This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

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.

Methods: Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups. Results: Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°. Conclusions TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.

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.

Methods: Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups. Results: Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°. Conclusions TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.

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.

This study aimed to investigate the correlation between changes in intestinal toxicity and compositional alterations of Euphorbiae Ebracteolatae Radix(commonly known as Langdu) before and after milk processing, and to explore the detoxification mechanism of milk processing. Mice were intragastrically administered the 95% ethanol extract of raw Euphorbiae Ebracteolatae Radix, milk-decocted(milk-processed), and water-decocted(water-processed) Euphorbiae Ebracteolatae Radix. Fecal morphology, fecal water content, and the release levels of inflammatory cytokines tumor necrosis factor-α(TNF-α) and interleukin-1β(IL-1β) in different intestinal segments were used as indicators to evaluate the effects of different processing methods on the cathartic effect and intestinal inflammatory toxicity of Euphorbiae Ebracteolatae Radix. LC-MS/MS was employed to analyze the small-molecule components in the raw product, the 95% ethanol extract of the milk-processed product, and the milky waste(precipitate) formed during milk processing, to assess the impact of milk processing on the chemical composition of Euphorbiae Ebracteolatae Radix. The results showed that compared with the blank group, both the raw and water-processed Euphorbiae Ebracteolatae Radix significantly increased the fecal morphology score, fecal water content, and the release levels of TNF-α and IL-1β in various intestinal segments(P<0.05). Compared with the raw group, all indicators in the milk-processed group significantly decreased(P<0.05), while no significant differences were observed in the water-processed group, indicating that milk, as an adjuvant in processing, plays a key role in reducing the intestinal toxicity of Euphorbiae Ebracteolatae Radix. Mass spectrometry results revealed that 29 components were identified in the raw product, including 28 terpenoids and 1 acetophenone. The content of these components decreased to varying extents after milk processing. A total of 28 components derived from Euphorbiae Ebracteolatae Radix were identified in the milky precipitate, of which 27 were terpenoids, suggesting that milk processing promotes the transfer of toxic components from Euphorbiae Ebracteolatae Radix into milk. To further investigate the effect of milk adjuvant processing on the toxic terpenoid components of Euphorbiae Ebracteolatae Radix, transmission electron microscopy(TEM) was used to observe the morphology of self-assembled casein micelles(the main protein in milk) in the milky precipitate. The micelles formed in casein-terpenoid solutions were characterized using particle size analysis, fluorescence spectroscopy, ultraviolet spectroscopy, and Fourier-transform infrared(FTIR) spectroscopy. TEM observations confirmed the presence of casein micelles in the milky precipitate. Characterization results showed that with increasing concentrations of toxic terpenoids, the average particle size of casein micelles increased, fluorescence intensity of the solution decreased, the maximum absorption wavelength in the UV spectrum shifted, and significant changes occurred in the infrared spectrum, indicating that interactions occurred between casein micelles and toxic terpenoid components. These findings indicate that the cathartic effect of Euphorbiae Ebracteolatae Radix becomes milder and its intestinal inflammatory toxicity is reduced after milk processing. The detoxification mechanism is that terpenoid components in Euphorbiae Ebracteolatae Radix reassemble with casein in milk to form micelles, promoting the transfer of some terpenoids into the milky precipitate.
Animals ; Mice ; Milk/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Male ; Tumor Necrosis Factor-alpha/immunology* ; Intestines/drug effects* ; Interleukin-1beta/immunology* ; Tandem Mass Spectrometry ; Female

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*

OBJECTIVES: To explore the measures to improve the follow-up rate of preterm infants after discharge, and to evaluate the effectiveness of these measures using quality improvement methodology. METHODS: The follow-up status of preterm infants discharged from March to May 2017 was used as the baseline before quality improvement, and a specific quality improvement goal for the follow-up rate was proposed. The Pareto chart was used to analyze the causes of follow-up failure, and a key driver diagram was constructed based on the links involved in improving follow-up rate. The causes of failure were analyzed to determine the key links and intervention measures for quality improvement, and the follow-up rate was monitored weekly using a control chart until the quality improvement goal was achieved. RESULTS: The follow-up rate of preterm infants after discharge was 57.92% (117/202) at baseline before quality improvement, and the quality improvement goal was set to increase the follow-up rate of preterm infants from baseline to more than 80% within 12 months. The Pareto chart analysis showed that the main causes of follow-up failure were deficiencies in follow-up file management and irregular follow-up times (33.70%, 31/92), insufficient follow-up education and poor communication (25.00%, 23/92), and the inability to meet the diverse needs of parents (18.48%, 17/92). Based on the key links for quality improvement and the main causes of follow-up failure, the following intervention measures were adopted: (1) strengthen follow-up publicity and education; (2) build a follow-up team; and (3) establish a follow-up platform and system. The control chart indicated that with the implementation of the above intervention measures, the weekly follow-up rate increased to 74.09% (306/413) in July 2017 and 83.09% (511/615) in December 2017, finally achieving the quality improvement goal. During the COVID-19 pandemic, the follow-up rate of preterm infants fluctuated between 23.54% (460/1 954) and 70.97% (1 931/2 721), and subsequently, it returned to pre-pandemic levels starting in February 2023. CONCLUSIONS The application of quality improvement methodology can help to formulate intervention measures based on the main causes of follow-up failure, thereby improving the follow-up rate of preterm infants after discharge. This quality improvement method is feasible and practical and thus holds promise for clinical application.
Humans ; Quality Improvement ; Infant, Premature ; Infant, Newborn ; Patient Discharge ; Follow-Up Studies ; Female ; Male