Search

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]

Amaç: Kanserlerde kemoterapötiklere gösterilen dirençte ve kanser gelişiminde apoptozisin önemli rolü vardır. Metformin tip 2 diyabet tedavisinde kullanılan güvenli, ucuz ve etkili bir ilaçtır. Ayrıca, antikanser etkilerinin olduğuna dair kanıtlar olmasına rağmen metforminin bu etkiyi hangi mekanizmalar ile gerçekleştirdiği açık değildir. Bu nedenle, çalışmamızda cilt kanseri hücrelerine metformin uygulamasının Bcl-2, kaspaz-3, bax, wee-1, AIF, grp78 ve gadd153 düzeyleri üzerine etkileri araştırıldı. Gereç ve Yöntem: İlgili protein düzeylerinin belirlenmesinde ELISA testleri kullanıldı. Bulgular: Çalışmamızda metforminin cilt kanseri hücrelerinde apoptozis proteinlerini (kaspaz-3, wee 1, gadd153, grp78 ve AIF) arttırdığı ve antiapoptotik markırı (bcl-2) azalttığı belirlendi. Sonuç: Sonuç olarak çalışmamız; metforminin melanom cilt kanseri üzerine apoptotik etkisinin olduğunu göstermiştir.

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]

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. Wereview 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. [ABSTRACT FROM AUTHOR]

Melanoma, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the most common skin cancers. The incidence rates of all three types of skin cancers have increased in the past three decades. Light pigmentary traits have been recognized as one of the host risk factors for skin cancer, but findings on associations between eye colors and risk of skin cancers have been inconsistent. We performed a prospective analysis to examine the association between eye colors and risk of skin cancers using the Health Professionals Follow-up Study (HPFS). Cox proportional hazard models were applied to estimate relative risks (RRs) and their 95% confidence intervals (CIs). Effect modifications due to hair color and skin reaction to sun were also examined. The HPFS included 35,662 males. During a median follow-up of 19 years (1988–2012), 445 melanoma, 1123 SCC, and 7198 BCC cases were documented. Compared to those whose eye colors were dark or brown, participants with hazel/green/medium and blue/light colors had a 24% (RR = 1.24, 95% CI: 1.06–1.45) and a 19% (RR = 1.19, 95% CI: 1.01–1.41) higher risk of SCC, respectively. Similarly, a higher risk of BCC was observed in participants with hazel/green/medium eye colors (RR = 1.16, 95% CI: 1.09–1.23) and blue/light eye colors (RR = 1.17, 95% CI: 1.10–1.25). We did not find significant associations between eye color and risk of melanoma. Lighter eye color was associated with increased risks of SCC and BCC among those with dark hair colors (p for interaction ≤ 0.02). In conclusion, in this large prospective study of men, we found that light eye colors were associated with higher risks of SCC and BCC, but not melanoma. Further studies are needed to confirm this association in other populations. [ABSTRACT FROM AUTHOR]

The diagnosis of melanoma skin cancer in the early stages is of vital importance owing to the fact that their effects on the prognosis of recovery. The made of these diagnoses are mostly done with visual evaluation of the skin. Therefore, the stated diagnosis of as a result of visual evaluation of the skin is a subjective assessment that because it depends on the doctor’s expertise. In this study, the segmentation of lesion regions in skin images is performed with K-mean clustering algorithm in order to increase the accuracy of diagnosis made by doctors. In the algorithm, the system is tested by selecting K center number 2 and 4 values. A special melanoma data set has been used during the testing. The analysis of the obtained values have been realized using Peak Signal Noise Ratio (PSNR) and Correlation Coefficient (CC) metrics. The performance of this study was evaluated by comparing Canny edge detection and Mean shift algorithm previously implemented by us. In this segmentation process, the selected center number is 4 in the K-average clustering algorithm and in this situation the highest PSNR value is 17,1591dB. According to the metric results, it was observed that the segmentation performed by using the K-mean clustering algorithm which has selected center number equals to the 4 yielded more successful results.

Melanoma is one of the most dangerous skin cancers in the world. It accounts for 55% of all deaths associated with skin cancer. Researchers believe that skin cancer increases the risk of other cancers if not diagnosed early. Therefore, prompt and timely diagnosis of this disease is very important for the successful treatment of the patient. This system can detect melanoma lethal carcinoma from other skin lesions without the need for surgery, with a low cost, accuracy of about 98.88% and specificity 99%. In this article, a new, intelligent and accurate software (Delphi) system has been used to diagnose melanoma skin cancer. To detect malignant melanoma, the ABCDT rule, asymmetry (A), boundary (B), color (C), diameter (D) and textural variation (T) of the lesion are calculated and finally, an artificial neural network (ANN) is used to obtain an accurate result. The ANN with Multi-Layer Perceptron (MLP) contains the five extraction Characteristics (ABCDT) of lesions is used as inputs, two hidden layers, and two outputs. Very good results were obtained using this method. It was observed that for a dataset of 180 dermoscopic lesion images including 80 malignant melanomas, 20 benign melanomas and 80 nevus lesions. Due to its automatic recognition and ability to be installed on a computer, this system can be very useful for dermatologists as well as the general public.

Skin-cancer is the commonest malignancy affecting huge proportion of the population, reaching heights in terms of morbidity. The treatment strategies are presently focusing on surgery, radiation and chemotherapy, which eventually cause destruction to unaffected cells. To overcome this limitation, wide range of nanoscaled materials have been recognized as potential carriers for delivering selective response to cancerous cells and neoplasms. Nanotechnological approach has been tremendously exploited in several areas, owing to their functional nanometric dimensions. The alarming incidence of skin cancer engenders burdensome effects worldwide, which is further awakening innovational medicinal approaches, accompanying target specific drug delivery tools for coveted benefits to provide reduced toxicity and tackle proliferative episodes of skin cancer. The developed nanosystems for anti-cancer agents include liposomes, ethosomes, nanofibers, solid lipid nanoparticles and metallic nanoparticles, which exhibit pronounced outcomes for skin carcinoma. In this review, skin cancer with its sub-types is explained in nutshell, followed by compendium of specific nanotechnological tools presented, in addition to therapeutic applications of drug-loaded nano systems for skin cancer. [Display omitted] • Skin-cancer is the commonest malignancy affecting huge population. • Nano-approaches play a pivotal role in exhibiting potent anti-carcinogenic mechanism. • Therapeutic applications of drug-loaded nano-systems for skin cancer has been discussed. • Nanomedicine provides the opportunities to improve survival rate of skin cancer. [ABSTRACT FROM AUTHOR]

Malignant melanoma is a lethal human skin cancer that is not easily treatable through traditional medicines, surgeries, and therapies. Millions of cases are recorded annually to cure physiological skin defects by chemotherapy that causes several adverse effects and challenges. Moreover, pathogenic infections might aggravate infection with subsequent ulceration or cutaneous melanoma. Accordingly, we synthesized nanodrug by loading chemotherapeutic agent, Fluorouracil (5FU), onto the reduced graphene oxide (rGO). We then extracted arabinoxylan (ARX) from the husk of Plantago Ovata and functionalized it into carboxymethylarabinoxylan (CMARX) and loaded synthesized nanodrug. We have crosslinked CMARX/nanodrug with different amount of tetraethylorthosilicate (TEOS) to prepare nanocomposite hydrogel rGO-5FU-CMARX system for melanoma skin cancer care and treatment. These nanocomposite hydrogel systems rGO-5FU-CMARX have exhibited different physicochemical properties. These properties were analyzed through FTIR, SEM, water contact angle, swelling in different media (aqueous and PBS) and biodegradation in PBS media. The in-vitro activities, i.e., drug delivery via Franz diffusion, antibacterial against S. aureus and P. aeruginosa and the anticancer activities was performed against Uppsala 87 Malignant Glioma (U-87) cell lines. Moreover, rGO-5FU-CMARX nanocomposite hydrogels displayed different antimicrobial and anticancer activities based on different crosslinking. Hence, an inventive rGO-5FU-CMARX based nanocomposite hydrogel drug-delivery system was developed to treat malignant melanoma skin cancer after bacterial infections.

