BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion.

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients.

: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images.

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ breast cancer, which is costly, tissue destructive, requires specialised platforms, and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively.

Isolation of Stable Borepin Radicals and Anions.

Borepin, a 7-membered boron-containing heterocycle, has become an emerging molecular platform for the development of new materials and optoelectronics. While electron-deficient borepins are well-established, reduced electron-rich species have remained elusive. Herein we report the first isolable, crystalline borepin radical (2 a, 2 b) and anion (3 a, 3 b) complexes, which have been synthesized by potassium graphite (KC ) reduction of cyclic(alkyl)(amino) carbene-dibenzo[b,d]borepin precursors.

AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes.

Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g.

Cation effects on dynamics of ligand-benzylated formazanate boron and aluminium complexes.

The dynamic processes present in ligand-benzylated formazanate boron and aluminium complexes are investigated using variable temperature NMR experiments and lineshape analyses. The observed difference in activation parameters for complexes containing either organic countercations (NBu4+) or alkali cations is rationalized on the basis of a different degree of ion-pairing in the ground state, and the data are in all cases consistent with a mechanism that involves pyramidal inversion at the nitrogens in the heterocyclic ring rather than homolytic N-C(benzyl) bond cleavage.