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Numerous advancements and significant progress have been made in computer methods for medical applications, alongside technological developments. Automatic image analysis plays a crucial role in the realm of medical diagnosis and therapy. Recent breakthroughs, especially in the field of medical image processing, have enabled the automatic detection of various characteristics, alterations, diseases, and degenerative conditions using skin scans. Utilizing image processing methods, skin image analysis is instrumental in the identification and monitoring of conditions manifesting through alterations in skin structure. Notably, accurate segmentation of cancerous regions from the background remains a challenging task in the area of melanoma image analysis. The primary objective of this study is to achieve exceptional precision in delineating melanoma boundaries. Leveraging the Bat Optimization algorithm, we determine the optimal threshold for melanoma segmentation, effectively identifying the most accurate cancerous area boundaries. To evaluate the results, standard metrics such as accuracy, sensitivity, specificity, Dice coefficient, and F1 score are employed. In this study, we applied the Bat Optimization algorithm to determine the optimal threshold value for segmenting melanoma skin cancer, effectively identifying the most accurate cancerous area boundaries. For result evaluation, we employed standard metrics including accuracy, sensitivity, specificity, Dice coefficient, and F1 score, which yielded impressive values of 99.8%, 98.99%, 98.87%, 98.45%, and 98.24%, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. Method: This research uses a hybrid deep learning model that combines two cutting‐edge approaches: Mask Region‐based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. Results: The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state‐of‐the‐art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. Conclusion: In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis. [ABSTRACT FROM AUTHOR]

Aims: To examine whether biologic disease‐modifying anti‐rheumatic drugs (bDMARDs) are associated with increased risk of malignancy among Israeli patients with rheumatoid arthritis (RA). Methods: We identified RA patients meeting specified inclusion and exclusion criteria from the Leumit healthcare services database between the years 2000 and 2017. Data were collected regarding bDMARD and conventional DMARD consumption, types of malignancies, and their temporal relation to RA diagnosis. The association between baseline variables and occurrence of malignancies was examined by Cox regression. Results: Among 4268 eligible RA patients, 688 (16.12%) were diagnosed with any malignancy. Melanoma skin cancer (MSC) was the most prevalent malignancy (148/688, 21.5%). The proportions out of all malignancies of MSC and non‐melanoma skin cancer (NMSC) were higher after than before RA diagnosis (24.7% vs 19.1%, p =.025 and 24.7% vs 13.0%, p =.021, respectively). A higher proportion of RA patients diagnosed with malignancy used bDMARDs in comparison with RA patients who were malignancy‐free (40.2% vs 17.5%, p <.001). After adjusting for demographic and clinical variables, bDMARDs were associated with an increased risk of malignancy (hazard ratio 1.42, 95% confidence interval 1.10‐1.78). Conclusions: Biologic DMARDs are associated with increased risk of malignancy among Israeli RA patients, presumably contributed by MSC and NMSC. MSC was the most prevalent type of malignancy in this cohort and may indicate a predisposition state among Israeli RA patients. [ABSTRACT FROM AUTHOR]

Background: The Black population has lower skin cancer incidence compared to White, Indian/Asian, and Mixed‐race populations in South Africa; however, skin cancer still exists in the Black population. The aim of this study is to identify risk factors associated with skin cancer among Black South Africans. Materials and Methods: A case‐control study was conducted. Cases were patients with keratinocyte cancers (KCs) and/or melanoma skin cancers (MSCs) and controls were cardiovascular patients. Sociodemographic exposures, environmental health variables, smoking, and HIV status were assessed. Stepwise logistic regression was used to identify risk factors associated with KCs and MSCs. Results: The KCs histological subtypes showed that there were more squamous cell carcinomas (SCCs) (78/160 in females, and 72/160 in males) than basal cell carcinomas (BCCs). The SCC lesions were mostly found on the skin of the head and neck in males (51%, 38/72) and on the trunk in females (46%, 36/78). MSC was shown to affect the skin of the lower limbs in both males (68%, 27/40) and females (59%, 36/61). Using females as a reference group, when age, current place of residency, type of cooking fuel used, smoking, and HIV status were adjusted for, males had an odds ratio (OR) of 2.04 for developing KCs (confidence interval [CI]: 1.08–3.84, p =.028). Similarly, when age, current place of residency, and place of cooking (indoors or outdoors) were adjusted for, males had an OR of 2.26 for developing MSC (CI: 1.19–4.29, p =.012). Conclusions: Differences in the anatomical distribution of KCs by sex suggest different risk factors between sexes. There is a positive association between being male, smoking, rural dwelling, and a positive HIV status with KCs and being male and rural dwelling with MSC. The rural dwelling was a newly found association with skin cancer and warrants further investigation. [ABSTRACT FROM AUTHOR]

