Antimicrobial Properties of Aloe vera Ethanol Extract as a Denture Disinfectant: An In Vitro Study.

Introduction The aim of this study was to evaluate the activity of an ethanolic extract of Aloe vera on Candida albicans and Staphylococcus aureus. Materials and methods A total of 42 heat-cured acrylic resin specimens were made and divided into three groups according to the disinfection method: (1) Corega disinfectant tablets; (2) ethanol extract of Aloe vera; and (3) distilled water (as a control group). Fresh Aloe vera whole leaves were washed with distilled water, chopped into small pieces, air-dried, and ground into powder. The powder was extracted with 95% ethanol. The acrylic specimens were contaminated with C. albicans and S. aureus, and then the specimens were immersed in study solutions for three minutes. The viable colonies were counted using the colony-forming units (CFU) method. Results The results showed a decrease in the number of C. albicans CFU for denture tablets and Aloe vera ethanoic extract groups compared to the negative control group. There were no significant statistical differences between the denture tablet group and the Aloe vera ethanolic extract group (P < 0.05). Aloe vera ethanolic extract groups significantly decreased the number of S. aureus CFU compared to the negative control group and less compared to the denture tablet, where significant statistical differences were found between the tablet group and the Aloe vera ethanolic extract group. Conclusions Within the limitations of this study, it was concluded that Aloe vera extract was effective against C. albicans and S. aureus when acrylic resin specimens were immersed for three minutes.

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors.

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

A Deep-Learning Framework for the Detection of Oil Spills from SAR Data.

Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.