scholarly journals Computational Sensing of Staphylococcus aureus on Contact Lenses Using 3D Imaging of Curved Surfaces and Machine Learning

ACS Nano ◽  
2018 ◽  
Vol 12 (3) ◽  
pp. 2554-2559 ◽  
Author(s):  
Muhammed Veli ◽  
Aydogan Ozcan
Keyword(s):  
Machine Learning ◽  
Staphylococcus Aureus ◽  
3D Imaging ◽  
Contact Lenses ◽  
Curved Surfaces ◽  
Computational Sensing

Measurement algorithm for real front and back curved surfaces of contact lenses

2020 ◽  
Vol 59 (28) ◽  
pp. 9051
Author(s):  
Kentaro Saeki ◽  
Decai Huyan ◽  
Mio Sawada ◽  
Yijie Sun ◽  
Akira Nakamura ◽  
...  
Keyword(s):  
Contact Lenses ◽  
Curved Surfaces ◽  
Measurement Algorithm

scholarly journals The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus aureus

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e55582 ◽  
Author(s):  
Joana Rosado Coelho ◽  
João André Carriço ◽  
Daniel Knight ◽  
Jose-Luis Martínez ◽  
Ian Morrissey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Staphylococcus Aureus

scholarly journals Holograms on the horizon?

2021 ◽  
Vol 64 (11) ◽  
pp. 14-16
Author(s):  
Chris Edwards
Keyword(s):  
Machine Learning ◽  
3D Imaging

Machine learning drives toward 3D imaging on the move.

scholarly journals Interactions between staphylococcal enterotoxins A and D and superantigen-like proteins 1 and 5 for predicting methicillin and multidrug resistance profiles among Staphylococcus aureus ocular isolates

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254519
Author(s):  
Min Lu ◽  
Jean-Marie Parel ◽  
Darlene Miller
Keyword(s):  
Machine Learning ◽  
Staphylococcus Aureus ◽  
Multidrug Resistance ◽  
Virulence Factors ◽  
Phenotypic Expression ◽  
Multidrug Resistant ◽  
Machine Learning Techniques ◽  
Strong Interactions ◽  
Antibiotics Resistance ◽  
Gene Profiles

Background Methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant (MDR) S. aureus strains are well recognized as posing substantial problems in treating ocular infections. S. aureus has a vast array of virulence factors, including superantigens and enterotoxins. Their interactions and ability to signal antibiotics resistance have not been explored. Objectives To predict the relationship between superantigens and methicillin and multidrug resistance among S. aureus ocular isolates. Methods We used a DNA microarray to characterize the enterotoxin and superantigen gene profiles of 98 S. aureus isolates collected from common ocular sources. The outcomes contained phenotypic and genotypic expressions of MRSA. We also included the MDR status as an outcome, categorized as resistance to three or more drugs, including oxacillin, penicillin, erythromycin, clindamycin, moxifloxacin, tetracycline, trimethoprim-sulfamethoxazole and gentamicin. We identified gene profiles that predicted each outcome through a classification analysis utilizing Random Forest machine learning techniques. Findings Our machine learning models predicted the outcomes accurately utilizing 67 enterotoxin and superantigen genes. Strong correlates predicting the genotypic expression of MRSA were enterotoxins A, D, J and R and superantigen-like proteins 1, 3, 7 and 10. Among these virulence factors, enterotoxin D and superantigen-like proteins 1, 5 and 10 were also significantly informative for predicting both MDR and MRSA in terms of phenotypic expression. Strong interactions were identified including enterotoxins A (entA) interacting with superantigen-like protein 1 (set6-var1_11), and enterotoxin D (entD) interacting with superantigen-like protein 5 (ssl05/set3_probe 1): MRSA and MDR S. aureus are associated with the presence of both entA and set6-var1_11, or both entD and ssl05/set3_probe 1, while the absence of these genes in pairs indicates non-multidrug-resistant and methicillin-susceptible S. aureus. Conclusions MRSA and MDR S. aureus show a different spectrum of ocular pathology than their non-resistant counterparts. When assessing the role of enterotoxins in predicting antibiotics resistance, it is critical to consider both main effects and interactions.

scholarly journals Essential Oils Biofilm Modulation Activity, Chemical and Machine Learning Analysis—Application on Staphylococcus aureus Isolates from Cystic Fibrosis Patients

2020 ◽  
Vol 21 (23) ◽  
pp. 9258
Author(s):  
Rosanna Papa ◽  
Stefania Garzoli ◽  
Gianluca Vrenna ◽  
Manuela Sabatino ◽  
Filippo Sapienza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cystic Fibrosis ◽  
Staphylococcus Aureus ◽  
Essential Oils ◽  
Machine Learning Algorithms ◽  
Bacterial Biofilm ◽  
Active Components ◽  
Biofilm Growth ◽  
Analysis Application ◽  
Multicomponent Nature

Bacterial biofilm plays a pivotal role in chronic Staphylococcus aureus (S. aureus) infection and its inhibition may represent an important strategy to develop novel therapeutic agents. The scientific community is continuously searching for natural and “green alternatives” to chemotherapeutic drugs, including essential oils (EOs), assuming the latter not able to select resistant strains, likely due to their multicomponent nature and, hence, multitarget action. Here it is reported the biofilm production modulation exerted by 61 EOs, also investigated for their antibacterial activity on S. aureus strains, including reference and cystic fibrosis patients’ isolated strains. The EOs biofilm modulation was assessed by Christensen method on five S. aureus strains. Chemical composition, investigated by GC/MS analysis, of the tested EOs allowed a correlation between biofilm modulation potency and putative active components by means of machine learning algorithms application. Some EOs inhibited biofilm growth at 1.00% concentration, although lower concentrations revealed different biological profile. Experimental data led to select antibiofilm EOs based on their ability to inhibit S. aureus biofilm growth, which were characterized for their ability to alter the biofilm organization by means of SEM studies.

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