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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]

Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC-2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models. [ABSTRACT FROM AUTHOR]

Common therapeutics in relation to melanoma and non-melanoma cancers include the use of kinase inhibitors. The long-term benefits of kinases, however, are limited by development of drug resistance. An alternative approach for treatment would be to focus on transcription factors. Cyclic AMP-regulatory element-binding protein (CREB) is a transcription factor that is commonly overactivated or overexpressed in many different cancers including skin cancer. Ultraviolet radiation (UVR), one of the main causes of skin cancer, can activate CREB in both melanocytes and keratinocytes. In addition, CREB has been found to be activated in skin cancers. Considering the prominent role that CREB plays in skin cancers, the studies reviewed herein raise the possibility of CREB as a potential prognostic and diagnostic marker of skin cancer and a novel target for therapeutic intervention. [ABSTRACT FROM AUTHOR]

BackgroundAlthough previous studies have indicated an association between low-density lipoprotein (LDL) and skin diseases, their causal effects remain inconclusive. This study aimed to assess the causal relationship between genetically proxied lipid-lowering drugs and skin cancers and psoriasis.MethodsTwo-sample Mendelian randomization (MR) analysis was performed using single-nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS). The inverse-variance weighted (IVW) method was used to determine causal relationships. The “leave-one-out” sensitivity test, Cochran’s Q-statistic and MR-Egger intercept were used to assess heterogeneity and horizontal pleiotropy.ResultsWe identified 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) and proprotein convertase subtilisin-kexin type 9 (PCSK9) as genetically proxied lipid-lowering drugs. Genetically proxied inhibition of HMGCR (stains) was causally associated with reduced risk of nonmelanoma skin cancer (OR 0.982, 95% CI 0.967–0.997, p = 0.016 by weighted median; OR 0.977, 95% CI 0.966–0.989, p

Skin cancer is the most common form of cancer. It is predicted that the total number of cases of cancer will double in the next fifty years. It is an expensive procedure to discover skin cancer types in the early stages. Additionally, the survival rate reduces as cancer progresses. The current study proposes an aseptic approach toward skin lesion detection, classification, and segmentation using deep learning and Harris Hawks Optimization Algorithm (HHO). The current study utilizes the manual and automatic segmentation approaches. The manual segmentation is used when the dataset has no masks to use while the automatic segmentation approach is used, using U-Net models, to build an adaptive segmentation model. Additionally, the meta-heuristic HHO optimizer is utilized to achieve the optimization of the hyperparameters of 5 pre-trained CNN models, namely VGG16, VGG19, DenseNet169, DenseNet201, and MobileNet. Two datasets are used, namely "Melanoma Skin Cancer Dataset of 10000 Images" and "Skin Cancer ISIC" dataset from two publicly available sources for variety purpose. For the segmentation, the best-reported scores are 0.15908, 91.95%, 0.08864, 0.04313, 0.02072, 0.20767 in terms of loss, accuracy, Mean Absolute Error, Mean Squared Error, Mean Squared Logarithmic Error, and Root Mean Squared Error, respectively. For the "Melanoma Skin Cancer Dataset of 10000 Images" dataset, from the applied experiments, the best reported scores are 97.08%, 98.50%, 95.38%, 98.65%, 96.92% in terms of overall accuracy, precision, sensitivity, specificity, and F1-score, respectively by the DenseNet169 pre-trained model. For the "Skin Cancer ISIC" dataset, the best reported scores are 96.06%, 83.05%, 81.05%, 97.93%, 82.03% in terms of overall accuracy, precision, sensitivity, specificity, and F1-score, respectively by the MobileNet pre-trained model. After computing the results, the suggested approach is compared with 9 related studies. The results of comparison proves the efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

Objectives: The present study used publicly available genome-wide association study (GWAS) summary data to perform three two-sample Mendelian randomization (MR) studies, aiming to examine the causal links between gut microbiome and BCC, melanoma skin cancer, ease of skin tanning. Methods: SNPs associated with exposures to basal cell carcinoma, melanoma skin cancer and ease of skin tanning from the genome-wide association study data of UK Biobank and MRC-IEU (MRC Integrative Epidemiology Unit), and the meta-analysis data from Biobank and MRC-IEU were used as instrumental variables (IVs). The casual estimates were assessed with a two-sample Mendelian randomisation test using the inverse-variance-weighted (IVW) method, Wald ratio, MR-Egger method, maximum likelihood, weighted median, simple mode, and weighted mode. Results: After the application of MR analysis, diffirent effects of multiple groups of gut microbiota was observed for BCC, melanoma skin cancer and ease of skin tanning. The relationships between the gut microbiome and BCC, melanoma skin cancer, ease of skin tanning were supported by a suite of sensitivity analyses, with no statistical evidence of instrument heterogeneity or horizontal pleiotropy. Further investigation is required to explore the relationship between between the gut microbiome and BCC, melanoma skin cancer, ease of skin tanning. Conclusion: Our study initially identified potential causal roles between the gut microbiome and BCC, melanoma skin cancer, ease of skin tanning, and highlighted the role of gut microbiome in the progression of basal cell carcinoma, melanoma skin cancer, ease of skin tanning. [ABSTRACT FROM AUTHOR]

Background: Observational studies have revealed relationships between circulating vitamin D concentrations and the risk of different types of cancer, although the potential causal relationship remains controversial. This study aims to investigate the presence of a causal relationship between circulating vitamin D concentrations and the risk of different types of cancer. Methods: Summary statistics from corresponding genome-wide association studies (GWASs) were used to investigate the causal relationship between circulating vitamin D concentrations and the risk of 14 cancers. A two-sample Mendelian randomization (MR) analysis using inverse-variance weighting (IVW) as the primary method was performed. Additionally, the results were verified using four other methods, including MR-Egger, weighted median, weighted mode, and simple mode. Multiple sensitivity analyses were conducted to ensure the robustness of our MR findings. Results: The MR analysis showed no causal relationship between circulating vitamin D concentrations and most types of cancer, except for a causal relationship with melanoma skin cancer (MSC) (odds ratio [OR]IVW = 1.003, 95% confidence intervals [CI] 1.001–1.005, P = 0.004). Conversely, reverse MR revealed a causal relationship between circulating vitamin D concentration and colorectal cancer (ORIVW = 0.398, 95% CI 0.195–0.813, P = 0.01; ORweighted median = 0.352, 95% CI 0.135–0.917, P = 0.03). Conclusions: Our findings provide support for a causal relationship between circulating vitamin D concentration and risk of MSC. Additionally, we found a causal relationship between circulating vitamin D concentration and risk of colorectal cancer in reverse-MR analysis. This evidence indicate that vitamin D is of great significance in the prevention and treatment of MSC and the prognosis of colorectal cancer. [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]

The purpose of this systematic review and meta-analysis was to compare the risk of non-melanoma skin cancer (NMSC) and melanoma development in renal transplant recipients who receive calcineurin inhibitors to that of patients treated with other immunosuppressive agents, and investigate the possible association between the type of maintenance immunosuppression and the incidence of NSMC and melanoma in this group of patients. The authors searched databases such as PubMed, Scopus, and Web of Science for articles that would help establish the influence of calcineurin inhibitors on skin cancer development. The inclusion criteria for the study consisted of randomized clinical trials, cohort studies, and case-control studies that compared patients who received kidney transplants and were treated with a calcineurin inhibitor (CNI), such as cyclosporine A (CsA) or tacrolimus (Tac), to those who received alternative immunosuppressants and did not receive a CNI. Seven articles were analyzed overall. The results revealed a correlation between CNI treatment in renal transplant recipients and increased total skin cancer risk (OR 1.28; 95% CI: 0.10–16.28; p < 0.01), melanoma risk (OR 1.09; 95% CI: 0.25–4.74; p < 0.01), and NMSC risk (OR 1.16; 95% CI: 0.41–3.26; p < 0.01). In conclusion, the calcineurin inhibitors used after kidney transplantation are associated with a higher risk of skin cancer—both non-melanoma and melanoma—when compared with other immunosuppressive therapies. This finding suggests that careful monitoring for skin lesions in post-transplant patients must be conducted. However, the decision on the kind of immunotherapy used should always be considered on an individual basis for each renal transplant recipient.

Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role in facilitating the timely detection of skin cancer and aiding in the accurate diagnosis and appropriate treatment of patients. However, the implementation of traditional machine learning approaches for skin disease diagnosis is impeded by privacy regulations, which necessitate centralized processing of patient data in cloud environments. To overcome the challenges associated with data privacy, federated learning emerges as a promising solution, enabling the development of privacy-aware healthcare systems for skin cancer diagnosis. This paper presents a comprehensive review that examines the obstacles faced by conventional machine learning algorithms and explores the integration of federated learning in the context of privacy-conscious skin cancer prediction healthcare systems. It provides discussion on the various datasets available for skin cancer prediction and provides a performance comparison of various machine learning and federated learning techniques for skin lesion prediction. The objective is to highlight the advantages offered by federated learning and its potential for addressing privacy concerns in the realm of skin cancer diagnosis. [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]

Nanoparticles open up new possibilities for treating skin problems. For the treatment of numerous skin problems, the topical route of administration has many advantages. This method avoids the hepatic first-pass impact, and many medications’ systemic availability is limited to skin cells like hair follicles, reducing undesired side effects and increasing localised therapeutic benefit. The skin's barrier function makes it difficult for nanoparticles to penetrate the tissue, even if the barrier is partially impaired in cases of damage or inflammation, such as in skin cancer. This could make nanoparticle penetration easier. Although a lot of work has gone into producing nanoparticles for topical distribution, there has been very little success in getting them to the clinic to treat skin malignancies. We review the various forms of skin malignancies and current clinical care strategies. Clinical therapy and management are also illustrated, explaining various skin cancer treatment options. In a brief manner, this study also emphasises the various nanoformulations that are being explored and reported, as well as the various types of nanoparticle systems. Hghlights Skin cancer is one of the widely occurring disease worldwide with varying mortality rates. Clinical supervision of skin cancer is crucial as it may lead to some serious consequences. Nanoparticle/nanovesicle systems would aid in better treatment and reduced adverse effects. Different nano systems are currently under development for the topical treatment of skin cancer. Penetration analysis of NPs is also important to develop a topical novel nanoformulation. Abbreviations: NMSC, non-melanoma skin cancer; MSC, melanoma skin cancer; BCC, basal cell carcinoma; cSCC, cutaneous squamous cell carcinoma; SC, stratum corneum; BCS, biopharmaceutics classification system; UV, ultraviolet; WHO, World Health Organization; AK, actnic keratosis; PDT, photo dynamic therapy; 5-FU, 5-fluorouracil; nm, nanometre; mV, millivolts; FDA, Food and drug administration; API, active pharmaceutical ingredient; NPs, nanoparticles; SLNs, solid-lipid nanoaparticles; NLCs, nanostructured lipid carriers; AuNPs, gold nanoparticles; QDs, quantumn dots; CNTs, carbon nanotubes

