ObjectiveTo construct a macro-evaluation tool for dampness syndrome （DS） animal model， which will provide a basis for experimental research on dampness syndrome in traditional Chinese medicine （TCM）. MethodsConceptual framework of this study was clarified through discussions within the core working group， and dimensions of the evaluation of the animal model of DS were identified according to TCM principles. We searched CNKI， Wanfang， VIP and SinoMed databases from the inception to June 30th， 2023， on experiments involving dampness syndrome animals to create a pool of items about DS animal models. The core items were selected and extracted for factor analysis and cluster analysis. An expert importance rating questionnaire was developed based on the results of the literature review， analyzing the distribution of item scores， importance averages， and coefficient of variation. Through a comprehensive analysis of literature， expert importance scoring， and specific expert opinions， items that did not meet anyone of the criteria of average importance rating ≥2.04， coefficient of variation ≤30%， or literature eva-luation frequency ≥2% were removed， thereafter， the macro-evaluation tool for DS animal model was preliminarily constructed. ResultsSpirit and body state， autonomic activity state， body surface characteristics， diet， urination and defecation， tongue manifestation， and motor behavior assessment were constructed as the seven dimensions in the evaluation of DS animal model. A total of 348 papers about animal experiments were included and analyzed， resulting in a saturated pool of 72 items， which was refined to 38 core items of DS animal models. Factor analysis obtained 16 common factors， which were further clustered into two categories， named dampness transforming from heat syndrome and dampness transforming from cold syndrome. The expert importance scoring showed that the Kendall harmony coefficient was 0.359 （P＜0.05） indicating a high level of agreement， coordination and reliability among the experts. Notably， 50% or more of the experts considered the items thick and greasy tongue coating， unclean perianal area， loose stools， lethargy， unformed stools， and listless expression as very important. The median scores for all items were 2.04（1.73， 2.37）， with a coefficient of variation ranging from 19.73% to 53.38%. After expert evaluation， the macro-evaluation tool for DS animal model in TCM with 33 items and corresponding criteria for assessing the formation of DS models was finally contructed. ConclusionThe Macro-evaluation tool for DS animal model in TCM is highly scientific， credible， and operable， and can be utilized in DS animal experiments after its characteristics are actually evaluated.

Paroxysmal nocturnal hemoglobinuria (PNH) is an acquired clonal hematopoietic stem cell disease caused by abnormal expression of glycosylphosphatidylinositol (GPI) on the cell membrane due to mutations in the phosphatidylinositol glycan class A(PIGA) gene. It is commonly characterized by intravascular hemolysis, repeated thrombosis, and bone marrow failure, as well as multiple systemic involvement symptoms such as renal dysfunction, pulmonary hypertension, swallowing difficulties, chest pain, abdominal pain, and erectile dysfunction. Due to the rarity of PNH and its strong heterogeneity in clinical manifestations, multidisciplinary collaboration is often required for diagnosis and treatment. Peking Union Medical College Hospital, relying on the rare disease diagnosis and treatment platform, has invited multidisciplinary clinical experts to form a unified opinion on the diagnosis and treatment of PNH, and formulated the Expert Consensus of Multidisciplinary Diagnosis and Treatment for Paroxysmal Nocturnal Hemoglobinuria (2024), with the hope of promoting the standardization of PNH diagnosis and treatment.

In recent years, the application of minimal residual disease (MRD) in solid tumors has gained widespread attention. MRD typically refers to the presence of residual cancer cells that remain undetectable by imaging after curative treatments, such as surgical resection. The presence of MRD post-surgery is significantly associated with an increased risk of tumor recurrence. In colorectal cancer, circulating tumor DNA (ctDNA) serves as an effective marker for assessing MRD, particularly in non-metastatic (stages I-III) colorectal cancer. As a real-time, accurate, and convenient biomarker, ctDNA can effectively predict tumor recurrence, guide postoperative adjuvant chemotherapy decisions, and provide crucial information for recurrence monitoring. The application prospects of ctDNA detection technology are vast, promising more precise and individualized treatment plans for colorectal cancer patients. This article comprehensively analyzes the progress in the application of ctDNA for detecting MRD in non-metastatic colorectal cancer patients, elaborates on its guiding role in clinical treatment decisions, and envisions the future development directions in this field.

