Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.     

A Novel N-Tert-Butyl Derivatives of Pseudothiohydantoin as Potential Target in Anti-Cancer Therapy

Tumors are currently more and more common all over the world; hence, attempts are being made to explain the biochemical processes underlying their development. The search for new therapeutic pathways, with particular emphasis on enzymatic activity and its modulation regulating the level of glucocorticosteroids, may contribute to the development and implementation of new therapeutic options in the treatment process. Our research focuses on understanding the role of 11β-HSD1 and 11β-HSD2 as factors involved in the differentiation and proliferation of neoplastic cells. In this work, we obtained the 9 novel N-tert-butyl substituted 2-aminothiazol-4(5H)-one (pseudothiohydantoin) derivatives, differing in the substituents at C-5 of the thiazole ring. The inhibitory activity and selectivity of the obtained derivatives in relation to two isoforms of 11β-HSD were evaluated. The highest inhibitory activity for 11β-HSD1 showed compound 3h, containing the cyclohexane substituent at the 5-position of the thiazole ring in the spiro system (82.5% at a conc. 10 µM). On the other hand, the derivative 3f with the phenyl substituent at C-5 showed the highest inhibition of 11β-HSD2 (53.57% at a conc. of 10 µM). A low selectivity in the inhibition of 11β-HSD2 was observed but, unlike 18β-glycyrrhetinic acid, these compounds were found to inhibit the activity of 11β-HSD2 to a greater extent than 11β-HSD1, which makes them attractive for further research on their anti-cancer activity.     

Red Blood Cell Stiffness and Adhesion Are Species-Specific Properties Strongly Affected by Temperature and Medium Changes in Single Cell Force Spectroscopy

Besides human red blood cells (RBC), a standard model used in AFM-single cell force spectroscopy (SCFS), little is known about apparent Young’s modulus (Ea) or adhesion of animal RBCs displaying distinct cellular features. To close this knowledge gap, we probed chicken, horse, camel, and human fetal RBCs and compared data with human adults serving as a repository for future studies. Additionally, we assessed how measurements are affected under physiological conditions (species-specific temperature in autologous plasma vs. 25 °C in aqueous NaCl solution). In all RBC types, Ea decreased with increasing temperature irrespective of the suspension medium. In mammalian RBCs, adhesion increased with elevated temperatures and scaled with reported membrane sialic acid concentrations. In chicken only adhesion decreased with higher temperature, which we attribute to the lower AE-1 concentration allowing more membrane undulations. Ea decreased further in plasma at every test temperature, and adhesion was completely abolished, pointing to functional cell enlargement by adsorption of plasma components. This halo elevated RBC size by several hundreds of nanometers, blunted the thermal input, and will affect the coupling of RBCs with the flowing plasma. The study evidences the presence of a RBC surface layer and discusses the tremendous effects when RBCs are probed at physiological conditions.     

Ultrasound Microbubbles Enhance the Efficacy of Insulin-Like Growth Factor-1 Therapy for the Treatment of Noise-Induced Hearing Loss

The application of insulin-like growth factor 1 (IGF-1) to the round window membrane (RWM) is an emerging treatment for inner ear diseases. RWM permeability is the key factor for efficient IGF-1 delivery. Ultrasound microbubbles (USMBs) can increase drug permeation through the RWM. In the present study, the enhancing effect of USMBs on the efficacy of IGF-1 application and the treatment effect of USMB-mediated IGF-1 delivery for noise-induced hearing loss (NIHL) were investigated. Forty-seven guinea pigs were assigned to three groups: the USM group, which received local application of recombinant human IGF-1 (rhIGF-1, 10 µg/µL) following application of USMBs to the RWM; the RWS group, which received IGF-1 application alone; and the saline-treated group. The perilymphatic concentration of rhIGF-1 in the USM group was 1.95- and 1.67- fold of that in the RWS group, 2 and 24 h after treatment, respectively. After 5 h of 118 dB SPL noise exposure, the USM group had the lowest threshold shift in auditory brainstem response, least loss of cochlear outer hair cells, and least reduction in the number of synaptic ribbons on postexposure day 28 among the three groups. The combination of USMB and IGF-1 led to a better therapeutic response to NIHL. Two hours after treatment, the USM group had significantly higher levels of Akt1 and Mapk3 gene expression than the other two groups. The most intense immunostaining for phosphor-AKT and phospho-ERK1/2 was detected in the cochlea in the USM group. These results suggested that USMB can be applied to enhance the efficacy of IGF-1 therapy in the treatment of inner ear diseases.     

