Application of recombinant human thrombopoietin in pregnant women with immune thrombocytopenia: a single-center experience of four patients and literature review.

The management of pregnant women with immune thrombocytopenia who fail to respond to corticosteroids and intravenous immunoglobulin is an intractable clinical challenge because of the limited availability of evidence-based information. Recombinant human thrombopoietin (rhTPO) is recommended for refractory immune thrombocytopenia (ITP). To date, however, few studies have investigated rhTPO treatment during pregnancy. We retrospectively reviewed four cases who were diagnosed with ITP and treated with rhTPO during pregnancy in our center from January 2015 to June 2020. Of the four cases, two (50%) responded to rhTPO treatment. No adverse events were noted in the newborns. Our findings indicate that rhTPO treatment is safe for patients with refractory gestational ITP, and that subcutaneous injection is a convenient delivery method that does not lead to adverse events. Thus, rhTPO may be a viable alternative treatment option for patients with refractory gestational ITP who do not respond to first-line therapies.

Tumor immune cell infiltration score based model predicts prognosis in multiple myeloma.

The tumor microenvironment plays an important role in various processes, including tumorigenesis, cancer progression, and metastasis. Immune signatures have been identified and verified for use in diagnosis and prognosis prediction. We used single-sample Gene Set Enrichment Analysis to evaluate tumor immune cell infiltration score (TIICs) and verify their prognostic significance in both training and validation cohorts and using this information to build a prognostic model. A total of 1281 samples were obtained for further evaluation of the immune enrichment scores of 28 immune cells, showing that Th17 cell contributed most significantly to survival. Using the median TIICs as a cutoff to divide the samples into two groups, we found that the high-TIICs group was associated with favorable outcomes in both the training and validation sets. We then constructed a prognostic model to predict the 6, 8, and 10-year survival outcomes. Further analysis showed that immune score and tumor purity were higher in the high-TIICs group, while the matrix score was lower in this group. Forty-two differentially expressed genes were identified between the two groups. This new prognostic model based on immune cell infiltration indicates the potential for TIICs in predicting prognosis and as targets for treatment.

The effect of consecutive ambient air pollution on the hospital admission from chronic obstructive pulmonary disease in the Chengdu region, China.

Hospitalisation risks for chronic obstructive pulmonary disease (COPD) have been attributed to ambient air pollution worldwide. However, a rise in COPD hospitalisations may indicate a considerable increase in fatality rate in public health. The current study focuses on the association between consecutive ambient air pollution (CAAP) and COPD hospitalisation to offer predictable early guidance towards estimates of COPD hospital admissions in the event of consecutive exposure to air pollution. Big data analytics were collected from 3-year time series recordings (from 2015 to 2017) of both air data and COPD hospitalisation data in the Chengdu region in China. Based on the combined effects of CAAP and unit increase in air pollutant concentrations, a quasi-Poisson regression model was established, which revealed the association between CAAP and estimated COPD admissions. The results show the dynamics and outbreaks in the variations in COPD admissions in response to CAAP. Cross-validation and mean squared error (MSE) are applied to validate the goodness of fit. In both short-term and long-term air pollution exposures, Z test outcomes show that the COPD hospitalisation risk is greater for men than for women; similarly, the occurrence of COPD hospital admissions in the group of elderly people (> 65 years old) is significantly larger than that in lower age groups. The time lag between the air quality and COPD hospitalisation is also investigated, and a peak of COPD hospitalisation risk is found to lag 2 days for air quality index (AQI) and PM10, and 1 day for PM2.5. The big data-based predictive paradigm would be a measure for the early detection of a public health event in post-COVID-19. The study findings can also provide guidance for COPD admissions in the event of consecutive exposure to air pollution in the Chengdu region.