This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation‐based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA‐DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background and Aim: The thiopurines are effective in the management of patients with inflammatory bowel disease (IBD), but the association between thiopurines use and the risk of skin cancer (including nonmelanoma skin cancer [NMSC] and melanoma skin cancer) has already been sufficiently reported. However, the results of these studies are inconsistent, and thus, the objective of our analysis was to explore whether thiopurines can lead to an excess risk of skin cancer in IBD patients. Methods: MEDLINE, EMBASE, and the Cochrane Library were searched to identify relevant studies that evaluated the risk of skin cancer in IBD patients treated with thiopurines. A random effects meta‐analysis was conducted to calculate the pooled incidence rate ratios as well as risk ratios (RRs). Subgroup analysis was performed to explore the potential source of heterogeneity. Results: Thirteen studies comprising 149 198 participants were included. The result suggested that thiopurines significantly increased the risk of overall skin cancer in IBD patients (random effects: RR = 1.80, 95% confidence interval [CI] 1.14–2.87, P = 0.013), among which NMSC showed an excess risk associated with thiopurines use (random effects: RR = 1.88, 95% CI 1.48–2.38, P < 0.001) while no increased risk was observed with respect to melanoma skin cancer (random effects: RR = 1.22, 95% CI 0.90–1.65, P = 0.206). Subgroup analysis regarding sample size and geographic distribution in skin cancer and follow‐up duration in NMSC reached statistical significance, while other subgroups showed no significance. Conclusion: Exposition of thiopurines in patients with IBD is associated with a higher risk of skin cancer. Routine skin screening and daily skin protective practice are recommended for these patients. [ABSTRACT FROM AUTHOR]

Microemulsion‐based animal oils, alone or associated with polymers have been extensively used in pharmacy, medicine and cosmetics, since the major lipid constituents of the oils show several biological activities. Despite showing antimicrobial activity, there are no reports in the literature regarding the effects of bullfrog oil on cytotoxic activity against tumor cells. The aim of the present study was to synthesize, characterise and evaluate the in vitro effects on melanoma cell line (B16F10) of bullfrog oil microemulsions associated or not with chitosan, surfactant and bullfrog oil (CSBO) and surfactant and bullfrog oil (SBO), respectively. The microemulsions were developed and their physical–chemical characteristics were evaluated by light microscopy, dynamic light scattering, atomic force microscopy and zeta potential. The microemulsions showed regular spherical shapes, high polydispersity and excellent (+82.2 ± 1.0 mV) to low (−16.0 ± 0.5 mV), colloidal stability. The systems significantly decreased the in vitro cell viability of melanoma skin cancer by up to 90.2% (CSBO) and 91.8% (SBO); while free bullfrog oil showed no effects. The results obtained from microemulsions of bullfrog oil indicate the potential of the microemulsions developed, alone or in combination with other chemotherapeutic agents, for future use in biomedical approaches aiming towards cancer therapy. [ABSTRACT FROM AUTHOR]

Several association studies and GWAS on melanoma skin cancer risk have reported statistically significant signals on 9p21.3 region, where MTAP gene maps. None of the associated SNPs identified in these studies lie in the coding region of the gene and the causative relation of risk alleles with melanoma predisposition has not been elucidated. MTAP has a tumor suppressor activity and epigenetic silencing has been described in melanoma cell lines. In the present study, we show that melanoma risk alleles correlate with a MTAP allele-specific hyper-methylation and down-regulation of gene expression. [ABSTRACT FROM AUTHOR]

Objectives: To assess the level of melanoma awareness and risk perception among ethnic minorities and to identify ways to enhance the relevance of melanoma educational materials for ethnic minorities. Methods: Twelve focus groups composed of participants from a single ethnicity [African-American ( n=40), Hispanic ( n=40), and Asian ( n=40)], participated in a 2 h discussion on melanoma and skin cancer and commented on an educational brochure by the American Cancer Society and reacted to photographs of melanoma on ethnic skin. Participants also evaluated the ability to sunburn and tan and the skin cancer risk of images of celebrities before and after the discussion. Additionally, participants assessed the skin tone of celebrities as very fair, fair, olive, light brown, dark brown, and very dark. The audiotape recordings of the 12 focus groups were transcribed and analyzed with the Non-numerical Unstructured Data Indexing Searching and Theorizing software for common themes. Results: The common themes were (1) lack of relevance of skin cancer to ethnic people, (2) understanding of skin cancer risk terminology is based on personal experience and what is acquired from the media, and (3) sources of health information for ethnic minorities are fragmented and physicians are not the primary source of information. Celebrity images representing the six skin tones were selected. Conclusions: Relevance of melanoma education to ethnic people may be improved by using 'melanoma skin cancer', photographs of early melanoma in people with dark skin, and providing guidance on how to inspect hands and feet for suspicious moles. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]