[Mechanism and clinical treatment strategy of heparin resistance].

Heparin resistance is becoming a hot issue of clinical concern. In critically ill patients, heparin resistance can lead to failure of anticoagulation therapy or increase the risk of major bleeding. Prompt recognition of heparin resistance can help to precisely adjust heparin dosage and avoid deterioration and adverse events. Heparin resistance can be mechanistically classified into the antithrombin-mediated and the non-antithrombin-mediated. Common etiologies include heparin-induced thrombocytopenia, severe infections such as severe COVID-19, treatment with extracorporeal circulation or extracorporeal membrane oxygenation (ECMO), and use of factor Xa reversal agents; heparin resistance is now often identified by the concordance of activated partial thromboplastin time (APTT) ratio with anti-FXa. Common clinical management strategies include antithrombin supplementation and replacement of anticoagulant drugs (e.g., direct thrombin inhibitors), but their safety and efficacy still need to be further validated.

[Annual review of venous thromboembolism in 2021].

In the past year, significant progress has been made in the field of venous thromboembolism (VTE) including risk assessment and anticoagulation prevention, diagnostic strategies and model exploration, new drug development and disease management. Particularly, major breakthroughs have been made in the prevention of VTE with FXI inhibitors and the prevention of novel coronavirus pneumonia with coagulation alterations and anticoagulation interventions. Here, we reviewed the progress and achievements in the field of VTE in the past year, aiming to provide evidence and ideas for the diagnosis, treatment and future studies of VTE.

Unsupervised Detection of Pulmonary Opacities for Computer-Aided Diagnosis of COVID-19 on CT Images

COVID-19 emerged towards the end of 2019 which was identified as a global pandemic by the world heath organization (WHO). With the rapid spread of COVID-19, the number of infected and suspected patients has increased dramatically. Chest computed tomography (CT) has been recognized as an efficient tool for the diagnosis of COVID-19. However, the huge CT data make it difficult for radiologist to fully exploit them on the diagnosis. In this paper, we propose a computer-aided diagnosis system that can automatically analyze CT images to distinguish the COVID-19 against to community-acquired pneumonia (CAP). The proposed system is based on an unsupervised pulmonary opacity detection method that locates opacity regions by a detector unsupervisedly trained from CT images with normal lung tissues. Radiomics based features are extracted insides the opacity regions, and fed into classifiers for classification. We evaluate the proposed CAD system by using 200 CT images collected from different patients in several hospitals. The accuracy, precision, recall, fl-score and AUC achieved are 95.5%, 100%, 91%, 95.1% and 95.9% respectively, exhibiting the promising capacity on the differential diagnosis of COVID-19 from CT images.