Antimicrobial resistance: Impacts, challenges, and future prospects

Journal of Medicine Surgery and Public Health, Journal Year: 2024, Volume and Issue: 2, P. 100081 - 100081

Published: March 2, 2024

Antimicrobial resistance (AMR) is a critical global health issue driven by antibiotic misuse and overuse in various sectors, leading to the emergence of resistant microorganisms. The history AMR dates back discovery penicillin, with rise multidrug-resistant pathogens posing significant challenges healthcare systems worldwide. antibiotics human animal health, as well agriculture, contributes spread genes, creating "Silent Pandemic" that could surpass other causes mortality 2050. affects both humans animals, treating infections. Various mechanisms, such enzymatic modification biofilm formation, enable microbes withstand effects antibiotics. lack effective threatens routine medical procedures lead millions deaths annually if left unchecked. economic impact substantial, projected losses trillions dollars financial burdens on agriculture. Artificial intelligence being explored tool combat improving diagnostics treatment strategies, although data quality algorithmic biases exist. To address effectively, One Health approach considers human, animal, environmental factors crucial. This includes enhancing surveillance systems, promoting stewardship programs, investing research development for new antimicrobial options. Public awareness, education, international collaboration are essential combating preserving efficacy future generations.

Language: Английский

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The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6

Published: Jan. 5, 2024

This paper presents a comprehensive analysis of the scholarly footprint ChatGPT, an AI language model, using bibliometric and scientometric methods. The study zooms in on early outbreak phase from when ChatGPT was launched November 2022 to June 2023. It aims understand evolution research output, citation patterns, collaborative networks, application domains, future directions related ChatGPT. By retrieving data Scopus database, 533 relevant articles were identified for analysis. findings reveal prominent publication venues, influential authors, countries contributing research. Collaborative networks among researchers institutions are visualized, highlighting patterns co-authorship. domains such as customer support content generation, examined. Moreover, identifies emerging keywords potential areas exploration. methodology employed includes extraction, various indicators, visualization techniques Sankey diagrams. provides valuable insights into ChatGPT's academia offers guidance further advancements. stimulates discussions, collaborations, innovations enhance capabilities impact across domains.

Language: Английский

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How trustworthy is ChatGPT? The case of bibliometric analyses

Cogent Engineering, Journal Year: 2023, Volume and Issue: 10(1)

Published: June 25, 2023

The introduction of the AI-powered chatbot ChatGPT by OpenAI has sparked much interest and debate among academic researchers. Commentators from different scientific disciplines have raised many concerns issues, especially related to ethics using these tools in writing publications. In addition, there been discussions about whether is trustworthy, effective, useful increasing researchers' productivity. Therefore, this paper, we evaluate ChatGPT's performance on tasks bibliometric analysis, comparing output provided with a recently conducted study same topic. findings show that are large discrepancies trustworthiness low particular area. researchers should exercise caution when as tool studies.

Language: Английский

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Analyzing the Scholarly Footprint of ChatGPT: Mapping the Progress and Identifying Future Trends

Published: June 29, 2023

This paper presents a comprehensive analysis of the scholarly footprint ChatGPT, an AI language model, using bibliometric and scientometric methods. The study aims to understand evolution research output, citation patterns, collaborative networks, application domains, future directions related ChatGPT. By analyzing data from Scopus database, 533 relevant articles were identified for analysis. findings reveal prominent publication venues, influential authors, countries contributing ChatGPT research. Collaborative networks among researchers institutions are visualized, highlighting patterns co-authorship. domains such as customer support content generation, examined. Moreover, identifies emerging keywords potential areas exploration. methodology employed includes extraction, various indicators, visualization techniques Sankey diagrams. provides valuable insights into ChatGPT's influence in academia offers guidance further advancements. stimulates discussions, collaborations innovations enhance capabilities impact across domains.

Language: Английский

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Comparative genomic and antimicrobial resistance profiles of Salmonella strains isolated from pork and human sources in Sichuan, China

Frontiers in Microbiology, Journal Year: 2025, Volume and Issue: 16

Published: March 3, 2025

Introduction Salmonella detection in retail pork is increasing, yet studies on its antimicrobial resistance (AMR) profiles and genomic characteristics remain limited. Moreover, it still unclear whether certain sequence types (STs) are consistently or rarely associated with as a transmission source. Sichuan province, the largest pork-production region China, provides critical setting to investigate these dynamics. Methods In this study, 213 strains isolated from human sources (2019–2021) underwent phenotypic AMR testing whole-genome sequencing (WGS). Results Resistance profiling revealed higher prevalence of pork-derived strains, particularly veterinary-associated antibiotics. We identified STs not observed such ST23 ( S . Oranienburg) poultry-commonly ST32 Infantis), suggesting potential non-pork routes for STs. To quantify type diversity within each sample source, we introduced index (ST = number different STs/ total isolates). The ST was 32% (49/153) human-derived isolates 20% (12/60) isolates. PERMANOVA analysis significant differences structural composition between human- p 0.001), indicating that may harbor specific more frequently. Discussion These findings highlight role reservoir STs, while also implying pathways. represents novel metric assessing across sources, offering better understanding genetic variation

