Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.

Fruits and vegetables are widely known to be rich in nutrients, antioxidants, vitamins, dietary fiber, minerals, and a bioactive molecule, making them an essential component of a balanced diet with multiple documented positive effects on human health. The probiotication of plant-based juices for the production of functional and nutraceutical food serves as a healthy alternative to dairy probiotics. They are cholesterol free, lack several dairy allergens, and also encourage ingestion for people with lactose intolerance. This review highlights valuable claims regarding the efficacy of different probiotic strains on various diseases. A comprehensive nutrition comparison and the preference of plant-based over dairy probiotic drinks is also discussed, supported with updated market trends of probiotic drinks (dairy and non-dairy based). An extensive compilation of current plant-based probiotic drinks that are available in markets around the world is listed as a reference. The fermentability of carbon sources by probiotic microorganisms is crucial in addressing the development of plant-based drinks. Therefore, the pathway involved in metabolism of sucrose, glucose, fructose, and galactose in fruit and vegetable juice was also underlined. Finally, the key factors in monitoring the quality of probiotic products such as total soluble solids, sugar consumption, titratable acidity, pH, and stability at low storage temperatures were outlined.

Food preservation is a method used to handle and treat food products to slow down food spoilage and subsequently reduce the risk of foodborne illness. Nowadays, the demand for natural preservatives over chemical preservatives in food is increasing due to the awareness of consuming healthy food products without the risk of harmful side effects. Thus, the research and development of preservation techniques, referred to as biopreservation, is growing rapidly. In biopreservation methods, microorganisms that are known as lactic acid bacteria (LAB) and their antimicrobial substances are used to extend shelf life and maintain the nutritional value of foods. Among the most studied LAB are from the genus Enterococcus, which produces a bacteriocin called enterocin. Bacteriocins are ribosomal-synthesized antimicrobial peptides that are capable of inhibiting the growth of pathogenic bacteria that cause spoilage in food. LAB is generally regarded as safe (GRAS) for human consumption. The current application of LAB, notably Enterococcus sp. in the biopreservation of meat and meat-based products was highlighted in this review. This report also includes information on the effects of enzymes, temperature, and pH on the stability of bacteriocin produced by Enterococcus sp. An extensive compilation of numerous industry procedures for preserving meat has also been emphasized, highlighting the benefits and drawbacks of each method.

Cancer is regarded as one of the most common and leading causes of death. Despite the availability of conventional treatments against cancer cells, current treatments are not the optimal treatment for cancer as they possess the possibility of causing various unwanted side effects to the body. As a result, this prompts a search for an alternative treatment without exhibiting any additional side effects. One of the promising novel therapeutic candidates against cancer is an antimicrobial peptide produced by bacteria called bacteriocin. It is a non-toxic peptide that is reported to exhibit potency against cancer cell lines. Experimental studies have outlined the therapeutic potential of bacteriocin against various cancer cell lines. In this review article, the paper focuses on the various bacteriocins and their cytotoxic effects, mode of action and efficacies as therapeutic agents against various cancer cell lines.

A simultaneous saccharification and fermentation (SSF) optimization process was carried out on pretreated empty fruit bunches (EFBs) by employing the Response Surface Methodology (RSM). EFBs were treated using sequential acid-alkali pretreatment and analyzed physically by a scanning electron microscope (SEM). The findings revealed that the pretreatment had changed the morphology and the EFBs’ structure. Then, the optimum combination of enzymes and microbes for bioethanol production was screened. Results showed that the combination of S. cerevisiae and T. harzianum and enzymes (cellulase and β-glucosidase) produced the highest bioethanol concentration with 11.76 g/L and a bioethanol yield of 0.29 g/g EFB using 4% (w/v) treated EFBs at 30 °C for 72 h. Next, the central composite design (CCD) of RSM was employed to optimize the SSF parameters of fermentation time, temperature, pH, and inoculum concentration for higher yield. The analysis of optimization by CCD predicted that 9.72 g/L of bioethanol (0.46 g/g ethanol yield, 90.63% conversion efficiency) could be obtained at 72 h, 30 °C, pH 4.8, and 6.79% (v/v) of inoculum concentration using 2% (w/v) treated EFBs. Results showed that the fermentation process conducted using the optimized conditions produced 9.65 g/L of bioethanol, 0.46 g/g ethanol yield, and 89.56% conversion efficiency, which was in close proximity to the predicted CCD model.