ObjectiveTo develop and validate a nomogram based on the albumin-bilirubin （ALBI） score for predicting the risk of severe perioperative complications in patients undergoing hepatectomy for hepatolithiasis. MethodsA retrospective analysis was conducted on the clinical data of 163 hepatolithiasis patients who underwent hepatectomy. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for severe perioperative complications. A nomogram prediction model was constructed and its performance was evaluated. ResultsAmong the 163 patients， 66 and 97 were classified into the low-grade and high-grade ALBI groups， respectively. Significant intergroup differences were observed in gender， total bilirubin， albumin levels， and the incidence of severe complications （P0.05）. Severe complications occurred in 40 patients. Independent risk factors included age 60 years （OR=5.49， P0.001）， high-grade ALBI （OR=8.30， P0.001）， history of biliary surgery （OR=2.60， P=0.035）， hepatectomy （segmentectomy）≥3 （OR=2.75， P=0.028）， and open surgical approach （OR=4.00， P=0.009）. A nomogram for predicting severe perioperative complications was successfully established. Internal validation showed that the model had an area under the ROC curve （AUC） of 0.865， which outperformed traditional single predictors. The calibration curve closely aligned with the ideal curve， with a mean absolute error （MAE） of 0.027. Decision curve analysis （DCA） demonstrated a net clinical benefit when the threshold probability exceeded 10%， superior to that of traditional predictors. ConclusionThe ALBI score-based nomogram is successfully developed and validated to predict the risk of severe perioperative complications in hepatolithiasis patients undergoing hepatectomy. The model demonstrated favorable predictive performance and high clinical utility， serving as an effective tool for both preoperative risk assessment and postoperative risk stratification.

Background: Malignant pleural effusion (MPE), a common complication of advanced cancers, is associated with poor prognosis and reduced quality of life. Although host–tumor interactions are known to drive MPE development, the associated immune dynamics during disease progression remain unclear. Using a Lewis lung carcinoma-induced MPE model in C57BL/6JNidfc mice, we systematically evaluated general parameters and immune cell changes at two-day intervals throughout disease progression. Results: The day of Lewis lung carcinoma cell injection into the pleural space was designated as day 0. By day 10 post-injection (p.i.), MPE-bearing mice exhibited ~ 10% body weight loss, marking the experimental endpoint. Pleural tumor mass and pleural effusion volume were minimal up to day 4 p.i. but increased sharply from day 6 onward.CD45⁺ immune cell counts rose over time, and days 6, 8, and 10 p.i. marked key stages of MPE progression. On day 6, B cells, T cells, and natural killer cells, but not macrophages and neutrophils, increased significantly compared to earlier timepoints. By day 8, all immune cell subsets except T cells exceeded day 6 levels, and at day 10, natural killer cell numbers declined while others continued to increase. Besides, the numbers of CD8⁺ T cells, Th1 cells, regulatory T cells, and M2 macrophages progressively increased from day 6 to 10. Based on these data, days 6 and 10 were defined as early and advanced MPE stages, respectively, with distinct immune phenotypes. In advanced MPE, CD8⁺ T cells displayed reduced IFN-γ, TNF-α, Granzyme B, Perforin, FasL, and Ki-67, but upregulated PD-1 and CTLA-4 relative to early stage. Similarly, Th1 cells showed decreased IFN-γ, TNF-α, and IL-2 production along with reduced Ki-67 expression. Advanced-stage M2 macrophages exhibited lower MHC-II levels and impaired phagocytosis, but higher PD-L1 and IL-10 production, while neutrophils showed reduced TNF-α release and phagocytic activity. Conclusions Our findings characterize the temporal immune dynamics associated with MPE progression in a mouse model, revealing a transition from an early immunostimulatory state to a late immunosuppressive state. This study enhances our understanding of MPE immunopathogenesis and provides a foundation for developing precise, stagespecific therapeutic strategies.

Objectives: . This study aimed to investigate the regulatory functions of gamma-aminobutyric acid (GABA)ergic neural circuits from the inferior colliculus (IC) to the medial geniculate body (MGB) in salicylate-induced tinnitus. Methods: . Mice were treated with salicylate to induce tinnitus, and tinnitus-like behaviors were evaluated via gap prepulse inhibition of acoustic startle. Using combined viral tracing methodologies, we identified and mapped the pathways and connections from the IC to the MGB. Furthermore, we employed Gq-coupled human M3 designer receptors exclusively activated by designer drugs (DREADDs) and Gi-coupled human M4 DREADDs to achieve targeted excitation or suppression of GABAergic neurons in the IC and MGB. Following the administration of clozapine N-oxide, which binds to these receptors, we modulated these neural circuits to assess their impact on tinnitus severity in a mouse model. Results: . Our findings demonstrated that mice exposed to salicylate exhibited tinnitus-like behaviors. GABAergic neurons projecting retrogradely from the MGB to the IC were primarily concentrated in the external nucleus of the IC. After clozapine N-oxide administration, chemogenetic activation of IC-MGB GABAergic neurons aggravated salicylate-induced tinnitus. Additionally, activation of GABAergic neurons between the IC and MGB induced the perception of tinnitus even without salicylate. However, chemogenetic inhibition of the IC-MGB GABAergic circuit did not reverse salicylate-induced tinnitus. Conclusion . These findings suggest that activation of the IC-MGB GABAergic neural circuit may contribute to tinnitus generation through a mechanism distinct from that of salicylate-induced tinnitus. This study provides novel insights into the mechanisms underlying tinnitus.

