The HIV envelope (Env) protein gp120 is protected from antibody recognition by a dense glycan shield. However, several of the recently identified PGT broadly neutralizing antibodies appear to interact directly with the HIV glycan coat. Crystal structures of Fabs PGT 127 and 128 with Man9 at 1.65 and 1.29 Å resolution, respectively, and glycan binding data delineate a specific high mannose binding site. Fab PGT 128 complexed with a fully-glycosylated gp120 outer domain at 3.25 Å reveals that the antibody penetrates the glycan shield and recognizes two conserved glycans as well as a short β-strand segment of the gp120 V3 loop, accounting for its high binding affinity and broad specificify. Furthermore, our data suggest that the high neutralization potency of PGT 127 and 128 IgGs may be mediated by cross-linking Env trimers on the viral surface.

Molecular targeting of cancer stem cells has therapeutic potential for efficient treatment of cancer although relatively few specific targets have so far been identified. Hypoxia-inducible factor was recently shown to regulate tumorigenic capacity of glioma stem cells under hypoxic condition. Surprisingly, we found that, under normoxia, HIF1α signaling was selectively activated in the stem cells of mouse lymphoma and human acute myeloid leukemia (AML). HIF1a ShRNA and HIF inhibitors abrogated the colony forming unit activity of mouse lymphoma and human AML CSCs. Importantly, the HIF inhibitor echinomycin efficiently eradicated mouse lymphoma and serially transplantable human AML in xenogeneic model by preferential elimination of CSCs. HIF1α maintains mouse lymphoma CSCs by repressing a negative feedback loop in the Notch pathway. Taken together, our results demonstrate an essential function of HIF1α-Notch interaction in maintaining CSCs and provide an effective approach to target CSCs for therapy of hematological malignancies.

We screened the electronic records of 2,799 patients admitted in Tongji Hospital from January 10th to February 18th, 2020. There were 375 discharged patients including 201 survivors. We built a prognostic prediction model based on XGBoost machine learning algorithm and then tested 29 patients (included 3 patients from other hospital) who were cleared after February 19th. Results:The mean age of the 375 patients was 58.83 years old with 58.7% of males. Fever was the most common initial symptom (49.9%), followed by cough (13.9%), fatigue (3.7%), and dyspnea (2.1%). Our model identified three key clinical features, i.e., lactic dehydrogenase (LDH), lymphocyte and High-sensitivity C-reactive protein (hs-CRP), from a pool of more than 300 features. The clinical route is simple to check and can precisely and quickly assess the risk of death. Therefore, it is of great clinical significance. : medRxiv preprint Conclusion:The three indices-based prognostic prediction model we built is able to predict the mortality risk, and present a clinical route to the recognition of critical cases from severe cases. It can help doctors with early identification and intervention, thus potentially reducing mortality.