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.

Background Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children and adolescents. Current research suggests that environmental factors, such as per- and polyfluoroalkyl substances (PFAS), may be associated with an increased risk of ADHD in offspring. However, most epidemiological evidence originates from non-Chinese populations, with limited research investigating the association between prenatal PFAS and ADHD-like symptoms in Chinese school-aged children. Objective To examine the association between prenatal PFAS exposure and ADHD-like symptoms in 7-year-old children. Methods Based on the Shanghai Birth Cohort, this study included 488 mother-child pairs. PFAS were measured in maternal serum during the second trimester using ultra-performance liquid chromatography-tandem mass spectrometry. The analysis focused on eight specific compounds from three PFAS categories [perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), and perfluorohexanesulfonic acid (PFHxS)]. The hyperactivity subscale of the Strengths and Difficulties Questionnaire (SDQ) was used to assess ADHD-like symptoms in children at age 7. Basic demographic information was collected via questionnaires and medical records. Negative binomial regression models were used to evaluate the relationship between individual exposures to the three typical PFAS categories (PFOA, PFOS, and PFHxS) and children's ADHD-like symptoms, while the bayesian kernel machine regression (BKMR) mixture model was employed to assess the overall effect of PFAS mixture exposure on ADHD-like symptoms. Results The detection rates of the eight target PFAS in maternal second-trimester serum were all above 85%, with PFOA exhibiting the highest median concentration (10.21 ng·mL⁻¹). Overall, 14.75% of the children exhibited ADHD-like symptoms. Results from the negative binomial regression models showed that second-trimester exposures to PFOA (IRR=1.15, 95%CI: 1.05, 1.26), n-PFOS (IRR=1.08, 95%CI: 1.01, 1.15), 6m-PFOS (IRR=1.13, 95%CI: 1.05, 1.21), and 1m-PFOS (IRR=1.11, 95%CI: 1.03, 1.18) were associated with an increased risk of ADHD-like symptoms in 7-year-old children. Stratified analyses suggested potentially statistically significant associations in girls [e.g., for PFOA, boys vs. girls: 1.09 (0.96, 1.23) vs. 1.25 (1.09, 1.44), P-int=0.31]. The BKMR mixture model indicated that the risk of ADHD-like symptoms trended upward with increasing PFAS mixture concentrations, although this association was statistically significant only among girls (P < 0.05). Conclusion Prenatal PFAS exposure may be associated with an increased risk of ADHD-like symptoms in 7-year-old children, and this adverse association appears to be more prominent in girls.

OBJECTIVE To provide guidance for the individualized use of antiepileptic drugs （AEDs） in patients with epilepsy or bipolar affective disorder.METHODS Clinical data of patients with epilepsy or bipolar affective disorder who received AEDs treatment and underwent therapeutic drug monitoring （TDM） at Xi’an International Medical Center Hospital from January 2023 to December 2025 were retrospectively collected. The baseline information， characteristics of AEDs utilization， implementation status of TDM and laboratory parameters were summarized. Spearman correlation analysis， univariate analysis and multivariate linear regression analysis were performed to identify the independent influencing factors for blood concentrations of AEDs.RESULTS A total of 1 572 patients were enrolled， including 660 males and 912 females. The majority were aged ≥60 years （30.15%）； 71.76% of patients were on monotherapy with AEDs， with valproic acid being the most commonly prescribed （51.97%）； combination therapy was predominantly a two-drug regimen （26.21%）， while three-drug regimens were rare （2.04%）. A total of 2 901 blood samples for TDM were collected， with the highest number of samples for valproic acid and levetiracetam （1 987 and 684 samples， respectively）. TDM for all five drugs was primarily conducted once； among which， the blood concentrations attainment rates exceeded 50% for patients taking valproic acid and levetiracetam， while the median blood concentrations for patients taking lamotrigine， oxcarbazepine， and carbamazepine were all below the lower limit of the therapeutic reference range. Based on the results of Spearman correlation analysis， univariate analysis， and multivariate linear regression analysis， patient gender， platelet count （PLT）， alanine transaminase （ALT）， aspartate transaminase （AST）， and total bilirubin （TBil） were found to be independent factors influencing blood concentration of valproic acid （unstandardized coefficients were －6.770 7， －0.078 6， －0.099 4， 0.083 2， －0.578 9， P ＜0.05）， while age and creatinine （CRE） were found to be independent factors influencing blood concentration of levetiracetam （unstandardized coefficients were 0.108 7， 0.047 8， P ＜0.05）.CONCLUSIONS Female gender and elevated AST levels are risk factors for increased blood concentration of valproic acid， while elevated PLT， ALT and TBil levels are associated with decreased blood concentration of valproic acid. Patients of advanced age and those with renal insufficiency bear a relatively high risk of in vivo levetiracetam accumulation.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

