OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

OBJECTIVE: To analyze the sequencing results, protein structure model, and impact of mutations on the dynamic stability of glycosyltransferase (GTA) in a case with Aw26 blood group subtype. METHODS: ABO phenotype was determined by serological testing (anti-A, anti-B, anti-H, and reverse typing). Potential variant of the ABO gene was identified by Sanger sequencing, and the haploid sequence of the variant site was analyzed by TOPOT-A cloning. Molecular models of the GTA was generated by PyMol, and 100-ns molecular dynamics (MD) was simulated with GROMACS software to assess the conformational stability using root mean square deviation (RMSD), radius of gyration (Rg), solvent-accessible surface area (SASA), hydrogen bonding, and binding free energy. RESULTS: Serological assays confirmed the proband as an Aw subtype, whose genotype was identified as ABO*Aw.26/ABO*O.01.02 with variants including p.Pro156Leu, p.Arg176His and p.Pro354ArgfsTer23. Haploid sequencing validated the results of direct sequencing. Molecular modeling showed that the p.Arg176His variant could reduce water-mediated hydrogen bonds from six (wild-type) to one (variant). MD simulation revealed the wild type system could achieve equilibrium within 10 ns (mean RMSD ≈ 0.30 nm), whilst the mutant system required 50 ns to equilibrate and exhibited greater fluctuation (mean RMSD ≈ 0.40 nm). Root mean square fluctuation (RMSF) analysis confirmed significantly increased flexibility in the mutant's N-terminal loop (residues 63-76). The mutant Rg displayed an expansion-contraction transition within 0 ~ 40 ns, and its SASA value has increased. The number of hydrogen bonds and binding energy of the mutant had decreased (wild-type: 5 to 8, binding energy: -11.53 kcal/mol; mutant: 2 to 5, binding energy:-8.52 kcal/mol). CONCLUSION An Aw26 subtype was identified. The p.Arg176His and p.Pro354Argfs*23p variants could synergistically compromise the structural stability of GTA and its substrate binding capacity by disrupting the hydrogen-bond network, increasing local flexibility, and reducing the overall conformational stability.
ABO Blood-Group System/chemistry* ; Humans ; Molecular Dynamics Simulation ; Models, Molecular ; Mutation ; Genotype ; Protein Conformation ; Glycosyltransferases/chemistry* ; Male

OBJECTIVES

To determine if there is a difference in the prevalence of abnormal spirometry in female adult residents of OLBAC who have significant and nonsignificant exposure to biomass fuel smoke

METHODS

A convenience sample of 54 adult female residents of OLBAC in San Mateo, Rizal, will be recruited in this analytical cross-sectional study. After enrollment, they will undergo a single spirometry procedure to determine their lung function status. The primary data to be collected from the experimental groups are FEV1, FVC and FEV1/ FVC ratio. The data will undergo descriptive and inferential analysis, and the lung function variable will be analyzed with logistic regression to account for confounding variables.

EXPECTED RESULTS

The descriptive data analysis will determine the mean values of lung function parameters (FEV1 and FVC) where long exposures may lead to an abnormal FVC compared to short or no exposure. The results in the inferential analysis may indicate a negative association between length of biomass fuel exposure and percentage predicted FVC among the sample, suggesting that more prolonged exposure to biomass fuel increases the risk of impaired lung function.

CONCLUSION

 

Human ; Female ; Adult: 25-44 Yrs Old ; Young Adult: 19-24 Yrs Old ; Adult ; Biomass ; Cross-sectional Studies ; Female ; Logistic Models ; Regression (psychology) ; Smoke ; Volition ; World Health Organization ; Spirometry

OBJECTIVE: To identify the risk factors related to the timing of patients receiving extracorporeal membrane oxygenation (ECMO) initiation and construct a risk prediction model for ECMO initiation timing. METHODS: Patients who received ECMO admitted to the Second Xiangya Hospital of Central South University from January 2020 to January 2024 were retrospectively collected. The case data mainly included physiological and biochemical indicators 1 hour before ECMO initiation. According to the outcome of the patients, they were divided into survival group and death group. Univariate and multivariate Logistic regression analysis were used to analyze the predictors of mortality risk in patients with ECMO, and a nomogram prediction model was constructed. The discrimination, calibration accuracy, and goodness of the model were evaluated by the receiver operator characteristic curve (ROC curve), calibration curve, and the Hosmer-Lemeshow test, respectively. Decision curve analysis (DCA) evaluated the clinical net benefit rate of the model. RESULTS: A total of 81 ECMO patients were included, including 59 males and 22 females; age range from 16 to 61 years old, with a median age of 56.0 (39.5, 61.5) years old; 20 patients received veno-arterial (V-A) ECMO, and 61 patients received veno-venous (V-V) ECMO; 23 patients ultimately survived and 58 patients died. Univariate analysis showed that age, blood urea nitrogen, serum creatinine, D-dimer, arterial blood carbon dioxide partial pressure, and prothrombin time of the death group were all higher than those of the survival group, while albumin was slightly lower than that of the survival group. There was a statistically significant difference in the direct cause of ECMO initiation between the two groups. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.069, 95% confidence interval (95%CI) was 1.015-1.125, P = 0.012], direct cause of ECMO initiation [with heart failure as the reference, return of spontaneous circulation (ROSC) after cardiopulmonary support (OR = 30.672, 95%CI was 1.265-743.638, P = 0.035), novel coronavirus infection (OR = 8.666, 95%CI was 0.818-91.761, P = 0.073), other severe pneumonia (OR = 4.997, 95%CI was 0.558-44.765, P = 0.150)], pre-ECMO serum creatinine (OR = 1.008, 95%CI was 1.000-1.016, P = 0.044), prothrombin time (OR = 1.078, 95%CI was 0.948-1.226, P = 0.252), and D-dimer (OR = 1.135, 95%CI was 1.047-1.231, P = 0.002) were entered into the final regression equation. A nomogram prediction model was developed based on these five factors. The area under the ROC curve (AUC) of the model was 0.889 (95%CI was 0.819-0.959), higher than the AUC of the sequential organ failure assessment (SOFA; AUC = 0.604, 95%CI was 0.467-0.742). The calibration curve showed good consistency between the model predictions and the observed results. The Hosmer-Lemeshow goodness-of-fit test showed that χ ² = 4.668, P = 0.792. DCA analysis showed that when the risk threshold was 0-0.8, the net benefit rate was greater than 0, which was significantly better than that of SOFA score. CONCLUSIONS The risk prediction model for the timing of ECMO initiation, constructed using five factors (age, direct cause of ECMO initiation, thrombin time, serum creatinine, and D-dimer), demonstrated good discrimination and calibration. It can serve as a pre-initiation assessment tool to identify and predict post-initiation mortality risk in ECMO patients.
Humans ; Extracorporeal Membrane Oxygenation ; Middle Aged ; Male ; Female ; Retrospective Studies ; Adult ; Risk Factors ; Adolescent ; Young Adult ; Logistic Models ; Nomograms ; ROC Curve ; Time Factors ; Risk Assessment

