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 develop and validate a clinically actionable prediction model for postoperative pulmonary complications (PPCs) in cardiac surgery patients, focusing on modifiable preoperative risk factors amenable to targeted optimization. Methods: In this prospective observational cohort study, 492 adults undergoing open-chest cardiac surgery between August 15, 2023 and December 31, 2023 were analyzed. Prespecified predictors included gas exchange variables, pulmonary function, inspiratory muscle strength, and physical performance. Univariable and multivariable logistic regression analyses were used to develop the prediction model. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Results: A total of 90 patients (14.1%) developed PPCs after surgery. Five independent predictors were identified: elevated arterial PaCO2 (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.00–1.26), oxygen desaturation (SpO2<93%) (OR 12.47, 95% CI 3.51–48.13), reduced gait speed (OR 0.17, 95% CI 0.04–0.71), lower FEV1/FVC ratio (OR 0.96, 95% CI 0.92–1.00), and diminished inspiratory muscle strength (MIP % predicted) (OR 0.96, 95% CI 0.92–0.99). The model demonstrated good discriminative ability with an AUC of 0.86 (95% CI 0.80–0.93) in the training cohort and 0.87 (95% CI 0.74–0.93) in the validation cohort. Conclusion This parsimonious model achieved high predictive accuracy using five modifiable physiological variables. By targeting abnormalities in gas exchange, pulmonary mechanics, muscle strength, and functional reserve, the model offers a practical tool to guide individualized prehabilitation strategies for reducing PPC risk in cardiac surgery patients.

Objective This study aims to assess the impact of four-dimensional cone-beam computed tomography (4D-CBCT) image guidance on setup error correction and target dose distribution optimization in patients receiving stereotactic body radiotherapy for lung cancer. Methods A total of 82 lung cancer patients treated with SBRT in our hospital between February 2023 and February 2025 were randomly selected as study subjects. Using a random number table, they were divided into two groups. The conventional group underwent positioning verification using traditional three-dimensional cone-beam CT, while the study group underwent verification using 4D-CBCT. Setup errors, target dose-volume parameters, organ at risk dose-volume parameters, and adverse reactions were compared between the two groups. Results The setup errors in the X, Y, and Z axes in the study group were 0.21, 0.19, and 0.23 mm, respectively, which were significantly lower than 0.58, 0.62, and 0.59 mm in the conventional group (P<0.05). The three-dimensional vector error was 0.34 (0.21, 0.48) mm in the study group, which was significantly lower than 0.98 (0.65, 1.32) mm in the conventional group (P<0.05). The rotational errors in left-right, anteroposterior, and superior-inferior directions in the study group were (0.25±0.12)°, (0.21±0.10)°, and (0.23±0.11)°, respectively; these values were significantly lower than (0.48±0.18)°, (0.42±0.15)°, and (0.45±0.16)° in the conventional group (P<0.05). The minimum dose received by 95% of the target volume [(59.45±0.85) Gy] and the percentage of the target volume receiving at least 100% of the prescription dose [(95.82±2.65)%] in the study group were higher than those in the conventional group [(58.90±1.20) Gy and (93.65±3.85)%]. The homogeneity index and conformity index in the study group were 1.07±0.03 and 1.15±0.05, respectively, which were lower than 1.12±0.05 and 1.23±0.08 in the conventional group (P<0.05). No significant differences were observed between the two groups in the volume of the lung receiving 5 Gy [(28.11±5.52)% vs. (28.85±6.24)%], the volume of the lung receiving 20 Gy [(12.84±3.56)% vs. (13.45±3.90)%], mean lung dose [(8.65±2.11) Gy vs. (8.95±2.34) Gy], maximum spinal cord dose [(18.54±3.62) Gy vs. (19.13±4.25) Gy], or mean heart dose[(14.85±4.22) Gy vs. (16.27±4.83) Gy](P>0.05). The incidence rates of radiation pneumonitis, radiation esophagitis, and skin reactions in the study group were 12.20%, 7.32%, and 4.88%, respectively, which were not significantly different from 17.07%, 9.76%, and 7.32% in the conventional group (P>0.05). Conclusion The 4D-CBCT can reduce setup errors, improve target dose accuracy and conformity in lung cancer patients undergoing SBRT, and minimize radiation exposure to normal tissues, making it worthy of clinical promotion.