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.)

Traditional methods of disease diagnosis can be time-intensive, error prone and invasive to the subject. These methods are also prejudiced by the doctor’s subjectivity. These issues can be resolved by using automated diagnosis methods. There is a considerable dearth of medical experts today, especially in the rural areas. The use of computing technology may help to assist in the diagnostic process. This paper proposes the utilization of computers to detect melanoma skin cancer. Melanoma skin cancer can be fatal, especially in its later stages. However, it shows a high recovery rate when it is detected in its early stages. Considering the lack of medical professionals, early diagnosis of melanoma may be tried using machine learning algorithms. This paper explores hybrid wavelet transform based melanoma identification using ensemble of machine learning algorithms. The hybrid wavelet transform is produced using Discrete Cosine Transform and Haar Wavelet Transform as its components. The sizes of both components are varied from 4x4 to 128x128 to obtain the hybrid wavelet transorm. Experimentation performed on the transformed dermoscopy skin images with machine learning algorithms and their ensembles gives rise to a total of 196 variations. Overall, if the average of the metrics accuracy, sensitivity and specificity is considered, the SVM algorithm using the hybrid transform of Haar 8x8 and DCT 64x64 gives the best performance, followed by the SVM algorithm using hybrid transform of Haar 128x128 and DCT 4x4.

Aim. To assess the effectiveness of preventive, diagnostic and therapeutic work of the oncological service of Krasnodar Region, analyze the epidemiological features of morbidity, mortality, indicators reflecting the diagnosis (the proportion of early detection, neglect) in patients with melanoma skin cancer for the period 2007–2017, and the survival of cancer patients in Krasnodar Region for the period 2007–2016.Materials and methods. Subjects of the study were patients with newly diagnosed melanoma skin cancer in Krasnodar Region for the period 2007–2017. Using an automated information and analytical system the Population Cancer Registry, survival of 3 961 patients with melanoma skin cancer for the period 2007–2016 was analyzed. Standardized rates of morbidity and mortality were calculated according to the accepted world standard with the same age groupings. Statistical processing of data according to the studied indicators was carried out using statistical software packages Statistica. The reliability of the difference in the relative indicators was determined by the t test of the Student, the significance level was adopted as p

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]

Studies show that the early (stage I) diagnosis of cancer increases the survival rate of patients, and significantly reduces treatment costs. Millimeter-wave radars can detect skin tumors based on the difference between the electrical properties of melanomas and healthy skin tissues. This paper proposes a low-power multitone continuous-wave radar design at 77GHz for biomedical imaging applications, to be used for skin cancer detection. The sensor implementation is based on a low-cost Miniature Hybrid Microwave Integrated Circuit (MHMIC) design. The entire sensor was integrated on a 0.127mm thin ceramic substrate. Two 16-element patch antenna arrays are implemented in the transmitter and receiver modules. The receiver is a six-port homodyne quadrature down-converter. The integrated power detectors are fabricated using HSCH-9161 Schottky diodes. The sensor was encapsulated in a metallic fixture with dimensions 35 by 45mm. Two cascaded amplifier circuits are designed and implemented for amplifying baseband voltages at the output of each power detector. The proposed radar design has the ability to detect the dielectic properties of tissues, making it suitable for the detection of melanomas with an accuracy on the order of tens of microns. [ABSTRACT FROM AUTHOR]

