scholarly journals The principles of rational chemotherapy of bacterial infections in poultry

2018 ◽  
Vol 20 (87) ◽  
pp. 45-49
Author(s):  
B. Tykałowski ◽  
A. Koncicki
Keyword(s):  
Antibiotic Resistance ◽  
Food Production ◽  
Bacterial Infections ◽  
Bacterial Species ◽  
Complex Problem ◽  
Chemotherapeutic Agents ◽  
The Public ◽  
Health Practitioners ◽  
Rational Use Of Antibiotics ◽  
Poultry Farming

Growing levels of microbial resistance to chemotherapeutic agents pose a threat to public health and constitute a global problem. The above can be often attributed to improper and excessive use of antibacterial drugs in veterinary and human medicine, animal breeding, agriculture and industry. To address this problem, veterinary and human health practitioners, animal breeders and the public have to be made aware of the consequences and threats associated with the uncontrolled use of antibacterial preparations. In recent years, many countries have implemented programs for monitoring antibiotic resistance which provide valuable information about the applied antibiotics and the resistance of various bacterial species colonizing livestock, poultry and the environment. Special attention should be paid to the sources and transmission routes of antibiotic resistance. There are no easy solutions to this highly complex problem. The relevant measures should address multiple factors, beginning from rational and controlled use of chemotherapeutic agents in veterinary practice, to biosecurity in animal farms, food production hygiene, and sanitary and veterinary inspections in the food chain. The tissues of treated birds should not contain antibiotic residues upon slaughter. Rational use of antibiotics should minimize the risk of drug resistance and decrease treatment costs without compromising the efficacy of treatment. Therefore, the key principles of antibiotic therapy of bacterial infections in poultry should be the adequate selection and dosage of the administered drug, a sound knowledge of the drug’s pharmacokinetic and pharmacodynamic properties, as well as a knowledge of the differences between bacteriostatic and bactericidal drugs and between time-dependent and concentration-dependent drugs. There is an urgent need to revise the existing approach to the use of chemotherapeutic agents in the treatment of poultry diseases, and to increase the awareness that antibiotics cannot compensate for the failure to observe the fundamental principles of biosecurity in all stages of poultry farming.

scholarly journals Factors favouring the evolution of multidrug resistance in bacteria

2020 ◽  
Vol 17 (168) ◽  
pp. 20200105
Author(s):  
Eliott Jacopin ◽  
Sonja Lehtinen ◽  
Florence Débarre ◽  
François Blanquart
Keyword(s):  
Antibiotic Resistance ◽  
Multidrug Resistance ◽  
Bacterial Infections ◽  
Large Fraction ◽  
Bacterial Species ◽  
Geographical Location ◽  
Host Population ◽  
Commensal Bacteria ◽  
Health Concern ◽  
Multiple Drugs

The evolution of multidrug antibiotic resistance in commensal bacteria is an important public health concern. Commensal bacteria such as Escherichia coli , Streptococcus pneumoniae or Staphylococcus aureus , are also opportunistic pathogens causing a large fraction of the community-acquired and hospital-acquired bacterial infections. Multidrug resistance (MDR) makes these infections harder to treat with antibiotics and may thus cause substantial additional morbidity and mortality. Here, we develop an evolutionary epidemiology model to identify the factors favouring the evolution of MDR in commensal bacteria. The model describes the evolution of antibiotic resistance in a commensal bacterial species evolving in a host population subjected to multiple antibiotic treatments. We combine statistical analysis of a large number of simulations and mathematical analysis to understand the model behaviour. We find that MDR evolves more readily when it is less costly than expected from the combinations of single resistances (positive epistasis). MDR frequently evolves when bacteria are in contact with multiple drugs prescribed in the host population, even if individual hosts are only treated with a single drug at a time. MDR is favoured when the host population is structured in different classes that vary in their rates of antibiotic treatment. However, under most circumstances, recombination between loci involved in resistance does not meaningfully affect the equilibrium frequency of MDR. Together, these results suggest that MDR is a frequent evolutionary outcome in commensal bacteria that encounter the variety of antibiotics prescribed in the host population. A better characterization of the variability in antibiotic use across the host population (e.g. across age classes or geographical location) would help predict which MDR genotypes will most readily evolve.

scholarly journals Impact of Biosynthesized Silver Nanoparticles on Bacterial Growth

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Jishnu Basu ◽  
Tiffany Grimes
Keyword(s):  
Cystic Fibrosis ◽  
Silver Nanoparticles ◽  
Antibiotic Resistance ◽  
Bacterial Growth ◽  
Industrial Production ◽  
Bacterial Infections ◽  
Bacterial Species ◽  
Bacterial Strains ◽  
Waste Products ◽  
Assimilatory Nitrate Reduction

