OBJECTIVE To investigate the mechanism of Huangqin decoction in improving ulcerative colitis （UC） through the gut microbiota-tryptophan metabolism-aryl hydrocarbon receptor （AhR） axis. METHODS Mice were randomly divided into normal group （normal saline）， model group （normal saline）， microbiota depletion-model group （normal saline）， microbiota depletion-Huangqin decoction group （9.1 g/kg， by crude drug， similarly hereinafter）， Huangqin decoction group and mesalazine group （positive control group， 0.4 g/kg）， with 6 mice in each group. Microbiota depletion was achieved by providing free access to a mixed antibiotics for 10 days. The UC model was induced by administering 2.5% dextran sulfate sodium solution for 7 days. After successful modeling， each treatment group received corresponding drugs or normal saline intragastrically once daily for 10 days. After the final administration， body weight change ratio， disease activity index （DAI） score， and colon length were evaluated； colon pathological changes were observed； serum levels of interleukin-6 （IL-6）， IL-10， IL-22， and tumor necrosis factor-α （TNF-α） were measured； the expressions of Occludin， zonula occluden-1 （ZO-1）， and AhR in colon tissue were detected； fecal samples were subjected to high-throughput sequencing to analyze targeted tryptophan metabolomics. RESULTS Compared with the model group， Huangqin decoction group showed reduced infiltration of inflammatory cells in the colon tissue and restoration of the intestinal mucosal structure. Body weight change ratio， colon length， serum content of IL-10， the expressions of Occludin， ZO-1 and AhR in colon tissue and the contents of tryptophan metabolites indole-3-propionic acid （IPA）， N -acetylserotonin （NAS） and indole-3-acetic acid （IAA） were all significantly increased （ P ＜0.05）； DAI score， serum levels of IL-6， TNF-α， and IL-22 and the content of tryptophan metabolite indole-3-ethanol were significantly decreased （ P ＜0.05）； gut microbiota structure was improved， with increased relative abundances of beneficial bacteria such as Lactobacillus ， and decreased relative abundances of pathogenic bacteria such as Escherichia-Shigella . However， after antibiotic-induced microbiota depletion， although Huangqin decoction significantly increased the content of NAS in the feces of mice， the expression of AhR protein in colon tissue did not increase concurrently. CONCLUSIONS Huangqin decoction can repair the intestinal mucosal barrier in UC mice by regulating the gut microbiota and promoting the production of IPA and IAA， thereby activating AhR. This suggests that an intact gut microbiota is an important prerequisite for Huangqin decoction to exert its AhR-regulating effects.

BACKGROUND:Degenerative scoliosis is defined as a condition that occurs in adulthood with a coronal cobb angle of the spine>10° accompanied by sagittal deformity and rotational subluxation,which often produces symptoms of spinal cord and nerve compression,such as lumbar pain,lower limb pain,numbness,weakness,and neurogenic claudication.The finite element method is a mechanical analysis technique for computer modelling,which can be used for spinal mechanics research by building digital models that can realistically restore the human spine model and design modifications. OBJECTIVE:To review the application of finite element method in the etiology and treatment of degenerative scoliosis. METHODS:The literature databases CNKI,PubMed,and Web of Science were searched for articles on the application of finite element method in degenerative scoliosis published before October 2023.Search terms were"finite element analysis,biomechanics,stress analysis,degenerative scoliosis,adult spinal deformity"in Chinese and English.Fifty-four papers were finally included. RESULTS AND CONCLUSION:(1)The biomechanical findings from the degenerative scoliosis model constructed using the finite element method were identical to those from the in vivo experimental studies,which proves that the finite element method has a high practical value in degenerative scoliosis.(2)The study of the etiology and treatment of degenerative scoliosis by the finite element method is conducive to the prevention of the occurrence of the scoliosis,slowing down the progress of the scoliosis,the development of a more appropriate treatment plan,the reduction of complications,and the promotion of the patients'surgical operation.(3)The finite element method has gradually evolved from a single bony structure to the inclusion of soft tissues such as muscle ligaments,and the small sample content is increasingly unable to meet the research needs.(4)The finite element method has much room for exploration in degenerative scoliosis.

