Rapid quantification of grapeseed oil multiple adulterations using near-infrared spectroscopy coupled with a novel double ensemble modeling method

As a high-quality edible oil, grapeseed oil is often adulterated with low-price/quality vegetable oils. A novel ensemble modeling method is proposed for quantitative analysis of grapeseed oil adulterations combined with near-infrared (NIR) spectroscopy. The method combines Monte Carlo (MC) sampling and whale optimization algorithm (WOA) to build numerous partial least squares (PLS) sub-models, named MC-WOA-PLS. A total of 80 adulterated grapeseed oil samples were prepared by mixing grapeseed oil with soybean oil, palm oil, cottonseed oil, and corn oil with the designed mass percentages. NIR spectra of the 80 samples were measured in a transmittance mode in the range of 12,000-4000 cm-1. Parameters in MC-WOA-PLS including the number of latent variables (LVs) in PLS, iteration number of WOA, whale number, number of PLS sub-models, and percentage of training subsets were optimized. To validate the prediction performance of the model, root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean squared error of prediction (RMSEP), correlation coefficient (R), residual predictive deviation (RPD), and standard deviation (S.D.) were used. Compared with PLS, standard normal variate-PLS (SNV-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS), randomization test-PLS (RT-PLS), variable importance in projection-PLS (VIP-PLS), and WOA-PLS, MC-WOA-PLS achieves the best prediction accuracy and stability for quantification of the five pure oils in adulterated grapeseed oil samples.

Discretized butterfly optimization algorithm for variable selection in the rapid determination of cholesterol by near-infrared spectroscopy

The blood cholesterol level is strongly associated with cardiovascular disease. It is necessary to develop a rapid method to determine the cholesterol concentration of blood. In this study, a discretized butterfly optimization algorithm-partial least squares (BOA-PLS) method combined with near-infrared (NIR) spectroscopy is firstly proposed for rapid determination of the cholesterol concentration in blood. In discretized BOA, the butterfly vector is described by 1 or 0, which represents whether the variable is selected or not, respectively. In the optimization process, four transfer functions, i.e., arctangent, V-shaped, improved arctangent (I-atan) and improved V-shaped (I-V), are introduced and compared for discretization of the butterfly position. The partial least squares (PLS) model is established between the selected NIR variables and cholesterol concentrations. The iteration number, transfer functions and the performance of butterflies are investigated. The proposed method is compared with full-spectrum PLS, multiplicative scatter correction-PLS (MSC-PLS), max-min scaling-PLS (MMS-PLS), MSC-MMS-PLS, uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Results show that the I-V function is the best transfer function for discretization. Both preprocessing and variable selection can improve the prediction performance of PLS. Variable selection methods based on BOA are better than those based on statistics. Furthermore, I-V-BOA-PLS has the highest predictive accuracy among the seven variable selection methods. MSC-MMS can further improve the prediction ability of I-V-BOA-PLS. Therefore, BOA-PLS combined with NIR spectroscopy is promising for the rapid determination of cholesterol concentration in blood.

Variational Mode Decomposition for Raman Spectral Denoising

As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two artificial noised signals and two Raman spectra of inorganic materials, i.e., MnCo ISAs/CN and Fe-NCNT. For comparison, empirical mode decomposition (EMD), Savitzky-Golay (SG) smoothing, and discrete wavelet transformation (DWT) are also investigated. At the same time, signal-to-noise ratio (SNR) was introduced as evaluation indicators to verify the performance of the proposed method. The results show that compared with EMD, VMD can significantly improve mode mixing and the endpoint effect. Moreover, the Raman spectrum by VMD denoising is more excellent than that of EMD, SG smoothing and DWT in terms of visualization and SNR. For the small sharp peaks, some information is lost after denoising by EMD, SG smoothing, DWT and VMD while VMD loses fewest information. Therefore, VMD may be an alternative method for Raman spectral denoising.

Development and validation of a multiphase CT radiomics nomogram for the preoperative prediction of lymphovascular invasion in patients with gastric cancer

AIM

To develop a nomogram to predict lymphovascular invasion (LVI) in gastric cancer by integrating multiphase computed tomography (CT) radiomics and clinical risk factors.

MATERIALS AND METHODS

One hundred and seventy-two gastric cancer patients (121 training and 51 validation) with preoperative contrast-enhanced CT images and clinicopathological data were collected retrospectively. The clinical risk factors were selected by univariate and multivariate regression analysis. Radiomic features were extracted and selected from the arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images of each patient. Clinical risk factors, radiomic features, and integration of both were used to develop the clinical model, radiomic models, and nomogram, respectively.

RESULTS

Radiomic features from AP (n=6), VP (n=6), DP (n=7) CT images and three selected clinical risk factors were used for model development. The nomogram showed better performance than the AP, VP, DP, and clinical models in the training and validation datasets, providing areas under the curves (AUCs) of 0.890 (95% CI: 0.820-0.940) and 0.885 (95% CI:0.765-0.957), respectively. All models indicated good calibration, and decision curve analysis proved that the net benefit of the nomogram was superior to that of the clinical and radiomic models throughout the vast majority of the threshold probabilities.

CONCLUSIONS

The nomogram integrating multiphase CT radiomics and clinical risk factors showed favourable performance in predicting LVI of gastric cancer, which may benefit clinical practice.

Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil

The quantification of single oil in high order edible blend oil is a challenging task. In this research, a novel swarm intelligence algorithm, discretized whale optimization algorithm (WOA), was first developed for reducing irrelevant variables and improving prediction accuracy of hexanary edible blend oil samples. The WOA is inspired by hunting strategy of humpback whales, which mainly includes three behaviors, i.e., encircling prey, bubble-net attacking and searching for prey. In discretized WOA, positions of whales were updated and then discretized by arctangent function. The whale population performance, iteration number and whale number of WOA were investigated. To validate the performance of selected variables, partial least squares (PLS) was used to build model and predict single oil contents in hexanary blend oil. Results show that WOA-PLS can provide the best prediction accuracy compared with full-spectrum PLS, continuous wavelet transform-PLS (CWT-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Furthermore, CWT-WOA-PLS can further produce better results with fewer variables compared with WOA-PLS.

Spectral denoising based on Hilbert–Huang transform combined with F-test

Due to the influence of uncontrollable factors such as the environment and instruments, noise is unavoidable in a spectral signal, which may affect the spectral resolution and analysis result. In the present work, a novel spectral denoising method is developed based on the Hilbert–Huang transform (HHT) and F-test. In this approach, the original spectral signal is first decomposed by empirical mode decomposition (EMD). A series of intrinsic mode functions (IMFs) and a residual (r) are obtained. Then, the Hilbert transform (HT) is performed on each IMF and r to calculate their instantaneous frequencies. The mean and standard deviation of instantaneous frequencies are calculated to further illustrate the IMF frequency information. Third, the F-test is used to determine the cut-off point between noise frequency components and non-noise ones. Finally, the denoising signal is reconstructed by adding the IMF components after the cut-off point. Artificially chemical noised signal, X-ray diffraction (XRD) spectrum, and X-ray photoelectron spectrum (XPS) are used to validate the performance of the method in terms of the signal-to-noise ratio (SNR). The results show that the method provides superior denoising capabilities compared with Savitzky–Golay (SG) smoothing.

A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends

Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field.

Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants

The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offcinarum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy.

Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil

In this study, near infrared (NIR) spectroscopy combined with chemometrics was used for the quantitative analysis of corn oil in binary to hexanary edible blend oil. Sesame oil, soybean oil, rice oil, sunflower oil and peanut oil were mixed with corn oil subsequently to form binary, ternary, quaternary, quinary and hexanary blend oil datasets. NIR spectra for the five order blend oil datasets were measured in a transmittance mode in the range of 12000-4000 cm^-1. Partial least square (PLS) was used to build models for the five datasets. Six spectral preprocessing methods and their combinations were investigated to improve the prediction performance. Furthermore, the optimal preprocessing-PLS models were further optimized by uninformative variable elimination (UVE), Monte Carlo uninformative variable elimination (MCUVE) and randomization test (RT) variable selection methods. The optimal models acquire root mean square error of prediction (RMSEP) of 1.7299, 2.2089, 2.3742, 2.5608 and 2.6858 for binary, ternary, quaternary, quinary and hexanary blend oil datasets, respectively. The determination coefficients of prediction set (R²_P) and residual predictive deviations (RPDs) for the five datasets are all above 0.93 and 3. Results show that the prediction accuracy is gradually decreased with the increasing of mixture order of blend oil. However, with proper spectral preprocessing and variable selection, the optimal models present good prediction accuracy even for the higher order blend oil. It demonstrates that NIR technology is feasible for determining the pure oil contents in binary to hexanary blend oil.

Rapid determination of hemoglobin concentration by a novel ensemble extreme learning machine method combined with near-infrared spectroscopy

A novel ensemble extreme learning machine (ELM) approach that combines Monte Carlo (MC) sampling and least absolute shrinkage and selection operator (LASSO), named as MC-LASSO-ELM, is proposed to determine hemoglobin concentration of blood. It employs MC sampling to randomly select samples from the training set and LASSO further to choose variables from selected samples to establish plenty of ELM sub-models. The final prediction is obtained by combining the predictions of these sub-models. Combined with near-infrared spectroscopy, MC-LASSO-ELM is used to determine the hemoglobin concentration of blood. Compared with ELM, MC-ELM and LASSO-ELM, MC-LASSO-ELM can obtain the best stability and highest accuracy.

The expressions of miR-151a-5p and miR-23b in lung cancer tissues and their effects on the biological functions of lung cancer A549 cells

OBJECTIVE

To investigate the expressions of miR-151a-5p and miR-23b in lung cancer tissues and their effects on the biological functions of lung cancer A549 cells.

PATIENTS AND METHODS

Samples of lung cancer tissue (55 cases) and pericarcinomatous tissue (55 cases) were collected in thoracic surgery in our hospital from May 2017 to November 2018. The expression levels of miR-151a-5p and miR-23b in lung cancer tissues and pericarcinomatous tissues were detected by RT-PCR. Lung cancer cells A549 were transfected. Before transfection, the cells were divided into a negative control group (NC group, transfected with miRNA NC), a group transfected with miR-151a-5p inhibitor and a group transfected with miR-23b inhibitor. MTS Cell Proliferation Colorimetric Assay Kit (CCK8) was used to detect cell proliferation and draw the growth curve. Transwell chamber was used to detect the invasion ability in vitro, and BD flow cytometry was used to detect apoptosis in each group.

