Bio-nanotechnology has emerged as an efficient and competitive methodology for the production of added-value nanomaterials (NMs). This review article gathers knowledge gleaned from the literature regarding the biosynthesis of sulfur-based chalcogenide nanoparticles (S-NPs), such as CdS, ZnS and PbS NPs, using various biological resources, namely bacteria, fungi including yeast, algae, plant extracts, single biomolecules, and viruses. In addition, this work sheds light onto the hypothetical mechanistic aspects, and discusses the impact of varying the experimental parameters, such as the employed bio-entity, time, pH, and biomass concentration, on the obtained S-NPs and, consequently, on their properties. Furthermore, various bio-applications of these NMs are described. Finally, key elements regarding the whole process are summed up and some hints are provided to overcome encountered bottlenecks towards the improved and scalable production of biogenic S-NPs.

Pigmented skin lesion identification is essential for detecting harmful pathologies related to this large organ, especially cancer. An analysis of the different methods and projects developed to diagnose these illnesses throughout the years showed that they had become very useful tools to identify melanoma, dermatofibroma, and basal cell carcinoma, among other types of cancer, are seen through the use of new computer-aided technologies. The most common diagnosis is based on dermoscopy and the dermatologist expertise that can improve accuracy with image detection techniques and classification by computer. Therefore, this study aims to develop software models able to detect and classify skin cancer. The following work is based on the use of dermoscopy images obtained from the HAM10000 dataset, a database with 10000 images previously tested and validated for research use. The main process is divided into three relevant parts: image segmentation, feature extraction (FE) using ten different pre-trained Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM) to establish a classification model. According to the results, the models of classification performed very well using the image segmentation step, showing average accuracies between 80.67% (Xception) and 90% (Alexnet). In contrast to the process without using image segmentation, where no method reached 60%. AlexNet plus SVM model showed the minor running time and presented the higher accuracy rate (90.34%) for the correct identification and classification of the seven categories of cutaneous lesions taken into account.