Homo- or heterodimeric compounds that affect dimeric protein function through interaction between monomeric moieties and protein subunits can serve as valuable sources of potent and selective drug candidates. Here, we screened an in-house dimeric natural product collection, and panepocyclinol A (PecA) emerged as a selective and potent STAT3 inhibitor with profound anti-tumor efficacy. Through cross-linking C712/C718 residues in separate STAT3 monomers with two distinct Michael receptors, PecA inhibits STAT3 DNA binding affinity and transcription activity. Molecular dynamics simulation reveals the key conformation changes of STAT3 dimers upon the di-covalent binding with PecA that abolishes its DNA interactions. Furthermore, PecA exhibits high efficacy against anaplastic large T cell lymphoma in vitro and in vivo, especially those with constitutively activated STAT3 or STAT3^Y640F. In summary, our study describes a distinct and effective di-covalent modification for the dimeric compound PecA to disrupt STAT3 function.

Tooth autotransplantation is a restoration technology that grafts the patient's own teeth to the missing tooth area, usually by using the third molar to replace a nonnormally functioning molar for the purpose of "turning waste into treasure". Robotic surgical assistance has been widely used in the fields of breast cancer, liver cancer, and orthopedics; however, its application in the dental field, particularly in tooth autotransplantation, remains relatively rare. This paper reports a case of tooth autotransplantation with the assistance of a domestic autonomous oral surgery robot, providing a reference for the application of robotic surgery assistance in tooth autotransplantation.
Humans ; Molar, Third/transplantation* ; Robotic Surgical Procedures/methods* ; Transplantation, Autologous

ObjectiveTo investigate the effect of the multidisciplinary team （MDT） management model in improving the prognosis of patients with cirrhotic portal hypertension. MethodsA total of 86 patients with cirrhotic portal hypertension who were admitted to Shenzhen Third People’s Hospital from May 2022 to July 2024 were enrolled， and according to whether the MDT treatment regimen was implemented， they were divided into execution group with 51 patients and non-execution group with 35 patients. Baseline clinical data were collected， and the patients were observed in terms of gastrointestinal bleeding， hepatic encephalopathy， liver cancer， and death from admission to the end of follow-up （January 2025）. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Wilcoxon rank-sum test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. The Kaplan-Meier method was used to plot survival curves for the cumulative incidence rates of endpoint events （gastrointestinal bleeding， hepatic encephalopathy， liver cancer， and death）， and the Log-rank test was used for comparison between groups. The Cox proportional-hazards regression model analysis was used to investigate the effect of MDT management on the prognosis of patients. ResultsThere were significant differences between the execution group and the non-execution group in diameter of the portal vein （t=1.216， P=0.017） and ascites （χ²=4.515， P=0.034） at baseline. The patients were followed up for 14.6±6.2 months， and the survival curve analysis showed that there was a significant difference in the cumulative incidence rate of gastrointestinal bleeding between the two groups （χ²=4.573， P=0.024）， while there were no significant differences in the incidence rates of other outcome events between the two groups （all P>0.05）. The Cox regression analysis showed that the execution group had a reduced risk of gastrointestinal bleeding （hazard ratio=0.262， 95% confidence interval： 0.110 — 0.630， P=0.003）. ConclusionImplementation of the MDT treatment regimen can significantly reduce the short-term risk of gastrointestinal bleeding in patients with cirrhotic portal hypertension， while its long-term benefits require further follow-up verification.

BACKGROUND:The highest incidence of spinal fracture is in the thoracolumbar segment,and its symptoms are back pain,posterior convexity deformity,activity limitation,or with spinal cord nerve injury causing lower limb pain,numbness,and even paraplegia and other complications.The finite element method is a digital computer modeling technique,which can simulate the physical model and carry out force analysis realistically.OBJECTIVE:To review the application of finite element method in thoracolumbar spine fractures.METHODS:We searched the Chinese and English literature databases PubMed,Web of Science,and CNKI for relevant literature on the application of the finite element analysis method in spinal thoracolumbar fracture published before March 2024.The search terms in Chinese and English were:finite element analysis methods,biomechanical phenomena,stress analysis,thoracolumbar fractures,spinal fractures.Finally,55 papers were included.RESULTS AND CONCLUSION:(1)The exploration of thoracolumbar fractures caused by different etiologies(osteoporotic,traumatic,and pathological)through the finite element method is conducive to a deeper understanding of the biomechanics of various types of thoracolumbar fractures,and to improve the individualized and fine-tuned treatment of thoracolumbar fractures.(2)The finite element analysis of a single sample or a small number of samples has the chance,and a larger number of samples are required for the future finite element analysis to reduce the chance caused by the sample.(3)The rigid structure of bones alone cannot meet the biomechanical working conditions of the integrity of the physical object,and future finite element models need to incorporate all the structures of the physical object(e.g.,soft tissues,such as muscles and ligaments)as far as possible.(4)The finite element method has been used in more studies on osteoporotic and traumatic thoracolumbar spine fractures,which will need to be more in-depth in the future,and less in the field of pathologic thoracolumbar fractures,which has a wider scope for future research.

Objectives:To investigate the prognostic value of the CHA2DS2-VASc and R2CHA2DS2-VASc scores in patients with atrial fibrillation(AF)and heart failure(HF).Methods:Patients with AF and HF from hospitals diagnosed by the Heart Failure Center in Guangdong Province between January 2017 and December 2021 were selected.Major adverse cardiovascular events(MACE)were used as the follow-up endpoint.Statistical methods such as the area under the receiver operating characteristic(ROC)curve(AUC),net reclassification index(NRI),and integrated discrimination improvement(IDI)were applied to evaluate the predictive value of the CHA2DS2-VASc and R2CHA2DS2-VASc scores in patients with AF and HF.Results:A total of 1 839 patients were enrolled in this study,comprising 703 patients in the MACE group and 1 136 patients in the non-MACE group.Compared with the non-MACE group,the MACE group exhibited significantly advanced age,higher prevalence of New York Heart Association class Ⅳ and coronary artery disease,lower diastolic blood pressure and estimated glomerular filtration rate levels,and elevated serum N-terminal pro-B-type natriuretic peptide concentrations(all P<0.05).Additionally,significantly lower proportions of patients in the MACE group received angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors,beta-blockers,mineralocorticoid receptor antagonists,or anticoagulant therapy(all P<0.05).Multivariable logistic regression analysis revealed that each 1-point increment in both CHA2DS2-VASc and R2CHA2DS2-VASc scores was associated with approximately 10%increased risk of MACE.ROC curve analysis demonstrated that the AUC values for predicting MACE in AF patients with HF were 0.555(95%CI:0.528-0.582,P<0.001)for CHA2DS2-VASc and 0.576(95%CI:0.549-0.608,P<0.001)for R2CHA2DS2-VASc,indicating marginally superior discriminatory capacity of the R2CHA2DS2-VASc score.Delong's test confirmed statistically significant differences between the two scoring systems(P=0.001).The R2CHA2DS2-VASc score demonstrated a NRI of 0.259(95%CI:0.166-0.352,P<0.001)and an IDI of 0.007(95%CI:0.005-0.010,P<0.001)compared with the conventional CHA2DS2-VASc score.Although the R2CHA2DS2-VASc score exhibited slightly better predictive accuracy and outcome discrimination capacity than the original scoring system,both scores demonstrated suboptimal clinical predictive performance.Conclusions:Both the R2CHA2DS2-VASc and CHA2DS2-VASc scores show suboptimal performance for predicting the risk of MACE in patients with AF and HF,and the predicting performance of R2CHA2DS2-VASc score is marginally superior to CHA2DS2-VASc score in this patient cohort.

Objective To evaluate and improve missing data imputation methods to enhance the performance of binary classification prediction model.Methods By simulating data missing scenes,the effects of direct elimination,mean imputation,random forest(RF)imputation algorithm,and multiple imputation-random forest(MI-RF)on the performance of the prediction model were jointly evaluated by receiver operating characteristic area under curve(AUC)and root mean square error(RMSE).Bayesian Network was introduced for the random forest imputation algorithm to optimize the imputation method using the correlations between variables.Results Under different missing proportions,both AUC and RMSE indicated that Bayesian network optimized random forest(BN-RF)imputation algorithm was better.In addition,when the missing proportion was 10％-20％,various imputation methods had roughly the same improvement effect for the prediction model;when the proportion of missing data was 30％-40％,compared to the mean imputation,except for the BN-RF,RF was more effective and its effect was slightly better than MI-RF;however,when the proportion of missing data was close to 50％,even if the model performance was still appropriate,the imputation data gradually deviated from the true data features,resulting in a decrease in the usability of the model.Conclusion The overall effect of BN-RF is satisfactory,and it should be chosen when random missing was 30％-40％.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.