A comprehensive study to reveal the potential of a more sustainable ultra-high performance enantioselective reversed-phase chromatography on Pirkle-type stationary phase, with Whelk-O1 as a case study.

In this study, we aimed to make enantioselective chromatography more sustainable, more sensitive, and compatible with aqueous formulations analysis and ESI-MS. To achieve this, we examined the effects of transitioning from normal-phase chromatography (which uses hydrocarbon-based solvents) to reversed-phase chromatography (using mobile phases based on water) using broad-spectrum Whelk-O1 columns as a critical study. For the first time, we holistically compared the thermodynamics and kinetics of the two elution modes in order to answer the question of whether same-column chemistry can effectively separate the compounds even in reversed-phase mode and found, unexpectedly, that reversed-phase chromatography using acetonitrile as the organic modifier was competitive from a kinetic standpoint. We also evaluated the effectiveness of three organic modifiers simultaneously on a sample of 11 molecules already resolved in NP conditions with different resolutions and achieved a resolution value of 1.5 for 91% and a resolution value of 2 for 82% of cases. Finally, we separated three racemates (within a k factor of 9) using only 480 µL of solvent per chromatographic run on a millibore column of 1 mm I.D., demonstrating that our approach allows for greener chromatographic separations.

A methodology to quantify the differences between alternative methods of heart rate variability measurement.

This work proposes a systematic procedure to report the differences between heart rate variability time series obtained from alternative measurements reporting the spread and mean of the differences as well as the agreement between measuring procedures and quantifying how stationary, random and normal the differences between alternative measurements are. A description of the complete automatic procedure to obtain a differences time series (DTS) from two alternative methods, a proposal of a battery of statistical tests, and a set of statistical indicators to better describe the differences in RR interval estimation are also provided. Results show that the spread and agreement depend on the choice of alternative measurements and that the DTS cannot be considered generally as a white or as a normally distributed process. Nevertheless, in controlled measurements the DTS can be considered as a stationary process.

Accuracy of heart rate variability estimation by photoplethysmography using an smartphone: Processing optimization and fiducial point selection.

This work compares several fiducial points to detect the arrival of a new pulse in a photoplethysmographic signal using the built-in camera of smartphones or a photoplethysmograph. Also, an optimization process for the signal preprocessing stage has been done. Finally we characterize the error produced when we use the best cutoff frequencies and fiducial point for smartphones and photopletysmograph and compare if the error of smartphones can be reasonably be explained by variations in pulse transit time. The results have revealed that the peak of the first derivative and the minimum of the second derivative of the pulse wave have the lowest error. Moreover, for these points, high pass filtering the signal between 0.1 to 0.8 Hz and low pass around 2.7 Hz or 3.5 Hz are the best cutoff frequencies. Finally, the error in smartphones is slightly higher than in a photoplethysmograph.

Real time heart rate variability assessment from Android smartphone camera photoplethysmography: Postural and device influences.

The aim of this paper is to present a smartphone based system for real-time pulse-to-pulse (PP) interval time series acquisition by frame-to-frame camera image processing. The developed smartphone application acquires image frames from built-in rear-camera at the maximum available rate (30 Hz) and the smartphone GPU has been used by Renderscript API for high performance frame-by-frame image acquisition and computing in order to obtain PPG signal and PP interval time series. The relative error of mean heart rate is negligible. In addition, measurement posture and the employed smartphone model influences on the beat-to-beat error measurement of heart rate and HRV indices have been analyzed. Then, the standard deviation of the beat-to-beat error (SDE) was 7.81 ± 3.81 ms in the worst case. Furthermore, in supine measurement posture, significant device influence on the SDE has been found and the SDE is lower with Samsung S5 than Motorola X. This study can be applied to analyze the reliability of different smartphone models for HRV assessment from real-time Android camera frames processing.