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.

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

ObjectiveThe aim of this study was to investigate the prophylactic effects of caloric restriction (CR) on lipopolysaccharide (LPS)-induced septic cardiomyopathy (SCM) and to elucidate the mechanisms underlying the cardioprotective actions of CR. This research aims to provide innovative strategies and theoretical support for the prevention of SCM. MethodsA total of forty-eight 8-week-old male C57BL/6 mice, weighing between 20-25 g, were randomly assigned to 4 distinct groups, each consisting of 12 mice. The groups were designated as follows: CON (control), LPS, CR, and CR+LPS. Prior to the initiation of the CR protocol, the CR and CR+LPS groups underwent a 2-week acclimatization period during which individual food consumption was measured. The initial week of CR intervention was set at 80% of the baseline intake, followed by a reduction to 60% for the subsequent 5 weeks. After 6-week CR intervention, all 4 groups received an intraperitoneal injection of either normal saline or LPS (10 mg/kg). Twelve hours post-injection, heart function was assessed, and subsequently, heart and blood samples were collected. Serum inflammatory markers were quantified using enzyme-linked immunosorbent assay (ELISA). The serum myocardial enzyme spectrum was analyzed using an automated biochemical instrument. Myocardial tissue sections underwent hematoxylin and eosin (HE) staining and immunofluorescence (IF) staining. Western blot analysis was used to detect the expression of protein in myocardial tissue, including inflammatory markers (TNF-α, IL-9, IL-18), oxidative stress markers (iNOS, SOD2), pro-apoptotic markers (Bax/Bcl-2 ratio, CASP3), and SIRT3/SIRT6. ResultsTwelve hours after LPS injection, there was a significant decrease in ejection fraction (EF) and fractional shortening (FS) ratios, along with a notable increase in left ventricular end-systolic diameter (LVESD). Morphological and serum indicators (AST, LDH, CK, and CK-MB) indicated that LPS injection could induce myocardial structural disorders and myocardial injury. Furthermore, 6-week CR effectively prevented the myocardial injury. LPS injection also significantly increased the circulating inflammatory levels (IL-1β, TNF-α) in mice. IF and Western blot analyses revealed that LPS injection significantly up-regulating the expression of inflammatory-related proteins (TNF-α, IL-9, IL-18), oxidative stress-related proteins (iNOS, SOD2) and apoptotic proteins (Bax/Bcl-2 ratio, CASP3) in myocardial tissue. 6-week CR intervention significantly reduced circulating inflammatory levels and downregulated the expression of inflammatory, oxidative stress-related proteins and pro-apoptotic level in myocardial tissue. Additionally, LPS injection significantly downregulated the expression of SIRT3 and SIRT6 proteins in myocardial tissue, and CR intervention could restore the expression of SIRT3 proteins. ConclusionA 6-week CR could prevent LPS-induced septic cardiomyopathy, including cardiac function decline, myocardial structural damage, inflammation, oxidative stress, and apoptosis. The mechanism may be associated with the regulation of SIRT3 expression in myocardial tissue.

Optical imaging is highly valued for its superior temporal and spatial resolution. This is particularly important in near-infrared II (NIR-II, 1 000-3 000 nm) imaging, which offers advantages such as reduced tissue absorption, minimal scattering, and low autofluorescence. These characteristics make NIR-II imaging especially suitable for deep tissue visualization, where high contrast and minimal background interference are critical for accurate diagnosis and monitoring. Currently, inorganic fluorescent probes—such as carbon nanotubes, rare earth nanoparticles, and quantum dots—offer high brightness and stability. However, they are hindered by ambiguous structures, larger sizes, and potential accumulation toxicity in vivo. In contrast, organic fluorescent probes, including small molecules and polymers, demonstrate higher biocompatibility but are limited by shorter emission wavelengths, lower quantum yields, and reduced stability. Recently, gold clusters have emerged as a promising class of nanomaterials with potential applications in biocatalysis, fluorescence sensing, biological imaging, and more. Water-soluble gold clusters are particularly attractive as fluorescent probes due to their remarkable optical properties, including strong photoluminescence, large Stokes shifts, and excellent photostability. Furthermore, their outstanding biocompatibility—attributed to good aqueous stability, ultra-small hydrodynamic size, and high renal clearance efficiency—makes them especially suitable for biomedical applications. Gold clusters hold significant potential for NIR-II fluorescence imaging. Atomic-precision gold clusters, typically composed of tens to hundreds of gold atoms and measuring only a few nanometers in diameter, possess well-defined three-dimensional structures and clear spatial coordination. This atomic-level precision enables fine-tuned structural regulation, further enhancing their fluorescence properties. Variations in cluster size, surface ligands, and alloying elements can result in distinct physicochemical characteristics. The incorporation of different atoms can modulate the atomic and electronic structures of gold clusters, while diverse ligands can influence surface polarity and steric hindrance. As such, strategies like alloying and ligand engineering are effective in enhancing both fluorescence and catalytic performance, thereby meeting a broader range of clinical needs. In recent years, gold clusters have attracted growing attention in the biomedical field. Their application in NIR-II imaging has led to significant progress in vascular, organ, and tumor imaging. The resulting high-resolution, high signal-to-noise imaging provides powerful tools for clinical diagnostics. Moreover, biologically active gold clusters can aid in drug delivery and disease diagnosis and treatment, offering new opportunities for clinical therapeutics. Despite the notable achievements in fundamental research and clinical translation, further studies are required to address challenges related to the standardized synthesis and complex metabolic behavior of gold clusters. Resolving these issues will help accelerate their clinical adoption and broaden their biomedical applications.