The purpose of this systematic review and meta-analysis was to compare the risk of non-melanoma skin cancer (NMSC) and melanoma development in renal transplant recipients who receive calcineurin inhibitors to that of patients treated with other immunosuppressive agents, and investigate the possible association between the type of maintenance immunosuppression and the incidence of NSMC and melanoma in this group of patients. The authors searched databases such as PubMed, Scopus, and Web of Science for articles that would help establish the influence of calcineurin inhibitors on skin cancer development. The inclusion criteria for the study consisted of randomized clinical trials, cohort studies, and case-control studies that compared patients who received kidney transplants and were treated with a calcineurin inhibitor (CNI), such as cyclosporine A (CsA) or tacrolimus (Tac), to those who received alternative immunosuppressants and did not receive a CNI. Seven articles were analyzed overall. The results revealed a correlation between CNI treatment in renal transplant recipients and increased total skin cancer risk (OR 1.28; 95% CI: 0.10–16.28; p < 0.01), melanoma risk (OR 1.09; 95% CI: 0.25–4.74; p < 0.01), and NMSC risk (OR 1.16; 95% CI: 0.41–3.26; p < 0.01). In conclusion, the calcineurin inhibitors used after kidney transplantation are associated with a higher risk of skin cancer—both non-melanoma and melanoma—when compared with other immunosuppressive therapies. This finding suggests that careful monitoring for skin lesions in post-transplant patients must be conducted. However, the decision on the kind of immunotherapy used should always be considered on an individual basis for each renal transplant recipient. [ABSTRACT FROM AUTHOR]

The impact of β-CD on 4 benzophenones (BPs) [namely, BP, HBP, d-HBP, t-HBP] and its resultant effect on the ultraviolet protection factor (UPF) of poplin cotton fabric are tested. Drastic enhancement in the UPF values for BPs:β-CD complexes treated fabrics is noticed (than the untreated/fabric treated with free BPs). The impact of β-CD on the tautomerism of hydroxyl substituted BPs are demonstrated by investigatingthe guest (BPs:absorbers)-host(β-CD:enhancer) process. Orientation of -OH substituted benzene towards 1° rim and positioning of C = O group of BPs in the middle of β-CD could be attributed to the steric effect driven preference of the guest molecules for achieving a rigid fit. Rigid fit rendered by β-CD improves the photostability of BPs and dissipates UVR efficiently through keto-enol tautomerization. With the highest UPF (= 51) d-HBP:β-CD complex is identified as a potential application material for producing sun-protective clothing. [ABSTRACT FROM AUTHOR]

Skin cancer has been one of the major worldwide public health issue with more than 1 million cases every year. Skin cancer is classified into three categories: Basal Cell Carcinoma, Melanoma and Squamous Cell Carcinoma [ 1 ]. Melanoma is the most critical category of skin cancer with very thin chances of recovery and survival of the patient. Early diagnosis of skin cancer can drastically improve survival rate to as high as 95 percent. This served as a motivation to contribute to this noble cause using technology based solutions. In the process of diagnosis for the disease, the process is divided into four basic components: image processing including hair removal, noise removal, sharpening, and increasing the resolution of the image given to the skin dimension. Recent developments in identification of skin cancer technology uses machine learning and in-depth based reading segmentation algorithms. Most used algorithms are: InceptionV3, ResNet, VGGNet. This paper suggests an artificial skin cancer screening process using techniques like image processing and machine learning. Image super-resolution (ISR) techniques recreate an image with high resolution or sequence from visual LR images. An approach using deep learning on the Image super resolution was used to boost the accuracy of the convolutional neural network model. This model was developed using the Keras backend and tested the model by modifying the layers of neural network which are used for training. The model is built on publicly sourced dataset from the ISIC data archives. [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]

Aim: Apoptotic pathways are important for resistance to chemotherapeutics drugs and cancer development. Metformin is a safe, inexpensive and effective drug used to treat type 2 diabetes. Furthermore, although there is evidence of anticancer effects, the mechanisms by which metformin exerts the effects are unclear. Therefore, it was aimed to investigate Bcl-2, kaspaz-3, bax, wee-1, AIF, grp78 and gadd153 levels in metformin treated skin cancer cells. Materials and Methods: The skin cancer cells (B16F10) were treated with metformin and kaspase-3, bcl-2, wee 1, gadd153, grp78 and AIF levels were analyzed by using ELISA. Result: Treatment of metformin increased of proapoptotic proteins (kaspase-3, bcl-2, wee 1, gadd153, grp78 and AIF) and decreased antiapoptotic protein (bcl-2). Conclusion: In conclusion, this study showed that metformin has apoptotic effect on melanoma skin cancer. [ABSTRACT FROM AUTHOR]

