New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

Therapeutic and cosmeceutical role of glycosylated natural products in dermatology

Natural products (NPs) remain the primary source of pharmacologically active candidates for drug discovery. Since time immemorial, NPs have attracted considerable attention because of their beneficial skin effects. Moreover, there has been a great interest in using such products for the cosmetics industry in the past few decades, bridging the gap between modern and traditional medicine. Terpenoids, Steroids, and Flavonoids having glycosidic attachment have proven biological effects with a positive impact on human health. NPs derived glycosides are mainly found in fruits, vegetables, and plants, and most of them have a special reverence in traditional and modern medicine for disease prevention and treatment. A literature review was performed using scientific journals, Google scholar, Scifinder, PubMED, and Google patents. These scientific articles, documents, and patents establish the significance of glycosidic NPs in the areas of dermatology. Considering the human inclination to the usage of NPs rather than synthetic or inorganic drugs (especially in the area of skin care), in the present review we have discussed the worth of NP glycosides in beauty care and skin-related therapeutics and the mechanistic pathways involved.

Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning

A medical center in the smart cities of the future needs data security and confidentiality to treat patients accurately. One mechanism for sending medical data is to send information to other medical centers without preserving confidentiality. This method is not impressive because in treating people, the privacy of medical information is a principle. In the proposed framework, the opinion of experts from other medical centers for the treatment of patients is received and consider the best therapy. The proposed method has two layers. In the first layer, data transmission uses blockchain. In the second layer, blocks related to patients' records analyze by machine learning methods. Patient records place in a block of the blockchain. Block of patient sends to other medical centers. Each treatment center can recommend the proposed type of treatment and blockchain attachment and send it to all nodes and treatment centers. Each medical center receiving data of the patients, then predicts the treatment using data mining methods. Sending medical data between medical centers with blockchain and maintaining confidentiality is one of the innovations of this article. The proposed method is a binary version of the HHO algorithm for feature selection. Another innovation of this research is the use of majority voting learning in diagnosing the type of disease in medical centers. Implementation of the proposed system shows that the blockchain preserves data confidentiality of about 100%. The reliability and reliability of the proposed framework are much higher than the centralized method. The result shows that the accuracy, sensitivity, and precision of the proposed method for diagnosing heart disease are 92.75%, 92.15%, and 95.69%, respectively. The proposed method has a lower error in diagnosing heart disease from ANN, SVM, DT, RF, AdaBoost, and BN.

Cytoplasmic CK1ε Protein Expression Is Correlated With Distant Metastasis and Survival in Patients With Melanoma

BACKGROUND/AIM

Casein kinase 1 epsilon (CK1ε) is a member of the casein kinase 1 family, which includes highly conserved and ubiquitous serine/threonine protein kinases. Recent research has revealed that CK1ε plays an important role in a variety of human cancer types; however, its role in human melanoma remains unclear. The aim of this study was to elucidate the clinical role of CK1ε in patients with melanoma.

PATIENTS AND METHODS

Samples from 34 patients with melanoma were analyzed by immunohistochemical staining. Formalin-fixed paraffin-embedded tissue microarrays were also examined by two histopathologists to assess CK1ε protein expression in humans.

RESULTS

Cytoplasmic CK1ε protein expression was significantly lower in tumor tissue than in normal tissue. Lack of cytoplasmic CK1ε protein was significantly correlated with distant metastasis (p=0.022) and poorer survival (p=0.030). However, Kaplan-Meier survival analysis revealed that elevated expression of cytoplasmic CK1ε protein was not significantly associated with the overall survival of patients with melanoma. Univariate and multivariate analyses demonstrated that lack of cytoplasmic CK1ε protein expression was related to distant metastasis (p<0.001 and p=0.004), showing that CK1ε was a prognostic factor.

CONCLUSION

CK1ε protein expression might serve as a prognostic indicator in the treatment of patients with melanoma.

FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy

Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segment the lesion of diabetic retinopathy effectively with the existing U-Net model. In this paper, we proposed a new lesion segmentation model named FFU-Net (Feature Fusion U-Net) that enhances U-Net from the following points. Firstly, the pooling layer in the network is replaced with a convolutional layer to reduce spatial loss of the fundus image. Then, we integrate multiscale feature fusion (MSFF) block into the encoders which helps the network to learn multiscale features efficiently and enrich the information carried with skip connection and lower-resolution decoder by fusing contextual channel attention (CCA) models. Finally, in order to solve the problems of data imbalance and misclassification, we present a Balanced Focal Loss function. In the experiments on benchmark dataset IDRID, we make an ablation study to verify the effectiveness of each component and compare FFU-Net against several state-of-the-art models. In comparison with baseline U-Net, FFU-Net improves the segmentation performance by 11.97%, 10.68%, and 5.79% on metrics SEN, IOU, and DICE, respectively. The quantitative and qualitative results demonstrate the superiority of our FFU-Net in the task of lesion segmentation of diabetic retinopathy.