A segmentation-based algorithm for classification of benign and malignancy Thyroid nodules with multi-feature information.

The aim of this study is to propose a new diagnostic model based on "segmentation + classification" to improve the routine screening of Thyroid nodule ultrasonography by utilizing the key domain knowledge of medical diagnostic tasks. A Multi-scale segmentation network based on a pyramidal pooling structure of multi-parallel void spaces is proposed. First, in the segmentation network, the exact information of the underlying feature space is obtained by an Attention Gate. Second, the inflated convolutional part of Atrous Spatial Pyramid Pooling (ASPP) is cascaded for multiple downsampling. Finally, a three-branch classification network combined with expert knowledge is designed, drawing on doctors' clinical diagnosis experience, to extract features from the original image of the nodule, the regional image of the nodule, and the edge image of the nodule, respectively, and to improve the classification accuracy of the model by utilizing the Coordinate attention (CA) mechanism and cross-level feature fusion. The Multi-scale segmentation network achieves 94.27%, 93.90% and 88.85% of mean precision (mPA), Dice value (Dice) and mean joint intersection (MIoU), respectively, and the accuracy, specificity and sensitivity of the classification network reaches 86.07%, 81.34% and 90.19%, respectively. Comparison tests show that this method outperforms the U-Net, AGU-Net and DeepLab V3+ classical models as well as the nnU-Net, Swin UNetr and MedFormer models that have emerged in recent years. This algorithm, as an auxiliary diagnostic tool, can help physicians more accurately assess the benign or malignant nature of Thyroid nodules. It can provide objective quantitative indicators, reduce the bias of subjective judgment, and improve the consistency and accuracy of diagnosis. Codes and models are available at https://github.com/enheliang/Thyroid-Segmentation-Network.git.

A novel ultrasound image diagnostic method for thyroid nodules.

The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules.

Intrathecal circHIPK3 shRNA alleviates neuropathic pain in diabetic rats.

Neuropathic pain is one of the most common diabetic complications and significantly decrease the quality of life. The aetiology of the painful diabetic neuropathic pain is not fully clear. Circular RNAs (circRNAs) have been identified as miRNA sponges and involved in various biological processes, including pain. CircHIPK3 is a circRNA that have been shown to be an oncogene or tumor suppressor to regulate cancer cells growth by sponging multiple miRNAs. However, the role of circHIPK3 in diabetic neuropathic pain remains unknown. The aim of the present study was to elucidate the possible role of circHIPK3 in the control of diabetic neuropathic pain. We found that circHIPK3 are highly abundant in serum from diabetes patients who suffered from neuropathic pain and in dorsal root ganglion from STZ-induced diabetes rats. Upregulation of circHIPK3 was positively associated with grade neuropathic pain in patients with type 2 diabetes. Silencing circHIPK3 alleviated neuropathic pain in diabetic rats, which was involved in neuroinflammation. Further mechanistic investigation demonstrated that circHIPK3 interacted with miR-124 and negatively regulated its expression. MiR-124 inhibitor can reverse circHIPK3 knockdown-mediated alleviation of neuropathic pain and inhibition of neuroinflammation in diabetic rats. We present the first evidence that intrathecal circHIPK3 shRNA treatment can be used to treat neuropathic pain of diabetic rats.

miR-1297 Promotes Cell Proliferation of Non-Small Cell Lung Cancer Cells: Involving in PTEN/Akt/Skp2 Signaling Pathway.

Phosphatase and tensin homolog deleted on chromosome ten (PTEN) is a lipid and protein phosphatase and possesses an antitumor effect in lung cancers. miRNAs are reportedly abnormally expressed in human lung cancers. However, whether miRNA contributes to PTEN expression in non-small cell lung cancers (NSCLCs) has not been clearly clarified. In the present study, we found that miR-1297 probably binds with 3'UTR sequence of PTEN and negatively regulated the levels of PTEN in NSCLC cells. First, the expression levels of PTEN and Skp2 were detected by western blotting in NSCLC specimens and paired normal tissue specimens. The results showed that decreased levels of PTEN were detected in NSCLC tissues, compared with paired control tissues (**p < 0.01). The expression levels of PTEN were conversely correlated with the levels of Skp2 in clinical NSCLC specimens and NSCLC cell line. Transfection with miR-1297 mimic significantly promoted cell viability of A549 cells and NCI-H460 cells by downregulating the level of PTEN and upregulating the expression of Skp2. Interestingly, knockdown of Skp2 did not affect the expression of PTEN in A549 cells. Thus, miR-1297 might work as an oncogene by regulating PTEN/Akt/Skp2 signaling pathway in NSCLC cells. PTEN and Skp2 might be the potential targets in the clinical therapy of lung cancers.