A novel treatment approach for retinoblastoma by targeting epithelial growth factor receptor expression with a shRNA lentiviral system

Objective(s): Non-invasive treatment options for retinoblastoma (RB), the most common malignant eye tumor among children, are lacking. Epithelial growth factor receptor (EGFR) accelerates cell proliferation, survival, and invasion of many tumors including RB. However, RB treatment by targeting EGFR has not yet been researched. In the current study, we investigated the effect of EGFR down-regulation on RB progression using shRNA lentiviral vectors. Materials and Methods: EGFR expression in Weri-Rb-1 cells was down-regulated by EGFR shRNA-bearing lentiviral vectors. Cell death, proliferation, cell cycle as well as invasion after EGFR down-regulation were determined. Further signaling pathway analysis was done by Western blot. Results: Our results revealed that EGFR shRNA could specifically down-regulate EGFR expression and down-regulation of this protein promoted cell death. Further analysis on cell cycle demonstrated that EGFR down-regulation also suppressed cell proliferation by arresting cells at G1 phase. Invasion analysis showed that EGFR down-regulation suppressed cell invasion and was correlated with alteration in the expression of matrix metalloproteinases 2 and 9. Further signaling pathway analysis revealed that EGFR down-regulation mediated RB progression was through PI3K/AKT/mTOR signaling pathway. Conclusion: Our study revealed that EGFR down-regulation, through the PI3K/AKT/mTOR signaling pathway, could inhibit RB progression by promoting cell death while suppressing cell proliferation and invasion. The findings of our study indicated that down-regulation of EGFR using shRNA lentiviral vectors may offer a novel non-invasive treatment for RB

Auto Diagnostics of Lung Nodules Using Minimal Characteristics Extraction Technique

Computer-aided detection (CAD) systems provide useful tools and an advantageous process to physicians aiming to detect lung nodules. This paper develops a method composed of four processes for lung nodule detection. The first step employs image acquisition and pre-processing techniques to isolate the lungs from the rest of the body. The second stage involves the segmentation process using a 2D algorithm to affect every layer of a scan eliminating non-informative structures inside the lungs, and a 3D blob algorithm associated with a connectivity algorithm to select possible nodule shape candidates. The combinations of these algorithms efficiently eliminate the high rates of false positives. The third process extracts eight minimal representative characteristics of the possible candidates. The final step utilizes a support vector machine for classifying the possible candidates into nodules and non-nodules depending on their features. As the objective is to find nodules bigger than 4mm, the proposed approach demonstrated quite encouraging results. Among 65 computer tomography (CT) scans, 94.23% of sensitivity and 84.75% in specificity were obtained. The accuracy of these two results was 89.19% taking into consideration that 45 scans were used for testing and 20 for training. The rate of false positives was 0.2 per scan

PIDNET: Polar Transformation Based Implicit Disentanglement Network for Truncation Artifacts

The interior problem, a persistent ill-posed challenge in CT imaging, gives rise to truncation artifacts capable of distorting CT values, thereby significantly impacting clinical diagnoses. Traditional methods have long struggled to effectively solve this issue until the advent of supervised models built on deep neural networks. However, supervised models are constrained by the need for paired data, limiting their practical application. Therefore, we propose a simple and efficient unsupervised method based on the Cycle-GAN framework. Introducing an implicit disentanglement strategy, we aim to separate truncation artifacts from content information. The separated artifact features serve as complementary constraints and the source of generating simulated paired data to enhance the training of the sub-network dedicated to removing truncation artifacts. Additionally, we incorporate polar transformation and an innovative constraint tailored specifically for truncation artifact features, further contributing to the effectiveness of our approach. Experiments conducted on multiple datasets demonstrate that our unsupervised network outperforms the traditional Cycle-GAN model significantly. When compared to state-of-the-art supervised models trained on paired datasets, our model achieves comparable visual results and closely aligns with quantitative evaluation metrics

A Novel Contrastive Self-Supervised Learning Framework for Solving Data Imbalance in Solder Joint Defect Detection

Poor chip solder joints can severely affect the quality of the finished printed circuit boards (PCBs). Due to the diversity of solder joint defects and the scarcity of anomaly data, it is a challenging task to automatically and accurately detect all types of solder joint defects in the production process in real time. To address this issue, we propose a flexible framework based on contrastive self-supervised learning (CSSL). In this framework, we first design several special data augmentation approaches to generate abundant synthetic, not good (sNG) data from the normal solder joint data. Then, we develop a data filter network to distill the highest quality data from sNG data. Based on the proposed CSSL framework, a high-accuracy classifier can be obtained even when the available training data are very limited. Ablation experiments verify that the proposed method can effectively improve the ability of the classifier to learn normal solder joint (OK) features. Through comparative experiments, the classifier trained with the help of the proposed method can achieve an accuracy of 99.14% on the test set, which is better than other competitive methods. In addition, its reasoning time is less than 6 ms per chip image, which is in favor of the real-time defect detection of chip solder joints

A Novel Contrastive Self-Supervised Learning Framework for Solving Data Imbalance in Solder Joint Defect Detection

Poor chip solder joints can severely affect the quality of the finished printed circuit boards (PCBs). Due to the diversity of solder joint defects and the scarcity of anomaly data, it is a challenging task to automatically and accurately detect all types of solder joint defects in the production process in real time. To address this issue, we propose a flexible framework based on contrastive self-supervised learning (CSSL). In this framework, we first design several special data augmentation approaches to generate abundant synthetic, not good (sNG) data from the normal solder joint data. Then, we develop a data filter network to distill the highest quality data from sNG data. Based on the proposed CSSL framework, a high-accuracy classifier can be obtained even when the available training data are very limited. Ablation experiments verify that the proposed method can effectively improve the ability of the classifier to learn normal solder joint (OK) features. Through comparative experiments, the classifier trained with the help of the proposed method can achieve an accuracy of 99.14% on the test set, which is better than other competitive methods. In addition, its reasoning time is less than 6 ms per chip image, which is in favor of the real-time defect detection of chip solder joints

Feasibility study on the introduction of Micro-CT technology for the identification of Radix Bupleuri and its adulterants

Background:Radix Bupleuri, a kind of Chinese herbal medicine with great clinical use, is often confused with its adulterants, and it is difficult to identify it without certain knowledge. The existing identification methods have their own drawbacks, so a new method is needed to realize the identification of Radix Bupleuri and its adulterants.Methods: We used Micro Computed Tomography (Micro-CT) to perform tomography scans on Radix Bupleuri and its adulterants, performed data screening and data correction on the obtained DICOM images, and then applied 3D reconstruction, data augmentation, and ResNext deep learning model for the classification study.Results: The DICOM images after data screening, data correction, and 3D reconstruction can observe the differences in the microstructure of Radix Bupleuri and its adulterants, thus enabling effective classification and analysis. Meanwhile, the accuracy of classification using the ResNext model reached 75%.Conclusion: The results of this study showed that Micro-CT technology is feasible for the authentication of Radix Bupleuri. The pre-processed and 3D reconstructed tomographic images clearly show the microstructure and the difference between Radix Bupleuri and its adulterants without damaging the internal structure of the samples. This study concludes that Micro-CT technology provides important technical support for the reliable identification of Radix Bupleuri and its adulterants, which is expected to play an important role in the quality control and clinical application of herbs

SPECIAL: Single-Shot Projection Error Correction Integrated Adversarial Learning for Limited-Angle CT

International audienceLimited-angle CT is an indispensable tool for some practical applications when the projection data can be only collected within a limited-angle range due to the constraints of scanning conditions. However, the limited-angle scanning mode will lead to severely degraded images with excessive artifacts. Meanwhile, existing methods fail to reconstruct satisfactory images in limited-angle CT because of the unguaranteed measurement consistency caused by serious projection missing. In this paper, we developed a method termed Single-shot Projection Error Correction Integrated Adversarial Learning (SPECIAL) progressive-improvement strategy, which could effectively combine the complementary information contained in the image domain and projection domain, and greatly improve the reconstructions at the expense of small computational cost. Specifically, enhanced adversarial learning is used in different stages to remove artifacts without losing high-frequency component. A projection error correction module is used to boost the performance in high-attenuation tissue restoration. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method could make a promising improvement on artifact removal, edge preservation and tiny structure restoration