Reversal of inflammatory reprogramming by vasodilator agents in pulmonary hypertension.

Background: Pulmonary arterial hypertension (PAH) is a deadly disease without effective non-invasive diagnostic and prognostic testing. It remains unclear whether vasodilators reverse inflammatory activation, a part of PAH pathogenesis. Single-cell profiling of inflammatory cells in blood could clarify these PAH mechanisms.

Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data.

Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data.

Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, it requires a large mount of individual cells with accurate and unbiased annotated types to train the identification models.

Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN.

Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data.

A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records.

Background: Electronic Medical Record (EMR) comprises patients' medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR.