Multivariate modeling techniques play important roles in the tablet manufacturing process.

In this work, microwave-assisted ionic liquids treatment, followed by hydro-distillation (MILT-HD), as an efficient extraction technology, was used to extract essential oil. The purpose for this was to use multivariate analysis (MVA) models to investigate the effects of potential critical process parameters on the extraction efficiency of essential oil, and explore the mechanism of ionic liquids (ILs). According to the design of experiment (DoE), under optimal process conditions, the extraction efficiency of essential oil was dramatically enhanced, and had low energy demands. Since little is known regarding those mechanisms, according to the non-covalent interaction analysis, the underlying mechanism for ILs improving extraction efficiency was explored based on the density functional theory (DFT). The results showed that ILs could form intense non-covalent bond interaction with cellulose. It helped destroy the network hydrogen bond structure of cellulose in plant cells and caused the essential oils in the cells to be more easily exposed to the extraction solution, thereby accelerating extraction efficiency. Based on this work, it is conducive to understand the MILT-HD process better and gain knowledge of the mechanism of ILs.

Background: MicroRNAs (miRNAs) have pivotal roles in gene regulation. Circulating miRNAs have been developed as novel candidate non-invasive biomarkers for diagnosis, prognosis, and treatment response for diseases. However, miRNAs that have causal effects on Parkinson’s Disease (PD) remain largely unknown. To investigate the causal relationships between miRNAs and PD, here we conduct a Mendelian randomization (MR) study.Methods: This study utilized the summary-level data of respective genome-wide association studies (GWAS) for 2083 miRNAs and seven PD-related outcomes to comprehensively reveal the causal associations between the circulating miRNAs and PD. Two-sample MR design was deployed and the causal effects were estimated with inverse variance weighted, MR-Egger, and weighted median. Comprehensively sensitive analyses were followed, including Cochran’s Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis, to validate the robustness of our results. Finally, we investigated the potential role of the MR significant miRNAs by predicting their target genes and functional enrichment analysis.Results: Inverse variance weighted estimates suggested that two miRNAs, miR-205-5p (β = −0.46, 95%CI: −0.690 to −0.229, p = 9.3 × 10⁻⁵) and miR-6800-5p (β = −0.389, 95%CI: −0.575 to −0.202, p = 4.32 × 10⁻⁵), significantly decreased the rate of cognitive decline among PD patients. In addition, eight miRNAs were nominally associated with more than three PD-related outcomes each. No significant heterogeneity of instrumental variables or horizontal pleiotropy was found. Gene Ontology (GO) analysis showed that the targets of these causal miRNAs were significantly enriched in cell cycle, apoptotic, and aging pathways.Conclusion: This MR study identified two miRNAs whose genetically regulated expression might have a causal role in the development of PD dementia. Our findings provided potential miRNA biomarkers to make better and early diagnoses and risk assessments of PD.

It is significant to analyze the blend homogeneity of cohesive powders during pharmaceutical manufacturing in order to provide the exact content of the active pharmaceutical ingredient (API) for each individual dose unit. In this paper, an online monitoring platform using an MEMS near infrared (NIR) sensor was designed to control the bin blending process of cohesive powders. The state of blend homogeneity was detected by an adaptive algorithm, which was calibration free. The online control procedures and algorithm’s parameters were fine-tuned through six pilot experiments and were successfully transferred to the industrial production. The reliability of homogeneity detection results was validated by 16 commercial scale experiments using 16 kinds of natural product powders (NPPs), respectively. Furthermore, 19 physical quality attributes of all NPPs and the excipient were fully characterized. The blending end time was used to denote the degree of difficulty of blending. The empirical relationships between variability of NPPs and the blending end time were captured by latent variable modeling. The critical material attributes (CMAs) affecting the blending process were identified as the particle shape and flowability descriptors of cohesive powders. Under the framework of quality by design (QbD) and process analytical technology (PAT), the online NIR spectroscopy together with the powder characterization facilitated a deeper understanding of the mixing process.