OBJECTIVE To construct three-class （insufficient， normal， excessive） and two-class （insufficient， normal） models for predicting plasma concentration of valproic acid （VPA）， and compare the performance of these two models， with the aim of providing a reference for formulating clinical medication strategies. METHODS The clinical data of 480 patients who received VPA treatment and underwent blood concentration test at the Xi’an International Medical Center Hospital were collected from November 2022 to September 2024 （a total of 695 sets of data）. In this study， predictive models were constructed for target variables of three-class and two-class models. Feature ranking and selection were carried out using XGBoost scores. Twelve different machine learning algorithms were used for training and validation， and the performance of the models was evaluated using three indexes: accuracy， F1 score， and the area under the working characteristic curve of the subject （AUC）. RESULTS XGBoost feature importance scores revealed that in the three-class model， the importance ranking of kidney disease and electrolyte disorders was higher. However， in the two-class model， the importance ranking of these features significantly decreased， suggesting a close association with the excessive blood concentration of VPA. In the three-class model， Random Forest method performed best， with F1 score of 0.704 0 and AUC of 0.519 3 on the test set； while in the two-class model， CatBoost method performed optimally， with F1 score of 0.785 7 and AUC of 0.819 5 on the test set. CONCLUSIONS The constructed three-class model has the ability to predict excessive VPA blood concentration， but its prediction and model generalization abilities are poor； the constructed two-class model can only perform classification prediction for insufficient and normal blood concentration cases， but its model performance is stronger.

The renin-angiotensin-aldosterone system (RAAS) is crucial for regulating blood pressure and maintaining fluid balance, while clock genes are essential for sustaining biological rhythms and regulating metabolism. There exists a complex interplay between RAAS and clock genes that may significantly contribute to the development of various cardiovascular and metabolic diseases. Although current literature has identified correlations between these two systems, the specific mechanisms of their interaction remain unclear. Moreover, the interaction patterns under different physiological and pathological conditions need further investigation. This review summarizes the synergistic roles of the RAAS and clock genes in cardiovascular diseases, explores their molecular mechanisms and pathophysiological connections, discusses the application of chronotherapy, and highlights potential future research directions, aiming to provide novel insights for the prevention and treatment of related diseases.
Humans ; Renin-Angiotensin System/genetics* ; Cardiovascular Diseases/genetics* ; CLOCK Proteins/physiology* ; Animals

In recent years, there has been a growing interest in the research on NOD-like receptor thermal protein domain associated protein 3(NLRP3) inflammasome inhibitors in the treatment of inflammatory diseases. The NLRP3 inflammasome is integral to the innate immune response, and its abnormal activation can lead to the release of pro-inflammatory cytokine, consequently facilitating the progression of various pathological conditions. Therefore, investigating the pharmacological inhibition pathway of the NLRP3 inflammasome represents a promising strategy for the treatment of inflammation-related diseases. Currently, the Food and Drug Administration(FDA) has not approved drugs targeting the NLRP3 inflammasome for clinical use due to concerns regarding liver toxicity and gastrointestinal side effects associated with chemical small molecule inhibitors in clinical trials. Natural small molecule compounds such as polyphenols, flavonoids, and alkaloids are ubiquitously found in animals, plants, and other natural substances exhibiting pharmacological activities. Their abundant sources, intricate and diverse structures, high biocompatibility, minimal adverse reactions, and superior biochemical potency in comparison to synthetic compounds have attracted the attention of extensive scholars. Currently, certain natural small molecule compounds have been demonstrated to impede the activation of the NLRP3 inflammasome via various action mechanisms, so they are viewed as the innovative, feasible, and minimally toxic therapeutic agents for inhibiting NLRP3 inflammasome activation in the treatment of both acute and chronic inflammatory diseases. Hence, this study systematically examined the effects and potential mechanisms of natural small molecule compounds derived from traditional Chinese medicine on the activation of NLRP3 inflammasomes at their initiation, assembly, and activation stages. The objection is to furnish theoretical support and practical guidance for the effective clinical application of these natural small molecule inhibitors.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/metabolism* ; Inflammation/drug therapy* ; Anti-Inflammatory Agents/therapeutic use* ; Humans ; Animals ; Disease Models, Animal ; Biological Products/therapeutic use* ; Drug Discovery ; Medicine, Chinese Traditional/methods*