Objective This study aims to assess the impact of four-dimensional cone-beam computed tomography (4D-CBCT) image guidance on setup error correction and target dose distribution optimization in patients receiving stereotactic body radiotherapy for lung cancer. Methods A total of 82 lung cancer patients treated with SBRT in our hospital between February 2023 and February 2025 were randomly selected as study subjects. Using a random number table, they were divided into two groups. The conventional group underwent positioning verification using traditional three-dimensional cone-beam CT, while the study group underwent verification using 4D-CBCT. Setup errors, target dose-volume parameters, organ at risk dose-volume parameters, and adverse reactions were compared between the two groups. Results The setup errors in the X, Y, and Z axes in the study group were 0.21, 0.19, and 0.23 mm, respectively, which were significantly lower than 0.58, 0.62, and 0.59 mm in the conventional group (P<0.05). The three-dimensional vector error was 0.34 (0.21, 0.48) mm in the study group, which was significantly lower than 0.98 (0.65, 1.32) mm in the conventional group (P<0.05). The rotational errors in left-right, anteroposterior, and superior-inferior directions in the study group were (0.25±0.12)°, (0.21±0.10)°, and (0.23±0.11)°, respectively; these values were significantly lower than (0.48±0.18)°, (0.42±0.15)°, and (0.45±0.16)° in the conventional group (P<0.05). The minimum dose received by 95% of the target volume [(59.45±0.85) Gy] and the percentage of the target volume receiving at least 100% of the prescription dose [(95.82±2.65)%] in the study group were higher than those in the conventional group [(58.90±1.20) Gy and (93.65±3.85)%]. The homogeneity index and conformity index in the study group were 1.07±0.03 and 1.15±0.05, respectively, which were lower than 1.12±0.05 and 1.23±0.08 in the conventional group (P<0.05). No significant differences were observed between the two groups in the volume of the lung receiving 5 Gy [(28.11±5.52)% vs. (28.85±6.24)%], the volume of the lung receiving 20 Gy [(12.84±3.56)% vs. (13.45±3.90)%], mean lung dose [(8.65±2.11) Gy vs. (8.95±2.34) Gy], maximum spinal cord dose [(18.54±3.62) Gy vs. (19.13±4.25) Gy], or mean heart dose[(14.85±4.22) Gy vs. (16.27±4.83) Gy](P>0.05). The incidence rates of radiation pneumonitis, radiation esophagitis, and skin reactions in the study group were 12.20%, 7.32%, and 4.88%, respectively, which were not significantly different from 17.07%, 9.76%, and 7.32% in the conventional group (P>0.05). Conclusion The 4D-CBCT can reduce setup errors, improve target dose accuracy and conformity in lung cancer patients undergoing SBRT, and minimize radiation exposure to normal tissues, making it worthy of clinical promotion.

Objective This study aims to assess the impact of four-dimensional cone-beam computed tomography (4D-CBCT) image guidance on setup error correction and target dose distribution optimization in patients receiving stereotactic body radiotherapy for lung cancer. Methods A total of 82 lung cancer patients treated with SBRT in our hospital between February 2023 and February 2025 were randomly selected as study subjects. Using a random number table, they were divided into two groups. The conventional group underwent positioning verification using traditional three-dimensional cone-beam CT, while the study group underwent verification using 4D-CBCT. Setup errors, target dose-volume parameters, organ at risk dose-volume parameters, and adverse reactions were compared between the two groups. Results The setup errors in the X, Y, and Z axes in the study group were 0.21, 0.19, and 0.23 mm, respectively, which were significantly lower than 0.58, 0.62, and 0.59 mm in the conventional group (P<0.05). The three-dimensional vector error was 0.34 (0.21, 0.48) mm in the study group, which was significantly lower than 0.98 (0.65, 1.32) mm in the conventional group (P<0.05). The rotational errors in left-right, anteroposterior, and superior-inferior directions in the study group were (0.25±0.12)°, (0.21±0.10)°, and (0.23±0.11)°, respectively; these values were significantly lower than (0.48±0.18)°, (0.42±0.15)°, and (0.45±0.16)° in the conventional group (P<0.05). The minimum dose received by 95% of the target volume [(59.45±0.85) Gy] and the percentage of the target volume receiving at least 100% of the prescription dose [(95.82±2.65)%] in the study group were higher than those in the conventional group [(58.90±1.20) Gy and (93.65±3.85)%]. The homogeneity index and conformity index in the study group were 1.07±0.03 and 1.15±0.05, respectively, which were lower than 1.12±0.05 and 1.23±0.08 in the conventional group (P<0.05). No significant differences were observed between the two groups in the volume of the lung receiving 5 Gy [(28.11±5.52)% vs. (28.85±6.24)%], the volume of the lung receiving 20 Gy [(12.84±3.56)% vs. (13.45±3.90)%], mean lung dose [(8.65±2.11) Gy vs. (8.95±2.34) Gy], maximum spinal cord dose [(18.54±3.62) Gy vs. (19.13±4.25) Gy], or mean heart dose[(14.85±4.22) Gy vs. (16.27±4.83) Gy](P>0.05). The incidence rates of radiation pneumonitis, radiation esophagitis, and skin reactions in the study group were 12.20%, 7.32%, and 4.88%, respectively, which were not significantly different from 17.07%, 9.76%, and 7.32% in the conventional group (P>0.05). Conclusion The 4D-CBCT can reduce setup errors, improve target dose accuracy and conformity in lung cancer patients undergoing SBRT, and minimize radiation exposure to normal tissues, making it worthy of clinical promotion.

