Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.

In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis.

Application of chemometrics for detection and modeling of adulteration of fresh cow milk with reconstituted skim milk powder using voltammetric fingerpriting on a graphite/ SiO2 hybrid electrode.

In the present work, an analytical approach for the voltammetric detection and prediction of adulteration of fresh cow milk with reconstituted skim milk powder is developed. After precipitation of milk proteins upon addition of ethanol and centrifugation, the supernatant liquid of the samples was analyzed by cyclic voltammetry on a novel graphite/SiO2 hybrid working electrode (GSiHE) using LiClO4 as electrolyte. Under these conditions, fresh milk samples gave broadened peaks/plateaus in both forward and backward potential scanning, attributed mainly to oxidases. Such peaks were not evident in the case of reconstituted skim milk powder samples due to inactivation of enzymes and breakdown of certain antioxidants caused by heat and pressure-treatments. The differences between fresh and reconstituted skim milk powder samples in their voltammetric profile were exploited for the detection of fresh milk adulteration by submitting voltammetric data to chemometrics. As datapoints, the differences between forward and backward current values, recorded at the same potentials, were determined and submitted to multivariate analysis. Principal Component Analysis (PCA) provided a clear differentiation between fresh milk and reconstituted skim milk powder samples. Soft independent modeling of class analogy (SIMCA) was employed to model the class of fresh milks, using samples from 12 commercially available fresh milk brands. Prediction of fresh milk adulteration with reconstituted skim milk powders was achieved by means of Partial Least Squares (PLS) regression analysis. Detection limit of the technique was found to be below 6% (v/v) and the linearity of model in terms of observed/predicted values was confirmed up to 100% (v/v). Validation and applicability of both SIMCA and PLS models were confirmed using a suitable test set, consisting of commercial fresh milk and skim milk powder samples as well as synthetic adulterated fresh milk samples.

Inability to act was associated with an extended delay prior to care-seeking, in patients with an acute myocardial infarction.

BACKGROUND: The out-of-hospital mortality in patients with acute myocardial infarction remains unchanged in contrast to a decrease in inhospital mortality. Interventions aiming to shorten patient delay have been largely unsuccessful. A deeper understanding is apparently needed on patients' appraisal prior to care-seeking. AIM: To investigate whether appraisal processes influence patient delay, and if the questionnaire 'Patients' appraisal, emotions and action tendencies preceding care seeking in acute myocardial infarction' (PA-AMI) could discriminate between patients with prolonged care-seeking and those with a short delay. METHODS: A cross-sectional study including 326 acute myocardial infarction patients filling out the validated questionnaire PA-AMI. The impact of subscales on delay was analysed by projection to latent structures regression. Discrimination opportunities between patients with short and long delays were analysed by projection to latent structures discriminant analysis. RESULTS: The subscales 'perceived inability to act' and 'symptom appraisal' had a major impact on patient delay (P<0.0001). 'Perceived inability to act' had its main influence in patients with a delay exceeding 12 hours, and 'symptom appraisal' had its main influence in patients with a delay shorter than one hour. CONCLUSION: Appraisal processes influence patient delay. Acute myocardial infarction patients with a prolonged delay were, besides a low perceived symptom severity and urgency to seek medical care, characterised by a perceived loss of control and ability to act. Therefore, future interventions aimed at decreasing delay should pay attention to appraisal processes, and perceived inability to act may be a sign of a health threat and therefore a signal to seek medical care.

