Performance of a series of novel N-substituted acrylamides in capillary electrophoresis of DNA fragments

DNA separations by capillaryelectrophoresis in viscous solutions of novel polymers, made with \u3a9-hydroxyl, N-substitutedacrylamides (notably N-acryloyl amino propanol, AAP and N-acryloyl amino butanol, AAB) are evaluated. Whereas in standard poly(acrylamide), at 6% concentration, the theoretical plate number (N) does not exceed 500 000, in 6% poly(AAP) N reaches 922 000 and in 6% poly(AAB) N values as high as 1 200 000 are obtained. Also, copolymers of AAP and AAB give N values in excess of 1 million plates. The two novel monomers (AAP and AAB) remain extremely stable during alkaline hydrolysis and display very good hydrophilicity, while being devoid of the noxious habit of auto-polymerization and auto-reticulation exhibited by the previous monomer of this series (N-acryloyl amino ethoxy ethanol). The reasons for such a good performance of the \u3a9-substituted acrylamide derivatives could be that their polymers may form hydrogen bonds via their distal -OH group during DNA separation

Rapid evaluation of oxidized fatty acid concentration in virgin olive oils using Fourier-Transform Infrared Spectroscopy and Multiple Linear Regression

Fourier-transform infrared spectroscopy (FTIR), followed by multivariate treatment of spectral data, was used to evaluate the oxidised fatty acid (OFA) concentration in virgin olive oil samples characterised by different oxidative status. The entire FTIR spectra (4000–700 cmÿ1) of oils were divided in 25 wavelength regions. The normalised absorbances of the peak areas within these regions were used as predictors. In order to predict the OFA concentration, multiple linear regression (MLR) models were performed. After a cube root transformation of data, an MLR model constructed using eight predictors was able to predict OFA concentration with an average error of 17%. The main wavelength regions selected to construct this MLR model corresponded to =C–H (trans and cis, stretching), –C–H (CH2, stretching asym), O–H (bending in plane), C–O (stretching), –H–C=C–H– (cis?) and =CH2 (wagging), due to the fact that these regions were those more affected by oxidation. This FTIR method is an extremely quick and simple procedure for OFA determination which can be easily automatised

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

International audienceTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding: US National Institutes of Health and European Society of Intensive Care Medicine