With the frequent occurrence of respiratory viral infectious diseases,global public health is facing major challenges,and finding effective prevention and treatment methods has become a top priority.As an important part of traditional Chinese medicine,Chinese medicine has shown unique advantages in antiviral treatment.This article aims to review the modern scientific research pro-gress of Chinese medicine against respiratory viral infectious diseases,explore its advantages in clinical application and how to deeply understand the scientific connotation of Chinese medicine intervention in the whole-life cycle of viruses through multidisciplinary cross-integration,and systematically analyze the research strategy of the antiviral infection mechanism of Chinese medicine compound.It pro-vides a reference for the reserve of candidate Chinese medicine for future prevention and control of virus safety risks,which is of great significance for effectively supporting the prevention,control and treatment of new and emerging viral infectious diseases in China.

Objective:To investigate the effect of the virtual simulation system-based teaching method for the experimental course of oral local nerve block anesthesia in improving the effect of traditional teaching methods.Methods:One hundred and eighteen undergraduate dental students were randomly divided into two groups, the experimental group was taught using a virtual simulation-based system, and the control group was taught using traditional teaching. The results of the teaching were comprehensively evaluated through course feedback questionnaires, analysis of theoretical test scores, evaluations of the trainees administering and receiving anesthesia on the current anesthesia, and faculty evaluations of the success of the anesthesia, and t-tests and chi-square tests were performed using SPSS 23.0.Results:There was no significant difference in baseline level between the two groups. The students in the experimental group thought that the learning was more vivid ( t=4.24, P=0.005) and had more self-confidence in local anesthesia ( t=4.99, P<0.001). The students in the experimental group felt less needle tip jitter during injection ( t=2.22, P=0.048) and better contact with the medial surface of the mandible ( t=2.22, P=0.020). The students who received anesthesia reported less pain during injection ( t=1.99, P=0.029) and better anesthesia of the inferior alveolar nerve ( t=3.36, P=0.039) in the experimental group. Teacher assessment revealed that the experimental group had a significantly lower failure rate of inferior alveolar nerve block than the control group ( χ2=4.40, P=0.036). Conclusions:The virtual simulation system can optimize the experimental teaching of oral local nerve block anesthesia and can achieve a satisfactory teaching effect.

Objective To compare the efficiency of nomogram and different machine learning algo-rithms for constructing the dental caries predictive models for middle-aged and elderly people.Methods The multi-stage stratified random sampling method was used to select 510 middle-aged and elderly people from Nanning City,Guigang City and Chongzuo City as the research subjects for conducting the questionnaire sur-vey and oral cavity examination.The univariate analysis and Lasso regression were used to screen the related variables,and the multivariate logistic regression analysis was used to determine the final independent influen-cing factors.Based on the salient features,the nomogram predictive model was established,and the seven ma-chine learning algorithms,including linear discriminant analysis (LDA),partial least squares (PLS),range Doppler algorithm (RDA),generalized linear models (GLM),random forest (RF),support vector machine (SVM) kernel function (SVM-Radial),and SVM linear kernel function (SVM-Linear),were used to construct the seven kinds of dental caries risk predictive models.The area under the receiver operating characteristic (ROC) curve (AUC) was adopted to evaluate the predictive performance of various models and the predictive performance of models constructed using different variable screening methods.Results The detection rate of dental caries in middle-aged and elderly people was 71.18％.After feature screening,the five predictive factors were ultimately retained,which were the age (OR=0.945,95％CI:0.917-0.973),brushing frequency (OR=0.688,95％CI:0.475-0.997),whether having teeth cleaning in the past one year (OR=0.303,95％CI:0.103-0.890),number of remaining teeth (OR=1.062,95％CI:1.038-1.087) and oral health assess-ment tool (OHAT) score (OR=1.363,95％CI:1.234-1.505).The results of comparison of various models showed that the predictive model constructed by the RF algorithm performed the best,the median of AUC was 0.747,followed by the nomogram,and the median of AUC was 0.733.The median of AUCs in the predic-tion model constructed by single factor+Lasso+multivariate logistic (Lasso+logistic) screening independent variables were higher than those constructed by RF algorithm screening independent variables.ConclusionBased on Lasso+logistic screening variables,RF provide more reliable predictive efficiency in predicting dental caries in middle-aged and elderly people than nomogram and the other machine learning algorithms.

Objective To develop a mental fatigue detection method for spaceflight environments in response to the mental fatigue problem that may arise during the on-orbit period of astronauts.Methods In the ground-based research,we used a 36-hour sleep deprivation experiment to induce mental fatigue state.Then the electroencephalogram(EEG)data and questionnaire scores were collected from 45 subjects.Finally,a series of EEG features relevant to mental fatigue were calculated and a mental fatigue detection method constructed with Random Forest Classifier to differentiate the mental fatigue level.Results The average accuracy over 85%was achieved in both ground-based and space-based experimental data and each evaluation index had consistent trend.Conclusion The mental fatigue detection method based on EEG signals is suited to the differences in environment and tasks,and shows precise and stable performance in both ground-based experiment and space-based one,which is feasible to be applied in spaceflight environments in the future.