RESULTS

The expression levels of miR-151a-5p and miR-23b in lung cancer tissues were significantly higher than those in pericarcinomatous tissues (p<0.001). After 48 h to 72 h, the cell growth of both the miR-151a-5p inhibitor group and the miR-23b inhibitor group was significantly lower than that of the NC group (p<0.001). The numbers of invasion of miR-151a-5p inhibitor group and miR-23b inhibitor group were significantly lower than that of NC group (p<0.00). The apoptosis rates of miR-151a-5p inhibitor group and miR-23b inhibitor group were significantly higher than that of NC group (p<0.001).

CONCLUSIONS

Both miR-151a-5p and miR-23b are highly expressed in lung cancer, and the inhibition of miR-151a-5p and miR-23b can restrain the proliferation, invasion and migration of lung cancer A549 cells, thereby promoting the apoptosis of lung cancer A549 cells.

A new ensemble modeling method for multivariate calibration of near infrared spectra

Ensemble modeling has gained increasing attention for improving the performance of quantitative models in near infrared (NIR) spectral analysis. Based on Monte Carlo (MC) resampling, least absolute shrinkage and selection operator (LASSO) and partial least squares (PLS), a new ensemble strategy named MC-LASSO-PLS is proposed for NIR spectral multivariate calibration. In this method, the training subsets for building the sub-models are generated by sampling from both samples and variables to ensure the diversity of the models. In detail, a certain number of samples as sample subsets are randomly selected from training set. Then, LASSO is used to shrink the variables of the sample subset to form the training subset, which is used to build the PLS sub-model. This process is repeated N times and N sub-models are obtained. Finally, the predictions of these sub-models are used to produce the final prediction by simple average. The prediction ability of the proposed method was compared with those of LASSO-PLS, MC-PLS and PLS models on the NIR spectra of corn, blend oil and orange juice samples. The superiority of MC-LASSO-PLS in prediction ability is demonstrated.

Weighted multiscale support vector regression for fast quantification of vegetable oils in edible blend oil by ultraviolet-visible spectroscopy

Weighted multiscale support vector regression combined with ultraviolet-visible (UV-Vis) spectra for quantitative analysis of edible blend oil is proposed. In the approach, UV-Vis spectra of the training set are decomposed into a certain number of intrinsic mode functions (IMFs) and a residue by empirical mode decomposition (EMD) at first. Then support vector regression (SVR) sub-models are built on each IMF and residue. For prediction set, the spectra are decomposed as done on the training set and the final predictions are obtained by integrating SVR sub-model predictions by weighted average. The weight of the sub-model is the reciprocal of the fourth power of the root mean square error of cross-validation (RMSECV). For predicting peanut oil in binary blend oil and sesame oil in ternary blend oil, the proposed method has superiority in root mean square error of prediction (RMSEP) and correlation coefficient (R) compared with SVR and partial least squares (PLS).

Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs

Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 Angelicae Pubescentis Radix (APR), 38 Chuanxiong Rhizoma (CR), 35 Atractylodis Macrocephalae Rhizoma (AMR) and 39 Angelicae Dahuricae Radix (ADR), were collected and divided into the training and prediction sets. Principal component analysis (PCA) was used for observing the sample cluster tendency of the calibration set. Different preprocessing methods were investigated and the optimal preprocessing combination was selected according to spectral signal characteristics and three-dimensional PCA (3D PCA) clustering results. The final discriminant model was built using extreme learning machine (ELM). The exploratory studies on the raw spectra and their 3D PCA scores indicate that the classification of the five herbs cannot be achieved by PCA of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to different degrees. Furthermore, their combination in the order of CWT + autoscaling + SG smoothing can enhance the spectral resolution and obtain the best clustering result. These results are also validated using ELM models of raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved in both the calibration set and the prediction set. Therefore, the developed method could be used as a rapid, economic and effective method for discriminating the five herbs used in this study.

The shuttling mechanism of foldaxanes: more than just translocation and rotation

Tailoring the structures of nanomachines to achieve specific functions is one of the major challenges in chemistry. Disentangling the different movements of nanomachines is critical to characterize their functions. Here, the motions within one kind of molecular machine, a foldaxane, composed of a foldamer with a spring-like conformation on an axle have been examined at the molecular level. With the aid of molecular dynamics simulations and enhanced sampling methods, the free-energy landscape characterizing the shuttling of the foldaxane has been drawn. The calculated free-energy barrier, amounting to 20.7 kcal mol^-1, is in good agreement with experiments. Further analysis reveals that the predominant contribution to the free-energy barrier stems from the disruption of the hydrogen bonds between the foldamer and the thread. In the absence of hydrogen bonding interactions between the terminals of the foldamer and the thread, shrinkage and swelling movements of the foldamer have been identified and investigated in detail. By deciphering the intricate mechanism of how the foldaxane shuttles, our understanding of motions within molecular machines is expected to be improved, which will, in turn, assist the construction of molecular machines with specific functions.

Modeled changes to the Great Plains low-level jet under a realistic irrigation application

Low-level jets (LLJs) are relatively fast-moving streams of air that form in the lower troposphere and are a common phenomenon across the Great Plains (GP) of the United States. LLJs play an important role in moisture transport and the development of nocturnal convection in the spring and summer. Alterations to surface moisture and energy fluxes can influence the planetary boundary layer (PBL) development and thus LLJs. One important anthropogenic process that has been shown to affect the surface energy budget is irrigation. In this study, we investigate the effects of irrigation on LLJ development across the GP by incorporating a dynamic and realistic irrigation scheme into the Weather Research and Forecasting (WRF) model. WRF simulations were conducted with and without the irrigation scheme for the exceptionally dry summer of 2012 over the GP. The results show irrigation-introduced changes to LLJ features both over and downstream of the most heavily irrigated regions in the GP. There were statistically significant increases to LLJ speeds in the simulation with the irrigation parameterization. Decreases to the mean jet core height on the order of 50 m during the overnight hours were also simulated when irrigation was on. The overall frequency of jet occurrences increased over the irrigated regions by 5-10%; however, these differences were not statistically significant. These changes were weaker than those reported in earlier studies based on simple representations of irrigation that unrealistically saturate the soil columns over large areas over a long period of time, which highlights the importance and necessity to represent human activity more accurately in modeling studies.

