An HPLC-DAD-MS/MS method was developed for rapid analysis and identification of degradation products of buagafuran. Buagafuran and degradation products were separated on a Zorbax C8 column (5 microm, 4.6 mm x 150 mm) using acetonitrile-water (78 : 22) as mobile phase. The elutes were detected with diode array detector and tandem mass spectrometer via electrospray ionization source in positive ion mode. According to analysis of the retention time, UV spectra and MS, MS/MS data, combined with the possible degradation reaction of buagafuran, the structures of main degradation products were inferred. The results showed that six main degradation products were oxidation or peroxidation productions of buagafuran. Degradation product A was a double bond epoxidation product of buagafuran, degradation products B, C, D and E were the further oxidation products of degradation product A, degradation product F was a peroxidation product of buagafuran. The results indicated that the established method was effective in the rapid identification of the degradation products of buagafuran.

A fast atom bombardment mass spectrometric method to predict and detect the antioxidant ability of phenolic compounds was developed to accelerate the pace of finding the antioxidant with higher effect and low toxicity. The effect of experimental conditions on the relative peak intensity ratio of M+· ion to [ M+H]+ion in the FAB mass spectra of the compound was investigated, including matrix, scan time and concentration. The correlation of antioxidant activity with the I ( M+· )/I ( [ M+H ]+) value of flavonoids obtained in FAB mass spectra was studied. Then the antioxidant activity of 12 phenolic compounds was predicted using the above method and the results were compared with those obtained from thiobarbituric acid ( TBA) method. The results show that the I( M+· )/I( [ M+H]+) value of the phenolic compound obtained from FAB mass spectra could reflect their antioxidant activity, which could help to accelerate the development of the antioxidant drug.

The factor analysis method applied in imaging mass spectrometry data analysis was studied. The imaging mass spectrometric data were obtained by air flow-assisted ionization imaging mass spectrometry method. The sample contained some symbols which were drawn on slides using three different inks ( red, blue, black) . The imaging data analyzed by factor analysis method were divided into the background, black, blue and red factor. The results showed that the scores of m/z=443. 2, 478. 4, 322. 2(344. 2) in red, blue, black factor respectively were much larger than others. Therefore, they were markers of three inks. The results accorded with actual condition well and proved that the application of factor analysis in imaging mass spectrometric data analysis was feasible. The data analysis results of factor analysis and principal component analysis were compared. The results showed that the target sample markers could be extracted by factor analysis simply and quantitatively. It was of great potential in biomarker extraction, diseases diagnose and pharmacological analysis.

To screen the harmful substance 5-hydroxymethyl furfural content in commercially available traditional Chinese medicine injection which are commonly used, and to preliminarily evaluate the quality of these injections, 5-hydroxymethyl furfural was taken as an index. The contents of 5-hydroxymethyl furfural in 56 samples which consist of 23 kinds of traditional Chinese medicine injections and glucose injection were determined using LC-MS/MS, and 5-hydroxymethyl furfural was detected in 52 of these samples. The minimal content was 0.0038 microg x L(-1) and the maximum content was 1420 microg x mL(-1). The contents of 5-hydroxymethyl furfural were significantly different in traditional Chinese medicine injection which came from different kinds, manufacturers or batches. The results showed the quality difference of commercially available traditional Chinese medicine injection is significant taking 5-hydroxymethyl furfural content as assessment index. More attention should be paid to the safety of 5-hydroxymethyl furfural in traditional Chinese medicine injection, and unified limitation standard should be set to improve medication safety of traditional Chinese medicine injection.

