Identification of a RAD51B enhancer variant for susceptibility and progression to glioma.

BACKGROUND: RAD51B plays a significant role in homologous recombination-mediated repair of DNA double-strand breaks. Many enhancer variants are involved in cancer development and progression. However, the significance of enhancer variants of RAD51B in glioma susceptibility and progression remains unclear. METHODS: A case-control study consisting of 1056 individuals was conducted to evaluate the associations of enhancer variants of RAD51B with glioma susceptibility and progression. Sequenom MassARRAY technology was used for genotyping. The function of enhancer variants was explored by biochemical assays. RESULTS: A significantly decreased risk of glioma was associated with rs6573816 GC genotype compared with rs6573816 GG genotype (OR = 0.66, 95% CI 0.45-0.97; P = 0.034). Multivariable Cox regression revealed that rs6573816 was significantly associated with glioma progression in a sex-dependent manner. Worse PFS was found in the male patients with high grade glioma carrying rs6573816 GC or CC genotype (HR = 2.28, 95% CI 1.14-4.57; P = 0.020). The rs6573816 C allele repressed enhancer activity by affecting transcription factor POU2F1 binding, which resulted in lower expression of RAD51B. Remarkably attenuated expression of RAD51B was observed following POU2F1 knockdown. Consistently, positive correlation between the expression of POU2F1 and RAD51B was found in lymphoblastic cells and glioma tissues. CONCLUSIONS: These results indicate that an enhancer variant of RAD51B rs6573816 influences enhancer activity by changing a POU2F1 binding site and confers susceptibility and progression to glioma.

An insertion variant of MGMT disrupts a STAT1 binding site and confers susceptibility to glioma.

BACKGROUND: O6-methylguanine-DNA methyltransferase (MGMT) is a pivotal enzyme for repairing DNA alkylation damage. Epigenetic modification of MGMT has been well known as a promising prognostic biomarker for glioma. However, the significance of genetic variations of MGMT in glioma carcinogenesis has not been fully elucidated. METHODS: The associations between expression quantitative trait loci (eQTLs) of MGMT and glioma susceptibility were evaluated in a case-control study of 1056 individuals. The function of susceptibility locus for glioma was explored with a set of biochemical assays, including luciferase reporter gene, EMSA and supershift EMSA, ChIP, and siRNA knockdown. RESULTS: We found that rs11016798 TT genotype was associated with a significantly decreased risk of glioma (OR = 0.57, 95% CI 0.39-0.85; P = 0.006). Stratification analyses indicated that the association between rs11016798 and glioma was more pronounced in males (OR = 0.62, 95% CI 0.40-0.97; P = 0.035), older subjects (OR = 0.46, 95% CI 0.27-0.80; P = 0.006), WHO grade IV glioma (OR = 0.58, 95% CI 0.35-0.96; P = 0.033), and IDH wildtype glioma (OR = 0.43, 95% CI 0.21-0.88; P = 0.022). We characterized an insertion variant rs10659396 in the upstream of MGMT as a causative variant. The risk allele rs10659396 ins allele was demonstrated to downregulate MGMT expression by disrupting a STAT1 binding site. Knockdown of STAT1 remarkably attenuated MGMT expression. Moreover, the rs10659396 allele-specific positive correlation was observed between the expression of STAT1 and MGMT in population. CONCLUSIONS: The study demonstrates that an insertion variant of MGMT rs10659396 confers susceptibility to glioma by downregulating MGMT expression through disrupting a STAT1 binding site.

Genetic Predisposition to Glioma Mediated by a MAPKAP1 Enhancer Variant.

