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The carcinogenic role of FASN by regulating lipid metabolism reprogramming has been well-established in multiple tumors. However, whether mechanisms during intrahepatic cholangiocarcinoma (ICC) progression, such as circRNAs, regulate FASN expression remains unknown. Here we demonstrate a lipid metabolism-related circRNA, circMBOAT2 (hsa_circ_0007334 in circBase), frequently upregulated in ICC tissues, and positively correlated with ICC malignant features. CircMBOAT2 knockdown inhibits the growth and metastasis of ICC cells. Mechanistically, circMBOAT2 combines with PTBP1 and protects PTBP1 from ubiquitin/proteasome-dependent degradation, impairing the function of PTBP1 to transfer FASN mRNA from the nucleus to the cytoplasm. Moreover, circMBOAT2 and FASN have the same effect on fatty acid profile, unsaturated fatty acids instead of saturated fatty acids are primarily regulated and associated with malignant behaviors of ICC cells. The levels of lipid peroxidation and ROS were significantly higher when FASN was knocked down and recovered when circMBOAT2 was overexpressed. Our results identified that circMBOAT2 was upregulated in ICC and promoted progression by stabilizing PTBP1 to facilitate FASN mRNA cytoplasmic export, which altered lipid metabolic profile and regulated redox homeostasis in ICC, suggesting that circMBOAT2 may serve as an available therapeutic target for ICC with active lipid metabolism.

Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 46 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits. Here the authors combine an experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes to provide a comprehensive map of Atrial Fibrillation risk variants and genes.

Cell type-specific analysis is crucial for uncovering biological insights hidden in bulk tissue data, yet single-cell or single-nuclei approaches are often cost-prohibitive for large samples. We introduce EPIC-unmix, a novel two-step empirical Bayesian method combining reference single-cell/single-nuclei and bulk RNA-seq data to improve cell type-specific inference, accounting for the difference between reference and target datasets. Under comprehensive simulations, we demonstrate that EPIC-unmix outperforms alternative methods in accuracy. Applied to Alzheimer’s disease brain RNA-seq data, EPIC-unmix identifies multiple differentially expressed genes in a cell type-specific manner, and empowers cell type-specific eQTL analysis.

BackgroundLenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be further explored. We aimed to develop a radiomics model to noninvasively predict the efficacy of LPI therapy.MethodsClinical data of patients with HCC receiving LPI therapy were collected in our institution. The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. The clinical-radiomics model was constructed by integrating clinical and radiomics scores through logistic regression analysis.Results151 patients were enrolled in this study (2:1 randomization, 101 and 50 in the training and validation cohorts), of which three achieved complete response, 69 showed partial response, 46 showed stable disease, and 33 showed progressive disease. The objective response rate, disease control rate, and conversion resection rates were 47.7, 78.1 and 23.2%. 14 features were selected from the initially extracted 1223 for radiomics model construction. The area under the curves of the radiomics model (0.900 for training and 0.893 for validation) were comparable to that of the clinical-radiomics model (0.912 for training and 0.892 for validation), and both were superior to the clinical model (0.669 for training and 0.585 for validation). Meanwhile, the radiomics model can categorize participants into high-risk and low-risk groups for progression-free survival (PFS) and overall survival (OS) in the training (HR 1.913, 95% CI 1.121 to 3.265, p=0.016 for PFS; HR 4.252, 95% CI 2.051 to 8.816, p=0.001 for OS) and validation sets (HR 2.347, 95% CI 1.095 to 5.031, p=0.012 for PFS; HR 2.592, 95% CI 1.050 to 6.394, p=0.019 for OS).ConclusionThe promising machine learning radiomics model was developed and validated to predict the efficacy of LPI therapy for patients with HCC and perform risk stratification, with comparable performance to clinical-radiomics model.

MicroRNA-27a/b are small non-coding RNAs which are reported to regulate inflammatory response and cell proliferation. Although some studies have demonstrated that miR-27b is down-regulated in the oral specimens of patients suffering with oral lichen planus (OLP), the molecular mechanism of miR-27b decrease remains a large mystery, and the expression of miR-27a in OLP is not well explored. Here, we demonstrated both miR-27a and miR-27b, compared with healthy controls, were reduced in the oral biopsies, serum and saliva samples derived from OLP patients. The reductions of miR-27a/b were also confirmed in the lipopolysaccharide (LPS)- or activated CD4 + T cell-treated human oral keratinocytes (HOKs). Furthermore, we found vitamin D receptor (VDR) binding sites in the promoters of miR-27a/b genes and verified this finding. We also tested miR-27a/b levels in the oral epithelium from paricalcitol-treated, vitamin D deficient or VDR knockout mice. In the rescue experiments, we confirmed vitamin D and VDR inhibited LPS- or activated CD4 + T cell-induced miR-27a/b reductions in HOKs. In sum, our results show that vitamin D/VDR signaling induces miR-27a/b in oral lichen planus.

