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Ochi, K.

Publications and source records attributed to Ochi, K..

2 recordsLinked to original sources

Quantum Dot-Based Non-invasive Monitoring of Sinusoidal Endothelial Scavenger Function in Steatohepatitis

Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive form, metabolic dysfunction-associated steatohepatitis (MASH), represent urgent medical challenges. Current non-invasive assessments rely on serum indices or tissue stiffness, and none visualizes functional changes within the liver, hindering drug discovery and patient management. In this study, we developed a non-invasive technique using semiconductor quantum dots, as blood-borne foreign particle mimics, to evaluate the scavenger function of liver sinusoidal endothelial cells (LSECs) via near-infrared optical imaging. Progression from MASLD to MASH induced a significant decline in the scavenger capability of LSECs, resulting in a marked reduction of intrahepatic quantum dot retention. Mechanistically, we demonstrated that quantum dots are recognized and internalized by scavenger receptor class A (SR-A) on LSECs, and that SR-A-mediated uptake progressively diminishes with increasing MASH severity. Crucially, quantum dot retention levels significantly correlated with both the progression and regression of MASH pathology. These findings demonstrate that quantum dot-based optical evaluation of LSEC scavenger function offers a highly sensitive, non-invasive diagnostic platform for staging MASH and assessing therapeutic efficacy.

bioengineering↗

Artificial Intelligence Enables the Label-Free Identification of Chronic Myeloid Leukemia Cells with Mitochondrial Morphological Alterations

Long-term tyrosine kinase inhibitor (TKI) treatment for patients with chronic myeloid leukemia (CML) causes various adverse events. Achieving a deep molecular response (DMR) is necessary for discontinuing TKIs and attaining treatment-free remission. Thus, early diagnosis is crucial as a lower DMR achievement rate has been reported in high-risk patients. Therefore, we attempted to identify CML cells using a novel technology that combines artificial intelligence (AI) with flow cytometry and investigated the basis for AI- mediated identification. Our findings indicate that BCR-ABL1-transduced cells and leukocytes from patients with CML showed significantly fragmented mitochondria and decreased mitochondrial membrane potential. Additionally, BCR-ABL1 enhanced the phosphorylation of Drp1 via the mitogen-activated protein kinase pathway, inducing mitochondrial fragmentation. Finally, the AI identified cell line models and patient leukocytes that showed mitochondrial morphological changes. Our study suggested that this AI- based technology enables the highly sensitive detection of BCR-ABL1-positive cells and early diagnosis of CML.

cancer biology↗