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.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

The chemical constituents in dried roots of Atractylodes macrocephala were investigated in this study. Through utilizing normal-phase silica gel and ODS column chromatography, TLC, and semi-preparative HPLC, 4 new sesquiterpenoids were purified from the ethyl acetate extract of A. macrocephala. By various spectroscopic techniques, such as 1D and 2D NMR, HR-ESI-MS, IR, UV, and CD, their structures were identified as atractylmacron A (1), 9β-hydroxyasterolide (2), atractylenolide H (3), atractylenolide J (4). Compound 1 possesses a rare 6/7 bicyclic skeleton.

Retinal displacement refers to the strong fluorescent lines parallel to the retinal vessels that are detected through autofluorescence examination after rhegmatogenous retinal detachment(RRD)surgery. Actually, even if patients with RRD achieve macroscopic structural reattachment after the operation, the visual function of some patients remains suboptimal. This is associated with the incomplete recovery of retinal function, and retinal displacement is one of the critical influencing factors. This paper reviews the related concepts of retinal displacement and systematically summarizes the incidence of retinal displacement after RRD surgery and its impact on function, the possible mechanisms of retinal displacement, and the influence of various factors on the occurrence of retinal displacement reported in the recent 5 a. It is conducive to enabling surgeons to conduct better design and planning for retinal reattachment surgeries, then achieve higher integrity of retinal function recovery, and enable patients to obtain better postoperative visual function.

Objective:To investigate the alteration and regulatory of macrophage subtypes and the underlying mechanisms of cellular interactions in mouse photoaged skin.Methods:Immune cell type identification was performed by estimating relative subpopulations of RNA transcripts (CIBERSORT) on 18 samples from the public dataset GSE58915. A total of 15 healthy male C57BL/6J mice aged 6-8 weeks were exposed to an animal UV-radiation chamber for 4 weeks (4W-UV group) and 8 weeks (8W-UV group). Skin samples were collected for hematoxylin-eosin staining, Masson staining, immunohistochemistry and immunofluorescence to evaluate skin architecture, inflammatory status and macrophage infiltration. Dermal fibroblasts of passages 3-5 were irradiated daily at 36 mW/cm2 for 7 days to establish a photoaged model; senescence-associated indicators were detected by β-galactosidase staining and Western blot. A co-culture system of photoaged fibroblasts and mouse monocyte-macrophages was then constructed; phagocytosis assays and flow cytometry were employed to determine the phagocytic capacity and polarization of monocyte-macrophages.Results:The number of M1 macrophages in mouse skin increased with UV-radiation duration; M1 counts in the 8W-UV and 4W-UV groups were (17.2±4.7) and (10.3±2.1) cells/HPF, respectively, both higher than the (3.8±0.7) cells/HPF observed in the control group (both P<0.01). Monocyte-macrophages treated with supernatant from photoaged fibroblasts exhibited enhanced phagocytic activity and a higher proportion of CD86-positive cells. Conclusions:Prolonged UV radiation aggravates photoaging and increases M1-macrophage infiltration in skin tissue. Cytokines secreted by photoaged fibroblasts induce M1 polarization of macrophages.