The University of Tokyo · Medicine
Professor Yuqi Zhou's research lab specializes in biomedical data science and intelligent diagnostics, focusing on the application of machine learning and imaging technologies to understand platelet dynamics in thrombotic and inflammatory diseases. The lab integrates high-throughput imaging flow cytometry, convolutional neural networks, and multi-modal single-cell analysis to classify platelet aggregates and immune cells in complex biological samples, particularly in the context of COVID-19 and cardiovascular disorders. A key research direction involves leveraging AI-driven image analysis to uncover disease-specific cellular phenotypes and improve diagnostic accuracy. The lab also explores the optimization of scientific research efficiency in higher education institutions through data-driven evaluation models.
Figures are computed from collected data and may differ slightly.
Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to c
Nanocrystals technology for water insoluble drugs delivery has been expanding exponentially, as a robust approach, over the last ten years. Drug nanocrystals are sub-micron colloidal dispersion system of pure drug nanoparticles. Compared to the traditional pharmaceutical dosage forms, nano-crystals formulation presented many extraordinary properties such as enhanced dissolution rate and saturation solubility, high drug loading, excellent reproducibility of oral absorption, improved proportionali
Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived sing
In very old population diagnosed with pulmonary thromboembolism, worse laboratory results, atypical symptoms and physical signs were common. Mortality was very high and comorbid conditions were their features compared to younger patients. PaO2 < 60 mmHg, eGFR < 60 mL/min/1.73m2 and malignancy were positive mortality predictors for all-cause death in very old patients with pulmonary thromboembolism while anticoagulation as first therapy was negative mortality predictors.
Based on the global principal component factor analysis and BCC-DEA model, this paper constructs an evaluation system for research efficiency of higher education institutions, and obtains the evaluation scores of each university based on the relevant data of Liaoning Province in 2020. The results show that: the overall scientific research efficiency of higher education institutions in Liaoning Province is high, but there are still some inefficient colleges and universities, and the management me
ABSTRACT Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on der
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