OBJECTIVE To systematically review the current application of discrete choice experiment （DCE） in the value assessment and preference measurement of orphan medicinal product （OMP）， and to provide a reference for the standardized use of this methodology in China. METHODS The systematic search was conducted across Chinese and English databases including CNKI， Wanfang Data， VIP， CBM， PubMed， Web of Science， Medline， and Embase. Original studies that employed DCE to evaluate the value or preferences related to OMP were included. The methodological quality and reporting completeness of the included studies were assessed using the ISPOR Conjoint Analysis Checklist and the DIRECT Checklist， respectively. Respondent populations， attribute setting， and the relative importance of attributes were summarized and analyzed. RESULTS Eight eligible studies were included； all studies demonstrated high-quality reporting and methodological rigor. Respondents comprised the general public， patients/caregivers， policymakers， and other stakeholders. The number of DCE attributes ranged from 4 to 13 （median＝7.5）. Through thematic synthesis， these attributes were categorized into three dimensions， namely “disease-related” “treatment-related” and “economic/financial-related” along with 14 secondary criteria. The most frequently included secondary criteria were treatment efficacy （13 occurrences）， disease severity （9 occurrences）， safety （7 occurrences）， unmet medical need （6 occurrences）， and treatment cost （5 occurrences）. Rankings of relative importance identified treatment efficacy as the most valued criterion across most studies， followed by health insurance financing. CONCLUSIONS DCE applications in the value assessment of OMP have begun to converge on a relatively consistent core attribute framework and selection preference. Future research should further promote the use of DCE to inform attribute and criterion selection in multi-criteria decision analysis frameworks for OMP.

ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

ObjectiveTo investigate the association between neutrophil-to-lymphocyte and platelet ratio （NLPR） and recompensation in patients with hepatitis B cirrhotic ascites， and to establish an individualized risk prediction model. MethodsThe patients with hepatitis B cirrhotic ascites who were hospitalized in Department of Gastroenterology， The General Hospital of Western Theater Command of Chinese PLA， from January 2015 to December 2022 were enrolled. General information and laboratory markers were collected， and NLPR was calculated. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test or the chi-square test with correction was used for comparison of categorical data between two groups. The subjects were randomly divided into a training set and a validation set at a ratio of 7∶3. In the training set， univariate and multivariate binary Logistic regression analyses were used to investigate the independent influencing factors for recompensation in patients with hepatitis B cirrhotic ascites， and a nomogram was established； the receiver operating characteristic （ROC） curve was used to assess the value of the new model in predicting recompensation in patients with hepatitis B cirrhotic ascites， and the Delong test was used for comparison of the area under the ROC curve （AUC）. The calibration curve and the decision curve were plotted for the model， and the model was assessed in terms of degree of fitting and predictive benefits. ResultsA total of 360 patients were enrolled， among whom134 achieved recompensation. There were 252 patients in the training set and 108 patients in the validation set， and there were no significant differences in baseline characteristics between the two groups （all P>0.05）. The Logistic regression analysis showed that the onset of hepatic encephalopathy （odds ratio ［OR］=0.066， 95% confidence interval ［CI］： 0.008 — 0.545， P=0.012）， NLPR （OR=0.950， 95%CI： 0.912 — 0.989， P=0.012）， alpha-fetoprotein （OR=1.012， 95%CI： 1.005 — 1.020， P<0.001）， and albumin （OR=1.096， 95%CI： 1.031 — 1.166， P=0.003） were independent influencing factors for recompensation in patients with hepatitis B cirrhotic ascites. The above four factors were included in a nomogram predictive model， which had an AUC of 0.776， a sensitivity of 66.5%， and a specificity of 76.3% in the training set and an AUC of 0.746， a sensitivity of 63.4%， and a specificity of 75.7% in the validation set， while Model for End-Stage Liver Disease score， Child-Pugh score， and albumin-bilirubin score had an AUC of 0.574， 0.628， and 0.621， respectively. The nomogram model had a better performance than the other three scores in predicting recompensation in patients with hepatitis B cirrhotic ascites （Z=4.191， 3.369， and 3.527， P<0.001， P=0.001， and P<0.001）. The calibration curve and the decision curve showed that the model had a good degree of fitting， and the decision made using this model could bring net benefits. ConclusionNLPR has a good value in predicting recompensation in patients with hepatitis B cirrhotic ascites， and the nomogram model established can help to predict recompensation in such patients in clinical practice.

