Objective To construct a nomogram to predict the prognosis of patients with gallbladder cancer(GBC).Methods The clinicopathological data of GBC patients were extracted from the SEER database,and the independent prognostic factors of GBC patients were analyzed by Cox regression,and a nomogram was constructed.Finally,the column diagrams in the training queue and validation queue are verified.Results Age,T stage,M stage,histological grade,radiotherapy,surgery and tumor size were independent prognostic factors in GBC patients,and the differences were statistically significant(P＜0.05).In the training cohort,the C index was 0.735(95％CI=0.721～0.749),and the AUC values at 1,3 and 5 years were 0.821,0.820 and 0.833,respectively.In the verification group,the C index was 0.733(95％CI=0.711～0.755),and the AUC values for 1,3 and 5 years were 0.816,0.807 and 0.827,respectively.The calibration curve shows that the predicted values of the nomogram are in good agreement with the observed values.The decision curve shows that the nomogram model has better prediction ability than TNM staging system.Conclusion The constructed dynamic prognosis nomogram of GBC patients has high accuracy and reliability.

Objective: To investigate the changes of microorganisms and metabolites in joint effusion of patients with Rheumatoid arthritis(RA), Osteoarthritis(OA) and Urarthritis(UA). To provide new ideas for the study of the effect of microbiota on the pathogenesis of arthritis. Methods: Joint effusion samples were collected from 20 patients with RA, 20 patients with OA, and 20 patients with UA. 16S rRNA gene sequencing and untargeted ultra-high performance Liquid chromatography-mass spectrometry(LC-MS) were used to explore the differences in microorganisms and metabolites among the three groups. Pearson correlation analysis was used to detect the correlation between effusion microbiota and metabolites. Results: There were differences in microbial diversity and microbiota composition among the three groups. Combined with VIP>1 from OPLS-DA andP<0.05 from two-tailed Students t-test, 45 differential metabolites(Between RA and OA groups), 38 differential metabolites(Between UA and OA groups) and 16 differential metabolites(Between RA and UA groups), were identified. GO analysis and KEGG pathway analysis showed that the differential metabolic pathways among the three groups were mainly concentrated in citric acid cycle(TCA cycle), nucleotide metabolism, amino acid metabolism and glycolysis pathway. Correlation analysis of joint effusion microbiota and metabolites suggested that bacteria enriched in the three groups of joint effusion, such asPrevotella,Clostridium ruminosus,Prevotellaceae＿UCG-001, were related to many key metabolites such as lysozyme, uric acid, glucose, and L-glutamine. Conclusion This study shows that there are a variety of bacterial flora in joint cavity effusion of RA, OA, and UA patients, and the differential metabolites produced by them are involved in the pathogenesis of the three types of arthritis by affecting a variety of metabolic pathways.

Clinical trials of drugs for rare diseases face special challenges such as a limited number of patients,difficult recruitment,long trial period,and frequent video interviews during the trial.Therefore,in the clinical operation of rare diseases,a decentralized clinical trials(DCT)model based on the"patient-cen-tred"research and development concept is implemented.With the help of decentralized elements and digital health technology,the barriers of geographical restrictions can be overcome and subjects do not have to be limit-ed to traditional clinical trial sites(hospitals/research centers),which can significantly reduce the burden on subjects,increase their representation,and obtain a wider range of scientific research data.To guide the indus-try's scientific and standardized application of DCT in the research and development of drugs for rare diseases,the Center for Drug Evaluation of the National Medical Products Administration(NMPA)organized the stake holders to draft the Technical Guideline for the Application of Decentralized Clinical Trials in the Research and Development of Drugs for Rare Diseases.This guideline provides scientific recommendations for the development and implementation of DCT for rare disease drugs.It aims to solve the difficult and key problems during rare disease drug research and development,improve the efficiency and optimize patient experience.This article,combining the research and development concepts in the guideline,explains the current research and develop-ment thinking on the application of DCT in the research and development of rare disease drugs,with a view of providing reference for the industry.

