Aim of the study was to determine the characteristics and prognosis, and to identify the risk factors for mortality in patients with primary Sjögren syndrome (pSS) with interstitial lung disease (pSS-ILD). A total of 1422 patients with SS were screened and 178 patients with pSS-ILD were recruited. The medical records and outcomes were retrospectively reviewed. Overall survival and case control study were performed to explore the predictors of death. Among 178 pSS-ILD patients, 87.1% were women. Mean age was 61.59 ± 11.69-year-old. Median disease duration was 72.0 (24.0, 156.0) months. Nonspecific interstitial pneumonia was the predominant high-resolution computed tomography pattern (44.9%). Impairment in diffusion capacity was the most common abnormality of pulmonary function test (75.8%) and the most severe consequence. Type 1 respiratory failure and hypoxia were observed in 15.0% and 30.0% patients, respectively. Mean survival time after confirmation of pSS-ILD diagnosis was 9.0 (6.8, 13.0) years. The 10-year survival rate for all patients with pSS-ILD was 81.7%. Forty-four (24.7%) of 178 patients died during the follow-up period. The most predominant cause of death was respiratory failure (n = 27). Twenty-seven patients died of ILD and formed study group. The 78 patients who survived formed control group. Age and smoking were risk factors for mortality in patients with pSS-ILD. In addition, severity of ILD, as reflected by high-resolution computed tomography, pulmonary function test, and arterial blood gas, was an independent risk factor. However, inflammation status (erythrocyte sedimentation rate, C-reactive protein) and anti-Sjögren syndrome–related antigen A and anti-Sjögren syndrome–related antigen B were not. ILD is a severe complication of pSS. Age, smoking, and severity of lung involvement are more critical for prognosis rather than inflammation status and autoantibodies.

BackgroundThrombocytopenia is a common manifestation of antiphospholipid syndrome (APS), and is a main concern for bleeding on the standard treatment of low dose aspirin (LDA) and low molecular weight heparin (LMWH) in obstetric APS (OAPS).ObjectiveThis study assesses the possible relationship between thrombocytopenia during the first trimester and adverse pregnancy outcomes (APOs) in OAPS patients.MethodsA case-control study was conducted at Peking University People’s Hospital, Beijing, China. The clinical, immunologic, and pregnancy outcomes of the OAPS patients were collected. Univariate and multivariate logistic regression analyses were applied to assess the relationship between APOs and thrombocytopenia in the first trimester.ResultsA total of 115 participants were included in the analysis. There were no difference on antepartum and postpartum hemorrhage between the two groups. The gestational age in the thrombocytopenia group was less than that in the control group (34.12 ± 8.44 vs. 37.44 ± 3.81 weeks, p = 0.002). Hypocomplementemia, double aPL positive, and high titers of anti-β2 glycoprotein I were more frequent in APS patients with thrombocytopenia (p < 0.05). Compared to the control group, thrombocytopenia in the first trimester was correlated with SGA (12.12% vs. 31.25%, p = 0.043), premature birth <37 weeks (16.16% vs 43.75%, p = 0.010) and intrauterine fetal death (2.02% vs 12.50%, p = 0.033). Thrombocytopenia in first-trimester independently increased the risk of preterm birth <37 weeks (OR = 5.40, 95% CI: 1.35-21.53, p = 0.02) after adjusting for demographic and laboratory factors. After adding medication adjustments, these factors above become insignificant (p > 0.05). Of note, the number of platelets increased after delivery in 14 thrombocytopenia patients with live fetuses (p = 0.03).ConclusionThis study demonstrates that thrombocytopenia in the first trimester increases the risks of preterm birth in women with APS. The effective OAPS treatments may improve pregnancy outcomes and not increase the risk of antepartum and postpartum hemorrhage.

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In recent years, machine learning has achieved good results in the field of asset prices. Compared with traditional data analysis and technical analysis, using machine learning methods can show unique advantages in various aspects. In this paper, we combine the correlation between bull and bear markets and bitcoin and gold prices in the market, and apply a random forest model to predict them. The results of the study show that the random forest has high explanations and the accuracy of the model predictions are above 0.9, indicating that the model is a good fit for bitcoin and gold price predictions; bitcoin price is volatile and not suitable as a long-term investment, and it is suitable for gold at the beginning of each year.