Molecular Epidemiological Survey of Prophages in MRSA Isolates in Taiwan

Discrimination of Methicillin-resistant Staphylococcus aureus by MALDI-TOF Mass Spectrometry with Machine Learning Techniques in Patients with Staphylococcus aureus Bacteremia

Early administration of proper antibiotics is considered to improve the clinical outcomes of Staphylococcus aureus bacteremia (SAB), but routine clinical antimicrobial susceptibility testing takes an additional 24 h after species identification. Recent studies elucidated matrix-assisted laser desorption/ionization time-of-flight mass spectra to discriminate methicillin-resistant strains (MRSA) or even incorporated with machine learning (ML) techniques. However, no universally applicable mass peaks were revealed, which means that the discrimination model might need to be established or calibrated by local strains’ data. Here, a clinically feasible workflow was provided. We collected mass spectra from SAB patients over an 8-month duration and preprocessed by binning with reference peaks. Machine learning models were trained and tested by samples independently of the first six months and the following two months, respectively. The ML models were optimized by genetic algorithm (GA). The accuracy, sensitivity, specificity, and AUC of the independent testing of the best model, i.e., SVM, under the optimal parameters were 87%, 75%, 95%, and 87%, respectively. In summary, almost all resistant results were truly resistant, implying that physicians might escalate antibiotics for MRSA 24 h earlier. This report presents an attainable method for clinical laboratories to build an MRSA model and boost the performance using their local data.

Therapeutic Potential of Green Synthesized Metallic Nanoparticles against Staphylococcus aureus

BACKGROUND

The recent treatment challenges posed by the widespread emergence of pathogenic Multidrug-Resistant (MDR) bacterial strains are a cause of huge health troubles worldwide. Infections caused by MDR organisms are associated with longer period of hospitalization, increased mortality, and inflated healthcare costs. Staphylococcus aureus is one of these MDR organisms identified as an urgent threat to human health by the World Health Organization. Infections caused by S. aureus may range from simple cutaneous infestations to life threatening bacteremia. S. aureus infections get easily escalated in severely ill, hospitalized and or immunocompromised patients with incapacitated immune system. Also, in HIV-positive patients S. aureus ranks amongst one of the most common comorbidities where it can further worsen a patient's health condition. At present anti-staphylococcal therapy is reliant typically on chemotherapeutics that are gathering resistance and pose unfavorable side-effects. Thus, newer drugs are required that can bridge these shortcomings and aid effective control against S. aureus.

OBJECTIVE

In this review, we summarize drug resistance exhibited by S. aureus and lacunae in current anti-staphylococcal therapy, nanoparticles as an alternative therapeutic modality. The focus lays on various green synthesized nanoparticles, their mode of action and application as potent antibacterial compounds against S. aureus.

CONCLUSION

Use of nanoparticles as anti-bacterial drugs has gained momentum in recent past and green synthesized nanoparticles, which involves microorganisms and plants or their byproducts for synthesis of nanoparticles offer a potent, as well as environment friendly solution in warfare against MDR bacte.