Litchi Pericarp Extract Treats Type 2 Diabetes Mellitus by Regulating Oxidative Stress, Inflammatory Response, and Energy Metabolism.

Litchi pericarp is rich in polyphenols, and demonstrates significant biological activity. This study assessed the therapeutic effects of litchi pericarp extract (LPE) on type 2 diabetes mellitus in db/db mice. The results showed that LPE ameliorated symptoms of glucose metabolism disorder, oxidative stress, inflammatory response, and insulin resistance in db/db mice. The mechanistic studies indicated that LPE activates adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) and suppresses the protein expression of phosphoenolpyruvate carboxykinase (PEPCK), thereby reducing hepatic gluconeogenesis. Additionally, LPE facilitates the translocation of nuclear factor erythroid2-related factor 2 (Nrf2) into the cell nucleus, initiating the transcription of antioxidant factors superoxide dismutase (SOD) and NAD(P)H: quinone oxidoreductase 1 (NQO1), which alleviate oxidative stress and reduce oxidative damage. Furthermore, LPE blocks nuclear factor kappa-B (NF-κB) nuclear translocation and subsequent inflammatory response initiation, thereby reducing inflammation. These findings indicate that LPE addresses type 2 diabetes mellitus by activating the AMPK energy metabolic pathway and regulating the Nrf2 oxidative stress and NF-κB inflammatory signaling pathways.

Study on the lignin-derived sp2-sp3 hybrid hard carbon materials and the feasibility for industrial production.

Hard carbon has been widely used in anode of lithium/sodium ion battery, electrode of supercapacitor, and carbon molecular sieve for CO2 capture and hydrogen storage. In this study the lignin derived hard carbon products are investigated, and the conclusions are abstracted as follows. (1) The lignin derived hard carbon products consist of microcrystal units of sp2 graphene fragments, jointed by sp3 carbon atoms and forming sp2-sp3 hybrid hard carbon family. (2) From the lignin precursors to the sp2-sp3 hybrid hard carbon products, most carbon atoms retain their original electron configurations (sp2 or sp3) and keep their composition in lignin. (3) The architectures of lignin-derived hard carbon materials are closely dependent on the forms of their lignin precursors, and could be preformed by different pretreatment techniques. (4) The carbonization of lignin precursors follows the mechanism "carbonization in situ and recombination nearby". (5) Due to the high carbon ratio and abundant active functional groups in lignin, new activation techniques could be developed for control of pore size and pore volume. In general lignin is an excellent raw material for sp2-sp3 hybrid hard carbon products, a green and sustainable alternative resource for phenolic resin, and industrial production for lignin derived hard carbon products would be feasible.

Raman time-delay in attosecond transient absorption of strong-field created krypton vacancy.

Strong field ionization injects a transient vacancy in the atom which is entangled to the outgoing photoelectron. When the electron is finally detached, the ion is populated at different excited states with part of coherence information lost. The preserved coherence of matter after interacting with intense short pulses has important consequences on the subsequent nonequilibrium evolution and energy relaxation. Here we employ attosecond transient absorption spectroscopy to measure the time-delay of resonant transitions of krypton vacancy during their creation. We have observed that the absorptions by the two spin-orbit split states are modulated at different paces when varying the time-delay between the near-infrared pumping pulse and the attosecond probing pulse. It is shown that the coupling of the ions with the remaining field leads to a suppression of ionic coherence. Comparison between theory and experiments uncovers that coherent Raman coupling induces time-delay between the resonant absorptions, which provides insight into laser-ion interactions enriching attosecond chronoscopy.

Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis.

BACKGROUND: Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting. METHODS: We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People's Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets. RESULTS: The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process. CONCLUSIONS: Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.

Arrhythmia, also known as an irregular heartbeat, is a common cardiovascular disease. Sometimes the presence of an arrhythmia can increase the risk of more serious heart conditions. Long-term monitoring of the heartbeat enables arrhythmia to be more easily diagnosed. To accurately detect arrhythmia, we developed a computational model that was able to detect six common types of arrhythmias from readings of the heart rate obtained using a device connected to a mobile phone. We showed that our model could diagnose these arrhythmias in over 270,000 people living in China. Our diagnostic system could enable arrhythmias to be diagnosed more easily outside of hospitals and therefore improve access to healthcare, particularly for those in remote settings.

Detection and quantification of etomidate and metomidate in human hairs by ultraperformance liquid chromatography with triple quadrupole mass spectrometry (UPLC-MS/MS).

