Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy.

There has been a persistent demand for an innovative modality in real-time histologic imaging, distinct from the conventional frozen section technique. We developed an artificial intelligence-driven real-time evaluation model for gastric cancer tissue using confocal laser endomicroscopic system. The remarkable performance of the model suggests its potential utilization as a standalone modality for instantaneous histologic assessment and as a complementary tool for pathologists' interpretation.

Real-time Histological Evaluation of Gastric Cancer Tissue by Using a Confocal Laser Endomicroscopic System.

BACKGROUND/AIM: The need for instant histological evaluation of fresh tissue, especially in cancer treatment, remains paramount. The conventional frozen section technique has inherent limitations, prompting the exploration of alternative methods. A recently developed confocal laser endomicroscopic system provides real-time imaging of the tissue without the need for glass slide preparation. Herein, we evaluated its applicability in the histologic evaluation of gastric cancer tissues. MATERIALS AND METHODS: A confocal laser endomicroscopic system (CLES) with a Lissajous pattern laser scanning, was developed. Fourteen fresh gastric cancer tissues and the same number of normal gastric tissues were obtained from advanced gastric cancer patients. Fluorescein sodium was used for staining. Five pathologists interpreted 100 endomicroscopic images and decided their histologic location and the presence of cancer. Following the review of matched hematoxylin and eosin (H&E) slides, their performance was evaluated with another 100 images. RESULTS: CLES images mirrored gastric tissue histology. Pathologists were able to detect the histologic location of the images with 65.7% accuracy and differentiate cancer tissue from normal with 74.7% accuracy. The sensitivity and specificity of cancer detection were 71.9% and 76.1%. Following the review of matched H&E images, the accuracy of identifying the histologic location was increased to 92.8% (p<0.0001), and that of detecting cancer tissue was also increased to 90.9% (p<0.001). The sensitivity and specificity of cancer detection were enhanced to 89.1% and 93.2% (p<0.0001). CONCLUSION: High-quality histological images were immediately acquired by the CLES. The operator training enabled the accurate detection of cancer and histologic location raising its potential applicability as a real-time tissue imaging modality.

Direct lineage reprogramming of mouse fibroblasts to functional midbrain dopaminergic neuronal progenitors.

The direct lineage reprogramming of somatic cells to other lineages by defined factors has led to innovative cell-fate-change approaches for providing patient-specific cells. Recent reports have demonstrated that four pluripotency factors (Oct4, Sox2, Klf4, and c-Myc) are sufficient to directly reprogram fibroblasts to other specific cells, including induced neural stem cells (iNSCs). Here, we show that mouse fibroblasts can be directly reprogrammed into midbrain dopaminergic neuronal progenitors (DPs) by temporal expression of the pluripotency factors and environment containing sonic hedgehog and fibroblast growth factor 8. Within thirteen days, self-renewing and functional induced DPs (iDPs) were generated. Interestingly, the inhibition of both Jak and Gsk3ß notably enhanced the iDP reprogramming efficiency. We confirmed the functionality of the iDPs by showing that the dopaminergic neurons generated from iDPs express midbrain markers, release dopamine, and show typical electrophysiological profiles. Our results demonstrate that the pluripotency factors-mediated direct reprogramming is an invaluable strategy for supplying functional and proliferating iDPs and may be useful for other neural progenitors required for disease modeling and cell therapies for neurodegenerative disorders.