Objective:To prepare rabbit polyclonal antibody against human FAM21 and analyze antibody specificity.Methods:Using the plasmid encoding human FAM21 full-length gene as a template,the nucleotide sequence of its 2 431～3 006 base was amplified by PCR and connected to the pGEX-6p-1 prokaryotic expression vector to construct pGEX-6P-1-FAM21 recombinant plasmid expressing the 811～1 002 amino acid fragment of FAM21.The recombinant plasmid was transformed into BL21(DE3)compe-tent Escherichia coli and was expressed inductively,and the protein was purified using GST fusion protein purification magnetic beads.The purified GST fusion protein was used as an antigen to immunize New Zealand rabbits,and the collected antiserum was purified by an agarose column containing GST protein.The specificity of antibody was detected by Western blot and immunofluorescence assay in stable FAM21 knockdown HeLa cells.Results:The pGEX-6p-1-FAM21 prokaryotic expression plasmid was successfully constructed and induced to express in BL21(DE3)competent Escherichia coli.The purified GST fusion protein had a molecular weight of approxi-mately 50 kD,and the purified antibody titer from immunized New Zealand rabbits was greater than 1∶128 000,with high specificity.Conclusion:The pGEX-6p-1-FAM21 prokaryotic expression plasmid is successfully constructed,and the rabbit polyclonal antibody against human FAM21 is prepared for Western blot and immunofluorescence assay.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

Objective:To establish a Zika virus-infected suckling SD rat model with type Ⅰ interferon receptor deficiency（SD-Ifnar1 -/-［cc］）and provide a novel animal model for investigating Zika virus pathogenesis and developing therapeutic strategies. Methods:Seventeen-day-old male SD-Ifnar1 -/-［cc］rat pups were randomly divided into experimental and control groups（ n=6）. The experimental group received an intraperitoneal injection of Zika virus strain SZ-wiv01（5×10 4 PFU/rat，200 μl），while the control group received an equal volume of phosphate-buffered saline（PBS）. Animals were euthanized via CO 2 asphyxiation on days 12 and 15 post-infection（dpi），and brain，spleen，liver，and testis tissues were harvested. Viral loads and cytokine expression levels were quantified using quantitative real-time PCR qRT-PCR），and histopathological evaluation was performed via HE staining. Results:qRT-PCR analysis revealed no detectable Zika virus RNA（Ct >35）in control tissues. In the experimental group，viral RNA（Ct <35）was detected in the brain，spleen，liver，and testis by day 12，with stable viral loads across tissues by day 15（ P>0.05）. Cytokine profiling demonstrated significant upregulation in the brain at day 12：IFN-β（5.58-fold， P<0.01），IL-6（7.11-fold， P<0.01），and CCL5（3.79-fold， P<0.01）. By day 15，these levels remained elevated（IFN-β：3.07-fold；IL-6：4.04-fold；CCL5：5.22-fold；all P<0.01）. In the liver，IFN-β mRNA decreased to 20% of the control level by day 15（ P<0.05），while IL-6 declined to 24% and CCL5 mRNA dropped to 38% by day 12. No significant cytokine changes were observed in the spleen（ P>0.05）. Testicular tissues exhibited reduced IFN-β mRNA levels（43% of control at day 12，31% at day 15； P<0.05）. Histopathological analysis revealed marked splenomegaly with disrupted splenic corpuscle architecture and lymphocyte depletion，significant inflammatory cell infiltration in hepatic portal areas，and testicular structural disorganization with inflammatory infiltration in Zika-infected rats. Conclusions:The SD-Ifnar1 -/-［cc］suckling rat model is successfully established and validated. This model recapitulates systemic Zika virus infection，tissue-specific pathology，and immune response dynamics，providing a robust platform for elucidating viral pathogenesis and advancing antiviral drug development.