PURPOSE: Intravenous narcotic agents, such as etomidate and metomidate, has been widely spread and abused in the world, including in Korea and China; thus, it is important to establish validated and sensitive analytical method for these compounds. Human hair as a biological sample has various advantages, including a wide detection window of drugs, compared to other typical samples, such as urine and blood in investigation. The purpose of this communication is to develop a reliable and useful method for the simultaneous detection and quantification of etomidate and metomidate in human hair samples by ultraperformance liquid chromatography combined with triple quadrupole mass spectrometry (UPLC-MS/MS), and to apply it for authentic samples in abuse cases. METHODS: The hair samples were washed with a detergent solution, followed by with water and acetone. After drying, they were cut into approximately 2 mm sections and then ground to powder by a low-temperature grinder. The 20 mg of hair powder plus internal standard in 1 mL of methanol was vortexed and then centrifuged to obtain the supernatant layer, followed by subjecting to analysis. RESULTS: The coefficient of determination (r2) values of the calibration curves of etomidate and metomidate in the hair samples were both more than 0.99 in the range of 1-500 ng/mg and 1-500 pg/mg, respectively. The limits of detection and lower limits of quantification were 0.5 and 1 pg/mg, respectively, for the both target compounds. Other tested validation data were all satisfactory. Etomidate and metomidate could be detected in the all hair samples and cigarette oil, which were seized by the police. The concentrations of etomidate and metomidate obtained from 10 samples from suspects were 5.48-45.7 ng/mg and 3.60-377 pg/mg, respectively. The concentrations of etomidate and metomidate in the cigarette oil were 95.8 µg/mg and 2.8 µg/mg, respectively. CONCLUSIONS: In this study, a simple and reliable analytical method for etomidate and metomidate in the human hair has been established. To the best of our knowledge, this is the first report to establish a method for the simultaneous detection and quantification of etomidate and metomidate in the human hair, and to apply it to authentic samples seized in authentic cases.

Study on the microcrystal cellulose and the derived 2D graphene and relative carbon materials.

Microcrystal cellulose (MCC) is a green and sustainable resource that widely exists in various lignocellulose species in percentage 10% to 30%. The fine powder of MCC is often discarded in industrial productions that use lignocellulose as feedstock. The crystal structure of two types of MCC (sugarcane pith and bamboo pith) and their derived carbon materials are studied, and the key findings are summarized as follows. (1) In the MCC refined from sugarcane pith, there are large amount of cellulose 2D crystal, which can be converted to valuable 2D graphene crystal. (2) In the MCC refined from bamboo pith there are large amount of cluster microcrystal cellulose, which can be converted to soft and elastic graphene microcrystal (GMC). (3) The 2D cellulose in MCC of sugarcane pith has large surface area and is easily to be degraded to sugars by acid-base hydrolysis reaction, which can be carbonized to Fullerenes-like carbon spheres. (4) The crystal structures of MCC derived carbon materials are strongly impacted by the crystal structures of MCC, and the carbonization reaction of MCC follows "in situ carbonization" and "nearby recombination" mechanism. In general, the results from this study may open a new way for value-added applications of microcrystal cellulose.

Saponins of Tomato Extract Improve Non-Alcoholic Fatty Liver Disease by Regulating Oxidative Stress and Lipid Homeostasis.

The present study investigated the impact of saponins of tomato extract (STE) on non-alcoholic fatty liver disease (NAFLD). The findings demonstrated that introducing STE in NAFLD mice revealed promising results in ameliorating symptoms of oxidative stress, lipid metabolism disorders, visceral fat deposition and fatty liver disease. Moreover, the mechanistic studies have demonstrated that STE delivers its effects by activating adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK), thereby suppressing downstream protein expression associated with fatty acid synthesis. In such conditions, lipid metabolism can be improved. Simultaneously, STE enhanced nuclear factor erythroid 2-related factor 2 (Nrf2) and entry into the nucleus and initiated the transcription of downstream antioxidant factors, thereby relieving oxidative stress induced by a high-fat diet and lowering oxidative damage to the liver. Such results imply that the administration of STE can be regarded as a viable treatment option for NAFLD, providing a mechanism that can regulate the AMPK and Nrf2 signaling pathways.

Design and Synthesis of a Polyketone Building Block with Vinyl Groups-9,10-Diethyl-9,10-ethenoanthracene-2,3,6,7(9H,10H)-tetraone-and a Preliminary Photoelectrical Property Study of Its Azaacene Derivatives.

