Characteristics of catalytic destruction of dichloromethane and ethyl acetate mixture over HxPO4-RuOx/CeO2 catalyst.

Catalytic destruction is an ascendant technology for the abatement of volatile organic compounds (VOCs) originating from solvent-based industrial processes. The varied composition tends to influence each VOC's catalytic behavior in the reaction mixture. We investigated the catalytic destruction of multi-component VOCs including dichloromethane (DCM) and ethyl acetate (EA), as representatives from pharmaceutical waste gases, over co-supported HxPO4-RuOx/CeO2 catalyst. A mutual inhibitory effect relating to concentrations because of competitive adsorption was verified in the binary VOCs oxidation and EA posed a more negative effect on DCM oxidation owing to EA's superior adsorption capacity. Preferential adsorption of EA on acidic sites (HxPO4/CeO2) promoted DCM activation on basic sites (O2-) and the dominating EA oxidation blocked DCM's access to oxidation centers (RuOx/CeO2), resulting in boosted monochloromethane yield and increased chlorine deposition for DCM oxidation. The impaired redox ability of Ru species owing to chlorine deposition in turn jeopardized deep oxidation of EA and its by-products, leading to increased gaseous by-products such as acetic acid originating from EA pyrolysis. Notably, DCM at low concentration slightly promoted EA conversion at low temperatures with or without water, consistent with the enhanced EA adsorption in co-adsorption analyses. This was mainly due to that DCM impeded the shielding effect of hydrolysate deposition from rapid EA hydrolysis depending on the decreased acidity. Moreover, water benefited EA hydrolysis but decreased CO2 selectivity while the generated water derived from EA was likely to affect DCM transformation. This work may provide theoretical guidance for the promotion of applied catalysts toward industrial applications.

Simulating the ionic liquid mixing with organic-solvent clarifies the mixture's SFG spectral behavior and the specific surface region originating SFG.

Molecular dynamics (MD) simulation of the green ionic liquid [C4mim][PF6] mixed with polar benzonitrile (BNZ) solvent provides detailed insights into their structural and dynamic properties, essential for electrochemistry and materials science applications. The simulations we carried out at varying mole fractions (XBZN) reveal the mixtures' physical, structural, and dynamic properties, with radial, spatial, and combined distribution functions, highlighting the effective interaction through H-bonding involved. The simulation indicates that BZN stacks on the cation butyl tail, providing a significant explanation for the unique experimental observations (following). Adding BZN causes the mixture's liquid dynamics to increase linearly at low XBZN and exponentially at high XBZN, with a notable singular transition at 0.5XBZN. Comprehensive efforts were made to verify and support experimental sum frequency generation (SFG) spectroscopy by simulating the surface structure of the mixtures. Consequently, the simulated BZN stacking structure explains (1) the absence of the C≡N vibrational mode in the SFG spectrum for XBZN < 0.8, and (2) the gradual diminishing of the CH3 SFG signal, which disappears as XBZN approaches 0.5. Finally, this research removes a persistent ambiguity, proving that only the molecular moieties on the surface generate the SFG vibrational signal, while those in the subsurface do not.

Benzo(a)anthracene Targeting SLC1A5 to Synergistically Enhance PAH Mixture Toxicity.

Human exposure to polycyclic aromatic hydrocarbons (PAHs) as mutagenic and carcinogenic pollutants in the environment often occurs in the form of mixtures. Although the mixture effects of PAHs have been previously recognized, the toxicological mechanisms to explain them still remain quite unclear. This study combined metabolomics and chemical proteomics methods to comprehensively understand the mixture effects of a PAH mixture including benzo(a)anthracene (BaA), benzo(b)fluoranthene (BbF), benzo(a)pyrene (BaP), and chrysene (CHR). Among them, BaA has shown a strong synergistic effect with other PAHs. Interestingly, BaA alone is not a potent oxidative stress inducer in liver cells but dose-dependently amplifies oxidative damage caused by the PAH mixture. Global metabolomics analysis results revealed damage to the antioxidant glutathione synthesis, which was caused by the glutamine depletion caused by BaA in the mixture. Subsequently, the label-free chemical proteomics and cellular thermal shift analysis (CETSA) demonstrated that the PAH mixture altered the thermal shift of glutamine transporter SLC1A5. Furthermore, Western blotting and the isothermal titration calorimetry (ITC) interaction measurements showed nanomolar KD values between BaA and SLC1A5. Overall, this study showed that BaA synergistically contributed to PAH mixture induced oxidative damage by targeting SLC1A5 to inhibit glutamate transport into cells, resulting in the inhibition of glutathione synthesis.

Synthesis and optimization of molecular weight of chitosan and carboxymethyl cellulose based polyurethanes.

For the first time in this research, using a mixture design approach, polyurethanes (PUs) based on chitosan (CSN) and carboxymethyl cellulose (CMC) were synthesized to develop a high molecular weight polymer. In the synthesis process, a reaction between isophorone diisocyanate (IPDI) and hydroxyl-terminated polybutadiene was carried out to synthesize a prepolymer containing free NCO groups at the corners. This prepolymer was further reacted with changing moles ratio of CSN and CMC following the principles of statistical mixture design. The structural confirmation of the developed PUs was carried out through spectroscopic techniques (FTIR and NMR). The molecular weights of the PU specimens were characterized using gel permeation chromatography. The findings demonstrated that the interaction between CMC and CSN led to a notable increase in the molecular weights of the samples, supported by a significant p-value of 0.006. Additionally, the analysis of variance (ANOVA) disclosed that the employed mixture design and the resulting interaction model effectively account for 98 % of the total variation observed in the molecular weights. The sample labeled as PUS-3 (CMC0.50:CSN0.50) emerged as the most significant formulation, exhibiting a noteworthy 27.9 % improvement in the polymer molecular weight compared to the base sample, denoted as PUS-1 (CMC1.00:CSN0.00).

Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs.

BACKGROUND: Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis. METHODS: In this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application. RESULTS: We extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme. CONCLUSIONS: The proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo , respectively.

Enhancing DHA supplementation adherence: A Bayesian approach with finite mixture models and irregular interim schedules in adaptive trial designs.

Docosahexaenoic acid (DHA) supplementation has proven beneficial in reducing preterm births. However, the challenge lies in addressing nonadherence to prescribed supplementation regimens-a hurdle that significantly impacts clinical trial outcomes. Conventional methods of adherence estimation, such as pill counts and questionnaires, usually fall short when estimating adherence within a specific dosage group. Thus, we propose a Bayesian finite mixture model to estimate adherence among women with low baseline red blood cell phospholipid DHA levels (<6%) receiving higher DHA doses. In our model, adherence is defined as the proportion of participants classified into one of the two distinct components in a normal mixture distribution. Subsequently, based on the estimands from the adherence model, we introduce a novel Bayesian adaptive trial design. Unlike conventional adaptive trials that employ regularly spaced interim schedules, the novelty of our proposed trial design lies in its adaptability to adherence percentages across the treatment arm through irregular interims. The irregular interims in the proposed trial are based on the effect size estimation informed by the finite mixture model. In summary, this study presents innovative methods for leveraging the capabilities of Bayesian finite mixture models in adherence analysis and the design of adaptive clinical trials.

Experimentation on Sargassum wightii as a flash powder igniter for attaining eco-friendly combustion on pyrotechnics.

