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.

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

Objective:To investigate which indicator is more advantageous when using arterial oxygen saturation(SaO2)and fingertip pulse oxygen saturation(SpO2)for blood oxygen detection in patients with hyperleukocytic acute leukemia(HAL).Methods:In this prospective research,the difference between SaO2 and SpO2 of 18 HAL patients(observation group)and 14 patients(control group),as well as the relationship between the difference and white blood cell(WBC)counts were analyzed.Results:SaO2 was lower than SpO2 in the observation group(P＜0.05),and SpO2-SaO2 difference was positively correlated with WBC counts(r=0.47).However,there was no statistical difference between SaO2 and SpO2 in the control group.SaO2 and PO2 showed a downward trend with the prolongation of detection time after arterial blood was collected in the observation group,but there was no statistical difference.There was no downward trend of SaO2 and PO2 in the control group.Conclusion:HAL patients have a phenomenon where SaO2 is lower than SpO2,that is pseudohypoxemia,and this phenomenon may be caused by excessive consumption of oxygen by the leukemia cells in vitro.SpO2 can be monitored bedside in real time and is non-invasive,it is a better way to detect the blood oxygen status of HAL patients.

This study investigated the mechanism by which kaempferol(KAE) affected intervertebral disc degeneration(IDD) through the p38 mitogen-activated protein kinase(p38 MAPK) signaling pathway. Rats were randomly divided into five groups: control group, model group, low-dose KAE group, medium-dose KAE group, and high-dose KAE group. An IDD model was established by needle puncture of the caudal intervertebral discs. Four weeks post-surgery, the rats were administered KAE via gavage for 8 consecutive weeks. Magnetic resonance imaging(MRI) was performed, and samples were collected. In vitro, an inflammation model of nucleus pulposus cells(NPCs) induced by tumor necrosis factor-alpha(TNF-α) was constructed. Anisomycin was used to activate the p38 MAPK signaling pathway. NPCs were divided into blank, model, KAE, agonist, and KAE + agonist groups. After 1 day of treatment, cell proliferation activity was assessed using the CCK-8. Protein expression levels were determined by Western blot, and mRNA expression was measured by real-time quantitative polymerase chain reaction. Cell apoptosis was detected by TUNEL staining, and immunofluorescence staining was used to detect type Ⅱ collagen and matrix metalloproteinase 3(MMP3). In vivo results indicated significant improvement in the degree of IDD in the treatment groups compared to the model group, with the medium-dose group showing more pronounced therapeutic effects than the low-and high-dose groups. In vitro results demonstrated that KAE treatment significantly enhanced NPC proliferation activity, down-regulated the expression levels of Bcl-2-associated X protein(Bax), interleukin-6(IL-6), interleukin-17A(IL-17A), MMP3, and a disintegrin and metalloproteinase with thrombospondin motifs 5, and inhibited the phosphorylation of p38 MAPK pathway-related proteins. Activation of the p38 MAPK signaling pathway by anisomycin reduced the therapeutic effects of KAE. The study concluded that KAE could improve the proliferation activity of degenerated NPCs, reduce inflammation levels, and slow the progression of IDD in rats, and the mechanism was likely related to the regulation of the p38 MAPK signaling pathway.
Animals ; p38 Mitogen-Activated Protein Kinases/genetics* ; Kaempferols/pharmacology* ; Intervertebral Disc Degeneration/genetics* ; Rats ; Rats, Sprague-Dawley ; Male ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Nucleus Pulposus/drug effects* ; Signal Transduction/drug effects* ; Humans ; MAP Kinase Signaling System/drug effects*

