Structural and dynamical insights revealed the anti-glioblastoma potential of withanolides from Withania coagulans against vascular endothelial growth factor receptor (VEGFR).

Context: Glioblastoma (GBM), well known as grade 4 tumors due to its progressive malignant features such as vascular proliferation and necrosis, is the most aggressive form of primary brain tumor found in adults. Mutations and amplifications in the vascular endothelial growth factor receptor (VEGFR) contribute to almost 25% of GBM tumors. And thus, VEGFR has been declared the primary target in glioblastoma therapeutic strategies.

Long-read transcriptome sequencing of CLL and MDS patients uncovers molecular effects of mutations.

Mutations in splicing factor 3B subunit 1 () frequently occur in patients with chronic lymphocytic leukemia (CLL) and myelodysplastic syndromes (MDSs). These mutations have different effects on the disease prognosis with beneficial effect in MDS and worse prognosis in CLL patients. A full-length transcriptome approach can expand our knowledge on mutation effects on RNA splicing and its contribution to patient survival and treatment options.

Development, Evaluation, and Molecular Dynamics Study of Ampicillin-Loaded Chitosan-Hyaluronic Acid Films as a Drug Delivery System.

Periodontitis is an inflammatory periodontal disease defined by the progressive loss of tissues surrounding the tooth. Ampicillin is an antibiotic for managing and treating specific bacterial infections, including periodontitis. Periodontal pockets occur due to periodontal disease progression and act as a natural reservoir that is easily reachable for the insertion of a delivery system, and the amount of drug to be released has a major role in the efficiency of treatment of the disease.

Cancer drug sensitivity estimation using modular deep Graph Neural Networks.

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drugs components that are tailored to the transcriptomic profile of a given primary tumor. The SMILES representation of molecules that is used by state-of-the-art drug-sensitivity models is not conducive for neural networks to generalize to new drugs, in part because the distance between atoms does not generally correspond to the distance between their representation in the SMILES strings. Graph-attention networks, on the other hand, are high-capacity models that require large training-data volumes which are not available for drug-sensitivity estimation.