Method For Quantification Research Articles

The Development of an Isotope Dilution Mass Spectrometry Method for Interleukin-6 Quantification.

Inflammatory responses and tumor developments are closely related, with interleukin-6 (IL-6) playing important roles in both processes. IL-6 has been extensively identified as a potential tumor biomarker. This study developed an isotope dilution mass spectrometry (IDMS) method for quantifying IL-6 based on signature peptides. These peptides were screened by excluding those with missed cleavage or post-translational modification. The method's accuracy was verified using amino acid-based IDMS, in which purified IL-6 protein samples were quantified after hydrolyzing them into amino acids, and no significant difference was observed (p-value < 0.05). The method demonstrated good linearity and sensitivity upon testing. The specificity and matrix effect of the method were verified, and a precision study showed that the coefficient of variation was less than 5% for both the intra-day and inter-day tests. Compared to immunoassays, this method offers distinct advantages, such as the facilitation of multi-target analysis. Furthermore, the peptides used in this study are much more convenient for storage and operation than the antibodies or purified proteins typically used in immunoassays.

Impact of smoking intensity and device cleaning on IQOS emissions: comparison with an array of cigarettes

SignificanceIQOS is a heated tobacco product that has been widely advertised by Philip Morris International (PMI) as a reduced-exposure product compared with cigarettes. Reduced exposure results from reduced emission of...

An evidence theory-based uncertainty quantification method of building thermal parameters for accurate reliability assessment of building air-conditioning design loads

Probabilistic methods are frequently employed in the reliability assessment of building air-conditioning design loads to quantify the uncertainty associated with building thermal parameters. However, its implementation requires some assumptions because of inadequate data in the early stages of design. These assumptions cause the uncertainty quantification results of the building thermal parameters to differ from reality, which may lead to a significant overestimation or underestimate of reliability. Therefore, an evidence theory-based method was proposed to quantify the uncertainty of the building thermal parameters. In this method, evidence theory was employed instead of probability theory because the former could make full use of small sample information to achieve objective uncertainty quantification. Taking an office building in Tianjin, China, as an example, the new method was tested, and its effectiveness was verified. The results showed that the uncertainty quantification results of the building thermal parameters based on the new method included those of the benchmark method. Under small sample conditions, the reliability assessment results based on the new method were closer to those of the benchmark compared with the traditional method.

Flow Cytometry Analysis of Microglial Phenotypes in the Murine Brain During Aging and Disease.

Microglia, the brain's primary resident immune cell, exists in various phenotypic states depending on intrinsic and extrinsic signaling. Distinguishing between these phenotypes can offer valuable biological insights into neurodevelopmental and neurodegenerative processes. Recent advances in single-cell transcriptomic profiling have allowed for increased granularity and better separation of distinct microglial states. While techniques such as immunofluorescence and single-cell RNA sequencing (scRNA-seq) are available to differentiate microglial phenotypes and functions, these methods present notable limitations, including challenging quantification methods, high cost, and advanced analytical techniques. This protocol addresses these limitations by presenting an optimized cell preparation procedure that prevents ex vivo activation and a flow cytometry panel to distinguish four distinct microglial states from murine brain tissue. Following cell preparation, fluorescent antibodies were applied to label 1) homeostatic, 2) disease-associated (DAM), 3) interferon response (IRM), and 4) lipid-droplet accumulating (LDAM) microglia, based on gene markers identified in previous scRNA-Seq studies. Stained cells were analyzed by flow cytometry to assess phenotypic distribution as a function of age and sex. A key advantage of this procedure is its adaptability, allowing the panel provided to be enhanced using additional markers with an appropriate cell analyzer (i.e., Cytek Aurora 5 laser spectral flow cytometer) and interrogating different brain regions or disease models. Additionally, this protocol does not require microglial cell sorting, resulting in a relatively quick and straightforward experiment. Ultimately, this protocol can compare the distribution of microglial phenotypic states between various experimental groups, such as disease state or age, with a lower cost and higher throughput than scRNA-seq. Key features • Analysis of microglial phenotypes from murine brain without the need for cell sorting, imaging, or scRNA-seq. • This protocol can distinguish between homeostatic, disease-associated (DAM), lipid-droplet accumulating (LDAM), and interferon response (IRM) microglia from any murine brain region and/or disease model of interest. • This protocol can be modified to incorporate additional markers of interest or dyes when using a cell analyzer capable of multiple color detections.

Development and Validation of Ultra-Performance Liquid Chromatography (UPLC) Method for Simultaneous Quantification of Hydrochlorothiazide, Amlodipine Besylate, and Valsartan in Marketed Fixed-Dose Combination Tablet

Fixed-dose combination therapy is considered a practical approach in the treatment of various diseases, as it can simultaneously target different mechanisms of action that achieve the required therapeutic efficacy through a synergistic effect. A combination of hydrochlorothiazide (HTZ), amlodipine (AMD), and valsartan (VLS) has been created for the treatment of hypertension. Therefore, the aim of this study was to develop an optimized UPLC method for the simultaneous quantification of this combination. A DoE at a level of 32 was used to investigate the effects of column temperature (20, 30, and 40 °C) and formic acid concentration (0.05, 0.15, and 0.25%) on the retention time of each active pharmaceutical ingredient (API), the peak area, and the peak symmetry, as well as the resolution between HTZ-AMD and AMD-VLS peaks. The optimized analytical method was validated and used to extract the three APIs from the marketed product. The optimized analytical condition with a column temperature of 27.86 °C and a formic acid concentration of 0.172% showed good separation of the three APIs in 1.62 ± 0.006, 3.59 ± 0.002, and 3.94 ± 0.002 min for HTZ, AMD, and VST, respectively. The developed method was linear with the LOQ for a HTC, AMD, and VST of 0.028, 0.038, and 0.101 ppm, respectively. Moreover, the developed assay was sustainable and robust, with an RSD % of less than 2%. The application of this method in the extraction of HTZ, AMD, and VST from the Exforge® marketed product showed good separation with a measurable drug content of 23.5 ± 0.7, 9.68 ± 0.1, and 165.2 ± 5.2 mg compared to the label claims of 25/10/160 for HTZ, AMD, and VST, respectively.

Development, validation and application of a UPLC-MS/MS method for simultaneous quantification of OPC-61815 and its metabolites tolvaptan, DM-4103 and DM-4107 in human plasma

OPC-61815 is an intravenous formulation vasopressin antagonist designed to treat heart failure patients, especially who have difficulty in oral intake. Tolvaptan together with DM-4103 and DM-4107 are considered as the major metabolites of OPC-61815 biotransformed in the liver via cytochrome P450 (CYP) 3A. An efficient and robust ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for quantification of OPC-61815 and its three metabolites in human plasma was developed and fully validated. To our best knowledge, it was the first published method that simultaneously quantified all of these four analytes in only one run. Simple and rapid sample preparation procedure and very short UPLC-MS/MS run time (3.5 min) offered OPC-61815 and its metabolites relatively high throughput detection, which was greatly beneficial to further clinical bio-sample analysis. The method showed good linearity and sufficient sensitivity in the range of 2.00–1000 ng/mL with a low limit of quantitation (2.00 ng/mL) for each analyte. For samples with concentrations above 1000 ng/mL, 100-fold dilution with blank plasma before sample preparation was accepted. High precision and accuracy, high selectivity and satisfactory recovery of this method were demonstrated. For all of the four analytes, no significant matrix effect or carry-over was observed. The stability of analytes and internal standards under different conditions were evaluated to ensure they were stable during the whole period of storage, preparation and detection. Also, re-injection reproducibility was investigated. In addition, the conversion test showed that almost no OPC-61815 converted into DM-4103 and DM-4107 during sample processing, while attention should be paid to the concentration difference between OPC-61815 and tolvaptan in bioanalysis. The developed UPLC-MS/MS method was successfully applied to an open, single and multiple dose administration phase I trial for monitoring the pharmacokinetics of OPC-61815. This work provided a promising way for further pharmacokinetic study of OPC-61815.

Semi-automated software improves interrater reliability and reduces processing time of magnetic resonance imaging-based exocrine pancreatic assessments in pediatric patients

ObjectivesMagnetic resonance (MR) imaging with secretin stimulation (MR-PFTs) is a non-invasive test for pancreatic exocrine function based on assessing the volume of secreted bowel fluid in vivo. Adoption of this methodology in clinical care and research is largely limited to qualitative assessment of secretion as current methods for secretory response quantification require manual thresholding and segmentation of MR images, which can be time-consuming and prone to interrater variability. We describe novel software (PFTquant) that preprocesses and thresholds MR images, performs heuristic detection of non-bowel fluid objects, and provides the user with intuitive semi-automated tools to segment and quantify bowel fluid in a fast and robust manner. We evaluate the performance of this software on a retrospective set of clinical MRIs.MethodsTwenty MRIs performed in children (< 18 years) were processed independently by two observers using a manual technique and using PFTquant. Interrater agreement in measured secreted fluid volume was compared using intraclass correlation coefficients, Bland-Altman difference analysis, and Dice similarity coefficients.ResultsInterrater reliability of measured bowel fluid secretion using PFTquant was 0.90 (0.76–0.96 95% C.I.) with − 4.5 mL mean difference (-39.4–30.4 mL 95% limits of agreement) compared to 0.69 (0.36–0.86 95% C.I.) with − 0.9 mL mean difference (-77.3–75.5 mL 95% limits of agreement) for manual processing. Dice similarity coefficients were better using PFTquant (0.88 +/- 0.06) compared to manual processing (0.85 +/- 0.10) but not significantly (p = 0.11). Time to process was significantly (p < 0.001) faster using PFTquant (412 +/- 177 s) compared to manual processing (645 +/- 305 s).ConclusionNovel software provides fast, reliable quantification of secreted fluid volume in children undergoing MR-PFTs. Use of the novel software could facilitate wider adoption of quantitative MR-PFTs in clinical care and research.

Leveraging explainable AI for informed building retrofit decisions: Insights from a survey

Accurate predictions of building energy consumption are essential for reducing the energy performance gap. While data-driven energy quantification methods based on machine learning deliver promising results, the lack of Explainability prevents their widespread application. To overcome this, Explainable Artificial Intelligence (XAI) was introduced. However, to this point, no research has examined how effective these explanations are concerning decision-makers, i.e., property owners. To address this, we implement three transparent models (Linear Regression, Decision Tree, QLattice) and apply four XAI methods (Partial Dependency Plots, Accumulated Local Effects, Local Interpretable Model-Agnostic Explanations, Shapley Additive Explanations) to an Artificial Neural Network using a real-world dataset of 25,000 residential buildings. We evaluate their Prediction Accuracy and Explainability through a survey with 137 participants considering the human-centered dimensions of explanation satisfaction and perceived fidelity. The results quantify the Explainability-Accuracy trade-off in building energy consumption forecasting and how it can be counteracted by choosing the right XAI method to foster informed retrofit decisions. For research, we set the foundation for further increasing the Explainability of data-driven energy quantification methods and their human-centered evaluation. For practice, we encourage using XAI to reduce the acceptance gap of data-driven methods, whereby the XAI method should be selected carefully, as the Explainability within the methods varies by up to 10 %.

Unsupervised quantitative structural damage identification method based on BiLSTM networks and probability distribution model

Current unsupervised structural damage identification methods mainly focus on unsupervised damage detection, i.e., detecting whether damage occurs, but rarely involve damage localization and quantification in practical civil engineering structures. Thus, based on bidirectional long short-term memory (BiLSTM) networks and a probability distribution model, i.e., generalized extreme value distribution (GEVD), this paper proposes an unsupervised structural damage detection, localization, and quantification method. The response correlations model among sensors under healthy conditions was built by employing the BiLSTM networks for reconstructing response data. Then, reconstruction error between reconstructed and true data is used as damage-sensitive features and thus is applied to detect and localized damage. In addition, a stable damage quantification result can be obtained based on the probability distribution model of damage-sensitive features. The proposed method is validated by using the data obtained from a numerical steel beam model and a practical long-span cable-stayed bridge. The research results demonstrate that the proposed method can effectively detect, localize, and quantify damage in an unsupervised manner, even with a limited number of sensors and low-order modal information in acceleration data. The proposed method possesses the capability to autonomously furnish alerts regarding structural damage location and severity under Environmental and Operational Conditions.

One-Year Monitoring of Prevalence and Diversity of Dairy Propionic Acid Bacteria in Raw Milk by Means of Culture-Dependent and Culture-Independent Methods.