OBJECTIVE: To explore the correlation between albumin (Alb) combined with diuretic treatment and the mortality risk of septic patients with pre-existing congestive heart failure based on the United States Critical Care Medical Information Database-IV (MIMIC-IV), and to conduct the external validation. METHODS: A retrospective cohort study was conducted. The clinical data of septic patients with pre-existing congestive heart failure admitted to the intensive care unit (ICU) from 2008 to 2019 in the MIMIC-IV 2.0 were extracted, including demographic characteristics, comorbidities, laboratory indicators on the first day of ICU admission, severity of illness, treatment measures, etc. For external validation, clinical data were collected from septic patients with pre-existing congestive heart failure admitted to the ICU of the Second People's Hospital of Shenzhen from October 2022 to December 2023. The patients were divided into Alb alone group and Alb combined with diuretic group. The ICU mortality was defined as the primary outcome event, and the 30-day and 60-day mortality were defined as the secondary outcomes. Multivariate Cox proportional hazard regression analysis was conducted to investigate the relationship between Alb combined with diuretic treatment and the mortality risk of ICU and 30 days in septic patients with pre-existing congestive heart failure, and subgroup analysis was performed. Kaplan-Meier survival curve was plotted to compared the 60-day cumulative survival rate between the Alb alone group and Alb combined with diuretic group. RESULTS: (1) Analysis results of data from MIMIC-IV: a total 1 754 patients were enrolled, of which 378 in the Alb alone group, and 1 376 in the Alb combined with diuretic group. Compared with the Alb alone group, the patients in the Alb combined with diuretic group had significantly lower ICU, 30-day, and 60-day mortality [ICU mortality: 19.11% (263/1 376) vs. 30.42% (115/378), 30-day mortality: 18.90% (260/1 376) vs. 32.54% (123/378), 60-day mortality: 24.49% (337/1 376) vs. 39.15% (148/378), all P < 0.05]. Based on the multivariate Cox proportional hazard regression adjusted models considering demographic characteristics, comorbidities, laboratory indicators, severity of illness, and treatment measures, it was shown that the use of Alb combined with diuretic was significantly associated with a reduced risk death of ICU and 30 days [ICU mortality risk: hazard ratio (HR) = 0.597, 95% confidence interval (95%CI) was 0.460-0.774, P < 0.001; 30-day mortality risk: HR = 0.557, 95%CI was 0.433-0.716, P < 0.001]. Subgroup analysis revealed that after adjusting for variables, regardless of gender, age, and whether or not patients had comorbidities such as hypertension, diabetes, severe liver disease, acute renal insufficiency, and sequential organ failure assessment (SOFA) score, the ICU mortality risk was significantly reduced in patients treated with Alb combined with diuretic (all HR < 1, P < 0.05), with no interaction observed (all P > 0.05). Kaplan-Meier survival curve showed the 60-day cumulative survival rate of patients in the Alb combined with diuretic group was significantly higher than that in the Alb alone group (Log-rank test: χ ² = 49.62, P < 0.05). (2) External validation: a total of 385 patients were enrolled, of which 144 in the Alb alone group, and 241 in the Alb combined with diuretic group. Compared with the Alb alone group, the patients of the Alb combined with diuretic group had significantly lower ICU, 30-day, and 60-day mortality [ICU mortality: 19.92% (48/241) vs. 31.25% (45/144), 30-day mortality: 19.09% (46/241) vs. 28.47% (41/144), 60-day mortality: 24.07% (58/241) vs. 34.03% (49/144), all P < 0.05]. The results of multivariate Cox proportional hazard regression analysis, subgroup analysis, and Kaplan-Meier survival curve analysis were consistent with the data analysis of the MIMIC-IV database. CONCLUSIONS Combination therapy of Alb and diuretic was associated with reduced mortality risk in septic patients with pre-existing congestive heart failure.
Humans ; Heart Failure/mortality* ; Retrospective Studies ; Sepsis/drug therapy* ; Intensive Care Units ; Diuretics/therapeutic use* ; Male ; Female ; Aged ; Middle Aged ; Proportional Hazards Models ; Hospital Mortality

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

OBJECTIVE: Comorbid pain and depression are common but remain difficult to treat. Electroacupuncture (EA) can effectively improve symptoms of depression and relieve pain, but its neural mechanism remains unclear. Therefore, we used resting-state functional magnetic resonance imaging (rs-fMRI) to detect cerebral changes after initiating a mouse pain model via constriction of the infraorbital nerve (CION) and then treating these animals with EA. METHODS: Forty male C57BL/6J mice were divided into 4 groups: control, CION model, EA, and sham acupuncture (without needle insertion). EA was performed on the acupoints Baihui (GV20) and Zusanli (ST36) for 20 min, once a day for 10 consecutive days. The mechanical withdrawal threshold was tested 3 days after the surgery and every 3 days after the intervention. The depressive behavior was evaluated with the tail suspension test, open-field test, elevated plus maze (EPM), sucrose preference test, and marble burying test. The rs-fMRI was used to detect the cerebral changes of the functional connectivity (FC) in the mice following EA treatment. RESULTS: Compared with the CION group, the mechanical withdrawal threshold increased in the EA group at the end of the intervention (P < 0.05); the immobility time in tail suspension test decreased (P < 0.05); and the times of the open arm entry and the open arm time in the EPM increased (both P < 0.001). There was no difference in the sucrose preference or marble burying tests (both P > 0.05). The fMRI results showed that EA treatment downregulated the amplitude of low-frequency fluctuations and regional homogeneity values, while these indicators were elevated in brain regions including the amygdala, hippocampus and cerebral cortex in the CION model for comorbid pain and depression. Selecting the amygdala as the seed region, we found that the FC was higher in the CION group than in the control group. Meanwhile, EA treatment was able to decrease the FC between the amygdala and other brain regions including the caudate putamen, thalamus, and parts of the cerebral cortex. CONCLUSION EA can downregulate the abnormal activation of neurons in the amygdala and improve its FC with other brain regions, thus exerting analgesic and antidepressant effects. Please cite this article as: Yin X, Zeng XL, Lin JJ, Xu WQ, Cui KY, Guo XT, Li W, Xu SF. Brain functional changes following electroacupuncture in a mouse model of comorbid pain and depression: a resting-state functional magnetic resonance imaging study. J Integr Med. 2025; 23(2): 159-168.
Animals ; Electroacupuncture ; Male ; Magnetic Resonance Imaging ; Depression/diagnostic imaging* ; Mice, Inbred C57BL ; Brain/diagnostic imaging* ; Disease Models, Animal ; Mice ; Pain/diagnostic imaging* ; Acupuncture Points

