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.

OBJECTIVE To explore the ameliorative effect and potential mechanism of vitexin on inflammation in ulcerative colitis （UC） mice. METHODS The UC mice model was established by continuous administration of 3% dextran sulfate sodium solution for 5 days. Mice with successful modeling were randomly divided into UC group， vitexin low- and high-dose groups （vitexin-L and vitexin-H groups， 40， 80 mg/kg）， mesalazine group （400 mg/kg）， and vitexin-H+recombinant Jagged canonical Notch ligand 1 （rJagged-1） group （vitexin-H+rJagged-1 group， 80 mg/kg vitexin+1 mg/kg rJagged-1）， with 12 mice in each group. Another 12 normal mice were used as the control （CK） group. Mice in each group were administered the corresponding drugs or the corresponding drugs and normal saline by gavage and intraperitoneal injection once daily for 7 consecutive days. General conditions were observed during the experiment. At 24 h after the last administration， the disease activity index （DAI） score was evaluated. Colonic histopathological morphology was observed and scored. Macrophage polarization levels in the spleen and colon tissues were measured. The protein expressions of interleukin-6 （IL-6）， IL-10， tumor necrosis factor-α （TNF-α）， transforming growth factor-β 1 （TGF-β 1 ）， Jagged-1， Notch1 and Notch intracellular domain （NICD） in colonic tissues were determined. RESULTS Compared with the UC group， the symptoms （reduced food and water intake， dull fur， etc.） and pathological changes （epithelial cell shedding， inflammatory cell infiltration， etc.） were significantly improved in the vitexin-L， vitexin-H and mesalazine groups. DAI scores， colonic histopathological scores， M1 macrophage contents in spleen tissue， M1/M2 macrophage ratios， M1 macrophage proportions in colon tissue， and protein expressions of IL-6， TNF-α， Jagged-1， Notch1 and NICD in colon tissue were significantly decreased （ P ＜0.05）. Meanwhile， the M2 macrophage contents in spleen tissue， M2 macrophage proportions in colon tissue， and protein expressions of IL-10 and TGF-β 1 in colon tissue were significantly increased （ P ＜0.05）. Moreover， the improvement effects in the vitexin-H and mesalazine groups were significantly superior to those in the vitexin-L group （ P ＜0.05）. Compared with the vitexin-H group， the above symptoms and pathological changes were aggravated， and all quantitative indicators were significantly reversed in the vitexin-H+rJagged-1 group （ P ＜0.05）. CONCLUSIONS Vitexin can ameliorate the inflammation of UC mice， which is associated with its inhibition of the Jagged-1/Notch1 pathway and regulation of macrophage polarization （inhibition of M1-type polarization and promotion of M2-type polarization）.

The chronicity of infectious diseases is an important field in the collaborative research of traditional Chinese and Western medicine. The warm disease theory of pathogen invading nutrient and blood aspects in traditional Chinese medicine （TCM） takes the struggle between healthy Qi and pathogenic Qi and cementation of Yin as the core pathogenesis， providing a unique theoretical framework for explaining the common pathology of infectious chronic diseases. This theory originated from Yin-Yang interaction in the Internal Classic and was enriched with WU Youke's theory of intruding pathogen interacting and lingering in blood vessels and YE Tianshi's theory of long-term illness entering collaterals. Combining the theory with modern medical knowledge， our team has condensed the dynamic pathogenesis model of deficiency （nutrient and blood aspects） and excess （pathogen） interacting in the blood collaterals of Yin aspect， the core feature of which is the four-dimensional interactions of cause （pathogen characteristics）， location （three Yin locations of diseases）， nature （deficiency and excess）， and potential （transmission trend）. The common pathology of infectious chronic diseases is reflected in interactions. That is， the interactions between nutrient and blood deficiency （immune exhaustion and metabolic disorder） and pathogen excess （pathogen persistence and fibrous hyperplasia） in the liver collaterals （Jueyin）， kidney collaterals （Shaoyin）， lung collaterals （Taiyin） and other blood collaterals of Yin aspect form the pathological damage characterized by immune inflammatory response-continuous tissue damage with excessive repair. Taking the inheritance and innovative development of classics as the main line， this paper systematically discusses the scientific connotation of the theory of pathogen invading nutrient and blood aspects and the paths of inheritance and innovation and clarifies the original significance of this theory in the chronic development of infectious diseases. Furthermore， taking clinical diseases as an example， this paper reflects the guiding value of this classical theory in the modern diagnosis and treatment of infectious diseases with integrated traditional Chinese and Western medicine and the application potential of this theory in solving complex medical problems through the construction of the innovative paradigm of precise diagnosis and treatment with integrated traditional Chinese and Western medicine.

As a new generation of time-of-flight mass spectrometry,multiple-reflection time-of-flight mass spectrometry(MR-TOF-MS)has been increasingly applied in the fields such as nuclear physics,chemistry,and biology due to its ultra-high resolution and rapid analysis capabilities.However,the analytical performance of MR-TOF-MS largely depends on the ion bunch state entering the mass analyzer.In this study,a rectangular ion trap(RIT)was developed,designed and processed using printed circuit board technology,as an ion accumulating and focusing device for MR-TOF mass analyzer.Compared to traditional ion traps composed of two sets of planar electrodes,this RIT had higher voltage utilization efficiency,resulting in more efficient ion collection and focusing.The ions were cooled to a sufficiently small bunch for precise mass measurement with MR-TOF-MS mass spectrometry in only 1 ms of cooling time in the RIT,then orthogonally ejected to the MR-TOF mass spectrometer for mass analysis.Experimental results indicated that the working cycle,ion flux,and ion focusing state of the RIT fully met the requirements of the MR-TOF mass analyzer.When coupled with the MR-TOF mass analyzer,the RIT enabled MR-TOF-MS to achieve a mass resolution of 1.5×105.

Objective:To investigate the dynamic changes of plasma high-mobility group box 1 (HMGB1) and its correlation with functional outcome and symptomatic intracranial hemorrhage (sICH) after endovascular treatment (EVT) in patients with acute large vessel occlusion stroke (ALVOS).Methods:Patients with ALVOS admitted to the Department of Neurology, Zengcheng District, Nanfang Hospital, Southern Medical University from June 2021 to April 2023 were included retrospectively. Plasma HMGB1 before EVT and at 6, 24, and 48 hours after procedure was detected, and the dynamic changes of plasma HMGB1 were compared and analyzed. The primary endpoint was the functional outcome evaluated using the modified Rankin Scale at 90 days of onset. A score of 0-2 was defined as good outcome and >2 was defined as poor outcome. The secondary endpoint was sICH, which was defined as the occurrence of hemorrhagic infarction after EVT and an increase of ≥4 in the National Institutes of Health Stroke Scale (NIHSS) score from baseline. Multivariate logistic regression analysis was used to evaluate the predictive value of HMGB1 for poor outcome and sICH. Results:A total of 73 patients with ALVOS received EVT were included. There were 54 males (74.0%), aged 62±12 years. The median time from onset to door was 90 minutes (interquartile range, 40-180 minutes), and the median time from onset to femoral artery puncture was 181 minutes (interquartile range, 140-280 minutes). Twenty-nine patients (39.7%) underwent bridging intravenous thrombolysis (IVT). At 90 days after onset, 37 patients (50.7%) had poor outcome, and 12 (16.4%) died during follow-up. Eleven patients (15.1%) developed sICH. After EVT, plasma HMGB1 showed a temporal increase, reaching its peak at 48 hours (median, 102.57 μg/L). Subgroup analysis showed that HMGB1 in the bridging IVT group at 6 hours ( P<0.05) and 24 hours ( P<0.05) after procedure were significantly higher than that at baseline. The non-bridging IVT group showed a significant increase at 6 hours after procedure ( P<0.05). There was no statistically significant difference in HMGB1 between the bridging IVT group and the non-bridging IVT group at the same time point. Multivariate logistic regression analysis showed that after adjusting for age, ischemic heart disease, triglycerides, uric acid, baseline NIHSS score, and sICH, the third quartile (adjusted odds ratio 7.087, 95% confidence interval 1.243-40.419; P=0.027) and fourth quartile (adjusted odds ratio 7.544, 95% confidence interval 1.260-45.172; P=0.027) of plasma HMGB1 were independent risk factors for poor outcome at 6 hours after procedure. The postoperative plasma HMGB1 in the sICH group was significantly higher than that in the non-sICH group ( P<0.05), but multivariate analysis showed no independent correlation between plasma HMGB1 and sICH. Conclusion:The elevation of plasma HMGB1 in patients with ALVOS at 6 hours after EVT is independently associated with poor outcome at 90 days after onset, but not with sICH.