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

Robust nickel silicate catalysts with high Ni loading for CO2 methanation

CO₂ is the main component of greenhouse gases and also an important carbon source. The hydrogenation of CO₂ to methane using Ni-based catalysts can not only alleviate CO₂ emissions but also obtain useful fuels. However, Ni-based catalysts face one major problem of the sintering of Ni nanoparticles in the process of CO₂ methanation. Thus, this work has synthesized a series of efficient and robust nickel silicate catalysts (NiPS−X) with different nickel content derived from nickel phyllosilicate by the hydrothermal method. It was found that the Ni loading plays a critical role in the structure and catalytic performance of the NiPS−X catalysts. The catalytic performance gradually increases with the increase of Ni loading. In particular, the highly dispersed NiPS-1.6 catalyst with a high Ni loading of 34.3 wt% could obtain the CO₂ conversion greater than 80%, and the methane selectivity was close to 100% for 48 h at 330 °C and the GHSV of 40,000 mL g⁻¹ h⁻¹. The excellent catalytic property can be assigned to the high dispersion of Ni nanoparticles and the strong interaction between the active component and the carrier, which is derived from a unique layered silicate structure with lots of nickel phyllosilicate and a large number of Lewis acid sites.     

Facile Surfactant-, Reductant-, and Ag Salt-free Growth of Ag Nanoparticles with Controllable Size from 35 to 660 nm on Bulk Ag Materials

Morphologically and dimensionally controlled growth of Ag nanocrystals has long been plagued by surfactants or capping agents that complicate downstream applications, unstable Ag salts that impaired the reproducibility, and multistep seed injection that is troublesome and time-consuming. Here, we report a one-pot electro-chemical method to fast (∼2 min) produce Ag nanoparticles from commercial bulk Ag materials in a nitric acid solution, eliminating any need for surfactants or capping agents. Their size can be easily manipulated in an unprecedentedly wide range from 35 to 660 nm. Furthermore, the Ag nanoparticles are directly grown on the Ag substrate, highly desirable for promising applications such as catalysis and plasmonics. The mechanistic studies reveal that the concentration of Ag⁺ in the diffusion layer nearby the surface, controlled by the magnitude and duration of voltage, is critical in governing the nanoparticle formation (<1.3 mM) and its dimensional adjustability.     

Combination of RSM and NSGA-II algorithm for optimization and prediction of thermal conductivity and viscosity of bioglycol/water mixture containing SiO2 nanoparticles

The fluids containing nanoparticles have enhanced thermo-physical characteristics in comparison with conventional fluids without nanoparticles. Thermal conductivity and viscosity are thermo-physical properties that strongly determine heat transfer and momentum. In this study, the response surface method was firstly used to derive an equation for the thermal conductivity and another one for the viscosity of bioglycol/water mixture (20:80) containing silicon dioxide nanoparticles as a function of temperature as well as the volume fraction of silicon dioxide. Then, NSGA-II algorithm was used for the optimization and maximizing thermal conductivity and minimizing the nanofluid viscosity. Different fronts were implemented and 20th iteration number was selected as Pareto front. The highest thermal conductivity (0.576 W/m.K) and the lowest viscosity (0.61 mPa.s) were obtained at temperature on volume concentration of (80 °C and 2%) and (80 °C without nanoparticle) respectively. It was concluded that the optimum thermal conductivity and viscosity of nanofluid could be obtained at maximum temperature (80 °C) or a temperature close to this temperature. An increase in the volume fraction of silicon dioxide led to the enhancement of thermal conductivity but the solution viscosity was also increased. Therefore, the optimum point should be selected based on the system requirement.     

