Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis.

Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling.

Structural basis of human γ-secretase inhibition by anticancer clinical compounds.

Aberrant activation of Notch signaling, mediated by the Notch intracellular domain (NICD), is linked to certain types of cancer. The NICD is released through γ-secretase-mediated cleavage of the Notch receptor. Therefore, development of a γ-secretase inhibitor (GSI) represents an anticancer strategy.

Optical sensor for fast and accurate lung cancer detection with tissue autofluorescence and diffuse reflectance spectroscopy.

Background: Cancer is a severe threat to human health, and surgery is a major method of cancer treatment. This study aimed to develop an optical sensor for fast cancer tissue.

Methods: The tissue autofluorescence spectrum and diffuse reflectance spectrum were obtained by using a laboratory-developed optical sensor system.

Improvement of adhesive properties of modified epoxy-novolac resin by acrylonitrile-butadiene rubber grafted poly(chromium methacrylate).

The purpose of this work was to improve the adhesive properties of modified epoxy-novolac resin by acrylonitrile-butadiene rubber (NBR) grafted poly(chromium methacrylate). Chromium methacrylate was prepared by reaction of basic chromium sulfate with sodium methacrylate. Acrylonitrile-butadiene rubber grafted poly(chromium methacrylate) (GNBR) was successfully prepared by solution graft copolymerization to improve the adhesive properties of epoxy-novolac resin.

PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction.

X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination.