Objective: To analyze the change trends in the body shape indicators and proportions of students in Harbin from 2010 to 2024, and to investigate ergonomic compatibility of functional dimensions of school desks and chairs with current student shape indicators, so as to provide a reference for revising furniture standards of desks and chairs. Methods: Between September and November of both 2010 and 2024, a combination of convenience sampling and stratified cluster random sampling was conducted across three districts in Harbin, yielding samples of 6 590 and 6 252 students, respectively. Anthropometric shape indicators cluding height, sitting height, crus length, and thigh length-and their proportional changes were compared over the 15-year period. The 2024 data were compared with current standard functional dimensions of school furniture. The statistical analysis incorporated t-test and Mann-Whitney U- test. Results: From 2010 to 2024, average height increased by 1.8 cm for boys and 1.5 cm for girls; sitting height increased by 1.5 cm for both genders; crus length increased by 0.3 cm for boys and 0.4 cm for girls; and thigh length increased by 0.5 cm for both genders. The ratios of sitting height to height, and sitting height to leg length increased by less than 0.1 . The difference between desk chair height and 1/3 sitting height ranged from 0.4-0.8 cm. Among students matched with size 0 desks and chairs, 22.0% had a desk to chair height difference less than 0, indicating that the desk to chair height difference might be insufficient for taller students. The differences between seat height and fibular height ranged from -1.4 to 1.1 cm; and the differences between seat depth and buttock popliteal length ranged from -9.8 to 3.4 cm. Among obese students, the differences between seat width and 1/2 hip circumference ranged from -20.5 to -8.7 cm, while it ranged from -12.2 to -3.8 cm among non obese students. Conclusion Current furniture standards basically satisfy hygienic requirements; however, in the case of exceptionally tall and obese students, ergonomic accommodations such as adaptive seating allocation or personalized adjustments are recommended to meet hygienic requirements.

Traditional Chinese medicine （TCM） therapy has unique clinical advantages in the treatment of atrial fibrillation， mainly reflected in five aspects， improving quality of life， enabling early diagnosis and treatment， promoting cardiac rehabilitation， making up for the limitations of Western medicine， and improving the success rate of catheter ablation. However， there is insufficient evidence in current clinical research. Based on the current status of TCM research in the treatment of atrial fibrillation， it is suggested that future studies should focus on standardized research on syndrome differentiation and classification. This can be achieved through clinical epidemiological surveys， expert consensus， and other methods to establish a unified syndrome differentiation and classification standard for atrial fibrillation. Clinical efficacy evaluation indicators should be standardized， and core outcome measures for clinical research on TCM treatment of atrial fibrillation should be developed through systematic reviews， patient interviews， and other methods. Additionally， clinical research design， implementation， and data management should be improved. By leveraging modern information technologies such as artificial intelligence， the scientific and standardized nature of TCM intervention research on atrial fibrillation can be enhanced， ultimately improving the quality of research.

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.

Taxifolin (Tax) has been proved to be a medicinal edible substance with protective effects against alcoholic liver injury, however, its poor hydrophilicity and permeability have hindered the clinical application of Tax. In this study, we prepared taxifolin-phosphatidylcholine/sodium deoxycholate/PVP-K30 micells (Tax-MLs). Box-Behnken test was used to obtain the optimal preparation process, and Tax-MLs were characterised by transmission electron microscopy and fourier transform infrared spectroscopy. Physicochemical parameters such as proximate micelle concentration, equilibrium solubility and oil-water partition coefficient were determined, and the release pattern of Tax-MLs was investigated by in vitro digestion simulation. Alcoholic liver injury model to explore the in vivo efficacy of Tax-MLs. The results showed that the average particle size, polydispersity index (PDI) and zeta potential of Tax-MLs were 36.90 ± 4.57 nm, 0.194 ± 0.01 and -32.6 ± 0.35 mV, respectively, and the uniform size and distribution of Tax-MLs were observed by transmission electron microscopy. The formation of Tax-MLs was proved by differential scanning calorimetry and fourier transform infrared spectroscopy. In terms of physicochemical properties, the solubility of Tax-MLs in water increased by 92.02 times compared with Tax, and its oil-water partition coefficient in water increased from 0.43 to 1.14, which proved that Tax-MLs could improve its solubility and permeability. The in vivo pharmacodynamic results showed that compared with the Tax group, Tax-MLs low, medium and high dose groups showed a significant reduction in liver indices and serum levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) (P < 0.05), and enhanced the activities of superoxide dismutase (SOD) and glutathione (GSH), and lowered the levels of malondialdehyde (MDA) more significantly in the hepatic tissues. Tax-MLs effectively improved drug solubility and permeability, and enhanced the protective effect against alcoholic liver injury. Animal experiments were conducted with approval from the Animal Ethics Committee of Changchun University of Traditional Chinese Medicine (approval number: 2023601).

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

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.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.