Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Traditional Chinese medicine has been paid more and more attention in the development of modern healthcare,and its clinical diagnosis and treatment have broad prospects and enormous potential.However,the current traditional Chinese medicine diagnosis and treatment model have serious shortcomings in service capacity and,diagnosis,and treatment effect.The rapid development of big data and artificial intelli-gence technology provides an opportunity for the iterative upgrade of traditional Chinese medicine diagnosis and treatment models.This article reviewed the current situation of artificial intelligence empowering tradi-tional Chinese medicine clinical diagnosis and treatment,clarified the problems and challenges faced by artifi-cial intelligence technology in data integration,data quality,and data analysis in traditional Chinese medicine clinical diagnosis and treatment,and proposed to empower from the aspects of disciplinary integration,data quality optimization,data privacy protection,and promotion and application,so as to provide reference for im-proving the effectiveness of traditional Chinese medicine clinical diagnosis and treatment.

The adulteration and counterfeiting of herbal ingredients in medicinal and food homology (MFH) have a serious impact on the quality of herbal materials, thereby endangering human health. Compared to pharmaceutical drugs, health products derived from traditional Chinese medicine (TCM) are more easily accessible and closely integrated into consumers' daily life. However, the authentication of the authenticity of TCM ingredients in MFH has not received sufficient attention. The lack of clear standards emphasizes the necessity of conducting systematic research in this area. This study utilized DNA barcoding technology, combining ITS2, psbA-trnH, matK as universal barcode sequences, and DX, HH, JYH as specific barcode sequences, to identify the authenticity of commercial medicinal and food homology scented herbal teas. The aim was to investigate the authenticity of scented herbal tea products circulating in the market. The research results revealed that among 180 scented herbal tea samples, DNA barcodes were successfully obtained from 164 samples, while the remaining samples either lacked the target components or suffered severe DNA degradation, resulting in failed amplification. Combined with morphological identification studies, it was found that out of the 180 samples, 141 were authentic, accounting for 78.33% of the total samples, while 31 samples showed adulteration and 8 samples lacked the target components, accounting for 21.67% of the total samples. Additionally, water testing revealed that 5 samples of Carthamus tinctorius herbal tea exhibited the phenomenon of weight adulteration. This study exposed the presence of adulteration and weight adulteration in scented herbal tea products, highlighting the need for regulatory authorities to expedite the establishment of quality standards in scented herbal tea industry. These findings provide valuable insights for the rapid development of the scented herbal tea industry.

OBJECTIVE To elucidate the mechanisms responsible for the inhibitory effects of limonene on the proliferation of non-small cell lung cancer(NSCLC) by non-targeted metabolomics and additional approaches. METHODS The CCK-8 assay was utilized to evaluate the inhibitory effects of limonene on NSCLC A549 cell viability and to ascertain the IC50. In vitro experiments, encompassing colony formation, flow cytometry, iron content assessment, and mitochondrial staining, were conducted to assess the anti-lung cancer and iron-induced cell death effects of limonene. Metabolomic analysis was employed to identify potential pathways influenced by limonene, and Western blotting was carried out to validate pivotal proteins within these pathways. RESULTS In comparison to the control group, the limonene-treated group demonstrated a significant, dose-dependent reduction in A549 cell proliferation and colony formation. Optical microscopy revealed cellular detachment and pronounced changes in cellular morphology following exposure to limonene. Limonene induced apoptosis in A549 cells and arrested them in the G0-G1 phase of the cell cycle. Confocal microscopy unveiled diminished mitochondrial fluorescence and an augmented intracellular iron content, indicative of the classical phenomenon of ferroptosis. Metabolomic investigations unveiled divergent metabolic pathways, including glutathione(GSH) metabolism, arginine biosynthesis, D-glutamine and D-glutamate metabolism, as well as cysteine and methionine metabolism, with many of them intricately linked to intracellular GSH synthesis. Western blotting experiments underscored a marked reduction in the levels of SLC40A1, SLC7A11(xCT), and GPX4 proteins within the cells post-limonene treatment. CONCLUSION Limonene may induce ferroptosis in lung cancer cells by reducing GSH synthesis and increasing Fe2+ levels.

OBJECTIVE To obtain stronger cytotoxic activity of panaxadiol derivatives. METHODS The 3-amino panaxadiol was prepared by the bioelectronic isosteric principle, and then 18 derivatives of cinnamic acid, NO donor and other types of panaxadiol derivatives were synthesized, among them, 12 compounds had not been reported in the literature, and their structures had been confirmed by 1H-NMR, 13C-NMR and mass spectrometry. These compounds were evaluated for their cytotoxic activity by MTS assay against human leukemia cell line HL-60, liver cancer cell line SMMC-7721, lung cancer cell line A-549, breast cancer cell line MCF-7, and colon cancer cell line SW480. RESULTS These results showed that compounds 6c, 7 as well as 7j exhibited potent inhibitory activities against all five tumor cells, especially the IC50 values of compound 7 against HL-60 and SMMC-7721cells were 3.41 and 4.51 μmol·L−1, respectively. It was significantly superior to panaxadiol in cytotoxicity. CONCLUSION These results show that 7 and 7j can be used as promising lead compounds for further research.

Objective To establish a high performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS)for the simultaneous determination of gefitinib,erlotinib,nillotinib and imatinib plasma concentrations and analyze the results.Methods The plasma samples were treated with acetonitrile precipitation and separated by Diamonsil C18 column(150 mm ×4.6 mm,3.5 μm)with mobile phase of 0.1％formic acid water(A)-0.1％formic acid acetonitrile(B).The flow rate of gradient elution was 0.7 mL·min-1,and the column temperature was 40 ℃ and the injection volume was 3 μL.Using arotinib as the internal standard,the scanning was carried out by using electrospray ionization source in positive ionization mode with multi-reaction monitoring.The specificity,standard curve,lower limit of quantitation,precision,accuracy,recovery rate,matrix effect and stability of the method were investigated.The concentrations of imatinib and erlotinib in 20 patients with chronic myelogenous leukemia(CML)and gefitinib and erlotinib in 3 patients with non-small cell lung cancer were measured.Results The standard curves of the four drugs were as follows,gefitinib:y=2.536 × 10-3x+9.362 × 10-3(linear range 20-2 000 ng·mL-1,R2=0.996 6);erlotinib:y=3.575× 10-3x+7.406 × 10-3(linear range 50-5 000 ng·mL-1,R2=0.994 9);nilotinib:y=1.945 x 10-3x+0.015 643(linear range 50-5 000 ng·mL-1,R2=0.990 6);imatinib:y=4.56 x 10-3x+0.010 451(linear range 100～104 ng·mL-1,R2=0.9963).RSD of intra-day and inter-day were less than 10％,and the accuracy ranged from 90％to 110％,and the recovery rates were 91.35％to 98.93％(RSD＜10％);the matrix effect ranged from 91.64％to 107.50％(RSD＜10％).Determination of 23 patients showed that the blood concentration of nilotinib ranged from 623.76 to 2 934.13 ng·mL-1,and the blood concentration of imatinib ranged from 757.77 to 2 637.71 ng·mL-1,and the blood concentration of gefitinib ranged from 214.76 to 387.40 ng·mL-1.The serum concentration of erlotinib was 569.57 ng·mL-1.Conclusion The method of this research is simple,fast,sensitive and dedicated,which can be monitored by the concentration of clinical blood.