Implementable assay for monitoring minimum residual disease after radical treatment for colorectal cancer.

Considering the cost and invasiveness of monitoring postoperative minimal residual disease (MRD) of colorectal cancer (CRC) after adjuvant chemoradiotherapy (ACT), we developed a favorable approach based on methylated circulating tumor DNA to detect MRD after radical resection. Analyzing the public database, we identified the methylated promoter regions of the genes FGD5, GPC6, and MSC. Using digital polymerase chain reaction (dPCR), we termed the "amplicon of methylated sites using a specific enzyme" assay as "AMUSE." We examined 180 and 114 pre- and postoperative serial plasma samples from 28 recurrent and 19 recurrence-free pathological stage III CRC patients, respectively. The results showed 22 AMUSE-positive of 28 recurrent patients (sensitivity, 78.6%) and 17 AMUSE-negative of 19 recurrence-free patients (specificity, 89.5%). AMUSE predicted recurrence 208 days before conventional diagnosis using radiological imaging. Regarding ACT evaluation by the reactive response, 19 AMUSE-positive patients during their second or third blood samples showed a significantly poorer prognosis than the other patients (p = 9E-04). The AMUSE assay stratified four groups by the altered patterns of tumor burden postoperatively. Interestingly, only 34.8% of cases tested AMUSE-negative during ACT treatment, indicating eligibility for ACT. The AMUSE assay addresses the clinical need for accurate MRD monitoring with universal applicability, minimal invasiveness, and cost-effectiveness, thereby enabling the timely detection of recurrences. This assay can effectively evaluate the efficacy of ACT in patients with stage III CRC following curative resection. Our study strongly recommends reevaluating the clinical application of ACT using the AMUSE assay.

Tumor suppressive role of the epigenetic master regulator BRD3 in colorectal cancer.

Bromodomain and extraterminal domain (BET) family proteins are epigenetic master regulators of gene expression via recognition of acetylated histones and recruitment of transcription factors and co-activators to chromatin. Hence, BET family proteins have emerged as promising therapeutic targets in cancer. In this study, we examined the functional role of bromodomain containing 3 (BRD3), a BET family protein, in colorectal cancer (CRC). In vitro and vivo analyses using BRD3-knockdown or BRD3-overexpressing CRC cells showed that BRD3 suppressed tumor growth and cell cycle G1/S transition and induced p21 expression. Clinical analysis of CRC datasets from our hospital or The Cancer Genome Atlas revealed that BET family genes, including BRD3, were overexpressed in tumor tissues. In immunohistochemical analyses, BRD3 was observed mainly in the nucleus of CRC cells. According to single-cell RNA sequencing in untreated CRC tissues, BRD3 was highly expressed in malignant epithelial cells, and cell cycle checkpoint-related pathways were enriched in the epithelial cells with high BRD3 expression. Spatial transcriptomic and single-cell RNA sequencing analyses of CRC tissues showed that BRD3 expression was positively associated with high p21 expression. Furthermore, overexpression of BRD3 combined with knockdown of, a driver gene in the BRD family, showed strong inhibition of CRC cells in vitro. In conclusion, we demonstrated a novel tumor suppressive role of BRD3 that inhibits tumor growth by cell cycle inhibition in part via induction of p21 expression. BRD3 activation might be a novel therapeutic approach for CRC.

Ensemble detection of hand joint ankylosis and subluxation in radiographic images using deep neural networks.

The modified total Sharp score (mTSS) is often used as an evaluation index for joint destruction caused by rheumatoid arthritis. In this study, special findings (ankylosis, subluxation, and dislocation) are detected to estimate the efficacy of mTSS by using deep neural networks (DNNs). The proposed method detects and classifies finger joint regions using an ensemble mechanism. This integrates multiple DNN detection models, specifically single shot multibox detectors, using different training data for each special finding. For the learning phase, we prepared a total of 260 hand X-ray images, in which proximal interphalangeal (PIP) and metacarpophalangeal (MP) joints were annotated with mTSS by skilled rheumatologists and radiologists. We evaluated our model using five-fold cross-validation. The proposed model produced a higher detection accuracy, recall, precision, specificity, F-value, and intersection over union than individual detection models for both ankylosis and subluxation detection, with a detection rate above 99.8% for the MP and PIP joint regions. Our future research will aim at the development of an automatic diagnosis system that uses the proposed mTSS model to estimate the erosion and joint space narrowing score.

Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.

Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model's insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73-4.10; log-rank p < 0.001). This remained consistent after adjusting for risk factors (adjusted HR, 2.57; 95% CI 1.62-4.10; p < 0.001). AI-CTRCD score enhanced prediction beyond known factors (time-dependent AUC for 2 years: 0.78 with AI-CTRCD score vs. 0.74 without; p = 0.005). In conclusion, the AI model robustly stratified CTRCD risk from baseline ECG.

Childhood interstitial lung diseases: current understanding of the classification and imaging findings.

Childhood interstitial lung diseases (chILDs) encompass a diverse group of disorders with a high mortality rate and severe respiratory morbidities. Recent investigations have revealed that the classification of adult ILDs is not valid for chILDs, particularly for ILDs of early onset. Therefore, Children's Interstitial Lung Disease Research Cooperative of North America proposed a new classification of chILDs for affected children under 2 years of age, and later another classification for affected individuals between 2 and 18 years of age. In this review, we provide an overview of the imaging findings of chILDs by classification. Most infantile ILDs have unique clinical, radiological, and molecular findings, while the manifestation of pediatric ILDs overlaps with that of adult ILDs.

Redox dynamics and surface structures of an active palladium catalyst during methane oxidation.

Catalysts based on palladium are among the most effective in the complete oxidation of methane. Despite extensive studies and notable advances, the nature of their catalytically active species and conceivable structural dynamics remains only partially understood. Here, we combine operando transmission electron microscopy (TEM) with near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) and density functional theory (DFT) calculations to investigate the active state and catalytic function of Pd nanoparticles (NPs) under methane oxidation conditions. We show that the particle size, phase composition and dynamics respond appreciably to changes in the gas-phase chemical potential. In combination with mass spectrometry (MS) conducted simultaneously with in situ observations, we uncover that the catalytically active state exhibits phase coexistence and oscillatory phase transitions between Pd and PdO. Aided by DFT calculations, we provide a rationale for the observed redox dynamics and demonstrate that the emergence of catalytic activity is related to the dynamic interplay between coexisting phases, with the resulting strained PdO having more favorable energetics for methane oxidation.

A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.

Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis.

BACKGROUND: Cell-cell interaction factors that facilitate the progression of adenoma to sporadic colorectal cancer (CRC) remain unclear, thereby hindering patient survival. METHODS: We performed spatial transcriptomics on five early CRC cases, which included adenoma and carcinoma, and one advanced CRC. To elucidate cell-cell interactions within the tumour microenvironment (TME), we investigated the colocalisation network at single-cell resolution using a deep generative model for colocalisation analysis, combined with a single-cell transcriptome, and assessed the clinical significance in CRC patients. FINDINGS: CRC cells colocalised with regulatory T cells (Tregs) at the adenoma-carcinoma interface. At early-stage carcinogenesis, cell-cell interaction inference between colocalised adenoma and cancer epithelial cells and Tregs based on the spatial distribution of single cells highlighted midkine (MDK) as a prominent signalling molecule sent from tumour epithelial cells to Tregs. Interaction between MDK-high CRC cells and SPP1+ macrophages and stromal cells proved to be the mechanism underlying immunosuppression in the TME. Additionally, we identified syndecan4 (SDC4) as a receptor for MDK associated with Treg colocalisation. Finally, clinical analysis using CRC datasets indicated that increased MDK/SDC4 levels correlated with poor overall survival in CRC patients. INTERPRETATION: MDK is involved in the immune tolerance shown by Tregs to tumour growth. MDK-mediated formation of the TME could be a potential target for early diagnosis and treatment of CRC. FUNDING: Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Science Research; OITA Cancer Research Foundation; AMED under Grant Number; Japan Science and Technology Agency (JST); Takeda Science Foundation; The Princess Takamatsu Cancer Research Fund.

Improvement of Ionization Efficiency and Application of Structural Analysis for MALDI-TOFMS by Derivatization of Polyacrylic Acid.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) is a suitable method for polymer analysis. MALDI is a soft ionization technique that can generate mainly singly charged ions. Therefore, the polymer's molecular weight distribution is easy to analyze, facilitating the calculation of the number average molecular weight and weight average molecular weight and polydispersity. However, there are polymers that are difficult to detect by MALDI-TOFMS. For example, polyacrylic acid includes carboxylic acid in the main chain, which is difficult to measure due to its low ionization efficiency. As a solution, the ionization efficiency was improved by methylation. In this technical report, we introduce a method to utilize derivatization to determine the degree of polymerization by accurate mass spectrometry (MS). Furthermore, the structures of both ends of the polymers were estimated by tandem time-of-flight MS.