Direct-determination of high-concentration sulfate by serial differential spectrophotometry with multiple optical pathlengths.

Direct-determination is a fast and free of contamination method for analysis of substances in water samples. However, direct-determination of SO42- with high concentration in environmental systems is still challenging due to deviation from Beer-Lambert law is generally observed when a substance with high concentration is directly determined, resulting in poor accuracy, sensitivity, and narrow linear range. In this study, a simple and rapid method for the direct-determination of SO42- with high concentration was proposed. Serial high-absorbance differential spectrophotometry was applied to incrementally widen the determination range under different optical pathlengths. In this process, the effects of optical pathlength and reference concentration on sensitivity were further investigated. The results showed that SO42- could be accurately quantified within a concentration range of 0-4.10 g/L, and the determination range by this method was 10-fold and 19.5-fold wider than those by conventional differential spectrophotometry and conventional spectrophotometry, respectively. And the applicable ranges of sensitivity were obtained at various optical pathlengths by adjusting the reference solution concentration. This approach exhibited several advantages over conventional methods, including high accuracy, excellent precision, low cost, less time consumption, and easy operation. This method is promising and can provide accurate and reliable data support for environmental monitoring and pollution control.

Novel application of published risk factors for methicillin-resistant S. aureus in acute bacterial skin and skin structure infections.

Methicillin-resistant Staphylococcus aureus acute bacterial skin and skin structure infections (MRSA ABSSSIs) are associated with a significant clinical and economic burden; however, rapid identification of MRSA remains a clinical challenge. This study aimed to use a novel method of predictive modeling to determine those at highest risk of MRSA ABSSSIs. Risk factors for MRSA ABSSSI were derived from a combination of previously published literature and multivariable logistic regression of individual patient data (IPD) using the 'adaptation method.' A risk-scoring tool was derived from weight-proportional integer-adjusted coefficients of the predictive model. Likelihood ratios were used to adjust posterior probability of MRSA. Risk factors were identified from 12 previously published studies and adapted based on IPD (n = 231). Risk factors were: history of diabetes with obesity (adapted odds ratio [aOR] = 1.1), prior antibiotics (90 days) (aOR = 2.6), chronic kidney disease/hemodialysis (aOR = 1.4), intravenous drug use (aOR = 2.8), previous MRSA exposure/infection (12 months) (aOR = 2.8), previous hospitalization (12 months) (aOR = 7.5), and HIV/AIDS (aOR = 4.0). Baseline prevalence of MRSA was 42.7%. Scores ranged from 0 - 8 points. Post-test probability of MRSA: score 0 = 35.0%; score 1 - 2 = 45.0%; score 3 = 63.0%. The newly derived risk-scoring tool is proof-of-concept of the adaptation method. This study is hypothesis generating and such a tool remains to be validated for clinical use.