Objective To compare the performance of 7 Chinese-developed GenAI models (DeepSeek, Wenxin Yiyan, Tongyi, Doubao, Kimi, Hunyuan, and iFLYTEK Spark) in responding to common diseases of the pediatric genitourinary system and to explore their potential applications.Methods A total of 80 questions covering basic disease information, diagnosis, treatment, and prognosis related to “hypospadias, hydronephrosis, and cryptorchidism” were developed as prompts.The models were tested under 3 modes:“quick thinking, ”“deep thinking, ” and “deep thinking + internet access.” The results were evaluated in a blinded review by two pediatric urologists.The responses were graded into 4 levels—A, B, C, and D—based on accuracy, completeness, and relevance.The proportions of responses rated as A and A+B were statistically analyzed and compared across the different models/modes.Results The “deep thinking” mode of DeepSeek demonstrated the best performance, with 88% of responses rated as A and 96% rated as A+B.DeepSeek (deep thinking + internet access), Doubao (quick thinking), Hunyuan (deep thinking), and Doubao (deep thinking + internet access) followed closely, also showing excellent response capabilities.Among the top 5 models/modes, significant differences were observed in the proportion of Alevel responses (P＜0.05), while differences in the proportion of A+B-level responses were not statistically significant (P＞0.05).Conclusion Certain Chinese-developed GenAI models, represented by DeepSeek (deep thinking), demonstrate significant application potential.However, they still cannot consistently provide recommendations rated at the A+B level (recommended grade or higher) with 100% accuracy.In clinical practice, both doctors and patients should view GenAI as an auxiliary tool for education and information acquisition rather than as a final decision-making authority.All information must be reviewed and confirmed by professional physicians.

Background Water serves as an indispensable resource for human survival and constitutes one of the primary means through which humans are exposed to environmental chemicals. Consequently, the safety of drinking water is critical to safeguarding public health. Objective To analyze the levels of the 10 metal/metalloid indicators [Al, Fe, Mn, Cu, Zn, As, Cd, Cr(VI), Pb, and Hg] in drinking water from a region of the Fuchun River Basin, and to evaluate the health risks in drinking water through oral ingestion. Methods In accordance with the national Standard Examination Method for Drinking Water – Part 2: Collection and Preservation of Water Samples (GB/T 5750-2006 and GB/T 5750-2023), a total of 2016 drinking water samples were collected from urban and rural water supply systems in a region of the Fuchun Basin from 2017 to 2024. Two batches of water samples were collected annually during the dry and wet seasons, with each batch comprising 72 samples from the urban water supply system and 54 samples from the rural water supply system. These samples were analyzed according to the Standard Examination Method for Drinking Water – Part 6: Metal and Metalloid Indicators (GB/T 5750-2006 and GB/T 5750-2023) and the results were compared with the limits specified in the Standards for Drinking Water Quality (GB 5749-2022). Health risks were evaluated using the U.S. Environmental Protection Agency (EPA) health risk assessment model. Results All tested metal/metalloid elements in the drinking water samples of the area met the national standards. The results of risk assessment showed that the non-carcinogenic risks associated with oral intake of drinking water, ranked from highest to lowest, were as follows: As>Cr(VI)>Pb>Cd>Hg>Mn>Cu>Zn>Fe>Al, with values of 4.55×10⁻², 2.79×10⁻², 2.06×10⁻², 1.20×10⁻², 4.95×10⁻³, 4.58×10⁻³, 2.85×10⁻³, 1.95×10⁻³, 1.52×10⁻³, and 9.21×10⁻⁴, respectively. The non-carcinogenic risk of each indicator was less than 1, which suggested no potential risk to population health. The carcinogenic risks ranked as Cr(VI)>As>Cd>Pb, with values of 4.18×10⁻⁵, 2.05×10⁻⁵, 3.85×10⁻⁶, and 2.45×10⁻⁷, respectively. The carcinogenic risks of Cr(VI), As, and Cd were between 10⁻⁶ and 10⁻⁴, which suggested an acceptable carcinogenic risk, while the carcinogenic risk of Pb was relatively negligible. Children's total non-carcinogenic/total carcinogenic risks were comparable to those of adult males, both higher than those of adult females. Additionally, rural areas exhibited higher non-carcinogenic/carcinogenic risks for Cr(VI), while those risks for As and Pb were lower than those in urban water samples, and those risks for Cd were comparable between the two. Conclusion The concentrations of the 10 metal/metalloid indicators in drinking water from the studied region in the Fuchun River Basin comply with the national standards from 2017 to 2024. There is a certain carcinogenic risk associated with oral intake of drinking water, but it is still within the acceptable range. In terms of non-carcinogenic risk, no potential threat to population health is anticipated.

