This study aims to explore the pharmacodynamic material basis of Jinbei Oral Liquid(JBOL) against idiopathic pulmonary fibrosis(IPF) based on serum pharmacochemistry and network pharmacology. The ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS/MS) technology was employed to analyze and identify the components absorbed into rat blood after oral administration of JBOL. Combined with network pharmacology, the study explored the pharmacodynamic material basis and potential mechanism of JBOL against IPF through protein-protein interaction(PPI) network construction, "component-target-pathway" analysis, Gene Ontology(GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis. First, a total of 114 compounds were rapidly identified in JBOL extract according to the exact relative molecular mass, fragment ions, and other information of the compounds with the use of reference substances and a self-built compound database. Second, on this basis, 70 prototype components in blood were recognized by comparing blank serum with drug-containing serum samples, including 28 flavonoids, 25 organic acids, 4 saponins, 4 alkaloids, and 9 others. Finally, using these components absorbed into blood as candidates, the study obtained 212 potential targets of JBOL against IPF. The anti-IPF mechanism might involve the action of active ingredients such as glycyrrhetinic acid, cryptotanshinone, salvianolic acid B, and forsythoside A on core targets like AKT1, TNF, and ALB and thereby the regulation of multiple signaling pathways including PI3K/AKT, HIF-1, and TNF. In conclusion, JBOL exerts the anti-IPF effect through multiple components, targets, and pathways. The results would provide a reference for further study on pharmacodynamic material basis and pharmacological mechanism of JBOL.
Drugs, Chinese Herbal/pharmacokinetics* ; Animals ; Tandem Mass Spectrometry ; Network Pharmacology ; Rats ; Chromatography, High Pressure Liquid ; Rats, Sprague-Dawley ; Male ; Idiopathic Pulmonary Fibrosis/metabolism* ; Humans ; Administration, Oral ; Protein Interaction Maps/drug effects* ; Signal Transduction/drug effects*

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

This study adopted a three-dimensional "effect-dose-mechanism" evaluation system to screen the optimal regimen of Yuxuebi Tablets(YXB) combined with ibuprofen(IBU) for chronic musculoskeletal pain(CMP) intervention and elucidate its pharmacological mechanism, so as to provide a scientific basis for the clinical application of the regimen. The experiments were conducted using 8-week-old ICR mice, which were randomly divided into sham operation(sham) group, model(CFA) group, IBU group, YXB group, stasis paralysis tablets combined with ibuprofen low-dose group(IBU-L-YXB), stasis paralysis combined with ibuprofen high-dose group(IBU-H-YXB), stasis paralysis tablets combined with ibuprofen high-dose with ibuprofen discontinuation on the 10th day of administration(IBU-10-YXB), and stasis paralysis tablets combined with ibuprofen high-dose with ibuprofen halving on the 10th day of administration(IBU-1/2-YXB) group. An animal model was established using the CFA plantar injection method. On D0(the second day post-modeling), the success of model establishment was assessed, followed by continuous drug administration for 18 consecutive days from D1 to D18. During this period, mechanical pain threshold was measured by the Von Frey test; thermal hyperalgesia was detected by the hot plate test, and depression-like behavior was observed by the tail suspension test. After treatment, peripheral blood was collected from all groups for complete blood biochemical analysis, and the injected feet of the sham, CFA, IBU, YXB, IBU-YXB, and IBU-10-YXB groups were subjected to oxylipin metabolomics analysis. Immunofluorescence double staining was further performed to detect the co-expression of key oxylipin metabolic enzymes(COX2, LTA4H, and 5/12/15-LOX) and macrophage marker CD68 in the sham, CFA, IBU, and YXB-L/M/H groups. Subsequently, confirmatory analysis of positive indicators was conducted in the sham, CFA, IBU, YXB, IBU-YXB, and IBU-10-YXB groups. On D6(acute phase), mechanical pain sensitivity data showed that compared with the CFA group, only the three combination groups(IBU-YXB, IBU-10-YXB, and IBU-1/2-YXB) exhibited significantly increased paw withdrawal thresholds. On D17(chronic phase), only the IBU-10-YXB group showed a mechanical pain threshold significantly higher than all other monotherapy and combination groups. On D17, thermal pain data showed that compared with the CFA group, all groups except IBU-1/2-YXB had significantly prolonged paw withdrawal latency. On D18, tail suspension data showed that compared with the CFA group, the YXB, IBU-YXB, and IBU-10-YXB groups had significantly reduced immobility time. In summary, IBU-10-YXB stably improved the core symptoms of acute and chronic inflammatory pain. Complete blood count data showed that compared with the sham group, the CFA group had significantly increased mean platelet volume(MPV), while compared with the CFA group, the IBU-YXB and IBU-10-YXB groups had significantly reduced MPV. Moreover, the platelet distribution width(PDW) of the IBU-10-YXB group was further reduced compared with the CFA group. These data suggest that the IBU-10-YXB combination regimen has superior effects on inflammation and blood circulation improvement compared with other treatment groups. At the mechanistic level, each treatment group differentially regulated pro-inflammatory and pro-resolving oxylipin(SPM). Specifically, compared with the CFA group, the IBU and IBU-YXB groups significantly inhibited the synthesis of the prostaglandin family downstream of COX2, reducing pro-inflammatory oxylipins PGD2 and 6-keto-PGF1α but inhibiting PGE1 and PGE2, which played positive roles in peripheral circulation, vasodilation, and inflammation resolution. Compared with the CFA group, the YXB group tended to inhibit the pro-inflammatory oxylipin LTB4 downstream of LTA4H and increase SPMs such as LXA4. The IBU-10-YXB group bidirectionally regulated pro-inflammatory oxylipins and SPMs. Compared with IBU, IBU-10-YXB significantly inhibited the pro-inflammatory mediator 5-HETE. Meanwhile, IBU-10-YXB broadly upregulated SPMs, as evidenced by significant upregulation of LXA4 compared with the CFA group, significant upregulation of LXA5 compared with the IBU and IBU-YXB groups, significant upregulation of RvD1 compared with the CFA group and all other treatment groups, and significant upregulation of RvD5 compared with the sham group. Immunofluorescence double staining results were as follows: compared with the CFA group, the IBU group specifically inhibited the oxylipin metabolic enzyme COX2. In the YXB group, COX2, LTA4H, and 5/12-LOX were significantly inhibited. Within the optimal analgesic dose range, YXB's inhibitory effects on COX2 and LTA4H were dose-dependent, while its inhibitory effects on 5/12-LOX were inversely dose-dependent. The two combination groups(IBU-YXB and IBU-10-YXB) inhibited COX2 and LTA4H without significantly affecting 5-LOX, while IBU-10-YXB further significantly inhibited 12-LOX. These results suggest that the IBU-10-YXB combination regimen effectively maintains stable inhibition of COX2, LTA4H, and 12-LOX while enhancing 5-LOX expression. This combinatorial strategy effectively suppresses pro-inflammatory oxylipins and promotes SPM biosynthesis, overcoming IBU's analgesic ceiling effect and its blockade of pain resolution pathways while compensating for YXB's inability to effectively intervene in acute pain and inflammation. Therefore, it achieves more stable anti-inflammatory, analgesic, and antidepressant effects.
Animals ; Ibuprofen/administration & dosage* ; Mice ; Mice, Inbred ICR ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Musculoskeletal Pain/immunology* ; Tablets ; Humans ; Chronic Pain/metabolism* ; Drug Therapy, Combination ; Disease Models, Animal

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.