Objective:To establish HPLC fingerprint and methods for determining the contents of four iridoid glycosides of Gentiana rigescens; To evaluate the quality of Gentiana rigescens from different origins; To improve the quality control level of Gentiana rigescens medicinal materials.Methods:Using 15 batches of Gentiana rigescens from the main production areas and authentic production areas as raw materials, the common mode of HPLC fingerprints of Gentiana rigescens was established, and the chemical components of the common peaks were identified. Referring to the common mode of fingerprints, similarity analysis was conducted on the fingerprints of Gentiana rigescens from different origins. Using chemometric methods, cluster analysis (HCA), principal component analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed on 15 batches of Gentiana rigescens, with the common peak area of fingerprint as the variable. The contents of four types of iridoid glycosides in Gentiana rigescens were determined. Combined with the fingerprints and the content results of four types of iridoid glycosides, the quality of Gentiana rigescens from different origins was evaluated.Results:The fingerprints of Gentiana rigescens contained 9 common peaks, with 4 identified iridoid glycosides. The similarity of the fingerprints of 15 batches of Gentiana rigescens ranged from 0.962 to 0.999. HCA and PCA divided the 15 batches of Gentiana rigescens into two categories. OPLS-DA analyzed 3 significantly different components, namely gentiopicroside, peak 7, and loganic acid. The content determination results showed that the average contents of loganic acid, swertiamarin, and gentiopicroside in Gentiana rigescens from Dali Bai Autonomous Prefecture and Yunnan Province were the highest, and the total amount of four iridoid glycosides was also significantly higher than that from other regions, indicating that the overall quality of Gentiana rigescens from Dali Bai Autonomous Prefecture and Yunnan Province was relatively good.Conclusion:This method is simple, fast, accurate, and can provide reference for improving the quality standards of Gentiana rigescens.

Objective: To construct a deep learning model based on convolutional neural network(CNN)-long short term memory network(LSTM)-Attention to explore the correlation between meteorological and clinical factors and the incidence of ischemic stroke. Methods: A fusion model CNN-LSTM-Attention based on CNN, LSTM, and Attention was constructed by incorporating clinical data and meteorological data of ischemic stroke inpatients. The predictive performance of the model was evaluated by maximum prediction error and root mean square error(RMSE). The impact of different lag days on prediction performance was investigated by selecting lag periods ranging from 1 to 7 days. Results: In both short-term and long-term predictions, the CNN-LSTM-Attention fusion model(short-term: 1.5 and 0.6; long-term: 8.3 and 2.5) showed superior maximum prediction bias and RMSE compared to the LSTM model(short-term: 2.8 and 1.2; long-term: 19.5 and 5.5) and the CNN-LSTM model(short-term: 2.0 and 0.8; long-term: 11.2 and 3.3). After incorporating lag days, the maximum prediction deviation and RMSE for lags of 3 days(short-term: 0.7 and 0.4; long-term: 5.5 and 1.9) and 5 days(short-term: 0.8 and 0.3; long-term: 6.5 and 2.0) in both short-term and long-term forecasts were smaller than lags of 0 days(short-term: 1.5 and 0.6; long-term: 8.3 and 2.5). The maximum prediction deviation and RMSE in the short-term forecast were greater than lag 0 days for both lag 1 days(1.5 and 0.8) and lag 7 days(1.9 and 0.9). In the long-term forecast, the two indicators for lag 1 days(6.8 and 2.4) were lower than those for lag 0 days but higher than those for lag 3 days and 5 days. The maximum prediction deviation for lag 7 days(7.5) was lower than that for lag 0 days, but the RMSE(2.7) is higher than that for lag 0 days. Conclusion The established CNN-LSTM-Attention model demonstrates significant predictive value for the onset of ischemic stroke and can provide reference for the rational allocation of medical resources.

Background: Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM). Methods: We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently. Results: Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (–1.80% vs. –0.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (–1.93% vs. –0.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (–19.64% vs. –10.87%). Notably, glycemia risk index (GRI) (–3.74; 95% CI, –6.34 to –1.14; P<0.01) was also improved with AID therapy. Conclusion AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.