Mitogen-activated protein kinase-associated protein 1 (MAPKAP1) is a unique component of the mechanistic target of rapamycin (MTOR) pathway which plays a pivotal role in carcinogenesis. The role of enhancer variant in carcinogenesis receives increased attentions. However, the significance of enhancer variants of MAPKAP1 in glioma has not yet been investigated. The associations of enhancer variants of MAPKAP1 with glioma susceptibility were evaluated in a cohort of 400 glioma patients and 651 controls. The function of glioma susceptibility locus was examined by a set of biochemical assays. We found that an enhancer variant of MAPKAP1 rs473426 was associated with a significantly increased risk of glioma in a dominant manner (OR 1.53, 95% CI 1.13-2.06; P = 0.006). The association for rs1339499 located in the same enhancer approached the borderline of significance after multiple testing correction (OR 0.74, 95% CI 0.56-0.98; P = 0.037). Furthermore, cumulative associations of rs473426 and rs1339499 with glioma risk were observed (P = 0.011). Functional analyses showed that the risk allele rs473426 C downregulated the regulatory activity of enhancer by reducing the binding affinity of a transcriptional activator NFΙC, which resulted in lower gene expression both in vitro and in vivo. These results demonstrate for the first time that enhancer variant of MAPKAP1 confers susceptibility to glioma by downregulation of MAPKAP1 expression, and provide further evidence highlighting MAPKAP1 as a cancer suppressor in glioma carcinogenesis.

[Online Analysis of Dynamic Trend Regression and Endpoint Determination for Chinese Traditional Medicine Extraction Process Based on Ultraviolet spectroscopy].

With the transition of Chinese traditional medicine manufacture industry, modernization has become the inexorable tendency in its future development. However, during the current Chinese traditional medicine producing process, the lack of online monitoring leads to the lagging of quality detection, as well as quality differences between products. In this paper, aiming at realizing online monitoring and end point automatic determination for Chinese traditional medicine (CTM) extraction unit, which is one of the most important units in CTM producing, ultraviolet (UV) spectroscopy is applied to build UV absorbance dynamic model based on the Lambert-Beer law and the CTM extraction kinetic model which presents a new method of UV absorbance dynamic analysis and endpoint determination, including curve regression, robustness analysis and endpoint calculation. In the experiment for online monitoring homalomena occulta extraction, the online UV spectral collection system for CTM extraction, developed by our laboratory, was applied for spectral collection; meanwhile, solid component contents in the offline samples were measured as reference. During the analysis, first of all, we pretreated the spectrum collected in the current period with interpolation and smoothing, and calculated the mean value within a UV region of 230.2～400 nm to form an absorbance mean value sequence with data obtained in the early measurements; then we verified the linear correlation between the sequence of absorbance and the concentrations of effective component in the solution, the linear correlation coefficient equals 0.982 8, showing a high linearity between UV spectra and solid component contents; finally, we regressed the absorbance mean value sequence with the dynamic model, analyzing its robustness and the extraction endpoint. Experimental results demonstrate that, the robustness analysis could recognize the bad points of measurement during the regression process, and improve the consistency between the regression and the original curves, raising its squared correlation coefficient to more than 0.99; meanwhile, with endpoint determination, we shortened the homalomena occulta extraction process from the original manually set 180 to 122 min effectively. The experiment above proves that this method with UV spectroscopy realizes online monitoring and automatic endpoint determination for the CTM extraction process, and is of significant importance for stabilizing production as well as improving economic benefit.

Identification of expression quantitative trait loci of RPTOR for susceptibility to glioma.

Expression quantitative trait loci (eQTLs) have been recognized to be more likely to associate with complex diseases including cancer. As an essential scaffold for MTOR complex 1, RPTOR is necessary for the MTOR-catalyzed phosphorylation. This study examined the associations between the eQTLs of RPTOR and glioma susceptibility. The eQTLs of RPTOR were obtained from GTEx eQTL Browser. Associations were estimated by logistic regression models. On the basis of analysis of 138 cases with glioma and 327 cancer-free population controls, we demonstrated that the eQTL of RPTOR, rs7502563, was significantly associated with a decreased glioma risk [odds ratio (OR) = 0.59, 95 % confidence interval (CI) = 0.38-0.89, P = 0.0123] in a dominant manner. Stratified analyses indicated that the association between rs7502563 and glioma was more pronounced in females (OR = 0.40, 95 % CI = 0.20-0.80, P = 0.0091), older subjects (OR = 0.47, 95 % CI = 0.26-0.86, P = 0.0135), and subjects with high-grade glioma (OR = 0.45, 95 % CI = 0.27-0.77, P = 0.0031). Moreover, an interest gradual decrease in OR with higher grade glioma was observed. Further analysis of the extracted data from GTEx eQTL Browser found that rs7502563 G allele was associated with significantly higher expression of RPTOR in all HapMap populations. Our results demonstrate for the first time that the eQTL of RPTOR, rs7502563, is susceptible to glioma.

