Publications by authors named "Chaojie Shi"

Article Synopsis
  • - Generalizing real-world data poses a significant challenge for machine learning (ML), with most efforts focusing on algorithm improvements rather than data quality, which is crucial for ML success.
  • - The authors tackled the complex task of predicting reorganization energy (RE) for organic semiconductors by developing various data evaluation and filtering methods to create a reliable dataset of 15,989 molecules.
  • - They proposed an ensemble framework combining two deep learning models, which demonstrated improved robustness and generalization across different organic semiconductor structures, thereby setting a new standard for ML application in this field.
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Article Synopsis
  • Scientists created a smart computer program called deep learning to quickly guess the light properties of materials, which is super important for making new inventions.
  • This new program works better than eight other models and can accurately predict key light features like how materials absorb and emit light.
  • Using their predictions, researchers successfully made a new molecule that gives off deep blue light, proving their program is reliable and useful for developing new materials.
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Achieving timely and effective hemorrhage control is imperative for the survival of individuals with severe bleeding. Hemostatic materials, by enhancing the natural cell-based coagulation response, are essential tools in modern and military medical practice for controlling bleeding, especially in emergency and surgical settings. Here, we report a new type of composite hemostatic material with two different aluminosilicate-based components, kaolin and zeolite, which synergistically work together in different stages of the coagulation cascade reactions.

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Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately predict the cocrystal density, we develop a graph neural network (GNN)-based deep learning framework by considering three key factors of machine learning (data quality, feature presentation, and model architecture). The result shows that different stoichiometric ratios of molecules in cocrystals can significantly influence the prediction performances, highlighting the importance of data quality.

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To solve the problem of strong adhesion and excessive blood loss caused by the use of hydrophilic zeolite gauze (Z-Gauze) in uncontrollable bleeding, we have modified the surface of commercial Z-Gauze with a paraffin coating and prepared a hydrophobic dressing PZ-Gauze. After paraffin coating, the adhesion of Z-Gauze was reduced without an obvious decrease in coagulation activity. The clotting time of the hydrophobic PZ-Gauze was reduced from 378.

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Objective: To investigate the relationship between asymmetric prominent hypointense vessels (prominent vessel sign, PVS) on susceptibility-weighted imaging (SWI) and leptomeningeal collateralization in patients with acute ischemic stroke due to large vessel occlusion.

Methods: We retrospectively enrolled patients with M1 segment occlusion of the middle cerebral artery who underwent emergency magnetic resonance imaging and digital subtraction angiography within 24 hours from stroke onset. The extent of PVS on SWI was assessed using the Alberta Stroke Program Early CT Score (ASPECTS).

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The objective of this study is to find out the potential influence of miR-301a in an experimental cerebral ischemia-reperfusion (I/R) rat model through targeting NDRG2. Rats with cerebral I/R injury were constructed and classified into model, miR-301a inhibitor, miR-301a mimic, NC (negative control), siNDRG2, NDRG2, and miR-301a inhibitor + si-NDRG2 groups, as well as another sham group. Cerebral infarct volume and cell apoptosis were observed by TTC staining and TUNEL staining.

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