Unveiling the helicity switching mechanism of a rigid two-tiered stacked architecture

Conformational inversion of foldamers has been shown to transmit signals across the lipid membrane. Helicity switching is critical to fulfilling this function. Despite the importance of the conformational inversion, the mechanism that underlies the helicity switching process remains unclear. In the present contribution, a rigid two-tiered stacked architecture (2T) has been investigated at the atomic level using molecular simulations. The architecture consists of two conjugated cores and three flexible side chains. Two- and three-dimensional free-energy landscapes characterizing the isomerization of 2T reveal a four-stage helicity switching process. Four stages involve the flipping of three peripheral aromatic rings in the top tier and rotating of the bottom tier relative to the top one. The highest barrier hampering the transition between right-handed and left-handed helices emerges as the second benzene ring flips. Structural analysis shows that the ring flipping stretches the side chain, which leads to the deformation of conjugated cores, twist of dihedral angles within side chains, and the reorientation of amine moieties attached to chains. By deciphering the intricate mechanism whereby the rigid stacked architecture isomerizes, our understanding of the helicity switching is expected to be improved, helping in turn the construction of novel functional helices.

Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics

Traditional methods for identification of Panax notoginseng (PN) such as high performance liquid chromatography (HPLC) and gas chromatography (GC) are time-consuming, laborious and difficult to realize rapid and online analysis. In this research, the feasibility of identification and quantification of PN with rhizoma curcumae (RC), Curcuma longa (CL) and rhizoma alpiniae offcinarum (RAO) are investigated by using near infrared (NIR) spectroscopy combined with chemometrics. Five chemical pattern recognition methods including hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), artificial neural networks (ANN), support vector machine (SVM) and extreme learning machine (ELM) are used to build identification model of the dataset with 109 samples of PN and its three adulterants. Then seven datasets of binary, ternary and quaternary adulterations of PN are designed, respectively. Five multivariate calibration methods, i.e., principal component regression (PCR), support vector regression (SVR), partial least squares regression (PLSR), ANN and ELM are used to build quantitative model and compared for each dataset, separately. Finally, in order to further improve the prediction accuracy, SG smoothing, 1st derivative, 2nd derivative, continuous wavelet transform (CWT), standard normal variate (SNV), multiple scatter correction (MSC) and their combinations are investigated. Results show that PLS-DA and SVM can achieve 100% classification accuracy for identification of 109 PN with its three adulterants. PLSR is an optimal calibration method by comprehensive consideration of prediction accuracy, over-fitting and efficiency for the quantitative analysis of seven adulterated datasets. Furthermore, the predictive ability of the PLSR model for PN contents can be improved obvious by pretreating the spectra by the optimal preprocessing method, with correlation coefficients of which all higher than 0.99.

Correction: Unveiling the helicity switching mechanism of a rigid two-tiered stacked architecture

Correction for ‘Unveiling the helicity switching mechanism of a rigid two-tiered stacked architecture’ by Peng Liu et al., RSC Adv., 2019, 9, 1501–1508.

Deciphering the helicity switching mechanism: a case study of the rigid three-tiered stacked architecture

Understanding the switching mechanism of helical molecular cages is critical in regulating their functions of asymmetric catalysis and enantioseparation. The helical inversion of a three-tiered stacked architecture was investigated by employing molecular dynamics simulations combined with free-energy calculations. A two-dimensional free-energy landscape characterizing the spinning processes of the top and bottom tiers around the z axis was determined using the extended adaptive biasing force method. The free-energy barrier in the least free-energy pathway was estimated to be 17.6 kcal mol-1, in excellent agreement with experimental measurements. Further analysis revealed that the barrier was caused by geometric deformation, weakening of π-π stacking between aromatic rings, and the re-orientation of polarized amine moieties. The present contribution takes a step toward understanding the dynamic helicity-based functions related to asymmetric reactions and optical resolution.

Tailoring optical pulling force on gain coated nanoparticles with nonlocal effective medium theory

We study the optical scattering force on the coated nanoparticles with gain core and nonlocal plasmonic shell in the long-wavelength limit, and demonstrate negative optical force acting on the nanoparticles near the symmetric and/or antisymmetric surface plasmon resonances. To understand the optical force behavior, we propose nonlocal effective medium theory to derive the equivalent permittivity for the coated nanoparticles with nonlocality. We show that the imaginary part of the equivalent permittivity is negative near the surface resonant wavelength, resulting in the negative optical force. The introduction of nonlocality may shift the resonant wavelength of the optical force, and strengthen the negative optical force. Two examples of Fano-like resonant scattering in such coated nanoparticles are considered, and Fano resonance-induced negative optical force is found too. Our findings could have some potential applications in plasmonics, nano-optical manipulation, and optical selection.