Study on quality grade evaluation of Andrographis Herba based on principal component clustering and PLS regression / 中草药

Objective: To combine macroscopical characteristic indices and chemical indices of Andrographis Herba to evaluate its quality grade. Methods: Both macroscopical characteristic indices and chemical indices (the content of four active diterpenoids and the content of ethanol-soluble extractives) of different batches of Andrographis Herba were determined. The macroscopical characteristic indices were encoded using the method of numerical taxonomy, and the content of four active diterpenoids were determined by HPLC. To screen out the appropriate indices for classification, the correlational analyses were conducted between encoded macroscopical characteristic indices and chemical indices. The quality grade was made by principal component clustering analysis according these evaluation indices, and then was analyzed through partial least squares discriminant analysis (PLS-DA). Furthermore, a partial least squares (PLS) regression was constructed for the quality grade prediction of Andrographis Herba. Results: It showed that the samples could be divided into three grades according to the principal component clustering analysis, and was reasonable evaluating by PLS-DA. The PLS regression model for quality grade of Andrographis Herba was constructed as follows: grade Y=3.761-0.020×the leaf content-0.388×the content of andrographolide-1.117×the content of neoandrographolide-0.274×the content of deoxyandrographolide-0.287×the content of 14-deoxy-11,12-didehydro-andrographolide-0.302×the content of four active diterpenoids-0.104×the content of ethanol-soluble extractives-0.015×the color of stem-0.008 4×the color of leaf-0.003×the diameter of base part of stem+0.020×the number of branch+0.137×the diameter of the upper stem+0.011×plant height, if Y=0.7-1.3, the predicted quality was grade A, if Y=1.7-2.3, then B grade, and if Y=2.7-3.3, C grade or qualified product. Conclusion: The model of grade evaluation we constructed using principal component clustering analysis combing with PLS regression analysis performed well, which was applicable in evaluating the quality grade of Andrographis Herba and other traditional Chinese medicines. It also provided a new strategy for study on grade standards of traditional Chinese medicines.

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. We present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of the spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO3-), total acid (H+), neodymium (Nd3+), sodium (Na+), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.

Construction of Prediction Models for the Transient Receptor Potential Vanilloid Subtype 1 (TRPV1)-Stimulating Activity of Ginger and Processed Ginger Based on LC-HRMS Data and PLS Regression Analyses.

To construct a model formula to evaluate the thermogenetic effect of ginger (Zingiber officinale Roscoe) from the ingredient information, we established transient receptor potential vanilloid subtype 1 (TRPV1)-stimulating activity prediction models by using a partial least-squares projections to latent structures (PLS) regression analysis in which the ingredient data from liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and the stimulating activity values for TRPV1 receptor were used as explanatory and objective variables, respectively. By optimizing the peak extraction condition of the LC-HRMS data and the data preprocessing parameters of the PLS regression analysis, we succeeded in the construction of a TRPV1-stimulating activity prediction model with high precision ability. We then searched for the components responsible for the TRPV1-stimulating activity by analyzing the loading plot and s-plot of the model, and we identified [6]-gingerol (1) and hexahydrocurcumin (3) as TRPV1-stimulating activity components.

Metabolic alterations of lutein, ß-carotene and chlorophyll a during germination of two soybean sprout varieties.

The metabolic changes of lutein, ß-carotene and chlorophyll a during germination of the soybean (Glycine max (L.) Merr.) sprout varieties, 'Pungsannamulkong' and 'Bosug', have been studied. Seeds were germinated at 20 °C with 80% humidity in the darkness and sampled at 2 day intervals for 10 days. Partial least squares (PLS) scores plot showed that the responses of three metabolites during germination were linearly linked with each other except for day 2 in both varieties. PLS loading plots indicated that lutein content in whole sprout and cotyledon was closely associated with germination in 'Pungsannamulkong' while the chlorophyll a content in whole sprouts was highly linked with germination in 'Bosug'. Heatmap analyses revealed that lutein and ß-carotene levels, but not those of chlorophyll a, accumulated in whole soybean sprouts and cotyledon. While hypocotyls did not accumulate lipophilic pigments during germination, the accumulation of lutein and ß-carotene in the cotyledons was greater in 'Pungsannamulkong' than in 'Bosug' sprouts. In addition, the contents of lutein, ß-carotene and chlorophyll a increased from those in the seeds. Overall, the metabolic changes of lutein, ß-carotene and chlorophyll a during germination are affected not only by variety but also by organ type.