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.

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.

Objective:To analyze the trends and research hotspots in English publications from China′s anesthesiology discipline in international journals from 2000 to 2023 using the bibliometric analysis.Methods:Based on the results retrieved from the Science Citation Index Expanded database covering English publications from China′s anesthesiology discipline in international journals from January 2000 to December 2023, CiteSpace 6.3.R1 and VOSviewer 1.6.20.0 were employed to analyze the annual publication counts, institutions, authors, co-cited references, and keywords, etc. with knowledge graph generated.Results:A total of 53, 039 articles published in English from January 2000 to December 2023 were included. The annual number of articles published in English showed an overall increasing trend year by year. Basic research articles exhibited a phase of slow growth during 2000-2007, entered a period of accelerated growth from 2008, and demonstrated a slightly decreased trend by 2023. The number of clinical research articles increased rapidly since 2008, was equivalent to the basic research articles by 2020, surpassed basic research articles by 2023 and became the mainstream research direction of anesthesiology discipline. Among clinical research publications, case series studies predominated from 2000 to 2022, and cohort studies exceeded case series for the first time in 2023, ranking second only to the randomized controlled trials. Research hotspots in anesthesiology discipline predominantly focused on the relationship of neuropsychiatry to anesthesia, pain management, cardiovascular surgery and organ injury, COVID-19 pandemic and patient safety. In terms of journals, Medicine was the journal with the largest number of publications, with 1, 345 publications in 2023 and an impact factor of 1.3. American journals dominated the top 15 highly productive journals, and Anesthesiology had the highest h-index (60) and g-index (98) with an impact factor of 9.1. In terms of domestic high-yield institutions, Shanghai Jiao Tong University topped the list, publishing about 1, 200 papers between 2000 and 2023, followed by Sichuan University (about 850) and Capital Medical University (about 720). Conclusions:China′s anesthesiology research has demonstrated a meteoric rise in both the number of papers published and cited frequency, reflecting ascending scientific academic levels and innovation capacity in the the international arena. While acknowledging this quantitative expansion, recognition is warranted that enhancing research quality and international impact remains imperative to narrow the gap with advanced nations and gradually achieve advancement.

Objective: To explore the breeding and genotyping of CCR2 knockout mice, and to verify the applicability of the polymerase chain reaction(PCR) method for genotype detection of CCR2 knockout mice. Methods: The introduced CCR2 pure male mice and wild-type female mice were mated and bred to produce the offspring generation, the obtained F1 generation heterozygous mice were continued to be mated. DNA was extracted by clipping the tail tissues of the mice at the age of 2 weeks, the target gene fragment was amplified by PCR, and the genotypic results were determined by agarose gel electrophoresis. The proportion of purebred progeny carrying the CCR2 knockout gene was increased by genetic crosses, the effect of CCR2 knockout in the progeny mice was verified by using Western blot against major immune cells and key organs, and flow cytometry was used to detect whether the knockout of the CCR2 gene had any effect on the function of the immune system by targeting the major immune cells. Results: CCR2 knockout mice were successfully bred and characterized, and three genotypes of F2 generation mice were obtained: CCR2+/+, CCR2+/-, and CCR2-/-. The offspring genotypes were identified by PCR, and Western blot showed extremely low CCR2 protein expression in CCR2 knockout mice. Flow analysis showed that CCR2 knockdown reduced the expression of CD4+T and Th1 cells in mouse spleen-derived T cells, but did not affect macrophage function. Conclusion Correct breeding and identification are important ways to get the pure CCR2 knockout mice, and PCR method for identifying mouse genotypes is simple, fast and reliable.

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.

Objective:To analyze the trends and research hotspots in English publications from China′s anesthesiology discipline in international journals from 2000 to 2023 using the bibliometric analysis.Methods:Based on the results retrieved from the Science Citation Index Expanded database covering English publications from China′s anesthesiology discipline in international journals from January 2000 to December 2023, CiteSpace 6.3.R1 and VOSviewer 1.6.20.0 were employed to analyze the annual publication counts, institutions, authors, co-cited references, and keywords, etc. with knowledge graph generated.Results:A total of 53, 039 articles published in English from January 2000 to December 2023 were included. The annual number of articles published in English showed an overall increasing trend year by year. Basic research articles exhibited a phase of slow growth during 2000-2007, entered a period of accelerated growth from 2008, and demonstrated a slightly decreased trend by 2023. The number of clinical research articles increased rapidly since 2008, was equivalent to the basic research articles by 2020, surpassed basic research articles by 2023 and became the mainstream research direction of anesthesiology discipline. Among clinical research publications, case series studies predominated from 2000 to 2022, and cohort studies exceeded case series for the first time in 2023, ranking second only to the randomized controlled trials. Research hotspots in anesthesiology discipline predominantly focused on the relationship of neuropsychiatry to anesthesia, pain management, cardiovascular surgery and organ injury, COVID-19 pandemic and patient safety. In terms of journals, Medicine was the journal with the largest number of publications, with 1, 345 publications in 2023 and an impact factor of 1.3. American journals dominated the top 15 highly productive journals, and Anesthesiology had the highest h-index (60) and g-index (98) with an impact factor of 9.1. In terms of domestic high-yield institutions, Shanghai Jiao Tong University topped the list, publishing about 1, 200 papers between 2000 and 2023, followed by Sichuan University (about 850) and Capital Medical University (about 720). Conclusions:China′s anesthesiology research has demonstrated a meteoric rise in both the number of papers published and cited frequency, reflecting ascending scientific academic levels and innovation capacity in the the international arena. While acknowledging this quantitative expansion, recognition is warranted that enhancing research quality and international impact remains imperative to narrow the gap with advanced nations and gradually achieve advancement.