OBJECTIVE: Treating peripheral nerve injury (PNI) presents a clinical challenge due to limited axon regeneration. Strychni Semen, a traditional Chinese medicine, is clinically used for numbness and hemiplegia. However, its role in promoting functional recovery after PNI and the related mechanisms have not yet been systematically studied. METHODS: A mouse model of sciatic nerve crush (SNC) injury was established and the mice received drug treatment via intragastric gavage, followed by behavioral assessments (adhesive removal test, hot-plate test and Von Frey test). Transcriptomic analyses were performed to examine gene expression in the dorsal root ganglia (DRGs) from the third to the sixth lumbar vertebrae, so as to identify the significantly differentially expressed genes. Immunofluorescence staining was used to assess the expression levels of superior cervical ganglia neural-specific 10 protein (SCG10). The ultra-trace protein detection technique was used to evaluate changes in gene expression levels. RESULTS: Strychni Semen and its active compounds (brucine and strychnine) improved functional recovery in mice following SNC injury. Transcriptomic data indicated that Strychni Semen and its active compounds initiated transcriptional reprogramming that impacted cellular morphology and extracellular matrix remodeling in DRGs after SNC, suggesting potential roles in promoting axon regeneration. Imaging data further confirmed that Strychni Semen and its active compounds facilitated axon regrowth in SNC-injured mice. By integrating protein-protein interaction predictions, ultra-trace protein detection, and molecular docking analysis, we identified myeloperoxidase as a potentially critical factor in the axon regenerative effects conferred by Strychni Semen and its active compounds. CONCLUSION Strychni Semen and its active compounds enhance sensory function by promoting axonal regeneration after PNI. These findings establish a foundation for the future applications of Strychni Semen and highlight novel therapeutic strategies and drug targets for axon regeneration. Please cite this article as: Zhang Y, Zhao XY, Liu MT, Zhou ZC, Cheng HB, Jiang XH, Zheng YR, Chen Z. Strychni Semen and its active compounds promote axon regeneration following peripheral nerve injury by suppressing myeloperoxidase in the dorsal root ganglia. J Integr Med. 2025; 23(2): 169-181.
Animals ; Nerve Regeneration/drug effects* ; Mice ; Peripheral Nerve Injuries/physiopathology* ; Male ; Ganglia, Spinal/enzymology* ; Axons/physiology* ; Peroxidase/antagonists & inhibitors* ; Mice, Inbred C57BL ; Drugs, Chinese Herbal/pharmacology* ; Disease Models, Animal ; Strychnine/pharmacology*

