ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

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 design and synthesize rifamycin S derivatives and load them into milk exosomes to evaluate their in vitro antimicrobial activity.METHODS Rifamycin S derivatives were synthe-sized and characterized by mass spectrometry and NMR.Using the dilution assay method,the inhibitory activity of each rifamycin S derivatives molecule against Staphylococcus aureus and Pseudomonas aerugi-nosa was determined,and the IC50 was calculated.Derivatives molecules with excellent antimicrobial activity were selected and loaded into milk exosomes using the ultrasonication method,resulting in the preparation of milk exosome-loaded rifamycin S derivatives.The antimicrobial activity against Staphylo-coccus aureus was determined using the dilution assay method.The inhibitory effect of the exosome-loaded rifamycin S derivatives on Staphylococcus aureus residing within macrophages was detected using the plate colony counting method.RESULTS Three rifamycin S derivatives were successfully designed and synthesized,which demonstrated superior antimicrobial activity against Staphylococcus aureus(the parent compound's antimicrobial activity is merely from 1/20 to 1/80 of that of the three rifamycin S derivatives)and Pseudomonas aeruginosa(the parent compound's antimicrobial activity is only 1/14 and 1/9 of that of compound 1 and compound 3)compared to the parent compound.The loading of milk exosomes with the rifamycin S derivatives compound 3 was successfully achieved,with a loading efficiency of 10.9％.The antimicrobial activity of the compound after exosome loading was significantly enhanced against Staphylococcus aureus in vitro and against Staphylococcus aureus residing within macrophages(P＜0.01).CONCLUSION The designed and synthesized derivatives of rifamycin S possess stronger anti-microbial activity,and their antibacterial efficacy against both extracellular and intracellular bacteria can be further enhanced after loading into exosomes.

Objective To evaluate the efficacy and safety of high-power,short-duration radiofrequency catheter ablation for the treatment of persistent atrial fibrillation.Methods This retrospective study included 392 patients diagnosed with persistent atrial fibrillation who underwent catheter radiofrequency ablation at Suzhou Kowloon Hospital,Shanghai Jiao Tong University School of Medicine,from January 2019 to December 2023.Of these,256 patients were treated with high-power,short-duration ablation,and 136 patients with low-power,long-duration ablation.The following parameters were compared:radiofrequency ablation time,total procedure time,single-circle pulmonary vein isolation rate,immediate procedural success rate,number of ablation points,and perioperative complications(including pericardial tamponade,pseudoaneurysm,arteriovenous fistula,stroke,etc.).Follow-up assessments were conducted at 3,6,and 12 months post-surgery to evaluate the 12-month sinus rhythm maintenance rate.Results The ablation time in the high-power group was significantly shorter than that in the low-power group[(14.6±2.3)min vs.(30.3±4.2)min,P＜0.001],as was the total procedure time[(113.8±24.8)min vs.(128.5±26.7)min,P=0.001].There were no significant differences between the two groups in terms of pulmonary vein isolation rate(97.7％vs.94.9％,P=0.823),number of ablation points[(71.2±8.0)vs.(74.3±14.3),P=0.168],or perioperative complications(3.1％vs.4.4％,P=0.571).Regarding the maintenance rate of sinus rhythm at 12 months post-operation,the high-power group showed a higher rate than the low-power group,but no statistically significant difference was observed(82.8％vs.79.4％,P=0.399).Conclusions High-power,short-duration radiofrequency catheter ablation can improve procedural efficiency in the treatment of persistent atrial fibrillation.Its efficacy and safety are similar to those of the low-power,long-duration technique.

Single-cell metabolite analysis at the small molecule level reveals intercellular heterogeneity and molecular diversity,especially living cell metabolite analysis which can provide more accurate biochemical information.In this study,a comprehensive single-cell metabolomics mass spectrometry analysis platform was constructed based on continuous ultrasonic sample introduction,aiming to improve the utilization rate of single cells and the efficiency of mass spectrometry detection.This platform utilized mechanical motion generated by a miniaturized ultrasound module,which minimally affected cell integrity and viability,enabling cell suspension and dispersion for up to 60 min,with cell viability exceeding 70%.By comparing cell suspension densities and the cell number of mass spectrometry detections between static and ultrasound groups,the results showed that the ultrasound treatment significantly reduced cell sedimentation rate and increased single-cell mass spectrometry detection efficiency.Applying this platform to single-cell analysis of cell line of mouse cerebellar astrocytes(C8D1A)and mouse glioma(GL261)cells achieved clustering and differential analysis of different cell types,demonstrating the method's potential in analyzing cellular heterogeneity and identifying cells.This approach promised to provide new insights and solutions for single-cell analysis.