Objective: To investigate the clinical characteristics and the risk factors of severe human metapneumovirus (hMPV)-associated community acquired pneumonia (CAP) in children. Methods: A retrospective case summary was conducted. From December 2020 to March 2022, 721 children who were diagnosed with CAP and tested positive for hMPV nucleic acid by PCR-capillary electrophoresis fragment analysis of nasopharyngeal secretions at the Yuying Children's Hospital, the Second Affiliated Hospital of Wenzhou Medical University were selected as the research objects. The clinical characteristics, epidemiological characteristics and mixed pathogens of the two groups were analyzed. According to CAP diagnostic criteria, the children were divided into the severe group and the mild group. Chi-square test or Mann-Whitney rank and contrast analysis was used for comparison between groups, while multivariate Logistic regression was applied to analyze the risk factors of the severe hMPV-associated CAP. Results: A total of 721 children who were diagnosed with hMPV-associated CAP were included in this study, with 397 males and 324 females. There were 154 cases in the severe group. The age of onset was 1.0 (0.9, 3.0) years, <3 years old 104 cases (67.5%), and the length of hospital stay was 7 (6, 9) days. In the severe group, 67 children (43.5%) were complicated with underlying diseases. In the severe group, 154 cases (100.0%) had cough, 148 cases (96.1%) had shortness of breath and pulmonary moist rales, and 132 cases (85.7%) had fever, 23 cases (14.9%) were complicated with respiratory failure. C-reactive protein (CRP) was elevated in 86 children (55.8%), including CRP≥50 mg/L in 33 children (21.4%). Co-infection was detected in 77 cases (50.0%) and 102 strains of pathogen were detected, 25 strains of rhinovirus, 17 strains of Mycoplasma pneumoniae, 15 strains of Streptococcus pneumoniae, 12 strains of Haemophilus influenzae and 10 strains of respiratory syncytial virus were detected. Six cases (3.9%) received heated and humidified high flow nasal cannula oxygen therapy, 15 cases (9.7%) were admitted to intensive care unit, and 2 cases (1.3%) received mechanical ventilation. In the severe group, 108 children were cured, 42 children were improved, 4 chlidren were discharged automatically without recovery and no death occurred. There were 567 cases in the mild group. The age of onset was 2.7 (1.0, 4.0) years, and the length of hospital stay was 4 (4, 6) days.Compared with the mild group, the proportion of children who age of disease onset <6 months, CRP≥50 mg/L, the proportions of preterm birth, congenital heart disease, malnutrition, congenital airway malformation, neuromuscular disease, mixed respiratory syncytial viruses infection were higher (20 cases (13.0%) vs. 31 cases (5.5%), 32 cases (20.8%) vs. 64 cases (11.3%), 23 cases (14.9%) vs. 44 cases (7.8%), 11 cases (7.1%) vs. 18 cases (3.2%), 9 cases (5.8%) vs. 6 cases (1.1%), 11 cases (7.1%) vs. 12 cases (2.1%), 8 cases (5.2%) vs. 4 cases (0.7%), 10 cases (6.5%) vs. 13 cases (2.3%), χ²=0.42, 9.45, 7.40, 4.94, 11.40, 8.35, 3.52, 6.92, all P<0.05). Multivariate Logistic regression analysis showed that age<6 months (OR=2.51, 95%CI 1.29-4.89), CRP≥50 mg/L (OR=2.20, 95%CI 1.36-3.57), prematurity (OR=2.19, 95%CI 1.26-3.81), malnutrition (OR=6.05, 95%CI 1.89-19.39) were the independent risk factors for severe hMPV-associated CAP. Conclusions: Severe hMPV-associated CAP is most likely to occur in infants under 3 years old and has a higher proportion of underlying diseases and co-infection. The main clinical manifestations are cough, shortness of breath and pulmonary moist rales, fever. The overall prognosis is good. Age<6 months, CRP≥50 mg/L, preterm birth, malnutrition are the independent risk factors for severe hMPV-associated CAP.
Infant ; Male ; Female ; Humans ; Child ; Infant, Newborn ; Child, Preschool ; Retrospective Studies ; Cough ; Coinfection ; Premature Birth ; Respiratory Sounds ; Metapneumovirus ; Pneumonia, Viral/epidemiology* ; Respiratory Syncytial Virus, Human ; Community-Acquired Infections/epidemiology* ; Risk Factors ; Dyspnea ; Malnutrition

