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Abstract Background RNA m5C methylation has been extensively implicated in the occurrence and development of tumors. As the main methyltransferase, NSUN2 plays a crucial regulatory role across diverse tumor types. However, the precise impact of NSUN2-mediated m5C modification on breast cancer (BC) remains unclear. Our study aims to elucidate the molecular mechanism underlying how NSUN2 regulates the target gene HGH1 (also known as FAM203) through m5C modification, thereby promoting BC progression. Additionally, this study targets at preliminarily clarifying the biological roles of NSUN2 and HGH1 in BC. Methods Tumor and adjacent tissues from 5 BC patients were collected, and the m5C modification target HGH1 in BC was screened through RNA sequencing (RNA-seq) and single-base resolution m5C methylation sequencing (RNA-BisSeq). Methylation RNA immunoprecipitation-qPCR (MeRIP-qPCR) and RNA-binding protein immunoprecipitation-qPCR (RIP-qPCR) confirmed that the methylation molecules NSUN2 and YBX1 specifically recognized and bound to HGH1 through m5C modification. In addition, proteomics, co-immunoprecipitation (co-IP), and Ribosome sequencing (Ribo-Seq) were used to explore the biological role of HGH1 in BC. Results As the main m5C methylation molecule, NSUN2 is abnormally overexpressed in BC and increases the overall level of RNA m5C. Knocking down NSUN2 can inhibit BC progression in vitro or in vivo. Combined RNA-seq and RNA-BisSeq analysis identified HGH1 as a potential target of abnormal m5C modifications. We clarified the mechanism by which NSUN2 regulates HGH1 expression through m5C modification, a process that involves interactions with the YBX1 protein, which collectively impacts mRNA stability and protein synthesis. Furthermore, this study is the first to reveal the binding interaction between HGH1 and the translation elongation factor EEF2, providing a comprehensive understanding of its ability to regulate transcript translation efficiency and protein synthesis in BC cells. Conclusions This study preliminarily clarifies the regulatory role of the NSUN2-YBX1-m5C-HGH1 axis from post-transcriptional modification to protein translation, revealing the key role of abnormal RNA m5C modification in BC and suggesting that HGH1 may be a new epigenetic biomarker and potential therapeutic target for BC.

ObjectiveThe research aims to develop an advanced and precise lung cancer screening model based on Convolutional Neural Networks (CNN).MethodsBased on the health medical big data platform of Shandong University, we developed a VGG16-Based CNN lung cancer screening model. This model was trained using the Computed Tomography scans data of patients from Pingyi Traditional Chinese Medicine Hospital in Shandong Province, from January to February 2023. Data augmentation techniques, including random resizing, cropping, horizontal flipping, color jitter, random rotation and normalization, were applied to improve model generalization. We used five-fold cross-validation to robustly assess performance. The model was fine-tuned with an SGD optimizer (learning rate 0.001, momentum 0.9, and L2 regularization) and a learning rate scheduler. Dropout layers were added to prevent the model from relying too heavily on specific neurons, enhancing its ability to generalize. Early stopping was implemented when validation loss did not decrease over 10 epochs. In addition, we evaluated the model’s performance with Area Under the Curve (AUC), Classification accuracy, Positive Predictive Value (PPV), and Negative Predictive Value (NPV), Sensitivity, Specificity and F1 score. External validation used an independent dataset from the same hospital, covering January to February 2022.ResultsThe training and validation loss and accuracy over iterations show that both accuracy metrics peak at over 0.9 by iteration 15, prompting early stopping to prevent overfitting. Based on five-fold cross-validation, the ROC curves for the VGG16-Based CNN model, demonstrate an AUC of 0.963 ± 0.004, highlighting its excellent diagnostic capability. Confusion matrices provide average metrics with a classification accuracy of 0.917 ± 0.004, PPV of 0.868 ± 0.015, NPV of 0.931 ± 0.003, Sensitivity of 0.776 ± 0.01, Specificity of 0.962 ± 0.005 and F1 score of 0.819 ± 0.008, respectively. External validation confirmed the model’s robustness across different patient populations and imaging conditions.ConclusionThe VGG16-Based CNN lung screening model constructed in this study can effectively identify lung tumors, demonstrating reliability and effectiveness in real-world medical settings, and providing strong theoretical and empirical support for its use in lung cancer screening.

