Anticancer effect of a single-chain variable fragment against pro-matrix metalloproteinase-7 in colon cancer.

Disrupting the interaction between matrix metalloproteinase-7 (MMP-7) and syndecan-2 (SDC-2) can yield anticancer effects in colon cancer cells. Here, a single-chain variable fragment (scFv) targeting the pro-domain of MMP-7 was generated as a potential candidate anticancer agent. Among the generated scFvs, those designated 1B7 and 1C3 showed the strongest abilities to inhibit the ability of MMP-7 pro-domain to directly interact with SDC-2 in vitro and decrease the cancer activities of human HT29 colon adenocarcinoma cells.

Improving functional correlation of quantification of interstitial lung disease by reducing the vendor difference of CT using generative adversarial network (GAN) style conversion.

Objective: To assess whether CT style conversion between different CT vendors using a routable generative adversarial network (RouteGAN) could minimize variation in ILD quantification, resulting in improved functional correlation of quantitative CT (QCT) measures.

Methods: Patients with idiopathic pulmonary fibrosis (IPF) who underwent unenhanced chest CTs with vendor A and a pulmonary function test (PFT) were retrospectively evaluated. As deep-learning based ILD quantification software was mainly developed using vendor B CT, style-converted images from vendor A to B style were generated using RouteGAN.

A Study on the Development of Information and Communication Technology-Based Oral Functional Rehabilitation Exercise Program Content for Elderly People.

: This study was conducted to develop information and communication technology (ICT)-based oral functional rehabilitation exercise (OFRE) program content to effectively improve the oral function of the elderly people. : After selecting evidence-based effective OFRE items through systematic review, the final items were constructed through the validity evaluation of detailed items through an expert Delphi survey. The items were composed in a simple content form that can be performed directly and applied to ICT-based mobile applications.

C-ter100 peptide derived from Vibrio vEP-45 protease acts as a pathogen-associated molecular pattern to induce inflammation and innate immunity.

The bacterium Vibrio vulnificus causes fatal septicemia in humans. Previously, we reported that an extracellular metalloprotease, vEP-45, secreted by V. vulnificus, undergoes self-proteolysis to generate a 34 kDa protease (vEP-34) by losing its C-terminal domain to produce the C-ter100 peptide.

The Life Functioning Scale: A Measurement Tool Developed to Assess the Physical Functioning Abilities of Community-Dwelling Adults Aged 50 Years or Older.

Background: This study aimed to develop an instrument for assessing physical functioning among adults aged 50 years or older living in the community.

Methods: Based on a review of various national health surveys and cohort studies, a 144-item bank was constructed for assessing physical functioning. Focus group interviews were conducted among adults aged 50 years or older to investigate their level of understanding of 60 selected items, followed by a pretest of the items on a nationally representative sample (n=508).

Evaluation of retrieval accuracy and visual similarity in content-based image retrieval of chest CT for obstructive lung disease.

The aim of our study was to assess the performance of content-based image retrieval (CBIR) for similar chest computed tomography (CT) in obstructive lung disease. This retrospective study included patients with obstructive lung disease who underwent volumetric chest CT scans. The CBIR database included 600 chest CT scans from 541 patients.

Everolimus exerts anticancer effects through inhibiting the interaction of matrix metalloproteinase-7 with syndecan-2 in colon cancer cells.

Previous work showed that matrix metalloproteinase-7 (MMP-7) regulates colon cancer activities through an interaction with syndecan-2 (SDC-2) and SDC-2-derived peptide that disrupts this interaction and exhibits anticancer activity in colon cancer. Here, to identify potential anticancer agents, a library of 1,379 Food and Drug Administration (FDA)-approved drugs that interact with the MMP-7 prodomain were virtually screened by protein-ligand docking score analysis using the GalaxyDock3 program. Among five candidates selected based on their structures and total energy values for interacting with the MMP-7 prodomain, the known mechanistic target of rapamycin kinase (mTOR) inhibitor, everolimus, showed the highest binding affinity and the strongest ability to disrupt the interaction of the MMP-7 prodomain with the SDC-2 extracellular domain in vitro.

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting.

Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive care units (ICUs). Purpose To develop and validate a deep learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change by using longitudinal follow-up data.

Technical Feasibility of Quantitative Measurement of Various Degrees of Small Bowel Motility Using Cine Magnetic Resonance Imaging.

Objective: Cine magnetic resonance imaging (MRI) has emerged as a noninvasive method to quantitatively assess bowel motility. However, its accuracy in measuring various degrees of small bowel motility has not been extensively evaluated. We aimed to draw a quantitative small bowel motility score from cine MRI and evaluate its performance in a population with varying degrees of small bowel motility.

MuSiC-ViT: A multi-task Siamese convolutional vision transformer for differentiating change from no-change in follow-up chest radiographs.

A major responsibility of radiologists in routine clinical practice is to read follow-up chest radiographs (CXRs) to identify changes in a patient's condition. Diagnosing meaningful changes in follow-up CXRs is challenging because radiologists must differentiate disease changes from natural or benign variations. Here, we suggest using a multi-task Siamese convolutional vision transformer (MuSiC-ViT) with an anatomy-matching module (AMM) to mimic the radiologist's cognitive process for differentiating baseline change from no-change.

Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease.

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software.

Materials And Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions.

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.

Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA).

Interstitial lung abnormalities (ILA) on routine chest CT: Comparison of radiologists' visual evaluation and automated quantification.

Purpose: We aimed to evaluate the performance of a fully automated quantitative software in detecting interstitial lung abnormalities (ILA) according to the Fleischner Society guidelines on routine chest CT compared with radiologists' visual analysis.

Method: This retrospective single-centre study included participants with ILA findings and 1:2 matched controls who underwent routine chest CT using various CT protocols for health screening. Two thoracic radiologists independently reviewed the CT images using the Fleischner Society guidelines.

Artificial Intelligence in Lung Imaging.

Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace.

Multi-domain CT translation by a routable translation network.

To unify the style of computed tomography (CT) images from multiple sources, we propose a novel multi-domain image translation network to convert CT images from different scan parameters and manufacturers by simply changing a routing vector.Unlike the existing multi-domain translation techniques, our method is based on a shared encoder and a routable decoder architecture to maximize the expressivity and conditioning power of the network.Experimental results show that the proposed CT image conversion can minimize the variation of image characteristics caused by imaging parameters, reconstruction algorithms, and hardware designs.

Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma.

Objective: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC).

Materials And Methods: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019.

Structure of the human galanin receptor 2 bound to galanin and Gq reveals the basis of ligand specificity and how binding affects the G-protein interface.

Galanin is a neuropeptide expressed in the central and peripheral nervous systems, where it regulates various processes including neuroendocrine release, cognition, and nerve regeneration. Three G-protein coupled receptors (GPCRs) for galanin have been discovered, which is the focus of efforts to treat diseases including Alzheimer's disease, anxiety, and addiction. To understand the basis of the ligand preferences of the receptors and to assist structure-based drug design, we used cryo-electron microscopy (cryo-EM) to solve the molecular structure of GALR2 bound to galanin and a cognate heterotrimeric G-protein, providing a molecular view of the neuropeptide binding site.