Phthalocyanine-Porphyrin Uniform Orthogonal Conjugated Oligomer for NIR Photothermal-Photodynamic Synergistic Antibacterial Treatment DOI Creative Commons
Wei Liu,

Wanru Zhao,

Gaoqiang Ma

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 15, 2024

Abstract With the increase of antibiotic resistance worldwide, there is an urgent demand to develop new antibacterial agent and approaches address threat human health posed by ineffectiveness traditional antibiotics. In this work, orthogonal conjugated uniform oligomer bactericide SiPc-ddCPP was constructed between silicon phthalocyanine porphyrin via amide bond, which can effectively treat infection through photodynamic-photothermal combined therapy without considering drug resistance. Compared with organic photothermal agents induced unstable H-aggregation blue-shifted absorption fluorescence/ROS quenching, orthogonal-structured nanoparticle shows remarkably stability NIR effect (η = 31.15%) along fluorescence ROS generation, due photoinduced intramolecular energy transfer within SiPc-ddCPP. Antibacterial studies have shown that both Gram-positive Gram-negative bacteria could be efficiently annihilated in a few minutes synergistic PDT-PTT satisfactory bacterial targeting. These results suggest multifunctional bactericide, afford approach targeted anti-inflammation conquer crisis

Language: Английский

Antimicrobial resistance: Impacts, challenges, and future prospects DOI Creative Commons
Sirwan Khalid Ahmed, Safin Hussein, Karzan Qurbani

et al.

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

Citations

197
A review on the screening methods for the discovery of natural antimicrobial peptides DOI Creative Commons
Bin Yang,

Hongyan Yang,

Jianlong Liang

et al.

Journal of Pharmaceutical Analysis, Journal Year: 2024, Volume and Issue: 15(1), P. 101046 - 101046

Published: July 18, 2024

Natural antimicrobial peptides (AMPs) are promising candidates for the development of a new generation antimicrobials to combat antibiotic-resistant pathogens. They have found extensive applications in fields medicine, food, and agriculture. However, efficiently screening AMPs from natural sources poses several challenges, including low efficiency high antibiotic resistance. This review focuses on action mechanisms AMPs, both through membrane non-membrane routes. We thoroughly examine various highly efficient AMP methods, whole-bacterial adsorption binding, cell chromatography (CMC), phospholipid membrane-mediated capillary electrophoresis (CE), colorimetric assays, thin layer (TLC), fluorescence-based screening, genetic sequencing-based analysis, computational mining databases, virtual methods. Additionally, we discuss potential developmental enhancing discovery. provides comprehensive framework identifying within complex product systems.

Language: Английский

Citations

47
Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance DOI Creative Commons
Angela Cesaro, Samuel C. Hoffman, Payel Das

et al.

npj Antimicrobials and Resistance, Journal Year: 2025, Volume and Issue: 3(1)

Published: Jan. 7, 2025

Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning deep assist in pathogen detection, resistance prediction, drug These tools improve stewardship identify effective compounds such as antimicrobial peptides small molecules. This review explores AI applications diagnostics, therapy, discovery, emphasizing both strengths areas needing improvement.

Language: Английский

Citations

6
The Role of Artificial Intelligence and Machine Learning in Predicting and Combating Antimicrobial Resistance DOI Creative Commons
Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 27, P. 423 - 439

Published: Jan. 1, 2025

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised deep reinforcement natural language processing are some main tools used this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, epidemiological for predicting AMR outbreaks. Although relatively new fields, numerous case studies offer substantial evidence their successful application outbreaks with greater accuracy. These provide insights into discovery novel antimicrobials, repurposing existing drugs, combination therapy through analysis molecular structures. In addition, AI-based decision support systems real-time guide healthcare professionals improve prescribing antibiotics. also outlines how AI surveillance, analyze trends, enable early outbreak identification. Challenges, ethical considerations, privacy, model biases exist, however, continuous development methodologies enables play significant combating

Language: Английский

Citations

4
Navigating Antibacterial Frontiers: A Panoramic Exploration of Antibacterial Landscapes, Resistance Mechanisms, and Emerging Therapeutic Strategies DOI Creative Commons

Krittika Ralhan,

Kavita A. Iyer, Leilani Lotti Díaz

et al.

ACS Infectious Diseases, Journal Year: 2024, Volume and Issue: 10(5), P. 1483 - 1519

Published: May 1, 2024

The development of effective antibacterial solutions has become paramount in maintaining global health this era increasing bacterial threats and rampant antibiotic resistance. Traditional antibiotics have played a significant role combating infections throughout history. However, the emergence novel resistant strains necessitates constant innovation research. We analyzed data on antibacterials from CAS Content Collection, largest human-curated collection published scientific knowledge, which proven valuable for quantitative analysis knowledge. Our focuses mining Collection recent publications (since 2012). This article aims to explore intricate landscape research while reviewing advancement traditional emerging strategies. By delving into resistance mechanisms, paper highlights need find alternate strategies address growing concern.

Language: Английский

Citations

14
From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance – a Comprehensive Review DOI Creative Commons
José Manuel Pérez de la Lastra, Samuel J. T. Wardell, Tarun Pal

et al.

