Downregulation of SMAD4 protects HaCaT cells against UVB-induced damage and oxidative stress through the activation of EMT.

As a member of the SMAD family, SMAD4 plays a crucial role in several cellular biological processes. However, its function in UVB radiation-induced keratinocyte damage is not yet clarified. Our study aims to provide mechanistic insight for the development of future UVB protective therapies and therapeutics involving SMAD4. HaCaT cells were treated with UVB, and the dose dependence and time dependence of UVB were measured. The cell function of UVB-treated HaCaT cells and the activity of epithelial-mesenchymal transition (EMT) after overexpression or silencing of SMAD4 was observed by flow cytometry, quantitative reverse transcription PCR (qRT-PCR) and Western Blots (WB). We found that a significant decrease in SMAD4 was observed in HaCaT cells induced by UVB. Our data confirm SMAD4 as a direct downstream target of miR-664. The down-regulation of SMAD4 preserved the viability of the UVB-treated HaCaT cells by inhibiting autophagy or apoptosis. Furthermore, the silencing of SMAD4 activated the EMT process in UVB-treated HaCaT cells. Down-regulation of SMAD4 plays a protective role in UVB-treated HaCaT cells via the activation of EMT.

Classification and Diagnosis of Residual Thyroid Tissue in SPECT Images Based on Fine-Tuning Deep Convolutional Neural Network.

Patients with thyroid cancer will take a small dose of 131I after undergoing a total thyroidectomy. Single-photon emission computed tomography (SPECT) is used to diagnose whether thyroid tissue remains in the body. However, it is difficult for human eyes to observe the specificity of SPECT images in different categories, and it is difficult for doctors to accurately diagnose the residual thyroid tissue in patients based on SPECT images. At present, the research on the classification of thyroid tissue residues after thyroidectomy is still in a blank state. This paper proposes a ResNet-18 fine-tuning method based on the convolutional neural network model. First, preprocess the SPECT images to improve the image quality and remove background interference. Secondly, use the preprocessed image samples to fine-tune the pretrained ResNet-18 model to obtain better features and finally use the Softmax classifier to diagnose the residual thyroid tissue. The method has been tested on SPECT images of 446 patients collected by local hospital and compared with the widely used lightweight network SqueezeNet model and ShuffleNetV2 model. Due to the small data set, this paper conducted 10 random grouping experiments. Each experiment divided the data set into training set and test set at a ratio of 3:1. The accuracy and sensitivity rates of the model proposed in this paper are 96.69% and 94.75%, which are significantly higher than other models (p < 0.05). The specificity and precision rates are 99.6% and 99.96%, respectively, and there is no significant difference compared with other models. (p > 0.05). The area under the curve of the proposed model, SqueezeNet, and ShuffleNetv2 are 0.988 (95% CI, 0.941-1.000), 0.898 (95% CI, 0.819-0.951) (p = 0.0257), and 0.885 (95% CI, 0.803-0.941) (p = 0.0057) (p < 0.05). We prove that this thyroid tissue residue classification system can be used as a computer-aided diagnosis method to effectively improve the diagnostic accuracy of thyroid tissue residues. While more accurately diagnosing patients with residual thyroid tissue in the body, we try our best to avoid the occurrence of overtreatment, which reflects its potential clinical application value.

A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis.

An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (SVM) classifier. A coarse-to-fine scanning method was developed to detect tomatoes, followed by a proposed False Color Removal (FCR) method to remove the false-positive detections. Non-Maximum Suppression (NMS) was used to merge the overlapped results. Compared with other methods, the proposed algorithm showed substantial improvement in tomato detection. The results of tomato detection in the test images showed that the recall, precision, and F1 score of the proposed method were 90.00%, 94.41 and 92.15%, respectively.