Chemical profile of anti-epidemic sachet based on multiple sample preparation coupled with gas chromatography-mass spectrometry analysis combined with an embedded peaks resolution method and their action mechanisms.

The anti-epidemic sachet (Fang Yi Xiang Nang, FYXN) in traditional Chinese medicine (TCM) can prevent COVID-19 through volatile compounds that can play the role of fragrant and dampness, heat-clearing and detoxifying, warding off filth and pathogenic factors. Nevertheless, the anti-(mutant) SARS-CoV-2 compounds and the compounds related to the mechanism in vivo, and the mechanism of FYXN are still vague. In this study, the volatile compound set of FYXN was constructed by gas chromatography-mass spectrometry (GC-MS) based on multiple sample preparation methods, which include headspace (HS), headspace solid phase microextraction (HS-SPME) and pressurized liquid extraction (PLE). In addition, selective ion analysis (SIA) was used to resolve embedded chromatographic peaks present in HS-SPME results. Preliminary analysis of active compounds and mechanism of FYXN by network pharmacology combined with disease pathway information based on GC-MS results. A total of 96 volatile compounds in FYXN were collected by GC-MS analysis. 39 potential anti-viral compounds were screened by molecular docking. 13 key pathways were obtained by KEGG pathway analysis (PI3K-Akt signaling pathway, HIF-1 signaling pathway, etc.) for FYXN to prevent COVID-19. 16 anti-viral compounds (C95, C91, etc.), 10 core targets (RELA, MAPK1, etc.), and 16 key compounds related to the mechanism in vivo (C56, C30, etc.) were obtained by network analysis. The relevant pharmacological effects of key pathways and key compounds were verified by the literature. Finally, molecular docking was used to verify the relationship between core targets and key compounds, which are related to the mechanism in vivo. A variety of sample preparation methods coupled with GC-MS analysis combined with an embedded peaks resolution method and integrated with network pharmacology can not only comprehensively characterize the volatile compounds in FYXN, but also expand the network pharmacology research ideas, and help to discover the active compounds and mechanisms in FYXN.

The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures.

The spread of SARS-CoV-2, which causes the disease COVID-19, is difficult to control as some positive individuals, capable of transmitting the disease, can be asymptomatic. Thus, it remains critical to generate noninvasive, inexpensive COVID-19 screening systems. Two such methods include detection canines and analytical instrumentation, both of which detect volatile organic compounds associated with SARS-CoV-2. In this study, the performance of trained detection dogs is compared to a noninvasive headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) approach to identifying COVID-19 positive individuals. Five dogs were trained to detect the odor signature associated with COVID-19. They varied in performance, with the two highest-performing dogs averaging 88% sensitivity and 95% specificity over five double-blind tests. The three lowest-performing dogs averaged 46% sensitivity and 87% specificity. The optimized linear discriminant analysis (LDA) model, developed using HS-SPME-GC-MS, displayed a 100% true positive rate and a 100% true negative rate using leave-one-out cross-validation. However, the non-optimized LDA model displayed difficulty in categorizing animal hair-contaminated samples, while animal hair did not impact the dogs' performance. In conclusion, the HS-SPME-GC-MS approach for noninvasive COVID-19 detection more accurately discriminated between COVID-19 positive and COVID-19 negative samples; however, dogs performed better than the computational model when non-ideal samples were presented.

A simple and efficient derivatization strategy combined with switchable solvent liquid-liquid microextraction hydroxychloroquine methyl acetate-d3 -based quadruple isotope dilution gas chromatography mass spectrometry for the determination of hydroxychloroquine sulfate in biological fluids.

RATIONALE: A derivatization switchable solvent liquid-liquid microextraction quadruple isotope dilution gas chromatography mass spectrometry (D-SS-LLME-ID4 -GC/MS) method is presented for the determination of hydroxychloroquine sulfate in human biofluids. METHODS: While mixing type/period and concentration of NaOH were optimized via a univariate optimization approach, a multivariate optimization approach was used to determine optimum values for relatively more important parameters such as volumes of derivatization agent (acetic anhydride), NaOH and switchable solvent. RESULTS: Under the optimum experimental conditions, limit of detection and limit of quantification were calculated as 0.03 and 0.09 mg/kg (mass based), respectively. An isotopically labelled material (hydroxychloroquine methyl acetate-d3 ) was firstly synthesized to be used in ID4 experiments which give highly accurate and precise recovery results. After the application of D-SS-LLME-ID4 , superior percent recovery results were recorded as 99.9 ± 1.6-101.3 ± 1.2 for human serum, 99.9 ± 1.7-99.8 ± 1.8 for urine and 99.6 ± 1.5-101.0 ± 1.1 for saliva samples. CONCLUSIONS: The developed D-SS-LLME-ID4 -GC/MS method compensates the complicated matrix effects of human biofluids and provides highly accurate quantification of an analyte with precise results.

Development of a solid phase microextraction gas chromatography tandem mass spectrometry methodology for the analysis of sixty personal care products in hydroalcoholic gels - hand sanitizers - in the context of COVID-19 pandemic.

Because of the coronavirus pandemic, hydroalcoholic gels have become essential products to prevent the spread of COVID-19. This research aims to develop a simple, fast and sustainable microextraction methodology followed by gas chromatography tandem mass spectrometry (GC-MS/MS) to analyze simultaneously 60 personal care products (PCPs) including fragrances allergens, synthetic musks, preservatives and plasticizers in hand sanitizers. Micro-matrix-solid-phase dispersion (µMSPD) and solid-phase microextraction (SPME) were compared with the aim of obtaining high sensitivity and sample throughput. SPME demonstrated higher efficiency being selected as sample treatment. Different dilutions of the sample in ultrapure water were assessed to achieve high sensitivity but, at the same time, to avoid or minimize matrix effect. The most critical parameters affecting SPME (fibre coating, extraction mode and temperature) were optimized by design of experiments (DOE). The method was successfully validated in terms of linearity, precision and accuracy, obtaining recovery values between 80 and 112% for most compounds with relative standard deviation (RSD) values lower than 10%. External calibration using standards prepared in ultrapure water demonstrated suitability due to the absence of matrix effect. Finally, the simple, fast and high throughput method was applied to the analysis of real hydroalcoholic gel samples. Among the 60 target compounds, 39 of them were found, highlighting the high number of fragrance allergens, at concentrations ranging between 0.01 and 217 µg g-1. Most of the samples were not correctly labelled attending cosmetic Regulation (EU) No 1223/2009, and none of them followed the World Health Organization (WHO) recommendation for hand sanitizers formulation.

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza.

BACKGROUND: Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection. METHODS: We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery. FINDINGS: A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations. INTERPRETATION: This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing. FUNDING: None.

A multiple-method comparative study using GC-MS, AMDIS and in-house-built software for the detection and identification of "unknown" volatile organic compounds in breath.

The human respiratory system is a highly complex matrix that exhales many volatile organic compounds (VOCs). Breath-exhaled VOCs are often "unknowns" and possess low concentrations, which make their analysis, peak digging and data processing challenging. We report a new methodology, applied in a proof-of-concept experiment, for the detection of VOCs in breath. For this purpose, we developed and compared four complementary analysis methods based on solid-phase microextraction and thermal desorption (TD) tubes with two GC-mass spectrometer (MS) methods. Using eight model compounds, we obtained an LOD range of 0.02-20 ng/ml. We found that in breath analysis, sampling the exhausted air from Tedlar bags is better when TD tubes are used, not only because of the preconcentration but also due to the stability of analytes in the TD tubes. Data processing (peak picking) was based on two data retrieval approaches with an in-house script written for comparison and differentiation between two populations: sick and healthy. We found it best to use "raw" AMDIS deconvolution data (.ELU) rather than its NIST (.FIN) identification data for comparison between samples. A successful demonstration of this method was conducted in a pilot study (n = 21) that took place in a closed hospital ward (Covid-19 ward) with the discovery of four potential markers. These preliminary findings, at the molecular level, demonstrate the capabilities of our method and can be applied in larger and more comprehensive experiments in the omics world.

Response Surface Methodology to Optimize the Isolation of Dominant Volatile Compounds from Monofloral Greek Thyme Honey Using SPME-GC-MS.

This study aimed at an experimental design of response surface methodology (RSM) in the optimization of the dominant volatile fraction of Greek thyme honey using solid-phase microextraction (SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS). For this purpose, a multiple response optimization was employed using desirability functions, which demand a search for optimal conditions for a set of responses simultaneously. A test set of eighty thyme honey samples were analyzed under the optimum conditions for validation of the proposed model. The optimized combination of isolation conditions was the temperature (60 °C), equilibration time (15 min), extraction time (30 min), magnetic stirrer speed (700 rpm), sample volume (6 mL), water: honey ratio (1:3 v/w) with total desirability over 0.50. It was found that the magnetic stirrer speed, which has not been evaluated before, had a positive effect, especially in combination with other factors. The above-developed methodology proved to be effective in the optimization of isolation of specific volatile compounds from a difficult matrix, like honey. This study could be a good basis for the development of novel RSM for other monofloral honey samples.

Implementing an integrated multi-technology platform for drug checking: Social, scientific, and technological considerations.

The illicit drug overdose crisis in North America continues to devastate communities with fentanyl detected in the majority of illicit drug overdose deaths. The COVID-19 pandemic has heightened concerns of even greater unpredictability in the drug supplies and unprecedented rates of overdoses. Portable drug-checking technologies are increasingly being integrated within overdose prevention strategies. These emerging responses are raising new questions about which technologies to pursue and what service models can respond to the current risks and contexts. In what has been referred to as the epicenter of the overdose crisis in Canada, a multi-technology platform for drug checking is being piloted in community settings using a suite of chemical analytical methods to provide real-time harm reduction. These include infrared absorption, Raman scattering, gas chromatography with mass spectrometry, and antibody-based test strips. In this Perspective, we illustrate some advantages and challenges of using multiple techniques for the analysis of the same sample, and provide an example of a data analysis and visualization platform that can unify the presentation of the results and enable deeper analysis of the results. We also highlight the implementation of a various service models that co-exist in a research setting, with particular emphasis on the way that drug checking technicians and harm reduction workers interact with service users. Finally, we provide a description of the challenges associated with data interpretation and the communication of results to a diverse audience.

A two-tiered system of analysis to tackle rice fraud: The Indian Basmati study.

Demand for high quality Basmati rice has increased significantly in the last decade. This commodity is highly vulnerable to fraud, especially in the post COVID-19 era. A unique two-tiered analytical system comprised of rapid on-site screening of samples using handheld portable Near-infrared NIR and laboratory confirmatory technique using a Head space gas chromatography mass spectrometry (HS-GC-MS) strategy for untargeted analysis was developed. Chemometric models built using NIR data correctly predicted nearly 100% of Pusa 1121 and Taraori, two high value types of Basmati, from potential adulterants. Furthermore, rice VOC profile fingerprints showed very good classification (R2 >0.9, Q2 > 0.9, Accuracy > 0.99) for these high quality Basmati varieties from potential adulterant varieties with aldehydes identified as key VOC marker compounds. Using a two-tiered system of a rapid method for on-site screening of many samples alongside a laboratory-based confirmatory method can classify Basmati rice varieties, protecting the supply chain from fraud.