Autism spectrum disorder (ASD) is a neurodevelopmental condition with a steadily rising global prevalence, yet effective pharmacological interventions remain notably limited, highlighting an urgent need for safe, accessible, and mechanism-based therapeutic strategies. Physical exercise has emerged as a promising non-pharmacological intervention that ameliorates both core symptoms—social communication deficits and restricted repetitive behaviors—and associated features including cognitive dysfunction and motor impairments, in children and adolescents with ASD. However, the molecular mechanisms mediating these beneficial effects remain incompletely defined, impeding the development of evidence-based exercise prescriptions and biomarker-driven rehabilitation protocols. MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs that post-transcriptionally regulate approximately 60% of protein-coding genes. Within the central nervous system, miRNAs orchestrate diverse neurobiological processes including neural progenitor proliferation, neuronal differentiation, dendritic spine morphogenesis, synaptic plasticity, and neuroinflammatory homeostasis. Notably, miRNAs are remarkably stable in biological fluids and can be packaged into extracellular vesicles, rendering them attractive candidates as both mechanistic mediators and non-invasive peripheral biomarkers. Among the hundreds of miRNAs expressed in the brain, three—miR-132, miR-34a, and miR-146a—have emerged as particularly relevant to ASD pathophysiology. This review focuses on these three miRNAs for the following reasons: miR-132 is a master regulator of activity-dependent synaptic plasticity through its modulation of BDNF/MeCP2/PTEN signaling and has been consistently downregulated in ASD prefrontal cortex; miR-34a functions as a pro-apoptotic factor that suppresses Bcl-2-mediated neuronal survival pathways and is upregulated in ASD cerebellum; and miR-146a serves as a key brake on neuroinflammation via TLR7/IRAK1 signaling and shows region-specific dysregulation in ASD temporal lobe. We first summarize evidence from human post-mortem brain tissues and ASD animal models demonstrating the consistent dysregulation of these three miRNAs. Notably, the pathological consequences of these miRNA alterations—impaired synaptic plasticity, excessive neuronal apoptosis, and sustained neuroinflammation—are interconnected and collectively contribute to the heterogeneous symptomatology of ASD. We then present a synthesis of emerging evidence demonstrating that various exercise modalities, including swimming, treadmill running, and voluntary wheel running, can concurrently reverse these ASD-like behavioral phenotypes and normalize the expression of the three key miRNAs. These data provide the first direct experimental evidence linking exercise-induced miRNA modulation to ASD symptom improvement. On the basis of these findings, we propose an integrative “exercise-miRNA-ASD” framework wherein exercise functions as a multi-targeted modulator—simultaneously enhancing synaptic plasticity, promoting neuronal survival, and attenuating neuroinflammation—through coordinated regulation of the three miRNAs. Importantly, this framework is not merely descriptive but offers testable predictions: exercise-induced miRNA changes should be dose-dependent, show temporal correlation with behavioral improvements, and be blunted by miRNA-specific antagonists or CRISPR/Cas9-mediated knockout. Beyond its specific application to ASD, this framework has broader implications. The miR-132/BDNF, miR-34a/Bcl-2, and miR-146a/TLR7 pathways are not ASD-specific but represent fundamental neural stress and repair mechanisms that are dysregulated across Alzheimer’s disease (AD), traumatic brain injury, Parkinson’s disease (PD), and major depressive disorder. Exercise has been shown to modulate these same miRNAs in several of these conditions, suggesting that the “exercise-miRNA-neural function” axis may represent a conserved neuroprotective mechanism that transcends diagnostic boundaries. Thus, we propose that ASD serves as an ideal model disease for elucidating this universal mechanism, with findings potentially generalizable to other neurological disorders. We also critically evaluate current translational barriers: the near-absence of human clinical trials with serial miRNA profiling; the undefined dose-response relationships between exercise parameters and miRNA expression; the unresolved causality issue (current evidence demonstrates association, not causation); and the uncertain correlation between peripheral exosomal miRNA levels and brain miRNA dynamics. We argue that future research must prioritize CRISPR/Cas9-based miRNA manipulation in animal models combined with longitudinal exercise interventions to establish causality, and that cross-disease validation studies are essential to determine whether exercise-induced miRNA changes represent a shared neuroprotective signature or disease-specific responses. Ultimately, we envision a paradigm where a simple blood test measuring exosomal miR-132, miR-34a, and miR-146a levels could guide personalized exercise prescriptions, enabling precision rehabilitation for individuals with ASD and potentially other neurological conditions.

