ObjectiveRetinal vein occlusion （RVO） is the second most common vascular disease leading to vision loss. Since its pathogenesis remains unclear， current Western medical treatments primarily target complications such as macular edema and neovascularization. The main therapeutic approaches include intravitreal injections of anti-vascular endothelial growth factor （VEGF） agents or corticosteroids， laser photocoagulation， and pars plana vitrectomy. However， these treatments cannot fully reverse disease progression or structural damage. Traditional Chinese medicine （TCM） has unique advantages in the clinical diagnosis and treatment of RVO， and integrated Chinese and Western medicine approaches may offer better clinical outcomes. This study， based on the clinical manifestations of RVO， systematically reviews the existing literature and evaluates the alignment of current RVO animal models with clinical manifestations. The aim is to identify the characteristics and limitations of existing models and provide recommendations and prospects for developing RVO animal models featuring the combination of disease and syndrome. MethodsDatabases including CNKI， Wanfang Data， PubMed， and Web of Science were searched with the keywords of "retinal vein occlusion" and "animal model". Model characteristics were assessed based on the diagnostic criteria for diseases and syndromes in both TCM and Western medicine. The alignment of each model with clinical manifestations was analyzed and evaluated. ResultsThe available RVO models were primarily established via methods such as laser photocoagulation， photodynamic therapy， diathermy， intravitreal drug injection， and mechanical modeling. These models demonstrated moderate overall alignment with clinical manifestations， mainly reflecting disease characteristics. However， they generally lack representation of TCM syndrome features. ConclusionExisting RVO models are predominantly based on Western medicine and lack TCM syndrome features. Western medical treatments for RVO have certain limitations， while syndrome differentiation and treatment in TCM offer potential advantages. Future research should focus on developing disease-syndrome integrated animal models that incorporate both pathological features and TCM syndrome characteristics. This approach will enhance the design of RVO models and facilitate both basic and clinical research， which make it a scientifically valuable and necessary endeavor.

ObjectiveRetinal vein occlusion （RVO） is the second most common vascular disease leading to vision loss. Since its pathogenesis remains unclear， current Western medical treatments primarily target complications such as macular edema and neovascularization. The main therapeutic approaches include intravitreal injections of anti-vascular endothelial growth factor （VEGF） agents or corticosteroids， laser photocoagulation， and pars plana vitrectomy. However， these treatments cannot fully reverse disease progression or structural damage. Traditional Chinese medicine （TCM） has unique advantages in the clinical diagnosis and treatment of RVO， and integrated Chinese and Western medicine approaches may offer better clinical outcomes. This study， based on the clinical manifestations of RVO， systematically reviews the existing literature and evaluates the alignment of current RVO animal models with clinical manifestations. The aim is to identify the characteristics and limitations of existing models and provide recommendations and prospects for developing RVO animal models featuring the combination of disease and syndrome. MethodsDatabases including CNKI， Wanfang Data， PubMed， and Web of Science were searched with the keywords of "retinal vein occlusion" and "animal model". Model characteristics were assessed based on the diagnostic criteria for diseases and syndromes in both TCM and Western medicine. The alignment of each model with clinical manifestations was analyzed and evaluated. ResultsThe available RVO models were primarily established via methods such as laser photocoagulation， photodynamic therapy， diathermy， intravitreal drug injection， and mechanical modeling. These models demonstrated moderate overall alignment with clinical manifestations， mainly reflecting disease characteristics. However， they generally lack representation of TCM syndrome features. ConclusionExisting RVO models are predominantly based on Western medicine and lack TCM syndrome features. Western medical treatments for RVO have certain limitations， while syndrome differentiation and treatment in TCM offer potential advantages. Future research should focus on developing disease-syndrome integrated animal models that incorporate both pathological features and TCM syndrome characteristics. This approach will enhance the design of RVO models and facilitate both basic and clinical research， which make it a scientifically valuable and necessary endeavor.

Perineural invasion (PNI) by tumor cells is a key phenotype of highly-invasive oral squamous cell carcinoma (OSCC). Since Schwann cells (SCs) and fibroblasts maintain the physiological homeostasis of the peripheral nervous system, and we have focused on cancer-associated fibroblasts (CAFs) for decades, it's imperative to elucidate the impact of CAFs on SCs in PNI⁺ OSCCs. We describe a disease progression-driven shift of PNI^- towards PNI⁺ during the progression of early-stage OSCC (31%, n = 125) to late-stage OSCC (53%, n = 97), characterized by abundant CAFs and nerve demyelination. CAFs inhibited SC proliferation/migration and reduced neurotrophic factors and myelin in vitro, and this involved up-regulated ER stress and decreased MAPK signals. Moreover, CAFs also aggravated the paralysis of the hind limb and PNI in vivo. Unexpectedly, leukemia inhibitory factor (LIF) was exclusively expressed on CAFs and up-regulated in metastatic OSCC. The LIF inhibitor EC330 restored CAF-induced SC inactivation. Thus, OSCC-derived CAFs inactivate SCs to aggravate nerve injury and PNI development.
Schwann Cells/metabolism* ; Mouth Neoplasms/metabolism* ; Humans ; Cancer-Associated Fibroblasts/metabolism* ; Animals ; Carcinoma, Squamous Cell/metabolism* ; Neoplasm Invasiveness/pathology* ; Male ; Female ; Mice ; Cell Movement/physiology* ; Cell Proliferation/physiology* ; Cell Line, Tumor ; Leukemia Inhibitory Factor/metabolism* ; Middle Aged

Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

Objective To construct a prognostic model of future asthma exacerbation risk in adults by com-bining novel biomarkers of serum chitinase-3-like protein 1(YKL-40),dipeptidyl peptidase-4(DPP4)and conventional predictors.Methods Patients with asthma in the non-acute exacerbation phase were recruited from the People's Hospital of Yubei District of Chongqing,from March 2022 to May 2023.Baseline clinical da-ta collected included medical history,forced expiratory volume in the first second(FEV1)/forced vital capacity(FVC),percentage of predicted forced expiratory volume in the first second(FEV1％pred),blood eosinophil count(EOS),blood neutrophil count(NEU),fractional exhaled nitric oxide(FeNO),serum YKL-40,and ser-um DPP4,etc.The patients were followed for one year to gather data on asthma acute exacerbations and their timings as defined in this study.A COX proportional hazards regression model was used to construct a prog-nostic model for future asthma exacerbations,with internal validation and results presentation.Results A to-tal of 224 patients with asthma completed the study.During the one-year follow-up period,102 patients experi-enced acute exacerbations as defined in this study.Based on univariate COX regression,stepwise regression for variable selection,clinical significance,and model simplicity,asthma control test(ACT)score group,number of asthma exacerbations in the past year group,log10(YKL-40),log10(FeNO),log10(EOS),and FEV1％pred were the following predictors were included in the final model.The overall C-statistic of the model was 0.795(95％CI:0.754-0.836),the area under the curve at the 52-week follow-up was 0.879(95％CI:0.834-0.924),and the Brier score at the 52-week follow-up was 0.142(95％CI:0.117-0.168).The calibration curve was close to a slope of 1,and bootstrap validation suggested good stability of the prediction model.The model was presented using a Nomogram and a dynamic scoring table in a web APP,which can be used to predict the risk of asthma exacerbations within 52 weeks for individual patients.Conclusion The prediction model based on serum YKL-40,EOS,FeNO,the number of asthma exacerbation in the past year group,FEV1％pred and ACT scores group can accurately predict the probability of acute attacks in 52 weeks of asthma patients.

