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Reply to Chung and to Rogliani and Calzetta.

Am J Respir Crit Care Med

October 2024

Imperial College London, National Heart and Lung Institute, London, United Kingdom of Great Britain and Northern Ireland.

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Design and Synthesis of Topoisomerases-Histone Deacetylase Dual Targeted Quinoline-Bridged Hydroxamates as Anticancer Agents.

J Med Chem

January 2025

Laboratory for Drug Design and Synthesis, Department of Pharmaceutical Sciences and Natural Products, School of Pharmaceutical Sciences, Central University of Punjab, Bathinda 151 401, India.

The multifactorial nature of cancer requires treatment that involves simultaneous targeting of associated overexpressed proteins and cell signaling pathways, possibly leading to synergistic effects. Herein, we present a systematic study that involves the simultaneous inhibition of human topoisomerases (hTopos) and histone deacetylases (HDACs) by multitargeted quinoline-bridged hydroxamic acid derivatives. These compounds were rationally designed considering pharmacophoric features and catalytic sites of the cross-talk proteins, synthesized, and assessed for their anticancer potential.

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Radon, a common radioactive indoor air pollutant, is the second leading cause of lung cancer in the United States. Knowledge about its distribution is essential for risk assessment and designing efficient protective regulations. However, the three current radon maps for the United States are unable to provide the up-to-date, high-resolution, and time-varying radon concentrations.

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Anti-programmed cell death 1 (PD-1) monoclonal antibodies (mAbs) have proven to be effective in treating various cancers, including colorectal, lung, and melanoma. Despite their clinical success, some patients develop resistance to mAbs, requiring co-treatments with radio- or chemotherapy. Interleukin-15 (IL-15) is an immunostimulatory cytokine that promotes immune cell production and proliferation.

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Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.

Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.

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