Even low levels of dairy propionic acid bacteria (dPAB) can cause cheese defects, resulting in severe economic losses for the producers of selected raw milk cheeses. Therefore, routine quality control of raw cheese milk for dPAB contamination is essential if propionic acid fermentation is undesired. Although knowledge of dPAB contamination of raw milk is important to understand cheese spoilage, long-term dPAB screening data are outdated, and studies taking into account different farm management parameters and their potential influence on dPAB levels are scarce. This study aims to provide insight into the dPAB levels of raw milk over time, to identify farm management factors that potentially influence dPAB levels, and to compare a cultural yeast extract lactate agar (YELA) and lithium glycerol agar (LGA) and a culture-independent method (qPCR) for dPAB quantification with respect to their applicability in routine quality control for the dairy industry. For this purpose, bulk tank milk from 25 dairy farms was screened for dPAB contamination over a one-year period. We were able to identify significant differences in the dPAB contamination levels in raw milk depending on selected farm-specific factors and observed relationships between the different types of milking systems and dPAB contamination levels in raw milk. When dPAB were quantified by cultivation on YELA, strong overgrowth of commensal microbiota impeded counting. Therefore, we conclude that quantification on LGA or by qPCR is preferable. Both methods, colony counting on LGA as well as quantification of dPAB using qPCR, have advantages for the application in (routine) quality control of raw milk, one being low-tech and inexpensive, the other being fast and highly specific, but the detection of (low level) dPAB contamination in raw milk remains a challenge.

Development and validation of stable isotope dilution LC–MS/MS method for simultaneous quantification of four Alternaria toxins in 15 food commodities

Alternaria toxins (ATs) are produced from Alternaria species that result in crop losses and harmful impacts on human health. A stable isotope dilution LC–MS/MS method was established to quantify four ATs in 15 food commodities: alternariol (AOH), alternariol monomethyl ether (AME), tentoxin (TEN), and tenuazonic acid (TeA). Based on systematically optimization of detection conditions and pre-processing steps, the limits of detection and limits of quantification of the four ATs ranged from 0.1 to 10 μg/kg and 0.2 to 30 μg/kg, respectively. The results showed that the recoveries of the four ATs were 72.0%–119.1%. The intra-precision and inter-precision ranged from 0.7% to 11.1% and 1.1% to 13.1%, respectively. The method was successfully applied to the determination of four ATs in 35 food samples, suggesting that this method could provide meaningful occurrence data to support the assessment of emerging ATs in food commodities.

Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy.

To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning. The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis. The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and -0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five-fold cross-validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365. The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

The ddeq Python library for point source quantification from remote sensing images (version 1.0)

Abstract. Atmospheric emissions from anthropogenic hotspots, i.e., cities, power plants and industrial facilities, can be determined from remote sensing images obtained from airborne and space-based imaging spectrometers. In this paper, we present a Python library for data-driven emission quantification (ddeq) that implements various computationally light methods such as the Gaussian plume inversion, cross-sectional flux method, integrated mass enhancement method and divergence method. The library provides a shared interface for data input and output and tools for pre- and post-processing of data. The shared interface makes it possible to easily compare and benchmark the different methods. The paper describes the theoretical basis of the different emission quantification methods and their implementation in the ddeq library. The application of the methods is demonstrated using Jupyter notebooks included in the library, for example, for NO2 images from the Sentinel-5P/TROPOMI satellite and for synthetic CO2 and NO2 images from the Copernicus CO2 Monitoring (CO2M) satellite constellation. The library can be easily extended for new datasets and methods, providing a powerful community tool for users and developers interested in emission monitoring using remote sensing images.

Endothelial Progenitor Cells: A Review of Molecular Mechanisms in the Pathogenesis and Endovascular Treatment of Intracranial Aneurysms.

This comprehensive review explores the multifaceted role of endothelial progenitor cells (EPCs) in vascular diseases, focusing on their involvement in the pathogenesis and their contributions to enhancing the efficacy of endovascular treatments for intracranial aneurysms (IAs). Initially discovered as CD34+ bone marrow-derived cells implicated in angiogenesis, EPCs have been linked to vascular repair, vasculogenesis, and angiogenic microenvironments. The origin and differentiation of EPCs have been subject to debate, challenging the conventional notion of bone marrow origin. Quantification methods, including CD34+ , CD133+ , and various assays, reveal the influence of factors, like age, gender, and comorbidities on EPC levels. Cellular mechanisms highlight the interplay between bone marrow and angiogenic microenvironments, involving growth factors, matrix metalloproteinases, and signaling pathways, such as phosphatidylinositol-3-kinase (PI3K) and mitogen-activated protein kinase (MAPK). In the context of the pathogenesis of IAs, EPCs play a role in maintaining vascular integrity by replacing injured and dysfunctional endothelial cells. Recent research has also suggested the therapeutic potential of EPCs after coil embolization and flow diversion, and this has led the development of device surface modifications aimed to enhance endothelialization. The comprehensive insights underscore the importance of further research on EPCs as both therapeutic targets and biomarkers in IAs.

Improved method for quantification of intact oxaliplatin by ultra high performance liquid chromatography-inductively coupled plasma mass spectrometry: Applications to clinical and speciation studies

Interest is increasing in the use of different liquid chromatography techniques coupled online to mass spectrometry for the quantification of platinum anticancer drugs in human plasma to inform cancer chemotherapy. We developed, validated and studied the application of a method for quantification of intact oxaliplatin in human plasma using ultra high performance liquid chromatography hyphenated to inductively coupled plasma mass spectrometry (UHPLC-ICP-MS). Plasma samples were processed instantly after collection from patients to preserve oxaliplatin speciation by methanol-deproteinization, and storage of diluted supernatants (plasma:methanol 1:2 v/v) at −80 °C. UHPLC separation of intact oxaliplatin and internal standard (carboplatin) was achieved using a C18 column and linear gradient mobile phase (Mobile phase A: water-methanol (97:3 v/v), 0.075 mM sodium dodecyl sulfate, 9.79 nM thallium adjusted to pH 2.5 with trifluoromethanesulfonic acid; Mobile phase B: 100 % methanol (v/v)) with ICP-MS detection to monitor platinum and thallium at m/z 195 and 205, respectively. The limit of quantification was 50 nM in methanol-deproteinized diluted plasma (1:2 v/v). Linearity was established for calibration standards ranging from 50 to 500 nM made in methanol-deproteinized diluted plasma (1:2 v/v), and for dilution of higher concentration samples in blank matrix containing internal standard (final dilution 1:29 v/v). Intra-day and inter-day accuracy ranged from 96.8 to 103 % of nominal concentration and precision from 0.62 to 2.49 % coefficient of variation. Recovery was complete and a matrix effect confirmed the requirement for matrix-matched standards. Intact oxaliplatin was stable during storage for at least 473 days, and during analysis, in methanol-deproteinized diluted plasma (1:2 v/v). The method was applied to determining the plasma concentrations of intact oxaliplatin in patients undergoing cancer chemotherapy, and studies of oxaliplatin degradation in vitro. This improved method based on UHPLC-ICP-MS will allow more specific, efficient and reliable quantification of intact oxaliplatin in human plasma.

Evaluation of [11C]UCB-A positron emission tomography in human brains

BackgroundIn preclinical studies, the positron emission tomography (PET) imaging with [11C]UCB-A provided promising results for imaging synaptic vesicle protein 2A (SV2A) as a proxy for synaptic density. This paper reports the first-in-human [11C]UCB-A PET study to characterise its kinetics in healthy subjects and further evaluate SV2A-specific binding.ResultsTwelve healthy subjects underwent 90-min baseline [11C]UCB-A scans with PET/MRI, with two subjects participating in an additional blocking scan with the same scanning procedure after a single dose of levetiracetam (1500 mg). Our results indicated abundant [11C]UCB-A brain uptake across all cortical regions, with slow elimination. Kinetic modelling of [11C]UCB-A PET using various compartment models suggested that the irreversible two-tissue compartment model best describes the kinetics of the radioactive tracer. Accordingly, the Patlak graphical analysis was used to simplify the analysis. The estimated SV2A occupancy determined by the Lassen plot was around 66%. Significant specific binding at baseline and comparable binding reduction as grey matter precludes the use of centrum semiovale as reference tissue.Conclusions[11C]UCB-A PET imaging enables quantifying SV2A in vivo. However, its slow kinetics require a long scan duration, which is impractical with the short half-life of carbon-11. Consequently, the slow kinetics and complicated quantification methods may restrict its use in humans.

Quantitative composition analysis of natural gas by high-pressure proton low-field NMR spectroscopy: Development of an improved experimental methodology

Natural gas (NG) has been proposed as a bridging fuel between fossil and renewable energy sources, playing a significant role in the decarbonization of the energy sector. Its commercial value and the integrity of the NG infrastructure depend on the NG physical properties, which are strongly related to its composition. Thus, the development of low-cost and reliable analytical methods for composition analysis of NG can enable a more efficient operation. Recently, we showed that high-pressure (HP) benchtop 1H NMR spectroscopy combined with indirect hard modelling (IHM) is an efficient and low-cost method for composition quantification of NG. The current work extends this methodology by introducing and testing novel experimental strategies for increasing the reliability and accuracy of the method. This new approach includes the use of a recently published high-pressure tube design and single-scan acquisition combined with novel quality control parameters to obtain highly reproducible peak shape 1H NMR spectra over long periods. The proposed methodology tested on binary, ternary, and more complex mixtures at 200 bar and 160 bar gives compositions in excellent agreement with the vendor certificates.

Carbon Emission Measurement of Prefabricated Buildings Based on LCA and Research on Energy Saving and Emission Reduction Strategy of Renewable Energy

In the face of diverse quantification methods for building carbon emissions, this article delves into the Life Cycle Assessment (LCA) approach to comprehensively measure the carbon emissions of prefabricated buildings throughout their lifespan. It meticulously identifies the carbon emission sources in prefabricated buildings and analyzes the measurement models relevant to their emissions in both physical and chemical stages. Prefabricated buildings hold profound implications for transforming the construction industry and advancing its sustainable development path. Examining the energy-saving characteristics and emission reduction potential of prefabricated buildings from a life-cycle perspective, this article analyzes the carbon emission measurement model during the prefabricated building transformation stage. This comprehensive analysis of the building atomization path and influential carbon emission factors lays a theoretical foundation for transitioning prefabricated buildings towards energy-saving and emission reduction strategies. Using the LCA method, carbon emissions were calculated, revealing a positive correlation with building size. Notably, through the prefabricated construction method detailed in this article, carbon emissions were significantly reduced by 30% compared to traditional construction methods.

Evaluating storage conditions and enhancement strategies on viral biomarker recovery for WBE applications.

Wastewater-based epidemiology (WBE) is a valuable disease surveillance tool. However, little is known on how factors such as transportation, storage, and wastewater characteristics influence the accuracy of the quantification methods. Hence, this study investigated the impact of storage temperatures and physicochemical characteristics of wastewater on SARS-CoV-2 and influenza A stability using droplet digital PCR. Additionally, strategies to enhance viral recovery were explored. Municipal influent wastewater stored between ±25 and -80 °C was assessed for a period of 84 days to determine viral degradation. Degradation up to 94.1% of influenza A and SARS-CoV-2 was observed in all samples with the highest at ±25 °C. Viral degradation was correlated to the changes in wastewater physicochemical characteristics. The low degradation observed of SARS-CoV-2 in the spiked pellets were indicative of viral adhesion to wastewater solids, which correlated with changes in pH. Ultrasonication frequencies ranging from 4 to 16 kHz, increased SARS-CoV-2 concentrations in the supernatant between 3.30 and 35.65%, indicating viral RNA attachment to wastewater solids. These results highlight the importance of additional pretreatment methods for maximizing RNA recovery from wastewater samples. Based on these findings, it was deduced that wastewater preservation studies are essential, and pretreatment should be included in the WBE methodology.

Quality Evaluation of Tripterygium Glycoside Tablets Based on Quantitative Band-Selective 2D 1H-13C HSQC and 1H NMR Fingerprinting.

Tripterygium glycoside tablets (TGTs) are preparations extracted and purified from Tripterygium wilfordii Hook. F and are extensively utilized in the treatment of autoimmune diseases, such as rheumatoid arthritis (RA). However, variations in production processes among manufacturers can lead to challenges in quality control and clinical utilization of TGTs. A band-selective 2D 1H-13C HSQC quantification method was applied for the determination of 13 active ingredients in TGTs. This method was validated following the guidelines of USP-NF 2022. The results demonstrated that the quantitative method exhibited excellent signal resolution, as well as sufficient accuracy, sensitivity, and stability. In addition, the 1H NMR spectra of TGTs from three manufacturers underwent analysis using principal component analysis and orthogonal partial least-squares discriminant analysis. The results revealed significant differences among the TGTs from the three manufacturers, with manufacturer 2 and manufacturer 3 demonstrating superior product consistency compared to manufacturer 1. A quality evaluation system for TGTs was developed based on band-selective 2D 1H-13C HSQC and 1H NMR, encompassing both quality markers and fingerprinting. This system offers reliable approaches and insights for enhancing the quality control of natural products.