Hongyan Hou

Background: Interstitial lung disease (ILD) is a major cause of morbidity and mortality in idiopathic inflammatory myopathies (IIMs). Although Gender-Age-Physiology (GAP) staging is widely used for prognostic stratification, it relies on pulmonary function testing, underscoring the need for practical markers to facilitate timely risk stratification. Methods: We conducted a retrospective cohort study of 47 IIM patients with ILD at Tongji Hospital. ILD was confirmed by baseline high-resolution CT and severity was staged using the GAP index. Patients were categorized as GAP stage I (0–3 points, n = 25) or GAP stages II-III (4–8 points, n = 22). Routine laboratory parameters and anti-MDA5/anti-Ro52 profiles were compared between groups. Multivariable logistic regression was performed with GAP stages II-III (vs GAP stage I) as the outcome to evaluate the association between antibody-defined subgroups and ILD severity. Results: Patients with GAP stages II-III were older than those with GAP stage I (median 58.0 vs 52.0 years, p = 0.030) and had a higher 28-day mortality (27.27% vs 0%). Anti-MDA5 positivity was more frequent in GAP stages II-III ILD than in GAP stage I (72.7% vs 40.0%, p = 0.024). Compared with GAP stage I, GAP stages II-III were characterized by reduced absolute lymphocyte counts and lymphocyte proportions, increased neutrophil proportions, elevated fibrinogen levels, and lower uric acid, while most liver and muscle injury markers were comparable. Fungal infections were more common in GAP stages II-III than in stage I (45.5% vs 16.0%, p = 0.028). In multivariable logistic regression (reference: double-negative), concurrent anti-MDA5/anti-Ro52 positivity independently associated with GAP stages II-III ILD (adjusted OR 12.03, 95% CI 1.53–94.73, p = 0.018). Conclusions: Routinely available laboratory indices combined with anti-MDA5/anti-Ro52 profiling may aid rapid identification of IIM patients at high risk for GAP stages II-III ILD.

Objectives: To determine the prevalence and clinical associations of rare HEp 2 indirect immunofluorescence ANA patterns and to evaluate their relationships with disease categories, ANA titers, and expression form. Methods: We retrospectively analyzed 366,524 ANA tests performed between 2018 and 2024 using pattern definitions based on the 2018 ICAP update. Rare patterns were defined as those occurring in less than 1 % of ANA positive cases and represented by more than 30 samples. Clinical diagnoses were categorized as autoimmune, metabolic, infectious, neoplastic, or unclassified. Results: Among 81,860 ANA positive cases (22.3 %), 1,769 (2.2 %) showed rare patterns. The most frequent rare patterns were AC-22 (0.7 %), AC-23 (0.41 %), AC-25 (0.25 %), AC-26 (0.23 %) and AC-29 (0.16 %). AC-29 was largely confined to autoimmune diseases, particularly systemic sclerosis, and was often observed at high titers. For AC-26, higher titers were more frequently observed among autoimmune disease cases, suggesting titer dependent enrichment. By contrast, AC-22 and AC-23 were more frequently observed in metabolic or infectious diseases and were mainly characterized by low titers (≤1:320). Cytoplasmic and mitotic patterns more commonly appeared as mixed patterns and showed broader distributions across disease categories. Among patients with systemic sclerosis exhibiting the AC-29 pattern, 96.2 % were positive for anti-Scl-70 antibodies. Conclusions: Rare ANA patterns show distinct disease and titer profiles. Recognition of these patterns may enhance diagnostic accuracy when interpreted in conjunction with clinical and serological findings.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is a tick‐borne viral illness with high mortality, yet early risk stratification remains challenging. Recent evidence suggests that B‐cell dysregulation contributes to disease severity. Methods: A cohort of 168 patients with confirmed SFTS was retrospectively analyzed. Flow cytometry was used to quantify B‐cell subsets in peripheral blood at admission. Laboratory markers, viral load, and B‐cell phenotypes were evaluated for prognostic relevance using univariate and multivariate Cox regression analyses. Receiver operating characteristic (ROC) curves and nomogram models were employed to assess predictive value. Results: Deceased patients exhibited significantly higher viral loads, elevated proinflammatory cytokines (interleukin‐6 (IL‐6), IL‐10, and tumor necrosis factor‐alpha (TNF‐α)), and markers of multiorgan dysfunction. Immunophenotyping revealed a reduction in naïve B‐cells (IgD ⁺ CD27 ⁻ ), alongside expansion of double negative B‐cells (DNBs) (IgD ⁻ CD27 ⁻ ) in fatal cases. Furthermore, viral load was positively correlated with inflammatory cytokines and dysfunctional B‐cell subsets, suggesting that impaired humoral immunity contributes to persistent hyperinflammation in severe SFTS. Multivariate Cox regression analysis identified higher viral load (HR = 2.193, p < 0.001), older age (HR = 1.073, p < 0.001), and increased proportion of DNBs (HR = 1.024, p = 0.035) as independent predictors of 28‐day mortality. A combined prognostic model integrating these variables achieved excellent performance (AUC = 0.906, 95% CI: 0.814–0.967), significantly surpassing individual markers and enabling early identification of high‐risk SFTS patients. Conclusion: This study demonstrates that integrating B‐cell subset profiling with laboratory indicators significantly improves early prognostic assessment in SFTS. These findings provide insights into immune‐pathological mechanisms and support timely risk stratification and intervention to reduce mortality.

