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A new fluorogenic based aminonaphthalimide-functionalized Fe(3)O(4)@SiO(2) core/shell magnetic nanoparticles 1 has been prepared, and its abilities to sense and separate metal ions were evaluated by fluorophotometry. The nanoparticles 1 exhibited a high affinity and selectivity for Hg(2+) and CH(3)Hg(+) ions over competing metal ions.

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Fluorescein-functionalized silica nanoparticles (1) , no significant fluorescence intensity changes were observed in the experiments with the other metal ions. These findings

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PurposeThe prevalence of macrolide-resistant Mycoplasma pneumoniae (MRMP) has increased worldwide. The aim of this study was to estimate the proportion of MRMP in a tertiary hospital in Korea, and to find potential laboratory markers that could be used to predict the efficacy of macrolides in children with MRMP pneumonia.MethodsA total of 95 patients with M. pneumoniae pneumonia were enrolled in this study. Detection of MRMP was based on the results of specific point mutations in domain V of the 23S rRNA gene. The medical records of these patients were reviewed retrospectively and the clinical course and laboratory data were compared.ResultsThe proportion of patients with MRMP was 51.6% and all MRMP isolates had the A2063G point mutation. The MRMP group had longer hospital stay and febrile period after initiation of macrolides. The levels of serum C-reactive protein (CRP) and interleukin-18 in nasopharyngeal aspirate were significantly higher in patients who did not respond to macrolide treatment. CRP was the only significant factor in predicting the efficacy of macrolides in patients with MRMP pneumonia. The area under the curve for CRP was 0.69 in receiver operating characteristic curve analysis, indicating reasonable discriminative power, and the optimal cutoff value was 40.7 mg/L.ConclusionThe proportion of patients with MRMP was high, suggesting that the prevalence of MRMP is rising rapidly in Korea. Serum CRP could be a useful marker for predicting the efficacy of macrolides and helping clinicians make better clinical decisions in children with MRMP pneumonia.

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Despite ample research on the association between indoor air pollution and allergic disease prevalence, public health and environmental policies still lack predictive evidence for developing a preventive guideline for patients or vulnerable populations mostly due to limitation of real-time big data and model predictability. Recent popularity of IoT and machine learning techniques could provide enabling technologies for collecting real-time big data and analyzing them for more accurate prediction of allergic disease risks for evidence-based intervention, but the effort is still in its infancy. This pilot study explored and evaluated the feasibility of a deep learning algorithm for predicting asthma risk. It is based on peak expiratory flow rates (PEFR) of 14 pediatric asthma patients visiting the Korea University Medical Center and indoor particulate matter PM10 and PM2.5 concentration data collected at their residence every 10 minutes using a PM monitoring device with a low-cost sensor between September 1, 2017 and August 31, 2018. We interpolated the PEFR results collected twice a day for each patient throughout the day so that it can be matched to the PM and other weather data. The PEFR results were classified into three categories such as 'Green' (normal), 'Yellow' (mild to moderate exacerbation) and 'Red' (severe exacerbation) with reference to their best peak flow value. Long Short-Term Memory (LSTM) model was trained using the first 10 months of the linked data and predicted asthma risk categories for the next 2 months during the study period. LSTM model is found to predict the asthma risk categories better than multinomial logistic (MNL) regression as it incorporates the cumulative effects of PM concentrations over time. Upon successful modifications of the algorithm based on a larger sample, this approach could potentially play a groundbreaking role for the scientific data-driven medical decision making. INDEX TERMS Asthma, indoor particulate matter, deep learning, peak expiratory flow rates, real-time monitoring.