Polyketone compounds are powerful building blocks to synthesize various organic functional materials. Despite that a great many number of planar and non-planar polyketone building blocks have been developed, one issue is that generally there are only ketone functional groups on the molecular skeleton, which will constrain their transformation and further limit the development of functional materials. In this work, we report the design and synthesis of a building block 9,10-diethyl-9,10-ethenoanthracene-2,3,6,7(9H,10H)-tetraone with additional vinyl functional groups. In addition, its azaacene derivatives were also synthesized, and their preliminary physicochemical properties were studied.

Quantification of the benzimidazole opioid analog isotonitazene in human hair using liquid chromatography-tandem mass spectrometry.

Benzimidazole opioids were originally developed from the late 1950s to 1970s as analgesics for medical use, although a lot of them could not be approved as licit medicines because of their severe side effects and physical dependence. Such benzimidazole opioid analogs as abused drug, however, have recently been found in illicit drug markets throughout the world. Isotonitazene is one such benzimidazole opioids, whose analgesic potency can be as much as 500 times greater than that of morphine, according to previous animal studies. In line with this potency, a couple of hundred fatalities related to it were reported to date. In this study, a well validated method for the quantification of isotonitazene in human hair samples using liquid chromatography (LC)-tandem mass spectrometry (MS/MS) was established, and could be applied to authentic samples which were seized by the police security bureau. Isotonitazene concentrations in the seized hair averaged 6.11 pg/mg. The LLOQ and LOD of this method were 1.25 and 2.5 pg/mg, respectively; the calibration curve of the substance in hair samples showed a good linearity in the concentration range of 2.5-250 pg/mg (r > 0.999); the extraction recovery rates were 87.3-105% in the tested range; the inter- and intra-day precisions and accuracies (%biases) were not greater than 9.09% for each determination. Isotonitazene in human hair showed good stability at room temperature and under dark storage conditions for 30 days. As for matrix effect in hair samples, moderate ion suppression of target substances could be found. This is the first report for the analysis of isotonitazene in human hair samples.

Study on material basis and anti-hypertensive metabolomics of different extraction methods of the Uncaria rhynchophylla Scrophularia Formula.

Hypertension is one of the most challenging public health problems worldwide. Previous studies suggested that the Uncaria rhynchophylla Scrophularia Formula (URSF), a medical institution preparation of the affiliated Hospital of Shandong University of Traditional Chinese Medicine, is effective for essential hypertension. However, the efficacy of URSF for hypertension remains unclear. We aimed to clarify the anti-hypertensive mechanism of the URSF. The material basis of URSF was identified by the LC-MS. We also evaluated the antihypertensive efficacy of URSF on SHR rats by body weight, blood pressure and biochemical indicators. The LC-MS spectrometry-based serum non-targeted metabolomics was used to seek potential biomarkers and relevant pathways for URSF in the treatment of SHR rats. 56 biomarkers were metabolically disturbed in SHR rats in the model group compared with the control group. After URSF intervention, 13 biomarkers showed a recovery in the optimal method compared with the other three groups. We identified 3 metabolic pathways in which URSF is involved: the arachidonic acid metabolism pathway, the niacin and nicotinamide metabolism pathway, and the purine metabolism pathway. These discoveries offer a basis for the study of URSF for the treatment of hypertension.

Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine.

Backgrounds: Dyslipidemia is a prominent risk factor for cardiovascular diseases and one of the primary independent modifiable factors of diabetes and stroke. Statins can significantly improve the prognosis of dyslipidemia, but its side effects cannot be ignored. Traditional Chinese Medicine (TCM) has been used in clinical practice for more than 2000 years in China and has certain traits in treating dyslipidemia with little side effect. Previous research has shown that Mutual Obstruction of Phlegm and Stasis (MOPS) is the most common dyslipidemia type classified in TCM. However, how to compose diagnostic factors in TCM into diagnostic rules relies heavily on the doctor's experience, falling short in standardization and objectiveness. This is a limit for TCM to play its advantages of treating dyslipidemia with MOPS. Methods: In this study, the syndrome diagnosis in TCM was transformed into the prediction and classification problem in artificial intelligence The deep learning method was employed to build the classification prediction models for dyslipidemia. The models were built and trained with a large amount of multi-centered clinical data on MOPS. The optimal model was screened out by evaluating the performance of prediction models through loss, accuracy, precision, recall, confusion matrix, PR and ROC curve (including AUC). Results: A total of 20 models were constructed through the deep learning method. All of them performed well in the prediction of dyslipidemia with MOPS. The model-11 is the optimal model. The evaluation indicators of model-11 are as follows: The true positive (TP), false positive (FP), true negative (TN) and false negative (FN) are 51, 15, 129, and 9, respectively. The loss is 0.3241, accuracy is 0.8672, precision is 0.7138, recall is 0.8286, and the AUC is 0.9268. After screening through 89 diagnostic factors of TCM, we identified 36 significant diagnosis factors for dyslipidemia with MOPS. The most outstanding diagnostic factors from the importance were dark purple tongue, slippery pulse and slimy fur, etc. Conclusions: This study successfully developed a well-performing classification prediction model for dyslipidemia with MOPS, transforming the syndrome diagnosis problem in TCM into a prediction and classification problem in artificial intelligence. Patients with dyslipidemia of MOPS can be accurately recognized through limited information from patients. We also screened out significant diagnostic factors for composing diagnostic rules of dyslipidemia with MOPS. The study is an avant-garde attempt at introducing the deep-learning method into the research of TCM, which provides a useful reference for the extension of deep learning method to other diseases and the construction of disease diagnosis model in TCM, contributing to the standardization and objectiveness of TCM diagnosis.

Machine learning in TCM with natural products and molecules: current status and future perspectives.

Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

Knowledge Mapping of Drug Repositioning's Theme and Development.

Background: In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology: The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results: The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion: The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.

Quantification of a new recreational drug 2-methoxyqualone in human hair using gas chromatography-tandem mass spectrometry.

A method for identification and quantification of 2-methoxyqualone, an newly emerging quinazolinone derivative recreational drug, in human scalp hair was established using gas chromatography (GC)-tandem mass spectrometry (MS/MS). In this report, authentic cases are presented, in which the suspects were seized by police security bureau; the police in China requested our laboratory to identify and quantify the involved drug(s) of abuse in the hair samples of the suspects. After washing and cryo-grinding the authentic hair samples, the target compound was extracted with methanol, and the solvent layer was evaporated to dryness. The residue was reconstituted in methanol and analyzed by GC-MS/MS. 2-Methoxyqualone concentrations in the hair were between 35.1 and 116 pg/mg. The calibration curve of the substance in hair samples showed a good linearity in the concentration range of 10-1000 pg/mg (r > 0.998); the extraction recovery rate, 88.8-105.6 %; the interday and intraday precisions and accuracies (biases), not greater than 8.9 %. 2-Methoxyqualone in human hair had good stability under three different storage conditions at room (20 °C), refrigerated (4 °C) and frozen (- 20 °C) temperatures for at least 7 days. In the present report, simple and rapid quantification method for 2-methoxyqualone in human scalp hair have been established using GC-MS/MS and it could successfully be applied to authentic forensic toxicological cases. To our knowledge, this is the first report for quantification of 2-methoxyqualone in human hair samples.

The next addiction-causing drug class 4-quinazolinone derivatives: analyses of methaqualone analogs including recently discovered 2-methoxyqualone by different modes of mass spectrometry.

PURPOSE: The information on analytical methods for 4-quinazolinone recreational drugs, such as methaqualone, etaqualone and 2-methoxyqualone, is almost scant. In this study, product ion spectra of gas chromatography-tandem mass spectrometry (GC-MS/MS) with different collision energies were presented for these drugs. Because 2-methoxyqualone is a new recreational drug discovered in dubious tablets very recently, much more detailed data obtained by different types of mass spectrometry instruments, and quantification data of 2-methoxyqualone in the tablet together with its validation were demonstrated. METHODS: The methods for analyses were GC-MS/MS, high-resolution ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry and liquid chromatography-tandem mass spectrometry. RESULTS: The GC-MS/MS product ion spectra of the three compounds with different collision energies have not been reported before. They were very useful to tentatively identify unknown compounds. If a reference standard is available, the final identification and quantification can be achieved by measurements of product ion spectra and in selected reaction monitoring mode very easily by GC-MS/MS. The final identification and quantification for the new 2-methoxyqualone were performed in this way. The content of the compound was 69.8 ± 0.5% (w/w) in the tablet. Acetaminophen and caffeine coexisted in the tablet with approximate concentrations at 10 and 5%, respectively. CONCLUSIONS: In this article, we have presented product ion spectra of methaqualone, etaqualone and 2-methoxyqualone at different collision energies by GC-MS/MS for the first time. In addition, this is the first paper to describe the details of quantification of 2-methoxyqualone in the authentic seized product.

Strigolactones positively regulate Verticillium wilt resistance in cotton via crosstalk with other hormones.

Verticillium wilt caused by Verticillium dahliae is a serious vascular disease in cotton (Gossypium spp.). V. dahliae induces the expression of the CAROTENOID CLEAVAGE DIOXYGENASE 7 (GauCCD7) gene involved in strigolactone (SL) biosynthesis in Gossypium australe, suggesting a role for SLs in Verticillium wilt resistance. We found that the SL analog rac-GR24 enhanced while the SL biosynthesis inhibitor TIS108 decreased cotton resistance to Verticillium wilt. Knock-down of GbCCD7 and GbCCD8b genes in island cotton (Gossypium barbadense) decreased resistance, whereas overexpression of GbCCD8b in upland cotton (Gossypium hirsutum) increased resistance to Verticillium wilt. Additionally, Arabidopsis (Arabidopsis thaliana) SL mutants defective in CCD7 and CCD8 putative orthologs were susceptible, whereas both Arabidopsis GbCCD7- and GbCCD8b-overexpressing plants were more resistant to Verticillium wilt than wild-type (WT) plants. Transcriptome analyses showed that several genes related to the jasmonic acid (JA)- and abscisic acid (ABA)-signaling pathways, such as MYELOCYTOMATOSIS 2 (GbMYC2) and ABA-INSENSITIVE 5, respectively, were upregulated in the roots of WT cotton plants in responses to rac-GR24 and V. dahliae infection but downregulated in the roots of both GbCCD7- and GbCCD8b-silenced cotton plants. Furthermore, GbMYC2 suppressed the expression of GbCCD7 and GbCCD8b by binding to their promoters, which might regulate the homeostasis of SLs in cotton through a negative feedback loop. We also found that GbCCD7- and GbCCD8b-silenced cotton plants were impaired in V. dahliae-induced reactive oxygen species (ROS) accumulation. Taken together, our results suggest that SLs positively regulate cotton resistance to Verticillium wilt through crosstalk with the JA- and ABA-signaling pathways and by inducing ROS accumulation.

Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method.

The objective of this study is to construct a multi-department symptom-based automatic diagnosis model. However, it is difficult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously. Inspired by the thought of "knowledge graph is model", this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data, and incrementally injecting it into the knowledge graph. Therefore, incremental learning and injection are used to solve the data collection problem, and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems. First, an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion. Then, an adaptive neural network model is constructed for each dataset, and the data is used for statistical initialization and model training. Finally, the weights and biases of the learned neural network model are updated to the knowledge graph. It is worth noting that for the incremental process, we consider both the data and class increments. We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets. Compared with the classical model, the proposed model improves the diagnostic accuracy of the three datasets by 5%, 2%, and 15% on average, respectively. Meanwhile, the model under incremental learning has a better ability to resist forgetting.

Quantitation of sibutramine in human hair using gas chromatography-isotope dilution tandem mass spectrometry.

PURPOSE: An analytical method for quantitation of sibutramine in human hair using gas chromatography (GC)-isotope dilution tandem mass spectrometry (MS/MS) was newly established. In this article, a case is presented, in which a 3.5-year-old male child accidentally ingested chocolate-like product containing sibutramine, showing various symptoms; he could survived the crisis. About 1 month after the incident, his scalp hair sample was subjected to analysis for the causative sibutramine. METHOD: After cryo-grinding for the hair sample, target compound was extracted with methanol, and the solvent layer was evaporated to dryness. The residue was reconstituted in methanol and analyzed by GC-MS/MS, using the selected reaction monitoring (SRM) mode with a deuterated isotope internal standard. RESULTS: The substance was identified as sibutramine; its concentration in the hair sample of the child was 58.6 pg/mg. The calibration curve of sibutramine in hair samples had a good linear relationship in the concentration range of 20-200 pg/mg (r > 0.99); the extraction recovery rate 85.2-91.8%; the interday and intraday precision and accuracy (bias) examined not greater than 9.6%. Sibutramine in human hair had good stability under 3 different storage conditions at room (20 °C), refrigerated (4 °C) and frozen ( - 20 °C) temperatures for at least 7 days. CONCLUSIONS: It should be expected that the method established in this study would contribute to rapid determinations of sibutramine. To our knowledge, this is the first report describing quantitation of sibutramine in an authentic human hair sample by GC-MS/MS.