ObjectiveTo identify potential influencing factors of urate crystal deposition at ankle/foot in patients with hyperuricemia （HUA）， and to analyze the predictive value of inflammatory indicators for urate crystal deposition in patients with different traditional Chinese medicine （TCM） syndromes， so as to provide potential reference for clinical risk assessment and individualized TCM intervention. MethodsA retrospective study was carried out with the enrollment of 231 HUA patients from The Third Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine between January 2021 and December 2024. The enrolled patients were further divided into a crystal deposition-positive group （143 cases） and a crystal deposition-negative group （88 cases） according to the results of dual-energy computed tomography （CT）. Sociodemographic data， living habits， serum uric acid levels， and inflammatory indicators of the enrolled patients were collcted， and TCM syndrome differentiation was performed. Furthermore， univariate analysis was used to compare inter-group differences in clinical characteristics. MMultivariate Logistic regression was applied to identify the influencing factors of urate crystal deposition. In addition， the receiver operating characteristic （ROC） curves were plotted to evaluate the predictive efficacy of inflammatory indicators for crystal deposition across different TCM syndromes. ResultsThere were statistically significant inter-group differences in the proportion of males， age， body mass index， proportion of mental labor， rate of low water intake， and rate of high-sugar beverage consumption （P<0.05），whereas no significant difference in low exercise intensity was found between the two groups. Furthermore， compared with the negative group， the positive group had higher serum uric acid level， neutrophil-to-lymphocyte ratio （NLR）， and platelet-to-lymphocyte ratio （PLR）， but lower systemic immune-inflammation index （SIRI） （P<0.05）. Regarding the distribution of TCM syndromes， the positive group was dominated by the dampness-heat accumulation syndrome （55/143，38.46%）， while the negative group was mainly characterized by the phlegm-turbidity obstruction syndrome （44/88，50.00%）. Multivariate Logistic regression analysis revealed that high-sugar beverage consumption， elevated NLR， and elevated PLR were risk factors for urate crystal deposition ［odd ratio （OR） = 8.002， 5.377， 1.034， respectively； 95% CI 1.572-40.732， 2.179-13.270， 1.013-1.054，all P<0.05］， while SIRI was a protective factor （OR = 0.869， 95% CI 0.778-0.971， P<0.05）. In the positive group， patients with the dampness-heat accumulation syndrome exhibited the highest NLR， while the lowest PLR and SIRI， showing statistically significant differences with those of other syndromes （all P<0.05）. In addition， ROC curve analysis indicated that for the dampness-heat accumulation syndrome， the combined "NLR + PLR" model had an area under the curve （AUC） of 0.901 （95% CI 0.850-0.951， P<0.01）， with a sensitivity of 89.1% and a specificity of 79.5%； for the blood stasis-heat obstruction syndrome， the combined "NLR + PLR" model had an AUC of 0.880 （95% CI 0.825-0.934， P<0.01）， with a sensitivity of 100.0% and a specificity of 67.3%； for the liver-kidney Yin-deficiency syndrome， the single PLR model had an AUC of 0.842 （95% CI 0.731-0.952， P<0.01）， with a sensitivity of 83.3% and a specificity of 84.0%. ConclusionUrate crystal deposition in HUA patients exhibits intimate associations with high-sugar beverage consumption as well as elevated NLR and PLR levels. Meanwhile， TCM syndrome differentiation has potential correlation with inflammatory characteristics. The inflammatory indicator-based prediction model constructed based on TCM syndromes exhibits good predictive value.

Objective: To explore the intervention effects of different exercise modalities on executive function in school aged children, providing references for formulating exercise prescriptions to promote school aged children executive function. Methods: A systematic literature search was conducted using PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP and CBM to identify studies on the effects of exercises on executive function in school aged children. The search period spanned from the database inception to August 2025. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias of the included studies. Network Meta analysis was performed using Stata 16.0. Results: A total of 51 studies involving 5 710 school aged children and seven modalities of exercises(control group,small ball sports,large ball sports,aerobic exercise,combined exercise,sport game,gymnastic and dance sports) were finally included. Network Meta analysis showed that, in terms of inhibitory control development of school aged children, small ball sports ( SMD =-0.98,95% CI =-1.32 to -0.64 ), large ball sports ( SMD =-0.64,95% CI =-0.91 to -0.36), and mixed sports ( SMD =-0.26,95% CI =-0.50 to -0.02]) were more effective than the control group; in terms of working memory, largeball sports ( SMD =-1.26,95% CI =-1.88 to -0.65) and small ball sports ( SMD =-0.91,95% CI =-1.64 to -0.19) were superior to the control group; in terms of improving cognitive flexibility, large ball sports ( SMD =-1.02,95% CI =-1.28 to -0.76), gymnastics and dance sports ( SMD =-0.80,95% CI =-1.21 to -0.40), and small ball sports ( SMD =-0.75,95% CI =-1.15 to -0.36) were more effective than the control group (all P <0.05). Surface under cumulative ranking curve(SUCRA) showed that small ball sports had the highest value (SUCRA=98.9%) in improving inhibitory control, while large ball sports achieved the highest score(SUCRA=92.6%) in enhancing working memory, and large ball sports also had the highest value(SUCRA=94.7%) in promoting cognitive flexibility. Conclusions For developing inhibitory control in school aged children, small ball sports are recommended as a priority. For enhancing working memory and cognitive flexibility, large ball sports are recommended.

