Objective: This work aimed to explore the possibility of using Fourier-transform infrared (FTIR) spectroscopy and chemometrics to develop multivariate models to authenticate the "halal-ity" of pharmaceutical excipients with controversial halal status (e.g., magnesium stearate).

Materials and Methods: The FTIR spectral fingerprints of the substance were used to build principal component analysis (PCA) models. The effects of different spectral pretreatment processes such as auto-scaling, baseline correction, standard normal variate (SNV), first, and second derivatives were evaluated. The optimization of the model performance was established to ensure the sensitivity, specificity, and accuracy of the predicted models.

Results: Significant peaks corresponding to the properties of the compound were identified. For both bovine and plant-derived magnesium stearate, the peaks associated can be seen within the regions 2900cm-1 (C-H), 2800cm-1 (CH3), 1700cm-1 (C=O), and 1000-1300cm-1 (C-O). There was not much difference observed in the FTIR raw spectra of the samples from both sources. The quality and accuracy of the classification models by PCA and soft independent modeling classification analogy (SIMCA) have shown to improve using spectra optimized by first derivative followed by SNV smoothing.

METHODS: Healthy participants consumed pure forms of a non-nutritive sweetener (NNS) mixed with water that were standardized to doses of 14% (0.425 g) of the acceptable daily intake (ADI) for aspartame and 20% (0.136 g) of the ADI for sucralose every day for two weeks. Blood samples were collected and analysed for glucose, insulin, active glucagon-like peptide-1 (GLP-1), and leptin.

RESULTS: Seventeen participants (10 females and 7 males; age 24 ± 6.8 years; BMI 22.9 ± 2.5 kg/m2) participated in the study. The total area under the curve (AUC) values of glucose, insulin, active GLP-1 and leptin were similar for the aspartame and sucralose treatment groups compared to the baseline values in healthy participants. There was no change in insulin sensitivity after NNS treatment compared to the baseline values.

Objective: The objective of this study was to investigate the prevalence and pattern of medications associated with geriatric syndromes (MAGSs) among the discharged elderly patients (≥65 years old).

Materials and Methods: This is a cross-sectional study that was conducted at a Malaysian teaching hospital from October to December 2018. The discharge medications of geriatric patients were reviewed to identify MAGSs using Beers criteria, Lexicomp drug information handbook, and the United States Food and Drug Administration (USFDA) drug inserts. Chi-square test was used to compare MAGS prescribed between categories. Spearman's rank-order correlation was used to test the correlation between the presence of MAGS and the number of discharge medications. A binomial logistic regression was applied to determine the predictors of prescribing MAGSs.

Results: A total of 400 patients (mean ± standard deviation [SD] age, 72.0 ± 5.0 years) were included, and 45.3% of them were females. The most common diseases were hypertension followed by diabetes mellitus. The mean ± SD number of discharge medications per patient was 4.2 ± 2.5. The MAGSs were prescribed in 51.7% of the patients, and 54 patients were discharged with more than one MAGSs. The most commonly prescribed MAGSs were opioid analgesics, vasodilators, and β-blockers, which are associated with falls, depression, and delirium. Polypharmacy was found in 138 patients, and it was significantly associated with the presence of MAGSs (P < 0.001). No significant differences were found in prescribing MAGSs based on the patients' gender, race, and age.