Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis.

Objective: Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease with a poor prognosis. This study aims to explore the correlation between the spatial distribution of lymphocyte aggregates and the prognosis of MIBC using deep learning.

Methods: Whole-slide images (WSIs) and clinical information of MIBC patients were collected from The Cancer Genome Atlas (TCGA) and the HBlaU079Su01 microarray.

Multi-omics data reveal that SAA1 + fibroblasts exacerbate periodontitis by regulating macrophage inflammation and chemotaxis.

Background: Traditional techniques are limited in their ability to analyze the complex interaction mechanisms among multiple cell types within the periodontal microenvironment, thereby restricting the development of targeted therapies for periodontitis (PD). Utilizing multiomics technologies to investigate the interaction networks of key cell clusters can systematically uncover regulatory mechanisms and identify critical therapeutic targets.

Methods: Through integrative analysis of single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk transcriptome datasets from periodontal tissues, we systematically characterized the spatial architecture and intercellular communication networks within the inflammatory periodontal microenvironment, identifying a functionally serum amyloid A1 + fibroblasts (SAA1 + Fib) that critically drives disease progression.

A deep learning model could screen for coronary heart disease from a "pseudo-normal" electrocardiogram.

Background: This study aimed to develop a deep learning model (DLM) for rapid screening of coronary heart disease (CHD) using "pseudo-normal" electrocardiograms (ECGs), particularly focusing on patients who present with normal or near-normal ECGs at admission.

Methods: This study utilized standard 12-lead ECGs from CHD and non-CHD patients collected at the Second Affiliated Hospital of Nanchang University (SAH) between September 2017 and May 2019. These data sets were employed for training and validation of the DLM.

Fibroblast-derived versican exacerbates periodontitis progression by regulating macrophage migration and inflammatory cytokine secretion.

Objective: Versican (VCAN), a prominent extracellular matrix component upregulated in inflammatory diseases, demonstrates context-specific regulatory mechanisms. Periodontitis, a chronic inflammatory disease leading to periodontal tissue destruction and tooth loss, the pathological role of it remains poorly defined. Our study aims to examine VCAN-mediated mechanisms in periodontitis.

Biological age prediction and NAFLD risk assessment: a machine learning model based on a multicenter population in Nanchang, Jiangxi, China.

Background: The objective was to develop a biological age prediction model (NC-BA) for the Chinese population to enrich the relevant studies in this population. And to investigate the association between accelerated age and NAFLD.

Methods: On the basis of the physical examination data of people without noninfectious chronic diseases (PWNCDs) in Nanchang, Jiangxi, China, the biological age measurement method was developed via three feature selection methods (all-subset regression, LASSO regression (LR), and recursive feature elimination) and three machine learning algorithms (generalized linear model (GLM), support vector machine, and deep generalized linear model (deep GLM)).

Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.

Rationale And Objectives: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.

Materials And Methods: A total of 724 pathologically confirmed early invasive lung adenocarcinoma patients were retrospectively included from two centers. Clinical and CT semantic features of the patients were collected, and 3D radiomics features were extracted from nonenhanced CT images.

Integrated analysis of scRNA-seq and bulk RNA-seq identifies as a candidate biomarker associated with chemoresistance in HGSOC.

Background: High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice.

Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.

Objectives: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis.

Materials And Methods: Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL.

TRIM65 knockout inhibits the development of HCC by polarization tumor-associated macrophages towards M1 phenotype via JAK1/STAT1 signaling pathway.

Background & Aims: Tumor-associated macrophages (TAMs) are main components of immune cells in tumor microenvironment (TME), and play a crucial role in tumor progression. Tripartite motif-containing protein 65 (TRIM65) has been associated with tumor progression. However, whether TRIM65 regulate the interaction of tumor cell and TAMs in HCC and the underlying mechanisms remain unknown.

Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer.

Background: Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors.

Osteopontin Promotes Macrophage M1 Polarization by Activation of the JAK1/STAT1/HMGB1 Signaling Pathway in Nonalcoholic Fatty Liver Disease.

Background And Aims: Osteopontin (OPN) is reported to be associated with the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the function of OPN in NAFLD is still inconclusive. Therefore, our aim in this study was to evaluate the role of OPN in NAFLD and clarify the involved mechanisms.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.

Objective: Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI).

Methods: We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs.

ARL13B promotes angiogenesis and glioma growth by activating VEGFA-VEGFR2 signaling.

Background: Tumor angiogenesis is essential for solid tumor progression, invasion and metastasis. The aim of this study was to identify potential signaling pathways involved in tumor angiogenesis.

Methods: Genetically engineered mouse models were used to investigate the effects of endothelial ARL13B(ADP-ribosylation factor-like GTPase 13B) over-expression and deficiency on retinal and cerebral vasculature.

Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity.

The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system.

Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes.

Background: Hepatocellular carcinoma (HCC) is a cancer with a poor prognosis. Many recent studies have suggested that pyroptosis is important in tumour progression. However, the role of pyroptosis-related genes (PRGs) in HCC remains unclear.

Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma.

Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.

Remediation of -Linked Macrothrombocytopenia With Ezetimibe Therapy.

To investigate refractory hypercholesterolemia, a female patient and relatives were subjected to whole-genome sequencing. The proband was found to have compound heterozygous substitutions p. Arg446Gln and c.

QSOX2 Is an E2F1 Target Gene and a Novel Serum Biomarker for Monitoring Tumor Growth and Predicting Survival in Advanced NSCLC.

Background: Quiescin Q6 sulfhydryl oxidase 2 (QSOX2), an enzyme that can be directly secreted into the extracellular space, is known to be associated with oxidative protein folding. However, whether is abnormally expressed in non-small cell lung cancer (NSCLC) and its role in tumor growth remains unclear.

Methods: Real-time quantitative PCR (qPCR), immunohistochemistry (IHC), bioinformatics analyses were applied to analyze the expression pattern and prognostic significance of QSOX2 in NSCLC.

Positive feedback of SuFu negating protein 1 on Hedgehog signaling promotes colorectal tumor growth.

Hedgehog (Hh) signaling plays a critical role in embryogenesis and tissue homeostasis, and its deregulation has been associated with tumor growth. The tumor suppressor SuFu inhibits Hh signaling by preventing the nuclear translocation of Gli and suppressing cell proliferation. Regulation of SuFu activity and stability is key to controlling Hh signaling.

A multi-effective and long-acting immunotherapy through one single hydrogel based injection.

A dual-effective (photothermal and immune) therapy employing gold nanorods (AuNRs) with a drug (two macrophage migration inhibitory factor (MIF) inhibitors) sustained release hydrogel was designed in this paper. The subsequent cellular and animal studies demonstrated that the proposed therapy can not only inhibit the proliferation, migration, and recurrence of cancer cells, but also improve the immune function (increase the infiltration of CD8 killer T cells in tumors) without the traditional multiple injections of expensive immune drugs.

Clinical and genomic landscape of hepatocellular carcinoma subtypes with various proportions of nonleukocyte stromal cells.

Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies and inflicts high mortality worldwide. The effect of tumor microenvironment components on HCC oncogenesis remains unknown. In particular, the nonleukocyte portion of the stromal fraction (SF) is poorly understood.

Comprehensive Analysis of a Long Noncoding RNA-Associated Competing Endogenous RNA Network in Wilms Tumor.

Long noncoding RNA (lncRNA) plays crucial roles in various biological processes of different cancers, especially acting as a competing endogenous RNA (ceRNA). However, the role of lncRNA-mediated ceRNA in Wilms tumor (WT), which is the most common malignant kidney cancer in children, remains unknown. In present study, RNA sequence profiles and clinical data of 125 patients with WT consisting of 119 tumor and 6 normal tissues from Therapeutically Applicable Research To Generate Effective Treatments database were analyzed.

Exploration of the effects of the CYCLOPS gene RBM17 in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most lethal and malignant tumours worldwide. New therapeutic targets for HCC are urgently needed. CYCLOPS (copy number alterations yielding cancer liabilities owing to partial loss) genes have been noted to be associated with cancer-targeted therapies.

Tautomerase Activity-Lacking of the Macrophage Migration Inhibitory Factor Alleviates the Inflammation and Insulin Tolerance in High Fat Diet-Induced Obese Mice.

Macrophage migration inhibitory factor (MIF) has multiple intrinsic enzymatic activities of the dopachrome/phenylpyruvate tautomerase and thiol protein oxidoreductase, and plays an important role in the development of obesity as a pro-inflammatory cytokine. However, which enzymatic activity of MIF is responsible for regulating in obesity are still unknown. In the present study, we investigated the roles of the tautomerase of MIF in high fat diet (HFD)-induced obesity using MIF tautomerase activity-lacking (MIF) mice.

A Pooling Genome-Wide Association Study Identifies Susceptibility Loci and Signaling Pathways of Immune Thrombocytopenia in Chinese Han Population.

Immune thrombocytopenia (ITP) is an acquired bleeding disease due to immune-mediated destruction of antilogous platelets and ineffective thrombopoiesis. Although the etiology of ITP remains unknown, genetic variants are thought to predispose individuals to the disease. Several candidate gene analyses have identified several loci that increased ITP susceptibility, but no systematic genetic analysis on a genome-wide scope.