Objective To investigate the effects of forsythinol(Fo)on the expression of matrix metalloproteinase-2(MMP2)in hepatoma cells through Janus kinase 2/signal transduction and transcriptional activator 3(JAK2/STAT3)signaling pathway.Methods SMMC-7721 cells were divided into experimental-L,-M,-H groups,control group,inhibitor group and activator group.The control group was added with equal volume dimethyl sulfoxide(DMSO);the experimental-L,-M,-H groups were treated with 50,200,500 μg·mL-1 Fo;and the inhibitor group was added with 50 μmol·L-1 JAK2/STAT3 inhibitor AG490 based on the experimental-M group.In the activator group,10 μmol·L-1 JAK2/STAT3 activator Broussonin E was added to the experimental-M group.Apoptosis was detected by deoxynucleotide terminal transferase-mediated dUTP notch end labeling(TUNEL);protein expression was detected by Western blot;real-time quantitative polymerase chain reaction(qRT-PCR)was used to detect mRNA levels.Results The apoptosis rates of control group,experimental-M group,inhibitor group and activator group were(19.94±4.88)％,(27.04±5.27)％,(15.36±3.40)％and(46.66±7.89)％,respectively;the relative expression levels of phosphorylated JAK2 protein were 1.00±0.13,0.73±0.11,1.33±0.17 and 0.26±0.07,respectively;the relative expression levels of phosphorylated STAT3 protein were 1.00±0.12,0.27±0.04,0.88±0.13 and 0.12±0.04,respectively;the mRNA relative expression levels of MMP2 were 1.00±0.14,0.68±0.08,1.17±0.17 and 0.51±0.09,respectively.Compared with experimental-M group and control group,inhibitor group and activator group,there were statistically significant differences(P＜0.05,P＜0.001).Conclusion Fo promotes apoptosis of hepatocellular carcinoma cells,and its mechanism may be related to the effect of Fo on the expression of MMP2 by regulating JAK2-STAT3 signaling pathway.

Periodontitis is a common chronic inflammatory disease that causes the periodontal bone destruction and may ultimately result in tooth loss.With the progression of periodontitis,the osteoimmunology microenvironment in periodontitis is damaged and leads to the formation of pathological alveolar bone resorption.CD301b+macrophages are specific to the osteoimmunology microenvironment,and are emerging as vital booster for conducting bone regeneration.However,the key upstream targets of CD301b+macrophages and their potential mechanism in periodontitis remain elusive.In this study,we concentrated on the role of Tim4,a latent upstream regulator of CD301b+macrophages.We first demonstrated that the transcription level of Timd4(gene name of Tim4)in CD301b+macrophages was significantly upregulated compared to CD301b-macrophages via high-throughput RNA sequencing.Moreover,several Tim4-related functions such as apoptotic cell clearance,phagocytosis and engulfment were positively regulated by CD301b+macrophages.The single-cell RNA sequencing analysis subsequently discovered that Cd301b and Timd4 were specifically co-expressed in macrophages.The following flow cytometric analysis indicated that Tim4 positive expression rates in total macrophages shared highly synchronized dynamic changes with the proportions of CD301b+macrophages as periodontitis progressed.Furthermore,the deficiency of Tim4 in mice decreased CD301b+macrophages and eventually magnified alveolar bone resorption in periodontitis.Additionally,Tim4 controlled the p38 MAPK signaling pathway to ultimately mediate CD301b+macrophages phenotype.In a word,Tim4 might regulate CD301b+macrophages through p38 MAPK signaling pathway in periodontitis,which provided new insights into periodontitis immunoregulation as well as help to develop innovative therapeutic targets and treatment strategies for periodontitis.

OBJECTIVES: Several countries have successfully eliminated measles, and China is making significant strides toward achieving this goal. This study focused on investigating the patterns of measles infections in Zhejiang Province, China, as well as control measures. The objective was to provide valuable insights that could contribute to the development of nationwide elimination strategies. METHODS: We analyzed measles surveillance data from 2005 to 2022 in Zhejiang Province. We utilized a joinpoint regression model to examine trends in measles. Additionally, we employed SaTScan version 9.5 to identify spatial-temporal clusters. Finally, we used an age-period-cohort model to assess the effects of age, period, and cohort. RESULTS: The age-standardized incidence rate (ASIR) of measles infection in Zhejiang Province from 2005 to 2022 was 5.24 per 100,000, showing a consistent and significant downward trend with an annual percentage change of -24.93% (p<0.05). After 2020, the ASIR for measles infection fell to below 0.1 per 100,000. The majority of measles cases occurred in individuals either without an immunization history or with an unknown immunization status, representing 41.06% and 41.40% of the cases from 2010 to 2022, respectively. According to data from the National Measles Surveillance System, the annual rate of discarded measles cases from 2009 to 2014, and the annual rate of discarded measles and rubella cases from 2015 to 2022, were both above 2 per 100,000, indicating the high sensitivity of the measles surveillance system. CONCLUSIONS The significant reduction in measles incidence from 2005 to 2022 demonstrates substantial progress in Zhejiang Province towards the elimination of measles.

OBJECTIVES: Several countries have successfully eliminated measles, and China is making significant strides toward achieving this goal. This study focused on investigating the patterns of measles infections in Zhejiang Province, China, as well as control measures. The objective was to provide valuable insights that could contribute to the development of nationwide elimination strategies. METHODS: We analyzed measles surveillance data from 2005 to 2022 in Zhejiang Province. We utilized a joinpoint regression model to examine trends in measles. Additionally, we employed SaTScan version 9.5 to identify spatial-temporal clusters. Finally, we used an age-period-cohort model to assess the effects of age, period, and cohort. RESULTS: The age-standardized incidence rate (ASIR) of measles infection in Zhejiang Province from 2005 to 2022 was 5.24 per 100,000, showing a consistent and significant downward trend with an annual percentage change of -24.93% (p<0.05). After 2020, the ASIR for measles infection fell to below 0.1 per 100,000. The majority of measles cases occurred in individuals either without an immunization history or with an unknown immunization status, representing 41.06% and 41.40% of the cases from 2010 to 2022, respectively. According to data from the National Measles Surveillance System, the annual rate of discarded measles cases from 2009 to 2014, and the annual rate of discarded measles and rubella cases from 2015 to 2022, were both above 2 per 100,000, indicating the high sensitivity of the measles surveillance system. CONCLUSIONS The significant reduction in measles incidence from 2005 to 2022 demonstrates substantial progress in Zhejiang Province towards the elimination of measles.