Journal of Medical Systems, Journal Year: 2024, Volume and Issue: 48(1)

Published: Aug. 1, 2024

Abstract The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims explore the role AI/ML in AMR management, with focus on identifying pathogens, understanding patterns, predicting treatment outcomes, discovering new antibiotic agents. Recent advancements enabled efficient analysis large datasets, facilitating reliable prediction trends responses minimal human intervention. ML can analyze genomic data identify genetic markers associated resistance, enabling development targeted strategies. Additionally, techniques show promise optimizing drug administration developing alternatives traditional antibiotics. By analyzing patient clinical these technologies assist healthcare providers diagnosing infections, evaluating their severity, selecting appropriate therapies. While integration settings is still its infancy, quality algorithm suggest that widespread adoption forthcoming. conclusion, holds improving management outcome.

Language: Английский

Citations

12
Therapeutic Peptide Development Revolutionized: Harnessing the Power of Artificial Intelligence for Drug Discovery DOI Creative Commons
Samaneh Hashemi,

Parisa Vosough,

Saeed Taghizadeh

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(22), P. e40265 - e40265

Published: Nov. 1, 2024

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and expansion effective medicinal compounds. The novel functional properties peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals biotechnology advance drug development rapidly reduce costs. Short-chain inhibit a wide range pathogens great potential for targeting diseases. To address challenges synthesis sustainability, methods, namely machine learning, must be integrated into their production. Learning can use complicated computations select active toxic compounds metabolic activity. Through this comprehensive review, we investigated method as tool finding peptide-based drugs providing more accurate analysis through introduction predictable databases selection development.

Language: Английский

Citations

8
ESKAPE pathogens and associated quorum sensing systems: New targets for novel antimicrobials development DOI Creative Commons
Christiana Eleojo Aruwa,

Theolyn Chellan,

Nosipho Wendy S’thebe

et al.

Health Sciences Review, Journal Year: 2024, Volume and Issue: 11, P. 100155 - 100155

Published: March 11, 2024

Globally, antimicrobial (AMR) or multi-drug resistance (MDR) constitutes a current health challenge that is predicted to cause increased infections rates with adverse socioeconomic impacts through increase in healthcare costs. In addition, the group of Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp. (ESKAPE) pathogens debilitating (community nosocomial) are classed as priority 1 AMR pathogens. This systematic report therefore aimed at providing detailed coverage new targets for novel antimicrobials development against MDR ESKAPE mitigate future spread improve public indices. The prevalent bacterial show high quinolones, lactams, cephalosporins, carbapenems other antibiotic groups, ability form biofilms linked various quorum sensing systems (QSSs) boost their virulence. These QS pathways have become viable drug design efforts development. Also, since antibiotics discovery has waned past decade, emergence advanced computational modelling technologies design, repurposing may yet bridge gap. As such, this work we provided comprehensive overview using relevant, included data findings on pathogens, QSSs target agents' development, contributions tools heart advancements roles bioprospecting developing 'druggable' candidates therapies anti-biofilm, anti-quorum activities AMR, biofilm QS-related pathogenicity factors.

Language: Английский

Citations

6
AI-driven antimicrobial peptides for drug development DOI

Y. K. Arora,

H. B. Lalwani, Ajay Kumar

et al.

Methods in microbiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Citations

0
Global insights into MRSA bacteremia: a bibliometric analysis and future outlook DOI Creative Commons
Jiayi Lin, Jia-Kai Lai, Jianyi Chen

et al.

Frontiers in Microbiology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 22, 2025

Background Methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) pose a significant challenge to global public health, characterized by high morbidity and mortality rates, particularly in immunocompromised patients. Despite extensive research, the rapid development of MRSA antibiotic resistance has outpaced current treatment methods, increasing difficulty treatment. Therefore, reviewing research on BSIs is crucial. Methods This study conducted bibliometric analysis, retrieving analyzing 1,621 publications related from 2006 2024. The literature was sourced Web Science Core Collection (WoSCC), data visualization trend analysis were performed using VOSviewer, CiteSpace, Bibliometrix software packages. Results showed that primarily concentrated United States, China, Japan. States leads output influence, with contributions institutions such as University California system Texas system. journal most Antimicrobial Agents Chemotherapy, while cited publication Vincent JL’s article “Sepsis European Intensive Care Units: SOAP Study” published Critical Medicine 2006. Cosgrove SE’s “Comparison Mortality Associated Methicillin-Resistant Methicillin-Susceptible Bacteremia: A Meta-analysis” had co-citations. Key trends include MRSA’s mechanisms, application new diagnostic technologies, impact COVID-19 studies. Additionally, artificial intelligence (AI) machine learning are increasingly applied diagnosis treatment, phage therapy vaccine have become future hotspots. Conclusion remain major health challenge, especially severity resistance. Although progress been made treatments further validation required. Future will rely integrating genomics, AI, drive personalized Strengthening cooperation, resource-limited countries, be key effectively addressing BSIs.

Language: Английский

Citations

0