomics signature, from 0.720 to 0.824 for the MRI-clinical nomogram, from 0.843 to 0.932 for the MRI-clinical-radiomics nomogram. The optimal predictive factors in the radiomics signature, MRI-clinical nomogram, and MRI-clinical-radiomics nomogram were one texture feature of diffusion-weighted imaging (DWI), two clinicopathologic features together with one MRI morphologic finding, and the DWI-based texture feature together with the two clinicopathologic features plus the one MRI morphologic finding, respectively. The MRI-clinical-radiomics nomogram with CA 15-3 included achieved the highest AUC compared with the radiomics signature (0.868 vs. 0.806, P <0.001) and MRI-clinical nomogram (0.868 vs. 0.761; P <0.001). In addition, the MRI-clinical-radiomics nomogram without CA 15-3 showed a higher performance than that of the radiomics signature (AUC, 0.852 vs. 0.806, P = 0.016) and the MRI-clinical nomogram (AUC, 0.852 vs. 0.761, P = 0.007). The MRI-clinical-radiomics nomograms showed good discrimination and good calibration. Decision curve analysis demonstrated that the MRI-clinical-radiomics nomograms were clinically useful. Conclusion: The MRI-clinical-radiomics nomograms developed in our study showed high predictive performance, which can be used to predict the axillary NSLN status in SLN-positive breast cancer patients before surgery.

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.

HLA-DR Expression Level in CD8+ T Cells Correlates With the Severity of Children With Acute Infectious Mononucleosis.

Background: This study aimed to assess the host immune signatures associated with EBV infection and its clinical value in indicating the severity of children with acute infectious mononucleosis (IM). Methods: Twenty-eight pediatric patients with IM aged 3-8 years were enrolled. The immune phenotypes and cytokine secretion capability of T cells were detected. Results: The percentages and absolute numbers of CD3+ and CD8+ T cells were significantly increased in IM patients compared with HCs. The percentages of Naïve CD4+ and CD8+ T cells were decreased but with increased percentages of memory CD4+ and CD8+ T subsets. Our results showed the upregulation of active marker HLA-DR, TCR-αß, and inhibitory receptors PD-1, TIGIT in CD8+ T cells from IM patients, which suggested that effective cytotoxic T cells were highly against EBV infection. However, EBV exposure impaired the cytokine (IFN-γ, IL-2, and TNF-α) secretion capability of CD4+ and CD8+ T cells after stimulation with PMA/ionomycin in vitro. Multivariate analysis revealed that the percentage of HLA-DR+ CD8+ T cells was an independent prognostic marker for IM. The percentage of HLA-DR+ CD8+ T cells was significantly correlated with high viral load and abnormal liver function results. Conclusion: Robust expansion and upregulation of HLA-DR in CD8+ T cells, accompanied with impaired cytokine secretion, were typical characteristics of children with acute IM. The percentage of HLA-DR+ CD8+ T cells might be used as a prominent marker not only for the early diagnosis but also for indicating the severity of IM.

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.

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.