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 provide technical support for improving recognition rate of adverse drug events （ADEs） related to traditional Chinese medicine （TCM） formula granules and decoction pieces among inpatient patients. METHODS By referencing the global trigger tool （GTT） whitepaper， literature on adverse reactions to TCM， and expert review opinions， ADE trigger items for TCM formula granules and decoction pieces used in the inpatients were established. GTT was applied to analyze ADEs in inpatients who had used TCM formula granules and decoction pieces in our hospital from August 2013 to August 2023， utilizing the Chinese Hospital Pharmacovigilance System. The effectiveness of GTT and the characteristics of these ADEs were analyzed. RESULTS A total of forty-eight triggers were established， including thirty-two laboratory test indexes， thirteen clinical symptoms， and three antidotes. Among the 1 682 patients included， GTT identified 652 potential ADEs， 284 true positive ADEs，with a trigger rate of 38.76% and a positive predictive value of 43.56%. After review by the auditor， 278 cases of ADEs were finally confirmed， with an incidence rate of 16.53%， significantly higher than the number of spontaneously reported ADEs during the same period （0）. The 278 cases of ADEs were mostly grade 1 （223 cases）， mainly involving hepatobiliary system， gastrointestinal system， blood- lymphatic system， etc；a total of 219 types of TCMs are involved，and the top five suspected TCMs used at a frequency higher than 1% were Poria cocos， Codonopsis pilosula， Atractylodes macrocephala， fried Glycyrrhiza uralensis， and Scutellaria baicalensis. CONCLUSIONS The established GTT can improve the recognition rate of ADEs for hospitalized patients using traditional Chinese medicine formula granules and decoction pieces.

Objective To analyse the clinical efficiency of endolymphatic sac surgery (ESS) in the management of Meniere’s disease (MD). Methods A retrospective analysis was conducted on 274 patients with MD who were hospitalized for treatment in Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University from January 2009 to August 2023. All patients received lifestyle management and drug treatment such as diuretics. For those whose conditions were not well controlled 3 to 6 months after the initial treatment, intratympanic glucocorticoid (ITG) or ESS treatment was carried out. Six months after the treatment, the classes of vertigo relief and hearing changes in the patients were evaluated. After adjusting the confounding factors through propensity score matching (PSM), the impact of ESS on the prognosis of MD patients was evaluated. Results Among 274 patients, 194 and 80 patients underwent ITG and ESS, respectively. Eighty patients were enrolled into each group after PSM. Before and after PSM, the rate of patients reaching vertigo relief class A in ESS group was higher than that in the ITG group (P＝0.004); there was no significant difference in hearing preservation between the two groups. Kaplan-Meier curve analysis showed that vertigo relief in the ESS group was better than that in the ITG group (P=0.029); there was no statistically significant difference in hearing preservation between the two groups. Conclusion When the initial treatment for patients with MD is ineffective, choosing ESS is more beneficial than ITG for controlling vertigo.

To guide transfusion practice in critically ill children who often need plasma and platelet transfusions, the Transfusion and Anemia Expertise Initiative-Control/Avoidance of Bleeding (TAXI-CAB) developed Recommendations and Expert Consensus for Plasma and Platelet Transfusion Practice in Critically Ill Children. This guideline addresses 53 recommendations related to plasma and platelet transfusion in critically ill children with 8 kinds of diseases, laboratory testing, selection/treatment of plasma and platelet components, and research priorities. This paper introduces the specific methods and results of the recommendation formation of the guideline.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