Objective:To investigate the association between albumin-corrected anion gap(ACAG)and short-to long-term death out-comes in patients with acute pancreatitis(AP).Methods:This retrospective study was based on the Medical Information Mart for Inten-sive Care-IV database,and the adult patients who were diagnosed with AP and were admitted to the intensive care unit were enrolled in this study.Cox regression risk analysis,receiver operating charac-teristic(ROC)curve analysis,Kaplan-Meier survival curve analy-sis,restricted cubic spline,and subgroup analysis were used to in-vestigate the value of ACAG in predicting the death outcome of AP patients.Results:A total of 444 patients were enrolled in this study,and according to the death status of the patients on day 28 after ad-mission,the patients were divided into survival group with 412 pa-tients and death group with 32 patients,with a mortality rate of 7.2%on day 28 after admission.The multivariate Cox regression analysis showed that ACAG was an independent predictive factor for all-cause mortality rate on day 28 after admission in AP patients(hazard ratio[HR]=1.18,95%CI=1.05-1.32),while it was not an in-dependent predictive factor for death outcome on days 90(HR=1.05,95%CI=0.97-1.14)and 180(HR=1.01,95%CI=0.94-1.09)and at 1 year(HR=1.02,95%CI=0.95-1.10).The ROC curve analysis showed that ACAG had an area under the ROC curve(AUC)of 0.732(95%CI=0.632-0.832)in predicting 28-day death outcome,which was better than that of AG(AUC=0.665,95%CI=0.550-0.781)and serum albumin(Alb)(AUC=0.655,95%CI=0.550-0.761)and was similar to that of Sequential Organ Failure Assessment(SOFA)score(AUC=0.745,95%CI=0.651-0.838).The ROC curve showed that the optimal cut-off value of ACAG was 21.375.Based on the cut-off value of ACAG of 21.375,the patients were divided into high-value group and normal-value group,and the Kaplan-Meier curve analysis showed that the patients with a high level of ACAG had a significantly higher mortality rate than those with normal ACAG(P<0.001).The subgroup analysis showed that the results were stable.Conclusion:ACAG can be used as an independent pre-dictive factor for all-cause mortality rate on day 28 after admission in AP patients,with a better efficacy than AG and Alb and a similar efficacy to SOFA.However,it is not significantly associated with 90-day,180-day,and 1-year death outcomes in AP patients.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective To investigate the characteristics and differences of congenital endowment in senile dementia population based on the theory of five circuits and six qi.Methods Based on the cross-sectional data of China Health and Retirement Longitudinal Survey(CHARLS)in 2018,the dementia status of the population aged 60 and above in China was evaluated by using the Mini-Mental State Examination(MMSE),and the five-circuit and six-qi features at birth in the senile dementia population were analyzed by descriptive statistics and Chi-square goodness-of-fit test.Results A total of 854 patients with senile dementia were included.The five-circuit and six-qi features at birth in the senile dementia population were as follows:most of them were born at the heavenly stem of Bing while the least at the heavenly stem of Ji and Geng(P＜0.001),most of them were born at the earthly branch of Wei while the least at the earthly branch of Zi(P＜0.001),most of them were born at the yearly circuit of excessive water circuit while the least at the yearly circuit of excessive gold circuit and deficient earth circuit(P＜0.001),and most of the patients were born at sitan of taiyin damp-earth and zaiquan of taiyang cold-water while the least at sitan of jueyin wind-wood and zaiquan of shaoyang ministerial fire;no statistically significant differences were found in the dominant qi and guest qi(P＞0.05);most of the patients were born in the year of combination of circuit and qi being Shunhua while the least in the year of combination of circuit and qi being same celestial correspondence(P＜0.001),and the patients born in the year of Shunhua usually were frequently distributed in heavenly-stem and earthly-branch year of Jiawu(P＜0.001).Conclusion There is a certain relationship between the congenital endowment at birth and the incidence of senile dementia in the population of senile dementia.The circuit-qi features at birth for the prevalence of senile dementia are the yearly circuit of excessive water circuit,sitan of taiyin damp-earth and zaiquan of taiyang cold-water,and the year of the combination of circuit and qi being Shunhua.The population born at the time with the above circuit-qi features are prone to suffer the injury of the kidneys,the heart,and the spleen,and then result into illness.