A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.

Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In this study, we propose a novel application of machine learning in the automated screening of microinfarcts and microhemorrhages.

The BIM deletion polymorphism potentiates the survival of leukemia stem and progenitor cells and impairs response to targeted therapies.

One sixth of human cancers harbor pathogenic germline variants, but few studies have established their functional contribution to cancer outcomes. Here, we developed a humanized mouse model harboring a common East Asian polymorphism, the BIM deletion polymorphism (BDP), which confers resistance to oncogenic kinase inhibitors through generation of non-apoptotic splice isoforms. However, despite its clear role in mediating bulk resistance in patients, the BDP's role in cancer stem and progenitor cells, which initiate disease and possess altered BCL-2 rheostats compared to differentiated tumor cells, remains unknown.

Key derivation function: key-hash based computational extractor and stream based pseudorandom expander.

The key derivation function is a specific cryptographic algorithm that transforms private string and public strings into one or more cryptographic keys. The cryptographic keys are essential for protecting electronic data during transmission on the internet. This function is designed based on a computational extractor and pseudorandom expander and is typically constructed using various cryptography ciphers such as stream ciphers, keyed-hash message authentication codes, and block ciphers.

Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.

Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Disease (AD). This multidisciplinary study addresses the current limitations on neuropathology by leveraging a machine learning (ML) pipeline to perform a granular quantification of Aβ deposits and assess their distribution in the temporal lobe. Utilizing 131 whole-slide-images from consecutive autopsied cases at the University of California Davis Alzheimer Disease Research Center, our objectives were threefold: (1) Validate an automatic workflow for Aβ deposit quantification in white matter (WM) and gray matter (GM); (2) define the distributions of different Aβ deposit types in GM and WM, and (3) investigate correlates of Aβ deposits with dementia status and the presence of mixed pathology.