Constant exposure to harmful substances from both inside and outside the body can mess up the body's natural ways of keeping itself in balance. This can cause severe skin damage, including basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma. However, plant-derived compounds found in fruits and vegetables have been shown to protect against skin cancer-causing free radicals and other harmful substances. It has been determined that these dietary phytochemicals are effective in preventing skin cancer and are widely available, inexpensive, and well-tolerated. Studies have shown that these phytochemicals possess anti-inflammatory, antioxidant, and antiangiogenic properties that can aid in the prevention of skin cancers. In addition, they influence crucial cellular processes such as angiogenesis and cell cycle control, which can halt the progression of skin cancer. The present paper discusses the benefits of specific dietary phytochemicals found in fruits and vegetables, as well as the signaling pathways they regulate, the molecular mechanisms involved in the prevention of skin cancer, and their drawbacks., Competing Interests: All authors report no conflicts of interest relevant to this article., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Anorectal mucosal melanoma (AMM) is an infrequent and highly aggressive form of mucosal melanoma. Its rarity makes it challenging to clinically diagnose, and its initial symptoms are typically nonspecific such as rectal/anal bleeding (the most common symptom), anal pain, or the presence of an anal mass. The prognosis for this condition is generally poor, and its incidence appears to be increasing each year. AMMs often go undetected and/or are already metastasized at the time of diagnosis. We present a case report of a patient who initially presented with nonspecific symptoms of anemia and blood per rectum, and was later found to have stage IV melanoma of the anorectal region. There is a notable scarcity of literature on this disease, resulting in a lack of a comprehensive understanding of its nature. Most available information consists of isolated case reports rather than comprehensive studies. Although surgical resection remains the primary treatment approach, the majority of patients (over 80%) will die due to distant metastasis within five years after undergoing surgery. The five-year survival rate for anorectal melanoma is estimated to be between 6% and 22%., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Amoako-Teming et al.)

This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, California, United States), we developed a model that can accurately differentiate between these lesions based on their distinctive features. The dataset was divided into training (80%), validation (10%), and testing (10%) sets to optimize model performance and ensure its generalizability. Our findings demonstrate the potential of machine learning algorithms in enhancing the efficiency and accuracy of melanoma and nevi detection, with the developed model exhibiting robust performance metrics. Nonetheless, limitations exist due to the potential lack of comprehensive representation of melanoma and nevi cases in the dataset, and variations in image quality and acquisition methods, which may influence the model's performance in real-world clinical settings. Therefore, further research, validation studies, and integration into clinical practice are necessary to ensure the reliability and generalizability of these models. This study underscores the promise of artificial intelligence in advancing dermatologic diagnostics, aiming to improve patient outcomes by supporting early detection and treatment initiation for melanoma., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Riazi Esfahani et al.)

Primary malignant melanoma of the small intestine is rare and infrequent. However, the small bowel is a relatively common metastatic destination for cutaneous melanoma. Given the fact that primary small intestinal melanoma is a controversial and rare diagnosis, we present a case in which the initial finding suggested a primary tumor. However, the patient was later diagnosed with a small left thigh melanoma after the diagnosis of primary malignant melanoma of the small bowel was established. As a result, we emphasize that all primary small intestinal melanoma must be thoroughly investigated for an alternative primary lesion. Additionally, we question if the diagnosis of primary malignant melanoma of the small bowel needs to be re-classified as small bowel melanoma of unknown primary, especially in cases in which the primary lesion is unidentified., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Galindo et al.)

