Data processing and learning has become a spearhead for the advancement of medicine. Computational pathology is burgeoning subspecialty that promises a better-integrated solution to whole-slide images, multi-omics data and clinical informatics as innovative approach for patient care. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.

The breast cancer immune microenvironment was analyzed with the nanoString GeoMx® Digital Spatial Profiler (DSP) in cases from the Carolina Breast Cancer Study. Basal-like breast cancers showed increased expression of markers for regulatory T cells. The results were highly reproducible between whole sections and tissue microarrays.

The authors developed a deep-learning-based ductal carcinoma in situ (DCIS) grading system that achieved performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.

Dimethylarginine dimethylamino hydrolase-1 (DDAH-1), as the critical enzyme responsible for asymmetric dimethylarginine (ADMA) degradation, serves as a protective factor for ischemic stroke. DDAH-1 protects ischemia-induced disruption of blood-brain-barrier via regulating ADMA level and preventing tight junction proteins degradation. The supplementation of L-arginine helps restore the function of DDAH-1.

Most current biomedical datasets are rectangular in shape and have few missing data, but the sample sizes are very large. Rigorous analyses of these huge datasets demand considerably more efficient and more accurate machine-learning algorithms to classify outcomes. This paper aims to determine the performance and efficiency of classifying multi-category outcomes of such rectangular data.

This manuscript describes a methodology to quantify the abnormalities in digital cytology images. This automatic AI-system incorporates deep learning structures, mathematical algorithms, and image processing methods to locate and segment abnormal and suspicious cells. Characterized as more informative, objective, and reproducible, it has the potential to assist clinical practice.

The findings of the present study demonstrate that inflammasome NLRP3 deficiency did not attenuate, but enhanced hepatocellular steatosis, injury and death, inflammation, and fibrosis, as well as insulin resistance in both liver and adipose tissues. This effect is probably due to an enhanced inflammatory response with elevated monocyte chemotactic protein-1 and M1 microphages.