Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.

Objective: To investigate the objective characteristics of tongue manifestation in different stages of damp-heat syndrome in diabetic kidney disease (DKD). Methods: A cross-sectional study enrolled 134 patients with DKD G3-5 stages who met the diagnostic criteria for damp-heat syndrome in DKD. The patients were treated at Dongzhimen Hospital, Beijing University of Chinese Medicine, from May 2023 to January 2024. The patients were divided into three groups: DKD G3, DKD G4, and DKD G5 stage, with 53, 33, and 48 patients in each group, respectively. Clinical general data (gender, age, and body mass index) and damp-heat syndrome scores were collected from the patients. The YZAI-02 traditional Chinese medicine (TCM) AI Tongue Image Acquisition Device was used to capture tongue images from these patients. The accompanying AI Open Platform for TCM Tongue Diagnosis of the device was used to analyze and extract tongue manifestation features, including objective data on tongue color, tongue quality, coating color, and coating texture. Clinical data and objective tongue manifestation characteristics were compared among patients with DKD G3-5 based on their DKD damp-heat syndrome status. Results: No statistically significant difference in gender or body mass index was observed among the three patient groups. The DKD G3 stage group had the highest age (P<0.05). The DKD G3 stage group had a lower score for symptoms of poor appetite and anorexia(P<0.05) than the DKD G5 group. No statistically significant difference was observed in damp-heat syndrome scores among the three groups. Compared with the DKD G5 stage group, the DKD G3 stage group showed a decreased proportion of pale color at the tip and edges of the tongue (P<0.05). The DKD G4 stage group exhibited an increased proportion of crimson at the root of the tongue, a decreased proportion of thick white tongue coating at the root, a decreased proportion of pale color at the tip and edges of the tongue, an increased hue value (indicating color tone) of the tongue color in the middle, an increased brightness value (indicating color lightness) of the tongue coating color in the middle, and an increased thickness of the tongue coating (P<0.05). No statistically significant difference was observed in other tongue color proportions, color chroma values, body characteristics, coating color proportions, coating color chroma values, and coating texture characteristics among the three groups. Conclusion Tongue features differ in different stages of DKD damp-heat syndrome in multiple dimensions, enabling the inference that during the DKD G5 stage, the degree of qi and blood deficiency in the kidneys, heart, lungs, liver, gallbladder, spleen, and stomach is prominent. Dampness is more likely to accumulate in the lower jiao, particularly in the kidneys, whereas heat evil in the spleen and stomach is the most severe. These insights provide novel ideas for the clinical treatment of DKD.

Objective To predict the radiation impact of discharging wastewater containing uranium within the specified limit generated during the normal operation of a new production line at a nuclear fuel plant on the receiving water body and its downstream, and to provide a reference for the management of radioactive liquid effluent discharge from nuclear facilities. Methods Based on the technical guidelines for environmental impact assessment, literature on radiation environmental impact assessment, and data collected from on-site investigations, appropriate hydrological parameters and prediction models were selected to analyze and predict the variation pattern of radioactive nuclide uranium along the receiving water body and the radiation exposure of nearby residents. Results The maximum increase in uranium concentration in the receiving water body and its downstream caused by the discharge of uranium-containing wastewater was 1.14 μg/L. The maximum predicted concentration was 2.75 μg/L after adding the background data of the water body. The resulting maximum individual annual effective dose for the public was 1.49 × 10⁻⁴ mSv/a. Conclusion The maximum predicted uranium concentration in the receiving water body and its downstream is lower than the uranium concentration limit of 30 μg/L specified in the Standards for Drinking Water Quality (GB 5749-2022). The maximum individual annual effective dose for the public is much lower than the control value of 0.2 mSv/a specified in the Radiation Protection Regulations for Uranium Processing and Fuel and Fuel Manufacturing Facilities (EJ 1056-2018). The radiation impact is acceptable.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective To study the chemical constituents of Fallopia aubertii L.Henry Holub. Methods The chemical constituents of Fallopia aubertii L.Henry Holub. were separated and purified by online two-dimensional preparative liquid chromatography and identified by physical and chemical constants and spectral analysis. The inhibitory activities on xanthine oxidase were determined by ultraviolet spectrophotometry. Results Ten compounds were isolated from the extract of Fallopia aubertii L.Henry Holub, including isotachioside（1）, 3,4,5-trimethoxyphenyl-（6'-O-galloyl）-O-β-D-Glucopyranoside（2）, 1-hydroxy-,4,5-1-O-[6'-O-（4''-carboxy-1'',3'',5'trihydrotrimethoxyphenylxy）-phenyl]-β-D-glucopyranoside（3）, myricetrin（4）, myricetin（5）, rutin（6）, quercetin-3-O-β-D-galactoside（7）, quercetin-3-O-β-D-glucopyranoside（8）, lyciumideA（9）, and N-trans-Feruloyltyramine（10）. The inhibitory activity test results showed that the IC₅₀ of compound 5 was 15.92 μmol/L, and the IC₅₀ of compound 6 was 87.36 μmol/L. Conclusion Compounds 1,2,3,4 and 8 were isolated from Medicago polymorpha for the first time. Compounds 5 and 6 had xanthine oxidase inhibitory activity.

