Search Results (2)

Conventional therapy is commonly used for the treatment of inflammatory skin conditions, but undesirable effects, such as erythema, dryness, skin thinning, and resistance to treatment, may cause poor patient compliance. Therefore, patients may seek complementary treatment with herbal plant products including essential oils (EOs). This scoping review aims to generate a broad overview of the EOs used to treat inflammatory skin conditions, namely, acne vulgaris, dermatitis and eczema, psoriasis, and rosacea, in a clinical setting. The quality, efficacy, and safety of various EOs, as well as the way in which they are prepared, are reviewed, and the potential, as well as the limitations, of EOs for the treatment of inflammatory skin conditions are discussed. Twenty-nine eligible studies (case studies, uncontrolled clinical studies, and randomized clinical studies) on the applications of EOs for inflammatory skin conditions were retrieved from scientific electronic databases (PubMed, Embase, Scopus, and the Cochrane Library). As an initial result, tea tree (Melaleuca alternifolia) oil emerged as the most studied EO. The clinical studies with tea tree oil gel for acne treatment showed an efficacy with fewer adverse reactions compared to conventional treatments. The uncontrolled studies indicated the potential efficacy of ajwain (Trachyspermum ammi) oil, eucalyptus (Eucalyptus globulus) oil, and cedarwood (Cedrus libani) oil in the treatment of acne, but further research is required to reach conclusive evidence. The placebo-controlled studies revealed the positive effects of kānuka (Kunzea ericoides) oil and frankincense (Boswellia spp.) oil in the treatment of psoriasis and eczema. The quality verification of the EO products was inconsistent, with some studies lacking analyses and transparency. The quality limitations of some studies included a small sample size, a short duration, and the absence of a control group. This present review underscores the need for extended, well-designed clinical studies to further assess the efficacy and safety of EOs for treating inflammatory skin conditions with products of assured quality and to further elucidate the mechanisms of action involved. Full article

Accurate object classification and position estimation is a crucial part of executing autonomous pick-and-place operations by a robot and can be realized using RGB-D sensors becoming increasingly available for use in industrial applications. In this paper, we present a novel unified framework for object detection and classification using a combination of point cloud processing and deep learning techniques. The proposed model uses two streams that recognize objects on RGB and depth data separately and combines the two in later stages to classify objects. Experimental evaluation of the proposed model including classification accuracy compared with previous works demonstrates its effectiveness and efficiency, making the model suitable for real-time applications. In particular, the experiments performed on the Washington RGB-D object dataset show that the proposed framework has 97.5% and 95% fewer parameters compared to the previous state-of-the-art multimodel neural networks Fus-CNN, CNN Features and VGG3D, respectively, with the cost of approximately 5% drop in classification accuracy. Moreover, the inference of the proposed framework takes 66.11%, 32.65%, and 28.77% less time on GPU and 86.91%, 51.12%, and 50.15% less time on CPU in comparison to VGG3D, Fus-CNN, and CNN Features. The potential applicability of the developed object classification and position estimation framework was then demonstrated on an experimental robot-manipulation setup realizing a simplified object pick-and-place scenario. In approximately 95% of test trials, the system was able to accurately position the robot over the detected objects of interest in an automatic mode, ensuring stable cyclic execution with no time delays. Full article