[Component analysis of the circulating fluid in an adsorption tower in a P-xylene unit based on raman spectral decomposition].

In order to achieve fast and accurate online analysis of the circulating fluid in an adsorption tower in a p-xylene unit, the Raman spectral analysis method is adopted. However, the Raman spectra of the pure components included in the circulating fluid overlap together, and the concentration of each component varies obviously, the present Raman analysis technology needs a large amount of training samples. Therefore, this paper applies Raman spectral decomposition method in component analysis of the circulating fluid. First of all, the Raman spectra of the pure components and the spectra of a few training samples must be measured, and baseline subtraction and mean normalization are applied to obtain pretreated Raman spectra. Then the characteristic wave number range, 680-880 cm(-1), is chosen, and the Raman spectral decomposition method is adopted, to get decomposition coefficients of each component for each training sample. Next, the mathematical model between coefficients and concentrations of each component are built based on all training samples. For a testing sample, the above spectral pretreatment and the spectral decomposition for the same wave number range is adopted, then the decomposition coefficients of each component can be obtained. Based on the built mathematical model, the concentrations of all components can be predicted. Experimental results show that the standard prediction errors for the concentration of toluene, ethylbenzene, p-xylene, m-xylene, o-xylene and p-diethylbenzene are 0.301%, 0.088%, 0.563%, 0.384%, 0.366% and 0.536% respectively. The above method provides a methodological basis for the online analysis of the circulating fluid in adsorption towers.

Risk factors for hypoglycemia in patients with type 2 diabetes mellitus after intensive insulin therapy and blood glucose monitoring strategy.

Background: To explore the risk factors for hypoglycemia in patients with type 2 diabetes mellitus (T2DM) after intensive insulin therapy and the blood glucose monitoring strategy. Methods: A total of 172 T2DM patients diagnosed from March 2019 to March 2021 were randomly divided into training (n=115) and test sets (n=57), and given intensive insulin therapy. After treatment, the training set was divided into hypoglycemia (n=35) and non-hypoglycemia groups (n=80). Univariate and multivariate logistic regression analyses were performed. Then 120 patients were divided into instantaneous scanning glucose test system monitoring (observation) and glucometer monitoring (control) groups. The diagnostic values of the two methods were validated using receiver operator characteristic curves. Results: Course of disease, body mass index, fasting C-peptide and creatinine were independent risk factors for hypoglycemia, while instantaneous scanning glucose test system monitoring and glucometer monitoring were protective factors (P<0.05). The model had high predictive value. The observation group had shorter time of blood glucose reaching the standard, smaller insulin dose, and lower risk of hypoglycemia than those of the control group (P<0.05). Conclusion: During intensive insulin therapy by multiple subcutaneous injections, the clinical effect of instantaneous scanning glucose test system on T2DM patients is better than that of glucometer.

Utilizing AAV-mediated LEAPER 2.0 for programmable RNA editing in non-human primates and nonsense mutation correction in humanized Hurler syndrome mice.

BACKGROUND: The endogenous adenosine deaminases acting on RNA (ADAR) have been harnessed to facilitate precise adenosine-to-inosine editing on RNAs. However, the practicability of this approach for therapeutic purposes is still ambiguous due to the variable expression of intrinsic ADAR across various tissues and species, as well as the absence of all-encompassing confirmation for delivery methods. RESULTS: In this study, we demonstrate that AAV-mediated delivery of circular ADAR-recruiting RNAs (arRNAs) achieves effective RNA editing in non-human primates at dosages suitable for therapy. Within a time frame of 4 to 13 weeks following infection, the editing efficiency in AAV-infected cells can reach approximately 80%, with no discernible toxicity, even at elevated dosages. In addition, when AAV-delivered circular arRNAs are systematically administered to a humanized mouse model of Hurler syndrome, it rectifies the premature stop codon precisely and restores the functionality of IDUA enzyme encoded by the Hurler causative gene in multiple organs. CONCLUSIONS: These discoveries considerably bolster the prospects of employing AAV-borne circular arRNAs for therapeutic applications and exploratory translational research.

