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.

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.

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.

[Objective] To explore the strategy of blood management in patients with massive transfusion in the frigid plateau region. [Methods] The treatment process of a patient with liver rupture in the frigid plateau region was analyzed, and the blood management strategy of the frigid plateau region was discussed in combination with the difficulties of blood transfusion and literature review. [Results] The preoperative complete blood count (CBC) test results of the patient were as follows: RBC 3.14×10¹²/L, Hb 10⁶ g/L, HCT 30.40%, PLT 115.00×10⁹/L; coagulation function: PT 18.9 s, FiB 1.31 g/L, DD > 6 μg/mL, FDP 25.86 μg/mL; ultrasound examination and imaging manifestations suggested liver contusion and laceration / intraparenchymal hematoma, splenic contusion and laceration, and massive blood accumulation in the abdominal cavity; it was estimated that the patient's blood loss was ≥ 2 000 mL, and massive blood transfusion was required during the operation; red blood cell components were timely transfused during the operation, and the blood component transfusion was guided according to the patient's CBC and coagulation function test results, providing strong support and guarantee for the successful treatment of the patient. The patient recovered well after the operation, and the CBC test results were as follows: RBC 4.32×10¹²/L, Hb 144 g/L, HCT 39.50%, PLT 329.00×10⁹/L; coagulation function: APTT 29.3 s, PT 12.1 s, FiB 2.728 g/L, DD>6 μg/mL, FDP 25.86 μg/mL. The patient was discharged after 20 days, and regular follow-up reexamination showed no abnormal results. [Conclusion] Individualized blood management strategy should comprehensively consider the patient’s clinical symptoms, the degree of hemoglobin decline, dynamic coagulation test results and existing treatment conditions. Efficient and reasonable patient blood management strategies can effectively improve the clinical outcomes of massive transfusion patients in the frigid plateau region.

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.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.