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The thermal infrared (TIR) data from the Medium Resolution Spectral Imager II (MERSI-2) on the Chinese meteorological satellite FY-3D have high spatiotemporal resolution. Although the MERSI-2 land surface temperature (LST) products have good application prospects, there are some deviations in the TIR band radiance from MERSI-2. To accurately retrieve LSTs from MERSI-2, a method based on a cross-calibration model and split window (SW) algorithm is proposed. The method is divided into two parts: cross-calibration and LST retrieval. First, the MODTRAN program is used to simulate the radiation transfer process to obtain MERSI-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data, establish a cross-calibration model, and then calculate the actual brightness temperature (BT) of the MERSI-2 image. Second, according to the characteristics of the near-infrared (NIR) bands, the atmospheric water vapor content (WVC) is retrieved, and the atmospheric transmittance is calculated. The land surface emissivity is estimated by the NDVI-based threshold method, which ensures that both parameters (transmittance and emissivity) can be acquired simultaneously. The validation shows the following: 1) The average accuracy of our algorithm is 0.42 K when using simulation data; 2) the relative error of our algorithm is 1.37 K when compared with the MODIS LST product (MYD11A1); 3) when compared with ground-measured data, the accuracy of our algorithm is 1.23 K. Sensitivity analysis shows that the SW algorithm is not sensitive to the two main parameters (WVC and emissivity), which also proves that the estimation of LST from MERSI-2 data is feasible. In general, our algorithm exhibits good accuracy and applicability, but it still requires further improvement.

This study aimed to investigate the associations of anti-C1q antibodies with systemic lupus erythematosus (SLE) disease activity and lupus nephritis (LN) in northeast of China. Ninety patients with SLE, 37 patients with other autoimmune diseases, and 40 healthy donors in northeast of China were enrolled. Serum anti-C1q antibodies were measured by ELISA with 20 RU/ml as the threshold of positive results. The prevalence and levels of anti-C1q antibodies in SLE group (50%, 20.54 ± 34.67 RU/ml) were significantly higher than those in autoimmune disease and healthy control groups (P < 0.05), yet no significant difference between LN patients and non-LN lupus patients (57.14% vs 41.46%, P > 0.05; 25.92 ± 39.94 vs 13.07 ± 27.39 RU/ml, P > 0.05). Anti-C1q antibody levels were positively correlated with levels of Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) scores, anti-dsDNA, and anti-cardiolipin and negatively correlated with serum C3 and C4 (P < 0.05). The prevalence of anti-Sm and anti-nucleosome increased in anti-C1q-positive lupus patients (P < 0.05). Compared with anti-C1q-negative lupus patients, patients with 20-40 RU/ml anti-C1q antibodies had comparable disease activity (P > 0.05); patients with 40-80 RU/ml anti-C1q antibodies had significantly lower levels of serum complement (P < 0.05); patients with above 80 RU/ml anti-C1q antibodies had much more severe hypocomplementemia, increased SLEDAI scores, and higher incidence of hematuria and proteinuria (P < 0.05). Furthermore, the specificity and positive predictive value of 80 RU/ml anti-C1q antibodies for LN was 97.56% and 87.50%, respectively. In conclusion, anti-C1q antibodies are associated with SLE and LN disease activity, and the contribution hinges on the titers. Moreover, high-level anti-C1q antibodies are valuable for diagnosing LN.

To use meta-analysis to determine the accuracy of anti-cyclic citrullinated peptide (CCP) antibody in diagnosis of patients with rheumatoid arthritis (RA) in a Chinese population, we searched MEDLINE and CNKI databases for studies published in English or Chinese between January 2000 and June 2010. Two investigators independently evaluated studies for inclusion, data extraction, and quality assessment. We used a random-effects model to combine estimates of sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio (DOR). One hundred and eighteen studies met our inclusion criteria. All studies were of high quality. The summary estimates for anti-CCP antibody in the diagnosis of RA in a Chinese population were as follows: sensitivity 0.65 (95% confidence interval (CI) 0.65-0.66), specificity 0.95 (95% CI 0.95-0.96), positive likelihood ratio (LR+) 15.84 (95% CI 13.55-18.54), negative likelihood ratio (LR-) 0.33 (95% CI 0.31-0.35), and diagnostic odds ratio (DOR) 51.60 (95% CI 43.64-61.01). With high specificity and moderate sensitivity, anti-CCP antibody tests play an important role in conforming the diagnosis of RA in a Chinese population.