Volatile organic compounds for early detection of prostate cancer from urine.

Prostate cancer (PCa) is one of the most common cancers in men worldwide. Early diagnosis of PCa is extremely challenging due to the lack of effective diagnostic methods. The study presented here aims to evaluate whether urine volatile organic compounds (VOCs) can be used as an emerging diagnostic biomarker for PCa. Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect VOCs in urine samples from 66 patients with PCa and to comparatively analyze samples from 87 patients with non-cancerous controls (NCs). A total of 86 substance peak heights were detected in urine samples from all patients. Analysis using four machine learning algorithms suggested that the diagnosis of PCa could be effectively facilitated. Ultimately, diagnostic models were constructed based on the four VOCs selected. The AUC for the RF and SVM model were 0.955 and 0.981, respectively. Both the NN and DT diagnostic models also achieved an AUC of 0.8 or more, but their sensitivity or specificity was poor compared to the RF and SVM models.

Preliminary discussion on the mechanism of active components of Radix Aconiti kusnezoffii in the treatment of migraine based on network pharmacology.

To investigate the mechanism of action of Cao Wu in the treatment of migraine from the perspective of network pharmacology. The Swiss Target Prediction Database and CTD database were used to predict the target information of Cao Wu. Human Genome Database gene cards were used to find migraine-related target genes. The target protein interaction network and the "active ingredient-target" network were obtained by combining Cytoscape 3.7.1 software and R language. Enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes pathway and gene function analysis (GO) were performed using the R language to preliminarily explore the multiple pharmacological mechanisms of Radix Aconiti kusnezoffii. Forty-three indicators were identified. A total of 176 migraine targets were identified through the genecards database and OMIM database. Radix Aconiti kusnezoffii targets were compared with migraine targets and 12 overlapping targets were extracted. The protein interaction network of the overlapping targets was analyzed to identify the key targets for the drug to affect the disease. In addition, Kyoto Encyclopedia of Genes and Genomes pathway and go function enrichment analyses were performed on the overlapping targets to explore the therapeutic mechanism of migraine. The treatment of migraine with herbal woo is characterized by multi-component, multi-target, and multi-channel, which exerts complex network regulation through the interaction between different targets, providing a new idea and basis for further exploring the mechanism of action of herbal woo in the treatment of migraine.

A Panel of Bile Volatile Organic Compounds Servers as a Potential Diagnostic Biomarker for Gallbladder Cancer.

As no reliable diagnostic methods are available, gallbladder cancer (GBC) is often diagnosed until advanced stages, resulting in a poor prognosis. In the present study, we assessed whether volatile organic compounds (VOCs) could be used as a diagnostic tool for GBC. The VOCs in bile samples collected from 32 GBC patients were detected by gas chromatography-ion mobility spectrometry (GC-IMS), and 54 patients with benign gallbladder diseases (BGD) were used as controls. Both principal component analysis and unsupervised hierarchical clustering analysis gave a clear separation of GBC and BGD based on the bile VOC data collected from GC-IMS. A total of 12 differentially expressed VOCs were identified, including four upregulated (cyclohexanone, 2-ethyl-1-hexanol, acetophenone, and methyl benzoate) and eight downregulated [methyl acetate, (E)-hept-2-enal, hexanal, (E)-2-hexenal, (E)-2-pentenal, pentan-1-ol, 1-octen-3-one, and (E)-2-octenal] in GBC compared with BGD. ROC analysis demonstrated a 12-VOC panel con-structed by four machine learning algorithms, which was superior to the traditional tumor marker, CA19-9. Among them, support vector machines and linear discriminant analysis provided the highest AUCs of 0.972, with a sensitivity of 100% and a specificity of 94.4% in the diagnosis of GBC. Collectively, VOCs might be used as a potential tool for the diagnosis of GBC.

Identification of Ophiopogonis Radix from different producing areas by headspace-gas chromatography-ion mobility spectrometry analysis.