Background Forkhead-box protein P1 (FOXP1) has been proposed to have both oncogenic and tumor-suppressive properties, depending on tumor heterogeneity. However, the role of FOXP1 in intrahepatic cholangiocarcinoma (ICC) has not been previously reported. Methods Immunohistochemistry was performed to detect FOXP1 expression in ICC and normal liver tissues. The relationship between FOXP1 levels and the clinicopathological characteristics of patients with ICC was evaluated. Finally, in vitro and in vivo experiments were conducted to examine the regulatory role of FOXP1 in ICC cells. Results FOXP1 was significantly downregulated in the ICC compared to their peritumoral tissues ( p  < 0.01). The positive rates of FOXP1 were significantly lower in patients with poor differentiation, lymph node metastasis, invasion into surrounding organs, and advanced stages ( p  < 0.05). Notably, patients with FOXP1 positivity had better outcomes (overall survival) than those with FOXP1 negativity ( p  < 0.05), as revealed by Kaplan–Meier survival analysis. Moreover, Cox multivariate analysis showed that negative FOXP1 expression, advanced TNM stages, invasion, and lymph node metastasis were independent prognostic risk factors in patients with ICC. Lastly, overexpression of FOXP1 inhibited the proliferation, migration, and invasion of ICC cells and promoted apoptosis, whereas knockdown of FOXP1 had the opposite role. Conclusion Our findings suggest that FOXP1 may serve as a novel outcome predictor for ICC as well as a tumor suppressor that may contribute to cancer treatment.

As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy. Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medical treatment, radiation processing, and so on. With the development of artificial intelligence and the proposal of \"Industry 4.0\", more and more artificial intelligence technologies are introduced into the nuclear industry chain to improve production efficiency, reduce operation cost, improve operation safety, and realize risk avoidance. Meanwhile, deep learning, as an important technology of artificial intelligence, has made amazing progress in theoretical and applied research in the nuclear industry, which vigorously promotes the development of informatization, digitization, and intelligence of the nuclear industry. In this paper, we first simply comb and analyze the intelligent demand scenarios in the whole industrial chain of the nuclear industry. Then, we discuss the data types involved in the nuclear industry chain. After that, we investigate the research status of deep learning in the application fields corresponding to different data types in the nuclear industry. Finally, we discuss the limitation and unique challenges of deep learning in the nuclear industry and the future direction of the intelligent nuclear industry.

Circular RNAs (circRNAs) have been implicated in tumorigenesis and progression of various cancers. However, the underlying mechanisms of circRNAs in hepatocellular carcinoma (HCC) have not been fully elucidated. Herein, a new oncogenic circRNA, hsa_circ_0070039 (circNUP54), was identified to be significantly upregulated in HCC through circRNA sequencing. As verified in 68 HCC samples, circNUP54 overexpression was correlated with aggressive cancerous behaviors and poor outcomes. Moreover, the function experiments showed that knockdown of circNUP54 inhibited the malignant progression of HCC in vitro and in vivo, whereas overexpression of circNUP54 had the opposite role. Mechanistic investigations carried out by RNA pull-down, RNA immunoprecipitation, and immunofluorescence revealed that circNUP54 interacted with the RNA-binding protein Hu-antigen R (HuR) and promoted its cytoplasmic export. The cytoplasmic accumulation of HuR stabilized the downstream BIRC3 mRNA through its binding to the 3′ UTR region. Consequently, the encoded protein of BIRC3, cellular inhibitor of apoptosis 2 (cIAP2), proceeded to activate the NF-κB signal pathway and ultimately contributed to HCC progression. In addition, depletion of BIRC3 rescued the pro-tumorigenic effect of circNUP54 on HCC cells. Overall, this study demonstrated that circNUP54 facilitates HCC progression via regulating the HuR/BIRC3/NF-κB axis, which may serve as a promising therapeutic target for HCC treatment.

Lemon, as an important cash crop with rich nutritional value, holds significant cultivation importance and market demand worldwide. However, lemon diseases seriously impact the quality and yield of lemons, necessitating their early detection for effective control. This paper addresses this need by collecting a dataset of lemon diseases, consisting of 726 images captured under varying light levels, growth stages, shooting distances and disease conditions. Through cropping high-resolution images, the dataset is expanded to 2022 images, comprising 4441 healthy lemons and 718 diseased lemons, with approximately 1–6 targets per image. Then, we propose a novel model lemon surface disease YOLO (LSD-YOLO), which integrates Switchable Atrous Convolution (SAConv) and Convolutional Block Attention Module (CBAM), along with the design of C2f-SAC and the addition of a small-target detection layer to enhance the extraction of key features and the fusion of features at different scales. The experimental results demonstrate that the proposed LSD-YOLO achieves an accuracy of 90.62% on the collected datasets, with mAP@50–95 reaching 80.84%. Compared with the original YOLOv8n model, both mAP@50 and mAP@50–95 metrics are enhanced. Therefore, the LSD-YOLO model proposed in this study provides a more accurate recognition of healthy and diseased lemons, contributing effectively to solving the lemon disease detection problem.

The pursuit of optimal neural network architectures is foundational to the progression of Neural Architecture Search (NAS). However, the existing NAS methods suffer from the following problem using traditional search strategies, i.e., when facing a large and complex search space, it is difficult to mine more effective architectures within a reasonable time, resulting in inferior search results. This research introduces the Generative Pre-trained Transformer NAS (GPT-NAS), an innovative approach designed to overcome the limitations which are inherent in traditional NAS strategies. This approach improves search efficiency and obtains better architectures by integrating GPT model into the search process. Specifically, we design a reconstruction strategy that utilizes the trained GPT to reorganize the architectures obtained from the search. In addition, to equip the GPT model with the design capabilities of neural architecture, we propose the use of the GPT model for training on a neural architecture dataset. For each architecture, the structural information of its previous layers is utilized to predict the next layer of structure, iteratively traversing the entire architecture. In this way, the GPT model can efficiently learn the key features required for neural architectures. Extensive experimental validation shows that our GPT-NAS approach beats both manually constructed neural architectures and automatically generated architectures by NAS. In addition, we validate the superiority of introducing the GPT model in several ways, and find that the accuracy of the neural architecture on the image dataset obtained from the search after introducing the GPT model is improved by up to about 9%.