An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy.

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Rapid detection of fertilizer information based on Raman spectroscopy and machine learning.

The rapid detection of fertilizer nutrient information is a crucial element in enabling intelligent and precise variable fertilizer application. However, traditional detection methods possess limitations, such as the difficulty in quantifying multiple components and cross-contamination. In this study, a rapid detection method was proposed, leveraging Raman spectroscopy combined with machine learning, to identify five types of fertilizers: K2SO4, (CO(NH2)2, KH2PO4, KNO3, and N:P:K (15-15-15), along with their concentrations. Qualitative and quantitative models of fertilizers were constructed using three machine learning algorithms combined with five spectral preprocessing methods. Two variable selection methods were used to optimize the quantitative model. The results showed that the classification accuracy of the five fertilizer solutions obtained by random forest (RF) was 100 %. Moreover, in terms of regression, partial least squares regression (PLSR) outperformed extreme learning machine (ELM) and least squares support vector machine (LSSVM), yielding prediction Rp2 within the range of 0.9843-0.9990 and a root mean square error in the range of 0.0486-0.1691. In addition, this study evaluated the impact of different water types (deionized water, well water, and industrial transition water) on the detection of fertilizer information via Raman spectroscopy. The results showed that while different water types did not notably affect the identification of fertilizer nutrients, they did exert a pronounced effect on the quantification of concentrations. This study highlights the efficacy of combining Raman spectroscopy with machine learning in detecting fertilizer nutrients and their concentration information effectively.

Evaluation of the shear stability of aerobic granular sludge from a pilot-scale membrane bioreactor: Establishment of a quantitative method.

This work established a quantitative method to access the shear stability of aerobic granular sludge (AGS) and validated its feasibility by using the mature AGS from a pilot-scale (50 tons/day) membrane bioreactor (MBR) for treating real municipal wastewater. The results showed that the changing rate (ΔS) of the peak area (S) of granule size distribution (GSD) exhibited an exponential relationship (R2≥0.76) with the shear time (y=a-b·cx), which was a suitable indicative index to reflect the shear stability of different AGS samples. The limiting granule size (LGS) was defined and proposed to characterize the equilibrium size for AGS after being sheared for a period of time, whose value in terms of Dv50 showed high correlation (R2=0.92) with the parameter a. The free Ca2+ (28.44-34.21 mg/L) in the influent specifically interacted with polysaccharides (PS) in the granule's extracellular polymeric substance (EPS) as a nucleation site, thereby inducing the formation of Ca precipitation to enhance its Young's modulus, while Ca2+ primarily interacted with PS in soluble metabolic product (SMP) during the initial granulation process. Furthermore, the Young's modulus significantly affected the parameter a related to shear stability (R2=0.99). Since the parameter a was more closely related (R2=1.00) to ΔS than that of the parameter b or c, the excellent correlation (R2=0.99) between the parameter a and the wet density further verified the feasibility of this method.

QSAR: Using the Past to Study the Present.

Quantitative structure-activity relationships (QSAR) is a method for predicting the physical and biological properties of small molecules; it is in use in industry and public services. However, as any scientific method, it is challenged by more and more requests, especially considering its possible role in assessing the safety of new chemicals. To answer the question whether QSAR, by exploiting available knowledge, can build new knowledge, the chapter reviews QSAR methods in search of a QSAR epistemology. QSAR stands on tree pillars, i.e., biological data, chemical knowledge, and modeling algorithms. Usually the biological data, resulting from good experimental practice, are taken as a true picture of the world; chemical knowledge has scientific bases; so if a QSAR model is not working, blame modeling. The role of modeling in developing scientific theories, and in producing knowledge, is so analyzed. QSAR is a mature technology and is part of a large body of in silico methods and other computational methods. The active debate about the acceptability of the QSAR models, about the way to communicate them, and the explanation to provide accompanies the development of today QSAR models. An example about predicting possible endocrine-disrupting chemicals (EDC) shows the many faces of modern QSAR methods.

Development, Use, and Validation of (Q)SARs for Predicting Genotoxicity and Carcinogenicity: Experiences from Italian National Institute of Health Activities.

In silico approaches are now increasingly accepted in several areas of toxicology to rapidly assess chemical hazard without the need for animal testing. Among in silico tools, quantitative and qualitative structure-activity approaches ((Q)SARs) are the most typically applied methods to predict hazard in the absence of experimental data. This paper provides an overview of different protocols that can be applied while dealing with (Q)SARs in different scenarios, namely, (Q)SAR development, use, and validation. Examples of protocols adopted in the three scenarios are reported, derived from the authors' experience in working at the Predictive Toxicology unit of the Italian National Institute of Health, focusing on the endpoints of carcinogenicity and genotoxicity.The illustrated activities are in line with the Institute's mission, the main center of research, control, and technical-scientific advice on public health in Italy.

MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.

MolPredictX is a free-access web tool in which it is possible to analyze the prediction of biological activity of chemical molecules. MolPredictX has been available online to the general public for just over a year and has now gone through its first update. We also developed its version for android, being the first free app capable of predicting biological activities. MolPredictX is available for free at https://www.molpredictX.ufpb.br/ , and its mobile application version can be obtained from Google Play.

Reflectance spectroscopy analysis and lithium content estimation in lithium-rich rocks and stream sediments: Insights from Tuanjie Peak, Western Kunlun, China.

Lithium, a rare metal of strategic importance, has garnered heightened global attention. This investigation delves into the laboratory visible-near infrared and short-wavelength infrared reflectance (VNIR-SWIR 350 nm-2500 nm) spectral properties of lithium-rich rocks and stream sediments, aiming to elucidate their quantitative relationship with lithium concentration. This research seeks to pave new avenues and furnish innovative technical solutions for probing sedimentary lithium reserves. Conducted in the Tuanjie Peak region of Western Kunlun, Xinjiang, China, this study analyzed 614 stream sediments and 222 rock specimens. Initial steps included laboratory VNIR-SWIR spectral reflectance measurements and lithium quantification. Following the preprocessing of spectral data via Savitzky-Golay (SG) smoothing and continuum removal (CR), the absorption positions (Pos2210nm, Pos1910nm) and depths (Depth2210, Depth1910) in the rock spectra, as well as the Illite Spectral Maturity (ISM) of the rock samples, were extracted. Employing both the Successive Projections Algorithm (SPA) and genetic algorithm (GA), wavelengths indicative of lithium content were identified. Integrating the lithium-sensitive wavelengths identified by these feature selection methods, A quantitative predictive regression model for lithium content in rock and stream sediments was developed using partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN). Spectral analysis indicated that lithium is predominantly found in montmorillonite and illite, with its content positively correlating with the spectral maturity of illite and closely related to Al-OH absorption depth (Depth2210) and clay content. The SPA algorithm was more effective than GA in extracting lithium-sensitive bands. The optimal regression model for quantitative prediction of lithium content in rock samples was SG-SPA-CNN, with a correlation coefficient prediction (Rp) of 0.924 and root-mean-square error prediction (RMSEP) of 0.112. The optimal model for the prediction of lithium content in stream sediment was SG-SPA-CNN, with an Rp and RMSEP of 0.881 and 0.296, respectively. The higher prediction accuracy for lithium content in rocks compared to sediments indicates that rocks are a more suitable medium for predicting lithium content. Compared to the PLSR and SVR models, the CNN model performs better in both sample types. Despite the limitations, this study highlights the effectiveness of hyperspectral technology in exploring the potential of clay-type lithium resources in the Tuanjie Peak area, offering new perspectives and approaches for further exploration.

Comparing Three Ultrasound-Based Techniques for Diagnosing and Grading Hepatic Steatosis in Metabolic Dysfunction-Associated Steatotic Liver Disease.

RATIONALE AND OBJECTIVES: To compare the diagnostic accuracy and grading ability of ultrasound-derived fat fraction (UDFF), controlled attenuation parameters (CAP), and hepatic/renal ratio (HRR) for hepatic steatosis in metabolic dysfunction-associated steatotic liver disease (MASLD) using magnetic resonance imaging proton density fat fraction (PDFF) as the gold standard. METHODS: Patients suspected of having MASLD in our hospital between October 2023 and May 2024 were divided into the MASLD group and the control group. All patients underwent UDFF, CAP, and PDFF examinations. HRR was measured during routine ultrasound examination. In statistical analysis, we initially assessed the correlation between UDFF, CAP, HRR, and general characteristics of subjects with PDFF. Subsequently, receiver operating characteristic curve were employed to evaluate and compare the diagnostic performance of UDFF, CAP, and HRR for different grades of hepatic steatosis in MASLD. Their area under the curve, optimal cut-off value, sensitivity, and specificity were also determined. Finally, predictive factors determined hepatic steatosis in MASLD (PDFF≥6%) were identified through binary logistic regression analysis. RESULTS: 115 individuals were ultimately included in the MASLD group, while 102 were included in the control group. UDFF, CAP, and HRR were all positively correlated with PDFF. Among them, UDFF exhibited the strongest correlation with PDFF (ρ = 0.91). Furthermore, in the comparison of diagnostic efficacy among different grades of hepatic steatosis, UDFF outperformed CAP and HRR (p < 0.05). However, there were no statistically significant differences in AUCs between CAP and HRR across all three grades. The AUCs for UDFF in ≥S1, ≥S2, and ≥S3 were 0.99 (95% CI 0.97 to 1.00), 0.96 (95% CI 0.93 to 0.98), and 0.97 (95% CI 0.94 to 0.99), respectively. The optimal thresholds for UDFF are determined as follows: ≥ 6% for grade S1; ≥ 15% for grade S2; and ≥ 23% for grade S3. Multivariate analysis revealed that only age, UDFF, and CAP were important influencing factors for hepatic steatosis in MASLD. CONCLUSION: The diagnostic accuracy of UDFF surpassed that of CAP and HRR in the detection and grading of hepatic steatosis in MASLD.

Test-retest and inter-rater reliability of two devices measuring tactile mechanical detection thresholds in healthy adults: Semmes-Weinstein monofilaments and the cutaneous mechanical stimulator.

INTRODUCTION/AIMS: Limitations exist in evaluating mechanical detection thresholds (MDTs) due to a lack of dependable electronic instruments designed to assess Aß fibers and measure MDTs across different body areas. This study aims to evaluate the test-retest and inter-rater reliability of the cutaneous mechanical stimulator (CMS), an electronic tactile stimulator, in quantifying MDTs. METHODS: Using a test-retest design, participants underwent assessments of MDTs using Semmes-Weinstein monofilaments (SWM) and the CMS. This study included 27 healthy volunteers (mean age 24.07 ± 3.76 years). Two raters assessed MDTs using SWM and the CMS at two stimulation sites (the left hand and foot) in two experimental sessions approximately 2 weeks apart. RESULTS: MDTs using SWM and the CMS showed excellent reliability on the hand (intraclass correlation coefficient [ICC] = .84) and foot (ICC = .90). A comparison of results obtained at the two sessions showed that MDTs on the hand displayed good reliability for both SWM (ICC = .63) and the CMS (ICC = .73), whereas MDTs on the foot displayed fair reliability for SWM (ICC = .50) and the CMS (ICC = .42). MDTs exhibited good inter-rater reliability with SWM (ICC = .66) and excellent inter-rater reliability with the CMS (ICC = .82) on the hand, as well as showing fair inter-rater reliability with SWM (ICC = .53) and good inter-rater reliability with the CMS (ICC = .60) on the foot. DISCUSSION: The CMS showed superior inter-rater reliability, indicating its potential as a valuable tool for assessing tactile sensitivity in research and clinical settings.

POSE: POSition Encoding for accelerated quantitative MRI.

Quantitative MRI utilizes multiple acquisitions with varying sequence parameters to sufficiently characterize a biophysical model of interest, resulting in undesirable scan times. Here we propose, validate and demonstrate a new general strategy for accelerating MRI using subvoxel shifting as a source of encoding called POSition Encoding (POSE). The POSE framework applies unique subvoxel shifts along the acquisition parameter dimension, thereby creating an extra source of encoding. Combining with a biophysical signal model of interest, accelerated and enhanced resolution maps of biophysical parameters are obtained. This has been validated and demonstrated through numerical Bloch equation simulations, phantom experiments and in vivo experiments using the variable flip angle signal model in 3D acquisitions as an application example. Monte Carlo simulations were performed using in vivo data to investigate our method's noise performance. POSE quantification results from numerical Bloch equation simulations of both a numerical phantom and realistic digital brain phantom concur well with the reference method, validating our method both theoretically and for realistic situations. NIST phantom experiment results show excellent overall agreement with the reference method, confirming our method's applicability for a wide range of T1 values. In vivo results not only exhibit good agreement with the reference method, but also show g-factors that significantly outperforms conventional parallel imaging methods with identical acceleration. Furthermore, our results show that POSE can be combined with parallel imaging to further accelerate while maintaining superior noise performance over parallel imaging that uses lower acceleration factors.

Quantitative expression of LNAPL pollutant concentrations in capillary zone by coupling multiple environmental factors based on random forest algorithm.

The capillary zone plays a crucial role in migration and transformation of pollutants. Light nonaqueous liquids (LNAPLs) have become the main organic pollutant in soil and groundwater environments. However, few studies have focused on the concentration distribution characteristics and quantitative expression of LNAPL pollutants within capillary zone. In this study, we conducted a sandbox-migration experiment using diesel oil as a typical LNAPL pollutant, with the capillary zone of silty sand as the research object. The variation characteristics of LNAPL pollutants (total petroleum hydrocarbon) concentration and environmental factors (moisture content, electrical conductivity, pH, and oxidationreduction potential) were essentially consistent at different locations with the same height. These characteristics differed within range of 10.0-50.0 cm and above 60.0 cm from groundwater. A model for quantitative expression of concentrations was constructed by coupling multiple environmental factors of 968 sets-7744 data via random forest algorithm. The goodness of fit (R2) for both training and test sets was greater than 0.90, and the mean absolute percentage error (MAPE) was less than 16.00 %. The absolute values of relative errors in predicting concentrations at characteristic points were less than 15.00 %. The constructed model can accurately and quantitatively express and predict concentrations in capillary zone.

Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305.

In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R2 =0.71, Q2LOO=0.68, mean absolute error [MAE]training=0.54). The model was further validated using the test set (Q2F1=0.77, Q2F2=0.75, MAEtest=0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R2 and Q2F1 values exceeding 0.7, Q2LOO values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new, unidentified chemical substances. These findings represent the significance of the RASAR method in enhancing predictivity for new unknown chemicals due to the incorporation of similarity functions in the RASAR descriptors, independent of a specific algorithm.

Biomagnification potential and health risks of organophosphate esters in prey to humpback dolphins based on dietary correction.

Information on the biomagnification of organophosphate esters (OPEs) is limited, and the results are inconclusive, mainly because precise predatorprey relationships have not been determined. Herein, we first evaluated the biomagnification potential and dietary exposure risk of 15 OPEs in 14 prey species (n = 234) to Indo-Pacific humpback dolphins from the northern South China Sea using quantitative fatty acid signature analysis (QFASA). QFASA identified Chinese gizzard shad as the primary prey of dolphins. Among the 15 OPEs, 86.7 % (13/15) had a diet-adjusted biomagnification factor (BMFQFASA) greater than 1, indicating the biomagnification potential between dolphins and their diet. Moreover, BMFQFASA exhibited a considerable positive correlation with the log octanolwater partitioning coefficient of OPEs, indicating that lipophilicity may affect the bioamplification of OPEs. Risk assessments showed that although current OPE levels may not pose substantial health risks to dolphins via diet intake, the nondiet-adjusted hazard quotient/hazard index underestimated the exposure risk of OPEs to this vulnerable dolphin species. This study provides novel evidence regarding the biomagnification and dietary exposure risks of OPEs in cetaceans, emphasizing the importance of estimating the dietary composition of predators in such analyses.

A new parameter for quantitatively characterizing antibiotic hormesis: QSAR construction and joint toxic action judgment.

Antibiotics usually induce the hormetic effects on bacteria, featured by low-dose stimulation and high-dose inhibition, which challenges the central belief in toxicity assessment and environmental risk assessment of antibiotics. However, there are currently no ideal parameters to quantitatively characterize hormesis. In this study, an effective area in hormesis (AH) was developed to quantify the biphasic dose-responses of single antibiotics (sulfonamides (SAs), sulfonamides potentiators (SAPs), and tetracyclines (TCs)) and binary mixtures (SAs-SAPs, SAs-TCs, and SAs-SAs) to the bioluminescence of Aliivibrio fischeri. Using Ebind (the lowest interaction energy between antibiotic and target protein) and Kow (octanol-water partition coefficient) as the structural descriptors, the reliable quantitative structure-activity relationship (QSAR) models were constructed for the AH values of test antibiotics and mixtures. Furthermore, a novel method based on AH was established to judge the joint toxic actions of binary antibiotics, which mainly exhibited synergism. The results also indicated that SAPs (or TCs) contributed more than SAs in the hormetic effects of antibiotic mixtures. This study proposes a new quantitative parameter for characterizing and predicting antibiotic hormesis, and considers hormesis as an integrated whole to reveal the combined effects of antibiotics, which will promote the development of risk evaluation for antibiotics and their mixtures.

Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns.

Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δß >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.

Establishment of trypsinogen-2 Amplification Luminescent Proximity Homogeneous Assay and its Application in Acute Pancreatitis.

We aim to develop an amplified luminescence proximity homogeneous assay (AlphaLISA) for quantification of trypsinogen-2 levels in human serum for the diagnosis of acute pancreatitis. Based on new amplified luminescence proximity homogeneity assay (AlphaLISA) method, carboxyl-modified donor and acceptor beads were coupled to capture and detection antibodies. A double antibody sandwich immunoassay was used to detect the concentration of trypsinogen-2 in serum. The method had good linearity (> 0.998). The intra - analysis precision was between 1.54% and 2.20% (< 10%), the inter-analysis precision was between 3.17% and 6.94% (< 15%), and the recovery was between 96.23% and 103.45%. The cross-reactivity of carbohydrate antigen 242 (CA242) and T-cell immunoglobulin mucin-3 (Tim-3) were 0.09% and 0.93%, respectively. The detection time only needed 15 min. The results of trypsinogen-2-AlphaLISA and time-resolved fluorescence immunoassay were consistent (ρ = 0.9019). In addition, serum trypsinogen-2 concentration in patients with acute pancreatitis [239.23 (17.83-807.58) ng/mL] was significantly higher than that in healthy controls [20.54 (12.10-39.73) ng/mL]. When the cut-off value was 35.38ng/mL, the sensitivity and specificity were 91.8% and 96.67%, and the positive detection rate was 91.80%. We have successfully established a trypsinogen-2-AlphaLISA method, which can promote the timely diagnosis of acute pancreatitis.

Human Keratinocyte Response to 4,4'-Methylene Diphenyl Diisocyanate-Glutathione Conjugate Exposure.

Workplace exposure to diisocyanates like 4,4'-methylene diphenyl diisocyanate can cause occupational asthma (MDI-OA), and the underlying biological pathways are still being researched.Although uncertainty remains, evidence supports the hypothesis that dermal exposure to MDI plays an important role in the development of MDI-OA.Gene expression, proteomics, and informatics tools were utilized to characterize changes in expression of RNA and protein in cultured human HEKa keratinocyte cells following exposure to conjugates of MDI with glutathione (MDI-GSH).RT-qPCR analysis using a panel of 39 candidate primers demonstrated 9 candidate genes upregulated and 30 unchanged.HPLC-MS/MS analysis of HEKa cell lysate identified 18,540 proteins across all samples Sixty proteins demonstrate statistically significant differential expression in exposed cells, some of which suggest activation of immune and inflammatory pathways.The results support the hypothesis that dermal exposures have the potential to play an important role in the development of MDI-OA. Furthermore, proteomic and gene expression data suggest multiple immune (adaptive and innate) and inflammatory pathways may be involved in the development of MDI-OA.