ObjectiveTo analyze the phylogroups of Klebsiella pneumoniae isolates from laboratory rats and mice housed at the Laboratory Animal Center Fudan University, optimize the identification method for Klebsiella pneumoniae, and perform virulence genes detection and antimicrobial susceptibility testing, thereby providing a practical basis for microbial detection strategies and prevention and control measures in laboratory animal facilities. MethodsBetween September 2023 and September 2025, ileocecal contents were collected from laboratory rats and mice purchased from laboratory animal suppliers, from which 11 strains of Klebsiella pneumoniae were isolated and cryopreserved. These strains were tested for β-lactamase genes using multiplex PCR for phylogrouping. Furthermore, representative strains from different phylogroups were selected for biochemical tests, mass spectrometry analysis, 16S rRNA gene and tyrB gene sequencing, as well as virulence genes detection and antimicrobial susceptibility testing. ResultsMultiplex PCR detection of β-lactamase genes revealed that among 11 strains, 4 were identified as Klebsiella pneumoniae (Kp1), 4 as Klebsiella quasipneumoniae (Kp2), and 3 as Klebsiella variicola (Kp3). Kp1-Kp3 could not be differentiated using biochemical tests, mass spectrometry analysis, or 16S rRNA gene sequencing, whereas multiplex PCR of β-lactamase genes and tyrB gene sequencing enabled phylogrouping of all three. There were differences in the detection rates of capsular genes (capsule associated gene G and magnesium transport system ATP-binding protein A) and siderophore genes (Klebsiella pneumoniae iron uptake system genes) among Kp1-Kp3; susceptibility to different antibiotics also varied considerably. ConclusionKlebsiella pneumoniae phylogroups isolated from commercially produced laboratory rats and mice are similar to those in human clinical samples and likewise include multiple phylogroups of the species complex. In this study, the identification methods of Kp1-Kp3 were optimized,furthermore selected virulence genes and antimicrobial resistance in Kp1-Kp3 were preliminarily investigated, which could provide precise reference for the treatment of infections caused by different phylogroups occurring in laboratory animal facilities.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

Psychological problems are significant factors that contribute to the occurrence of postoperative complications, reduced quality of life, and prolonged hospital stays. Lung cancer patients who require surgery often experience high psychological stress related to both the disease and the treatment, making them more susceptible to depression and anxiety. Therefore, timely intervention to address these psychological issues is crucial for promoting postoperative recovery and improving patients' quality of life. This paper will review the current status, impact, and interventions for depression and anxiety-related psychological problems in lung cancer surgery patients.