Cystic Fibrosis is a genetic disease which causes the production of viscous mucus in airways which limits airflow and creates the perfect conditions for bacterial growth. Unfortunately, deaths due to bacterial infections in Cystic Fibrosis patients have increased as bacterial strains have developed antibiotic resistance.  Researchers have found that silver nanoparticles offer a solution to growing antibiotic resistance due to how no resistance has been developed to them in clinical trials. Current research is focusing on the bio-synthesis of silver nanoparticles which does not produce the harmful waste products seen with the industrial production of silver nanoparticles. However, there is a lack of comparative research concerning the effectiveness of silver nanoparticles produced by different microorganisms, which is what the researcher’s work addressed. The researcher’s work primarily focused on determining how effective silver nanoparticles produced by different bacterial species were at inhibiting bacterial growth. Through the collection of nanoparticles via extracellular synthesis, antimicrobial assays were conducted to determine the efficacy of silver nanoparticles produced by different microorganisms. The results indicated that silver nanoparticles produced by B. subtilis were the most effective in inhibiting bacterial growth. This provides a crucial as research in the field should increasingly focus on bacteria which utilize assimilatory nitrate reduction like B. subtilis because of the increased efficacy of silver nanoparticles produced by this method in inhibiting bacterial growth in aerobic conditions. Advances in this area could increase the efficiency of nanoparticle production and make it viable for industrial production.

scholarly journals Co-Opting Host Receptors for Targeted Delivery of Bioconjugates—From Drugs to Bugs

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1479
Author(s):  
Kristen M. Tummillo ◽  
Karsten R.O. Hazlett
Keyword(s):  
Antibiotic Resistance ◽  
Targeted Delivery ◽  
Bacterial Infections ◽  
Vaccine Development ◽  
Bacterial Species ◽  
Cell Type ◽  
Cancer Treatments ◽  
Functional Elements ◽  
The Brain ◽  
Advanced Treatments

Bioconjugation has allowed scientists to combine multiple functional elements into one biological or biochemical unit. This assembly can result in the production of constructs that are targeted to a specific site or cell type in order to enhance the response to, or activity of, the conjugated moiety. In the case of cancer treatments, selectively targeting chemotherapies to the cells of interest limit harmful side effects and enhance efficacy. Targeting through conjugation is also advantageous in delivering treatments to difficult-to-reach tissues, such as the brain or infections deep in the lung. Bacterial infections can be more selectively treated by conjugating antibiotics to microbe-specific entities; helping to avoid antibiotic resistance across commensal bacterial species. In the case of vaccine development, conjugation is used to enhance efficacy without compromising safety. In this work, we will review the previously mentioned areas in which bioconjugation has created new possibilities and advanced treatments.

scholarly journals Predicting Antibiotic Resistance in Hospitalized Patients by Applying Machine Learning to Electronic Medical Records

2020 ◽  
Author(s):  
Ohad Lewin-Epstein ◽  
Shoham Baruch ◽  
Lilach Hadany ◽  
Gideon Y Stein ◽  
Uri Obolski
Keyword(s):  
Machine Learning ◽  
Antibiotic Resistance ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Bacterial Infections ◽  
Bacterial Species ◽  
Empiric Therapy ◽  
Hospitalized Patients ◽  
Empiric Antibiotic Therapy ◽  
Computerized Decision Support

Abstract Background Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning (ML) algorithms. However, they are scarcely used for empiric antibiotic therapy. Here, we predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using ML algorithms applied to patients’ electronic medical records (EMRs). Methods The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their EMRs. Five antibiotics were examined: ceftazidime (n = 2942), gentamicin (n = 4360), imipenem (n = 2235), ofloxacin (n = 3117), and sulfamethoxazole-trimethoprim (n = 3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble that combined all 3 algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis. Results The ensemble outperformed the separate models and produced accurate predictions on test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble yielded area under the receiver-operating characteristic (auROC) scores of 0.73–0.79 for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8–0.88. Variables’ effects on predictions were assessed and found to be consistent with previously identified risk factors for antibiotic resistance. Conclusions We demonstrate the potential of ML to predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapidly gained information regarding the infecting bacterial species can improve predictions substantially. Clinicians should consider the implementation of such systems to aid correct empiric therapy and to potentially reduce antibiotic misuse.

scholarly journals Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records

2020 ◽  
Author(s):  
Ohad Lewin-Epstein ◽  
Shoham Baruch ◽  
Lilach Hadany ◽  
Gideon Y Stein ◽  
Uri Obolski
Keyword(s):  
Machine Learning ◽  
Antibiotic Resistance ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Bacterial Infections ◽  
Bacterial Species ◽  
Hospitalized Patients ◽  
Machine Learning Algorithms ◽  
Ensemble Model ◽  
Machine Learning Models

AbstractBackgroundComputerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning algorithms. However, they are scarcely used for empiric antibiotic therapy. Here we accurately predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using machine learning algorithms applied to patients’ electronic medical records.MethodsThe data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their electronic medical records. Five antibiotics were examined: Ceftazidime (n=2942), Gentamicin (n=4360), Imipenem (n=2235), Ofloxacin (n=3117) and Sulfamethoxazole-Trimethoprim (n=3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble combining all three algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis.ResultsThe ensemble model outperformed the separate models and produced accurate predictions on a test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble model yielded area under the receiver-operating-characteristic (auROC) scores of 0.73-0.79, for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. The effects of different variables on the predictions were assessed and found consistent with previously identified risk factors for antibiotic resistance.ConclusionsOur study demonstrates the potential of machine learning models to accurately predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapid information regarding the infecting bacterial species can improve predictions substantially. The implementation of such systems should be seriously considered by clinicians to aid correct empiric therapy and to potentially reduce antibiotic misuse.40-word summaryMachine learning models were applied to large and diverse datasets of medical records of hospitalized patients, to predict antibiotic resistance profiles of bacterial infections. The models achieved high accuracy predictions and interpretable results regarding the drivers of antibiotic resistance.

scholarly journals Perceptions of Antibiotic Use and Resistance: Are Antibiotics the Dentists’ Anxiolytics?

Antibiotics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 735
Author(s):  
Julie Dormoy ◽  
Marc-Olivier Vuillemin ◽  
Silvia Rossi ◽  
Jean-Marc Boivin ◽  
Julie Guillet
Keyword(s):  
Public Health ◽  
Antibiotic Resistance ◽  
Qualitative Method ◽  
Oral Surgery ◽  
Antibiotic Use ◽  
Public Health Issue ◽  
Clinical Factors ◽  
Structured Interviews ◽  
Health Crisis ◽  
The Public

Background: Antibiotic resistance is a global health crisis. The aim of this study was to explore dentists’ perceptions of antibiotic resistance. Methods: A qualitative method was used. Seventeen dentists practising in the Nancy (Lorraine, France) region were surveyed. They were general practitioners or specialised in oral surgery, implantology, or periodontology. The practitioners took part in semi-structured interviews between September 2019 and July 2020. All of the interviews were transcribed in full and analysed thematically. Results: Four major themes have been selected: attitudes of the dentists in regard to the guidelines, clinical factors that influence prescriptions, non-clinical factors that influence prescriptions, and the perception of antibiotic resistance. The dentists stated that they were very concerned regarding the public health issue of antibiotic resistance. However, they often prescribe according to their own interests and habits rather than according to the relevant guidelines. Conclusions: Although dentists are generally well aware of antibiotic resistance, they often do not adequately appreciate the link between their prescribing habits and the phenomenon of antibiotic resistance. Regular updating of practitioners’ knowledge in this regard is necessary, but patients and the general public should also be made more aware of the issue.

scholarly journals Antimicrobial Resistance among Neonates with Bacterial Sepsis and Their Clinical Outcomes in a Tertiary Hospital in Kathmandu Valley, Nepal

2021 ◽  
Vol 6 (2) ◽  
pp. 56
Author(s):  
Bijendra Raj Raghubanshi ◽  
Karuna D. Sagili ◽  
Wai Wai Han ◽  
Henish Shakya ◽  
Priyanka Shrestha ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Neonatal Sepsis ◽  
Bacterial Culture ◽  
Bacterial Species ◽  
Antibiotic Sensitivity ◽  
College Teaching ◽  
Hospital Treatment ◽  
Resistant Bacteria ◽  
Coagulase Negative Staphylococcus ◽  
Cross Sectional

Globally, antibiotic resistance in bacteria isolated from neonatal sepsis is increasing. In this cross-sectional study conducted at a medical college teaching hospital in Nepal, we assessed the antibiotic resistance levels in bacteria cultured from neonates with sepsis and their in-hospital treatment outcomes. We extracted data of neonates with sepsis admitted for in-patient care from June 2018 to December 2019 by reviewing hospital records of the neonatal intensive care unit and microbiology department. A total of 308 neonates with sepsis were admitted of which, blood bacterial culture antibiotic sensitivity reports were available for 298 neonates. Twenty neonates (7%) had bacteriologic culture-confirmed neonatal sepsis. The most common bacterial species isolated were Staphylococcus aureus (8), followed by coagulase-negative Staphylococcus (5). Most of these bacteria were resistant to at least one first-line antibiotic used to manage neonatal sepsis. Overall, there were 7 (2%) deaths among the 308 neonates (none of them from the bacterial culture-positive group), and 53 (17%) neonates had left the hospital against medical advice (LAMA). Improving hospital procedures to isolate bacteria in neonates with sepsis, undertaking measures to prevent the spread of antibiotic-resistant bacteria, and addressing LAMA’s reasons are urgently needed.

