Locked Dimerized CXCL12 Exerts Radiosensitizing Effects in Head and Neck Cancer.

Background: Head and neck squamous cell carcinoma (HNSCC) presents significant treatment challenges, particularly in cases unrelated to human papillomavirus (HPV). The chemokine receptor CXCR4, interacting with its ligand CXCL12, plays a crucial role in tumor proliferation, metastasis, and treatment resistance. This study explores the therapeutic potential of engineered monomeric and dimerized CXCL12 variants (CXCL12 and CXCL12, respectively) in HNSCC and evaluates potential additive effects when combined with radiation therapy.

Type I interferon signaling promotes radioresistance in head and neck cancer.

Despite the promise of concurrent radiotherapy (RT) and immunotherapy in head and neck cancer (HNC), multiple randomized trials of this combination have had disappointing results. To evaluate potential immunologic mechanisms of RT resistance, we compared pre-treatment HNCs that developed RT resistance to a matched cohort that achieved curative status. Gene set enrichment analysis demonstrated that a pre-treatment pro-immunogenic tumor microenvironment (TME), including type II interferon [interferon gamma (IFNγ)] and tumor necrosis factor alpha (TNFα) signaling, predicted cure while type I interferon [interferon alpha (IFNα)] enrichment was associated with an immunosuppressive TME found in tumors that went on to recur.

Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks.

The utilization of numerical methods, such as computational fluid dynamics (CFD), has been widely established for modeling patient-specific hemodynamics based on medical imaging data. Hemodynamics assessment plays a crucial role in treatment decisions for the coarctation of the aorta (CoA), a congenital heart disease, with the pressure drop (PD) being a crucial biomarker for CoA treatment decisions. However, implementing CFD methods in the clinical environment remains challenging due to their computational cost and the requirement for expert knowledge.