Objective To observe the characteristics of gut microbiota in patients with type 2 diabetes mellitus(T2DM)with different traditional Chinese medicine(TCM)syndrome types,and to further explore the key microbial communities and functional differences affecting syndrome differentiation.Methods A total of 45 patients who visited the Department of Geriatrics,Hunan Provincial Hospital of Integrated Traditional Chinese and Western Medicine in 2023 were enrolled.These included 15 T2DM patients with qi-yin deficiency and blood stasis syndrome(Group A),15 T2DM patients with qi-yin deficiency syndrome(Group B),and 15 non-diabetic patients from the same period(Group C).Fecal samples were collected,and 16S rRNA sequencing and analysis were performed.Results 1)A total of 1 564 operational taxonomic units(OTUs)were obtained from the three groups of patients,with 224,127,and 351 unique OTUs identified in Groups A,B and C,respectively.2)Both α-and β-diversity analyses indicated differences among the gut microbiota of the three groups.For instance,in the α-diversity analysis,the Sobs index showed significant inter-group differences(P＜0.01).Group A(264.00±88.84)was significantly higher than Group B(145.90±87.0)(P＜0.01),while Group B was significantly lower than Group C(229.7±112.4)(P＜0.05).In the β-diversity analysis,the principal coordinate analysis(PCoA)indicated a clear separation among groups(R=0.1610,P＜0.01).The R values in the Anosim/Adonis analysis ranged from 0.144 to 0.196,and the R2 values ranged from 0.067 to 0.083,all indicating differences in inter-group comparisons(P＜0.01).3)At the phylum level,Firmicutes,Actinobacteriota,and Bacteroidota were predominant in all groups.Among them,Bacteroidota exhibited significant inter-group differences(P＜0.05),with its abundance in Group A being significantly higher than that in Group B(P＜0.01).4)Analysis of differences in microbiota composition,combined with linear discriminant analysis effect size(LEfSe)and Random Forest analysis,revealed that,at the genus level,the microbiota biomarkers between Group A and Group B were Parabacteroides,Bacteroides,g_unclassified_f_Lachnospiraceae,Roseburia,and Aspergillus,those between Group B and Group C were Erysipelotrichaceae_UCG-003 and Ruminococcus,and those between Group A and Group C were Parabacteroides,Anaerotruncus,and Oscillibacter.The results were validated by receiver operating characteristic(ROC)curve analysis,which suggested that the microbiota biomarkers between Group A and Group B(AUC=0.91;95％CI,0.80-1.00),Group B and Group C(AUC=0.84;95％CI,0.69-0.99),Group A and Group C(AUC=0.87;95％CI,0.75-0.99)had good diagnostic efficacy.5)The study identified 116 major pathways with inter-group differences through Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis.For example,the enrichment degree of ABC transporter pathway in Group A(2.58±0.36)was significantly lower than those in Group B(2.90±0.48)and Group C(3.11±0.66)(P＜0.05).These pathways were associated with metabolism and environmental information processing.g.Conclusion The differences in the gut microbiota characteristics and functions among patients with specific TCM syndromes of T2DM may provide references for TCM syndrome differentiation and therapeutic mechanisms.

Objective: To utilize machine learning(ML) algorithms to develop accurate and effective prediction models for TAC dose/weight-adjusted trough concentration(C0/D). Methods: Data were collected on 264 TAC blood concentration monitoring data from 72 patients undergoing kidney transplantation. The effects of population statistical data, clinical features, combined medication, and ultrasound feature parameters on TAC C0/D were analyzed. Features with a significance level less than 0.05 in the univariate analysis of TAC C0/D were selected for inclusion in the random forest(RF) algorithm to identify significant features. These features were interpreted using partial dependency plots. Five ML algorithms, including RF, support vector regression(SVR), extreme gradient boosting(XGBoost), decision trees(DT) and artificial neural networks(ANN), were employed to establish the TAC C0/D prediction model. Hyper-parameter tuning was performed on the RF model that performed the best. Results : Ten characteristic variables with importance scores>5 were retained and included in the ML model: transglutaminase, red blood cell count, blood urea nitrogen, weight, serum creatinine, renal segmental arterial resistance index, renal aortic resistance index, hematocrit, renal pelvic Young′s modulus value, and time after transplantation. The partial dependence plots showed that all 10 important variables screened were positively correlated with TAC C0/D. The tuned RF model outperformed the other models with aR2of 0.81, aRMSEof 43.93, and aMAEof 29.97. Conclusion The ML models demonstrate good performance in predicting TAC C0/D and provide innovative interpretations using partial dependence plot. The optimized RF model shows optimal performance and offers a novel tool for individualized medication adjustment for TAC in renal transplant patients.

