To analyze the occurrence of early complications after total hip arthroplasty (THA) in patients with systemic lupus erythematosus (SLE).

ObjectiveTo investigate the incidence rate of primary liver cancer （PLC） and the progression of liver fibrosis in chronic hepatitis B （CHB） patients with low-level viremia （LLV） （HBV DNA<2 000 IU/mL but ≥20 IU/mL） after treatment adjustment， and to provide more robust evidence for clinical practice. MethodsA retrospective analysis was performed for the clinical data of LLV patients who initially received nucleos（t）ide analogue （NAs） for at least 48 weeks at the Fifth Medical Center of PLA General Hospital from August 2007 to April 2017 and subsequently underwent NAs adjustment due to LLV， and according to the virologic response after 48 weeks of treatment adjustment， the patients were divided into LLV group and complete virological response （CVR） group （HBV DNA<20 IU/mL）. The patients were followed up once every 3‍ ‍—‍ ‍6 months till the primary endpoint event of PLC or October 2024. The incidence rate of PLC and the progression of liver fibrosis were observed， and the progression of liver fibrosis was defined as an increase of ≥1 grade in fibrosis-4 （FIB-4） index. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test was used for comparison of categorical data between groups. The Kaplan-Meier method was used to calculate the cumulative incidence rate of PLC， and the Log-rank test was used for comparison between groups； the Cox regression analysis was used to investigate the risk factors for PLC， and the Logistic regression analysis was used to investigate the influencing factors for the progression of liver fibrosis. ResultsA total of 307 patients were enrolled， with a mean age of 50.0 years， and the male patients accounted for 80.5%. After 48 weeks of treatment with the adjusted NAs regimen， 254 patients （82.7%） achieved CVR， and 53 patients （17.3%） still had LLV. For the LLV group， the incidence rate of PLC was 30.2% and the rate of liver fibrosis progression was 22.6%， while for the CVR group， the incidence rate of PLC was only 13.4%， and the rate of liver fibrosis progression was 7.5%. The multivariate regression analyses showed that LLV was an independent risk factor for the onset of PLC （hazard ratio=2.623， 95% confidence interval ［CI］： 1.315‍ ‍—‍ ‍5.234， P=0.006） and the progression of liver fibrosis （odds ratio=3.213， 95%CI： 1.385‍ ‍—‍ ‍7.455， P=0.007）. ConclusionActive adjustment of treatment is needed immediately after the diagnosis of LLV to improve CVR， and if LLV persists after treatment adjustment， it is necessary to enhance the monitoring of liver fibrosis progression and PLC， so as to facilitate early diagnosis and treatment.

ObjectiveTo investigate the clinical efficacy and potential mechanism of Shengmai Jiuxin decoction in the treatment of acute decompensated heart failure （ADHF） with the traditional Chinese medicine （TCM） pattern of Qi and Yin deficiency， Yang deficiency， and blood stasis. MethodsA total of 68 patients diagnosed with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type were randomly assigned to an observation group （34 cases） and a control group （34 cases）. Both groups received conventional Western medical treatment， while the observation group was additionally administered Shengmai Jiuxin decoction. Parameters compared before and after treatment included： TCM syndrome score， TCM syndrome efficacy， New York Heart Association （NYHA） functional classification， left ventricular ejection fraction （LVEF）， N-terminal pro-B-type natriuretic peptide （NT-proBNP）， six-minute walk distance （6MWD）， hypoxia-inducible factor-1 alpha （HIF-1α）， vascular endothelial growth factor A （VEGF-A）， Caspase-3， and the number of rehospitalizations for heart failure within one month after discharge. ResultsThere were no significant differences in sex， age， vital signs， or underlying diseases between the two groups. Compared with baseline， both groups exhibited significant reductions in TCM syndrome scores， NT-proBNP， and HIF-1α levels （P<0.01）， as well as significant increases in 6MWD， LVEF， VEGF-A， and Caspase-3 levels （P<0.05， P<0.01）. After treatment， the observation group showed significantly greater reductions in TCM syndrome score， NT-proBNP， HIF-1α， and Caspase-3 levels compared with the control group （P<0.05） and significantly greater increases in 6MWD， TCM syndrome efficacy， and VEGF-A levels （P<0.05）. No significant differences were observed between the groups in NYHA functional classification， LVEF， or the number of rehospitalizations for heart failure within one month after discharge. No drug-related adverse events were reported in either group during the treatment period. ConclusionShengmai Jiuxin decoction can improve cardiac function and clinical symptoms in patients with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type. Its mechanisms may be related to the regulation of the HIF-1 signaling pathway by modulating targets such as HIF-1α， VEGF-A， and Caspase-3.

ObjectiveTo investigate the clinical efficacy and potential mechanism of Shengmai Jiuxin decoction in the treatment of acute decompensated heart failure （ADHF） with the traditional Chinese medicine （TCM） pattern of Qi and Yin deficiency， Yang deficiency， and blood stasis. MethodsA total of 68 patients diagnosed with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type were randomly assigned to an observation group （34 cases） and a control group （34 cases）. Both groups received conventional Western medical treatment， while the observation group was additionally administered Shengmai Jiuxin decoction. Parameters compared before and after treatment included： TCM syndrome score， TCM syndrome efficacy， New York Heart Association （NYHA） functional classification， left ventricular ejection fraction （LVEF）， N-terminal pro-B-type natriuretic peptide （NT-proBNP）， six-minute walk distance （6MWD）， hypoxia-inducible factor-1 alpha （HIF-1α）， vascular endothelial growth factor A （VEGF-A）， Caspase-3， and the number of rehospitalizations for heart failure within one month after discharge. ResultsThere were no significant differences in sex， age， vital signs， or underlying diseases between the two groups. Compared with baseline， both groups exhibited significant reductions in TCM syndrome scores， NT-proBNP， and HIF-1α levels （P<0.01）， as well as significant increases in 6MWD， LVEF， VEGF-A， and Caspase-3 levels （P<0.05， P<0.01）. After treatment， the observation group showed significantly greater reductions in TCM syndrome score， NT-proBNP， HIF-1α， and Caspase-3 levels compared with the control group （P<0.05） and significantly greater increases in 6MWD， TCM syndrome efficacy， and VEGF-A levels （P<0.05）. No significant differences were observed between the groups in NYHA functional classification， LVEF， or the number of rehospitalizations for heart failure within one month after discharge. No drug-related adverse events were reported in either group during the treatment period. ConclusionShengmai Jiuxin decoction can improve cardiac function and clinical symptoms in patients with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type. Its mechanisms may be related to the regulation of the HIF-1 signaling pathway by modulating targets such as HIF-1α， VEGF-A， and Caspase-3.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

[This corrects the article DOI: 10.1016/j.apsb.2021.07.024.].