[Epidemiological investigation of hospitalized children with burn injury in a hospital of Qingdao]

Objective: To investigate epidemiological characteristics of hospitalized children with burn injury in the author's affiliation, so as to provide theoretical basis for developing prevention strategies of children with burn injury. Methods: Medical records of 384 and 596 hospitalized children with burn injury, aged 0 to 12-year-old, were collected respectively from January 2001 to December 2005 and January 2011 to December 2015. Percentage of children with burn injury to total hospitalized patients with burn injury in the same period of time, age, causes of injury, gender, injury month, residence, condition of first aid measures conforming to medical standard, time of admission post injury, burn degree, and operation condition of children with burn injury were analyzed. Data were processed with Mann-Whitney U test and Chi-square test. Results: From January 2001 to December 2005 and January 2011 to December 2015, percentages of children with burn injury to total hospitalized patients with burn injury in the same period of time were respectively 23.6% (384/1 626) and 25.4% (596/2 346) , with no statistically significant difference (χ(2)=1.653, P>0.05). Age of all children with burn injury was 1.0 (1.0, 2.0) year old from January 2011 to December 2015, obviously lower than that from January 2001 to December 2005[1.0 (1.0, 3.0) year old, Z=-3.257, P<0.01]. Ages of children with burn caused by hot liquid and electrical burn from January 2011 to December 2015 were obviously lower than those from January 2001 to December 2005 (with Z values respectively -4.248 and -2.040, P<0.05 or P<0.01). Compared with that from January 2001 to December 2005, age of children with burn caused by flame from January 2011 to December 2015 increased, with no statistically significant difference (Z=1.852, P>0.05). There was no statistically significant difference in gender of children with burn injury between the two periods of time (χ(2)=1.374, P>0.05). Burn injury of children in the two periods of time mainly occurred in Spring, and season of burn injury between the two periods of time was similar (χ(2)=1.177, P>0.05). There was statistically significant difference in residence of children with burn injury between the two periods of time (χ(2)=15.513, P<0.01). The number of children with burn injury of first aid measures conforming to medical standard and admission within 6 h post injury from January 2011 to December 2015 was obviously more than that from January 2001 to December 2005 (with χ(2) values respectively 7.434 and 43.961, P values below 0.01). Burn degrees of children with burn injury mainly were moderate in the two periods of time, and there was no statistically significant difference in burn degree and condition of operation between the two periods of time (with χ(2) values respectively 5.731 and 1.583, P values above 0.05). Conclusions: Burn of children is a social problem. We should make great efforts on popularization of prevention and treatment about burn of children, especially children with younger age in rural areas. We should publicize standard first aid measures of burn of children and advocate admission of burn of children within 6 h post burn injury for treatment.

Novel affinity membranes with macrocyclic spacer arms synthesized via click chemistry for lysozyme binding

Affinity membrane has great potential for applications in bioseparation and purification. Disclosed herein is the design of a novel affinity membrane with macrocyclic spacer arms for lysozyme binding. The clickable azide-cyclodextrin (CD) arms and clickable alkyne ethylene-vinyl alcohol (EVAL) chains are designed and prepared. By the azide-alkyne click reaction, the EVAL-CD-ligands affinity membranes with CD spacer arms in three-dimensional micro channels have been successfully fabricated. The FT-IR, XPS, NMR, SEM and SEM-EDS results give detailed information of structure evolution. The abundant pores in membrane matrix provide efficient working channels, and the introduced CD arms with ligands (affinity sites) provide supramolecular atmosphere. Compared with that of raw EVAL membrane, the adsorption capacity of EVAL-CD-ligands membrane (26.24mg/g) show a triple increase. The study indicates that three effects (inducing effect, arm effect, site effect) from CD arms render the enhanced performance. The click reaction happened in membrane matrix in bulk. The effective lysozyme binding and higher adsorption performance of affinity membranes described herein compared with other reported membranes are markedly related with the proposed strategy involving macrocyclic spacer arms and supramolecular working channels.

Simultaneous spectrophotometric determination of trace copper, nickel, and cobalt ions in water samples using solid phase extraction coupled with partial least squares approaches

A simultaneous spectrophotometric determination method for trace heavy metal ions based on solid-phase extraction coupled with partial least squares approaches was developed. In the proposed method, trace metal ions in aqueous samples were adsorbed by cation exchange fibers and desorbed by acidic solution from the fibers. After the ion preconcentration process, the enriched solution was detected by ultraviolet and visible spectrophotometer (UV-Vis). Then, the concentration of heavy metal ions were quantified by analyzing ultraviolet and visible spectrum with the help of partial least squares (PLS) approaches. Under the optimal conditions of operation time, flow rate and detection parameters, the overlapped absorption peaks of mixed ions were obtained. The experimental data showed that the concentration, which can be calculated through chemometrics method, of each metal ion increased significantly. The heavy metal ions can be enriched more than 80-fold. The limits of detection (LOD) for the target analytes of copper ions (Cu²⁺), cobalt ions (Co²⁺) and nickel ions (Ni²⁺) mixture was 0.10μgL^-1, 0.15μgL^-1 and 0.13μgL^-1, respectively. The relative standard deviations (RSD) were less than 5%. The performance of the solid-phase extraction can enrich the ions efficiently and the combined method of spectrophotometric detection and PLS can evaluate the ions concentration accurately. The work proposed here is an interesting and promising attempt for the trace ions determination in water samples and will have much more applied field.