Skin cancer is a high burden disease with a high impact on global health. Conventional therapies have several drawbacks; thus, the development of effective therapies is required. In this context, nanotechnology approaches are an attractive strategy for cancer therapy because they enable the efficient delivery of drugs and other bioactive molecules to target tissues with low toxic effects. In this review, nanotechnological tools for skin cancer will be summarized and discussed. First, pathology and conventional therapies will be presented, followed by the challenges of skin cancer therapy. Then, the main features of developing efficient nanosystems will be discussed, and next, the most commonly used nanoparticles (NPs) described in the literature for skin cancer therapy will be presented. Subsequently, the use of NPs to deliver chemotherapeutics, immune and vaccine molecules and nucleic acids will be reviewed and discussed as will the combination of physical methods and NPs. Finally, multifunctional delivery systems to codeliver anticancer therapeutic agents containing or not surface functionalization will be summarized. Unlabelled Image [ABSTRACT FROM AUTHOR]

Inflammatory bowel disease (IBD) is a chronic disorder that influences many aspects of a patient’s life. The medical treatment of the affected patient has been broadly investigated. Studies have shown that most physicians are familiar with treatment options, but they are not aware of proper preventive care for their patients. As most patients consider their gastroenterologists as their primary care provider, it is essential for gastroenterologists to inform the primary care providers of the unique needs of the patients with IBD, especially those receiving immunomodulators and biologics or are considered for such treatments. As most patients with are treated with long-term immune-suppressive drugs, and these drugs predispose the patients to many infectious diseases and cancers, it is important for primary care providers to document patients’ vaccination history comprehensively and screen for the cancers that these patients are encountered with, so that many of these disorders be prevented. Identification, safety, and proper timing of vaccinations, screening for cervical cancer, melanoma and non-melanoma skin cancers, osteoporosis, isdentification of depression and anxiety, smoking cessation, and care during traveling and flights are issues that will be discussed in this systemic review. [ABSTRACT FROM AUTHOR]

Melanoma is the most dangerous and lethal form of skin cancer, due to its ability to spread to different organs if it is not treated at an early stage. Conventional chemotherapeutics are failing as a result of drug resistance and weak tumor selectivity. Therefore, efforts to evaluate novel molecules for the treatment of skin cancer are necessary. Antimicrobial peptides have become attractive anticancer agents because they execute their biological activity with features such as a high potency of action, a wide range of targets, and high target specificity and selectivity. In the present study, the antiproliferative activity of the synthetic peptide ΔM4 on A375 human melanoma cells and spontaneously immortalized HaCaT human keratinocytes was investigated. The cytotoxic effect of ΔM4 treatment was evaluated through propidium iodide uptake by flow cytometry. The results indicated selective toxicity in A375 cells and, in order to further investigate the mode of action, assays were carried out to evaluate morphological changes, mitochondrial function, and cell cycle progression. The findings indicated that ΔM4 exerts its antitumoral effects by multitarget action, causing cell membrane disruption, a change in the mitochondrial transmembrane potential, an increase of reactive oxygen species, and cell cycle accumulation in S-phase. Further exploration of the peptide may be helpful in the design of novel anticancer peptides.

The American Cancer Society has recently stated that malignant melanoma is the most serious type of skin cancer, and it is almost 100% curable, if it is detected and treated early. In this paper, we present a fully automated neural framework for real-time melanoma detection, where a low-dimensional, computationally inexpensive but highly discriminative descriptor for skin lesions is derived from local patterns of Gabor-based entropic features. The input skin image is first preprocessed by filtering and histogram equalization to reduce noise and enhance image quality. An automatic thresholding by the optimized formula of Otsu’s method is used for segmenting out lesion regions from the surrounding healthy skin regions. Then, an extensive set of optimized Gabor-based features is computed to characterize segmented skin lesions. Finally, the normalized features are fed into a trained Multilevel Neural Network to classify each pigmented skin lesion in a given dermoscopic image as benign or melanoma. The proposed detection methodology is successfully tested and validated on the public PH2 benchmark dataset using 5-cross-validation, achieving 97.5%, 100% and 96.87% in terms of accuracy, sensitivity and specificity, respectively, which demonstrate competitive performance compared with several recent state-of-the-art methods.