Objective:To analyze serum inflammatory factors associated with antihistamine resistance in patients with chronic spontaneous urticaria (CSU) .Methods:A total of 88 CSU patients were enrolled from Guangzhou Dermatology Hospital from January 2022 to December 2024. All patients received antihistamine treatment according to the "Guideline for diagnosis and treatment of urticaria in China (2022) " . Based on the 7-day urticaria activity score (UAS7) after 4-week treatment, these patients were divided into an antihistamine-sensitive group and an antihistamine-resistant group. Serum levels of inflammatory factors at the initial visit were analyzed using the Olink-targeted proteomics technology. Specific biomarkers associated with antihistamine resistance were identified, and Spearman correlation analysis was carried out to analyze correlations among differentially expressed proteins. A logistic regression model was constructed based on the Olink proteomics data, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Measurement data were expressed as mean ± standard deviation or median (lower quartile, upper quartile) .Results:The 88 CSU patients aged 12 to 81 (38.78 ± 13.89) years, with the disease duration being 18 (7.00, 60.00) months. There were 32 patients in the antihistamine-sensitive group and 56 in the antihistamine-resistant group. No significant differences were found between the two groups in terms of age, disease duration, gender, or history of allergic diseases (all P > 0.05) . After 4 weeks of antihistamine treatment, the UAS7 score was significantly higher in the antihistamine-resistant group (25.00 [15.25, 31.00] points) than in the antihistamine-sensitive group (0.50 [0.00, 4.00] points; Z = -7.08, P < 0.001) . The Olink-targeted proteomics identified 5 differentially expressed proteins between the two groups: compared with the antihistamine-sensitive group, the antihistamine-resistant group showed > 2-fold higher expression of fibroblast growth factor 19 (FGF19) , interleukin-15 receptor subunit alpha (IL-15RA) , eotaxin (CCL11) , and monocyte chemoattractant protein-1 (MCP-1) ; in contrast, the expression of sulfotransferase 1A1 (ST1A1) in the antihistamine-sensitive group was 2.54 times that in the antihistamine-resistant group. Among the differentially expressed proteins, MCP-1 showed the highest specificity (1.00) for predicting antihistamine resistance, followed by CCL11 (0.97) . Correlation analysis revealed a significant positive correlation between MCP-1 and CCL11, and a significant negative correlation between IL-15RA and ST1A1. ROC curve analysis showed that MCP-1 and CCL11 had area under the curve values of 0.603 and 0.630, respectively, in predicting antihistamine resistance. Conclusion:MCP-1 and CCL11 may be potential biomarkers for predicting antihistamine resistance in CSU patients.

Objective:To understand the coverage and information consistency rate of National Immunization Program (NIP) vaccines among children born during 2020-2021 in Zhejiang Province, Chongqing City, and Shanxi Province (3 provinces) of China .Methods:A simple random sampling method was used to randomly select 3 counties (districts) from each of the 3 provinces, 5 townships from each county (district), and 5 villages from each township. Vaccination information for seven NIP vaccines was collected for children born between 2020 and 2021 in each village. The vaccination coverage, timely coverage, and consistency rates between the survey data and the Immunization Planning Information System data were analyzed.Results:A total of 1 117 children were investigated. The vaccination coverage for each dose of NIP vaccine ranged from 99.10% to 100.00%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 99.19% to 100.00%, 98.92% to 100.00%, and 99.20% to 100.00%, respectively. The timely coverage of each dose of NIP vaccine ranged from 89.79% to 99.82%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 94.09% to 99.73%, 89.52% to 99.73%, and 78.55% to 100.00%, respectively. The consistency rate of information on each dose of NIP vaccine ranged from 94.36% to 99.91%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 97.85% to 99.73%, 98.92% to 100.00%, and 86.06% to 100.00%, respectively.Conclusions:Coverage of NIP vaccines was generally high among children born during 2020-2021 in the 3 provinces of China, but there were regional differences in the timely coverage of some vaccine doses and the vaccination information consistency rate. It is necessary to strengthen the timely vaccination of children's vaccine booster doses and optimize the management of vaccination services.

