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 gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

Objective:To explore the factors influencing adherence to voice therapy among patients with voice disorders in China. Methods:Patients with voice disorders who visited the Voice Therapy Center at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, from February to May 2022 were enrolled in the study. Adherence was assessed using the URICA-Voice scale, while influencing factors were assessed through the Voice Handicap Index（VHI） scale and a general information questionnaire. Correlation analysis was conducted using univariate and multivariate logistic regression analysis. Results:A total of 247 patients were included in the study, comprising 57 males（23.08%） and 190 females（76.92%）. The results revealed that: ①Female patients demonstrated higher likelihood of being in the contemplation stage（OR=0.22） compared to males. ②Patients with a monthly family income per capita>6 000 yuan were more likely to be in the contemplation stage than those with<3 000 yuan with an OR = 13.94. ③High vocal-demand occupations increased contemplation stage probability（OR=7.70） compared to moderate-demand occupations. ④Residence within 30-minute commute predicted action/maintenance stages（OR=7.14） versus≥60-minute commute. ⑤Patients whose occupations had high voice demands were more likely to be in the action and maintenance stages than those with average voice demands, with an OR of 16.20. Conclusion:Gender, monthly family income per capita, occupational voice demands, and distance to the hospital significantly impact the URICA-Voice compliance stages of patients. Patients who are female, have higher family income, have occupations with high voice demands, and live closer to the hospital exhibit higher compliance with voice training.
Humans ; Male ; Female ; Voice Disorders/therapy* ; Patient Compliance ; Voice Training ; Surveys and Questionnaires ; China ; Middle Aged ; Adult ; Voice Quality ; Logistic Models ; Aged

ObjectiveTo screen key genes associated with neuroblastoma （NB） diagnosis and prognosis using the Gene Expression Omnibus （GEO） database， and to investigate the expression and clinical significance of nucleoporin 93 （NUP93） in NB tissues. MethodsNB gene chip data （GSE73517， GSE49710， GSE19274） were retrieved from the GEO database. Differentially expressed genes （DEGs） commonly upregulated in high-risk groups were screened. The R2 database was then used to assess the prognostic value of DEGs that were commonly upregulated in the MYCN amplification group. Finally， NUP93 expression levels in the tissues from 60 NB， 25 ganglioneuroblastoma （GNB）， and 26 ganglioneuroma （GN） cases were measured by immunohistochemistry . ResultsTwenty-five DEGs were identified as commonly upregulated in high-risk groups. Among these， 10 genes （SIVA1， NUP93， STIP1， LSM4， RAI14， MYOZ3， KNTC1， TNFRSF10B， TACC3 and CEP152） showed significantly higher expression in MYCN-amplified subgroups （P＜0.05）. Survival analysis revealed that high NUP93 expression was associated with shorter overall survival （HR = 4.0， 95% CI： 3.0，5.3， P = 1.80 × 10⁻³⁴）. Immunohistochemistry results revealed that NUP93 expression in NB tissues was significantly higher than in GNB and GN tissues （P＜0.001）. NUP93 expression was positively correlated with high mitosis-karyorrhexis index （MKI； P=0.040）， poor differentiation （P＜0.001）， and MYCN expression （r_s = 0.793， P ＜0.001）. ConclusionsHigh expression of NUP93 is associated with high MKI and poor differentiation， and predicts unfavorable prognosis in patients with NB， suggesting it may promote tumor progression by regulating MYCN. NUP93 has the potential to be a novel diagnostic biomarker and therapeutic target for NB.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

OBJECTIVE To promote the application of drones in emergency rescue and related fields， expand “low-altitude+ medical” rescue services， and advance the standardization of “low-altitude+medical” distribution services. METHODS The Consensus on Low-altitude Transport and Delivery Services for Emergency Medicines via Drones （2025 Edition） （hereinafter referred to as the Consensus） was jointly initiated by the Division of Therapeutic Drug Monitoring， Chinese Pharmacological Society and the Expert Committee on Precision Medication of the Guangdong Pharmaceutical Association. Guangzhou Red Cross Hospital served as the leading unit， organizing 53 multidisciplinary experts nationwide to participate in drafting and reviewing. A nominal group technique was employed to discuss and finalize the consensus outline， resulting in a preliminary draft. Delphi method was employed， and 11 external review experts were invited to conduct the evaluation. After the experts’ opinions were analyzed and integrated， the Consensus was finalized. RESULTS & CONCLUSIONS The finalized Consensus includes its purpose， principles， and applicable scenarios， basic requirements， and operational procedures for low-altitude transport and delivery of emergency medications； distribution requirements and precautions for controlled substances， fragile medications， and temperature-sensitive medications； and recommendations for emergency medications supplies suitable for the low-altitude transportation and distribution. The release of this Consensus is expected to provide guidance and support for the standardization of “low-altitude+medical” distribution services and the application of low-altitude economy in the healthcare sector.