OBJECTIVES: Several countries have successfully eliminated measles, and China is making significant strides toward achieving this goal. This study focused on investigating the patterns of measles infections in Zhejiang Province, China, as well as control measures. The objective was to provide valuable insights that could contribute to the development of nationwide elimination strategies. METHODS: We analyzed measles surveillance data from 2005 to 2022 in Zhejiang Province. We utilized a joinpoint regression model to examine trends in measles. Additionally, we employed SaTScan version 9.5 to identify spatial-temporal clusters. Finally, we used an age-period-cohort model to assess the effects of age, period, and cohort. RESULTS: The age-standardized incidence rate (ASIR) of measles infection in Zhejiang Province from 2005 to 2022 was 5.24 per 100,000, showing a consistent and significant downward trend with an annual percentage change of -24.93% (p<0.05). After 2020, the ASIR for measles infection fell to below 0.1 per 100,000. The majority of measles cases occurred in individuals either without an immunization history or with an unknown immunization status, representing 41.06% and 41.40% of the cases from 2010 to 2022, respectively. According to data from the National Measles Surveillance System, the annual rate of discarded measles cases from 2009 to 2014, and the annual rate of discarded measles and rubella cases from 2015 to 2022, were both above 2 per 100,000, indicating the high sensitivity of the measles surveillance system. CONCLUSIONS The significant reduction in measles incidence from 2005 to 2022 demonstrates substantial progress in Zhejiang Province towards the elimination of measles.

OBJECTIVES: Several countries have successfully eliminated measles, and China is making significant strides toward achieving this goal. This study focused on investigating the patterns of measles infections in Zhejiang Province, China, as well as control measures. The objective was to provide valuable insights that could contribute to the development of nationwide elimination strategies. METHODS: We analyzed measles surveillance data from 2005 to 2022 in Zhejiang Province. We utilized a joinpoint regression model to examine trends in measles. Additionally, we employed SaTScan version 9.5 to identify spatial-temporal clusters. Finally, we used an age-period-cohort model to assess the effects of age, period, and cohort. RESULTS: The age-standardized incidence rate (ASIR) of measles infection in Zhejiang Province from 2005 to 2022 was 5.24 per 100,000, showing a consistent and significant downward trend with an annual percentage change of -24.93% (p<0.05). After 2020, the ASIR for measles infection fell to below 0.1 per 100,000. The majority of measles cases occurred in individuals either without an immunization history or with an unknown immunization status, representing 41.06% and 41.40% of the cases from 2010 to 2022, respectively. According to data from the National Measles Surveillance System, the annual rate of discarded measles cases from 2009 to 2014, and the annual rate of discarded measles and rubella cases from 2015 to 2022, were both above 2 per 100,000, indicating the high sensitivity of the measles surveillance system. CONCLUSIONS The significant reduction in measles incidence from 2005 to 2022 demonstrates substantial progress in Zhejiang Province towards the elimination of measles.

Objective:To construct and validate a risk prediction model for immune checkpoint inhibitor-associated pneumonia (CIP) using machine learning algorithms and the nomogram, aiming to provide an accurate and intuitive method to assist nurses in screening people at high risk of developing CIP.Methods:This was a retrospective case -control study. A total of 230 oncology patients treated with immune checkpoint inhibitors attending Zhujiang Hospital of Southern Medical University from January 2019 to February 2022 were collected using the hospital's electronic medical record system. The prediction models were built using five machine learning algorithms and nomogram. The models were then validated on a separate test set, and their differentiation and stability were assessed using evaluation indices like AUC and accuracy rate.Results:Underlying lung disease, smoking history, serum albumin≤35 g/L and radiotherapy history were identified as important influencing factors of CIP in all six models. The AUC of K nearest neighbor, support vetor machines (SVM), naive Bayesian, decision tree and random forest models predicted CIP were 0.647, 0.696, 0.930, 0.870, and 0.934, respectively. The AUC of the model created by the nomogram was 0.813, which was lower than the best random forest model in the machine learning algorithm, but with good predictive performance (AUC=0.934).Conclusions:The nomogram model can assess the patient′s risk more intuitively, but the risk prediction model of CIP based on a machine learning algorithm has a higher diagnostic value. It is suggested that the accuracy and usefulness of the prediction model can be increased by combining the nomogram's foundation with the machine learning algorithm.