ibuprofen (ibuprofen, Nordmark / ibuprofen, Pacific / ibuprofen, Bracco)

Effects of the active parts of Yangxin Tongmai Formula on myocardial injury and spatial metabolic remodeling in rats with myocardial ischemia-reperfusion injury.

Ye Di D, Wang Yi Y, Zhao Ziyuan Z, Lin Caiyue C et al.

To investigate the protective effects of the active parts of Yangxin Tongmai Formula (apr-YTF) against myocardial ischemia-reperfusion injury (MIRI) in rats from the perspective of spatial metabolic remodeling. A rat model of MIRI with blood stasis syndrome was established. Rats were randomized into Control, Model, Vehicle, and apr-YTF groups. apr-YTF was administered by gavage for 14 days. Cardiac function and myocardial injury were assessed by echocardiography, serum myocardial enzyme assays, triphenyltetrazolium chloride (TTC) staining, and hematoxylin and eosin (HE) staining. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to analyze the spatial distribution patterns and metabolic profiles of differential metabolites in myocardial tissue. apr-YTF significantly improved left ventricular ejection fraction (LVEF) and left ventricular fractional shortening (LVFS), reduced serum creatine kinase (CK), creatine kinase-MB isoenzyme (CK-MB), lactate dehydrogenase (LDH), and lactate dehydrogenase 1 (LDH1) levels, decreased infarct size, and alleviated myocardial fibrosis and inflammatory infiltration. MALDI-MSI identified 1,282 metabolites, including 702 MIRI model-associated differential metabolic features, mainly including amino acids, nucleotides, and glycerophospholipids. The MIRI group exhibited extensive metabolic remodeling involving lipid, amino acid, nucleotide metabolism, and cofactor biosynthesis pathways. After apr-YTF intervention, 17 reversed differential metabolic features showed recovery trends. The active parts of Yangxin Tongmai Formula ameliorated myocardial injury and were associated with partial correction of spatial metabolic remodeling in rats with MIRI.

Driving Under the Influence of Cannabis Among US Young Adults Who Use Cannabis: Evidence From the 2021-2024 National Survey on Drug Use and Health.

Tang Yuni Y, Rudisill Toni Marie TM

To estimate the prevalence of driving under the influence of cannabis (DUIC) and identify associated factors among US young adult drivers reporting past-year cannabis use. This cross-sectional study analyzed pooled 2021-2024 National Survey on Drug Use and Health. The analytic data were restricted to drivers aged 18-25 years who reported past-year cannabis use (N = unweighted 17,141; weighted N = 10,814,381). The outcome was self-reported past-year DUIC. Independent variables included demographics, substance use, mental health, cannabis-related perceptions, and driving behaviors. These relationships were assessed by modified Poisson regression. The weighted prevalence of DUIC was 28.0%, representing approximately over three million young adults. DUIC prevalence increased with cannabis use frequency, from 2.51 times higher among those using cannabis 12-49 days (adjusted prevalence ratio [APR]: 2.51; 95% confidence interval [CI]: 2.29-2.75) to 3.63 times higher among those reporting use on 300-365 days (APR: 3.63; 95% CI: 3.60-3.76), compared with those using cannabis 1-11 days. Cannabis use disorder (APR: 2.34; 95% CI: 2.06-2.65), simultaneous alcohol and cannabis use (APR: 1.29; 95% CI: 1.28-1.30), and perceived easy cannabis availability (APR: 2.36; 95% CI: 2.33-2.39) were also associated with higher prevalence of DUIC. Nonenrollment in school and living in a state with a medical cannabis law were associated with lower DUIC prevalence. DUIC is highly prevalent among US young adults who use cannabis, with a clear graded association across categories of cannabis use frequency. Public health interventions should address frequent use, cannabis use disorder, alcohol-cannabis co-use, and perceived cannabis availability.

Corrigendum to "Quercetin counteracts monosodium glutamate to mitigate immunosuppression in the thymus and spleen via redox-guided cellular signaling" [Phytomedicine. 2024 Apr;126:155226].

Das Debasmita D, Banerjee Arnab A, Manna Krishnendu K, Sarkar Deotima D et al.

Predictive compaction modelling of ternary direct compression formulations.

Tait Theo T, Salehian Mohammad M, Aroniada Magdalini M, Shier Andrew P AP et al.

Developing directly-compressed formulations remains a resource-intensive task, requiring substantial experimental effort to characterise the compressibility and compactability of a formulation space. This study extends a global optimisation of mixture rules to a ternary formulation space (API-brittle filler-elastic filler) and investigates the potential for reducing experimental burden whilst maintaining the predictive accuracy of empirical compression and compaction models. Three grades each of paracetamol and ibuprofen, combined with a consistent placebo base, were used to evaluate the approach. The global optimisation outperformed the traditional line of best fit approach, achieving strong predictive performance for the Kawakita model (R2>0.94; RMSE<0.01) and more variable fits for the Ryshkewitch-Duckworth model (R2 = 0.93 - 0.95 and RMSE = 0.23 - 0.39 MPa for paracetamol; R2 = 0.70 - 0.83 and RMSE = 0.31 - 0.37 MPa for ibuprofen). The optimisations performance was found to improve when the training dataset considered only drug-loaded blends. The exploration of reducing experimental burden considered a Model-Based Design of Experiments (MBDoE) which was benchmarked against random experiment selection. Integrating MBDoE with optimised mixture rules reduced API consumption by over 30% across all formulations, with median savings of 75%-95% under Acceptable and Good performance thresholds. Savings decreased with increasing threshold stringency, with the greatest variability observed in ibuprofen formulations. The Kawakita model supported reductions across all threshold levels, whilst the Ryshkewitch-Duckworth model showed limited capacity beyond the Acceptable threshold. tThe MBDoE did not outperform the random selection of experiments, however the optimisation framework for populating empirical compression and compaction models offers a resource-efficient approach to predicting tablet porosity and tensile strength of ternary API loaded blends.