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Pollution can be broadly defined as the presence of contaminants or energy sources detrimental to ecosystems and human health. The human organism serves as a valuable indicator of ecosystem contamination. However, understanding physiological disorders and correlating specific contaminants with disease development is a complex and arduous task, necessitating extensive scientific research spanning years or even decades.

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Regenerative medicine and tissue engineering aim to restore or replace impaired organs and tissues using cell transplantation supported by scaffolds. Recently scientists are focusing on developing new biomaterials that optimize cellular attachment, migration, proliferation, and differentiation. Nanoparticles, such as graphene oxide (GO), have emerged as versatile materials due to their high surface-to-volume ratio and unique chemical properties, such as electrical conductivity and flexibility.

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: The objective of this study is to prospectively collect dosimetric and clinical data on vaginal cuff electronic brachytherapy and propose a protocol for the procedure. Twenty-five patients who had proven endometrial or cervical carcinoma and had undergone radical hysterectomy have been treated with vaginal cuff electronic brachytherapy. Treatment session durations and doses to the targets and the organs at risk have been extracted from the treatment planning software.

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Idiopathic pulmonary fibrosis (IPF) is a chronic and lethal interstitial lung disease (ILD) of unknown origin, characterized by limited treatment efficacy and a fibroproliferative nature. It is marked by excessive extracellular matrix deposition in the pulmonary parenchyma, leading to progressive lung volume decline and impaired gas exchange. The chemokine system, a network of proteins involved in cellular communication with diverse biological functions, plays a crucial role in various respiratory diseases.

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Deep learning image reconstruction (DLIR) has shown potential to enhance computed tomography (CT) image quality, but its impact on tumor visibility and adoption among radiologists with varying experience levels remains unclear. This study compared the performance of two deep learning-based image reconstruction methods, DLIR and Pixelshine, an adaptive statistical iterative reconstruction-volume (ASIR-V) method, and filtered back projection (FBP) across 33 contrast-enhanced CT staging examinations, evaluated by 20-24 radiologists. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for tumor and surrounding organ tissues across DLIR (Low, Medium, High), Pixelshine (Soft, Ultrasoft), ASIR-V (30-100%), and FBP.

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