Abstract Background RNA 5-methylcytosine (m5C) modification plays critical roles in the pathogenesis of various tumors. However, the function and molecular mechanism of RNA m5C modification in tumor drug resistance remain unclear. Methods The correlation between RNA m5C methylation, m5C writer NOP2/Sun RNA methyltransferase family member 2 (NSUN2) and EGFR-TKIs resistance was determined in non-small-cell lung cancer (NSCLC) cell lines and patient samples. The effects of NSUN2 on EGFR-TKIs resistance were investigated by gain- and loss-of-function assays in vitro and in vivo. RNA-sequencing (RNA-seq), RNA bisulfite sequencing (RNA-BisSeq) and m5C methylated RNA immunoprecipitation-qPCR (MeRIP-qPCR) were performed to identify the target gene of NSUN2 involved in EGFR-TKIs resistance. Furthermore, the regulatory mechanism of NSUN2 modulating the target gene expression was investigated by functional rescue and puromycin incorporation assays. Results RNA m5C hypermethylation and NSUN2 were significantly correlated with intrinsic resistance to EGFR-TKIs. Overexpression of NSUN2 resulted in gefitinib resistance and tumor recurrence, while genetic inhibition of NSUN2 led to tumor regression and overcame intrinsic resistance to gefitinib in vitro and in vivo. Integrated RNA-seq and m5C-BisSeq analyses identified quiescin sulfhydryl oxidase 1 (QSOX1) as a potential target of aberrant m5C modification. NSUN2 methylated QSOX1 coding sequence region, leading to enhanced QSOX1 translation through m5C reader Y-box binding protein 1 (YBX1). Conclusions Our study reveals a critical function of aberrant RNA m5C modification via the NSUN2-YBX1-QSOX1 axis in mediating intrinsic resistance to gefitinib in EGFR-mutant NSCLC.

With the rapid development of semiconductor technology, traditional equation-based modeling faces challenges in accuracy and development time. To overcome these limitations, neural network (NN)-based modeling methods have been proposed. However, the NN-based compact model encounters two major issues. Firstly, it exhibits unphysical behaviors such as un-smoothness and non-monotonicity, which hinder its practical use. Secondly, finding an appropriate NN structure with high accuracy requires expertise and is time-consuming. In this paper, we propose an Automatic Physical-Informed Neural Network (AutoPINN) generation framework to solve these challenges. The framework consists of two parts: the Physics-Informed Neural Network (PINN) and the two-step Automatic Neural Network (AutoNN). The PINN is introduced to resolve unphysical issues by incorporating physical information. The AutoNN assists the PINN in automatically determining an optimal structure without human involvement. We evaluate the proposed AutoPINN framework on the gate-all-around transistor device. The results demonstrate that AutoPINN achieves an error of less than 0.05%. The generalization of our NN is promising, as validated by the test error and the loss landscape. The results demonstrate smoothness in high-order derivatives, and the monotonicity can be well-preserved. We believe that this work has the potential to accelerate the development and simulation process of emerging devices.

Targeting immune checkpoints such as programmed cell death protein 1 (PD-1) and programmed death ligand-1 (PD-L1) have been approved for treating melanoma, gastric cancer (GC) and bladder cancer with clinical benefit. Nevertheless, many patients failed to respond to anti-PD-1/PD-L1 treatment, so it is necessary to seek an alternative strategy for traditional PD-1/PD-L1 targeting immunotherapy. Here with the data from The Cancer Genome Atlas (TCGA) and our in-house tissue library, PD-L1 expression was found to be positively correlated with the expression of ubiquitin-specific processing protease 7 (USP7) in GC. Furthermore, USP7 directly interacted with PD-L1 in order to stabilize it, while abrogation of USP7 attenuated PD-L1/PD-1 interaction and sensitized cancer cells to T cell killing in vitro and in vivo. Besides, USP7 inhibitor suppressed GC cells proliferation by stabilizing P53 in vitro and in vivo. Collectively, our findings indicate that in addition to inhibiting cancer cells proliferation, USP7 inhibitor can also downregulate PD-L1 expression to enhance anti-tumor immune response simultaneously. Hence, these data posit USP7 inhibitor as an anti-proliferation agent as well as a novel therapeutic agent in PD-L1/PD-1 blockade strategy that can promote the immune response of the tumor.

In this paper, we present a new modelling method to create 3D models. First, characteristic cross section curves are generated and approximated by generalized elliptic curves. Then, a vector-valued sixth-order partial differential equation is proposed, and its closed form solution is derived to create PDE surface patches from cross section curves where two adjacent PDE-surface patches are automatically stitched together. With the approach presented in this paper, C2 continuity between adjacent surface patches is well-maintained. Since surface creation of the model is transformed into the generation of cross sectional curves and few undetermined constants are required to describe cross sectional curves accurately, the proposed approach can save manual operations, reduce information storage, and generate 3D models quickly.

Isolation of islets from market-sized pigs is costly, with considerable islet losses from fragmentation occurring during isolation and tissue culture. Fetal and neonatal pigs yield insulin unresponsive islet-like cell clusters that become glucose-responsive after extended periods of time. Both issues impact clinical applicability and commercial scale-up. We have focused our efforts on a cost-effective scalable method of isolating viable insulin-responsive islets. Young Yorkshire pigs (mean age 20 days, range 4–30 days) underwent rapid pancreatectomy (