Objective: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick‐borne disease characterized by high morbidity and mortality rates. Timely detection and prognosis prediction are critical for implementing effective clinical interventions. This study aimed to develop a binary classification machine learning model utilizing early clinical and laboratory indicators to predict the prognosis of SFTS patients, facilitating early clinical decision‐making. Methods: We conducted a retrospective study including 233 SFTS patients diagnosed from October 2021 to May 2024. Clinical and laboratory data at initial diagnosis were collected and subjected to baseline analysis and correlation analysis to identify significant indicators. Using the area under the receiver operating characteristic curve as an indicator of model performance, select the analytical model among machine learning (LR) models, XGBOOST, LightGBM, and random forest. A binary classification machine learning model for predicting survival outcomes was constructed using a logistic regression algorithm in conjunction with identified metrics. The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). The model was externally validated using a separate cohort of 73 patients. Result: A total of 233 patients were included in this study, among whom 146 (62.7%) survived and 87 (37.3%) died, with 208 assigned to the internal cohort (177 to the training set and 31 to the test set) and the external validation cohort consisted of 73 patients, including 52 survivors (71.2%) and 21 mortality cases (28.8%). Based on the AUC, the LR model (0.750) is selected, where the values of XGBOOST, LightGBM, and random forest are 0.649, 0.661, and 0.716, respectively. The logistic regression model, incorporating age, lactate dehydrogenase (LDH), albumin, activated partial thromboplastin time (APTT), and platelet count, demonstrated robust predictive accuracy with an AUC of 0.913 (95% CI: 0.821–1.000) for the test set and 0.923 (95% CI: 0.842–1.000) for the external validation set. The model exhibited an accuracy of 0.863, with a sensitivity of 0.952 and an NPV of 0.977. The specificity and PPV were 0.827 and 0.690, respectively. These results indicate that the model maintains robust discriminative performance in an independent cohort, suggesting its potential utility as a screening tool with high sensitivity and favorable NPV. Conclusion: The model constructed using the first diagnostic indicators described above can accurately determine the patient’s prognosis, which can help the clinician intervene earlier as well as take the necessary life‐saving measures, and has the potential to improve the survival rate of SFTS patients.

Objective: To apply group‐based trajectory modeling (GBTM) to longitudinal real world data from patients with systemic lupus erythematosus (SLE) to identify distinct disease activity trajectories and factors associated with 6 month clinical status. Methods: A total of 91 patients with SLE treated between 2017 and 2024 were included. SLE Disease Activity Index 2000 (SLEDAI‐2K) scores were assessed at baseline, 3 months, and 6 months. GBTM was used to characterize longitudinal disease activity patterns. Heatmap clustering analysis was performed to compare clinical laboratory variables and SLEDAI‐2K scores across trajectory groups and to further explore their association with 6 month clinical status. Results: Two distinct disease activity trajectories were identified. Class 1, comprising patients with higher initial SLEDAI‐2K scores, was predominantly composed of nonremission cases (90.91%). Class 2, characterized by lower initial SLEDAI‐2K scores, showed a more balanced distribution of nonremission (46.81%) and remission (53.19%) patients. At 6 months, the remission subgroup had significantly lower SLEDAI‐2K scores and lower urinary biomarker levels, including 24 h urinary microalbumin (24 h‐UMA), 24 h urinary micrototal protein (24 h‐UMTP), UMA, and UMTP, than the nonremission subgroup. Conclusions: Longitudinal monitoring of SLEDAI‐2K trajectories may help identify patients at risk for persistent high disease activity or nonremission at 6 months and those who may benefit from closer monitoring and treatment adjustment. These findings support a stratified management approach in SLE based on trajectory profiles.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is marked by high case fatality and profound antiviral immune dysregulation, yet the clinical implications of changes in circulating T follicular helper (Tfh) cells remain unclear. Methods: We enrolled 79 patients with RT‐PCR‐confirmed SFTSV infection who were hospitalized from May to October 2023 and categorized them as acute‐phase survivors (AS) or acute‐phase deceased (AD). Frequencies of circulating Tfh subsets (Tfh1, Tfh2, Tfh17, and PD‐1 ⁺ Tfh) were measured by flow cytometry, and plasma SFTSV RNA was quantified by RT‐qPCR. Their relationships with clinical variables and prognosis were analyzed using correlation analysis, ROC analysis, Kaplan–Meier survival analysis, and forward‐selection multivariable logistic regression. Results: Compared with survivors, patients in the AD group showed greater inflammatory activation, more pronounced coagulation disturbances, and higher plasma viral loads. While overall circulating Tfh frequencies were elevated in AD patients, subset profiling demonstrated a clear bias toward Tfh2 expansion together with contraction of Tfh1 and Tfh17 populations. Among these subsets, Tfh2 had the best discriminatory value for mortality (AUC = 0.724). Lower Tfh1 and Tfh17 frequencies were associated with poorer 28‐day survival. Viral RNA levels were positively related to total Tfh and Tfh2 frequencies and negatively related to Tfh17. After adjustment for age, HLH status, and viral load, Tfh1 remained an independent correlate of acute‐phase mortality. Conclusions: Acute SFTSV infection is associated with substantial remodeling of the circulating Tfh compartment. Expansion of Tfh2 alongside reduction of Tfh1 and Tfh17 was linked to heavier viral burden and worse clinical outcome. The independent association between lower Tfh1 frequency and mortality suggests that Tfh profiling may be useful for early risk assessment and offers mechanistic insight into defective humoral immunity in severe SFTS.