Development and collaborative validation of an event-specific quantitative real-time PCR method for detection of genetically modified CC-2 maize.

As one of the developed genetically modified (GM) maize varieties in China, CC-2 has demonstrated promising commercial prospects during demonstration planting. The establishment of detection methods is a technical prerequisite for effective supervision and regulation of CC-2 maize. In this study, we have developed an event-specific quantification method that targets the junction region between the exogenous gene and the 5' flanking genomic DNA (gDNA) of CC-2. The accuracy and precision of this method were evaluated across high, medium, and low levels of CC-2 maize content, revealing biases within ±25% and satisfactory precision data. Additionally, we determined the limits of quantification of the method to be 0.05% (equivalent to 20 copies) of the CC-2 maize. A collaborative trial further confirmed that our event-specific method for detecting CC-2 produces reliable, comparable, and reproducible results when applied to five different samples provided by various sources. Furthermore, we calculated the expanded uncertainty associated with determining the content level of CC-2 in these samples.

High glucose induces DNA methyltransferase 1 dependent epigenetic reprogramming of the endothelial exosome proteome in type 2 diabetes.

In response to hyperglycemia, endothelial cells (ECs) release exosomes with altered protein content and contribute to paracrine signalling, subsequently leading to vascular dysfunction in type 2 diabetes (T2D). High glucose reprograms DNA methylation patterns in various cell/tissue types, including ECs, resulting in pathologically relevant changes in cellular and extracellular proteome. However, DNA methylation-based proteome reprogramming in endothelial exosomes and associated pathological implications in T2D are not known. Hence, in the present study, we used Human umbilical vein endothelial cells (HUVECs), High Fat Diet (HFD) induced diabetic mice (C57BL/6) and clinical models to understand epigenetic basis of exosome proteome regulation in T2D pathogenesis . Exosomes were isolated by size exclusion chromatography and subjected to tandem mass tag (TMT) labelled quantitative proteomics and bioinformatics analysis. Immunoblotting was performed to validate exosome protein signature in clinically characterized individuals with T2D. We observed ECs cultured in high glucose and aortic ECs from HFD mouse expressed elevated DNA methyltransferase1 (DNMT1) levels. Quantitative proteomics of exosomes isolated from ECs treated with high glucose and overexpressing DNMT1 showed significant alterations in both protein levels and post translational modifications which were aligned to T2D associated vascular functions. Based on ontology and gene-function-disease interaction analysis, differentially expressed exosome proteins such as Thrombospondin1, Pentraxin3 and Cystatin C related to vascular complications were significantly increased in HUVECs treated with high glucose and HFD animals and T2D individuals with higher levels of glycated hemoglobin. These proteins were reduced upon treatment with 5-Aza-2'-deoxycytidine. Our study shows epigenetic regulation of exosome proteome in T2D associated vascular complications.

Feasibility and acceptability of remote administration of the cold pressor test.

Objective: Quantitative sensory testing is often used to investigate pain in the context of experimental and clinical research studies. However, many of the devices used for QST protocols are only available in resource rich environments, thereby inadvertently limiting the possible pool of participants. Development of remote protocols for appropriate QST measures has the potential to reduce barriers to participation in research. Methods: Participants with insomnia and Crohn's disease were recruited as part of a clinical trial. We adapted a remote version of the cold pressor test for use during telehealth-based study assessments. Herein, we present data from the baseline assessments including an assessment of feasibility and acceptability of the task. Results: 100% of participants (N = 28) were able to complete the remote cold pressor test using a combination of materials from their homes and mailed by the study team. Temperature changes during the test were minimal and fairly evenly balanced between increases and decreases. Correlations between submersion time and both general and disease specific pain trended toward significance. Conclusions: We demonstrated that a remote version of the cold pressor test is feasible and acceptable in a clinical population and provided a step-by-step protocol for administration to facilitate use in other studies.