Ophiopogonis Radix can be divided into Zhemaidong (ZMD) and Chuanmaidong (CMD). The main planting areas of ZMD are Cixi City and Sanmen county. The quality and price of Ophiopogonis Radix from different producing areas are different. In this study, the headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) method is used to rapidly identify ZMD and CMD. The method is also used to identify ZMD from Cixi and Sanmen by analyzing volatile organic compounds (VOCs). A total of 58 VOCs was obtained from ZMD samples with more abundant signals of which 41 were identified. The peak intensities of all VOCs in ZMD and CMD, Cixi and Sanmen data were averaged and then those VOCs whose peak intensities were distributed outside of mean ± 2 standard deviation (µ ± 2σ) were selected as characteristic markers. We selected 14 characteristic markers to establish the characteristic fingerprint of ZMD and CMD, among the 14 VOCs, ZMD contained more eucalyptus oil compounds than CMD, CMD contained more volatile aldehydes than ZMD. We selected 12 characteristic markers for the establishment of the characteristic fingerprint of ZMD from Cixi and Sanmen. The principal component analysis (PCA) results indicated that both ZMD and CMD or ZMD from Cixi and Sanmen could be effectively divided. The ZMD and CMD as well as ZMD from Cixi and Sanmen were evaluated by partial least squares regression-discriminants analysis (PLS-DA) resulting to be excellent chemical descriptors for sample discrimination. One hundred percent classification rates for both PLS-DA calibration and prediction models were obtained. These results provided a reference for the traceability of species and origin and market standard of Ophiopogonis Radix. PRACTICAL APPLICATIONS: Ophiopogonis Radix can be divided into Zhejiang Ophiopogonis Radix (ZMD) and Sichuan Ophiopogonis Radix (CMD). As far as ZMD is concerned, its producing areas mainly include the traditional planting areas (Cixi City) and new growth areas (Sanmen county). In this paper, the HS-GC-IMS method was adopted to analyze VOCs in Ophiopogonis Radix from different producing areas and then we screen out the respective characteristic VOCs of ZMD and CMD as well as ZMD from Cixi and Sanmen. These characteristic VOCs can effectively identify ZMD and CMD as well as ZMD from Cixi City and Sanmen country to provide a scientific basis for the origin identification of Ophiopogonis Radix.

A modification of the bootstrapping soft shrinkage approach for spectral variable selection in the issue of over-fitting, model accuracy and variable selection credibility.

In this study, we proposed a new computational method stabilized bootstrapping soft shrinkage approach (SBOSS) for variable selection based on bootstrapping soft shrinkage approach (BOSS) which can enhance the analysis of chemical interest from the massive variables among the overlapped absorption bands. In SBOSS, variable is selected by the index of stability of regression coefficients instead of regression coefficients absolute value. In each loop, a weighted bootstrap sampling (WBS) is applied to generate sub-models, according to the weights update by conducting model population analysis (MPA) on the stability of regression coefficients (RC) of these sub-models. Finally, the subset with the lowest RMSECV is chosen to be the optimal variable set. The performance of the SBOSS was evaluated by one simulated dataset and three NIR datasets. The results show that SBOSS can select the fewer variables and supply the least RMSEP and latent variable number of the PLS model with the best stability comparing with methods of Monte Carlo uninformative variables elimination (MCUVE), genetic algorithm (GA), competitive reweighted sampling (CARS), stability of competitive adaptive reweighted sampling (SCARS) and BOSS.

Towards improvement in prediction of iodine value in edible oil system based on chemometric analysis of portable vibrational spectroscopic data.