Objective To evaluate the radiation dose of interventional procedure for children with congenital heart disease, and to analyze the differences in radiation dose and influencing factors. Methods A total of 94 children who underwent interventional procedure for congenital heart disease at a grade A tertiary hospital in Jinan, Shandong Province, China from June 2021 to September 2022 were included in this study. The patients were divided into three groups according to the type of procedure: ventricular septal defect occlusion group (VSD, 48 cases), patent ductus arteriosus occlusion group (PDA, 29 cases), and atrial septal defect occlusion group (ASD, 17 cases). The basic information of patients and postoperative dose reports were recorded. A statistical analysis was performed using SPSS software. Results The median cumulative air kerma (CAK) of VSD, PDA, and ASD was 100.5, 43.7, and 12.1 mGy, respectively. The median air kerma area product (KAP) of VSD, PDA, and ASD was 3.309, 1.313, and 0.540 Gy·cm², respectively. The median KAP·kg⁻¹ of VSD, PDA, and ASD was 0.179, 0.088, and 0.031 Gy·cm²·kg⁻¹, respectively. There were significant differences in fluoroscopy time, number of cine images, CAK, KAP, and KAP·kg⁻¹ among the three types of interventional procedures (P<0.05). Compared with PDA and ASD, VSD showed significantly higher fluoroscopy time, number of cine images, CAK, KAP, and KAP·kg⁻¹ (P<0.05). Multiple linear regression analysis found that age (B=52.445, P<0.05), weight (B=13.077, P<0.05), fluoroscopy time (B=0.425, P<0.05), tube current (B=0.872, P<0.05), and number of cine images (B=0.660, P<0.05) were positively correlated with KAP, while there was no significant association between height and KAP (P>0.05). Conclusion There are differences in radiation dose among the three types of procedures. Reducing fluoroscopy time, tube current, and number of cine images while meeting the procedure requirements is of great significance for reducing the radiation dose received by children.

OBJECTIVE: Clinical characteristics and outcome in COVID-19 with brucellosis patients has not been well demonstrated, we tried to analyze clinical outcome in local and literature COVID-19 cases with brucellosis before and after recovery. METHODS: We retrospectively collected hospitalization data of comorbid patients and prospectively followed up after discharge in Heilongjiang Infectious Disease Hospital from January 15, 2020 to April 29, 2022. Demographics, epidemiological, clinical symptoms, radiological and laboratory data, treatment medicines and outcomes, and follow up were analyzed, and findings of a systematic review were demonstrated. RESULTS: A total of four COVID-19 with brucellosis patients were included. One patient had active brucellosis before covid and 3 patients had nonactive brucellosis before brucellosis. The median age was 54.5 years, and all were males (100.0%). Two cases (50.0%) were moderate, and one was mild and asymptomatic, respectively. Three cases (75.0%) had at least one comorbidity (brucellosis excluded). All 4 patients were found in COVID-19 nucleic acid screening. Case C and D had only headache and fever on admission, respectively. Four cases were treated with Traditional Chinese medicine, western medicines for three cases, no adverse reaction occurred during hospitalization. All patients were cured and discharged. Moreover, one case (25.0%) had still active brucellosis without re-positive COVID-19, and other three cases (75.0%) have no symptoms of discomfort except one case fell fatigue and anxious during the follow-up period after recovery. Conducting the literature review, two similar cases have been reported in two case reports, and were both recovered, whereas, no data of follow up after recovery. CONCLUSION These cases indicate that COVID-19 patients with brucellosis had favorable outcome before and after recovery. More clinical studies should be conducted to confirm our findings.
Female ; Humans ; Male ; Middle Aged ; Brucellosis ; COVID-19 ; Retrospective Studies ; SARS-CoV-2 ; Treatment Outcome ; Case Reports as Topic

Background: The turnover rate of nurse aides in Taiwan is high. However, the predictors of turnover behavior in the newly employed are still unclear. Objective: To examine the predictors of turnover behavior in newly employed licensed nurse aides. Methods: A longitudinal study design was used and subjects were newly employed certified nurse aides from a nurse aid training association in Taiwan. A total of five questionnaire surveys were conducted. The questionnaire was mainly used to collect information on turnover behavior, personal socioeconomic background, workplace psychosocial hazards, worker health hazards, and musculoskeletal disorders. Results: A total of 300 participants were recruited in the study. Cox regression analysis results showed that short working experience (hazard ratio [HR] = 0.21, p < 0.01), work as non-home nurse aides (HR = 0.58, p = 0.01), low monthly salary (HR = 0.68, p < 0.01), high work mental load (HR = 1.01, p = 0.01), low workplace justice (HR = 0.97, p < 0.01), high workplace violence (HR = 1.60, p < 0.01), high burnout (HR = 1.01, p = 0.04), poor mental health (HR = 1.06, p = 0.04), and high total number of musculoskeletal disorder sites (HR = 1.08, p = 0.01) contribute to a higher risk of turnover. Conclusion The results indicated that employment period, work as a home nurse aide, monthly salary, work mental load, workplace justice, workplace violence, work-related burnout, mental health, and total number of musculoskeletal disorder sites are predictors of turnover behavior in newly employed certified nurse aides.