Clinical effect of azithromycin as an adjunct to non-surgical treatment of chronic periodontitis: a meta-analysis of randomized controlled clinical trials

The results of recent published studies focusing on the effect of azithromycin as an adjunct to scaling and root planing (SRP) in the treatment of chronic periodontitis are inconsistent. We conducted a meta-analysis of randomized controlled clinical trials to examine the effect of azithromycin combined with SRP on periodontal clinical parameters as compared to SRP alone. An electronic search was carried out on Pubmed, Embase and the Cochrane Central Register of Controlled Trials from their earliest records through December 28, 2014 to identify studies that met pre-stated inclusion criteria. Reference lists of retrieved articles were also reviewed. Data were extracted independently by two authors. Either a fixed- or random-effects model was used to calculate the overall effect sizes of azithromycin on probing depth, attachment level (AL) and bleeding on probing (BOP). Heterogeneity was evaluated using the Q test and I(2) statistic. Publication bias was evaluated by Begg's test and Egger's test. A total of 14 trials were included in the meta-analysis. Compared with SRP alone, locally delivered azithromycin plus SRP statistically significantly reduced probing depth by 0.99 mm (95% CI 0.42-1.57) and increased AL by 1.12 mm (95% CI 0.31-1.92). In addition, systemically administered azithromycin plus SRP statistically significantly reduced probing depth by 0.21 mm (95% CI 0.12-0.29), BOP by 4.50% (95% CI 1.45-7.56) and increased AL by 0.23 mm (95% CI 0.07-0.39). Sensitivity analysis yielded similar results. No evidence of publication bias was observed. The additional benefit of systemic azithromycin was shown at the initially deep probing depth sites, but not at shallow or moderate sites. The overall effect sizes of systemic azithromycin showed a tendency to decrease with time, and meta-regression analysis suggested a negative relation between the length of follow-up and net change in probing depth (r = -0.05, p = 0.02). This meta-analysis provides further evidence that azithromycin used as an adjunct to SRP significantly improves the efficacy of non-surgical periodontal therapy on reducing probing depth, BOP and improving AL, particularly at the initially deep probing depth sites.

Non-invasive prenatal testing for fetal aneuploidies in the first trimester of pregnancy

OBJECTIVES

To evaluate the feasibility of non-invasive prenatal testing (NIPT) of maternal plasma samples collected from pregnant Chinese women in early gestation, between 8 + 0 and 12 + 6 weeks' gestation.

METHODS

In this pilot study, 212 women with high-risk pregnancies were recruited at a single Chinese Hospital. Fetal aneuploidies associated with chromosomes 21, 18, 13, X and Y were detected by massively parallel sequencing of maternal plasma DNA samples. Invasive prenatal diagnosis by either chorionic villus sampling or amniocentesis and then karyotyping was offered to all women to confirm both positive and negative NIPT results. Fetal DNA fraction was also determined in male pregnancies, by the relative percentage of Y-chromosome sequences. All confirmed NIPT-negative pregnancies were followed up to birth and neonates were clinically evaluated for any symptoms of chromosomal disease.

RESULTS

Autosomal aneuploidies trisomy 21 (n = 2), 18 (n = 1) and 13 (n = 1) were detected by NIPT and confirmed by amniocentesis and karyotyping. There were one false-positive 45,X sample and two false-negative samples associated with fetal karyotypes 47,XXY and 45,X[16]/47,XXX[14]. In the 100 male pregnancies, the median fetal DNA fraction was 8.54% and there was a trend towards an increasing fetal fraction from 8 + 0 to 12 + 6 weeks' gestation. The majority (95%) of pregnancies had a fetal DNA fraction > 4%, which is generally the limit for accurate aneuploidy detection by NIPT. Across this early gestational time period, there was a weak inverse relationship (R(2)  = 0.186) between fetal DNA fraction and maternal weight.

CONCLUSIONS

NIPT is highly reliable and accurate when applied to maternal DNA samples collected from pregnant women in the first trimester between 8 + 0 and 12 + 6 weeks.

Effects of high hemodynamics upon the morphology of the walls of the great saphenous vein and splenic vein

AIM

Studies have shown that the incidence and development of pathological changes in the walls of the great saphenous vein and splenic vein are closely related to high venous pressure. Such changes are referred to as "vascular adaptive remodeling responses under high venous pressure". The proposition of the concept of vascular remodeling contributes to our knowledge of pathological changes in the venous wall (dilation of the venous lumen and thickening of the venous wall). In the present study, we compared the histomorphology and cytomorphology of the walls of varicose great saphenous veins (GSVs) and diseased splenic veins (SVs) to investigate the remodeling of the venous wall under high hemodynamic pressure.

METHODS

We collected 34 samples of varicose great saphenous veins and diseased splenic veins. Thirty-four samples of normal great saphenous veins and splenic veins were also collected (control group). Samples were made into slices and observed under light microscopy and electron microscopy. The thickness of the tunica intima and tunica media as well as the inner diameter of the venous lumen were measured.

RESULTS

Under light microscopy, the walls of varicose veins stained with H&E were unevenly thickened, and those of diseased splenic veins were evenly thickened; mucoid degeneration of the tunica intima of varicose veins was not obvious by Masson staining (2/20 cases). The boundary between the tunica intima and tunica media was clearly defined. Uneven hyperplasia of muscular connective tissues was observed. For the diseased splenic-vein group, mucoid degeneration of the tunica intima was obvious (8/14 cases), with an unclearly defined boundary between the tunica intima and tunica extima. Uneven hyperplasia of muscular connective tissues was also observed. Differences in the thickness and inner diameter of the tunica intima and tunica media between the great saphenous vein and the splenic vein were significantly different. Under electron microscopy, mitochondrial degeneration in endothelial cells was observed in both groups. Increased numbers of rough endoplasmic reticula in the cytoplasm of smooth muscle cells, ribosomes and mitochondria and decreased numbers of myofilaments were also observed.

