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 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 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 Bacteriophages as Potential Tools for Use in Antimicrobial Therapy and Vaccine Development

2021 ◽  
Vol 14 (4) ◽  
pp. 331
Author(s):  
Beata Zalewska-Piątek ◽  
Rafał Piątek
Keyword(s):  
Infectious Diseases ◽  
Targeted Delivery ◽  
Bacterial Infections ◽  
Large Scale ◽  
Target Genes ◽  
Phage Therapy ◽  
Vaccine Development ◽  
Fungal Infections ◽  
Bacterial Cells ◽  
Advantages And Disadvantages

The constantly growing number of people suffering from bacterial, viral, or fungal infections, parasitic diseases, and cancers prompts the search for innovative methods of disease prevention and treatment, especially based on vaccines and targeted therapy. An additional problem is the global threat to humanity resulting from the increasing resistance of bacteria to commonly used antibiotics. Conventional vaccines based on bacteria or viruses are common and are generally effective in preventing and controlling various infectious diseases in humans. However, there are problems with the stability of these vaccines, their transport, targeted delivery, safe use, and side effects. In this context, experimental phage therapy based on viruses replicating in bacterial cells currently offers a chance for a breakthrough in the treatment of bacterial infections. Phages are not infectious and pathogenic to eukaryotic cells and do not cause diseases in human body. Furthermore, bacterial viruses are sufficient immuno-stimulators with potential adjuvant abilities, easy to transport, and store. They can also be produced on a large scale with cost reduction. In recent years, they have also provided an ideal platform for the design and production of phage-based vaccines to induce protective host immune responses. The most promising in this group are phage-displayed vaccines, allowing for the display of immunogenic peptides or proteins on the phage surfaces, or phage DNA vaccines responsible for expression of target genes (encoding protective antigens) incorporated into the phage genome. Phage vaccines inducing the production of specific antibodies may in the future protect us against infectious diseases and constitute an effective immune tool to fight cancer. Moreover, personalized phage therapy can represent the greatest medical achievement that saves lives. This review demonstrates the latest advances and developments in the use of phage vaccines to prevent human infectious diseases; phage-based therapy, including clinical trials; and personalized treatment adapted to the patient’s needs and the type of bacterial infection. It highlights the advantages and disadvantages of experimental phage therapy and, at the same time, indicates its great potential in the treatment of various diseases, especially those resistant to commonly used antibiotics. All the analyses performed look at the rich history and development of phage therapy over the past 100 years.

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 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.

Быстродействующая атомно-силовая и сканирующая капиллярная микроскопия в решении задач материаловедения, биологии и медицины

2020 ◽  
Vol 13 (3-4) ◽  
pp. 222-228
Author(s):  
И.В. Яминский ◽  
А.И. Ахметова
Keyword(s):  
Antibiotic Resistance ◽  
Early Detection ◽  
Biomedical Research ◽  
Drug Screening ◽  
Targeted Delivery ◽  
High Speed ◽  
Scanning Probe ◽  
Biological Processes ◽  
Atomic Force ◽  
Probe Microscope

Разработка высокоэффективных режимов быстродействующего сканирующего зондового микроскопа, в первую очередь атомно-силовой и сканирующей капиллярной микроскопии, представляет особый интерес для успешного проведения биомедицинских исследований: изучения биологических процессов и морфологии биополимеров, определения антибио­тикорезистентности бактерий, адресной доставки биомакромолекул, скринингу лекарств, раннему обнаружению биологических агентов (вирусов и бактерий) и др. The development of highly efficient modes of a high-speed scanning probe microscope, primarily atomic force and scanning capillary microscopy, is of particular interest for successful biomedical research: studying biological processes and the morphology of biopolymers, determining antibiotic resistance of bacteria, targeted delivery of biomacromolecules, drug screening, early detection agents (viruses and bacteria), etc.

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 Trends in Research on Exosomes in Cancer Progression and Anticancer Therapy

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 326
Author(s):  
Dona Sinha ◽  
Sraddhya Roy ◽  
Priyanka Saha ◽  
Nabanita Chatterjee ◽  
Anupam Bishayee
Keyword(s):  
Cancer Progression ◽  
Targeted Delivery ◽  
Cancer Biology ◽  
Vaccine Development ◽  
Immune Escape ◽  
Immune Therapy ◽  
Bioactive Molecules ◽  
Successful Implementation ◽  
Scale Production ◽  
Exosome Biogenesis

Exosomes, the endosome-derived bilayered extracellular nanovesicles with their contribution in many aspects of cancer biology, have become one of the prime foci of research. Exosomes derived from various cells carry cargoes similar to their originator cells and their mode of generation is different compared to other extracellular vesicles. This review has tried to cover all aspects of exosome biogenesis, including cargo, Rab-dependent and Rab-independent secretion of endosomes and exosomal internalization. The bioactive molecules of the tumor-derived exosomes, by virtue of their ubiquitous presence and small size, can migrate to distal parts and propagate oncogenic signaling and epigenetic regulation, modulate tumor microenvironment and facilitate immune escape, tumor progression and drug resistance responsible for cancer progression. Strategies improvised against tumor-derived exosomes include suppression of exosome uptake, modulation of exosomal cargo and removal of exosomes. Apart from the protumorigenic role, exosomal cargoes have been selectively manipulated for diagnosis, immune therapy, vaccine development, RNA therapy, stem cell therapy, drug delivery and reversal of chemoresistance against cancer. However, several challenges, including in-depth knowledge of exosome biogenesis and protein sorting, perfect and pure isolation of exosomes, large-scale production, better loading efficiency, and targeted delivery of exosomes, have to be confronted before the successful implementation of exosomes becomes possible for the diagnosis and therapy of cancer.

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