Degradation of Plastic Beads Containing Low Density Polyethylene (LDPE) by Sequential Photolysis, Hydrolysis and Bacterial Isolates.

Plastic is an important part of today's human daily lifestyle, and it is classified as a "global pollutant" due to its durability. The natural degradation of plastic is extremely slow and will take a hundred years or more. The ultimate destinations of plastics as well as their effects on the ecosystem vary with the type of plastic and the rate of their degradation. In this study, an attempt was made to explain the degradation of low-density polyethylene (LDPE) plastic beads with the help of selected bacterial isolates in both laboratory and field conditions. 16 S rRNA gene sequencing further identified the bacterial isolates as Micrococcus luteus and Bacillus pumilus, obtained from the municipal waste disposal site near Anand, Gujarat, India. The beads were subjected to photolysis and hydrolysis for a predetermined amount of time in addition to biodegradation. After 60 days of treatment with Pseudomonas aeruginosa, Micrococcus luteus, and Bacillus pumilus in both laboratory and field conditions, a significant percentage decrease in the weight of LDPE beads was observed. Pseudomonas aeruginosa was taken as a positive control. Further, the rate of degradation was found to be accelerated in the presence of 10% starch.

An Approach to Track and Analyze the Trend of Antimicrobial Resistance Using Python: A Pilot Study for Anand, Gujarat, India-May 2022-August 2023.

The present work deals with the analysis and monitoring of bacterial resistance in using Python for the state of Gujarat, India, where occurrences of drug-resistant bacteria are prevalent. This will provide an insight into the portfolio of drug-resistant bacteria reported, which can be used to track resistance behavior and to suggest a treatment regime for the particular bacteria. The present analysis has been done using Python on Jupyter Notebook as the integrated development environment and its data analysis libraries such as Pandas, Seaborn, and Matplotlib. The data have been loaded from excel file using Pandas and cleaned to transform features into required format. Seaborn and Matplotlib have been used to create data visualizations and represent the data inexplicable manner using graphs, plots, and tables. This program can be used to study disaster epidemiology, tracking, analyzing, and surveillance of antimicrobial resistance with a proper system integration approach.