Spectrochemical analysis using LIBS and ICP-OES techniques of herbal medicine (Tinnevelly Senna leaves) and its anti-cancerous/antibacterial applications

Tinnevelly senna leaves are being applied to cure many diseases especially in developing countries and sub-Saharan region due to many bioactive compounds such as sennosides, phenols, and flavonoids. The conventional methods to isolate and analyze plant extracts biomolecules are not very effective as well cost effective as they require hazardous chemical solvents and reagents, which are time-consuming processes. The major objective of the present study is to investigate the feasibility of the Laser induced breakdown spectroscopy (LIBS) technique for rapid, eco-friendly, and multi-elemental analysis of Senna leaves extracts and study their antibacterial and anticancer potentials. The elegant LIBS technique was applied as a qualitative and quantitative method for Senna leaves sample’s elemental analysis and their biological activities were measured by evaluating anti-cancer and anti-bacterial analysis. The quantitative analysis of Senna leaves extracts was done using the calibration-free laser-induced breakdown spectroscopy (CF-LIBS) algorithm showing their appreciable content of several nutrient elements, and the obtained results were in close conformity with these achieved by using the standard analytical ICP OES technique. We studied the bactericidal efficacy of the Senna leaves extract against Staphylococcus aureus (S. aureus) by AWD assays and morphogenesis by scanning electron microscopy (SEM) and the anticancer activity was also investigated where different concentrations of Senna leaves extract were tested on cancer cells (HCT-116 and HeLa) and normal cells (HEK-293) using the cell metabolic activity MTT assay and Propidium iodide (PI) staining. We have also calculated the inhibitory concentration (IC50) value for the various extracts concentrations (25 µg/ml, 50 µg/ml, 100 µg/ml, 150 µg/ml, 200 µg/ml, and 225 µg/ml). We have found that IC₅₀ value for HCT-116 cells were 13.5 µg/ml, 17.5 µg/ml, 21.5 µg/ml, 22.5 µg/ml, 26 µg/ml and 33.5 µg/ml and for HeLa cells 15.25 µg/ml, 21.25 µg/ml, 23.5 µg/ml, 262.5 µg/ml, 36.25 µg/ml, and 39.50 µg/ml. The bactericidal efficacy of the Senna leaves extract showed significant inhibition against Gram-positive bacterium. Both MTT and PI analysis showed that Senna leaves extract induced profound inhibition on HCT-116 growth and proliferation. Additionally, Senna leaves extract did not exert an inhibitory influence on normal (HEK-293), which is non-cancerous cells. We suggest that the extract specifically targets the cancerous cells, which could be highly beneficial for the development of future safe anticancer and antibacterial drugs using these extracts.     

水载流-原子荧光光谱法同时测定土壤中痕量砷、汞

改变原子荧光光谱法以稀酸为载流的进样方式,以水为载流在新型双道原子荧光光谱仪上同时测定了土壤中的痕量砷(As)、汞(Hg)。采用王水水浴加热溶解样品,用50 g/L硫脲-抗坏血酸混合溶液预先将砷还原为As(Ⅲ),保持10%(体积分数)以上盐酸浓度,不转移直接定容静置后测定。优化了仪器工作条件,详细考察了以水为载流测定的可行性,选择了同时测定As、Hg所需的硼氢化钾浓度和盐酸浓度。节省了酸试剂用量,操作快速,单个样品测定仅需20 s左右。方法检出限As为0.005 mg/kg,Hg为0.0008 mg/kg,相对标准偏差(RSD,n=7)在1.0%~7.4%,经土壤国家标准物质和实际样品验证,适合土壤中痕量As、Hg的同时快速测定。