Carbon fiber-sampling combined flame ionization mass spectrometry for direct analysis of drugs in oral fluid.

Drug-impaired driving poses a significant risk of collisions and other hazardous accidents, emphasizing the urgent need for simple and rapid roadside detection methods. Oral fluid, as an easily collectible and non-invasive test material, has gained widespread use in detecting drug-impaired driving. In this study, we have devised a method for direct sampling using a carbon fiber bundle combined with flame ionization mass spectrometry. The essence of this method lies in the synergy between the adsorption properties of carbon fiber and the plasma characteristics of the flame. Leveraging the strong adsorption capabilities of the carbon fiber bundle allows for the use of a minimal sample size (<100 µL) during sampling, presenting a distinct advantage in the roadside inspection and sampling process. Throughout the flame ionization process, proteins and salts within the oral fluid matrix adhere well to the carbon fiber bundle, while small molecule targets can be efficiently desorbed and react with charged species in the flame, leading to ionization. The results demonstrate the successful development of carbon fiber-sampling combined flame ionization mass spectrometry, capable of qualitative and quantitative analysis of drugs in oral fluid without the need for sample pre-treatment. Its quantitative capabilities are sufficient for real sample detection, providing an effective analytical method for the roadside detection of drugs in oral fluids.

Rapid screening of acetylcholinesterase active contaminants in water: A solid phase microextraction-based ligand fishing approach.

Effect-directed analysis (EDA) has been increasingly used for screening toxic contaminants in the environment, but conventional EDA procedures are often time-consuming and labor-extensive. This challenges the use of EDA for toxicant identification in the scenarios when quick answers are demanded. Herein, a solid phase microextraction ligand fishing (SPME-LF) strategy has been proposed as a rapid EDA approach for identifying acetylcholinesterase (AChE) active compounds in water. The feasibility of ligand fishing techniques for screening AChE active chemicals from environmental mixtures was first verified by a membrane separation method. Then, SPME fibers were prepared through self-assembly of boronic acid groups with AChE via co-bonding and applied for SPME-LF. As AChE coated SPME fibers selectively enriched AChE-active compounds from water, comparing sorbing compounds by the SPME fibers with and without AChE coating can quickly distinguish AChE toxicants in mixtures. Compared with conventional EDA, SPME-LF does not require repeating sample separations and bioassays, endowing SPME-LF with the merits of low-cost, labor-saving, and user-friendly. It is believed that cost-efficient and easy-to-use SPME-LF strategy can potentially be a rapid EDA method for screening receptor-specific toxicants in aquatic environment, especially applicable in time-sensitive screening.

Pancreatic Paragonimiasis in Children: A Case Report.

ABSTRACT: A few pediatric cases of abdominal paragonimiasis have been described. Here we describe a case of pulmonary and abdominal paragonimiasis with involvement of the pancreas in a 9-year-old boy. The aim of this study was to analyze the clinical and radiological features of pancreatic paragonimiasis in children and raise the awareness of this disease.

Metabolic characterization of 25X-NBOH and 25X-NBOMe phenethylamines based on UHPLC-Q-Exactive Orbitrap MS in human liver microsomes.

