scholarly journals Inactivation of antibiotic-resistant bacteria by chlorine dioxide in soil and shifts in community composition

RSC Advances ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 6526-6532 ◽  
Author(s):  
M. S. Wu ◽  
X. Xu
Keyword(s):  
Staphylococcus Aureus ◽  
Community Composition ◽  
Chlorine Dioxide ◽  
Resistant Bacteria ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Resistant Species ◽  
And Shifts

Antibiotic-resistant bacteria are common widespread in soil and the most resistant species isStaphylococcus aureus.Sphingomonas,ArthrobacterandMassiliaare sensitive to ClO2.MicromonosporaceaeandThaumarchaeotaare more resistant to ClO2.

scholarly journals Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fatma Uysal Ciloglu ◽  
Abdullah Caliskan ◽  
Ayse Mine Saridag ◽  
Ibrahim Halil Kilic ◽  
Mahmut Tokmakci ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Deep Learning ◽  
Surface Enhanced Raman Spectroscopy ◽  
Resistant Bacteria ◽  
High Signal ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

AbstractOver the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Synthesis and antibiofilm activity of 1,2,3-triazole-pyridine hybrids against Staphylococcus aureus (MRSA)

2021 ◽  
Author(s):  
Tamer El Malah ◽  
Hanan A. Soliman ◽  
Bahaa A. Hemdan ◽  
Randa E. Abdel Mageid ◽  
Hany Nour
Keyword(s):  
Staphylococcus Aureus ◽  
Human Health ◽  
Resistant Bacteria ◽  
Antibiofilm Activity ◽  
Antibiotic Resistant Bacteria ◽  
Potential Threat ◽  
Antibiotic Resistant ◽  
Dihydropyridine Derivatives

Antibiotic-resistant bacteria are emerging at an alarming rate, posing a potential threat to human health. We synthesised alkyne-functionalised pyridines 3 and 4 via alkylation of substituted 2-oxo-1,2-dihydropyridine derivatives 1 and...

The impact of methicillin-resistant Staphylococcus aureus on ENT practice

2006 ◽  
Vol 120 (9) ◽  
pp. 713-717 ◽  
Author(s):  
I J Nixon ◽  
B J G Bingham
Keyword(s):  
Staphylococcus Aureus ◽  
Infection Control ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Methicillin Resistance ◽  
Resistant Bacteria ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Methicillin Resistant ◽  
The Impact

Antibiotic-resistant bacteria are increasingly common and present a major problem for the modern day ENT surgeon. This article reviews the development of methicillin resistance in Staphylococcus aureus and how it has come to affect ENT practice. We look at the evidence behind measures taken to help deal with methicillin-resistant Staphylococcus aureus (MRSA) and to prevent its spread. We go on to suggest a departmental guideline for infection control, which we hope can be implemented to help deal with the problems created by MRSA.

Particle-based immunochemical separation of methicillin resistant Staphylococcus aureus with indirect electrochemical detection of labeling oligonucleotides

2016 ◽  
Vol 8 (25) ◽  
pp. 5123-5128 ◽  
Author(s):  
K. Cihalova ◽  
D. Hegerova ◽  
S. Dostalova ◽  
P. Jelinkova ◽  
L. Krejcova ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Early Detection ◽  
Electrochemical Detection ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Resistant Bacteria ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Methicillin Resistant ◽  
Timely Treatment

Early detection of antibiotic-resistant bacteria causing inflammation in patients is a key for an appropriate and timely treatment.

scholarly journals Effects of a rifampicin pre-treatment on linezolid pharmacokinetics

2019 ◽  
Author(s):  
Fumiyasu Okazaki ◽  
Yasuhiro Tsuji ◽  
Yoshihiro Seto ◽  
Chika Ogami ◽  
Yoshihiro Yamamoto ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Metabolic Enzymes ◽  
Resistant Bacteria ◽  
Microsomal Enzymes ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Blood Concentrations ◽  
Methicillin Resistant ◽  
Vancomycin Resistant ◽  
Pre Treatment

AbstractLinezolid is an oxazolidinone antibiotic that effectively treats methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE). Since rifampicin induces other antibiotic effects, it is combined with linezolid in therapeutic regimes. However, linezolid blood concentrations are reduced by this combination, which increases the risk of the emergence of antibiotic-resistant bacteria. We herein demonstrated that the combination of linezolid with rifampicin inhibited its absorption and promoted its elimination, but not through microsomal enzymes. Our results indicate that the combination of linezolid with rifampicin reduces linezolid blood concentrations via metabolic enzymes.