Language: Английский

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Deep Learning in Disability Research: A Bibliometric Analysis

Digital engineering., Journal Year: 2025, Volume and Issue: unknown, P. 100046 - 100046

Published: April 1, 2025

Language: Английский

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Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Microorganisms, Journal Year: 2024, Volume and Issue: 12(5), P. 842 - 842

Published: April 23, 2024

Antimicrobial resistance is recognised as one of the top threats healthcare bound to face in future. There have been various attempts preserve efficacy existing antimicrobials, develop new and efficient manage infections with multi-drug resistant strains, improve patient outcomes, resulting a growing mass routinely available data, including electronic health records microbiological information that can be employed individualised antimicrobial stewardship. Machine learning methods developed predict from whole-genome sequencing forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose antibacterial treatments accelerate scientific discovery. Unfortunately, there an evident gap between number machine applications science effective implementation these systems. This narrative review highlights some outstanding opportunities offers when applied research related resistance. In future, tools may prove superbugs' kryptonite. aims provide overview publications aid researchers are looking expand their work approaches acquaint them current application techniques this field.

Language: Английский

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Predicting the distribution patterns of antibiotic-resistant microorganisms in the context of Jordanian cases using machine learning techniques

Journal of Applied Pharmaceutical Science, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Antimicrobial resistance (AMR) is identified as the fourth leading cause of mortality in Jordan. However, there a scarcity data addressing demographics and clinical characteristics associated with AMR against commonly used antibiotics Western To address this knowledge gap, retrospective analysis was undertaken on microbiology records at Al-Hussein/Salt Hospital Jordan West from October 2020 to December 2022 included 2893 reports. Two machine learning (ML) models, specifically categorization regression trees (CARTs) random forests (RFs) were trained using reports then utilized forecast for different categories antibiotics. The most isolated microorganisms Escherichia coli (53.3%), Klebsiella pneumoniae, Staphylococcus aureus. Bacterial strains belonging Enterococcus faecium, aureus, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species category demonstrated elevated levels resistance. RF model superior accuracy compared CART, exhibiting range 0.64–0.99. This finding suggests significant level dependability predictive capability models forecasting patterns. susceptible impact demographic factors such age, sex, bacterial species. study emphasized significance monitoring facilitate administration appropriate antibiotic therapy.

Language: Английский

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Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis

Technology and Health Care, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: May 26, 2024

BACKGROUND: The widespread use of antibiotics has led to a gradual adaptation bacteria these drugs, diminishing the effectiveness treatments. OBJECTIVE: To comprehensively assess research progress antibiotic resistance prediction models based on machine learning (ML) algorithms, providing latest quantitative analysis and methodological evaluation. METHODS: Relevant literature was systematically retrieved from databases, including PubMed, Embase Cochrane Library, inception up December 2023. Studies meeting predefined criteria were selected for inclusion. model risk bias assessment tool employed quality assessment, random-effects utilised meta-analysis. RESULTS: systematic review included total 22 studies with combined sample size 43,628; 10 ultimately in Commonly used ML algorithms random forest, decision trees neural networks. Frequently predictive variables encompassed demographics, drug history underlying diseases. overall sensitivity 0.57 (95% CI: 0.42–0.70; p< 0.001; I2= 99.7%), specificity 0.95 0.79–0.99; I2 = 99.9%), positive likelihood ratio 10.7 2.9–39.5), negative 0.46 0.34–0.61), diagnostic odds 23 7–81) area under receiver operating characteristic curve 0.78 0.74–0.81; 0.001), indicating good discriminative ability resistance. However, funnel plots suggested high publication studies. CONCLUSION: This meta-analysis provides current comprehensive evaluation predicting resistance, emphasising their potential application clinical practice. Nevertheless, stringent design reporting are warranted enhance credibility future Future should focus innovation incorporate more high-quality further advance this field.

Language: Английский

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Predicting Antimicrobial Resistance Trends Combining Standard Linear Algebra with Machine Learning Algorithms

Baltic Journal of Modern Computing, Journal Year: 2024, Volume and Issue: 12(1)

Published: Jan. 1, 2024

Antimicrobial resistance prediction is a pivotal ongoing research activity that currently being explored across various levels.In this context, we present the application of two methods model antimicrobial Neisseria gonorrhoeae on national level as an outcome socio-economic processes.The use different implementations principal component analysis combined with classification algorithms.Using these methods, generated forecasts concerning gonorrhoeae, using publicly available databases encompassing over 200 countries from 1998 to 2021.Both approaches exhibit similar mean absolute averages and correlations when comparing measurements predictions.Steps statistical applications are discussed, including population-weighted central tendencies, geographical correlations, time trends error reduction possibilities.

Language: Английский

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