Firecrackers are a vital element of cultural festivities that happen worldwide. However, the hazardous by-products they emit have a significant impact on environmental pollution, leading to the greenhouse effect and climate change. Aluminium powder serves as the fuel in the traditional flash powder mixture, nitrate of potassium serves as an oxidizing agent, and sulphur acts as the igniter at exact concentrations. The presence of sulfur in the flash powder mixture is critical as it acts as an igniter, contributing to the formation of sulfur dioxide, which can cause environmental harm. We carried out an experiment employing Sargassum wightii brown seaweed powder as a replacement for sulphur at specific amounts to lessen the effects of sulphur in flash powder. We discovered that Sargassum wightii brown seaweed powder may replace up to 50% of the sulphur significance in the flash powder mixture without impairing the flash powder's traditional performance. Our experiments included impact and friction sensitivity, SEM, and FTIR analyses to evaluate the improved flash powder composition. The results revealed that the modified flash powder mixture SP5 and SP10 emits less emission by 12% and 21%, and produces similar noise performance of 108 and 107 dB(A) to the normal flash powder composition (SP), affirming that the SP10 flash powder is a viable alternative. Moreover, in our relentless pursuit to mitigate the detrimental effects on our environment, we have ingeniously introduced a novel product-the Chinese cracker made from vegetable waste paper. Not only does this innovative solution address concerns regarding land pollution, but it also presents a sustainable approach to consumer goods.

Investigating the relationship of co-exposure to multiple metals with chronic kidney disease: An integrated perspective from epidemiology and adverse outcome pathways.

Systematic studies on the associations between co-exposure to multiple metals and chronic kidney disease (CKD), as well as the underlying mechanisms, remain insufficient. This study aimed to provide a comprehensive perspective on the risk of CKD induced by multiple metal co-exposures through the integration of occupational epidemiology and adverse outcome pathway (AOP). The study participants included 401 male mine workers whose blood metal, ß2-microglobulin (ß2-MG), and cystatin C (Cys-C) levels were measured. Generalized linear models (GLMs), quantile g-computation models (qgcomp), least absolute shrinkage and selection operator (LASSO), and bayesian kernel machine regression (BKMR) were utilized to identify critical nephrotoxic metals. The mean concentrations of lead, cadmium, mercury, arsenic, and manganese were 191.93, 3.92, 4.66, 3.11, 11.35, and 16.33 µg/L, respectively. GLM, LASSO, qgcomp, and BKMR models consistently identified lead, cadmium, mercury, and arsenic as the primary contributors to kidney toxicity. Based on our epidemiological analysis, we used a computational toxicology method to construct a chemical-genetic-phenotype-disease network (CGPDN) from the Comparative Toxicogenomics Database (CTD), DisGeNET, and GeneCard databases, and further linked key events (KEs) related to kidney toxicity from the AOP-Wiki and PubMed databases. Finally, an AOP framework of multiple metals was constructed by integrating the common molecular initiating events (reactive oxygen species) and KEs (MAPK signaling pathway, oxidative stress, mitochondrial dysfunction, DNA damage, inflammation, hypertension, cell death, and kidney toxicity). This is the first AOP network to elucidate the internal association between multiple metal co-exposures and CKD, providing a crucial basis for the risk assessment of multiple metal co-exposures.

Microwave healing properties and moisture sensitivity of asphalt mixture containing iron powder filler.

Through experimental tests, this paper investigated the effectiveness of microwave healing on asphalt mixtures containing iron powder (IP) filler to improve durability and enhance mechanical properties. The moisture sensitivity of the asphalt mixes with varying iron powder filler contents was measured using the modified Lottman test. For evaluating the asphalt mixture healing ability, two complementary methods were used: the fatigue-based method, derived from the ability of microwave healing of asphalt mixture samples damaged up to 50% level of indirect tensile fatigue (ITF), and the fracture-based method, obtained from the strength recovery rate of broken semi-circular samples by applying consecutive breaking-healing cycles. The results showed that the greater the quantity of iron powder filler in the asphalt sample, the greater the sample's indirect tensile strength and TSR ratio, indicating that IP had a positive effect on moisture sensitivity. The findings also indicated that utilizing iron powder as a filler positively strengthens the fatigue life, increases the toughness, and increases the microwave healing indices (both fatigue and failure approaches). Finally, according to the study results, substituting 70% iron powder as the filler is the most suitable option for improving the mechanical and self-healing characteristics of asphalt mixes.

Assessing the impact of different assumptions on the size of a Mixture Assessment Factor (MAF) for chemical mixtures in surface waters using data from three recent monitoring studies.