This study was aimed at investigating differentially expressed genes(DEGs)in Clonorchis sinensis(C.sinensis)meta-cercariae and newly excysted juveniles(NEJs)cultured in vitro for 1 hour or 3 hours,through transcriptomic analysis.Our objective was to explore the mechanisms underlying host invasion.Metacercariae were digested and isolated from Pseudorasbora parva infected with C.sinensis.The metacercariae excysted and developed into NEJs in vitro.Subsequently,the mRNA of metacercariae and NEJs cultured in vitro for 1 hour or 3 hours was extracted for transcriptomic sequencing analysis to screen for DEGs,and to conduct GO and KEGG analyses.A protein-protein interaction network(PPI)was constructed to identify hub genes.A total of 1 218 DEGs were de-tected.The main enriched GO terms of DEGs included transcription regulator activity and gated channel activity(primarily K+).The KEGG pathways significantly enriched in DEGs included cholesterol metabolism,lysosome,synthesis,secretion,and action of para-thyroid hormone.ZFAND4-2,BIRC6,and other genes were screened and identified as hub genes through PPI network analysis.Addi-tionally,abundant differential expression of cathepsin-related genes,including Cathepsin L and Cathepsin F,were observed before and after excystment in C.sinensis.Therefore,significant transcriptional level changes occurred in the metacercariae of C.sinensis be-fore and after excystation,and enrichment was observed primarily in signaling pathways,such as activation of growth and material me-tabolism,that regulate parasite growth and development.Meanwhile,biological events conducive to parasite invasion,migration,and adhesion were triggered.

Objective By observing the regulatory effect of Strengthening Spleen and Eliminating Cancer Formula on N6-methyladenosine(m6A)methyltransferase,To explore the effect of Strengthening Spleen and Eliminating Cancer Formula on inhibiting the IRX5 m6A level in colorectal cancer(CRC),the regulatory effect on N6-methyladenosine(m6A)methyltransferase was observed.Methods Clinically,m6A hypermethylated genes in colorectal cancer was analyzed by m6A sequencing of pathological tissues from five CRC patients after radical surgery,looking for protein detection indexes for validation.23 BALB/c nude mice were selected and injected with HCT116 cells to establish a nude-mouse transplantation model of human colorectal cancer.They were divided into Model group,Western medicine group(5-fluorouracil group),Chinese medicine group(Jianpi Xiaoai Formula low-dose group,Jianpi Xiaoai Formula high-dose group),with 6 rats in each group,5 rats in control group.The tumor volume of all groups was compared.The overall methylation level of m6A was detected by colorimetric method.The protein expression levels of METTL3,METTL14,and WTAP,in tumor were detected by Western blot.The SRAMP website was used to predict the m6A sites of IRX5.HCT116 cells were treated with oe-NC,oe-METTL3,sh-NC,and sh-METTL3.The expression of IRX5 protein was detected by Western blot.HCT116 cell line was treated with Jianpi Xiaoai Formula drug-containing serum,and transfected with oe-METTL3 and oe-IRX5.The group was set as followed:control group,Jianpi Xiaoai Formula drug-containing serum group,Jianpi Xiaoai Formula drug-containing serum group+oe-NC,Jianpi Xiaoai Formula drug-containing serum group+oe-METTL3,Jianpi Xiaoai Formula drug-containing serum group+oe-IRX5,cell cloning experiment and Transwell experiment were performed to detect cell proliferation,migration and invasion ability of each group.The protein expression levels of METTL3 and IRX5 were detected by Western blot.Results The results of m6A sequencing of genes showed that the m6A methylation level increased in patients with CRC,and the m6A methylation levels of SOX1 and IRX5 were significantly elevated.Compared with the model group,the tumor volume of Jianpi Xiaoyou Formula high-dose group,low-dose group and 5-Fu group decreased significantly(P<0.01),and the tumor inhibition effect was more obvious with the increase of Jianpi Xiaoai Formula concentration(P<0.01).The methylation level of m6A in Jianpi Xiaoai Formula high dose group,low dose group and 5-Fu group decreased significantly(P<0.01).The SRAMP website predicted that IRX5 contained multiple m6A sites.Overexpression of METTL3 promoted the expression of IRX5 protein(P<0.001),while knockdown of METTL3 inhibited the expression of IRX5 protein(P<0.001).The drug-containing serum of Jianpi Xiaoai Formula could inhibit the protein expression of METTL3 and IRX5(P<0.05)and inhibit the proliferation,migration and invasion of HCT116(P<0.01).Overexpression of METTL3 and IRX5 reversed the inhibitory effect of Jianpi Xiaoai Formula on HCT116 evil phenotype(P<0.01).Conclusion Jianpi Xiaoai Formula may inhibit METTL3 expression mediated IRX5 low expression to inhibit the progression of colorectal cancer.