OBJECTIVE To study the improvement effect and mechanism of Tripterygium wilfordii multiglucoside （TWM） on nephrotic syndrome in rats. METHODS The nephrotic syndrome model was established by intravenous injection of adriamycin via the tail vein. The modeling rats were randomly divided into the model group （distilled water）， prednisone group （10 mg/kg）， and TWM high- and low-dose groups （10 and 5 mg/kg， respectively）. Additionally， blank group （distilled water） without model induction was established. Each group consisted of 9 rats. Rats in each group were administered the corresponding drugs or distilled water by gavage， once a day， for 6 consecutive weeks. The histopathological morphology of kidney tissues in rats was observed； the levels of 24-hour urinary protein （24 h-UTP） and serum biochemical indicators [albumin （ALB）， blood urea nitrogen （BUN）， serum creatinine （SCr）， cholesterol （CHOL）， and triglyceride （TG）] in rats were determined； the levels of oxidative stress indicators [superoxide dismutase （SOD）， malondialdehyde （MDA）] in kidney tissue of rats were determined； expressions of hypoxia-inducible factor-1α （HIF-1α）/microRNA-155-5p （miR-155-5p）/nuclear factor erythriod 2- related factor 2 （Nrf2） signaling pathway-related mRNA and protein in the renal tissues of rats were detected. RESULTS Compared with the blank group， the rats in the model group exhibited disordered renal tissue structure， with a small amount of glomerular necrosis and edema of the renal tubular epithelial cells. 24 h-UTP， serum levels of SCr， BUN， CHOL and TG， MDA content， mRNA and protein expressions of HIF-1α and Keap1 as well as the expression of miR-155-5p in renal tissues were increased significantly （ P ＜0.05）. Serum level of ALB， SOD level in renal tissue as well as mRNA and protein expressions of Nrf2 were decreased significantly （ P ＜0.05）. Compared with the model group， TWM high-dose and low-dose groups exhibited significant improvements in renal injury， with notable reversals in the levels of the above quantitative indicators （ P ＜0.05）. CONCLUSIONS TWM can alleviate oxidative stress-induced damage and thereby improve nephrotic syndrome in rats by regulating the HIF-1α/miR-155-5p/Nrf2 signaling pathway.

OBJECTIVE To construct a predictive model for evaluating the efficacy of a triple antiemetic regimen （neurokinin- 1 receptor antagonist+5-hydroxytryptamine 3 receptor antagonist+dexamethasone） for preventing nausea and vomiting induced by highly emetogenic chemotherapy （HEC） based on interpretable deep learning algorithms. METHODS Clinical data of cancer patients who received HEC and were treated with the standard triple antiemetic regimen in the oncology department of Tianjin Medical University General Hospital from January 2018 to December 2022 were collected retrospectively. Demographic， clinical and metabolism-related variables were integrated. After data pre-processing， two deep learning algorithms （deep random forest and dense neural network） and four machine learning algorithms （support vector machine， categorical boosting， random forest and decision tree） were used to build predictive models. Subsequently， model performance evaluation and model interpretability analysis were conducted. RESULTS Among the six candidate models， the deep random forest model demonstrated the best predictive performance on the test set， with an area under the receiver operating characteristic curve of 0.850， an accuracy of 0.911， a precision of 0.805， a recall of 0.783， an F1 score of 0.793， and a Brier score of 0.075. Interpretability analysis revealed that creatinine clearance rate （Ccr） was the key predictive factor， and low Ccr levels， female gender， younger age， highly emetogenic drugs （particularly cisplatin-containing chemotherapy regimens）， and anticipatory nausea and vomiting were positively correlated with the risk of HEC-related nausea and vomiting. CONCLUSIONS The deep random forest model exhibits the best performance in predicting the efficacy of triple antiemetic regimen for preventing HEC-related nausea and vomiting. The key predictors in this model primarily include Ccr，anticipatory nausea and vomiting， gender， age， and highly emetogenic drugs.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Liumotang comes from the Yuan dynasty's Effective Prescription Handed Down for Generations of Physicians. It is composed of six medicinal materials： Arecae Semen， Aquilariae Lignum Resinatum， Aucklandiae Radix， Linderae Radix， Rhei Radix et Rhizoma， and Aurantii Fructus. It is a classical formula for treating abdominal pain due to Qi stagnation and constipation accompanied by heat. This study systematically collated the records of Liumotang in ancient medical books and modern clinical literature and conducted in-depth analysis and textual research on its formula source， main diseases， composition， dosage， medical books， container capacity， processing， preparation method， usage， drug basis， formula meaning， and other key information， so as to provide a powerful reference for the development and clinical application of compound preparations of the classical formula Liumotang. The results show that Liumotang was first seen in Effective Prescription Handed Down for Generations of Physicians， and many medical books of the past dynasties have imitated this. In terms of drug basis， the dried and mature seeds of the palm plant Areca catechu， resin-containing wood of the Daphneaceae plant Aquilaria sinensis， the dried roots of the Asteraceae plant woody Aucklandia lappa， the dried tuber root of the Lauraceae plant Lindera aggregata， the dried roots and rhizomes of the knotweed plant， R. palmatum， R.tangutikum， and R. officinale， and the dried and unripe fruits of the citrus genus C. aurantium and its cultivated varieties from the family Rutaceae were selected. In terms of dosage， through the textual research on bowls in the Ming and Qing dynasties， combined with the conversion of medicines and bowl capacity in the Qing dynasty， it was estimated that the dosage of each drug in the Yuan dynasty was 10.86 g. In the Ming and Qing dynasties， the dosage of drugs was mostly equal， but the dosage of drugs was somewhat different. In terms of processing， preparation method， and usage， in the medical books of the past dynasties， the processing of drugs has slightly changed， but raw drugs are used in all preparations. The preparation method and usage did not change much during the Yuan， Ming， and Qing dynasties， except for certain differences in dosage. In terms of syndrome， Liumotang was first used to treat abdominal pain due to Qi stagnation and constipation accompanied by heat. Medical books of the past dynasties often omit the symptoms of heat. In modern clinical practice， Liumotang is mainly used in the digestive system and urinary system diseases and is mostly used to treat constipation-predominant irritable bowel syndrome， biliary reflux gastritis， functional constipation， slow transit constipation， and other diseases， with no adverse reactions found yet. The above results provide a reliable scientific basis for the development and clinical treatment of Liumotang compound preparations.

