The most common problem of modern medicine is the development of increasing antifungal drug resistance nowadays including fungal infections of the skin and its appendages caused by dermatomycetes, yeast-like fungi, and molds so, there will be a need for alternative antifungal treatment. Natural substances with high antifungal properties like essential oils and their components show potential in this regard. Citrus lemon and Citrus sinensis essential oils were screened against Malassezia furfur by disc diffusion method and Minimum Inhibitory Concentration was determined by Modified Microdilution method. Citrus lemon (lemon) and Citrus sinensis (orange) show excellent inhibitory effects on the lipophilic, yeast-like fungus Malassezia furfur which causes Pityriasis versicolor, a chronic superficial fungal disease of the skin. The diameter of the inhibition zone was found to be 70 mm and 60 mm respectively. Malassezia furfur was found to be resistant against Clotrimazole & Ketoconazole. The minimum inhibitory concentration (MIC) of lemon and orange oil against Malassezia furfur was found to be 0.6 µl/ml and 0.8 µl/ml. These findings concluded that the use of Citrus lemon (lemon) and Citrus sinensis (orange) oil showed remarkable and excellent antifungal activity, and can be used as a traditional herbal medicine for the control of Pityriasis versicolor infection of the skin after undergoing clinical trials.
Biological data have grown exponentially due to single-cell multi-omics, high-content imaging, and large-scale clinical records; this has transformed modern biology. The landscape has shifted to one of data abundance, and the current approaches to computational analysis are not up to the task of dealing with the scale and complexity of this change. The information in big biological data can be characterized by the volume it occupies, velocity, variety, veracity, and value, and each of these features presents its own challenges to the researcher. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and natural language processing (NLP), has progressively turned into a requirement in tackling these issues. This chapter discusses how AI can be used to curate, represent, learn, and model the analysis of big biological data. Questions include data curation via methods to compress, represent, and outline big biological data, how to learn representations, how to model with big biological data, and how to translate into clinical problems. Using the conversation of genomics, proteomics, transcriptomics, structural biology, imaging, and electronic health records, this chapter reveals how AI is transformative in bioinformatics. In conclusion, the chapter points to enduring challenges, namely interpretability, data governance, and reproducibility, and provides a step into the future of explainable, trustworthy, and reproducible AI in biology.