Objective To analyze the influencing factors of survival of patients with airway stenosis requiring clinical interventions after lung transplantation. Methods Clinical data of 66 patients with airway stenosis requiring clinical interventions after lung transplantation were retrospectively analyzed. Univariate and multivariate Cox’s regression models were adopted to analyze the influencing factors of survival of all patients with airway stenosis and those with early airway stenosis. Kaplan-Meier method was used to calculate the overall survival and delineate the survival curve. Results For 66 patients with airway stenosis, the median airway stenosis-free time was 72 (52,102) d, 27% (18/66) for central airway stenosis and 73% (48/66) for distal airway stenosis. Postoperative mechanical ventilation time [hazard ratio (HR) 1.037, 95% confidence interval (CI) 1.005-1.070, P=0.024] and type of surgery (HR 0.400, 95%CI 0.177-0.903, P=0.027) were correlated with the survival of patients with airway stenosis after lung transplantation. The longer the postoperative mechanical ventilation time, the higher the risk of mortality of the recipients. The overall survival of airway stenosis recipients undergoing bilateral lung transplantation was better than that of their counterparts after single lung transplantation. Subgroup analysis showed that grade 3 primary graft dysfunction (PGD) (HR 4.577, 95%CI 1.439-14.555, P=0.010) and immunosuppressive drugs (HR 0.079, 95%CI 0.022-0.287, P<0.001) were associated with the survival of patients with early airway stenosis after lung transplantation. The overall survival of patients with early airway stenosis after lung transplantation without grade 3 PGD was better compared with that of those with grade 3 PGD. The overall survival of patients with early airway stenosis after lung transplantation treated with tacrolimus was superior to that of their counterparts treated with cyclosporine. Conclusions Long postoperative mechanical ventilation time, single lung transplantation, grade 3 PGD and use of cyclosporine may affect the survival of patients with airway stenosis after lung transplantation.