CONCLUSION

High hemodynamics affected the remodeling of varicose great saphenous veins and diseased splenic veins. The histomorphology of visceral veins showed more significant pathological changes than that of peripheral veins. Similar cytomorphological changes were observed in both groups.

Gene-gene interactions of IRF5, STAT4, IKZF1 and ETS1 in systemic lupus erythematosus

Interferon (IFN) activation signaling and T helper 17 (Th17)-cell/B-cell regulation play a critical role in the pathogenesis of systemic lupus erythematosus (SLE). Several studies have provided convincing evidence that polymorphisms in IRF5, STAT4, IKZF1 and ETS1 from these pathways may be involved in SLE by affecting gene expression or epistasis. We analyzed the genetic interaction in known SLE susceptibility loci from the four genes in northern Han Chinese. A total of 946 northern Han Chinese participated in this study (370 unrelated SLE patients and 576 healthy controls). Subjects underwent genotyping for the single-nucleotide polymorphisms (SNPs) rs2004640 in IRF5, rs7574865 in STAT4, rs4917014 in IKZF1 and rs1128334 in ETS1 by use of a TaqMan SNP genotyping assay and direct sequencing. Gene-gene interaction analysis involved direct counting, multifactor dimensionality reduction (MDR) and linear regression analysis. SLE patients and controls differed in allele frequencies of rs7574865, rs1128334 (P < 0.001) and rs4917014 (P < 0.01). Direct counting revealed that the frequency of risk homozygote combinations was higher for SLE patients than controls (P < 0.01). Furthermore, 2-, 3- and 4-way gene-gene epistasis in SLE was confirmed by parametric methods and MDR analysis. Gene expression analysis partially supported the findings. Our study confirmed the association of the IFN pathway or Th17/B-cells and the pathogenesis of SLE, and gene-gene interaction in this pathway may increase the risk of SLE.

Envelope surface ultrastructure and specific gravity of artificially fertilized Pacific cod Gadus macrocephalus eggs

The envelope surface ultrastructure and specific gravity of artificially fertilized eggs of the Pacific cod Gadus macrocephalus were examined. The unfertilized, demersal and slightly adhesive eggs of G. macrocephalus were almost spherical and had no oil globules. Wrinkled envelope surface with elaborated hexagonal reticulated patterns and type I micropyle were observed under a scanning electron microscope. The adhesiveness of the eggs was lost at the blastodermal-cap stage after fertilization. The micropylar canal was sealed by secretion of the perivitelline fluid, and the entire surface became rough. Numerous bacilli were deposited at the micropyle and the outer envelope surface at the late germ-ring stage and at the embryo five-eighths around the yolk stage. The micropyle was completely deformed at the embryo seven-eighths around the yolk stage. The specific gravity of the fertilized G. macrocephalus eggs ranged from c. 1·0316 to 1·0454. These values, however, sharply decreased towards the end stages of egg development to produce pelagic larvae. The ultrastructural changes in the micropyle and envelope surface of the G. macrocephalus eggs protected the embryo from microorganism infections and mechanical stress during the long incubation period. The adhesiveness and specific gravity of the eggs influenced their dispersion potential.

Temperature-mediated survival, development and hatching variation of Pacific cod Gadus macrocephalus eggs

Laboratory-validated data on the survival, development and hatching responses of fertilized Pacific cod Gadus macrocephalus eggs from the northern Japan stock were determined through an incubation experiment. The optimum temperature for survival until hatching ranged from 4 to 8°C. No significant difference in development rates was found between the populations from Mutsu Bay, Japan, and western Canadian coastal waters even though the samples may belong to different G. macrocephalus stocks. Gadus macrocephalus larvae hatched asynchronously from egg batches despite incubation under the same environment during their development. Both incubation temperature and temperature-mediated hatch rank affect size and yolk reserve. These data suggest that variations in water temperatures within an ecological range markedly influence the development rates, survival and hatching of the eggs, as well as the stage at hatch larvae of G. macrocephalus. Asynchronous hatching and the production of offspring with variable sizes and yolk reserves are considered evolutionary bet-hedging strategies that enable the species to maximize their likelihood of survival in an environment with variable temperatures.

Electrolytic partitioning of uranium and plutonium based on a new type of electrolytic mixer-settler

The design of a new type of electroreduction mixer-settler for the partitioning of uranium and plutonium during the Purex process, which is featured with E-shaped cathodes and U-shaped anodes in settling chamber, is described and the operational results achieved using this equipment are presented. The results show that this new type of mixer-settler has excellent separation performances. The flow rate of organic feed solution is 3 mL/min and the flow ratio of feed solution (1BF) to aqueous back extraction stream (1BX) and to organic wash stream (1BS) is 4/1/1. For an organic feed of 84 g/L uranium and 1.40 ⁓ 2.64 g/L plutonium, both the separation factor of plutonium from uranium and that of uranium from plutonium are apparently higher than 104

Visceral hypersensitivity in female but not in male serotonin transporter knockout rats

BACKGROUND

Visceral hypersensitivity occurs in irritable bowel syndrome (IBS), particularly in women. Serotonin signaling, including reduced serotonin transporter (SERT) expression, may be disrupted in IBS patients. We studied SERT gene knockout (KO) rats to determine if they exhibited sex-related alterations in visceral sensitivity.