[Fast spectral modeling based on Voigt peaks].

Indirect hard modeling (IHM) is a recently introduced method for quantitative spectral analysis, which was applied to the analysis of nonlinear relation between mixture spectrum and component concentration. In addition, IHM is an effectual technology for the analysis of components of mixture with molecular interactions and strongly overlapping bands. Before the establishment of regression model, IHM needs to model the measured spectrum as a sum of Voigt peaks. The precision of the spectral model has immediate impact on the accuracy of the regression model. A spectrum often includes dozens or even hundreds of Voigt peaks, which mean that spectral modeling is a optimization problem with high dimensionality in fact. So, large operation overhead is needed and the solution would not be numerically unique due to the ill-condition of the optimization problem. An improved spectral modeling method is presented in the present paper, which reduces the dimensionality of optimization problem by determining the overlapped peaks in spectrum. Experimental results show that the spectral modeling based on the new method is more accurate and needs much shorter running time than conventional method.

[Fast discrimination of edible vegetable oil based on Raman spectroscopy].

A novel method to fast discriminate edible vegetable oils by Raman spectroscopy is presented. The training set is composed of different edible vegetable oils with known classes. Based on their original Raman spectra, baseline correction and normalization were applied to obtain standard spectra. Two characteristic peaks describing the unsaturated degree of vegetable oil were selected as feature vectors; then the centers of all classes were calculated. For an edible vegetable oil with unknown class, the same pretreatment and feature extraction methods were used. The Euclidian distances between the feature vector of the unknown sample and the center of each class were calculated, and the class of the unknown sample was finally determined by the minimum distance. For 43 edible vegetable oil samples from seven different classes, experimental results show that the clustering effect of each class was more obvious and the class distance was much larger with the new feature extraction method compared with PCA. The above classification model can be applied to discriminate unknown edible vegetable oils rapidly and accurately.

Effect of dendrobium mixture in alleviating diabetic cognitive impairment associated with regulating gut microbiota.

Dendrobium mixture (DM) is a patent Chinese herbal formulation consisting of Dendrobii Caulis, Astragali Radix, Rehmanniae Radix as the main ingredients. DM has been shown to alleviate diabetic related symptoms attributed to its anti-hyperglycaemic and anti-inflammatory activities. However, the effect on diabetic induced cognitive dysfunction has not been investigated. This study aims to investigate the effect of DM in improving diabetic cognitive impairment and associated mechanisms. Our study confirmed the anti-hyperglycaemic effect of DM and showed its capacity to restore the cognitive and memory function in high fat/high glucose and streptozotocin-induced diabetic rats. The neuroprotective effect was manifested as improved learning and memory behaviours, restored blood-brain barrier tight junction, and enhanced expressions of neuronal survival related biomarkers. DM protected the colon tight junction, and effectively lowered the circulated proinflammatory mediators including tumour necrosis factor-α, interleukin-6 and lipopolysaccharides. In the gut microbiota, DM corrected the increase in the abundance of Firmicutes, the increase in the ratio of Firmicutes/Bacteroidetes, and the decrease in the abundance of Bacteroidetes in diabetic rats. It also reversed the abundance of Lactobacillus, Ruminococcus and Allobaculum genera. Short chain fatty acids, isobutyric acid and ethylmethylacetic acid, were negatively and significantly correlated to Ruminococcus and Allobaculum. Isovaleric acid was positively and significantly correlated with Lactobacillus, which all contributing to the improvement in glucose level, systemic inflammation and cognitive function in diabetic rats. Our results demonstrated the potential of DM as a promising therapeutic agent in treating diabetic cognitive impairment and the underlying mechanism may be associated with regulating gut microbiota.

[A fast classification method for petroleum products based on the Raman spectroscopy].

A fast and effective method for classification of petroleum products based on Raman spectroscopy is proposed. A knowledge base composed by Raman spectra of training samples, intra-class feature spectra and intra-class thresholds of all classes was firstly established. Then, correlation coefficients between the test sample and the intra-class feature spectra were calculated. If the maximal correlation coefficient of the test sample is larger than or equal to the corresponding intra-class threshold, the test sample is determined to belong to the corresponding class. For 96 petroleum product samples belonging to 7 classes and 4 unknown samples, the experimental results show that this method can accurately classify known test samples and can also find the unknown test samples. This method costs little calculation time and human interference. Moreover, it can be easily implemented in the practical application.