Objective:To study the clinical efficacy and impact on quality of life of the first administration of different doses of 131iodine ( 131I) therapy in patients with differentiated thyroid cancer (DTC) after surgical resection. Methods:A total of 97 DTC patients who underwent elective surgical resection at Hongqi Hospital Affiliated to Mudanjiang Medical University from June 2022 to June 2024 were selected and divided into a high-dose group ( n = 49) and a low-dose group ( n = 48) based on their first postoperative treatment with different doses of 131I. All patients underwent DTC endoscopic thyroidectomy. The high-dose group received > 150 - 200 mCi 131I treatment after surgery, while the low-dose group received 100 - 150 mCi 131I treatment after surgery. Both groups of DTC patients were followed up for 12 months. A comparison was conducted between the two groups regarding the success rate of clearing residual thyroid tissue (referred to as thyroid clearance), disease-free survival rate, recurrence rate, incidence of adverse reactions, and changes in quality of life before and after treatment. Results:There was no statistically significant difference in the success rate of thyroid clearance between the two groups of DTC patients (χ 2 = 1.43, P = 0.232). The disease-free survival rate of DTC patients in the low-dose group was higher than that in the high-dose group (χ 2 = 4.86, P = 0.027), and the recurrence rate was lower than that in the high-dose group (χ 2 = 4.86, P = 0.027). The incidence of adverse reactions in DTC patients in the low-dose group was lower than that in the high-dose group (χ 2 = 4.59, P = 0.032). The scores of the short form 36 health survey (SF-36) of both groups of DTC patients after treatment were higher than those before treatment ( P < 0.05), and the SF-36 scores of DTC patients in the low-dose group were higher than those in the high-dose group after treatment ( P < 0.001). The scores of the thyroid cancer specific quality of life (THYCA-QoL) of both groups of DTC patients after treatment were lower than those before treatment ( P < 0.05), and the THYCA-QoL scores of DTC patients in the low-dose group were lower than those in the high-dose group after treatment ( t = 4.48, P < 0.001). Conclusions:The initial treatment effect of different doses of 131I after DTC surgical resection is comparable, but the low-dose 131I can improve the disease-free survival rate, reduce adverse reactions, and improve the quality of life of patients, which is worthy of clinical practice.

Objective:To study the risk factors of thyroid nodules in patients with type 2 diabetes.Methods:Data of patients with type 2 diabetes with normal thyroid function admitted to the Department of Endocrinology of Heze Municipal Hospital from January to June 2024 were collected. Binary logistic regression was used to analyze the influencing factors of thyroid nodules in patients with type 2 diabetes, and the receiver operating characteristic curve (ROC curve) was used to evaluate the diagnostic efficacy of each influencing factor.Results:Among 162 patients with type 2 diabetes, 96 had thyroid nodules, accounting for 59.3%. The incidence of thyroid nodules in women was significantly higher than that in men (χ 2 = 4.56, P = 0.034). Multivariate logistic regression analysis showed that age (≥50 years old), overweight and obesity [body mass index (BMI)≥24.0 kg/m 2], high glycated hemoglobin (≥10%), and high total cholesterol ( > 6.5 mmol/L) were independent risk factors for thyroid nodules in patients with type 2 diabetes ( OR = 1.83, 1.67, 1.08, 3.65, P < 0.05), and men was an independent protective factor ( OR = 0.63, P = 0.039). The ROC curve results showed that total cholesterol and total cholesterol combined with glycated hemoglobin could distinguish patients with thyroid nodules from those without thyroid nodules, with AUC = 0.64 and 0.68, respectively, and the differences were statistically significant ( P < 0.05). Conclusions:The incidence of thyroid nodules in patients with type 2 diabetes is relatively high. Age, overweight and obesity, high glycated hemoglobin, and high total cholesterol are independent risk factors for thyroid nodules in patients with type 2 diabetes, and total cholesterol has the ability to distinguish patients with thyroid nodules.