OBJECTIVE: Prunella vulgaris L. has long been used for liver protection according to traditional Chinese medicine theory and has been proven by modern pharmacological research to have multiple potential liver-protective effects. However, its effects on non-alcoholic steatohepatitis (NASH) are currently uncertain. Our study explores the effects of P. vulgaris polysaccharides on NASH and intestinal homeostasis. METHODS: An aqueous extract of the dried fruit spikes of P. vulgaris was precipitated in an 85% ethanol solution (PVE85) to extract crude polysaccharides from the herb. A choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) was administrated to male C57BL/6 mice to establish a NASH animal model. After 4 weeks, the PVE85 group was orally administered PVE85 (200 mg/[kg·d]), while the control group and CDAHFD group were orally administered vehicle for 6 weeks. Quantitative real-time polymerase chain reaction analysis, Western blotting, immunohistochemistry and other methods were used to assess the impact of PVE85 on the liver in mice with NASH. 16S rRNA gene amplicon analysis was employed to evaluate the gut microbiota abundance and diversity in each group to examine alterations at various taxonomic levels. RESULTS: PVE85 significantly reversed the course of NASH in mice. mRNA levels of inflammatory mediators associated with NASH and protein expression of hepatic nucleotide-binding leucine-rich repeat and pyrin domain-containing protein 3 (NLRP3) were significantly reduced after PVE85 treatment. Moreover, PVE85 attenuated the thickening and cross-linking of collagen fibres and inhibited the expression of fibrosis-related mRNAs in the livers of NASH mice. Intriguingly, PVE85 restored changes in the gut microbiota and improved intestinal barrier dysfunction induced by NASH by increasing the abundance of Actinobacteria and reducing the abundance of Proteobacteria at the phylum level. PVE85 had significant activity in reducing the relative abundance of Clostridiaceae at the family levels. PVE85 markedly enhanced the abundance of some beneficial micro-organisms at various taxonomic levels as well. Additionally, the physicochemical environment of the intestine was effectively improved, involving an increase in the density of intestinal villi, normalization of the intestinal pH, and improvement of intestinal permeability. CONCLUSION PVE85 can reduce hepatic lipid overaccumulation, inflammation, and fibrosis in an animal model of CDAHFD-induced NASH and improve the intestinal microbial composition and intestinal structure. Please cite this article as: Zhu MJ, Song YJ, Rao PL, Gu WY, Xu Y, Xu HX. Therapeutic role of Prunella vulgaris L. polysaccharides in non-alcoholic steatohepatitis and gut dysbiosis. J Integr Med. 2025; 2025; 23(3): 297-308.
Animals ; Non-alcoholic Fatty Liver Disease/drug therapy* ; Male ; Dysbiosis/drug therapy* ; Mice, Inbred C57BL ; Gastrointestinal Microbiome/drug effects* ; Polysaccharides/therapeutic use* ; Prunella/chemistry* ; Mice ; Liver/metabolism* ; Plant Extracts/therapeutic use* ; Disease Models, Animal ; Diet, High-Fat

OBJECTIVE: To assess the safety and topical efficacy of prim-O-glucosylcimifugin (POG) and investigate the molecular mechanisms of its therapeutic effects in atopic dermatitis (AD). METHODS: The effects of POG on human keratinocyte cell viability and its anti-inflammatory properties were evaluated using cell counting kit-8 assay and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Subsequently, the impact of POG on the differentiation of cluster of differentiation (CD) 4⁺ T cell subsets, including T-helper type (Th) 1, Th2, Th17, and regulatory T (Treg), was examined through in vitro experiments. Network pharmacology analysis was used to elucidate POG's therapeutic mechanisms. Furthermore, the therapeutic potential of topically applied POG was further evaluated in a calcipotriol-induced mouse model of AD. The protein and transcript levels of inflammatory markers, including cytokines, lymphocyte-specific protein tyrosine kinase (Lck) mRNA, and LCK phosphorylation (p-LCK), were quantified using immunohistochemistry, RT-qPCR, and Western blot analysis. RESULTS: POG was able to suppress cell proliferation and downregulate the transcription of interleukin 4 (Il4) and Il13 mRNA. In vitro experiments indicated that POG significantly inhibited the differentiation of Th2 cells, whereas it exerted negligible influence on the differentiation of Th1, Th17 and Treg cells. Network pharmacology identified LCK as a key therapeutic target of POG. Moreover, the topical application of POG effectively alleviated skin lesions in the calcipotriol-induced AD mouse models without causing pathological changes in the liver, kidney or spleen tissues. POG significantly reduced the levels of Il4, Il5, Il13, and thymic stromal lymphopoietin (Tslp) mRNA in the AD mice. Concurrently, POG enhanced the expression of p-LCK protein and Lck mRNA. CONCLUSION Our research revealed that POG inhibits Th2 cell differentiation by promoting p-LCK protein expression and hence effectively alleviates AD-related skin inflammation. Please cite this article as: Zhao H, Ma X, Wang H, Ding XJ, Kuai L, Song JK, Zhang Z, Yang D, Gao CJ, Li B, Zhou M. Prim-O-glucosylcimifugin mitigates atopic dermatitis by inhibiting Th2 differentiation through LCK phosphorylation modulation. J Integr Med. 2025; 23(3): 309-319.
Dermatitis, Atopic/drug therapy* ; Animals ; Humans ; Cell Differentiation/drug effects* ; Phosphorylation/drug effects* ; Mice ; Th2 Cells/drug effects* ; Keratinocytes/drug effects* ; Disease Models, Animal ; Mice, Inbred BALB C ; Calcitriol/analogs & derivatives*