Objective To evaluate the clinical value of 18F-fibroblast-activation protein inhibitor(18F-FAPI)PET/CT in malignant tumors exhibiting low uptake of 18F-fluorodeoxyglucose(18F-FDG).Methods We prospectively analyzed 62 patients with malignant tumors and low 18F-FDG uptake who underwent 18F-FAPI PET/CT in the Affiliated Cancer Hospital and Institute of Guangzhou Medical University from January 2021 to November 2022.Patient demographics information,clinical and radiological data were collected.Tumor lesions were categorized based on 18F-FAPI and 18F-FDG uptake relative to surrounding tissues into low-,moderate-,and high-uptake,with high uptake indicating positivity.The number and tracer uptake levels of primary tumors and metastatic lesions visualized by both 18F-FDG and 18F-FAPI were recorded and compared.Results Of the 62 primary tumors,18(29.0%)showed low-uptake and 44(71.0%)moderate-uptake on 18F-FDG PET,while 1(1.6%),6(9.7%),and 55(88.7%)showed low,moderate,and high uptake on 18F-FAPI,respectively.The maximum standardized uptake value(SUVmax)for primary tumors was significantly higher with 18F-FAPI than with 18F-FDG(7.5±5.6 vs.3.9±2.1,P<0.01).The number of positive lymph node foci revealed by 18F-FAPI was noticeably higher than that by 18F-FDG(77 vs.38,P<0.01).A total of 16 distant organ metastases were identified,with 11(68.8%)detected by 18F-FDG and 15(93.8%)by 18F-FAPI,showing no significant difference in detection rates(P>0.05).Conclusions 18F-FAPI PET/CT effectively visualizes primary and metastatic lesions in malignant tumors with low 18F-FDG uptake,suggesting its potential as a promising radiotracer for such malignancies.

Objective To investigate the therapeutic effects and underlying mechanisms of Ganluqingwen formula on lipopolysaccharide(LPS)-induced acute lung injury/acute respiratory distress syndrome(ALI/ARDS)in mice.Methods Fifty ICR mice were randomly divided into five groups:control,model,and Ganluqingwen formula(GLQW)low dose(7.10 g/kg),medium dose(15.21 g/kg),and high dose(30.42 g/kg)groups,with 10 mice per group.On days 1-3,mice in GLQW groups were daily gavaged with the corresponding dose of GLQW,while control and model groups received equal volumes of saline.On day 4,ALI/ARDS was induced in model and GLQW groups using intraperitoneal injection of LPS(20 mg/kg),while control group received an equal volume of PBS.At 24 h post-treatment,survival rate,wet-to-dry weight ratio(W/D)and lung histological changes(HE staining)were observed.Serum levels of tumor necrosis factor(TNF)-α,interferon gamma(IFN-γ),interleukin(IL)-4,IL-10,IL-12,as well as lung tissue levels of TNF-α,IFN-γ,IL-1β,IL-4,IL-6,IL-10 were measured by ELISA.Western blotting was used to determine the expression levels of NOD-like receptor thermal protein domain associated protein 3(NLRP3),cystatinase-1(Caspase-1),apoptosis-associated speck-like protein(ASC),and membrane perforating protein Gasdermin D(GSDMD)in lung tissue.Results No significant differences in survival rates were observed among the groups(P>0.05).Compared with control group,ELISA and Western blotting results showed that lung tissue W/D,IFN-γ,TNF-α,IL-4,IL-12,IL-1β,IL-6,NLRP3,ASC,and Caspase-1,GSDMD and serum IFN-γ,TNF-α,IL-4,IL-12 levels were significantly higher(P<0.05),and IL-10 levels in lung tissue and serum were significantly lower in mice of model group(P<0.05).Compared with model group,lung tissue W/D,IFN-γ,TNF-α,IL-1β,IL-4,IL-6,IL-12,NLRP3,ASC,Caspase-1,and GSDMD,and serum IFN-γ,TNF-α,IL-4,and IL-12 levels were significantly lower(P<0.05),and lung tissue IL-10 levels were significantly higher(P<0.05)in GLQW low,medium,and high dose groups,with high-dose group showing significantly higher level in serum IL-10(P<0.05).Compared with GLQW low-dose group,the lung tissue levels of IFN-γ,IL-6,NLRP3,ASC,Caspase-1,and GSDMD,and serum TNF-α were significantly lower(P<0.05),and lung and serum IL-10 levels were significantly higher in GLQW high-dose group(P<0.05).HE staining results showed that lung structure was clear and normal in control group;part of the lung interstitium was congested and hemorrhagic,and some of the fine bronchial periphery was infiltrated with inflammatory cells in model group;the phenomena of lung interstitial congestion and hemorrhage were reduced,and the degree of infiltration of inflammatory cells was alleviated in GLQW low-,medium-,and high-dose groups.Conclusion Ganluqingwen formula can delay the development of ALI/ARDS in mice by inhibiting NLRP3/Caspase-1/GSDMD pathway,thereby suppressing cellular pyroptosis.