Novel Artificial Intelligence Combining Convolutional Neural Network and Support Vector Machine to Predict Colorectal Cancer Prognosis and Mutational Signatures From Hematoxylin and Eosin Images.

Reducing recurrence following radical resection of colon cancer without overtreatment or undertreatment remains a challenge. Postoperative adjuvant chemotherapy (Adj) is currently administered based solely on pathologic TNM stage. However, prognosis can vary significantly among patients with the same disease stage.

Preoperative evaluation of visceral pleural invasion in peripheral lung cancer utilizing deep learning technology.

Purpose: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.

Methods: This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.

Development of a 3D Vascular Network Visualization Platform for One-Dimensional Hemodynamic Simulation.

Recent advancements in computational performance and medical simulation technology have made significant strides, particularly in predictive diagnosis. This study focuses on the blood flow simulation reduced-order models, which provide swift and cost-effective solutions for complex vascular systems, positioning them as practical alternatives to 3D simulations in resource-limited medical settings. The paper introduces a visualization platform for patient-specific and image-based 1D-0D simulations.

Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues.

In recent years, the treatment of breast cancer has advanced dramatically and neoadjuvant chemotherapy (NAC) has become a common treatment method, especially for locally advanced breast cancer. However, other than the subtype of breast cancer, no clear factor indicating sensitivity to NAC has been identified. In this study, we attempted to use artificial intelligence (AI) to predict the effect of preoperative chemotherapy from hematoxylin and eosin images of pathological tissue obtained from needle biopsies prior to chemotherapy.

Mesoscopic-scale grain formation in HfO-based ferroelectric thin films and its impact on electrical characteristics.

Ferroelectric memory devices are expected for low-power and high-speed memory applications. HfO-based ferroelectric is attracting attention for its CMOS-compatibility and high scalability. Mesoscopic-scale grains, of which size is almost comparable to device size, are formed in HfO-based ferroelectric poly-crystalline thin films, which largely influences electrical characteristics in memory devices.

Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning.

Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction of recurrence after resection is clinically important.

A penalized spline fitting method to optimize geometric parameters of arterial centerlines extracted from medical images.

In order to grasp the spatial and temporal evolution of vascular geometry, three-dimensional (3D) arterial bending structure and geometrical changes of arteries and stent grafts (SG) must be quantified using geometrical parameters such as curvature and torsion along the vasculature centerlines extracted from medical images. Here, we develop a robust method for constructing smooth centerlines based on a spline fitting method (SFM) such that the optimized geometric parameters of curvature and torsion can be obtained independently of digitization noise in the images. Conventional SFM consists of the 3rd degree spline basis function and 2nd derivative penalty term.

Geometric analysis of ruptured and nonruptured abdominal aortic aneurysms.

Objective: The objective of this study was to use parameters to determine the geometric differences between ruptured abdominal aortic aneurysms (AAAs) and nonruptured AAAs.

Methods: Computed tomography data of 38 ruptured AAAs and 215 electively repaired (nonruptured) AAAs were collected from multiple institutes. We compared the ruptured AAA group and nonruptured AAA group with 1:1 matching by using the Mahalanobis distance, which was calculated using the patient's age, sex, and AAA diameter.

Morphological analysis using geometric parameters for splenic aneurysms.

Background Considering the unique characteristics of splenic artery aneurysms, we hypothesized that hemodynamic forces could play an important role in splenic artery aneurysm formation and that splenic artery geometry should be correlated with aneurysm development. Methods Tortuosity of the splenic artery was evaluated three-dimensionally by calculating the curvature using software and the original modeling system. We selected 54 splenic artery aneurysm patients who had undergone thin-slice computed tomography imaging with contrast.

Morphological Changes and Device Migration After Stent Graft Insertion - Clinical Application of an Image-Based Modeling System and Analysis With Geometric Parameters.

Background: Previously, we developed an image-based modeling system (V-Modeler) to investigate geometric changes in stent grafts (SGs) following their implantation for abdominal aortic aneurysms (AAAs). The aims of the present study were to improve this system for clinical use, to chronologically analyze postoperative morphological changes in SGs, and to demonstrate scenarios of SG migration.

Methods and results: Contrast-enhanced computed tomography data from 36 patients who underwent endovascular aneurysm repair (EVAR) for AAAs were used, with 72 centerline paths, in total, analyzed for bilateral SG legs.

Development of an Image-Based Modeling System to Investigate Evolutional Geometric Changes of a Stent Graft in an Abdominal Aortic Aneurysm.

Background: Quantification of geometric changes of the stent graft (SG) in abdominal aortic aneurysm has been required for follow up of endovascular aneurysm repair (EVAR). The aim was to develop an image-based modeling system (V-Modeler) to investigate these changes over time. METHODS AND RESULTS: V-Modeler was applied to investigate the migration of the SG.

Quantitative measurement of blood flow volume in the major intracranial arteries by using 123i-iodoamphetamine SPECT.

Purpose: The aim of this study was to establish the novel automatic method to quantify blood flow volumes of the major intracranial arteries by using SPECT.

Methods: We created the vascular templates to cover the territory supplied by the major intracranial arteries. Each blood flow volume was calculated as the regional cerebral blood flow on SPECT using this template × volume size of the template.

Cell-type specificity of the expression of Os BOR1, a rice efflux boron transporter gene, is regulated in response to boron availability for efficient boron uptake and xylem loading.

We describe a boron (B) transporter, Os BOR1, in rice (Oryza sativa). Os BOR1 is a plasma membrane-localized efflux transporter of B and is required for normal growth of rice plants under conditions of limited B supply (referred to as -B). Disruption of Os BOR1 reduced B uptake and xylem loading of B.

Saccharomyces cerevisiae Bor1p is a boron exporter and a key determinant of boron tolerance.

Boron is toxic to living organisms when present in excess. Saccharomyces cerevisiae Bor1p is a plasma membrane protein that decreases the intracellular concentration of boron and confers boron tolerance in yeasts. We investigated the detailed characteristics of boron transport by Bor1p and its roles in boron tolerance.

Roles of BOR1, DUR3, and FPS1 in boron transport and tolerance in Saccharomyces cerevisiae.

The roles of three membrane proteins, BOR1, DUR3, and FPS1, in boron (B) transport in yeast were examined. The boron concentration in yeast cells lacking BOR1 was elevated upon exposure to 90 mM boric acid, whereas cells lacking DUR3 or FPS1 showed lower boron concentrations. Compared with control cells, cells overexpressing BOR1 or FPS1 had a lower boron concentration, and cells overexpressing DUR3 had a higher boron concentration.

Isolation of Arabidopsis thaliana cDNAs that confer yeast boric acid tolerance.

An Arabidopsis thaliana cDNA library was introduced into a Saccharomyces cerevisiae mutant that lacks ScBOR1 (YNL275W), a boron (B) efflux transporter. Five cDNAs were identified that confer tolerance to high boric acid. The nucleotide sequence analysis identified the clones as a polyadenylate-binding protein, AtPAB2; a ribosomal small subunit protein, AtRPS20B; an RNA-binding protein, AtRBP47c'; and two Myb transcription factors, AtMYB13 and AtMYB68.

Arabidopsis boron transporter for xylem loading.

Boron deficiency hampers the productivity of 132 crops in more than 80 countries. Boron is essential in higher plants primarily for maintaining the integrity of cell walls and is also beneficial and might be essential in animals and in yeast. Understanding the molecular mechanism(s) of boron transport is crucial for alleviating boron deficiency.