Development and multi-institutional validation of an artificial intelligence-based diagnostic system for gastric biopsy.

To overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence-based system for the pathological diagnosis of gastric biopsies (AI-G), which is expected to work well in daily clinical practice in multiple institutes. The multistage semantic segmentation for pathology (MSP) method utilizes the distribution of feature values extracted from patches of whole-slide images (WSI) like pathologists' "low-power view" information of microscopy. The training dataset included WSIs of 4511 gastric biopsy tissues from 984 patients. In tissue-level validation, MSP AI-G showed better accuracy (91.0%) than that of conventional patch-based AI-G (PB AI-G) (89.8%). Importantly, MSP AI-G unanimously achieved higher accuracy rates (0.946 ± 0.023) than PB AI-G (0.861 ± 0.078) in tissue-level analysis, when applied to the cohorts of 10 different institutes (3450 samples of 1772 patients in all institutes, 198-555 samples of 143-206 patients in each institute). MSP AI-G had high diagnostic accuracy and robustness in multi-institutions. When pathologists selectively review specimens in which pathologist's diagnosis and AI prediction are discordant, the requirement of a secondary review process is significantly less compared with reviewing all specimens by another pathologist.

Characterization of polymorphic microsatellite markers for the Japanese endangered land snail Mandarina.

OBJECTIVE: Mandarina is an endangered land snail genus of the oceanic Ogasawara archipelago. On Chichijima Island, the largest inhabited island in Ogasawara, this genus is almost extinct in the wild due to predation by invasive species. Although ex situ conservation programs started in 2010, genetic diversity and population structure remain unclear due to a lack of genetic markers with sufficient genetic variation. In this study, we designed polymorphic microsatellite markers of Mandarina to enable genetic analysis and to develop appropriate conservation plans. RESULTS: Twenty-three polymorphic microsatellite markers were identified from the genomic DNA of wild samples of Mandarina mandarina. We assessed the genetic diversity of each marker. In 16 markers, neither linkage disequilibrium nor deviation from Hardy-Weinberg equilibrium was detected. These 16 markers were tested for multiplex PCR using low-density DNA extracted non-lethally from captive samples of M. mandarina, M. chichijimana and M. suenoae. Of the 16 markers, 15, 12 and 9 were usable for multiplex PCR, respectively. Genetic analysis using these microsatellite loci will be an important resource for the conservation of Mandarina.

The mutual history of Schlegel's Japanese gecko (Reptilia: Squamata: Gekkonidae) and humans inscribed in genes and ancient literature.

Knowing how the present distribution of organisms was formed is an essential issue in evolutionary ecology. Recently, the distribution of organisms on Earth has been significantly changed by human-mediated dispersal due to globalization. Therefore, significant attention has been paid to such processes. However, although humankind has taken considerable time to achieve modernization, the impact of ancient human activity on ecosystems has not yet been thoroughly studied. We hypothesized that ancient urban development and transitions had a non-negligible effect on species distribution. Inferring the impact of past human activity on ecosystems from ancient literature and verifying that impact by genetic analysis and human history is an effective means of tackling this problem. As geckos, a popular neighbor of human dwellings, are good material for this model, we performed this combination approach using Schlegel's Japanese gecko, Gekko japonicus. We show that G. japonicus migrated from China to the western Japanese archipelago before Christ. The gecko species dispersed itself from western to eastern the archipelago on a time scale of thousands of years. There are many synchronizations between the dispersal history of G. japonicus and the historical development of human society. It is suggested by such synchronizations that humans have influenced the distribution of G. japonicus many times throughout its dispersal history.

Universal encoding of pan-cancer histology by deep texture representations.

Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented the direct comparison and accumulation of large-scale datasets. Here, we show successful universal encoding of cancer histology by deep texture representations (DTRs) produced by a bilinear convolutional neural network. DTR-based, unsupervised histological profiling, which captures the morphological diversity, is applied to cancer biopsies and reveals relationships between histologic characteristics and the response to immune checkpoint inhibitors (ICIs). Content-based image retrieval based on DTRs enables the quick retrieval of histologically similar images using The Cancer Genome Atlas (TCGA) dataset. Furthermore, via comprehensive comparisons with driver and clinically actionable gene mutations, we successfully predict 309 combinations of genomic features and cancer types from hematoxylin-and-eosin-stained images. With its mounting capabilities on accessible devices, such as smartphones, universal encoding for cancer histology has a strong impact on global equalization for cancer diagnosis and therapies.