[Objective] To compare the clinical efficacy of super pulse thulium laser enucleation of the prostate (SPThuLEP) with "open tunnel" and transurethral holmium laser enucleation of the prostate (HoLEP) in the treatment of benign prostatic hyperplasia (BPH), in order to provide reference for the treatment options of BPH. [Methods] The clinical data of 112 BPH patients treated in our hospital during Jan.2023 and Jul.2023 were retrospectively analyzed, including 65 treated with SPThuLEP with "open tunnel" and 57 with HoLEP.The operation time, postoperative hemoglobin decrease, postoperative bladder irrigation, catheter indwelling time, hospitalization time and complications were compared between the two groups.The changes of maximum urine flow rate (Qmax), international prostate symptom score (IPSS), quality of life score (QoL), postvoid residual (PVR) and prostate-specific antigen (PSA) were compared between the two groups before operation and one month after operation. [Results] All operations were successful without conversion to open or transurethral plasmakinetic resection.The postoperative decrease of hemoglobin in SPThuLEP group was lower than that in HoLEP group [(13.12±6.72) g/L vs. (21.02±6.51) g/L], with statistical difference (P<0.05). There were no significant differences in the operation time [(63.35±15.73) min vs.(61.02±17.55) min], postoperative bladder irrigation time [(1.07±0.45) d vs. (1.06±0.36) d], catheter indwelling time [(2.98±0.56) d vs. (3.01±0.63) d] and hospitalization time [(3.63±0.61) d vs.(3.79±0.76) d] between the two groups (P>0.05). No blood transfusion, secondary bleeding or unplanned hospitalization occurred, and there were no serious complications such as transurethral electroresection syndrome (TURS), urethral stricture and urinary incontinence.One month after operation, the Qmax, IPSS, QoL, PVR and PSA of the two groups were significantly improved compared with those before operation (P<0.05), but with no statistical difference between the two groups (P>0.05). [Conclusion] SPThuLEP with "open tunnel" has comparable efficacy as HoLEP in the treatment of BPH.With advantages of small amount of bleeding and high safety, this minimally invasive technique can be widely popularized in clinical practice.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To investigate the anti-tumor effect of Job's tears oral solution on lung cancer mice.Methods Observe the histopathological morphology of the tumor;Flow cytometry detected the changes in the levels of CD4+T and CD8+T in splenic lymphocytes;Elisa detected the contents of immunoglobulins IgA,IgG,IL-2 and IFN-γ;Blood routine was detected;the kit determined the levels of liver and kidney glutathione peroxidase(GSH-Px),superoxide dismutase(SOD)and malondialdehyde(MDA)content;Serum alanine aminotransferase(ALT),alkaline phosphatase(ALB),azelaic transaminase(AST),urea nitrogen(BUN),and blood creatinine(CRE)were measured in each group of mice.The transcriptome was found differential genes and pathway enrichment was performed.Western blot was used to detect the expression of proteins related to IL-17 signaling pathway(STAT3,NF-κB,VEGF)and TNF signaling pathway(PI3K/AKT,MAPK,JNK).Results Tumor histopathological and morphological changes were obvious in each administration group,and the heterogeneity was gradually reduced.Compared with the cisplatin group,the levels of CD4+T,CD8+T,CD4+T/CD8+T,IL-2,IFN-γ,and IgG in the Job's tears group were significantly increased(P<0.01).The blood routine results:compared with the model group,WBC,RBC,HGB,PLT,Lym%and GR%in the Job's tears group decreased significantly(P<0.01);Compared with the cisplatin group,WBC,RBC,PLT,HGB and Lym%in the Job's tears group increased significantly(P<0.01).The antioxidant indexes of liver and kidney showed that the levels of GSH and SOD in the liver and kidney tissues of the Job's tears group increased significantly(P<0.01),and the level of MDA decreased significantly(P<0.01).Effects on liver and kidney function indexes in mice AST,ALT,BUN and CRE decreased significantly in the Job's tears group(P<0.01),and ALB level increased significantly in the Job's tears group(P<0.01).Transcriptome results,Job's tears high-dose group mainly exerted anti-tumor effects by affecting TNF signaling pathway and IL-17 signaling pathway.Western blot results,in the IL-17 signaling pathway,S-TAT3 and VEGF decreased in the cisplatin group and the Job's tears group compared with the model group(P<0.01),and NF-κB decreased in the Job's tears high-dose group(P<0.01);Compared with the cisplatin group,STAT3 and NF-κB were decreased in the Job's tears group(P<0.01);VEGF was decreased in the Job's tears low-dose group(P<0.01);In the TNF signaling pathway,PI3K and MAPK were decreased in the cisplatin group and Job's tears group(P<0.01);AKT and P-AKT were decreased in the Job's tears group(P<0.01).Compared with the cisplatin group,the Job's tears group AKT,PI3K,and MAPK decreased(P<0.01);P-AKT decreased in the high dose group of Job's tears(P<0.01).Conclusion High-dose Job's tears oral solution inhibits tumor proliferation,attenuates inflammatory response,enhances immunity,improves blood routine and reduces liver and kidney injury in lung cancer mice mainly by inhibiting IL-17 and TNF signaling pathway.

Psoriasis arthritis (PsA) is a chronic inflammatory musculoskeletal disease intricately linked to psoriasis (PsO), with a multifaceted etiology encompassing genetic, environmental, and immunological factors. Characterized by complex clinical manifestations, PsA often follows a protracted course with a propensity for relapses, potentially culminating in joint deformity and disability. The condition is further complicated by associated comorbidities such as inflammatory bowel disease, uveitis, cardiovascular disease, and metabolic syndrome, which significantly diminish patients′ quality of life. Early detection and screening of PsA are crucial for its management and prevention of adverse outcomes. However, in China, there is a notable deficiency in the recognition and early diagnosis of PsA, with missed or incorrect diagnoses being relatively common. The consensus comprises four overarching statements and sixteen detailed recommendations, with the overarching goal of enhancing the early diagnosis and treatment of PsA by clinical physicians, thereby improving patient outcomes.