A comprehensive AI model development framework for consistent Gleason grading.

BACKGROUND: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. METHODS: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. RESULTS: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. CONCLUSIONS: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.

Gleason grading is a well-accepted diagnostic standard to assess the severity of prostate cancer in patients' tissue samples, based on how abnormal the cells in their prostate tumor look under a microscope. This process can be complex and time-consuming. We explore how artificial intelligence (AI) can help pathologists perform Gleason grading more efficiently and consistently. We build an AI-based system which automatically checks image quality, standardizes the appearance of images from different equipment, learns from pathologists' feedback, and constantly improves model performance. Testing shows that our approach achieves consistent results across different equipment and improves efficiency of the grading process. With further testing and implementation in the clinic, our approach could potentially improve prostate cancer diagnosis and management.

Geographical distribution and ecological niche dynamics of Crassostrea sikamea (Amemiya, 1928) in China's coastal regions under climate change.

Global climate change drives species redistribution, threatening biodiversity and ecosystem heterogeneity. The Kumamoto oyster, Crassostrea sikamea (Amemiya, 1928), one of the most promising aquaculture species because of its delayed reproductive timing, was once prevalent in southern China. In this study, an ensemble species distribution model was employed to analyze the distribution range shift and ecological niche dynamics of C. sikamea along China's coastline under the current and future climate scenarios (RCP 2.6-8.5 covering 2050 s and 2100 s). The model results indicated that the current habitat distribution for C. sikamea consists of a continuous stretch extending from the coastlines of Hainan Province to the northern shores of Jiangsu Province. By the 2050 s, the distribution range will stabilize at its southern end along the coast of Hainan Province, while expanding northward to cover the coastal areas of Shandong Province, showing a more dramatic trend of contraction in the south and invasion in the north by the 2100 s. In RCP8.5, the southern end retracts to the coasts of Guangdong, whereas the northern end covers all of China's coastal areas north of 34°N. C. sikamea can maintain relatively stable ecological niche characteristics, while it may occupy different ecological niche spaces under future climate conditions. Significant niche expansion will occur in lower temperature. We concluded C. sikamea habitats are susceptible to climate change. The rapid northward expansion of C. sikamea may open new possibilities for oyster farming in China, but it will also have important consequences for the ecological balance and biodiversity of receiving areas. It's imperative that we closely examine and strategize to address these repercussions for a win-win situation.

Synthesis of Spirocyclohexadienones via Palladium-Catalyzed Dearomatization of Dibenzoxaborins.

The development of Pd(II)-catalyzed dearomatization transformation of dibenzoxaborins with alkynes triggered by transmetalation from boron to palladium has been achieved, leading to the synthesis of spirocyclohexadienones, an important skeleton demonstrating potential biomedical utility. The [3 + 2] spiroannulation exhibits remarkable regioselectivity and broad substrate scope under mild reaction conditions. This methodology employs dibenzoxaborin as a substrate to establish the formal dearomatization of 2-phenylphenol, which poses a formidable energy barrier to the destruction of aromaticity.