The types and quantities of new psychoactive substances synthesized based on structural modifications have increased rapidly in recent years and pose a great challenge to clinical and forensic laboratories. N-benzyl derivatives of phenethylamines, 25B-NBOH, 25E-NBOH, 25H-NBOH, and 25iP-NBOMe have begun to flow into the black market and have caused several poisoning cases and even fatal cases. The aim of this study was to avoid false negative results by detecting the parent drug and its metabolites to extend the detection window in biological matrices and provide basic data for the simultaneous determination of illegal drugs and metabolites in forensic and emergency cases. To facilitate the comparison of metabolic characteristics, we divided the four compounds into two groups of types, 25X-NBOH and 25X-NBOMe. The in vitro phase I and phase II metabolism of these four compounds was investigated by incubating 10 mg mL-1 pooled human liver microsomes with co-substrates for 180 min at 37 â„ƒ, and then analyzing the reaction mixture using ultrahigh-performance liquid chromatography-quadrupole/electrostatic field orbitrap mass spectrometry. In total, 70 metabolites were obtained for the four compounds. The major biotransformations were O-demethylation, hydroxylation, dehydrogenation, N-dehydroxybenzyl, N-demethoxybenzyl, oxidate transformation to ketone and carboxylate, glucuronidation, and their combination reactions. We recommended the major metabolites with high peak area ratio as biomarkers, B2-1 (56.61%), B2-2 (17.43%) and B6 (17.78%) for 25B-NBOH, E2-1 (42.81%), E2-2 (34.90%) and E8-2 (10.18%) for 25E-NBOH, H5 (49.28%), H2-1 (21.54%), and H1 (18.37%) for 25H-NBOH, P3-1 (10.94%), P3-2 (33.18%), P3-3 (14.85%) and P12-2 (23.00%) for 25iP-NBOMe. This is a study to evaluate their metabolic characteristics in detail. Comparative analysis of the N-benzyl derivatives of phenethylamines provided basic data for elucidating their pharmacology and toxicity. Timely analysis of the metabolic profiles of compounds with abuse potential will facilitate the early development of regulatory measures.

Negative mixing enthalpy solid solutions deliver high strength and ductility.

Body-centred cubic refractory multi-principal element alloys (MPEAs), with several refractory metal elements as constituents and featuring a yield strength greater than one gigapascal, are promising materials to meet the demands of aggressive structural applications1-6. Their low-to-no tensile ductility at room temperature, however, limits their processability and scaled-up application7-10. Here we present a HfNbTiVAl10 alloy that shows remarkable tensile ductility (roughly 20%) and ultrahigh yield strength (roughly 1,390 megapascals). Notably, these are among the best synergies compared with other related alloys. Such superb synergies derive from the addition of aluminium to the HfNbTiV alloy, resulting in a negative mixing enthalpy solid solution, which promotes strength and favours the formation of hierarchical chemical fluctuations (HCFs). The HCFs span many length scales, ranging from submicrometre to atomic scale, and create a high density of diffusive boundaries that act as effective barriers for dislocation motion. Consequently, versatile dislocation configurations are sequentially stimulated, enabling the alloy to accommodate plastic deformation while fostering substantial interactions that give rise to two unusual strain-hardening rate upturns. Thus, plastic instability is significantly delayed, which expands the plastic regime as ultralarge tensile ductility. This study provides valuable insights into achieving a synergistic combination of ultrahigh strength and large tensile ductility in MPEAs.

PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning.

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set with the ground-truth label included. However, in a more practical but challenging scenario, the annotator may miss the ground-truth and provide a wrong candidate set, which is known as the noisy PLL problem. To remedy this problem, we propose the PiCO+ framework that simultaneously disambiguates the candidate sets and mitigates label noise. Core to PiCO+, we develop a novel label disambiguation algorithm PiCO that consists of a contrastive learning module along with a novel class prototype-based disambiguation method. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. To handle label noise, we extend PiCO to PiCO+, which further performs distance-based clean sample selection, and learns robust classifiers by a semi-supervised contrastive learning algorithm. Beyond this, we further investigate the robustness of PiCO+ in the context of out-of-distribution noise and incorporate a novel energy-based rejection method for improved robustness. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning.

Global research trends in sexual health care: A bibliometric and visualized study.

No Patient or Public Contribution, because the relevant data of this article comes from the literature database. PURPOSE: The present study aimed to investigate the trends and research status of sexual healthcare. METHODS: We searched the Web of Science database for relevant articles concerning sexual healthcare, published between 2009 and 31 December 2022. Data collected include: the number of publications, authors, journals, countries, institutions, keywords. VOSviewer and CiteSpace were used to conduct the bibliometric study and visualise the analysis. RESULTS: A total of 1450 publications were included. The number of publications on sexual healthcare shows a fluctuating upward trend, and a stable core group of authors has been formed. The Journal of Clinical Nursing published the most articles on sexual healthcare (140 publications). The United States of America published the most articles (723, 49.86%). The research institution with the highest number of publications is the University of São Paulo. According to the keyword, timeline view and prominence mapping analysis, we believe that 'Female sexual health', 'HIV', 'LGBT' and 'Sexual Healthcare Services' may be new research hotspots in the field of sexual healthcare. CONCLUSION: This study describes the research status of sexual healthcare research over the past 14 years. The findings of this study can provide helpful reference and guidance for the development trend and research direction of sexual healthcare.

Fuse feeds as one: cross-modal framework for general identification of AMPs.

Antimicrobial peptides (AMPs) are promising candidates for the development of new antibiotics due to their broad-spectrum activity against a range of pathogens. However, identifying AMPs through a huge bunch of candidates is challenging due to their complex structures and diverse sequences. In this study, we propose SenseXAMP, a cross-modal framework that leverages semantic embeddings of and protein descriptors (PDs) of input sequences to improve the identification performance of AMPs. SenseXAMP includes a multi-input alignment module and cross-representation fusion module to explore the hidden information between the two input features and better leverage the fusion feature. To better address the AMPs identification task, we accumulate the latest annotated AMPs data to form more generous benchmark datasets. Additionally, we expand the existing AMPs identification task settings by adding an AMPs regression task to meet more specific requirements like antimicrobial activity prediction. The experimental results indicated that SenseXAMP outperformed existing state-of-the-art models on multiple AMP-related datasets including commonly used AMPs classification datasets and our proposed benchmark datasets. Furthermore, we conducted a series of experiments to demonstrate the complementary nature of traditional PDs and protein pre-training models in AMPs tasks. Our experiments reveal that SenseXAMP can effectively combine the advantages of PDs to improve the performance of protein pre-training models in AMPs tasks.

Metabolism of dipentylone in zebrafish and human liver microsomes determined by liquid chromatography-high resolution mass spectrometry.

The consumption of novel psychoactive substances (NPS) is exceedingly prevalent in society, as these substances are sold and distributed as "legal highs." One novel synthetic cathinone emerging in the market is 1-(1,3-benzodioxol-5-yl)-2-(dimethylamino) pentan-1-one (dipentylone). The goal of this work was to study the in vivo and in vitro metabolism of dipentylone in zebrafish and human liver microsomes (HLMs) by liquid chromatography-high resolution mass spectrometry (LC-HRMS). The zebrafish and HLM samples contained 14 dipentylone metabolites, specifically 12 phase Ⅰ metabolites and 2 phase Ⅱ metabolites. The main metabolic pathways included monohydroxylation (M1 and M2), N-dealkylation (M3), hydroxylation of the aromatic ring and dealkoxylation of M3 (M4), O-dealkylation (M5), N-dealkylation of M5 (M6), reduction of carboxide (M7), monohydroxylation of M5 (M8), dehydrogenation (M9), dealkoxylation (M10), N-dealkylation of M10 (M11), dealkoxylation of M9 (M12), glucuronidation of M5 (M13), and sulfation (M14). The monohydroxylated metabolite (M2) can be recommended as metabolic markers for dipentylone. This study is the first to identify a target compound for monitoring the abuse of dipentylone and to determine the essential chemical structure of the metabolites for further toxicological research.

Analysis of 15 anti-obesity drugs in urine using thermal-assisted paper spray mass spectrometry.

Anti-obesity drugs, used to suppress appetite and reduce fat absorption, have been circulated and traded illegally worldwide. The traditional methods of liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) for analyzing these drugs in in vivo samples require complex sample pretreatment and time-consuming procedures. To address this issue, a thermal-assisted paper spray mass spectrometry (PS-MS) method was developed in this study to analyze anti-obesity drugs in raw urine. By incorporating a heat source and optimizing the spray solvent and paper substrate, this technique demonstrates reduced matrix effect and higher sensitivity compared to traditional PS-MS methodology for direct analysis of anti-obesity drugs in urine samples. A temperature range of 100-200 °C can be set for screening anti-obesity drugs in urine samples, with the flexibility to adjust the temperature according to the specific drug being analyzed. The limits of detection (LODs) for these 15 anti-obesity drugs in urine ranged between 1 and 500 ng mL-1. Furthermore, the thermal-assisted PS-MS method exhibited good linearities (R2, 0.9903-0.9997) within the range from 10-100 to 1000 ng mL-1 for the direct quantitation of anti-obesity drugs in urine samples with an internal standard. Therefore, the thermal-assisted PS-MS technique may provide a novel approach for the direct analysis of drugs in complex samples.

Quantitative Analysis of Three Synthetic Cannabinoids MDMB-4en-PINACA, ADB-BUTINACA, and ADB-4en-PINACA by Thermal-Assisted Carbon Fiber Ionization Mass Spectrometry.

Recently, synthetic cannabinoids (SCs) have emerged as new psychoactive substances (NPS) and have been frequently added to e-liquids, leading to their abuse. In order to detect SCs in e-liquids quickly and accurately, a thermal-assisted carbon fiber ionization mass spectrometry technique has been developed. The introduction of a heat source helps to reduce the matrix effects. The results indicate that the ratio of the slope of the matrix curve (e-liquids matrix) and the standard curve (methanol solution) for SCs analysis is close to 1, indicating a minimized matrix effect of this method. Furthermore, this method exhibits good quantitative ability when applied to real samples. It does not require sample pretreatment and is sensitive enough to directly quantify SCs in e-liquids. Our method is characterized by the ability to achieve rapid and direct quantitative analysis with minimized matrix effects. It provides a rapid and simple method for analyzing SCs in e-liquids.

Physics Embedded Graph Convolution Neural Network for Power Flow Calculation Considering Uncertain Injections and Topology.

Probabilistic analysis tool is important to quantify the impacts of the uncertainties on power system operations. However, the repetitive calculations of power flow are time-consuming. To address this issue, data-driven approaches are proposed but they are not robust to the uncertain injections and varying topology. This article proposes a model-driven graph convolution neural network (MD-GCN) for power flow calculation with high-computational efficiency and good robustness to topology changes. Compared with the basic graph convolution neural network (GCN), the construction of MD-GCN considers the physical connection relationships among different nodes. This is achieved by embedding the linearized power flow model into the layer-wise propagation. Such a structure enhances the interpretability of the network forward propagation. To ensure that enough features are extracted in MD-GCN, a new input feature construction method with multiple neighborhood aggregations and a global pooling layer are developed. This allows us to integrate both global features and neighborhood features, yielding the complete features representation of the system-wide impacts on every single node. Numerical results on the IEEE 30-bus, 57-bus, 118-bus, and 1354-bus systems demonstrate that the proposed method achieves much better performance as compared to other approaches in the presence of uncertain power injections and system topology.

Genetic and neural mechanisms of sleep disorders in children with autism spectrum disorder: a review.

Background: The incidence of sleep disorders in children with autism spectrum disorder (ASD) is very high. Sleep disorders can exacerbate the development of ASD and impose a heavy burden on families and society. The pathological mechanism of sleep disorders in autism is complex, but gene mutations and neural abnormalities may be involved. Methods: In this review, we examined literature addressing the genetic and neural mechanisms of sleep disorders in children with ASD. The databases PubMed and Scopus were searched for eligible studies published between 2013 and 2023. Results: Prolonged awakenings of children with ASD may be caused by the following processes. Mutations in the MECP2, VGAT and SLC6A1 genes can decrease GABA inhibition on neurons in the locus coeruleus, leading to hyperactivity of noradrenergic neurons and prolonged awakenings in children with ASD. Mutations in the HRH1, HRH2, and HRH3 genes heighten the expression of histamine receptors in the posterior hypothalamus, potentially intensifying histamine's ability to promote arousal. Mutations in the KCNQ3 and PCDH10 genes cause atypical modulation of amygdala impact on orexinergic neurons, potentially causing hyperexcitability of the hypothalamic orexin system. Mutations in the AHI1, ARHGEF10, UBE3A, and SLC6A3 genes affect dopamine synthesis, catabolism, and reuptake processes, which can elevate dopamine concentrations in the midbrain. Secondly, non-rapid eye movement sleep disorder is closely related to the lack of butyric acid, iron deficiency and dysfunction of the thalamic reticular nucleus induced by PTCHD1 gene alterations. Thirdly, mutations in the HTR2A, SLC6A4, MAOA, MAOB, TPH2, VMATs, SHANK3, and CADPS2 genes induce structural and functional abnormalities of the dorsal raphe nucleus (DRN) and amygdala, which may disturb REM sleep. In addition, the decrease in melatonin levels caused by ASMT, MTNR1A, and MTNR1B gene mutations, along with functional abnormalities of basal forebrain cholinergic neurons, may lead to abnormal sleep-wake rhythm transitions. Conclusion: Our review revealed that the functional and structural abnormalities of sleep-wake related neural circuits induced by gene mutations are strongly correlated with sleep disorders in children with ASD. Exploring the neural mechanisms of sleep disorders and the underlying genetic pathology in children with ASD is significant for further studies of therapy.

Study of the Metabolic Profiles of "Indazole-3-Carboxamide" and "Isatin Acyl Hydrazone" (OXIZID) Synthetic Cannabinoids in a Human Liver Microsome Model Using UHPLC-QE Orbitrap MS.

Unregulated core structures, "isatin acyl hydrazones" (OXIZIDs), have quietly appeared on the market since China legislated to ban seven general core scaffolds of synthetic cannabinoids (SCs). The fast evolution of SCs presents clinical and forensic toxicologists with challenges. Due to extensive metabolism, the parent compounds are barely detectable in urine. Therefore, studies on the metabolism of SCs are essential to facilitate their detection in biological matrices. The aim of the present study was to elucidate the metabolism of two cores, "indazole-3-carboxamide" (e.g., ADB-BUTINACA) and "isatin acyl hydrazone" (e.g., BZO-HEXOXIZID). The in vitro phase I and phase II metabolism of these six SCs was investigated by incubating 10 mg/mL pooled human liver microsomes with co-substrates for 3 h at 37 °C, and then analyzing the reaction mixture using ultrahigh-performance liquid chromatography-quadrupole/electrostatic field orbitrap mass spectrometry. In total, 9 to 34 metabolites were detected for each SC, and the major biotransformations were hydroxylation, dihydrodiol formation (MDMB-4en-PINACA and BZO-4en-POXIZID), oxidative defluorination (5-fluoro BZO-POXIZID), hydrogenation, hydrolysis, dehydrogenation, oxidate transformation to ketone and carboxylate, N-dealkylation, and glucuronidation. Comparing our results with previous studies, the parent drugs and SC metabolites formed via hydrogenation, carboxylation, ketone formation, and oxidative defluorination were identified as suitable biomarkers.

Metabolic profiling of clonazolam in human liver microsomes and zebrafish models using liquid chromatography quadrupole Orbitrap mass spectrometry.

Clonazolam is a designer benzodiazepine with strong sedative and amnesic effects. As we all know, the detection of metabolites is the key to confirming the use of substances in the field of forensic toxicology. In order to better describe clonazolam metabolism completely, we performed the two different experiments exploiting the unique characteristics of the models used. In this study, in vivo and in vitro samples were analyzed with liquid chromatography-quadrupole/electrostatic field orbitrap mass spectrometry. The results showed that seven Phase I metabolites and one Phase II metabolite were detected in zebrafish model. The remaining Phase I and II metabolites were also found in the incubation solution of pooled human liver microsomes. The main types of metabolic reactions of clonazolam included hydroxylation, dealkylation, nitroreduction, dechlorination, N-Acetylation, and O-glucuronidation. In this paper, the main metabolites and metabolic pathways of clonazolam are clarified in detail in order to further improve the metabolic rule of clonazolam. Based on these results, to better detect and judge the abuse of clonazolam, we suggest that M1, its nitro reduction product, is used as its biomarker. The results of this study provide a theoretical basis for the pharmacokinetics and forensic medicine of clonazolam.

Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences.

Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6-9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.

LncRNA MEG3 attenuates the malignancy of retinoblastoma cells through inactivating PI3K /Akt/mTOR signaling pathway.

Retinoblastoma (RB) is the most common neoplasm found in the eye of children. There are increasing interests to develop targeted gene therapy for this disease. This study was performed to investigate the impact of long non-coding RNA (lncRNA) MEG3 on the biological features of RB cells. Vector overexpressing MEG3 was constructed and introduced into two RB cell lines. Transfected RB cells were assessed for proliferation, apoptosis, migration ability, expression levels of important genes in the PI3K/Akt/mTOR signaling pathway using qRT-PCR and Western blot analysis. Xenograft mouse models were constructed to determine the tumorigenicity of RB cells overexpressing MEG3. MEG3 mRNA level was significantly lower in RB cells than in non-cancer cells (p < 0.01). Overexpressing MEG3 resulted in significant reduction in cell proliferation (p < 0.05), migration (p < 0.01) and significant increase in apoptosis (p < 0.01). After overexpressing MEG3, p-PI3K, p-Akt and p-mTOR levels were significantly downregulated (p < 0.01). Furthermore, in the xenograft model, RB cells overexpressing MEG3 generated significantly smaller tumors as compared to RB cells that did not overexpress MEG3 (p < 0.05). Our data suggest that MEG3 increases apoptosis and reduces tumorigenicity of RB cells through inactivating the PI3K/Akt/mTOR pathway. Therefore, MEG3 could be further investigated as a potential new therapeutic agent and target for RB therapy.

Chiral analysis of dextromethorphan and levomethorphan in human hair by liquid chromatography-tandem mass spectrometry.

PURPOSE: Methorphan exists in two enantiomeric forms including dextromethorphan and levomethorphan. Dextromethorphan is an over-the-counter antitussive drug, whereas levomethorphan is strictly controlled as a narcotic drug. Chiral analysis of methorphan could, therefore, assist clinicians and forensic experts in differentiating between illicit and therapeutic use and in tracing the source of the drug. METHODS: A method for enantiomeric separation and quantification of levomethorphan and dextromethorphan in human hair was developed and validated using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Hair was extracted in hydrochloric acid/methanol (1:20, v/v). The supernatant were separated using a Supelco Astec Chirobiotic™ V2 column (250 × 2.1 mm, i.d., 5 µm particle size) and analyzed on a triple quadrupole linear ion trap mass spectrometer in multiple reaction monitoring mode. RESULTS: The limits of detection for dextromethorphan and levomethorphan were 2 and 1 pg/mg, respectively; the lower limit of quantification was 2 pg/mg for both drugs. Good linearity (r > 0.995) was observed for both analytes over the linear range. Precision values were below 10% for both analytes; accuracy values ranged from 87.5 to 101%. The extraction recoveries were 78.3-98.4%, and matrix effects were 70.5-88.6%. This method was applied to human hair samples from 120 people suspected of methorphan use to further distinguish the drug chirality. Dextromethorphan was detected in all 120 samples at a concentration range of 2.7-19,100 pg/mg, whereas levomethorphan was not detected in any sample. CONCLUSIONS: A sensitive quantitative method was established for the enantiomeric separation of dextromethorphan and levomethorphan in hair. This is the first study to achieve chiral analysis of methorphan in human hair.