A method for determination and targeted screening of diflorasone components in health products using ultra performance liquid chromatography-quadrupole time of flight mass spectrometry(UPLC-Q-TOF/MS)was established.Four representative diflorasone and esters(diflorasone,diflorasone diacetate,diflorasone-17-propionate,and diflorasone-21-propionate)were selected to optimize the pretreatment conditions,and 10 mL of extraction solvent dosage,15 min of extraction time and 5 g of salting-out agent as the optimal conditions were selected by response surface methodology.The results showed that the four analytes exhibited good linearity within the concentration range of 2.0?100 μg/L with the chromatographic peak area,and the correlation coefficients(R2)were all greater than 0.9990,while the results of recovery and relative standard deviation could satisfy the requirements of determination.The common characteristic ions of diflorasone and esters werem/z121 andm/z335,and their specific structures were obtained by analyzing the cleavage pathway based on the optimized determination conditions.A targeted screening method for other esters of diflorasone based on characteristic ions guidance strategy was established.This method had many advantages such as high efficiency,high sensitivity and good reproducibility,and could be used for targeted screening of diflorasone and esters in health products.The developed characteristic ion guided strategy could be employed to construct mass spectral databases for various glucocorticoids,enabling comprehensive targeted screening across a broad range of compounds.

Combined seawater Hf and Nd isotope compositions have been applied as reliable tracers of water mass mixing and weathering input changes.The establishment of standardized synchronous extraction methods for Fe-Mn oxyhydroxide in sediments,which serve as effective carriers for isotopic signals of ancient seawater,is a prerequisite and key for paleoceanography research.The research material of this study was the surface sediments of the Western Arctic Ocean obtained from China's 7th Arctic Scientific Expedition,and a reliable simultaneous Nd-Hf isotope extraction method in Fe-Mn oxyhydroxide fraction was established through systematic optimization of pretreatment procedures and key extraction parameters.This research focused on analyzing the effects of pretreatment(Milli-Q water pre-wash)and key extraction parameters(leaching time,solution/solid ratios,concentration of EDTA-2Na,and concentration of mixed solution of hydroxyamine hydrochloride(HH)and acetic acid(HAc))on element extraction efficiency and isotopic composition.The experimental results indicated that the Milli-Q water pre-wash was not necessary,Fe-Mn oxyhydroxide extraction could be directly performed.Nd extraction amount in Fe-Mn oxyhydroxide fraction was not sensitive to changes in extraction conditions,while the Hf extraction amount was significantly affected by leaching time,solution/solid ratios and concentration of EDTA-2Na.The optimal conditions for synchronously extracting Nd-Hf isotopes were as follows:adding 20 mL of a mixed solution of 0.005 mol/L HH+1.5%HAc+0.03 mol/L EDTA-2Na per gram of sample,and reaction at room temperature for 3 h under shaking.Through systematic verification of 87Sr/86Sr ratio,εNd value,εHf value,Al/Nd ratio and Al/Hf ratio,it was confirmed that this method could effectively avoid interferences from residual components and accurately obtain Nd-Hf isotope signals of ancient seawater preserved in sediments.The standardized method established in this work provided reliable technical support for reconstructing water mass mixing between the Arctic,North Atlantic,and North Pacific Oceans and riverine input histories.It also provided important reference for the extraction methods of Nd and Hf isotopes from marine sources in sediments from other sea areas around the world.

Aim To investigate the role of miR-130b-3p in regulating the USP47/NLRP3 inflammasome in airway remodeling associated with asthma and to explore its potential therapeutic value in asthma treat-ment.Methods An OVA-induced asthma mouse mod-el was established,and intervention with miR-130b-3p agomir was performed.Histological staining,quantita-tive real-time PCR,Western blot,immunofluorescence and flow cytometry were used to analyze the effects of miR-130b-3p on the expression of USP47,NLRP3,and related inflammatory factors,as well as the inflamma-some activity.Results miR-130b-3p was significantly downregulated in asthmatic mice,and its intervention significantly inhibited airway epithelial damage,inflam-matory cell infiltration,and collagen deposition.Addi-tionally,miR-130b-3p targeted USP47 and indirectly suppressed NLRP3 expression,leading to reduced in-flammasome activity and alleviated asthma-related in-flammatory responses.Conclusion miR-130b-3p re-duces asthma-related inflammatory responses by down-regulating USP47 expression and indirectly inhibiting NLRP3 inflammasome activity.

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.