To understand the background of Escherichia coli(E.coli)carried by artificially bred sika deer and the biological characteristics of the isolated strains,such as drug resistance and pathogenicity,in April 2024,we collected 184 fresh deer fecal samples from four deer farms in Luxiang Township,Shuangyang District,Changchun City,Jilin Province,for isolation and cultivation of E.coli.The isolates were tested for drug resistance and biochemical identification with a BD PhoenixTM-100 Automated Microbiology System.The virulence genes were detected with PCR,and the strains were molecularly typed with ERIC-PCR.A total of 165 E.coli strains were isolated from 184 samples of deer feces,with an isolation rate of 89.67%.Twenty strains had a drug resistance phenotype,and the drug resistance rate was 12.12%;these strains included 15 strains of multi-drug resistant bacteria and 11 strains of ESBL-producing bacteria.Virulence gene detection indicated that the sika deer isolates carried multiple diarrhea-associated virulence genes,such as EAST-1(12.12%),eae(1.21%),stx1(7.88%),stx2(7.27%),and STa(1.82%).ERIC-PCR demonstrated that the isolates showed high polymorphism.The ESBL-producing E.coli carried by sika deer are likely to spread drug resistance in the community and livestock population.Some isolates carried multiple diarrhea-associated virulence genes,thus posing a human transmission risk.Therefore,monitoring of drug resistance and virulence genes must be strengthened,and antibiotics must be used reasonably during the breeding process to avoid excessive use and misuse.

Objective:To explore the application and guiding value of bispectral index（BIS）in postoperative ventilator weaning in infants with congenital heart disease.Methods:A retrospective analysis was conducted on 81 cases of infants with congenital heart disease treated at Xuzhou Children's Hospital affiliated with Xuzhou Medical University from January 2022 to November 2023. The infants were divided into the successful weaning group（62 cases）and the failed weaning group（19 cases）based on the success of ventilator withdrawal. Univariate and multivariate Logistic analyses were performed on the clinical data of the two groups of infants，and ROC curves were plotted to analyze the predictive value of BIS for postoperative ventilator withdrawal failure in infants with congenital heart disease.Results:The mechanical ventilation time，ICU stay time，hospitalization time，and BIS values at 1 hour，2 hours，3 hours，4 hours，and 5 hours after sedation in the two groups of infants showed statistically significant differences（ P<0.05）. The results of the multivariate Logistic regression analysis indicated that BIS values at 1 hour，2 hours，3 hours，4 hours，and 5 hours after sedation are factors affecting the failure of postoperative ventilator weaning in infants with congenital heart disease（ P<0.05）. The ROC curve analysis revealed that the AUCs for predicting postoperative ventilator weaning failure in infants with congenital heart disease were 0.886，0.877，0.873，0.907，0.925，and 0.954 for BIS at 1 hour，2 hours，3 hours，4 hours，and 5 hours after sedation，and the combined prediction，respectively. The sensitivities and specificities were as follows：for BIS at 1 hour after sedation，94.7% and 67.7%；for BIS at 2 hours after sedation，84.2% and 93.5%；for BIS at 3 hours after sedation，84.2% and 83.9%；for BIS at 4 hours after sedation，89.5% and 79.0%；for BIS at 5 hours after sedation，84.2% and 85.5%；and for the combined prediction，100.0% and 90.3%. Conclusion:The use of BIS monitoring during the weaning of ventilators in infants after congenital heart disease surgery has certain predictive value for the success of the weaning process.

Complex liver resection (CLR) is a collective term for surgical procedures addre-ssing complex invasion of intrahepatic vasculobiliary structures that cannot be radically resected through conventional methods. The ex vivo liver resection and autotransplantation (ELRA) and its modified techniques have significantly enhanced the technical feasibility of CLR implementation. In recent years, advancements in modified ELRA techniques and derivative procedures, including conversion resection, in-situ hypothermic perfusion, and auxiliary liver transplantation, have further diversified CLR methodologies, offering more personalized treatment options for CLR candidates. Given the complexity of such cases and substantial variations in surgical approach selection, improving procedural safety and scalability remains a critical challenge in CLR practice. The authors review the current application of modified techniques based on ELRA in CLR, evaluate the clinical significance based on institutional experiences, and propose future directions and individual selection for advancing the safe implementation of CLR.