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This modeling study considers different screening strategies, contact tracing, and the severity of novel epidemic outbreaks for various population sizes, providing insight into multinational containment effectiveness of emerging infectious diseases, prior to vaccines development. During the period of the ancestral SARS-Cov-2 virus, contact tracing alone is insufficient to achieve outbreak control. Although universal testing is proposed in multiple nations, its effectiveness accompanied by other measures is rarely examined. Our research investigates the necessity of universal testing when contact tracing and symptomatic screening measures are implemented. We used a stochastic transmission model to simulate COVID-19 transmission, evaluating containment strategies via contact tracing, one-time high risk symptomatic testing, and universal testing. Despite universal testing having the potential to identify subclinical cases, which is crucial for non-pharmaceutical interventions, our model suggests that universal testing only reduces the total number of cases by 0.0009% for countries with low COVID-19 prevalence and 0.025% for countries with high COVID-19 prevalence when rigorous contact tracing and symptomatic screening are also implemented. These findings highlight the effectiveness of testing strategies and contact tracing in reducing COVID-19 cases by identifying subclinical cases.

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.

Acinetobacter baumannii, a Gram-negative bacterium, is an important nosocomial pathogen. Colistin-resistant A. baumannii is becoming a new concern, since colistin is one of the last-line antibiotics for infections by carbapenem-resistant A. baumannii. From 452 carbapenem-resistant isolates collected in a teaching hospital in Taipei, Taiwan, we identified seven that were resistant to colistin. Carbapenem resistance in these isolates is attributed to the presence of carbapenemase gene blaOXA-23 in their genomes. Colistin resistance is presumably conferred by mutations in the sensor kinase domain of PmrB found in these isolates, which are known to result in modification of colistin target lipid A via the PmrB–PmrA–PmrC signal transduction pathway. Overexpression of pmrC, eptA, and naxD was observed in all seven isolates. Colistin resistance mediated by pmrB mutations has never been reported in Taiwan. One of the seven isolates contained three mutations in lpxD and exhibited an altered lipopolysaccharide profile, which may contribute to its colistin resistance. No significant difference in growth rates was observed between the isolates and the reference strain, suggesting no fitness cost of colistin resistance. Biofilm formation abilities of the isolates were lower than that of the reference. Interestingly, one of the isolates was heteroresistant to colistin. Four of the isolates were significantly more virulent to wax moth larvae than the reference.

This retrospective observational study assessed the differences between monomicrobial and polymicrobial A. baumannii bacteremia and identified possible independent risk factors for 14-day mortality. There were 379 patients with A. baumannii bacteremia admitted to a tertiary care center in northern Taiwan between August 2008 and July 2015 enrolled for data analysis. Among them, 89 patients (23.5%) had polymicrobial bacteremia and 290 patients (76.5%) had monomicrobial bacteremia. No significant difference in 14-day mortality was observed between patients with monomicrobial and polymicrobial A. baumannii bacteremia (26.9% vs. 29.2%, p = 0.77). Logistic regression controlled for confounders demonstrated that polymicrobial bacteremia was not an independent predictor of mortality, whereas appropriate antimicrobial therapy was independently associated with reduced mortality. Higher 14-day mortality rates were observed in the polymicrobial bacteremic patients with concomitant isolation of Escherichia coli, Pseudomonas aeruginosa, and Enterobacter spp. from the bloodstream. Compared with patients with monomicrobial multidrug-resistant A. baumannii (MDRAb) bacteremia, those with MDRAb concomitant with Gram-negative bacilli bacteremia had a worse outcome. Polymicrobial A. baumannii bacteremia was not associated with a higher 14-day mortality rate than that of monomicrobial A. baumannii bacteremia, although more deaths were observed when certain Gram-negative bacteria were concomitantly isolated. Appropriate antimicrobial therapy remains an important life-saving measure for A. baumannii bacteremic patients.