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Multivariate regression models, including classical least square, principal component regression, and partial least square, combined with UV-Vis absorbance spectra, have been developed and validated to simultaneously determine furosemide and spironolactone in tablets. The central composite design method is used to design calibration and validation concentration sets for the models. The method was applied in the concentration ranges of 4.4–15.6 mg/L for spironolactone and 2.4–13.6 mg/L for furosemide. The method exhibited good correlation coefficients (R2) with low root mean square error of calibration values of 0.22, 0.16, and 0.17 mg/L for spironolactone and 0.20, 0.17, and 0.18 mg/L for furosemide, respectively. The root mean square error of prediction values for spironolactone and furosemide are less than 10% of the average concentration values, corresponding to 1 mg/L for spironolactone and 0.8 mg/L for furosemide. Precision assessment encompassed repeatability and intermediate precision, while comparison with the HPLC method revealed no statistically significant disparity (α = 0.05). Notably, classical least square, principal component regression, and partial least square methods exhibited successful simultaneous quantification of spironolactone and furosemide within the Spiromide-40 combination tablet. Additionally, the functions and code for the analytical methods implemented in R are provided, facilitating easy usage for the readers. [ABSTRACT FROM AUTHOR]

The aim of this study is to see if the centre of pressure (COP) measurements on the postural stability can be used to represent the electromyography (EMG) measurement on the activity data of lower limb muscles. If so, the cost-effective COP data measurements can be used to indicate the level of postural stability and lower limb muscle activity. The Hilbert-Huang Transform method was used to analyse the data from the experimental designed to examine the correlation between lower-limb muscles and postural stability. We randomly selected 24 university students to participate in eight scenarios and simultaneously measured their COP and EMG signals during the experiments. The Empirical Mode Decomposition was used to identify the intrinsic-mode functions (IMF) that can distinguish between the COP and EMG at different states. Subsequently, similarity indices and synchronization analyses were used to calculate the correlation between the lower-limb muscle strength and the postural stability. The IMF5 of the COP signals and the IMF6 of the EMG signals were not significantly different and the average frequency was 0.8 Hz, with a range of 0-2 Hz. When the postural stability was poor, the COP and EMG had a high synchronization with index values within the range of 0.010-0.015. With good postural stability, the synchronization indices were between 0.006 and 0.080 and both exhibited low synchronization. The COP signals and the low frequency EMG signals were highly correlated. In conclusion, we demonstrated that the COP may provide enough information on postural stability without the EMG data. [ABSTRACT FROM AUTHOR]

The aim of this study is to see if the centre of pressure (COP) measurements on the postural stability can be used to represent the electromyography (EMG) measurement on the activity data of lower limb muscles. If so, the cost-effective COP data measurements can be used to indicate the level of postural stability and lower limb muscle activity. The Hilbert-Huang Transform method was used to analyse the data from the experimental designed to examine the correlation between lower-limb muscles and postural stability. We randomly selected 24 university students to participate in eight scenarios and simultaneously measured their COP and EMG signals during the experiments. The Empirical Mode Decomposition was used to identify the intrinsic-mode functions (IMF) that can distinguish between the COP and EMG at different states. Subsequently, similarity indices and synchronization analyses were used to calculate the correlation between the lower-limb muscle strength and the postural stability. The IMF5 of the COP signals and the IMF6 of the EMG signals were not significantly different and the average frequency was 0.8 Hz, with a range of 0-2 Hz. When the postural stability was poor, the COP and EMG had a high synchronization with index values within the range of 0.010-0.015. With good postural stability, the synchronization indices were between 0.006 and 0.080 and both exhibited low synchronization. The COP signals and the low frequency EMG signals were highly correlated. In conclusion, we demonstrated that the COP may provide enough information on postural stability without the EMG data.