Mixture Assessment Factors (MAFs) have been proposed in the European Union (EU) as a rapid and simple way of protecting aquatic organisms from the combined effects of unintentional chemical mixtures when regulating industrial chemicals under the REACH (EU Regulation on the registration, evaluation, authorisation, and restriction of chemicals) program. A wide range of values has been suggested for the MAF including values of 20 or larger. In this paper we performed a series of case studies using composition data from 46,546 mixtures reported in three surveys of chemicals in EU surface waters. We determine that much of the evidence indicating a need for MAF values of five or greater is the result of assumptions on the impacts of future mitigations and screening assumptions used when determining combined risk. In this paper we present estimates of the MAF values that are based on more realistic assumptions for the impacts of future mitigation and mixture risk assessments that use data on the specific endpoints caused by chemicals and the modes-of-action (MoAs) by which the endpoints occur. We show that smaller MAFs may be sufficient to protect ecological receptors in >95 % of the mixtures reported in each of the three surveys. We also show that generic MAFs could be tailored to individual chemicals based on a chemical's endpoints and MoAs. Finally, we demonstrate that use of a large MAF could result in unnecessary concerns for chemicals. These findings suggest that caution should be taken in the use of large MAFs in regulations.

Association of perfluoroalkyl substances (PFAS) with liver function biomarkers in the highly exposed population of the Veneto Region.

INTRODUCTION: Epidemiological studies highlight the presence of associations between per- and polyfluoroalkyl substances (PFAS) exposure with liver damage. In 2013, PFAS contamination was discovered in Veneto (Italy), leading to the implementation of a Surveillance Program (SP). Our objective is to investigate the association between PFAS exposure and biomarkers of liver function using single-pollutant and mixture approaches, while exploring the sex-specific differences and the mediating role of obesity in the association. METHODS: The study included 42,094 subjects aged ≥20 years participating in the SP. We used generalized additive models to investigate the association between several PFAS and alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, adjusting for possible confounders and stratifying by sex. Results were back-transformed to show predicted percentage changes in outcomes per ln-unit increase in PFAS levels; furthermore, we explored the role of BMI in the abovementioned causal pathway, considering it as a potential confounder or mediator PFAS joint effect was investigated using Quantile G-computation. RESULTS: One ln-unit increase in PFHxS concentrations was associated with a 1.49% (95%CI: 0.87, 2.12) and a 0.84% (95% CI: 0.27, 1.40) increase in ALT levels, in males and females respectively; one ln-unit increase in PFOA concentrations was associated with a 1.03% (95%CI: 0.50, 1.55) increase in ALT levels in males, and a 0.52% (95% CI: 0.22, 0.82) and a 0.60% (95% CI: 0.25, 0.96) increase in AST levels in females and males. PFOS showed no association with ALT and AST levels. Quantile G-computation revealed that an interquartile increase in the PFAS mixture was associated with a 3.02% increase (95% CI: 1.65, 4.43) and a 1.65% (95% CI: 0.77, 2.5) increase in ALT levels, in females and males. Mediation analysis suggested that BMI suppressed the association between PFAS and ALT levels, with positive direct effects higher than the total effects. CONCLUSION: Our findings suggest sex-specific associations between PFAS exposure and liver function biomarkers and underscore the need for additional studies on potential mediators.

Discovery of the Alternaria mycotoxins alterperylenol and altertoxin I as novel immunosuppressive and antiestrogenic compounds in vitro.

Alternaria mycotoxins may pose significant challenges to food safety and public health due to the wide spectrum of reported adverse effects. Despite this, critical information on the immunomodulatory and antiestrogenic properties of most of these contaminants is still lacking. The present study aimed to identify the mycotoxins responsible for the immunosuppressive and antiestrogenic effects of a complex extract of Alternaria mycotoxins (CE) obtained by growing an Alternaria alternata strain on rice. Through a toxicity-guided fractionation procedure involving the production of CE-fractions by supercritical fluid chromatography and mycotoxin quantification by LC-MS/MS, the mycotoxins alternariol (AOH), tenuazonic acid (TeA), altertoxin I (ATX-I), and alterperylenol (ALTP) were identified as potential toxicologically relevant constituents contributing to the in vitro effects exerted by the extract. The assessment of the immunomodulatory effects, performed by applying the NF-κB reporter gene assay in THP1-Lucia™ monocytes, revealed the scarce contribution of AOH to the effects exerted by the CE. TeA showed no effect on the NF-κB pathway up to 250 µM, whereas ATX-I and ALTP suppressed the LPS-mediated pathway activation at concentrations ≥ 1 µM. The evaluation of antiestrogenic effects, performed in Ishikawa cells by applying the alkaline phosphatase assay, revealed the ability of ALTP (≥ 0.4 µM) and ATX-I (≥ 2 µM) to suppress the estrogen-dependent expression of enzyme activity. Given the risk of detrimental impacts stemming from alterations in endocrine and systemic immune responses by the investigated mycotoxins, further studies are needed to elucidate their underlying mechanisms of action and comprehensively evaluate the health risks posed by these toxins.

Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (µ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (µ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.

Is Passynski's Approach to Hydration Numbers Consistent with Thermodynamics?

Hydrophilic and hydrophobic phenomena occur in aqueous solutions. Despite the complex nature of the molecular interactions, the propensity of molecules and ions to hydration is sometimes characterized by a single "hydration number". Passynski's method for determining the hydration numbers in dilute aqueous solutions belongs to the group of methods based on the analysis of the isentropic compressibility of a mixture. Isentropic compressibility is a thermodynamic material constant; thus, the paper deals with Passynski's approach discussed in terms of thermodynamics. First, Passynski's assumptions were applied to the volume of the mixture. Subsequent strict thermodynamic derivation led to a formula for the hydration number which resembled that of Onori rather than the original one. Passynski's number turned out to be inconsistent with the thermodynamics and mechanics of fluids. This is a rather purely empirical measure of the slope of the dependence of isentropic compressibility on the solute mole fraction in a dilute aqueous solution. Being the quotient of the slope and the isentropic compressibility of pure water, Pasynski's numbers are more convenient to analyze and discuss than the slopes themselves. Conclusions about molecular interactions based on these numbers must be treated with considerable caution.

How relevant are sterols in the mode of action of prymnesins?

Prymnesins, produced by the haptophyte Prymnesium parvum, are considered responsible for fish kills when this species blooms. Although their toxic mechanism is not fully understood, membrane disruptive properties have been ascribed to A-type prymnesins. Currently it is suggested that pore-formation is the underlying cause of cell disruption. Here the hypothesis that A-, B-, and C-type prymnesins interact with sterols in order to create pores was tested. Prymnesin mixtures containing various analogs of the same type were applied in hemolysis and cytotoxicity assays using Atlantic salmon Salmo salar erythrocytes or rainbow trout RTgill-W1 cells. The hemolytic potency of the prymnesin types reflected their cytotoxic potential, with approximate concentrations reaching 50 % hemolysis (HC50) of 4 nM (A-type), 54 nM (C-type), and 600 nM (B-type). Variabilities in prymnesin profiles were shown to influence potency. Prymnesin-A (3 Cl) + 2 pentose + hexose was likely responsible for the strong toxicity of A-type samples. Co-incubation with cholesterol and epi-cholesterol pre-hemolysis reduced the potential by about 50 % irrespective of sterol concentration, suggesting interactions with sterols. However, this effect was not observed in RTgill-W1 toxicity. Treatment of RTgill-W1 cells with 10 µM lovastatin or 10 µM methyl-ß-cyclodextrin-cholesterol modified cholesterol levels by 20-30 %. Regardless, prymnesin cytotoxicity remained unaltered in the modified cells. SPR data showed that B-type prymnesins likely bound with a single exponential decay while A-types seemed to have a more complex binding. Overall, interaction with cholesterol appeared to play only a partial role in the cytotoxic mechanism of pore-formation. It is suggested that prymnesins initially interact with cholesterol and stabilize pores through a subsequent, still unknown mechanism possibly including other membrane lipids or proteins.

Changes of structural characteristics, functional properties and volatile compounds of Tenebrio molitor larvae protein after sustainable defatting process: Influence of the different volume ratios of n-hexane to ethanol.

This work aimed to study the effect of defatting via the mixture of n-hexane and ethanol under different volume ratio on the changes of structural characteristics, functional properties and volatile compounds of Tenebrio molitor larvae protein (TMLP). The results showed that 1:0.6 vol ratio of n-hexane to ethanol rendered the highest defatting rate (P < 0.05), as well as led to the highest EAA/AA contents, sulfhydryl contents, surface hydrophobicity, solubility, water/oil holding capacities and emulsifying properties of TMLP (P < 0.05). However, higher volume ratio of n-hexane to ethanol led to negative impacts on functionalities of TMLP. Moreover, the contents of aldehydes and hydrocarbons which rendered off-flavour to TMLP significantly decreased with the increasing volume ratio of n-hexane to ethanol (P < 0.05), while the contents of pleasure flavour (hydrocarbons and ester compounds) were obviously enhanced. This study provides an eco-friendly defatting method on the processing of TMLP with superior quality attributes.

Principal component analysis biplot visualization of electromyogram features for submaximal muscle strength grading.

BACKGROUND: Submaximal muscle strength grading is clinically significant to monitor the progress of rehabilitation. Especially muscle strength grading of core back muscles is challenging using the conventional manual muscle testing (MMT) methods. The muscles are crucial to recovery from back pain, spinal cord injury, stroke and other related diseases. The subjective nature of MMT, adds more ambiguity to grade fine progressions in submaximal strength levels involving 4-, 4 and 4+ grades. Electromyogram (EMG) has been widely used as a quantitative measure to provide insight into the progress of muscle strength. However, several EMG features have been reported in previous studies, and the selection of suitable features pertaining to the problem has remained a challenge. METHOD: Principal Component Analysis (PCA) biplot visualization is employed in this study to select EMG features that highlight fine changes in muscle strength spanning the submaximal range. Features that offer maximum loading in the principal component subspace, as observed in the PCA biplot, are selected for grading submaximal strength. The performance of the proposed feature set is compared with conventional Principal Component (PC) scores. Submaximal muscle strength grades of 4-, 4, 4+ or 5 are assigned using K-means and Gaussian mixture model clustering methods. Clustering performance of the two feature selection methods is compared using the silhouette score metric. RESULTS: The proposed feature set from biplot visualization involving Root Mean Square (RMS) EMG and Waveform Length in combination with Gaussian Mixture Model (GMM) clustering method was observed to offer maximum accuracy. Muscle-wise mean Silhouette Index (SI) scores (p < 0.05) of .81, .74 (Longissimus thoracis left, right) and .73, .77 (Iliocostalis lumborum left, right) were observed. Similarly grade wise mean SI scores (p < 0.05) of .80, .76, .73, and .981 for grades 4-, 4, 4+, and 5 respectively, were observed. CONCLUSION: The study addresses the problem of selecting minimum features that offer maximum variability for EMG assisted submaximal muscle strength grading. The proposed method emphasizes using biplot visualization to overcome the difficulty in choosing appropriate EMG features of the core back muscles that significantly distinguishes between grades 4-, 4, 4+ and 5.

Single and combined effects of titanium (TiO2) and zinc (ZnO) oxide nanoparticles in the rainbow trout gill cell line RTgill-W1.

Understanding the environmental impact of nanoparticle (NP) mixtures is essential to accurately assess the risk they represent for aquatic ecosystems. However, although the toxicity of individual NPs has been extensively studied, information regarding the toxicity of combined NPs is still comparatively rather scarce. Hence, this research aimed to investigate the individual and combined toxicity mechanisms of two widely consumed nanoparticles, zinc oxide (ZnO NPs) and titanium dioxide (TiO2 NPs), using an in vitro model, the RTgill-W1 rainbow trout gill epithelial cell line. Sublethal concentrations of ZnO NPs (0.1 µg mL-1) and TiO2 (30 µg mL-1) and a lethal concentration of ZnO NPs causing 10% mortality (EC10, 3 µg mL-1) were selected based on cytotoxicity assays. Cells were then exposed to the NPs at the selected concentrations alone and to their combination. Cytotoxicity assays, oxidative stress markers, and targeted gene expression analyses were employed to assess the NP cellular toxicity mechanisms and their effects on the gill cells. The cytotoxicity of the mixture was identical to the one of ZnO NPs alone. Enzymatic and gene expression (nrf2, gpx, sod) analyses suggest that none of the tested conditions induced a strong redox imbalance. Metal detoxification mechanisms (mtb) and zinc transportation (znt1) were affected only in cells exposed to ZnO NPs, while tight junction proteins (zo1 and cldn1), and apoptosis protein p53 were overexpressed only in cells exposed to the mixture. Osmoregulation (Na + /K + ATPase gene expression) was not affected by the tested conditions. The overall results suggest that the toxic effects of ZnO and TiO2 NPs in the mixture were significantly enhanced and could result in the disruption of the gill epithelium integrity. This study provides new insights into the combined effects of commonly used nanoparticles, emphasizing the importance of further investigating how their toxicity may be influenced in mixtures.

Identification of Primary Organophosphate Esters Contributing to Enhanced Risk of Gestational Diabetes Mellitus Based on a Case-Control Study.

Epidemiological studies on associations of organophosphate ester (OPE) exposure and gestational diabetes mellitus (GDM) risk, which remain rare and inconclusive, were carried out with a case-control population comprising 287 GDM and 313 non-GDM pregnant women recruited from Tianjin. The GDM group suffered distinctly higher serum concentrations of tri-n-butyl phosphate (TNBP), tri(2-butoxyethyl) phosphate (TBOEP), triphenyl phosphate (TPHP), tri-iso-propyl phosphate (TIPP), and tri(1-chloro-2-propyl) phosphate (TCIPP) than the healthy control group (p < 0.001). Traditional analysis methods employed for either individual or mixture effects found positive correlations (p < 0.05) between the concentrations of five OPEs (i.e., TNBP, TBOEP, TPHP, TIPP, and TCIPP) and the incidence of GDM, while 2-ethylhexyl diphenyl phosphate, tri(1-chloro-2-propyl) phosphate, and bis(2-ethylhexyl) phosphate exhibited opposite effects. Three machine learning methods considering the concurrence of OPE mixture exposure and population characteristics were applied to clarify their relative importance to GDM risk, among which random forest performed the best. Several OPEs, particularly TNBP and TBOEP ranking at the top, made greater contributions than some demographical characteristics, such as prepregnancy body mass index and family history of diabetes, to the occurrence of GDM. This was further validated by another independent case-control population obtained from Hangzhou.

The silent threat and countermeasures: Navigating the mixture risk of endocrine-disrupting chemicals on pregnancy loss in China.

Currently, many countries and regions worldwide face the challenge of declining population growth due to persistently low rates of female reproduction. Since 2017, China's birth rate has hit historic lows and continued to decline, with the death rate now equaling the birth rate. Concerns have emerged regarding the potential impact of environmental contaminants on reproductive health, including pregnancy loss. Endocrine-disrupting chemicals (EDCs) like phthalate esters (PAEs), bisphenol A (BPA), triclosan (TCS), and perfluoroalkyl substances (PFASs) have raised attention due to their adverse effects on biological systems. While China's 14th Five-Year Plan (2021-2025) for national economic and social development included the treatment of emerging pollutants, including EDCs, there are currently no national appraisal standards or regulatory frameworks for EDCs and their mixtures. Addressing the risk of EDC mixtures is an urgent matter that needs consideration from China's perspective in the near future. In this Perspective, we delve into the link between EDC mixture exposure and pregnancy loss in China. Our focus areas include establishing a comprehensive national plan targeting reproductive-aged women across diverse urban and rural areas, understanding common EDC combinations in women and their surrounding environment, exploring the relationship between EDCs and pregnancy loss via epidemiology, and reconsidering the safety of EDCs, particularly in mixtures and low-dose scenarios. We envision that this study could aid in creating preventive strategies and interventions to alleviate potential risks induced by EDC exposure during pregnancy in China.