Toward Finding the Role of Surface Iron Ions in Enhancing Oxygen-Evolution Reaction.

The oxygen evolution reaction (OER) in alkaline media is crucial for energy conversion technologies, and Fe-based catalysts have garnered significant attention for their efficacy. In this study, we provide an investigation of Fe-based catalysts under OER conditions using some techniques. Our findings reveal minimal structural alterations in the bulk FeHO framework during OER, indicating that the bulk structure remains largely intact.

Machine learning-based analysis of Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade.

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on  Ga-PSMA-11 PET/CT images in prostate cancer patients.

Differential privacy preserved federated transfer learning for multi-institutional Ga-PET image artefact detection and disentanglement.

Purpose: Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately.

A machine learning-based sonomics for prediction of thyroid nodule malignancies.

Objectives: This study aims to use ultrasound derived features as biomarkers to assess the malignancy of thyroid nodules in patients who were candidates for FNA according to the ACR TI-RADS guidelines.

Methods: Two hundred and ten patients who met the selection criteria were enrolled in the study and subjected to ultrasound-guided FNA of thyroid nodules. Different radiomics features were extracted from sonographic images, including intensity, shape, and texture feature sets.

Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.

Purpose: Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation.