Detection Of Metabolites Research Articles

Nuclear Magnetic Resonance for Targeted Metabolomics and Biochemical Sensor

NMR spectroscopy is quantitative, highly reproducible, non-selective and non-destructive. However, NMR costs and complexity hinder a use as point-of-care and biochemical sensor instrumentation. Low field NMR (LF-NMR) is an inexpensive and low footprint technique to obtain physical, chemical, electronic and structural information on small molecules, but suffers from poor spectral dispersion, especially when applied to the analysis of mixtures. Subspectral editing employing optimal control pulses is a suitable approach to cope with the severe signal superpositions in complex mixture spectra at low field.
 We have calculated the optimal control pulse shapes at 0.5 T NMR frequency using the Krotov algorithm. Downsizing the complexity of the algorithm from exponential to polynomial is shown to be possible by using a system approach with each system corresponding to a (small) molecule. In this way compound selective excitation pulses can be calculated. The signals of substructures of the cyclopentenone molecule were excited using optimal control pulses calculated by the Krotov algorithm demonstrating the feasibility of subspectral editing. Likewise, for a mixture of benzoic acid and alanine, editing of the signals of either benzoic acid or alanine employing optimal control pulses was shown to be possible.
 The obtained results are promising and can be extended to the targeted analysis of complex mixtures such as biofluids or metabolic samples at low field strengths opening access for benchtop NMR to point of care settings. Moreover, the LF-NMR was improved to allow a unique detection and reliable quantification of metabolites in biofluids like blood, urine, cerebrospinal fluid, or even tissue. In contrast to standard large scale NMR equipment the portable LF-NMR allows for an analysis of the chemical composition of biofluid samples directly at the patient (point-of-care).
 LF-NMR can be freely programmable for detecting and detecting and sensing sets of small molecules which deliver information on the health status of a person. Such an analytical tool would be useful for the prediction, diagnosis, monitoring and prognosis of diseases, and for supervision of therapeutic interventions.

Chemoproteomics Yields a Selective Molecular Host for Acetyl-CoA.

Chemoproteomic profiling is a powerful approach to define the selectivity of small molecules and endogenous metabolites with the human proteome. In addition to mechanistic studies, proteome specificity profiling also has the potential to identify new scaffolds for biomolecular sensing. Here, we report a chemoproteomics-inspired strategy for selective sensing of acetyl-CoA. First, we use chemoproteomic capture experiments to validate the N-terminal acetyltransferase NAA50 as a protein capable of differentiating acetyl-CoA and CoA. A Nanoluc-NAA50 fusion protein retains this specificity and can be used to generate a bioluminescence resonance energy transfer (BRET) signal in the presence of a CoA-linked fluorophore. This enables the development of a ligand displacement assay in which CoA metabolites are detected via their ability to bind the Nanoluc-NAA50 protein "host" and compete binding of the CoA-linked fluorophore "guest". We demonstrate that the specificity of ligand displacement reflects the molecular recognition of the NAA50 host, while the window of dynamic sensing can be controlled by tuning the binding affinity of the CoA-linked fluorophore guest. Finally, we show that the method's specificity for acetyl-CoA can be harnessed for gain-of-signal optical detection of enzyme activity and quantification of acetyl-CoA from cellular samples. Overall, our studies demonstrate the potential of harnessing insights from chemoproteomics for molecular sensing and provide a foundation for future applications in target engagement and selective metabolite detection.

Rapid Detection of Volatile Organic Metabolites in Urine by High-Pressure Photoionization Mass Spectrometry for Breast Cancer Screening: A Pilot Study.

Despite surpassing lung cancer as the most frequently diagnosed cancer, female breast cancer (BC) still lacks rapid detection methods for screening that can be implemented on a large scale in practical clinical settings. However, urine is a readily available biofluid obtained non-invasively and contains numerous volatile organic metabolites (VOMs) that offer valuable metabolic information concerning the onset and progression of diseases. In this work, a rapid method for analysis of VOMs in urine by using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) coupled with dynamic purge injection. A simple pretreatment process of urine samples by adding acid and salt was employed for efficient VOM sampling, and the numbers of metabolites increased and the detection sensitivity was improved after the acid (HCl) and salt (NaCl) addition. The established mass spectrometry detection method was applied to analyze a set of training samples collected from a local hospital, including 24 breast cancer patients and 27 healthy controls. Statistical analysis techniques such as principal component analysis, partial least squares discriminant analysis, and the Mann-Whitney U test were used, and nine VOMs were identified as differential metabolites. Finally, acrolein, 2-pentanone, and methyl allyl sulfide were selected to build a metabolite combination model for distinguishing breast cancer patients from the healthy group, and the achieved sensitivity and specificity were 92.6% and 91.7%, respectively, according to the receiver operating characteristic curve analysis. The results demonstrate that this technology has potential to become a rapid screening tool for breast cancer, with significant room for further development.

L-carnitine promotes liver regeneration after hepatectomy by enhancing lipid metabolism

BackgroundLipid metabolism plays an important role in liver regeneration, but its regulation still requires further research. In this study, lipid metabolites involved in mouse liver regeneration at different time points were sequenced and analyzed to study their influence on liver regeneration and its mechanism.MethodsOur experiment was divided into two parts. The first part examined lipid metabolites during liver regeneration in mice. In this part, lipid metabolites were sequentially analyzed in the livers of 70% mouse hepatectomy models at 0, 1, 3and 7 days after operation to find the changes of lipid metabolites in the process of liver regeneration. We screened L-carnitine as our research object through metabolite detection. Therefore, in the second part, we analyzed the effects of carnitine on mouse liver regeneration and lipid metabolism during liver regeneration. We divided the mouse into four groups: control group (70% hepatectomy group); L-carnitine group (before operation) (L-carnitine were given before operation); L-carnitine group (after operation)(L-carnitine were given after operation) and L-carnitine + perhexiline maleate (before operation) group. Weighing was performed at 24 h, 36 and 48 h in each group, and oil red staining, HE staining and MPO staining were performed. Tunnel fluorescence staining, Ki67 staining and serological examination.ResultsSequencing analysis of lipid metabolites in 70% of mouse livers at different time points after hepatectomy showed significant changes in carnitine metabolites. The results showed that, compared with the control group the mouse in L-carnitine group (before operation) at 3 time points, the number of fat drops in oil red staining was decreased, the number of Ki67 positive cells was increased, the number of MPO positive cells was decreased, the number of Tunnel fluorescence positive cells was decreased, and the liver weight was increased. Serum enzymes were decreased. Compared with control group, L-carnitine group (after operation) showed similar trends in all indexes at 36 and 48 h as L-carnitine group (before operation). L-carnitine + perhexiline maleate (before operation) group compared with control group, the number of fat drops increased, the number of Ki67 positive cells decreased, and the number of MPO positive cells increased at 3 time points. The number of Tunnel fluorescent positive cells increased and serum enzyme increased. However, both liver weights increased.ConclusionL-carnitine can promote liver cell regeneration by promoting lipid metabolism and reduce aseptic inflammation caused by excessive lipid accumulation.

Detection and identification of imperatorin metabolites in rat, dog, monkey, and human liver microsomes by ultra-high-performance liquid chromatography combined with high-resolution mass spectrometry and Compound Discoverer software.

Imperatorin, a furanocoumarin that widely exists in many umbelliferous herbs, has been demonstrated to have a variety of pharmacological effects, including anti-inflammatory, antiosteoporosis, and antitumor activities. The purpose of this study was to investigate the metabolism of imperatorin using liver microsomes. The metabolites were generated by individually incubating imperatorin with rat, dog, monkey, and human liver microsomes. To trap the reactive metabolites during microsomal metabolism, glutathione (GSH) was included in the incubation. A LC technique coupled with benchtop orbitrap MS with full mass/data-dependent tandem mass spectrometry acquisition mode was used to detect and identify the generated metabolites. The possible structures of the metabolites were characterized according to their accurate masses and fragment ions. Under the current conditions, a total of 10 metabolites, including four GSH adducts, were identified. The results indicated that imperatorin underwent extensive metabolic reactions including hydroxylation, oxidation, glucuronidation, and GSH conjugation. This study provides essential data on the metabolism of imperatorin, which will be helpful for us to understand the safety and efficacy of this bioactive compound.

