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.

Choline attenuates abdominal aortic coarctation-induced cardiac remodeling and cardiac dysfunction, by amelioration of circadian rhythm disruption and attenuation of calcium-handling protein defects. Modulation of vagal activity by choline may have therapeutic potential for cardiac remodeling and heart failure.

Human corneal keratocytes (HCK) are essential for maintaining corneal structure and transparency. This study shows that the natural antioxidant L-carnitine inhibits stromal scarring by suppressing injury-induced intrinsic transient receptor potential vanilloid type 1 (TRPV1) activity that is linked to induction of myofibroblast transdifferentiation in HCK cells. Blocking TRPV1 activation on keratocytes may therefore be a viable approach to suppress corneal opacification in a clinical setting.

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.