{"title":"使用 Python 跟踪和分析抗菌药耐药性趋势的方法：印度古吉拉特邦阿南德试点研究--2022 年 5 月至 2023 年 8 月。","authors":"Priyanshu Khound, Himanshu Pandya, Rupal Patel, Naimika Patel, Siddhi A Darji, Purvi Trivedi, Vandan Mehta, Avani Raulji, Devjani Banerjee","doi":"10.1089/mdr.2023.0057","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18701,"journal":{"name":"Microbial drug resistance","volume":" ","pages":"1-20"},"PeriodicalIF":2.3000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Approach to Track and Analyze the Trend of Antimicrobial Resistance Using Python: A Pilot Study for Anand, Gujarat, India-May 2022-August 2023.\",\"authors\":\"Priyanshu Khound, Himanshu Pandya, Rupal Patel, Naimika Patel, Siddhi A Darji, Purvi Trivedi, Vandan Mehta, Avani Raulji, Devjani Banerjee\",\"doi\":\"10.1089/mdr.2023.0057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":18701,\"journal\":{\"name\":\"Microbial drug resistance\",\"volume\":\" \",\"pages\":\"1-20\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Microbial drug resistance\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1089/mdr.2023.0057\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microbial drug resistance","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/mdr.2023.0057","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/28 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
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.
期刊介绍：
Microbial Drug Resistance (MDR) is an international, peer-reviewed journal that covers the global spread and threat of multi-drug resistant clones of major pathogens that are widely documented in hospitals and the scientific community. The Journal addresses the serious challenges of trying to decipher the molecular mechanisms of drug resistance. MDR provides a multidisciplinary forum for peer-reviewed original publications as well as topical reviews and special reports.
MDR coverage includes:
Molecular biology of resistance mechanisms
Virulence genes and disease
Molecular epidemiology
Drug design
Infection control.
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