Metal mixture and osteoporosis risk: Insights from plasma metabolite profiling

The pathophysiology of osteoporosis (OP) is influenced by exposure to nonessential harmful metals and insufficient or excessive intake of necessary metals. Investigating multiple plasma metals, metabolites, and OP risk among older adults may reveal novel clues of underlying mechanisms for metal toxicity on bone mass. A total of 294 adults ≥ 55 years from Wuhan communities were included. Plasma concentrations of 23 metals and metabolites were measured via inductively coupled plasma-mass spectrometry and global metabolite detection. To investigate the relationships between plasma metals, OP risk, and OP-related metabolites, three different statistical techniques were used: generalized linear regression model, two-way orthogonal partial least-squares analysis (O2PLS), and weighted quantile sum (WQS). The mean ages were 66.82 and 66.21 years in OP (n = 115) and non-OP (n = 179) groups, respectively. Of all 2999 metabolites detected, 111 differential between-group members were observed. The OP risk decreased by 58.5% (OR=0.415, 95% CI: 0.237, 0.727) per quartile increment in the WQS index indicative of metal mixture exposure. Consistency remained for bone mineral density (BMD) measurements. The O2PLS model identified the top five OP-related metabolites, namely, DG(18:2_22:6), 3-phenoxybenzoic acid, TG(16:1_16:1_22:6), TG(16:0_16:0_20:4), and TG(14:1_18:2_18:3), contributing most to the joint covariation between the metal mixture and metabolites. Significant correlations between each of them and the metal mixture were found using WQS regression. Furthermore, the five metabolites mediated the associations of the metal mixtures, BMD, and OP risk. Our findings shed additional light on the mediation functions of plasma metabolites in the connection between multiple metal co-exposure and OP pathogenesis and offer new insights into the probable mechanisms underpinning the bone effects of the metal mixture.

Human-relevant exposure to di-n-butyl phthalate tampers with the ovarian insulin-like growth factor 1 system and disrupts folliculogenesis in young adult mice.

Phthalates are compounds used in consumer and medical products worldwide. Phthalate exposure in women has been demonstrated by detection of phthalate metabolites in their urine and ovarian follicular fluid. High urinary phthalate burden has been associated with reduced ovarian reserve and oocyte retrieval in women undergoing assisted reproduction. Unfortunately, no mechanistic explanation for these associations is available. In short term in vivo and in vitro animal studies modeling human-relevant exposures to di-n-butyl phthalate (DBP), we have identified ovarian folliculogenesis as a target for phthalate exposures. In the present study, we investigated whether DBP exposure negatively influences insulin-like growth factor 1 (IGF1) signaling in the ovary and disrupts ovarian folliculogenesis. CD-1 female mice were exposed to corn oil (vehicle) or DBP (10 µg/kg/day, 100 µg/kg/day, or 1000 mg/kg/day) for 20-32 days. Ovaries were collected as animals reached the proestrus stage to achieve estrous cycle synchronization. Levels of mRNAs encoding IGF1 and 2 (Igf1 and Igf2), IGF1 receptor (Igf1r), and IGF-binding proteins 1-6 (Ifgbp1-6) were measured in whole ovary homogenates. Ovarian follicle counts and immunostaining for phosphorylated IGF1R protein (pIGF1R) were used to evaluate folliculogenesis and IGF1R activation, respectively. DBP exposure, at a realistic dose that some women may experience (100 µg/kg/day for 20-32 days), reduced ovarian Igf1 and Igf1r mRNA expression and reduced small ovarian follicle numbers and primary follicle pIGF1R positivity in DBP-treated mice. These findings reveal that DBP tampers with the ovarian IGF1 system and provide molecular insight into how phthalates could influence the ovarian reserve in females.

The sulfonamide-resistance dihydropteroate synthase gene is crucial for efficient biodegradation of sulfamethoxazole by Paenarthrobacter species.