The coronavirus disease 2019 (COVID-19) pandemic has dramatically impacted healthcare provision in the UK and skin cancer services have had to adapt to ensure continuity of safe care. As we return to "normality" we reflect on lessons learned and the impact of the pandemic on skin cancer services. We looked at data on Public Health Scotland Dashboard, which compiles data from 14 local health boards across Scotland, comparing melanoma and non-melanoma skin cancer diagnoses during the years 2020 and 2019 (pre-COVID-19 pandemic). We looked at skin cancer cases within the North Cancer Alliance (NCA) and all of Scotland. Within the NCA, 518 cases of melanoma were diagnosed in 2019, compared to 429 in 2020. Within Scotland, 1950 cases of melanoma were diagnosed in 2019, compared to 1605 in 2020. In 2019, 5103 non-melanoma skin cancer cases were diagnosed in NCA, compared to 4071 in 2020. In Scotland, 21,626 non-melanoma skin cancer cases were diagnosed in 2019, compared to 16,193 in 2020. The COVID-19 pandemic has had a significant impact on skin cancer care within the NCA and the whole of Scotland. A significantly lower number of melanoma and non-melanoma skin cancer cases have been diagnosed within the NCA and in Scotland in 2020 compared to 2019. The trend is similar between NCA and other areas of Scotland. We must raise awareness of melanoma and non-melanoma skin cancer to improve timely presentation of patients during a global health crisis and a multidisciplinary approach is needed to address this problem., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2022, Makaranka et al.)

This is a case report of new-onset bullous pemphigoid after the initiation of nivolumab for melanoma. Bullous pemphigoid is a rare immune-mediated adverse effect of nivolumab, with fewer than a hundred cases described. The patient initially developed a rash, which later progressed to respiratory symptoms, prompting the discontinuation of nivolumab. He was started on oral steroids, which improved his symptoms. However, while being tapered off the steroids, his rash reoccurred with the development of bullous pemphigoid. The diagnosis was confirmed by increased eosinophils and sub-epidermal vesicle formation compatible with bullous pemphigoid on skin biopsy. The patient was treated with steroids, mycophenolate, doxycycline, and niacinamide with significant improvement in his symptoms., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2022, Gotera et al.)

Background Seasonality of diagnosis occurs in many types of cancer and is well-established in non-melanoma (NMSC) and melanoma (MSC) skin cancers. Benign skin conditions have also been shown to demonstrate a similar seasonality pattern. Investigations into the seasonality of NMSC and MSC diagnoses are less common than benign skin conditions despite the high healthcare burden of the disease. In this study, we investigated if seasonality and monthly patterns of NMSC and MSC diagnoses are present in Eastern North Carolina. Methodology We observed and analyzed incident cancer diagnoses for patients visiting the Physicians East Dermatology clinic in Greenville, North Carolina, from 1983 to 2017 (n = 8,021 basal cell carcinomas (BCCs), n = 5,660 squamous cell carcinomas (SCCs), n = 451 MSCs, n = 14,132 total). Results Chi-square tests showed the highest rates of diagnosis for BCCs in August (9.85%), September (9.62%), and October (10.0%). For SCCs, the diagnosis rates were the highest in July (8.62%), August (9.63%), and October (9.58%). For MSCs, the diagnosis rates were the highest in May (9.98%), June (10.2%), and July (10.4%). Analysis of the differences between observed skin cancer diagnoses by month and equal distribution across all months in the event of no seasonality revealed peaks of skin cancer diagnoses corresponding to July through October for BCCs; July, August, and October for SCCs; and May through September for MSCs. Analysis of the patterns of diagnosis of this data over 34 years illustrated a continuously increasing pattern of diagnosis for all three cancer subtypes from 1983 to 2017. Conclusions This study identified a statistically significant pattern of seasonality in both NMSCs and MSCs, which was consistent with the findings of previous studies. Moving forward, further research should investigate the roles of temperature, quantified ultraviolet exposure, and geographic location and their relationships to seasonality., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2022, Samia et al.)