Background: Severe fever with thrombocytopenia syndrome (SFTS), an emerging tick-borne viral hemorrhagic fever, is characterized by high mortality rates. While neurological complications (eg, seizures, encephalitis) have been identified as adverse prognostic factors in severe cases, their association with viral replication, immune responses, and neuroinflammation remain poorly defined and urgently require systematic investigation. Method: A cohort of 277 patients with SFTS was included and stratified based on neurological symptoms. Clinical characteristics, laboratory results, and immune markers were compared between groups. Results: Neurological symptoms developed in 78 (28.2%) patients and were associated with significantly higher 28-day mortality. These patients had higher viral loads, elevated inflammatory cytokines (IL-6, IL-10, TNF-α, and ferritin), and more severe multi-organ dysfunction. Compared with survivors, nonsurvivors showed reduced platelet and T-cell counts, and disregulated B-cell subsets with increased plasmablasts and double-negative B cells. Viral load correlated with cytokine elevation, coagulopathy (prolonged APTT), and renal impairment (reduced eGFR). Multivariate Cox proportional hazards regression identified neurological symptoms (HR = 2.565; 95% CI: 1.641–4.011; P < .001) and viral load (HR = 1.785 per log₁₀ increase; 95% CI: 1.503–2.120; P < .001) as independent predictors of mortality. Conclusions: Neurological manifestations and elevated viral load play a central role in the progression of SFTS and are closely associated with adverse clinical outcomes. Considering neurological symptoms and immune profiles in prognostic assessments may improve early recognition of high-risk patients and inform clinical management.

Severe fever with thrombocytopenia syndrome virus (SFTSV) infection is associated with poor clinical outcomes and defective humoral immunity yet the immunometabolic mechanisms underlying B cell dysfunction remain incompletely defined. Through integrated single‐cell RNA sequencing and B cell receptor (BCR) repertoire profiling of peripheral B cells from SFTS patients, we dissected the molecular signatures between survivors and fatal cases. Functional validation and metabolic flux analysis were further performed. Seven transcriptionally distinct B cell subsets were identified. Fatal cases exhibited a marked expansion of CXCR3+Ki‐67+CXCR5‐ extrafollicular plasmablasts, coupled with depletion of naïve and memory B cells. These plasmablasts exhibited hyperactivation of interferon‐response genes (IFI27, ISG15), upregulation of inflammatory mediators (S100A8/A9), and contraction of BCR diversity, with skewed usage of λ‐light chains. Pseudotime trajectory analysis and metabolic scoring revealed progressive upregulation of oxidative phosphorylation, glycolysis and endoplasmic reticulum stress during terminal differentiation. In fatal cases, B cells exhibited suppressed antigen presentation capacity and impaired immunoglobulin gene expression, alongside heightened oxidative stress and elevated CD39 levels. Furthermore, analysis of SFTSV‐infected versus uninfected B cells revealed that infected plasmablasts displayed enhanced inflammatory and migratory features, including upregulated CXCR3 and CCR10 expression, suggesting direct viral modulation of B cell function and trafficking. Our study reveals that dysfunctional, metabolically reprogrammed plasmablasts underlie humoral immune failure in fatal SFTS. These findings provide mechanistic insight into B cell‐mediated immunopathogenesis and highlight potential targets for prognostic evaluation and immunomodulatory intervention.