Chlorhexidine Resistance in Antibiotic-Resistant Bacteria Isolated From the Surfaces of Dispensers of Soap Containing Chlorhexidine

2002 ◽  
Vol 23 (11) ◽  
pp. 692-695 ◽  
Author(s):  
Steven E. Brooks ◽  
Mary A. Walczak ◽  
Rizwanullah Hameed ◽  
Patrick Coonan
Keyword(s):  
Staphylococcus Aureus ◽  
Bacterial Contamination ◽  
Multidrug Resistant ◽  
Resistant Bacteria ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Gram Negative ◽  
Methicillin Resistant

AbstractBacterial contamination with pan-resistant Acinetobacter and Klebsiella, multidrug-resistant Pseudomonas, and methicillin-resistant Staphylococcus aureus (MRSA) was noted on the surfaces of dispensers of hand soap with 2% chlorhexidine. Gram-negative isolates could multiply in the presence of 1% chlorhexidine. In contrast, MRSA was inhibited in vitro by chlorhexidine at concentrations as low as 0.0019%.

scholarly journals Drug-resistant Staphylococcus Aureus Bacteria Detection with the Combination of Surface-enhanced Raman Spectroscopy and Deep Learning Techniques

2021 ◽  
Author(s):  
Fatma Uysal Ciloglu ◽  
Abdullah Caliskan ◽  
Ayse Mine Saridag ◽  
Ibrahim Halil Kilic ◽  
Mahmut Tokmakci ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Deep Learning ◽  
Surface Enhanced Raman Spectroscopy ◽  
Resistant Bacteria ◽  
Label Free ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Abstract Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door - antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with other traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared with other traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data. The proposed method is a label-free, easy implemented, and reliable technique with high sensitivity for clinical use.

scholarly journals Mursamacin: a novel class of antibiotics from soil-dwelling roundworms of Central Kenya that inhibits methicillin-resistant Staphylococcus aureus

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2431 ◽  
Author(s):  
Ryan Musumba Awori ◽  
Peter Njenga Ng'ang'a ◽  
Lorine Nanjala Nyongesa ◽  
Nelson Onzere Amugune
Keyword(s):  
Staphylococcus Aureus ◽  
Clinical Medicine ◽  
Modern Medicine ◽  
Resistant Bacteria ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Methicillin Resistant ◽  
Heat Stable ◽  
Antibiotic Compounds ◽  
Central Kenya

Antibiotic-resistant bacteria, also called “superbugs”, can at worst retrogress modern medicine to an era where even sore throats resulted in death. A solution is the development of novel types of antibiotics from untapped natural sources. Yet, no new class of antibiotic has been developed in clinical medicine in the last 30 years. Here, bacteria from insect-killingSteinernemaroundworms in the soils of Central Kenya were isolated and subjected to specific molecular identification. These were then assayed for production of antibiotic compounds with potential to treat methicillin-resistantStaphylococcus aureusinfections. The bacteria were identified asXenorhabdus griffiniaeand produced cell free supernatants that inhibitedS. aureus. Fermenting the bacteria for 4 days yielded a heat stable anti-staphylococcal class of compounds that at low concentrations also inhibited methicillin-resistantS. aureus. This class contained two major compounds whose identity remains unknown. ThusX. griffinaeisolated fromSteinernemaroundworms in Kenya have antimicrobial potential and may herald novel and newly sourced potential medicines for treatment of the world’s most prevalent antibiotic resistant bacteria.

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