scholarly journals Phage-Encoded Endolysins

Antibiotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 124
Author(s):  
Fatma Abdelrahman ◽  
Maheswaran Easwaran ◽  
Oluwasegun I. Daramola ◽  
Samar Ragab ◽  
Stephanie Lynch ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Cell Wall ◽  
Antibiotic Resistance ◽  
Bacterial Infections ◽  
Therapeutic Strategies ◽  
Therapeutic Application ◽  
Significant Evidence ◽  
Crucial Information

Due to the global emergence of antibiotic resistance, there has been an increase in research surrounding endolysins as an alternative therapeutic. Endolysins are phage-encoded enzymes, utilized by mature phage virions to hydrolyze the cell wall from within. There is significant evidence that proves the ability of endolysins to degrade the peptidoglycan externally without the assistance of phage. Thus, their incorporation in therapeutic strategies has opened new options for therapeutic application against bacterial infections in the human and veterinary sectors, as well as within the agricultural and biotechnology sectors. While endolysins show promising results within the laboratory, it is important to document their resistance, safety, and immunogenicity for in-vivo application. This review aims to provide new insights into the synergy between endolysins and antibiotics, as well as the formulation of endolysins. Thus, it provides crucial information for clinical trials involving endolysins.

scholarly journals Silencing Antibiotic Resistance with Antisense Oligonucleotides

Biomedicines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 416
Author(s):  
Saumya Jani ◽  
Maria Soledad Ramirez ◽  
Marcelo E. Tolmasky
Keyword(s):  
Antibiotic Resistance ◽  
Nucleic Acids ◽  
Antisense Oligonucleotides ◽  
Bacterial Infections ◽  
Genetic Disorders ◽  
Antibiotic Resistance Genes ◽  
Short Review ◽  
Rnase H ◽  
Biological Processes ◽  
Locked Nucleic Acids

Antisense technologies consist of the utilization of oligonucleotides or oligonucleotide analogs to interfere with undesirable biological processes, commonly through inhibition of expression of selected genes. This field holds a lot of promise for the treatment of a very diverse group of diseases including viral and bacterial infections, genetic disorders, and cancer. To date, drugs approved for utilization in clinics or in clinical trials target diseases other than bacterial infections. Although several groups and companies are working on different strategies, the application of antisense technologies to prokaryotes still lags with respect to those that target other human diseases. In those cases where the focus is on bacterial pathogens, a subset of the research is dedicated to produce antisense compounds that silence or reduce expression of antibiotic resistance genes. Therefore, these compounds will be adjuvants administered with the antibiotic to which they reduce resistance levels. A varied group of oligonucleotide analogs like phosphorothioate or phosphorodiamidate morpholino residues, as well as peptide nucleic acids, locked nucleic acids and bridge nucleic acids, the latter two in gapmer configuration, have been utilized to reduce resistance levels. The major mechanisms of inhibition include eliciting cleavage of the target mRNA by the host’s RNase H or RNase P, and steric hindrance. The different approaches targeting resistance to β-lactams include carbapenems, aminoglycosides, chloramphenicol, macrolides, and fluoroquinolones. The purpose of this short review is to summarize the attempts to develop antisense compounds that inhibit expression of resistance to antibiotics.

scholarly journals Tackling antimicrobial resistance in the community

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Tonkie-Crine ◽  
L Abel ◽  
O Van Hecke ◽  
K Wang ◽  
C Butler
Keyword(s):  
Antibiotic Resistance ◽  
Community Care ◽  
Respiratory Infections ◽  
Antibiotic Prescription ◽  
Future Research ◽  
Antibiotic Prescribing ◽  
Acute Respiratory Infections ◽  
Community Settings ◽  
The Public ◽  
Need Support

Abstract Antibiotic prescription is a major driver of antibiotic resistance. The majority of antibiotic prescribing occurs in community care settings, often for respiratory infections. A substantial proportion of prescriptions are issued not according to guidelines, particularly for acute respiratory infections which can be self-limiting. Prescribers in these settings need support to prescribe antibiotics more prudently. Patients and the public also need support to manage infections which are self-limiting. This presentation presents a summary of how antimicrobial stewardship (AMS) activities are being used in community settings. Firstly, types of community-level interventions are discussed, including those aimed at clinicians, patients and the public. Next, we assess interventions based on their effectiveness at reducing antibiotic prescriptions or use and their cost-effectiveness. Finally, we discuss directions for future research and consider how the research to date could influence policy.

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