Intensive vs non-intensive statin pretreatment before percutaneous coronary intervention in Chinese patients: A meta-analysis of randomized controlled trials.

BACKGROUND: The results of intensive statin pretreatment before percutaneous coronary intervention (PCI) is inconsistent between Chinese and Western populations, and there are no corresponding meta-analyses involving hard clinical endpoints in the available published literature. AIM: To evaluate the efficacy and safety of high-dose statin loading before PCI in Chinese patients through a meta-analysis. METHODS: Relevant studies were identified by searching the electronic databases of PubMed, Embase and Cochrane's Library to December 2019. The outcomes included an assessment of major adverse cardiovascular event (MACE), non-fatal myocardial infarction (MI), cardiac death, target vessel revascularization (TVR), myalgia /myasthenia and abnormal alanine aminotransferase (ALT) in all enrolled patients. Random effect model and fixed effect model were applied to combine the data, which were further analyzed by χ 2 test and I 2 test. The main outcomes were then analyzed through the use of relative risks (RR) and its 95% confidence interval (95%CI). RESULTS: Eleven studies involving 3123 individuals were included. Compared with patients receiving placebo or no statin treatment before surgery, intensive statin treatment was associated with a clear reduction of risk of MACE (RR = 0.44, 95%CI: 0.31-0.61, P < 0.00001). However, compared with the patients receiving moderate-intensity statin before surgery, no advantage to intensive statin treatment was seen (RR = 1.04, 95%CI: 0.82-1.31, P = 0.74). In addition, no significant difference was observed between intensive statin therapy and non-intensive statin therapy on the incidence of TVR (RR = 0.43, 95%CI: 0.18-1.02, P = 0.06) , myalgia /myasthenia (RR = 1.35, 95%CI: 0.30-5.95, P = 0.69) and abnormal alanine aminotransferase (RR = 1.47, 95%CI: 0.54-4.02, P = 0.45) except non-fatal MI (RR = 0.54, 95%CI: 0.33-0.88, P = 0.01). CONCLUSION: Compared with placebo or no statin pretreatment, intensive statin before PCI displayed reduced incidence of MACE. However, there was no significant benefit between high and moderate-intensity statin. In addition, no significant difference was observed between intensive statin therapy and non-intensive statin therapy on the incidence of TVR, myalgia/myasthenia and abnormal alanine aminotransferase except non-fatal MI.

LncRNA SNHG1 exerts a protective role in cardiomyocytes hypertrophy via targeting miR-15a-5p/HMGA1 axis.

Heart failure preceded by pathological cardiac hypertrophy is a leading cause of death. Long noncoding RNA small nucleolar RNA host gene 1 (SNHG1) was reported to inhibit cardiomyocytes apoptosis, but the role and underlying mechanism of SNHG1 in pathological cardiac hypertrophy have not yet been understood. This study was designed to investigate the role and molecular mechanism of SNHG1 in regulating cardiac hypertrophy. We found that SNHG1 was upregulated during cardiac hypertrophy both in vivo (transverse aortic constriction treatment) and in vitro (phenylephrine [PE] treatment). SNHG1 overexpression attenuated the cardiomyocytes hypertrophy induced by PE, while SNHG1 inhibition promoted hypertrophic response of cardiomyocytes. Furthermore, SNHG1 and high-mobility group AT-hook 1 (HMGA1) were confirmed to be targets of miR-15a-5p. SNHG1 promoted HMGA1 expression by sponging miR-15a-5p, eventually attenuating cardiomyocytes hypertrophy. There data revealed a novel protective mechanism of SNHG1 in cardiomyocytes hypertrophy. Thus, targeting of SNHG1-related pathway may be therapeutically harnessed to treat cardiac hypertrophy.

Chemometric Analysis of Elemental Fingerprints for GE Authentication of Multiple Geographical Origins.

The feasibility of combining elemental fingerprints and chemical pattern recognition methods for authentication of the geographical origins of a Chinese herb, Gastrodia elata BI. (GE), was studied in this paper. A total of 210 GE samples were collected from 7 different producing areas. The levels of 15 mineral elements in GE, including Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K, were determined using inductively coupled plasma mass spectrometry (ICP-MS). Using the autoscaled data of elemental fingerprints and partial least-squares discriminant analysis (PLSDA), two chemometrics strategies for multiclass classifications, One-Versus-Rest (OVR) and One-Versus-One (OVO), were studied and compared in discrimination of GE geographical origins. As a result, OVR-PLSDA and OVO-PLSDA could achieve the classification accuracy of 0.672 and 0.925, respectively. The results indicate that mineral elemental fingerprints coupled with chemometrics can provide a useful alternative method for simultaneous discrimination of multiple GE geographical origins.

Rapid discrimination of the geographical origins of an oolong tea (anxi-tieguanyin) by near-infrared spectroscopy and partial least squares discriminant analysis.

This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

Untargeted detection and quantitative analysis of poplar balata (PB) in Chinese propolis by FT-NIR spectroscopy and chemometrics.

