Integrative omics provide biological and clinical insights into acute respiratory distress syndrome.

PURPOSE: Acute respiratory distress syndrome (ARDS) is accompanied by a dysfunctional immune-inflammatory response following lung injury, including during coronavirus disease 2019 (COVID-19). Limited causal biomarkers exist for ARDS development. We sought to identify novel genetic susceptibility targets for ARDS to focus further investigation on their biological mechanism and therapeutic potential. METHODS: Meta-analyses of ARDS genome-wide association studies were performed with 1250 cases and 1583 controls in Europeans, and 387 cases and 387 controls in African Americans. The functionality of novel loci was determined in silico using multiple omics approaches. The causality of 114 factors potentially involved in ARDS development was assessed using Mendelian Randomization analysis. RESULTS: There was distinct genetic heterogeneity in ARDS between Europeans and African Americans. rs7967111 at 12p13.2 was functionally associated with ARDS susceptibility in Europeans (odds ratio = 1.38; P = 2.15 × 10-8). Expression of two genes annotated at this locus, BORCS5 and DUSP16, was dynamic but ultimately decreased during ARDS development, as well as downregulated in immune cells alongside COVID-19 severity. Causal inference implied that comorbidity of inflammatory bowel disease and elevated levels of C-reactive protein and interleukin-10 causally increased ARDS risk, while vitamin D supplementation and vasodilator use ameliorated risk. CONCLUSION: Our findings suggest a novel susceptibility locus in ARDS pathophysiology that implicates BORCS5 and DUSP16 as potentially acting in immune-inflammatory processes. This locus warrants further investigation to inform the development of therapeutic targets and clinical care strategies for ARDS, including those induced by COVID-19.

A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text.

Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-based medication entity recognition. We hypothesized that this approach could avoid the side effects of abundant negative samples and improve the performance of the named entity recognition from admission notes written in Chinese. Therefore, we applied this approach to a test set of 324 Chinese-written admission notes with manual annotation by medical experts. Our data demonstrated that this approach had a score of 94.2% in precision, 92.8% in recall, and 93.5% in F-measure for the recognition of traditional Chinese medicine drug names and 91.2% in precision, 92.6% in recall, and 91.7% F-measure for the recognition of Western medicine drug names. The differences in F-measure were significant compared with those in the baseline systems.