ObjectiveTo investigate the effect of curcumin on the cycle arrest of human colon cancer HCT116 cells and decipher the possible molecular mechanism. MethodThe methyl thiazolyl tetrazolium （MTT） method was employed to examine the effects of curcumin （0， 12.5， 25， 50， 75， 100 μmol·L^-1） and 5-fluorouracil （5-FU， 600 μmol·L^-1） on the proliferation of HCT116 cells at different time points （24， 48， 72 h）. Flow cytometry was employed to examine the cycle of HCT116 cells treated with curcumin （0， 25， 50， 75 μmol·L^-1） and 5-FU. Western blot was employed to determine the expression of proteins in the Janus kinase 1 （JAK1）/signal transducer and activator of transcription 1 （STAT1） /cyclin-dependent kinase inhibitor 1A （p21） pathway in HCT116 cells. The binding of STAT1 to p21 promoter region was detected by chromatin immunoprecipitation （ChIP）. Small interfering RNA （siRNA） was employed to measure the role of STAT1 in regulating the expression of p21 and that of JAK1 in regulating the activation of STAT1 by Western blot and cellular immunofluorescence， respectively. ResultCompared with the blank group， the HCT-116 cells treated with curcumin and 5-FU showed decreased viability （P<0.05）， increased proportions of cells in the G₀/G₁ phase （P<0.05）， decreased proportions of cells in the S phase and G₂/M phase （P<0.05）， down-regulated protein level of phosphorylated p21 （P<0.05）， and up-regulated protein level of p21 （P<0.05）. Compared with the curcumin group， the p21 siRNA+ curcumin group presented decreased proportion of cells in G₀/G₁ phase （P<0.05）. Compared with the blank group， curcumin elevated the level of phosphorylated STAT1 （p-STAT1） （P<0.05）. Compared with the curcumin group， the curcumin + STAT1 siRNA group showcased up-regulated protein level of p21 in HCT116 cells （P<0.05）. The mechanism study showed that curcumin treatment enhanced the enrichment of STAT1 in the p21 promoter region （P<0.05） compared with the blank group. Compared with the blank group， curcumin up-regulated the level of phosphorylated JAK1 （p-JAK1） （P <0.05）. Compared with the curcumin group， the curcumin + STAT1 siRNA group demonstrated up-regulated protein levels of p-STAT1 and p21 in HCT116 cells （P<0.05）. ConclusionCurcumin may induce the cycle arrest of human colon cancer HCT116 cells by activating the JAK1/STAT1/p21 signaling pathway.

OBJECTIVE To analyze the chemical constituents and components absorbed into plasma of the extract of Ardisia crenata and to elucidate its possible pharmacodynamic material basis. METHODS Overall， 12 rats were randomly assigned to the blank group （n＝6） and A. crenata group （n＝6） by the paired comparison method. The drug was administered once daily in the morning and afternoon for three days. Serum samples were prepared from serum after redosing on 4th day. The UPLC-QE-HF-MS/ MS was used to analyze and identify the chemical constituents in A. crenata extract and serum samples. Compound Discoverer 3.0 was employed for retention time correction， peak identification， and peak extraction. According to the secondary mass spectrometry information， the Thermo mzCloud online and Thermo mzVault local databases， referring to the relevant literature and control quality spectrum information were used to preliminarily identify the chemical constituents and components absorbed into plasma of A. crenata. RESULTS A total of 34 compounds were identified from the extract of A. crenata， mainly coumarins， flavonoids， organic acids， amino acids， including bergenin， quercetin， gallic acid， L-pyroglutamic acid， etc. Besides， 5 components absorbed into plasma were identified from serum samples: L-pyroglutamic acid， syringic acid， bergenin， cinnabar root saponin A， and mycophenolic acid. CONCLUSIONS L-pyroglutamic acid， syringic acid， bergenin， cinnabar root saponin A， and mycophenolic acid may act as the pharmacodynamic material basis of A. crenata.

Three carboline fluorescent probes F1-F3 were designed and synthesized, based on lead compound JYJ-19, an antifungal compound discovered previously by our group. The antifungal activity in vitro results showed that compound F1 had moderate antifungal activity (MIC₈₀ = 32 μg·mL^-1). The stokes shift of F1 is 70 nm. The fluorescent probe F1 has good optical properties and can be used for fluorescence imaging research. Subcellular localization experiments results showed that F1 was enriched in the mitochondria of fungal cells. The detection of intracellular reactive oxygen species levels shows that JYJ-19 enhances intracellular reactive oxygen species levels. The above results indicated that carboline compounds could exert antifungal effects by acting on fungal mitochondria.