This paper investigates the feasibility of using FT-NIR spectroscopy and chemometrics for rapid analysis of poplar balata (PB) in Chinese propolis. Because practical adulterations usually involve addition of certain known active components, together with commercial PB, the commonly targeted analysis methods are insufficient to identify PB-adulterated propolis. Untargeted analysis of PB was performed by developing class models of pure propolis using one-class partial least squares (OCPLS). Quantitative analysis of PB was performed using partial least squares regression (PLSR). For untargeted analysis, the most accurate OCPLS model was obtained with SNV spectra with sensitivity 0.960 and specificity 0.941. OCPLS could detect adulterations with 2% (w/w) or more PB. For quantitative analysis, the root mean squared error of prediction (RMSEP) value of PB was 0.902 (w/w, %) with SNV-PLS. FT-NIR spectrometry and chemometrics demonstrate potential for rapid analysis of PB adulterations in Chinese propolis.

Combining electronic tongue array and chemometrics for discriminating the specific geographical origins of green tea.

The feasibility of electronic tongue and multivariate analysis was investigated for discriminating the specific geographical origins of a Chinese green tea with Protected Designation of Origin (PDO). 155 Longjing tea samples from three subareas were collected and analyzed by an electronic tongue array of 7 sensors. To remove the influence of abnormal measurements and samples, robust principal component analysis (ROBPCA) was used to detect outliers in each class. Partial least squares discriminant analysis (PLSDA) was then used to develop a classification model. The prediction sensitivity/specificity of PLSDA was 1.000/1.000, 1.000/0.967, and 0.950/1.000 for longjing from Xihu, Qiantang, and Yuezhou, respectively. Electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of Longjing.

Rapid analysis of adulterations in Chinese lotus root powder (LRP) by near-infrared (NIR) spectroscopy coupled with chemometric class modeling techniques.

This paper develops a rapid analysis method for adulteration identification of a popular traditional Chinese food, lotus root powder (LRP), by near-infrared spectroscopy and chemometrics. 85 pure LRP samples were collected from 7 main lotus producing areas of China to include most if not all of the significant variations likely to be encountered in unknown authentic materials. To evaluate the model specificity, 80 adulterated LRP samples prepared by blending pure LRP with different levels of four cheaper and commonly used starches were measured and predicted. For multivariate quality models, two class modeling methods, the traditional soft independent modeling of class analogy (SIMCA) and a recently proposed partial least squares class model (PLSCM) were used. Different data preprocessing techniques, including smoothing, taking derivative and standard normal variate (SNV) transformation were used to improve the classification performance. The results indicate that smoothing, taking second-order derivatives and SNV can improve the class models by enhancing signal-to-noise ratio, reducing baseline and background shifts. The most accurate and stable models were obtained with SNV spectra for both SIMCA (sensitivity 0.909 and specificity 0.938) and PLSCM (sensitivity 0.909 and specificity 0.925). Moreover, both SIMCA and PLSCM could detect LRP samples mixed with 5% (w/w) or more other cheaper starches, including cassava, sweet potato, potato and maize starches. Although it is difficult to perform an exhaustive collection of all pure LRP samples and possible adulterations, NIR spectrometry combined with class modeling techniques provides a reliable and effective method to detect most of the current LRP adulterations in Chinese market.

The feasibility of using near-infrared spectroscopy and chemometrics for untargeted detection of protein adulteration in yogurt: removing unwanted variations in pure yogurt.

Untargeted detection of protein adulteration in Chinese yogurt was performed using near-infrared (NIR) spectroscopy and chemometrics class modelling techniques. sixty yogurt samples were prepared with pure and fresh milk from local market, and 197 adulterated yogurt samples were prepared by blending the pure yogurt objects with different levels of edible gelatin, industrial gelatin, and soy protein powder, which have been frequently used for yogurt adulteration. A recently proposed one-class partial least squares (OCPLS) model was used to model the NIR spectra of pure yogurt objects and analyze those of future objects. To improve the raw spectra, orthogonal projection (OP) of raw spectra onto the spectrum of pure water and standard normal variate (SNV) transformation were used to remove unwanted spectral variations. The best model was obtained with OP preprocessing with sensitivity of 0.900 and specificity of 0.949. Moreover, adulterations of yogurt with 1% (w/w) edible gelatin, 2% (w/w) industrial gelatin, and 2% (w/w) soy protein powder can be safely detected by the proposed method. This study demonstrates the potential of combining NIR spectroscopy and OCPLS as an untargeted detection tool for protein adulteration in yogurt.

Combining local wavelength information and ensemble learning to enhance the specificity of class modeling techniques: Identification of food geographical origins and adulteration.

Class modeling techniques are required to tackle various one-class problems. Because the training of class models is based on the target class and the origins of future test objects usually cannot be exactly predefined, the criteria for feature selection of class models are not very straightforward. Although feature reduction can be expected to improve class models performance, more features retained can provide a sufficient description of the sought-for class. This paper suggests a strategy to balance class description and model specificity by ensemble learning of sub-models based on separate local wavelength intervals. The acceptance or rejection of a future object can be explicitly determined by examining its acceptance frequency by sub-models. Considering the lack of information about sub-model independence, we propose to use a data-driven method to control the sensitivity of the ensemble model by cross validation. In this way, all the wavelength intervals are used for class description and the local wavelength intervals are highlighted to enhance the ability to detect out-of-class objects. The proposed strategy was performed on one-class partial least squares (OCPLS) and soft independent modeling of class analogy (SIMCA). By analysis of two infrared spectral data sets, one for geographical origin identification of white tea and the other for discrimination of adulterations in pure sesame oil, the proposed ensemble class modeling method was demonstrated to have similar sensitivity and better specificity compared with total-spectrum SIMCA and OCPLS models. The results indicate local spectral information can be extracted to enhance class model specificity.