Objective:To investigate the anemia conditions and characteristics of iron metabolism during different stages of pregnancy in women with different genotypes of thalassemia.Methods:This cohort study selected 3 303 singleton pregnant women who underwent regular prenatal examinations and genetic tests of thalassemia and were delivered at Maternal & Child Health Hospital of Guangxi Zhuang Autonomous Region from January 2019 to December 2023. According to the results of thalassemia gene testing, the women were divided into groups: those without thalassemia genes served as the control group (1 539 cases), and those with thalassemia genes (1 764 cases) were further divided based on genotype into the -α/αα group (326 cases), --/αα or -α/-α group (649 cases), point mutation α-thalassemia group (201 cases), β 0-thalassemia group (368 cases), β +-thalassemia group (91 cases), and α combined with β-thalassemia group (129 cases). Hemoglobin (Hb) and serum ferritin (SF) levels were measured in the first, second, and third trimester of pregnancy. Differences in anemia and iron reserves among the groups at different pregnancy stages were compared using repeated measures analysis of variance, LSD test, Kruskal-Wallis rank-sum test, and Bonferroni correction. Results:Compared to the first trimester, Hb levels decreased in the second and third trimester across all groups (LSD test, all P<0.05), and the severity of anemia increased (Bonferroni correction, all P<0.017). The severity of anemia varied among the groups at the same pregnancy stage ( Hfirst trimester=918.20, Hsecond trimester=1 224.50, Hthird trimester=980.19; all P<0.001), and Hb levels also differed ( Ffirst trimester=282.54, Fsecond trimester=352.31, Fthird trimester=239.02; all P<0.001). The β 0-thalassemia group had higher rates of moderate anemia in the first, second, and third trimester of pregnancy [38.6% (142/368), 85.3% (314/368), and 73.6% (271/368)] compared to other groups (Bonferroni correction, all P<0.002), and lower Hb levels [(102.1±8.9), (92.0±7.3), and (94.6±7.7) g/L] than other groups (LSD test, all P<0.05). As pregnancy progresses, SF levels in each group of pregnant women gradually decreased (LSD test, all P<0.05), and the degree of iron deficiency worsened (Bonferroni correction, all P<0.05). The iron deficiency rate in thalassemia pregnant women during the third trimester ranges from 21.5% (79/368) to 46.0% (150/326). The degree of iron deficiency varies among groups within the same gestational period ( Hfirst trimester=79.13, Hsecond trimester=203.98, Hthird trimester=130.55; all P<0.001), and SF levels also differ ( Ffirst trimester=17.28, Fsecond trimester=44.60, Fthird trimester=31.87; all P<0.001). Among them, the β 0-thalassemia group had the lowest iron deficiency rates in the second, and third trimesters [9.8% (36/368), and 21.5% (79/368)] (Bonferroni correction, all P<0.002). SF levels in the β 0-thalassemia and β +-thalassemia groups were higher than those in other groups during each gestational period (LSD test, all P<0.05). Conclusions:Pregnant women with thalassemia may experience varying degrees of iron deficiency during pregnancy, with the severity of iron deficiency and anemia increasing with gestational age. The degree of iron deficiency and anemia during pregnancy varies among pregnant women with different genotypes of thalassemia. Clinically, individualized management should be provided for pregnant women with thalassemia based on their genotypes, with dynamic monitoring of anemia and iron metabolism changes.