Objective:To retrospectively analyze the results of auditory examination,vestibular function examination and laboratory examination of 63 patients diagnosed as vestibular neuritis.Methods:A total of 63 patients diagnosed with vestibular neuritis hospitalized in the Department of Otolaryngology, Head and Neck Surgery of the Third Affiliated Hospital of Sun Yat-sen University, from October 2012 to December 2022 were recruited. All patients met the diagnostic criteria for the 2022 Bárány association vestibular neuritis. Clinical data and the results of pure tone audiometry, electrocochleogram, video electronystagmogram, caloric test, cervical vestibuloevoked myogenic potential（cVEMP）, ocular vestibuloevoked myogenic potential（oVEMP）, video head impulse test（vHIT） was collected.A total of 63 age-and sex-matched healthy subjects in the physical examination center were randomly selected as the control group. The differences of blood indexs and lipid metabolism indexes between the two groups were compared. Results:In patients with vestibular neuritis, 50 out of 63 patients presented normal threshold in pure tone audiometry, 8 out of 63 patients had bilateral high-frequency sensorineural hearing loss and 5 out of 63 patients had unilateral mild high-frequency sensorineural hearing loss, 56 out of 63 cases completed the electrocochleogram, of which 3 cases had a binaural-SP/AP amplitude ratio≥0.4, 5 cases had monaural amplitude ratio ≥0.4. Fifty-five out of 63 patients completed the caloric test with CP values greater than 30% in all. The ratio of patients completed cVEMP, oVEMP and vHIT were 46 cases, 22 cases and 30 cases, respectively. 17 out of 63 cases completed all the four vestibular function tests. According to these tests, 49 patients could determine the extent of injury，including 27 cases with unilateral superior vestibular nerve injury, 21 cases with unilateral superior and inferior vestibular nerve injury and 1 case with unilateral inferior vestibular nerve injury. There were significant differences in neutrophil value（P<0.001）, lymphocyte value（P<0.005）, neutrophil/lymphocyte ratio（P<0.001） and apolipoprotein A1（P<0.001） between patient group and control group. Inflammatory markers were risk factors for patients with vestibular neuritis. The OR values of neutrophil value and blood neutrophil/lymphocyte ratio were 1.81（1.38-2.37, P<0.001） and 2.11（1.41-3.16, P<0.001）, respectively. Apolipoprotein A1 was a protective factor for patients with vestibular neuritis, and the OR value was 0.004（0.001-0.042, P<0.001）. Conclusion:Electrocochleogram could be used in vestibular neuritis patients with normal pure tone threshold to test whether there is hidden hearing loss in these patients. Neutrophil value, lymphocyte value, neutrophil/lymphocyte ratio and apolipoprotein A1 were correlated with vestibular neuritis. The Neutrophil value and neutrophil/lymphocyte ratio were risk factors for morbidity.
Humans ; Vestibular Neuronitis/physiopathology* ; Retrospective Studies ; Female ; Male ; Audiometry, Pure-Tone ; Hearing Loss, Sensorineural/physiopathology* ; Middle Aged ; Adult ; Vestibular Function Tests ; Vestibular Evoked Myogenic Potentials ; Aged

Coronavirus-related diseases pose a significant challenge to the global health system. Given the diversity of coronaviruses and the unpredictable nature of disease outbreaks, the traditional "one bug, one drug" paradigm struggles to address the growing number of emerging crises. Therefore, there is an urgent need for therapeutic agents with broad-spectrum anti-coronavirus activity. Here, we provide evidence that ATV006, an anti-SARS-CoV-2 nucleoside analog targeting RNA-dependent RNA polymerase (RdRp), has broad antiviral activity against human and animal coronaviruses. Using mouse hepatitis virus (MHV) and human coronavirus NL63 (HCoV-NL63) as a model, we show that ATV006 has potent prophylactic and therapeutic activity against murine coronavirus infection in vivo. Remarkably, ATV006 successfully inhibits viral replication in mice even when administered 96 h after infection. Due to its oral bioavailability and potency against multiple coronaviruses, ATV006 has the potential to become a useful antiviral agent against SARS-CoV-2 and other circulating and emerging coronaviruses in humans and animals.

Chemotherapy is currently the mainstay of systemic management for triple-negative breast cancer (TNBC), but chemoresistance significantly impacts patient outcomes. Our research indicates that Doxorubicin (Dox)-resistant TNBC cells exhibit increased glycolysis and ATP generation compared to their parental cells, with this metabolic shift contributing to chemoresistance. We discovered that ALKBH3, an m¹A demethylase enzyme, is crucial in regulating the enhanced glycolysis in Dox-resistant TNBC cells. Knocking down ALKBH3 reduced ATP generation, glucose consumption, and lactate production, implicating its involvement in mediating glycolysis. Further investigation revealed that aldolase A (ALDOA), a key enzyme in glycolysis, is a downstream target of ALKBH3. ALKBH3 regulates ALDOA mRNA stability through m¹A demethylation at the 3'-untranslated region (3'UTR). This methylation negatively affects ALDOA mRNA stability by recruiting the YTHDF2/PAN2-PAN3 complex, leading to mRNA degradation. The ALKBH3/ALDOA axis promotes Dox resistance both in vitro and in vivo. Clinical analysis demonstrated that ALKBH3 and ALDOA are upregulated in breast cancer tissues, and higher expression of these proteins is associated with reduced overall survival in TNBC patients. Our study highlights the role of the ALKBH3/ALDOA axis in contributing to Dox resistance in TNBC cells through regulation of ALDOA mRNA stability and glycolysis.

[This corrects the article DOI: 10.1016/j.apsb.2024.03.030.].