[The application of piecewise direct standardization with SNV in calibration transfer of Raman spectra].

To implement calibration transfer between Raman spectrometers, an improved piecewise direct standardization (PDS) is proposed in the present paper. Standard normal variate (SNV) is firstly introduced to reduce the influence of spectral background and intensity corresponding to the master spectrometer and the slave spectrometer; then PDS algorithm is used to eliminate the differences between Raman spectra for a specific sample. Moreover, a new quantitative criterion, called transfer error rate, is proposed to evaluate the performance of calibration model transfer. This improved PDS is applied to Raman spectral analysis of gasoline. The result shows that the proposed algorithm not only needs a small quantity of transfer samples, but also obtains high transfer accuracy and strong model robustness.

[Fast recognition of gasoline brands based on the Raman spectroscopy].

A novel method for fast recognition of gasoline brands based on the Raman spectroscopy is presented. A classification model on the basis of product gasoline samples with known brands was established. The detailed modeling process includes measurement and pretreatment of Raman spectra of these samples, principal component analysis (PCA) to obtain loading vectors and score vectors of all known samples, and calculating each average score vector for all of the samples with the same brand. For a gasoline sample with unknown brand, first measure and preprocess its Raman spectrum with the same pretreatment algorithm, then calculate its score vector on the above loading vectors and its distances to the average score vectors for different brands, and finally determine the brand of the unknown sample by the minimum distance. For 45 product gasoline samples from different refinery, experimental results show that there are significant distances between different brands in the principal component space, and the above classification model can decide the brand of unknown gasoline samples rapidly and accurately.

[Fast analysis of benzene concentration in gasoline based on low-resolution dispersive Raman spectroscopy].

In order to fast analyze the benzene concentration in gasoline, a new measure method based on low-resolution dispersive Raman spectroscopy is proposed. There exist strong measurement noise and fluorescence background in dispersive Raman spectra, so the present paper applies the Savitzky-Golay smoothing filter to remove the measurement noise and uses iterative polynomial curve-fitting to reduce the fluorescence background. Based on ridge regression, principal component regression and partial least squares algorithm, three calibration models of the benzene concentration in gasoline are built and validated by a set of gasoline samples from a refinery. Experimental results show that their repeatability and reproducibility can satisfy the accuracy requirement specified by the standards SH/T0713-2002, regardless of applying what kind of calibration models. In addition to its low cost, small size, convenience to use and so on, the fast measure method based on low-resolution dispersive Raman spectroscopy can be widely applied to the routine analysis.

[Hydrocarbon group analysis of gasoline by Raman spectroscopy with outlier detection].

Hydrocarbon group of gasoline not only determines the quality of gasoline, but also directly relates to the impact of exhaust air on the environment. The present paper successfully applied Raman spectroscopy to the quantitative analysis of hydrocarbon group in gasoline. Contaminated samples were removed from calibration set by outlier detection, which effectively improved the partial least squares (PLS) prediction accuracy. The standard prediction errors of aromatics, olefin and oxygen content are 0.23, 0.52 and 0.143 respectively, and the corresponding multiple correlation coefficients of prediction (R2) are 0.987, 0.927 and 0.971. Experimental results show that the effectiveness of Raman spectroscopy for hydrocarbon group analysis in gasoline, and that the prediction accuracy is much better than near infrared spectroscopy or multidimensional gas chromatography. Quantitative calibrations based on Raman spectroscopy can also be used in the on-line analysis of gasoline production processes.

[Application of Raman spectra feature extraction in chemical fiber component qualitative identification].