Objective To explore the value of artificial intelligence federated learning system based on chest X-ray films for pathogen diagnosis of community-acquired pneumonia(CAP)in children.Methods Totally 900 cases of CAP children from 2 hospitals were retrospectively enrolled,including bacterial,viral and mycoplasma CAP(each n=300),and chest posteroanterior X-ray films were collected.Meanwhile,chest posteroanterior X-ray films of 5856 children from the publicly available dataset GWCMCx were collected,including 4273 CAP images and 1583 healthy chest images.All above 6756 images were divided into training set(n=5359)and validation set(n=1397)at the ratio of 8∶2.Then a pathogen diagnosis model of children CAP was established based on attention mechanism.Binary and ternary diagnostic algorithms were designed,and federated deployment training was performed.The efficacy of this system for pathogen diagnosis of children CAP was analyzed and compared with DenseNet model.Results Based on all data,the accuracy of the obtained artificial intelligence federated learning system model for diagnosing children CAP was 97.00%,with the area under the curve(AUC)of 0.990.Based on hospital data,the AUC of this system using single imaging data and clinical-imaging data for pathogen diagnosis of children CAP was 0.858 and 0.836,respectively,both better than that of DenseNet model(0.740,both P＜0.05).Conclusion The artificial intelligence federated learning system based on chest X-ray films could be used for pathogen diagnosis of children CAP.

In recent years, the application of minimal residual disease (MRD) in solid tumors has gained widespread attention. MRD typically refers to the presence of residual cancer cells that remain undetectable by imaging after curative treatments, such as surgical resection. The presence of MRD post-surgery is significantly associated with an increased risk of tumor recurrence. In colorectal cancer, circulating tumor DNA (ctDNA) serves as an effective marker for assessing MRD, particularly in non-metastatic (stages I-III) colorectal cancer. As a real-time, accurate, and convenient biomarker, ctDNA can effectively predict tumor recurrence, guide postoperative adjuvant chemotherapy decisions, and provide crucial information for recurrence monitoring. The application prospects of ctDNA detection technology are vast, promising more precise and individualized treatment plans for colorectal cancer patients. This article comprehensively analyzes the progress in the application of ctDNA for detecting MRD in non-metastatic colorectal cancer patients, elaborates on its guiding role in clinical treatment decisions, and envisions the future development directions in this field.

Tamoxifen(TAM)has been widely used for the treatment of ER+breast cancer.However,the inevitable emergence of resistance to tamoxifen obstructs the successful treatment of this cancer.The tumor suppressor gene N-myc downstream-regulated gene 2(NDRG2)plays a significant role in the de-velopment of ER+breast cancer.However,it is unclear whether NDRG2 participates in mediating TAM resistance in ER+breast cancer.Here,we investigate the expression of NDRG2 mRNA and protein in TAM-sensitive and TAM-resistant ER+breast cancer cells.The results of immunoblotting experiments re-vealed a negative correlation between NDRG2 expression and TAM resistance ability in ER+breast cancer cells(P＜0.001).CCK-8 cell viability assays and soft agar colony formation assays showed that NDRG2 overexpression in TAM resistant cells significantly reduced the TAM IC50 value and the soft agar colony formation rate(P＜0.001).For the mechanism,the ERAD reporter protein assays showed that NDRG2 overexpression upregulated the expression of the ERAD reporter protein CD3ε-YFP and increased the lev-els of spliced XBP1s mRNA,leading to severe endoplasmic reticulum stress in TAM resistant cells(P＜0.001).Immunoblot analysis confirmed that overexpression of NDRG2 significantly increased the level of phosphorylation of the endoplasmic reticulum stress sensor IRE 1α and the expression levels of its down-stream protein factors,including ERdj4,P58IPK,EDEM and PDIA5(P＜0.001).The in vivo xenograft tumor experiments in mice further verified that NDRG2 overexpression significantly inhibited the growth of resistant tumors,which enhanced the therapeutic effect of TAM(P＜0.001).These findings indicate that increasing NDRG2 expression and triggering severe endoplasmic reticulum stress upon TAM treatment can reverse the resistance of ER+breast cancer cells to TAM and inhibits the growth of ER+breast canc-er tumors.Our results provide valuable new insights and potential targets for improving the clinical man-agement of TAM-resistance and prognosis in ER+breast cancer.