ObjectiveThis study sought to investigate the impact of exosomes derived from LoVo cells (LoVo-Exos) in colorectal cancer (CRC) on tumor angiogenesis, as well as to elucidate the potential molecular mechanisms underlying their pro-angiogenic effects. MethodsLoVo-Exos were isolated via ultracentrifugation, and their internalization into recipient human umbilical vein endothelial cells (HUVECs) was visualized using confocal microscopy. The influence of LoVo-Exos on angiogenesis was assessed through an in vitro tube formation assay. Additionally, the pro-angiogenic effects of LoVo-Exos were evaluated in vivo using a matrix gluing assay in mice. To investigate the molecular mechanisms through which LoVo-Exos facilitate angiogenesis, Western blot analysis was employed to examine the transfer of pEGFR by LoVo-Exos into recipient cells. Both Western blot and ELISA were utilized to assess the expression levels of key signaling proteins within the EGFR-ERK pathway, as well as the expression of downstream angiogenic core molecules. Furthermore, the impact of EGFR knockdown and ERK inhibitor treatment on angiogenesis was evaluated, with subsequent analysis of the expression of downstream angiogenic core molecules following these interventions. ResultsConfocal microscopy demonstrated the internalization of LoVo-Exos into HUVECs. In vitro angiogenesis assays further indicated that LoVo-Exos significantly enhanced the formation of tubular structures in HUVECs. Additionally, macroscopic examination of subcutaneous matrix plug formation in mice revealed a substantial increase in vascular-like structures within the matrix plugs following the administration of LoVo-Exos, compared to the PBS control group. Hematoxylin and eosin (HE) staining revealed the presence of erythrocyte-filled microvessels within the matrix plugs combined with LoVo-Exos. Furthermore, immunohistochemical analysis demonstrated the expression of the endothelial cell marker CD31 in these matrix plugs. The presence of CD31-positive cells in the LoVo-Exos-treated matrix plugs was associated with a significant enhancement in the formation of luminal structures. These findings suggest that LoVo-Exos facilitate the in vivo development of vascular-like structures. Subsequent investigations demonstrated that LoVo-Exos facilitated the delivery of pEGFR to HUVEC, thereby enhancing angiogenesis. Conversely, LoVo-Exos with EGFR knockdown exhibited a diminished capacity to promote angiogenesis, an effect that was further attenuated by the ERK phosphorylation inhibitor U0126. Western blot analysis assessing the activation of the EGFR-ERK signaling pathway in HUVEC indicated that LoVo-Exos augmented angiogenesis through the activation of this pathway. Furthermore, analysis of the impact of LoVo-Exos on the expression of downstream angiogenic core molecules revealed an increase in interleukin-8 (IL-8) secretion in HUVEC. The enhancement observed was diminished in LoVo-Exos following EGFR knockdown, and this reduction was counteracted by the ERK phosphorylation inhibitor U0126. ConclusionThe underlying mechanism may involve the delivery of pEGFR in LoVo-Exos to HUVECs, leading to increased IL-8 secretion via the EGFR-ERK signaling pathway, thereby enhancing the angiogenic potential of HUVECs. This finding may offer new insights into the mechanisms underlying cancer metastasis.

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.