Cancer is a leading cause of death worldwide. Several classes of drugs are available to treat different types of cancer. Currently, researchers are paying significant attention to the development of drugs at the nanoscale level to increase their target specificity and to reduce their concentrations. Nanotechnology is a promising and growing field with multiple subdisciplines, such as nanostructures, nanomaterials, and nanoparticles. These materials have gained prominence in science due to their size, shape, and potential efficacy. Nanomedicine is an important field involving the use of various types of nanoparticles to treat cancer and cancerous cells. Synthesis of nanoparticles targeting biological pathways has become tremendously prominent due to the higher efficacy and fewer side effects of nanodrugs compared to other commercial cancer drugs. In this review, different medicinal plants and their active compounds, as well as green-synthesized metallic nanoparticles from medicinal plants, are discussed in relation to their anticancer activities.

Laboratory-based research on microbial fuel cells (MFCs) is often costly and limited to a small number of variables, making optimization challenging. However, machine learning (ML) offers a promising solution by enabling efficient multivariate principal component analysis (PCA) and multivariable optimization. These techniques can provide significant insights and optimization opportunities. The goal of this study is to propose an ML-based approach to explore the relationships between bioelectricity generation (in terms of voltage, power density (PD), current density (CD), and coulombic efficiency (CE)) and two key variables, chemical oxygen demand (COD) and pH, as well as to recommend their optimal combinations. Specifically, the objectives are to (1) integrate a laboratory-based MFC study with multivariate data analyses; (2) apply PCA to reduce data complexity by focusing on the principal components that account for the greatest variance, thus improving interpretability; and (3) identify the optimal combinations of COD and pH for maximizing bioelectricity generation. The PCA results demonstrated that COD positively influenced the generated voltage while having an inverse effect on CE. Additionally, both PD and CD increased with higher pH values. The optimal combination of COD and pH improved CD, PD, and CE; however, their optimal combination for generated voltage differed, with higher COD leading to higher voltage. The optimal predicted voltage, CD, PD, and CE of the study were 795.71 (mV), 1451.80 (mA/m²), 57.46 (mW/m²), and 4.85%, respectively. By integrating ML approaches, this study contributed to the optimization of bioelectricity generation from wastewater and offered valuable insights for researchers working in this field.

International Journal of Current Microbiology and Applied Sciences ISSN:2319-7692(Print), ISSN:2319-7706(Online) is a multidisciplinary peer-reviewed journal with reputable academics and experts as members of its Editorial Board. The Current Microbiology aims to publish all the latest and outstanding research articles. Review and letters in all areas of major importance to techniques of microbiology and applied research with publishes high quality of review and research articles on novel aspects of Microbiology including Environmental, Food, Agricultural, Medical, Pharmaceutical, Veterinary, Soil, Water and Biodeterioration.

The most common size measurements for agricultural produce, including fruits and vegetables, are length and width. While the length of any agricultural produce can be unique, the width varies continuously along its length. Single-width measurements alone are insufficient to accurately characterize the varying width profile, resulting in an inaccurate representation of the shape or mean dimension. Consequently, manual measurement of multiple and mean dimensions is laborious or impractical, and no information in this domain is available. Therefore, an efficient alternative computer vision measurement tool was developed utilizing ImageJ. Twenty sample sets, comprising fruits and vegetables, each representing different shapes, were selected and measured for length and multiple widths. A statistically significant minimum number of multiple widths was determined for practical measurements based on object shape. The “aspect ratio” (width/length) was identified to serve as an effective indicator of the minimum multiple width measurements. In general, 50 multiple width measurements are recommended; however, even 15 measurements would be satisfactory (1.0±0.6 deviation from 50 widths). The developed plugin was fast (734±365 CPU time/image), accurate (>=99.6%), cost-effective, and incorporated several user-friendly and helpful features. The study outcomes have practical applications in characterization, quality control, grading and sorting, and pricing determination of agricultural produce.