Sulfonamide antibiotics (SAs) are serious pollutants to ecosystems and environments. Previous studies showed that microbial degradation of SAs such as sulfamethoxazole (SMX) proceeds via a sad-encoded oxidative pathway, while the sulfonamide-resistant dihydropteroate synthase gene, sul, is responsible for SA resistance. However, the co-occurrence of sad and sul genes, as well as how the sul gene affects SMX degradation, was not explored. In this study, two SMX-degrading bacterial strains, SD-1 and SD-2, were cultivated from an SMX-degrading enrichment. Both strains were Paenarthrobacter species and were phylogenetically identical; however, they showed different SMX degradation activities. Specifically, strain SD-1 utilized SMX as the sole carbon and energy source for growth and was a highly efficient SMX degrader, while SD-2 did could not use SMX as a sole carbon or energy source and showed limited SMX degradation when an additional carbon source was supplied. Genome annotation, growth, enzymatic activity tests, and metabolite detection revealed that strains SD-1 and SD-2 shared a sad-encoded oxidative pathway for SMX degradation and a pathway of protocatechuate degradation. A new sulfonamide-resistant dihydropteroate synthase gene, sul918, was identified in strain SD-1, but not in SD-2. Moreover, the lack of sul918 resulted in low SMX degradation activity in strain SD-2. Genome data mining revealed the co-occurrence of sad and sul genes in efficient SMX-degrading Paenarthrobacter strains. We propose that the co-occurrence of sulfonamide-resistant dihydropteroate synthase and sad genes is crucial for efficient SMX biodegradation. KEY POINTS: • Two sulfamethoxazole-degrading strains with distinct degrading activity, Paenarthrobacter sp. SD-1 and Paenarthrobacter sp. SD-2, were isolated and identified. • Strains SD-1 and SD-2 shared a sad-encoded oxidative pathway for SMX degradation. • A new plasmid-borne SMX resistance gene (sul918) of strain SD-1 plays a crucial role in SMX degradation efficiency.

Brief Description and Application of Microneedle Biosensors

In recent years, tremendous advances have been made in microneedle (MN)-based biosensors. MN biosensors have been used as devices for developing non-invasive, continuous monitoring of patient health status due to their painless, non-blood contact characteristics. The development of biosensors based on an integrated microneedle platform for the detection of biomarkers in interstitial fluid is covered in this article. The combination of microneedle platforms with biosensors continues to create new opportunities for non-invasive detection and ongoing monitoring. The classification of MN microneedles and multiple MN biosensors for identifying various bodily fluid components, such as blood glucose, antibiotics, lactate, etc., are presented in this study. It reviews this next generation MN integrated circuit platform and explains the various directions of current MN integrated circuit platform development. These have been combined with electrochemical biosensor integration for quantitative detection of various metabolites, electrolytes, and other substances known to be present in interdermal tissue fluids. Furthermore, this paper explores some of the major issues and potential solutions for this new MN sensing technology.

Comparison of in vitro approaches for predicting the metabolism of the selective androgen receptor modulator RAD140

The identification of metabolites allows for the expansion of possible targets for anti-doping analysis. Especially for novel substances such as selective androgen receptor modulators (SARMs), information on metabolic fate is scarce. Novel approaches such as the organ on a chip technology may provide a metabolic profile that resembles human in vivo samples more closely than approaches that rely on human liver fractions only. In this study, the SARM RAD140 was metabolized by means of subcellular human liver fractions, human liver spheroids in an organ on a chip platform, and electrochemical (EC) conversion. The resulting metabolites were analyzed with LC-HRMS/MS and compared to a human doping control urine sample that yielded an adverse analytical finding for RAD140. A total of 16 metabolites were detected in urine, while 14, 13, and 7 metabolites were detected in samples obtained from the organ on a chip experiment, the subcellular liver fraction, and EC experiments, respectively. All tested techniques resulted in the detection of RAD140 metabolites. In the organ on a chip samples, the highest number of metabolites were detected. The subcellular liver fractions and organ on a chip techniques are deemed complementary to predict metabolites of RAD140, as both techniques produce distinct metabolites that are also found in an anonymized human in vivo urine sample.Graphical abstract

Volumetric Electron Transfer from Metabolites to Chemically Doped Polymer Electrodes

AbstractThe development of sensor electrode materials for the detection of metabolites will enable point‐of‐care diagnostic devices for the monitoring and treatment of metabolic diseases such as diabetes. Current state‐of‐the‐art glucose sensing electrodes employ the organic salt tetrathiafulvene tetracyanoquinodimethane (TTF TCNQ) to receive electrons directly from enzymatic reactions of glucose. However, TTF TCNQ is insoluble in most solvents, making it challenging to deposit high‐quality electrodes. Furthermore, its hydrophobicity hinders its interface with aqueous solutions in physiological environments. To overcome these issues, TCNQ derivatives are introduced into an electron‐rich and hydrophilic conjugated polymer. Thus, a polymeric electrode is demonstrated that is easily solution processible and can undergo volumetric direct electron transfer with glucose reactions throughout its bulk. This study further elucidates the electron transfer mechanism during chemical doping and metabolite sensing reactions to inform general design rules for this new class of glucose sensing materials.

The potential role of subseafloor fungi in driving the biogeochemical cycle of nitrogen under anaerobic conditions

Fungi represent the dominant eukaryotic group of organisms in anoxic marine sedimentary ecosystems, ranging from a few centimeters to ~ 2.5 km below seafloor. However, little is known about how fungi can colonize anaerobic subseafloor environments for tens of millions of years and whether they play a role in elemental biogeochemical cycles. Based on metabolite detection, isotope tracer and gene analysis, we examined the anaerobic nitrogen conversion pathways of 19 fungal species (40 strains) isolated from1.3 to 2.5 km coal-bearing sediments below seafloor. Our results show for the first time that almost all fungi possess anaerobic denitrification, dissimilatory nitrate reduction to ammonium (DNRA), and nitrification pathways, but not anaerobic ammonium oxidation (anammox). Moreover, the distribution of fungi with different nitrogen-conversion abilities in subseafloor sediments was mainly determined by in situ temperature, CaCO3, and inorganic carbon contents. These findings suggest that fungi have multiple nitrogen transformation processes to cope with their requirements for a variety of nitrogen sources in nutrient deficient anaerobic subseafloor sedimentary environments.

Molecular imprinted polymer capped luminescent metal -organic framework for the determination of pyrethroids and its metabolite

Monitoring the pyrethroids and its metabolite is of importance to estimate the potential risk of human exposure. Herein, a lanthanide luminescent metal of framework (Ln-MOF) was employed to construct fluorescent molecular imprinted polymer probe for the detection of pyrethroids metabolite, 3-phenoxybenzaldehyde (3-PBALD). Through a typical sol–gel imprinting process, the imprinted layer capped Ln-MOFs exhibited specific fluorescence quenching at 615 nm upon adding 3-PBALD. Under the optimum condition, a linear relationship was obtained in the range from 0.01 to 5 μg mL−1 and the limit of detection was 8 ng mL−1, which is superior to bare Ln-MOFs. In addition, the obtained probe showed good stability and repeatability as well as anti-interference. Furthermore, coupling with esterase hydrolysis strategy, the proposed fluorescent probe can be extended for the determination of various synthetic pyrethroids. In spiked apple and spinach samples, the recoveries ranged from 80.2% ∼116.7% and 80.8% ∼113.2%, respectively. Therefore, this fluorescent molecular imprinted polymer probe has potential for a broad-spectrum pyrethroids residue screening.

A comparison of high- and low-resolution gas chromatography-mass spectrometry for herbal product classification: A case study with Ocimum essential oils.

Selection of marker compounds for targeted chemical analysis is complicated when considering varying instrumentation and closely related plant species. High-resolution gas chromatography-mass spectrometry (GC-MS), via orbitrap detection, has yet to be evaluated for improved marker compound selection. This study directly compares high- and low-resolution GC-MS for botanical maker compound selection using Ocimum tenuiflorum L. (OT) and Ocimum gratissimum L. (OG) for botanical ingredient authentication. The essential oils of OT and OG were collected via hydrodistillation before untargeted chemical analysis with gas chromatography coupled to single-quadrupole (GC-SQ) and orbitrap (GC-Orbitrap) detectors. The Global Natural Products Social Molecular Networking (GNPS) software was used for compound annotation, and a manual search was used to find the 41 most common Ocimum essential oil metabolites. The GC-Orbitrap resulted in 1.7-fold more metabolite detection and increased dynamic range compared to the GC-SQ. Spectral matching and manual searching were improved with GC-Orbitrap data. Each instrument had differing known compound concentrations; however, there was an overlap of six compounds with higher abundance in OG than OT and three compounds with a higher abundance in OT than OG, suggesting consistent detection of the most variable compounds. An unsupervised principal component analysis (PCA) could not discern the two species with either dataset. GC-Orbitrap instrumentation improves compound detection, dynamic range, and feature annotation in essential oil analysis. However, considering both high- and low-resolution data may improve reliable marker compound selection, as GC-Orbitrap analysis alone did not improve unsupervised separation of two Ocimum species compared to GC-SQ data.

Stepwise Targeted Matching Strategy for Comprehensive Profiling of Xanthohumol Metabolites In Vivo and In Vitro Using UHPLC-Q-Exactive Orbitrap Mass Spectrometer.

Xanthohumol (XN), a natural prenylated flavonoid extracted and isolated from the hop plant (Humulus lupulus), possesses diverse pharmacological activities. Although the metabolites of XN have been investigated in the previous study, a comprehensive metabolic profile has been insufficient in vivo or in vitro until now. The current study was aimed at systematically elucidating the metabolic pathways of XN after oral administration to rats. Herein, a UHPLC-Q-Exactive Orbitrap MS was adopted for the potential metabolites detection. A stepwise targeted matching strategy for the overall identification of XN metabolites was proposed. A metabolic net (53 metabolites included) on XN in vivo and in vitro, as well as the metabolic profile investigation, were designed, preferably characterizing XN metabolites in rat plasma, urine, liver, liver microsomes, and feces. On the basis of a stepwise targeted matching strategy, the net showed that major in vivo metabolic pathways of XN in rats include glucuronidation, sulfation, methylation, demethylation, hydrogenation, dehydrogenation, hydroxylation, and so on. The proposed metabolic pathways in this research will provide essential data for further pharmaceutical studies of prenylated flavonoids and lay the foundation for further toxicity and safety studies.

Computational algorithm for search and detection of metabolites in CDF(GCxGCxMS) files

Metabolomics is the most novel of the omic sciences, which is responsible for the study and comparison of metabolites. In this study, gas chromatography mass spectrometry (GC-MS) and two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-TOFMS) were used for the identification of metabolites found in the blood, which, throw their results in CDF files for further analysis. Metabolic profiles of healthy patients were analyzed using multivariate data analysis techniques, which showed clear differences between the metabolites found, and compared with the universal Golm Metabolome Database (GMD), allowing them to be identified with a high percentage of coincidence. The results found reinforce the advantages of GCxGC-TOFMS and the role of metabolomics in the characterization of metabolites useful for different areas of biology.

Lateral Flow Strip Biosensors for Foodborne Pathogenic Bacteria via Direct and Indirect Sensing Strategies: A Review.

Rapid and sensitive detection of foodborne pathogenic bacteria is particularly important for the prevention and control of foodborne diseases. The lateral flow strip biosensor (LFSB) is one of the most promising point-of-care detection tools and has been widely used in food safety monitoring. This review introduces recent advances in the detection of foodborne pathogenic bacteria using LFSBs. According to different bacterial biomarkers, we summarize the direct and indirect sensing strategies of bacterial LFSBs. The direct sensing strategies for whole bacterial cells are divided into antibodies, antibody alternatives, and label-free according to the recognition elements. The indirect sensing strategies refer to the detection of bacterial nucleic acids and metabolites. Next, we compare and discuss the applications of direct and indirect sensing strategies. Finally, the existing challenges, future perspectives, and development directions are discussed, which will facilitate the theoretical innovation and practical application for bacterial LFSBs.

Human metabolism and basic pharmacokinetic evaluation of AP-238: A recently emerged acylpiperazine opioid.

As a consequence of recently implemented legal restrictions on fentanyl analogs, a new generation of acylpiperazine opioids appeared on the illicit drug market. AP-238 was the latest opioid in this series to be notified by the European Early Warning System in 2020 and was involved in an increasing number of acute intoxications. AP-238 metabolism was investigated to provide useful markers of consumption. For the tentative identification of the main phase I metabolites, a pooled human liver microsome assay was performed. Further, four whole blood and two urine samples collected during post-mortem examinations and samples from a controlled oral self-administration study were screened for anticipated metabolites. In total, 12 AP-238 phase I metabolites were identified through liquid chromatography-quadrupole time-of-flight mass spectrometry in the in vitro assay. All of these were confirmed in vivo, and additionally, 15 phase I and five phase II metabolites were detected in the human urine samples, adding up to a total of 32 metabolites. Most of these metabolites were also detected in the blood samples, although mostly with lower abundances. The main in vivo metabolites were built by hydroxylation combined with further metabolic reactions such as O-methylation or N-deacylation. The controlled oral self-administration allowed us to confirm the usefulness of these metabolites as proof of intake in abstinence control. The detection of metabolites is often crucial to documenting consumption, especially when small traces of the parent drug can be found in real samples. The in vitro assay proved to be suitable for the prediction of valid biomarkers of novel synthetic opioid intake.

Portable Mass Spectrometry Approach Combined with Machine Learning for Onsite Field Detection of Huanglongbing Disease.

Huanglongbing (HLB) is one of the most serious citrus diseases in the world. Rapid, onsite, and accurate field detection of HLB is a challenging task in analytical science for a long time. Herein, we have developed a novel HLB detection method that combines headspace solid phase microextraction with portable gas chromatography-mass spectrometry (PGC-MS) approach for onsite field detection of volatile metabolites of citrus leaves. Detectability and characteristics of HLB-affected metabolites from leaves were validated, and the important biomarkers were verified by authentic compounds. A machine learning approach based on random forest algorithm is established to model the volatile metabolites from healthy, symptomatic, and asymptomatic citrus leaves. In this work, a total of 147 citrus leaf samples were analyzed. Analytical performances of this newly developed method were investigated by in-field detection of various volatile metabolites. Results demonstrated limits of detection and quantification of 0.04-0.12 and 0.17-0.44 ng/mL for different metabolites, respectively. Linear calibration curves of various metabolites were established over a concentration dynamic range of at least three orders (R2 > 0.96). Good reproducibility was obtained for intraday (3.0-17.5%, n = 6) and interday precision (8.7-18.2%, n = 7). This new HLB field detection method provides a rapid detection with 6 min for each sample via a simple optimized procedure, including onsite sampling, PGC-MS analysis, and data process and provides a high accuracy (93.3%) for simultaneous identification of healthy, symptomatic, and asymptomatic trees. These data support the use of this new method for reliable field detection of HLB. Furthermore, metabolic pathways of HLB-affected metabolites were also proposed. Overall, our results not only provide a rapid and onsite field HLB detection method but also provide valuable information for understanding metabolic change of HLB infection.

Hollow Crystallization COF Capsuled MOF Hybrids Depict Serum Metabolic Profiling for Precise Early Diagnosis and Risk Stratification of Acute Coronary Syndrome.

Acute coronary syndrome (ACS), comprising unstable angina (UA) and acute myocardial infarction (AMI), is the leading cause of death worldwide. Currently, lacking effective strategies for classifying ACS hinders the prognosis improvement of ACS patients. Disclosing the nature of metabolic disorders holds the potential to reflect disease progress and high-throughput mass spectrometry-based metabolic analysis is a promising tool for large-scale screening. Herein, a hollow crystallization COF capsuled MOF hybrids (UiO-66@HCOF) assisted serum metabolic analysis is developed for the early diagnosis and risk stratification of ACS. UiO-66@HCOF exhibits unrivaled chemical and structural stability as well as endowing satisfying desorption/ionization efficiency in the detection of metabolites. Paired with machine learning algorithms, early diagnosis of ACS is achieved with the area under the curve (AUC) value of 0.945 for validation sets. Besides, a comprehensive ACS risk stratification method is established, and the AUC value for the discrimination of ACS from healthy controls, and AMI from UA are 0.890, and 0.928. Moreover, the AUC value of the subtyping of AMI is 0.964. Finally, the potential biomarkers exhibit high sensitivity and specificity. This study makes metabolic molecular diagnosis a reality and provided new insight into the progress of ACS.