ATRA treatment slowed P-selectin-mediated rolling of flowing HL60 cells in a mechano-chemical-dependent manner.

All-trans retinoic acid (ATRA)-induced differentiation of acute promyelocytic leukemia (APL) toward granulocytes may trigger APL differentiation syndrome (DS), but there is less knowledge about the mechano-chemical regulation mechanism of APL DS under the mechano-microenvironment. We found that ATRA-induced changes in proliferation, morphology, and adhesive molecule expression levels were either dose or stimulus time dependent. An optimal ATRA stimulus condition for differentiating HL60 cells toward neutrophils consisted of 1 × 10-6 M dose and 120 h of stimulus time. Under wall shear stresses, catch-slip bond transition governs P-selectin-mediated rolling for neutrophils and untreated or ATRA-treated (1 × 10-6 M, 120 h) HL60 cells. The ATRA stimuli slowed down the rolling of HL60 cells on immobilized P-selectin no matter whether ICAM-1 was engaged. The ß2 integrin near the PSGL-1/P-selectin axis would be activated within sub-seconds for each cell group mentioned above, thus contributing to slow rolling. A faster ß2 integrin activation rate and the higher expression levels of PSGL-1 and LFA-1 were assigned to induce the over-enhancement of ATRA-treated HL60 adhesion in flow, causing APL DS development. These findings provided an insight into the mechanical-chemical regulation for APL DS development via ATRA treatment of leukemia and a novel therapeutic strategy for APL DS through targeting the relevant adhesion molecules.

Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning.

The proposition of non-fullerene acceptors (NFAs) in organic solar cells has made great progress in the raise of power conversion efficiency, and it also broadens the ways for searching and designing new acceptor molecules. In this work, the design of novel NFAs with required properties is performed with the conditional generative model constructed from a convolutional neural network (CNN). The temporal CNN is firstly trained to be a good string-based molecular conditional generative model to directly generate the desired molecules. The reliability of generated molecular properties is then demonstrated by a graph-based prediction model and evaluated with quantum chemical calculations. Specifically, the global attention mechanism is incorporated in the prediction model to pool the extracted information of molecular structures and provide interpretability. By combining the generative and prediction models, thousands of NFAs with required frontier molecular orbital energies are generated. The generated new molecules essentially explore the chemical space and enrich the database of transformation rules for molecular design. The conditional generation model can also be trained to generate the molecules from molecular fragments, and the contribution of molecular fragments to the properties is subsequently predicted by the prediction model.

Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors.

Convolutional neural network (CNN) is employed to construct generative and prediction models for the design and analysis of non-fullerene acceptors (NFAs) in organic solar cells. It is demonstrated that the dilated causal CNN can be trained as a good string-based molecular generation model, and the diversity of the generated NFAs is influenced by the depth of convolutional layers. In the property prediction model, the features of NFAs are extracted from the string representations by the dilated CNN. Specially, the attention mechanism is adopted to pool the extracted information, from which the contributions of fragments to molecular properties can be obtained by calculating the corresponding weighted sum. The promising NFAs among the predicted molecules are further verified by quantum chemistry calculations. The proposed generative, prediction models and the theoretical calculations perform as a complete cycle from molecular generation and property prediction to verification, which offer a strategy for the application of CNN in material discovery.

[Localization diagnosis of chyluria by radionuclide lymphoscintigraphy].

OBJECTIVE: To study the effect of radionuclide lymphoscintigraphy, a new method of localization diagnosis of chyluria. METHODS: Radionuclide lymphoscintigraphy was used to examine 34 patients with chyluria and the results of radionuclide lymphoscintigraphy was compared with those of cystoscopy and lymphangiography. RESULTS: Among the 34 patients 85.3% of unilateral localization diagnosis by radionuclide lymphoscintigraphy was coincident with that by cystoscopy. The positive rate of bilateral localization diagnosis was higher than that by cystoscopy. CONCLUSION: A less invasive technique, radionuclide lymphoscintigraphy can be used as a new option for the localization diagnosis of chyluria.