Ansamycins, such as rifamycin and ansamitocin, usually consist of a group of structural similar components. Geldanamycin, a benzenic ansamycin, has been found to consist of four structural similar components. We analyzed the geldanamycin (GDM) preparation from Streptomyces hygroscopicus 17997 by LC-ESI(+)-MS/MS, and discovered five novel and one known GDM analogues in trace amounts. Based on the ESI(+)-MS/MS spectra of these GDM analogues, and the present understanding of GDM biosynthesis, we proposed the possible chemical structures of these GDM analogues. Three novel GDM analogues, all having the same molecular formula of C29H42N2O10, were GDM biosynthetic derivatives with one of the three C-C double bonds between C2-C3, C4-C5 and C8-C9 in GDM changed to mono-hydroxylated C-C single bond. The other two novel GDM analogues, having the same molecular formula of C28H38N2O8, were 17(or 12, or 4)-desmethoxylgeldanamycin and 4,5-dihydro-10,11-dehydrate-17-desmethyl-17-hydroxylgeldanamycin, respectively. The known GDM analogue, having the molecular formula of C29H42N2O9, was 4, 5-dihydrogeldanamycin, an intermediate in GDM biosynthesis. The discovery of novel GDM analogues provided us new insights in understanding the biosynthetic details of GDM, and clues of obtaining GDM derivatives by gene-disruption and combinatorial biosynthesis.
Anti-Bacterial Agents ; chemistry ; Benzoquinones ; analysis ; chemistry ; Chromatography, Liquid ; methods ; Lactams, Macrocyclic ; analysis ; chemistry ; Tandem Mass Spectrometry ; methods

Three-dimensional(3D)cell spheroid models combined with mass spectrometry imaging(MSI)enables innovative investigation of in vivo-like biological processes under different physiological and patho-logical conditions.Herein,airflow-assisted desorption electrospray ionization-MSI(AFADESI-MSI)was coupled with 3D HepG2 spheroids to assess the metabolism and hepatotoxicity of amiodarone(AMI).High-coverage imaging of＞1100 endogenous metabolites in hepatocyte spheroids was achieved using AFADESI-MSI.Following AMI treatment at different times,15 metabolites of AMI involved in N-desethylation,hydroxylation,deiodination,and desaturation metabolic reactions were identified,and according to their spatiotemporal dynamics features,the metabolic pathways of AMI were proposed.Subsequently,the temporal and spatial changes in metabolic disturbance within spheroids caused by drug exposure were obtained via metabolomic analysis.The main dysregulated metabolic pathways included arachidonic acid and glycerophospholipid metabolism,providing considerable evidence for the mechanism of AMI hepatotoxicity.In addition,a biomarker group of eight fatty acids was selected that provided improved indication of cell viability and could characterize the hepatotoxicity of AMI.The combination of AFADESI-MSI and HepG2 spheroids can simultaneously obtain spatiotemporal infor-mation for drugs,drug metabolites,and endogenous metabolites after AMI treatment,providing an effective tool for in vitro drug hepatotoxicity evaluation.

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures.By interacting with the microenvironment,tumor cells undergo dynamic changes in gene expression and metabolism,resulting in spatiotemporal variations in their capacity for proliferation and metastasis.In recent years,the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis.By preserving location information while obtaining a large number of gene and molecular data,spatially resolved metabolomics(SRM)and spatially resolved transcriptomics(SRT)approaches can offer new ideas and reliable tools for the in-depth study of tumors.This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques,as well as a review of their applications in cancer-related fields.

Objective:To explore the cognitive function characteristics of children with primary snoring (PS) and obstructive sleep apnea-hypopnea syndrome (OSAHS) using event-related potentials.Methods:From October 2020 to October 2022, 20 children with OSAHS, 20 children with PS, and 22 normal children were recruited for continuous performance task (CPT) and behavioral assessments. ERP and behavioral data were meticulously recorded, with measurements of N1, P2, N2, and P3 wave amplitudes and latencies at F3, Fz, and F4 electrode sites. Statistical analyses were conducted using one-way ANOVA and Kruskal-Wallis test via SPSS 25.0 software.Results:(1) Behavioural test: There was no statistically significant difference in terms of correct responses, response times, and false alarms among the three groups (all P>0.05). (2) F3 Lead: There were statistically significant differences in Go-P2 amplitude, Nogo-P2 amplitude, Nogo-P2 latency, Go-P3 amplitude, and Nogo-P3 latency among the three groups (all P<0.05). Specifically, the OSAHS group exhibited higher Go-P2 amplitude((15.03±5.12) μV vs (10.97±5.50)μV), Nogo-P2 amplitude((14.80±5.84) μV vs (9.67±4.79)μV), and Go-P3 amplitude((11.58±6.02) μV vs (7.49±4.89) μV) compared to the normal group. Additionally, the OSAHS and PS groups exhibited longer Nogo-P2 latency compared to the normal group((223.10±20.61) ms vs (208.00±23.09) ms, (230.60±13.61) ms vs (208.00±23.09) ms), as well as prolonged Nogo-P3 latency((459.20±34.26) ms vs (460.40±24.52) ms and (429.91±31.49) ms) (all P<0.05). Fz Lead: There were statistically significant differences in Go-N1, Go-P2, Nogo-P2, Go-P3, Nogo-N2 wave amplitudes, and Nogo-P3 latency among the three groups (all P<0.05). Compared to the normal group, the OSAHS group exhibited increased Go-P3 amplitude((9.07±5.68) μV vs (5.10±3.51) μV) and decreased Nogo-N2 amplitude((-8.80±5.97) μV vs (-12.84±4.86) μV). Moreover, both the OSAHS and PS groups had prolonged Nogo-P3 latency compared to the normal group((481.60±45.16) ms vs (435.13±28.17) ms and 484.00(443.50, 525.00) ms vs (435.13±28.17) ms) (both P<0.05). F4 Lead: There were statistically significant differences in Go-P2 and Nogo-P2 wave amplitudes among the three groups (all P<0.05). Compared to the normal group, the OSAHS group demonstrated increased Go-P2 amplitude((13.72±5.64) μV vs (9.70±4.59) μV) and Nogo-P2 amplitude((13.90±5.35) μV vs (9.64±3.74) μV) (both P<0.05). Conclusions:Both children with OSAHS and PS exhibit attentional cognitive impairments. However, children with OSAHS demonstrate more pronounced deficits in conflict monitoring, response inhibition, and executive functioning. The prolonged latency of the P3 wave serves as a sensitive electrophysiological marker for the early detection of neurocognitive impairment in children with sleep disordered breathing.

Against tumor-dependent metabolic vulnerability is an attractive strategy for tumor-targeted therapy.However,metabolic inhibitors are limited by the drug resistance of cancerous cells due to their metabolic plasticity and heterogeneity.Herein,choline metabolism was discovered by spatially resolved metab-olomics analysis as metabolic vulnerability which is highly active in different cancer types,and a choline-modified strategy for small molecule-drug conjugates(SMDCs)design was developed to fool tumor cells into indiscriminately taking in choline-modified chemotherapy drugs for targeted cancer therapy,instead of directly inhibiting choline metabolism.As a proof-of-concept,choline-modified SMDCs were designed,screened,and investigated for their druggability in vitro and in vivo.This strategy improved tumor targeting,preserved tumor inhibition and reduced toxicity of paclitaxel,through targeted drug delivery to tumor by highly expressed choline transporters,and site-specific release by carboxylesterase.This study expands the strategy of targeting metabolic vulnerability and provides new ideas of devel-oping SMDCs for precise cancer therapy.

Objective:To explore the application value of histogram and texture feature analysis based on CT images in distinguishing long bone osteosarcoma (OS) from Ewing sarcoma (ES).Methods:A retrospective collection of 25 patients with long bone osteosarcoma and 25 patients with Ewing sarcoma confirmed by surgery and pathology in National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Qilu Hospital of Shandong University and Nanjing Drum Tower Hospital, Nanjing University Medical School, from March 2018 to May 2023 was conducted. All patients were randomly divided into a training set (21 cases of OS and 19 cases of ES) and a validation set (4 cases of OS and 6 cases of ES) in an 8∶2 ratio. The region of interest (ROI) on CT images to extract texture feature parameters was manually sketched. Random forest and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature screening. Logistic regression (LR), random forest (RF), support vector machine (SVM) and K-nearest neighbor (KNN) classifiers were used to establish models respectively. Receiver operating characteristic (ROC)curve was drawn and area under the curve (AUC) was calculated to evaluate the diagnostic efficiency of the four models.Results:A total of 100 texture parameters were extracted from CT images, and 8 feature parameters (maximum 3D diameter, 10th percentile, kurtosis, maximum pixel intensity value, inverse normalization, grayscale level variance, long range high grayscale emphasis, and low grayscale area emphasis) were obtained through screening. Four classifiers were used to establish models, and the AUC values of the four models (LR, RF, SVM, KNN) in the validation group were 0.92, 0.79, 0.83, and 0.73, respectively. LR and SVM classifier algorithm trains models had high diagnostic efficiency, with an accuracy of 90%, sensitivity of 83%, specificity of 100%, and AUC of 92% for the LR classifier validation set; the accuracy of SVM classifier validation set was 80%, sensitivity was 67%, specificity was 100%, and AUC was 83%.Conclusions:LR and SVM models have high value in distinguishing OS and ES.