Optimization of psoriasis assessment system based on patch images

Psoriasis is a chronic inflammatory skin disease that occurs in various forms throughout the body and is associated with certain conditions such as heart disease, diabetes, and depression. The psoriasis area severity index (PASI) score, a tool used to evaluate the severity of psoriasis, is currently used in clinical trials and clinical research. The determination of severity is based on the subjective judgment of the clinician. Thus, the disease evaluation deviations are induced. Therefore, we propose optimal algorithms that can effectively segment the lesion area and classify the severity. In addition, a new dataset on psoriasis was built, including patch images of erythema and scaling. We performed psoriasis lesion segmentation and classified the disease severity. In addition, we evaluated the best-performing segmentation method and classifier and analyzed features that are highly related to the severity of psoriasis. In conclusion, we presented the optimal techniques for evaluating the severity of psoriasis. Our newly constructed dataset improved the generalization performance of psoriasis diagnosis and evaluation. It proposed an optimal system for specific evaluation indicators of the disease and a quantitative PASI scoring method. The proposed system can help to evaluate the severity of localized psoriasis more accurately.

Psoriasis Lesion Detection Using Hybrid Seeker Optimization Based Image Clustering

Background: In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a pre-requisite for quantifying the severity of this disease. However, segmentation of psoriatic lesion cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence. Objective: An alternative method for psoriatic lesion segmentation with the objective analysis is sought in the present work. The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique which possesses a higher probability of global convergence for psoriasis lesion segmentation. Method: A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms; namely Artificial Bee Colony and Seeker Optimization algorithm has been proposed. The initial population for the hybrid technique is obtained from the two conventional local-based approaches i.e. Fuzzy C-means and K-means clustering algorithms. Results: The initial population selection from the convergence of classical techniques reduces the effect of population dynamics on the final solution and hence yields precise lesion segmentation with Jaccard Index of 0.91 from 720 psoriasis images. Conclusion: The performance comparison reflects the superior performance of the proposed algorithm over other swarm intelligence and conventional clustering algorithms.

Emerging Pathophysiological Targets of Psoriasis for Future Therapeutic Strategies

Psoriasis is a chronic autoimmune skin disorder which involves complex interactions between genes, keratinocytes, T-cells and inflammatory cells. It affects 2-3% population worldwide. Molecular biology and cellular immunology of psoriasis, when linked with biotechnology and genetic studies can help researchers to understand the pathophysiology of psoriasis. T-cells activation, keratinocyte hyperproliferation, and angiogenesis are the core mechanisms entailed in the development of psoriasis lesion. Investigators are trying to overcome the challenges of complex pathophysiology pathways involved in this disorder. The different possible hypotheses for its pathophysiology such as growth factors, enzymes, inflammation, and genetic factors mediated pathophysiology have been described in the present review paper in detail. Clinically available drugs only control the symptoms of psoriasis but are not effective for the treatment of the disorder completely and are also associated with some side effects such as itching, renal disorders, hematologic, nonmelanoma skin cancer, pulmonary, gastrointestinal toxicity, etc. This paper made an effort to understand the pathophysiological targets, discuss the research done so far and the treatments available for the effective management of psoriasis.

The Model of PPARγ-Downregulated Signaling in Psoriasis

Interactions of genes in intersecting signaling pathways, as well as environmental influences, are required for the development of psoriasis. Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor and transcription factor which inhibits the expression of many proinflammatory genes. We tested the hypothesis that low levels of PPARγ expression promote the development of psoriatic lesions. We combined experimental results and network functional analysis to reconstruct the model of PPARγ-downregulated signaling in psoriasis. We hypothesize that the expression of IL17, STAT3, FOXP3, and RORC and FOSL1 genes in psoriatic skin is correlated with the level of PPARγ expression, and they belong to the same signaling pathway that regulates the development of psoriasis lesion.

THE MODEL OF PPARγ DOWNREGULATED SIGNALING IN PSORIASIS

ABSTRACTInteractions of genes in intersecting signaling pathways, as well as environmental influences, are required for the development of psoriasis. Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor and transcription factor which inhibits the expression of many proinflammatory genes. We tested the hypothesis that low levels of PPARγ expression promote the development of psoriatic lesions. We combined experimental results and network functional analysis to reconstruct the model of PPARγ downregulated signaling in psoriasis. We hypothesize that the expression of IL17, STAT3, FOXP3, and RORC and FOSL1 genes in psoriatic skin are correlated with the level of PPARγ expression and they belong to the same signaling pathway that regulates the development of psoriasis lesion.