METHODS

We measured serotonin in the colonic mucosa using HPLC and amperometric microelectrode techniques. Visceral sensitivity was assessed using the electromyographic visceromotor response (VMR) in response to colorectal balloon distention (CRD). We studied the electrophysiologic properties of colon projecting sensory neurons in vitro using whole-cell recordings.

KEY RESULTS

Mucosal serotonin levels were not different among male and female WT and SERT KO rats. Serotonin oxidation currents in vitro were larger (P < 0.05) in tissues from male and female SERT KO compared with WT rats. Oxidation currents in male and female WT, but not SERT KO, rats were increased (P < 0.05) by the SERT inhibitor fluoxetine (1 μmol L(-1) ). The VMR to CRD was increased in female but not in male SERT KO rats (P < 0.05); this response varied with the estrous cycle. Colon projecting sensory neurons from female SERT KO rats fired more action potentials compared with neurons from female WT rats. There were no differences in action potential firing in neurons from male WT and SERT KO rats.

CONCLUSIONS & INFERENCES

Increased colonic extracellular serotonin in female SERT KO rats is associated with visceral hypersensitivity and hyperexcitability of colon projecting sensory neurons. The SERT KO rat is a model for studying interactions between serotonin, sex and visceral sensation.

The identification of c-Kit-positive cells in the intestine of chicken

The ultrastructure of the interstitial cells of Cajal (ICC) has been examined in birds, but the distribution of these cells remains obscure because a suitable marker is lacking. In the present study, the identification and expression of c-Kit-positive cells in the chicken intestine were demonstrated by means of in situ hybridization histochemistry and the expression of the c-Kit gene by real-time quantitative PCR. Two types of cells stained positive for c-Kit mRNA. The first group consisted of spindle-shaped or bipolar cells identified as ICC. The ICC were found at a variety of locations: at the level of the myenteric plexus between the circular and longitudinal muscle and intermingled with smooth muscle cells within muscle bundles in the circular and longitudinal muscle layers. The ICC were also identified along the submucosal layer. The second group was composed of round-shaped cells, which resembled mast cells. Mast cells were mainly found in the lamina propria region as well as in the submucosal layer. The expression of the c-Kit gene by real-time quantitative PCR revealed the expression of c-Kit mRNA throughout the lamina muscularis and mucosa of the intestine; however, the quantitation was variable in different regions. This study reveals conclusively for the first time the distribution of ICC, quantifies the expression of c-Kit mRNA in the intestine of adult chicken, and also compares the c-Kit-positive cell types morphologically.

Ultrastructural identification of different subtypes of interstitial cells of Cajal in the chicken ileum

Ultrastructural characteristics of different subtypes of interstitial cells of Cajal (ICC) remain unclear in birds; however, birds have significant economical and scientific notability. Our aim was to describe and classify ICC in the chicken gut. The ileum of normal adult Three Yellow broiler chickens (n=10) were studied by transmission electron microscopy (TEM). The ICC were spindle- or stellate-shaped with ramified cell processes. They had numerous mitochondria, abundant intermediate filaments, fusiform nuclei (oval or indented), with a dense band of peripheral heterochromatin, and formed close contacts by true gap junctions with each other and with neighboring smooth muscle cells (SMC). The ICC were in close contact with enteric nerves, but true gap junctions were not found between them. A new subtype of ICC located in the lamina propria mucosae has been discovered. Some of the ICC showed typical features of SMC, including a basal lamina, caveolae, and dense bodies. Lacking intermediate filaments and caveolae distinguished them from the fibroblast-like cells showing well-developed secretory organelles, including coated vesicles and a patchy basal lamina. The ultrastructural features and distribution of ICC in chicken intestine is similar to mammals. They may play similar key regulatory roles in gastrointestinal motility. The new subtype of ICC discovered in the lamina propria mucosae may play a role in the regulation of secretion and absorption.

Climatology of persistent deep stable layers in Utah's Salt Lake Valley, USA

Abstract. The characteristics of winter season persistent deep stable layers (PDSLs) over Utah's Salt Lake Valley are examined using 30-year twice daily rawinsonde soundings. The results highlight the basic climatological characteristics of the PDSLs, including the strengths of the inversion, the frequency of the occurrence, and the duration of the events. The data analyses also reveal linear trend, interannual variability, as well as the relationship between the interannual variability of PDSLs and the variability of large-scale circulations. Finally, the study investigates the large-scale atmosphere conditions accompanying the formation and destruction of the PDSL episodes.

An improved boosting partial least squares method for near-infrared spectroscopic quantitative analysis

Boosting partial least squares (PLS) has been used for regression to improve the predictive accuracy of PLS models, however, there are still problems when the outliers exist in the calibration dataset. To make the method robust and enhance its prediction ability, an improved boosting PLS is proposed and applied in quantitative analysis of near-infrared (NIR) spectral datasets. In the method, a robust step is added to weaken the effect of the outliers on the model. On the other hand, the loss function defined with relative errors is suggested for updating the sampling weight during the boosting procedure. In addition, the ensemble prediction by the weighted mean of the models in the boosting series is found to be more effective than the commonly used weighted median. The performance of the improved method is tested with two large NIR datasets of industrial production. The method was found to have a marked superiority in robustness and prediction ability, particularly when outliers exist.