The main active compound, 3,4-dihydroxyacetophenone(3,4-DHAP), in the leaves of Ilex pubescens var. glaber, exhibits various pharmacological activities, including vasodilation and heart protection. Currently, natural extraction and chemical synthesis are the primary methods for obtaining 3,4-DHAP, but both approaches have inherent challenges. To address these problems, this study explored the whole-cell bioconversion of 1-(4-hydroxyphenol)-ethanol to 3,4-DHAP using recombinant Escherichia coli, cultivated in a green, cost-effective medium at room temperature and atmospheric pressure. Firstly, this study successfully constructed recombinant E. coli S1, which contained only the HpaBC gene, and recombinant E. coli S3, which contained both the Hped and HpaBC genes. The ability of S1 and S3 to synthesize 3,4-DHAP from their respective substrates was then evaluated through whole-cell bioconversion. Based on these results, the effects of four factors, i.e., substrate concentration, IPTG concentration, induction temperature, and transformation temperature, on the whole-cell bioconversion yield of S3 were investigated using an orthogonal experiment. The results showed that the factors influenced the yield in the following order: transformation temperature > induction temperature > IPTG concentration > substrate concentration. The optimal conditions were found to be a transformation temperature of 35 ℃, IPTG concentration of 0.1 mmol·L~(-1), induction temperature of 25 ℃, and substrate concentration of 10 mmol·L~(-1). Finally, the effect of transformation time on the yield of 3,4-DHAP was further examined under the optimal conditions. The results indicated that as the transformation time increased, the yield of 3,4-DHAP steadily increased. The highest yield of 260 mg·L~(-1) with a productivity of 17% was achieved after 72 hours of transformation. In conclusion, this study successfully achieved the whole-cell bioconversion of 1-(4-hydroxyphenol)-ethanol to 3,4-DHAP using recombinant E. coli for the first time, laying the groundwork for further optimization and development of the biosynthesis of 3,4-DHAP.
Escherichia coli/genetics* ; Acetophenones/chemistry* ; Ethanol/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Biotransformation

Colorectal cancer(CRC) is a common malignant tumor worldwide. Due to the treatment intolerance and side effects, CRC rank the top among various cancers regarding the incidence and mortality rates. Therefore, exploring new therapies is of great significance for the treatment of CRC. Aerobic glycolysis(AEG) plays an important role in the microenvironment formation, proliferation, metastasis, and recurrence of CRC and other tumor cells. It has been confirmed that intervening in the AEG pathway can effectively curb CRC. The active ingredients and compound prescriptions of traditional Chinese medicine(TCM) can effectively inhibit the proliferation, metastasis, and drug resistance and regulate the apoptosis of tumor cells by modulating AEG-associated transport proteins [eg, glucose transporters(GLUT)], key enzymes [hexokinase(HK) and phosphofructokinase(PFK)], key genes [hypoxia-inducible factor 1(HIF-1) and oncogene(c-Myc)], and signaling pathways(MET/PI3K/Akt/mTOR). Accordingly, they can treat CRC, reduce the recurrence, and improve the prognosis of CRC. Although AEG plays a key role in the development and progression of CRC, the specific mechanisms are not yet fully understood. Therefore, this article delves into the intrinsic connection of the targets and mechanisms of the AEG pathway with CRC from the perspective of tumor cell glycolysis and explores how active ingredients(oxymatrine, kaempferol, and dioscin) and compound prescriptions(Quxie Capsules, Jiedu Sangen Decoction, and Xianlian Jiedu Prescription) of TCM treat CRC by intervening in the AEG pathway. Additionally, this article explores the shortcomings in the current research, aiming to provide reliable targets and a theoretical basis for treating CRC with TCM.
Humans ; Colorectal Neoplasms/genetics* ; Drugs, Chinese Herbal/therapeutic use* ; Glycolysis/drug effects* ; Animals ; Medicine, Chinese Traditional ; Signal Transduction/drug effects*