Long non-coding RNA RMST serves as a diagnostic biomarker in patients with carotid artery stenosis and predicts the occurrence of cerebral ischemic event: A retrospective study.

OBJECTIVES: The purpose of this retrospective study is to explore the diagnostic and prognostic roles of serum RMST in carotid artery stenosis (CAS). METHODS: Serum levels of RMST were detected in CAS patients, and the relationship between degree of carotid stenosis and RMST levels was analyzed. The ROC curve was drawn to evaluate RMST value in predicting the risk of CAS. Then, all CAS patients received a 5-year follow-up. K-M curve was used to analyze the significance of RMST on prognosis of CAS patients. Multi-factor cox logistic regression analysis was conducted to evaluate independent factors for outcome of CAS patients. RESULTS: An increased RMST expression was certified in CAS patients when compared with healthy controls. The increase of serum RMST expression was related to high degree of carotid stenosis. In addition, serum RMST was a possible diagnosis and an independent influencing factor of prognosis in patients with CAS. CONCLUSIONS: Raised serum RMST level was found in patients with CAS. Detecting RMST expression levels was of high value for predicting the occurrence and outcomes in CAS.

The Clinical Significance of Lncrna GAS5 And Mir-222-3p in Carotid Artery Stenosis.

BACKGROUND: Carotid artery stenosis (CAS) is the major pathogen of cerebral infarction and brain death. Early detection and risk prediction could help the diagnosis and improve the outcome of patients. The clinical significance of lncRNA GAS5 (GAS5) and miR-222-3p in the diagnosis and prognosis of CAS was evaluated in this study to explore novel effective biomarkers of CAS. METHODS: A total of 72 CAS patients and 63 healthy individuals (control) were enrolled in this study. The expression levels of GAS5 and miR-222-3p in study subjects were detected using PCR. The ROC, Kaplan-Meier, and Cox regression analyses were carried out to estimate the diagnostic and prognostic value of GAS5 and miR-222-3p in CAS. The interaction between GAS5 and miR-222-3p was disclosed by the dual-luciferase reporter. RESULTS: The reduced expression of GAS5 and elevated expression of miR-222-3p were observed in CAS patients compared with the healthy controls, and a significant correlation between their expression levels in CAS was revealed. GAS5 and miR-222-3p could discriminate CAS patients from the healthy controls with high sensitivity and specificity. The GAS5 downregulation and miR-222-3p upregulation could predict the poor prognosis of CAS patients and may be associated with the severe development of patients. In human vascular smooth muscle cells, miR-222-3p could negatively regulate the luciferase activity of GAS5. CONCLUSION: Both GAS5 and miR-222-3p served as the diagnostic and prognostic biomarkers of CAS. The function of GAS5 might result from the regulation of miR-222-3p, which needs further validation.

Dissolution Kinetics of a BCS Class II Active Pharmaceutical Ingredient: Diffusion-Based Model Validation and Prediction.

In this work, a diffusion-theory-based model has been devised to simulate dissolution kinetics of a poorly water-soluble drug, ibuprofen. The model was developed from the Noyes-Whitney equation in which the dissolution rate term is a function of the remaining particulate surface area and the concentration gradient across the boundary layer. Other dissolution parameters include initial particle size, diffusion coefficient, material density, and diffusion boundary layer thickness. It is useful for predicting nonsink circumstances under which pure API polydisperse powders are suspended in a well-mixing tank. The model was used to compare the accuracy of simulations using spherical (single dimensional characteristic length) and cylindrical particle (multidimensional characteristic lengths) geometries, with and without size-dependent diffusion layer thickness. Experimental data was fitted to the model to obtain the diffusion layer thickness as well as used for model validation and prediction. The CSDs of postdissolution were also predicted with this model, demonstrating good agreement between theory and experiment.

Modelling and understanding powder flow properties and compactability of selected active pharmaceutical ingredients, excipients and physical mixtures from critical material properties.

The development of solid dosage forms and manufacturing processes are governed by complex physical properties of the powder and the type of pharmaceutical unit operation the manufacturing processes employs. Suitable powder flow properties and compactability are crucial bulk level properties for tablet manufacturing by direct compression. It is also generally agreed that small scale powder flow measurements can be useful to predict large scale production failure. In this study, predictive multilinear regression models were effectively developed from critical material properties to estimate static powder flow parameters from particle size distribution data for a single component and for binary systems. A multilinear regression model, which was successfully developed for ibuprofen, also efficiently predicted the powder flow properties for a range of batches of two other active pharmaceutical ingredients processed by the same manufacturing route. The particle size distribution also affected the compactability of ibuprofen, and the scope of this work will be extended to the development of predictive multivariate models for compactability, in a similar manner to the approach successfully applied to flow properties.