Repair of Osteochondral Defect with Acellular Cartilage Matrix and Thermosensitive Hydrogel Scaffold.

In the present study, acellular cartilage matrix (ACM) was modified with poly-l-lysine/hyaluronic acid (PLL/HA) multilayers via detergent-enzyme chemical digestion and layer-by-layer self-assembly technology. This modified ACM was then loaded with Transforming Growth Factor Beta 3 (TGF-β3) and incorporated into a thermosensitive hydrogel (TH) to create a HA/PLL-ACM/TH composite scaffold with sustained-release function. This study aimed to evaluate the efficacy of this novel composite scaffold in promoting chondrogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) and facilitating osteochondral defect repair.

Prognostic impact and reasons for variability by tumor location in gastric cancer.

Background: Gastric cancer (GC) is a highly prevalent gastrointestinal tract tumor. Several trials have demonstrated that the location of GC can affect patient prognosis. However, the factors determining tumor location remain unclear.

Reducing penumbral blur in computed tomography by learning the inverse finite focal spot model.

Penumbral blur is one of the major limitations of the high spatial resolution micro-CT, due to a nonideal large focal spot. Penumbral blur hinders the ability to resolve small features that may only be a few pixels in size. Reducing the focal spot size by decreasing the x-ray tube power is a straightforward solution, but it leads to prolonged scan durations.

Polyvinyl chloride nanoplastics suppress homology-directed repair and promote oxidative stress to induce esophageal epithelial cellular senescence and cGAS-STING-mediated inflammation.

Nanoplastics (NPs), which are characterized by plastic particles smaller than 1 μm, have emerged as pervasive environmental pollutants, raising concerns about their potential toxicity to living organisms. Numerous investigations have highlighted the tendency of NPs to accumulate in organs, resulting in toxic effects. Despite polyvinyl chloride (PVC) being one of the most prevalent NPs, its impact on the esophagus and the associated underlying mechanisms remain largely unknown.

PMMA nanoplastics induce gastric epithelial cellular senescence and cGAS-STING-mediated inflammation via ROS overproduction and NHEJ suppression.

The increasing environmental presence of nanoplastics (NPs) has raised concerns about their potential impact on biological systems. We investigated the repercussions of polymethyl methacrylate (PMMA) NPs exposure on normal gastric epithelial cells and revealed a pronounced increase in senescence-associated β-galactosidase activity and G1 phase cell cycle arrest. Our study demonstrated a dose-dependent increase in reactive oxygen species (ROS) and DNA damage, underscoring the pivotal role of ROS in PMMA NPs-mediated effects, a novel contribution to the existing body of knowledge dominated by polystyrene particles.

The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.

Background: Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent a novel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning.

Methods: Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice.

Comprehensive multimodal deep learning survival prediction enabled by a transformer architecture: A multicenter study in glioblastoma.

Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical, and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability.

Methods: We propose and evaluate a transformer-based nonlinear and nonproportional survival prediction model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with nonimaging data using cross-attention.

Principles of artificial intelligence in radiooncology.

Purpose: In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily.

PRL1 and PRL3 promote macropinocytosis via its lipid phosphatase activity.

PRL1 and PRL3, members of the protein tyrosine phosphatase family, have been associated with cancer metastasis and poor prognosis. Despite extensive research on their protein phosphatase activity, their potential role as lipid phosphatases remains elusive. We conducted comprehensive investigations to elucidate the lipid phosphatase activity of PRL1 and PRL3 using a combination of cellular assays, biochemical analyses, and protein interactome profiling.

System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy.

Background: Osteosarcoma (OS) is the most common bone malignant tumor in children, and its prognosis is often poor. Anoikis is a unique mode of cell death.However, the effects of Anoikis in OS remain unexplored.

A real-world disproportionality analysis of the FDA adverse event reporting system events for ibuprofen.

Introduction: Ibuprofen is commonly used as an over-the-counter (OTC) antipyretic and analgesic. As the frequency of its use has increased, there has been a corresponding increase in reports of associated adverse events (AEs). However, these events have not been systematically reported in the literature.

Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization.

To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e.

Discovery of novel macrocyclic derivatives as potent and selective cyclin-dependent kinase 2 inhibitors.

Cyclin-dependent kinase 2 (CDK2) is a member of CDK family of kinases (CDKs) that regulate the cell cycle. Its inopportune or over-activation leads to uncontrolled cell cycle progression and drives numerous types of cancers, especially ovarian, uterine, gastric cancer, as well as those associated with amplified CCNE1 gene. However, developing selective lead compound as CDK2 inhibitors remains challenging owing to similarities in the ATP pockets among different CDKs.

Metformin adverse event profile: a pharmacovigilance study based on the FDA Adverse Event Reporting System (FAERS) from 2004 to 2022.

Background: Metformin has the potential for treating numerous diseases, but there are still many unrecognized and unreported adverse events (AEs).

Methods: We selected data from the United States FDA Adverse Event Reporting System (FAERS) database from the first quarter (Q1) of 2004 to the fourth quarter (Q4) of 2022 for disproportionality analysis to assess the association between metformin and related adverse events.

Results: In this study 10,500,295 case reports were collected from the FAERS database, of which 56,674 adverse events related to metformin were reported.

Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer.

Background And Purpose: The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that is associated with additional radiation exposure and poor soft tissue contrast. To establish a magnetic resonance imaging (MRI) only workflow combining improved tissue contrast and high seed detectability, a deep learning-approach for automatic seed segmentation on MRI-scans was developed.

Material And Methods: Patients treated with I-125 seed brachytherapy received a postplan-CT and a 1.

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology.

Purpose: The potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology.

Methods: The 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4.

Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design.

We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation.

Revealing the Mechanisms of Byu dMar 25 in the Treatment of Alzheimer's Disease through Network Pharmacology, Molecular Docking, and In Vivo Experiment.

Background: Alzheimer's disease (AD) is the most common neurodegenerative disease, severely reducing the cognitive level and life quality of patients. Byu dMar 25 (BM25) has been proved to have a therapeutic effect on AD. However, the pharmacological mechanism is still unclear.

Research Progress on the Effect of Autophagy and Exosomes on Liver Fibrosis.

Chronic liver disease is a known risk factor for the development of liver cancer, and the development of microRNA (miRNA) liver therapies has been hampered by the difficulty of delivering miRNA to damaged tissues. In recent years, numerous studies have shown that hepatic stellate cell (HSC) autophagy and exosomes play an important role in maintaining liver homeostasis and ameliorating liver fibrosis. In addition, the interaction between HSC autophagy and exosomes also affects the progression of liver fibrosis.

Fibroblast growth factor 9 reduces TBHP-induced oxidative stress in chondrocytes and diminishes mouse osteoarthritis by activating ERK/Nrf2 signaling pathway.

Osteoarthritis (OA) is a degenerative and progressive disease that affects joints. Pathologically, it is characterized by oxidative stress-mediated excessive chondrocyte apoptosis and mitochondrial dysfunction. Fibroblast growth factor 9 (FGF9) has been shown to exert antioxidant effects and prevent degenerative diseases by activating ERK-related signaling pathways.

Deep learning for terahertz image denoising in nondestructive historical document analysis.

Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents.