ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.
Humans ; Artificial Intelligence ; Deep Learning ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Positron-Emission Tomography

Compared to traditional machine learning algorithms,which often suffer from low feature extraction efficiency,insufficient nonlinear pattern recognition capabilities and slow training speeds,the broad learning system(BLS)enhances the learning ability and efficiency by horizontally expanding the network structure.BLS offers advantages such as a simple structure,fast training speed,and strong generalization capabilities.While BLS has demonstrated potential in various fields,but its application in identification of complex samples has not been fully explored.This research investigated the feasibility of using BLS algorithm for identification of complex samples based on physicochemical indicators.Using the iris,wine,and breast cancer datasets,the length and width of petals and sepals of iris flowers,the physicochemical properties of wine,and the nuclear characteristics of breast cancer cells were used as input variables to establish BLS models for identifying iris species,wine varieties,and benign versus malignant nucleus.The model performance was evaluated by confusion matrices,accuracy,and runtime.Compared with partial least squares-discriminant analysis(PLS-DA),soft independent modeling of class analogies(SIMCA),and artificial neural networks(ANN),the results indicated that BLS demonstrated significant advantages in computational efficiency and recognition accuracy.Thus,BLS was an efficient and reliable method for identification of complex samples.

BACKGROUND:Although researchers have noted that fibroblast growth factor receptor 1 shows great potential in rheumatoid arthritis bone destruction,there is a lack of reviews related to the potential mechanisms of fibroblast growth factor receptor 1 in rheumatoid arthritis bone destruction. OBJECTIVE:To comprehensively analyze the mechanism of fibroblast growth factor receptor 1 in bone destruction in rheumatoid arthritis by reviewing the relevant literature at both home and abroad. METHODS:We searched the CNKI database using the Chinese search terms"fibroblast growth factor receptor 1,rheumatoid arthritis,bone destruction,bone cells,osteoblasts,osteoclasts,chondrocytes,macrophages,synovial fibroblasts,T cells,vascular endothelial cells."PubMed database was searched using the English search terms"fibroblast growth factor receptor 1,rheumatoid arthritis,bone destruction,osteocytes,osteoblasts,osteoclasts,chondrocytes,macrophages,synovial fibroblasts,T cells,endothelial cells."The search period focused on April 1992 to January 2024.After screening the literature by reading titles,abstracts,and full texts,a total of 82 articles were finally included for review according to inclusion and exclusion criteria. RESULTS AND CONCLUSION:Fibroblast growth factor receptor 1 was found to be widely expressed in bone tissue-associated cells,including osteoblasts,osteoclasts,and osteoclasts.Fibroblast growth factor receptor 1 affects bone remodeling and homeostasis by regulating the function of these cells,as well as promoting the onset and progression of bone destruction in rheumatoid arthritis.Fibroblast growth factor receptor 1 is involved in the inflammatory response of synovial fibroblasts and macrophages and regulates angiogenesis of endothelial cells in synovial tissues.Fibroblast growth factor receptor 1 promotes bone destruction in several ways.Fibroblast growth factor receptor 1 may be a potential causative agent of bone destruction in rheumatoid arthritis and provides a reference for further research on its therapeutic targets.

Objective:To investigate the distribution of CGG repeat numbers in the FMR1 gene among reproductive-age women in China, providing data reference for carrier screening and genetic counseling of Fragile X syndrome. Methods:This cross-sectional study recruited 16 610 reproductive-age women from 12 medical institutions between July 2022 and October 2023. Peripheral venous blood samples (3 mL) were collected, and genomic DNA was extracted. The number of CGG repeats in the FMR1 gene was determined using the triplet-primed polymerase chain reaction (TP-PCR) combined with capillary electrophoresis technology. Statistical analyses were performed to assess the prevalence and distribution of CGG repeat expansions. Results:Among 16 610 women of childbearing age, 5 684 (34.220%) women had the same number of CGG repeats in the two alleles of FMR1 gene, and 10 926 (65.780%) women had different numbers of repeats in the two alleles. Among the 33 220 FMR1 alleles in 16 610 women of reproductive age, the most common CGG repeat numbers were 29 [48.645% (16 160/33 220)] and 30 [26.276% (8 729/33 220)], while the most frequent CGG genotype was CGG 29/29 [24.726% (4 107/16 610)]. The CGG repeat numbers of FMR1 gene were normal in 16 498 women (99.326%). Among the 112 women (0.674%) with CGG repeat abnormities, 96 (0.578%) women were classified as intermediate carriers, 15 (0.090%) as premutation carriers, and 1 (0.006%) as a full mutation carrier, whose CGG genotype was (36, >200). Conclusion:In the general reproductive-age female population in China, the normal CGG repeat numbers of the FMR1 gene account for 99.326%, while the intermediate carrier rate is 0.578%, and the combined carrier rate of the premutation and full mutation types is 0.096%.

Objective To evaluate the efficacy and safety of obinutuzumab(OBZ)in the treatment of refractory primary membranous nephropathy(pMN).Methods The clinical data of 15 patients with refractory pMN who received OBZ treatment in Department of Nephrology of The First Affiliated Hospital of Naval Medical University between Jan.2022 and Sep.2024 were retrospectively analyzed,and they included basic information,relevant laboratory indexes,clinical and immunological outcomes,and adverse events.Results Among the 15 patients with refractory pMN,14(93.3％)were phospholipase A2 receptor(PLA2R)-associated membranous nephropathy(10 cases with positive PLA2R by renal biopsy and 4 cases with no recorded PLA2R results by renal biopsy but with positive serum PLA2R antibodies).During the follow-up period,all 15 patients achieved clinical remission,with 4(26.7％)patients achieving complete remission and 11(73.3％)patients achieving partial remission.Of the 12 patients with positive serum PLA2R antibodies,11 patients had continuously positive serum PLA2R antibodies before OBZ treatment,and 9(81.8％)patients achieved immunological remission after OBZ treatment.All the 15 patients had previously received immunosuppressive therapy,and all of them were classified as refractory pMN,with 7(46.7％)patients having been treated with cyclophosphamide combined with corticosteroids,2(13.3％)patients having been treated with calcineurin inhibitor combined with corticosteroids,11(73.3％)patients having received rituximab.During the treatment,2(13.3％)cases of adverse events were observed:1 patient experienced transient liver dysfunction,and the transaminase returned to normal after discontinuing atorvastatin;another patient developed a positive T-cell spot test for Tuberculosis infection during the intertreatment interval and successfully completed the subsequent OBZ treatment and achieved clinical remission after treatment with isoniazid and rifampicin.Conclusion OBZ demonstrates favorable clinical efficacy in the treatment of refractory pMN,with a low incidence of adverse events.

OBJECTIVES: To study the mechanism mediating the protective effect of asiaticoside (AS) against myocardial ischemia-reperfusion injury (MIRI) in rats. METHODS: Fifty SD rats were randomized into sham-operated group, MIRI model group and AS treatment group. AS treatment was administered at low, moderate and high doses by daily gavage for 2 weeks before MIRI modeling (n=10). Serum levels of lactate dehydrogenase (LDH), creatine kinase isoenzyme (CK-MB), interleukin-18 (IL-18) and IL-1β, the volume of myocardial infarction and ischemia, and myocardial pathologies of the rats were determined or observed. The protein expression levels of NLRP3, ASC, caspase-1, GSDMD, GSDMD-N, IL-1β and IL-18 in the myocardial tissues were detected using Western blotting. The changes in the expression levels of these proteins were also detected in H9C2 cells with AS pretreatment prior to hypoxia-reoxygenation (H/R) injury. RESULTS: The rats models of MIRI exhibited significant myocardial infarction and ischemia with increased serum levels of LDH and CK-MB and myocardial expressions of NLRP3, ASC, caspase-1, GSDMD, GSDMD-N, IL-1β and IL-18. AS pretreatment effectively reduced myocardial infarction volume in the rat models and significantly reduced serum LDH and CK-MB levels and the protein levels in the myocardial tissue in a dose-dependent manner. In the H9C2 cell model of H/R injury, AS pretreatment significantly suppressed the elevation of the protein expressions of NLRP3, ASC, caspase-1, GSDMD, GSDMD-N, IL-1β and IL-18. Molecular docking studies showed that AS had a strong binding affinity with NLRP3. CONCLUSIONS Asiaticoside can alleviate MIRI in rats possibly by inhibiting NLRP3 inflammasome-mediated pyroptosis.
Animals ; Myocardial Reperfusion Injury/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Pyroptosis/drug effects* ; Rats, Sprague-Dawley ; Rats ; Inflammasomes/metabolism* ; Triterpenes/pharmacology* ; Interleukin-18/metabolism* ; Male ; Interleukin-1beta/metabolism* ; Caspase 1/metabolism*