Iodine value (IV) is a significant parameter to illustrate the quality of edible oil. In this study, three portable spectroscopy devices were employed to determine IV in mixed edible oil system, a new Micro-Electro-Mechanical-System (MEMS) Fourier Transform Infrared Spectrometer (MEMS-FTIR), a MicroNIRTM1700 and an i-Raman Plus-785S. Quantitative model was built by Partial least squares (PLS) regression model and four variable selection methods were applied before PLS model, which are Monte Carlo uninformative variables elimination (MCUVE), competitive reweighted sampling (CARS), bootstrapping soft shrinkage approach (BOSS) and variable combination population analysis (VCPA). The coefficient of determination (R2), and the root mean square error prediction (RMSEP) were used as indicators for the predictability of the PLS models. In MicroNIRTM1700 dataset, MCUVE gave the lowest RMSEP (2.3440), in MEMS-FTIR dataset, CARS showed the best performance with RMSEP (2.2185), in i-Raman Plus-785S dataset, BOSS gave the lowest RMSEP (2.5058). They all had great improvements than full spectrum PLS model. Four variable selection methods take a smaller number of variables and perform significant superiority in prediction accuracy. It was demonstrated that three new portable instruments would be suitable for the on-site determination of edible oil quality in infrared and Raman field.

Superficial and deep changes of histology, texture and particle size distribution in broiler wooden breast muscle during refrigerated storage.

Recently the poultry industry faced an emerging muscle abnormality termed wooden breast (WB), the prevalence of which has dramatically increased in the past few years. Considering the incomplete knowledge concerning this condition and the lack of information on possible variations due to the intra-fillet sampling locations (superficial vs. deep position) and aging of the samples, this study aimed at investigating the effect of 7-d storage of broiler breast muscles on histology, texture, and particle size distribution, evaluating whether the sampling position exerts a relevant role in determining the main features of WB. With regard to the histological observations, severe myodegeneration accompanied by accumulation of connective tissue was observed within the WB cases, irrespective of the intra-fillet sampling position. No changes in the histological traits took place during the aging in either the normal or the WB samples. As to textural traits, although a progressive tenderization process took place during storage (P ≤ 0.001), the differences among the groups were mainly detected when raw meat rather than cooked was analyzed, with the WB samples exhibiting the highest (P ≤ 0.001) 80% compression values. In spite of the increased amount of connective tissue components in the WB cases, their thermally labile cross-links will account for the similar compression and shear-force values as normal breast cases when measured on cooked samples. Similarly, the enlargement of extracellular matrix and fibrosis might contribute in explaining the different fragmentation patterns observed between the superficial and the deep layer in the WB samples, with the superficial part exhibiting a higher amount of larger particles and an increase in particles with larger size during storage, compared to normal breasts.

Modeling and predicting the effect of temperature on the growth of Proteus mirabilis in chicken.

A predictive model to study the effect of temperature on the growth of Proteus mirabilis was developed. The growth data were collected under several isothermal conditions (8, 12, 16, 20, 25, 30, 35, 40, and 45°C) and were fitted into three primary models, namely the logistic model, the modified Gompertz model, and the Baranyi model. The statistical characteristics to evaluate the models such as R(2), mean square error, and Sawa's Bayesian information criteria (BIC) were used. Results showed that the Baranyi model performed best, followed by the logistic model and the modified Gompertz model. R(2) values for the secondary model derived from logistic, modified Gompertz, and Baranyi models were 0.965, 0.974, and 0.971, respectively. Bias factor and accuracy factor indicated that both the modified Gompertz and Baranyi models fitted the growth data better. Therefore, the Baranyi model was proposed to be the best predictive model for the growth of P. mirabilis.

[In vitro cytotoxicity test of medical ultrasonic couplant].

The cytotoxicity of medical ultrasonic couplant was tested by MTT assay and agar overlay test. By MTT assay, when the inoculum density was high, the cytotoxicity level was low, or vice versa. The cytotoxicity grade tested by agar overlay was not accord to MTT assay's too. MTT assay is suitable to test the cytotoxicity of medical ultrasonic couplant because it is quantitative and more sensitive, however, the experimental condition and the preparative method of extraction should be determined.