According to the characteristics of the textile fibers Raman spectra, a qualitative identification method based on Raman feature extraction is proposed. This fast method consists of spectrum measurement and spectral data processing algorithm, including spectrum preprocessing, feature extraction and matching recognition. It can be used to identify the components of fibers or fabrics, especially chemical fibers, which is an inspective difficulty in daily analytic work for its remarkable Raman feature. The authors performed an experiment to analyze 4 typical and widely used kinds of fibers as algorithm verification. They are terylene fiber, acrylic fiber, nylon fiber and rayon fiber. To identify the components of one test sample, first the authors set up feature tables of these 4 standard samples, which describe the features of their preprocessed spectra containing both position information and intensity information, then extract features of the test sample. The authors match these features with the tables and calculate the matching confidence coefficients of the results, which can be used to filter the unexpected matching results caused by accident and attain the final qualitative identification result. The experimental results confirm that this method is effective, efficient and expansible, which means it can be used to identify more actual fiber types by adding more standard spectra to the feature table database. In addition, it is a pure optical method, which needs only a small quantity of sample without any pretreatment. The whole identification process is damage-free, pollution-free and suitable for various kinds of fabrics. Compared to all existing methods, this Raman spectrum identification method can solve the limitation of efficiency, pollution, universality, and fill a gap in fabric inspection field.

Serum calcium as a biomarker of clinical severity and prognosis in patients with coronavirus disease 2019.

The aim of this study was to investigate the correlations between serum calcium and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). In this retrospective study, serum calcium levels, hormone levels and clinical laboratory parameters on admission were recorded. The clinical outcome variables were also recorded. From February 10 to February 28, 2020, 241 patients were enrolled. Of these patients, 180 (74.7%) had hypocalcemia on admission. The median serum calcium levels were 2.12 (IQR, 2.04-2.20) mmol/L, median parathyroid hormone (PTH) levels were 55.27 (IQR, 42.73-73.15) pg/mL, and median 25-hydroxy-vitamin D (VD) levels were 10.20 (IQR, 8.20-12.65) ng/mL. The serum calcium levels were significantly positively correlated with VD levels (P =0.004) but negatively correlated with PTH levels (P =0.048). Patients with lower serum calcium levels (especially ≤2.0 mmol/L) had worse clinical parameters, higher incidences of organ injury and septic shock, and higher 28-day mortality. The areas under the receiver operating characteristic curves of multiple organ dysfunction syndrome, septic shock, and 28-day mortality were 0.923 (P <0.001), 0.905 (P =0.001), and 0.929 (P <0.001), respectively. In conclusion, serum calcium was associated with the clinical severity and prognosis of patients with COVID-19. Hypocalcemia may be associated with imbalanced VD and PTH levels.

[Spectral wavelength selection based on PLS projection analysis].

In order to enhance the prediction accuracy of spectral analysis models and reduce their input number, this paper presents a simple and rapid wavelength selection method based on PLS projection correlation coefficients. These correlation coefficients are decided by both the changes in spectra data and the PLS regression coefficients between spectra matrix and concentration vector. Compared with the traditional wavelength selection method based on correlation analysis, the novel proposed method obviously improves the robustness of spectral analysis models and reduces their input number sharply. Applying the proposed method to 208 gasoline samples, the experimental results show that the number of calibration model input decreases to 30% of the original wavelength number, and the root mean square error of cross validation is reduced from 0.44 to 0.34. This method can be widely used in wavelength selection and data compression in spectral quantitative analysis.

[Near-infrared spectral quantitative analysis by combining classification with local PLS].

As a rapid analytical technology, near-infrared (NIR) spectroscopy has been developed fast in recent years. To improve the accuracy of near-infrared spectral quantitative analysis, the present paper first classifies a testing sample by a support vector machine classifier and selects some similar training samples of the same type to build the calibration model, than predicts the property of the testing sample. To avoid the negative influence of classification failure, a new hybrid algorithm (called H_PLS) was proposed. This algorithm consists of a local PLS model based on the same-type training samples (called C_PLS) and a local PLS model based on the total training samples (called D_PLS). H_PLS calculates the predictive value of the property for the testing sample by comparing the outputs of the two models. For a set of gasoline samples, experimental results show that the prediction accuracy of C_PLS is higher than that of D_PLS if there are no classification errors, otherwise the prediction accuracy of C_PLS will drop obviously. The novel proposed algorithm (H_PLS) combines the advantages of C_PLS and D_PLS. Using H_PLS, can increase from 0.9734 of D_PLS and 0.9656 of C_PLS to 0.9858 even though there are classification errors.