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.

What does FEXI measure?

Filter-exchange imaging (FEXI) has already been utilized in several biomedical studies for evaluating the permeability of cell membranes. The method relies on suppressing the extracellular signal using strong diffusion weighting (the mobility filter causing a reduction in the overall diffusivity) and monitoring the subsequent diffusivity recovery. Using Monte Carlo simulations, we demonstrate that FEXI is sensitive not uniquely to the transcytolemmal exchange but also to the geometry of involved compartments: complex geometry offers locations where spins remain unaffected by the mobility filter; moving to other locations afterwards, such spins contribute to the diffusivity recovery without actually permeating any membrane.

[F]FDG-PET/CT Radiomics and Artificial Intelligence in Lung Cancer: Technical Aspects and Potential Clinical Applications.

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices.

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.

Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers.

Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer.

Objectives: We evaluate the feasibility of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT).

Materials And Methods: This retrospective study included 53 LARC patients divided into a training set (Center#1, n = 36) and external validation set (Center#2, n = 17). T2-weighted (T2W) MRI was acquired for all patients, 2 weeks before and 4 weeks after nCRT.

Enhancement of chemoradiation by co-incorporation of gold nanoparticles and cisplatin into alginate hydrogel.

Nonspecificity and high toxicity limit the treatment efficacy and safety of chemoradiation therapy. Effective tumor targeting of anticancer drugs and radiosensitizing agents is highly desirable to amplify the efficacy of this standard cancer therapy approach. To achieve this goal, we exploited the synergy of cisplatin and gold nanoparticles (AuNPs) co-loaded into alginate hydrogel network, forming so-called ACA nanocomplex, and X-ray radiation.

Radiation Exposure and Bell's Palsy: A Hypothetical Association.

Bell's palsy is an idiopathic peripheral nerve palsy involving the facial nerve. It accounts for 60 to 75% of all cases of unilateral facial paralysis. The main mechanisms to induce BP remain unclear, but infection, ischemic condition and immunodeficiency may contribute to the development of Bell's palsy.

Investigating the Therapeutic Effects of Alginate Nanogel Co-loaded with Gold Nanoparticles and Cisplatin on U87-MG Human Glioblastoma Cells.

Background And Purpose: It has been well-known both gold nanoparticles (AuNPs) and cisplatin are potential radiosensitizers for radiotherapy of cancer. In this in vitro study, we investigated the chemoradiotherapeutic effects of alginate nanogel co-loaded with AuNPs and cisplatin (ACA) on U87-MG human glioblastoma cells.

Methods: Based on the accomplished pilot studies, U87-MG cells were incubated with ACA and cisplatin at 10% inhibitory concentration (IC10) for 4h.

Evaluation of autophagy induction on HEV 239 vaccine immune response in a mouse model.

Hepatitis E virus (HEV) infection remains a serious threat to life and productivity in developing world. Vaccine seems to be an effective, safe, and affordable approach to address HEV disease burden. The HEV genome consists of three open reading frames (ORFs).

Evaluation of possible relationship between COL4A4 gene polymorphisms and risk of keratoconus.

Purpose: Keratoconus (KC) is a genetically heterogeneous corneal dystrophy with unknown etiology that causes loss of visual acuity. Evidence has shown that corneas from patients with KC contain reduced amounts of total collagen proteins, and collagen type IV has been suggested as a candidate gene in KC pathogenesis. This study aimed to evaluate the possible associations between collagen type IV alpha-4 chain (COL4A4) polymorphisms (rs2229813 G/A, M1327V and rs2228555 A/G, V1516V) and susceptibility to KC.

Cross-sectional assessment of prevalence and correlates of blood-borne and sexually-transmitted infections among Afghan National Army recruits.

Background: Few data are available in Afghanistan to shape national military force health practices, particularly with regard to sexually-transmitted infections (STIs). We measured prevalence and correlates of HIV, syphilis, herpes simplex 2 virus (HSV-2), and hepatitis C virus (HCV) among Afghan National Army (ANA) recruits.

Methods: A cross-sectional sample of male ANA recruits aged 18-35 years were randomly selected at the Kabul Military Training Center between February 2010 and January 2011.