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.

The chemical constituents of Commiphora myrrha was investigated by column chromatography on silica gel, ODS, Sephadex LH-20, and semi-preparative HPLC. Their structures were elucidated by comprehensive spectroscopic methods including UV, IR, MS, NMR, as well as ECD calculation. Seven compounds were isolated from the dichloromethane-soluble fraction of C. myrrha and their structures were identified as(1S,2R,4S,5R,8S)-guaiane-2-hydroxy-7(11),10(15)-dien-6-oxo-12,8-olide(1), commipholide E(2), myrrhterpenoid H(3), myrrhterpenoid I(4), myrrhterpenoid E(5), 2α-methoxy-8α-hydroxy-6-oxogermacra-1(10),7(11)-dien-8,12-olide(6), 8,12-epoxy-1α,9α-hydroxy-eudesma-7,11-diene-6-dione(7). Compound 1 was a new compound and named myrrhterpenoid P. Compound 7 was isolated from Commiphora genus for the first time. Compounds 2, 5, and 6 significantly inhibited nitric oxide(NO) production in LPS-stimulated RAW264.7 cells, with IC_(50) values of(49.67±4.16),(40.80±1.27),(47.22±0.87) μmol·L~(-1), respectively [indomethacin as the positive control, with IC_(50) value of(63.92±2.60) μmol·L~(-1)].
Commiphora/chemistry* ; Animals ; Mice ; Resins, Plant/chemistry* ; Sesquiterpenes/isolation & purification* ; Molecular Structure ; Nitric Oxide ; Macrophages/metabolism* ; RAW 264.7 Cells ; Drugs, Chinese Herbal/pharmacology*

As a high-quality moxibustion material, Artemisia stolonifera has high economic value and research prospects. However, due to difficulties in seed germination, its wild germplasm resources are sparsely distributed in China. This study used young stem segments grown in the current year to investigate the effects of explant sterilization, different combinations and concentrations of plant growth regulators on the proliferation and rooting of adventitious shoots, with the aim of constructing an in vitro rapid propagation technology system for A. stolonifera. The results showed that the lowest contamination rate of 25.83% was achieved when sterilizing the stem segments by rinsing with running water for 30 min, soaking in 75% ethanol for 30 s, followed by a 5 min treatment with 0.1% HgCl_2, 10 min with 8% NaClO, and 10 min with 0.6% phytosaniline. There was no browning of the stem segments, and surface sterilization of the A. stolonifera stem segments was successfully achieved. In the induction culture phase, when the concentration of kinetin(KT) was 0.05 mg·L~(-1) and 6-benzylaminopurine(6-BA) was 0.05 mg·L~(-1), the adventitious shoot proliferation coefficient was 2.02, effectively promoting the proliferation and growth of A. stolonifera. In the rooting culture phase, 0.1 mg·L~(-1) 1-naphthaleneacetic acid(NAA) effectively induced A. stolonifera test-tube seedlings to root within a short period, achieving a rooting rate of 100%. The addition of a small amount of activated charcoal also promoted rooting and strengthened seedling growth. The survival rate of A. stolonifera seedlings transplanted into a substrate consisting of 90% nutrient soil and 10% perlite was 100%. This study established an efficient in vitro rapid propagation system for A. stolonifera, overcoming difficulties with seed germination, shortening the breeding cycle, and reducing production and planting costs. It provides technical support for the introduction, domestication, seedling propagation, germplasm conservation, and industrial development of A. stolonifera.
Artemisia/drug effects* ; Tissue Culture Techniques/methods* ; Plant Growth Regulators/pharmacology* ; Plant Stems/drug effects* ; Plant Shoots/drug effects*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

OBJECTIVE: To analyze the clinical features of acute myeloid leukemia patients with DEK-NUP214 fusion gene positive. METHODS: The DEK-NUP214 fusion gene was amplified by multi-nested PCR in 26 patients admitted to the First Affiliated Hospital of Soochow University from January 2018 to October 2023, and the disease course and post-transplant survival data were obtained by searching outpatient and inpatient medical records and telephone follow-up. RESULTS: The median follow-up time of pateints was 21.25（0.9-60.2） months. Among 26 patients with DEK-NUP214 fusion gene positive AML, 15 patients had FLT3-ITD gene mutation positive. One patient died after abandoning treatment due to non-remission of induction chemotherapy, one died due to infection, and 23 patients received allo-HSCT after achieving CR, of which one patient died within one month after transplantation due to multiple infections and one died due to severe pulmonary infection that did not respond to treatment. One patient received allo-HSCT in non-remission state and later died due to recurrence. CONCLUSION DEK-NUP214 fusion gene positive AML is a type of acute leukemia subtype with high risk and poor prognosis. Allo-HSCT treatment at the early stage of disease remission is the most effective way to improve the prognosis of patients.
Humans ; Leukemia, Myeloid, Acute/genetics* ; Poly-ADP-Ribose Binding Proteins ; Oncogene Proteins, Fusion/genetics* ; Nuclear Pore Complex Proteins/genetics* ; Oncogene Proteins/genetics* ; Chromosomal Proteins, Non-Histone/genetics* ; Male ; Female ; Adult ; Mutation ; Hematopoietic Stem Cell Transplantation ; Middle Aged

[This corrects the article DOI: 10.11909/j.issn.1671-5411.2017.08.005.].

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.