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.

As China accelerates its efforts in deep space exploration and long-duration space missions, including the operationalization of the Tiangong Space Station and the development of manned lunar missions, safeguarding astronauts’ physiological and cognitive functions under extreme space conditions becomes a pressing scientific imperative. Among the multifactorial stressors of spaceflight, microgravity emerges as a particularly potent disruptor of neurobehavioral homeostasis. Dopamine (DA) plays a central role in regulating behavior under space microgravity by influencing reward processing, motivation, executive function and sensorimotor integration. Changes in gravity disrupt dopaminergic signaling at multiple levels, leading to impairments in motor coordination, cognitive flexibility, and emotional stability. Microgravity exposure induces a cascade of neurobiological changes that challenge dopaminergic stability at multiple levels: from the transcriptional regulation of DA synthesis enzymes and the excitability of DA neurons, to receptor distribution dynamics and the efficiency of downstream signaling pathways. These changes involve downregulation of tyrosine hydroxylase in the substantia nigra, reduced phosphorylation of DA receptors, and alterations in vesicular monoamine transporter expression, all of which compromise synaptic DA availability. Experimental findings from space analog studies and simulated microgravity models suggest that gravitational unloading alters striatal and mesocorticolimbic DA circuitry, resulting in diminished motor coordination, impaired vestibular compensation, and decreased cognitive flexibility. These alterations not only compromise astronauts’ operational performance but also elevate the risk of mood disturbances and motivational deficits during prolonged missions. The review systematically synthesizes current findings across multiple domains: molecular neurobiology, behavioral neuroscience, and gravitational physiology. It highlights that maintaining DA homeostasis is pivotal in preserving neuroplasticity, particularly within brain regions critical to adaptation, such as the basal ganglia, prefrontal cortex, and cerebellum. The paper also discusses the dual-edged nature of DA plasticity: while adaptive remodeling of synapses and receptor sensitivity can serve as compensatory mechanisms under stress, chronic dopaminergic imbalance may lead to maladaptive outcomes, such as cognitive rigidity and motor dysregulation. Furthermore, we propose a conceptual framework that integrates homeostatic neuroregulation with the demands of space environmental adaptation. By drawing from interdisciplinary research, the review underscores the potential of multiple intervention strategies including pharmacological treatment, nutritional support, neural stimulation techniques, and most importantly, structured physical exercise. Recent rodent studies demonstrate that treadmill exercise upregulates DA transporter expression in the dorsal striatum, enhances tyrosine hydroxylase activity, and increases DA release during cognitive tasks, indicating both protective and restorative effects on dopaminergic networks. Thus, exercise is highlighted as a key approach because of its sustained effects on DA production, receptor function, and brain plasticity, making it a strong candidate for developing effective measures to support astronauts in maintaining cognitive and emotional stability during space missions. In conclusion, the paper not only underscores the centrality of DA homeostasis in space neuroscience but also reflects the authors’ broader academic viewpoint: understanding the neurochemical substrates of behavior under microgravity is fundamental to both space health and terrestrial neuroscience. By bridging basic neurobiology with applied space medicine, this work contributes to the emerging field of gravitational neurobiology and provides a foundation for future research into individualized performance optimization in extreme environments.

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.

INTRODUCTION: Cancer patients attending emergency departments (EDs) often present with acute symptoms and are frequently admitted. This study aimed to characterise the profile of oncology patients who were discharged from the ED. METHODS: This was a retrospective audit of patients with cancer-related diagnoses who presented to the ED at the Singapore General Hospital (SGH) over a 6-month period from 1 October 2018 to 31 March 2019 and were directly discharged from the ED. Data was extracted from the hospital's electronic medical record system. RESULTS: Of the 492 participants included in the study, the majority were triaged as Priority 2 (61.4%), while 30.7% were triaged as Priority 3, 6.9% as Priority 1 and 1.0% as Priority 4. There was no statistical difference between the National Early Warning scores across the different triage categories in these patients. The most common complaint was (44.3%), followed by genitourinary symptoms (19.5%) and those related to devices, catheters or stomas (17.3%). More investigations of all types were done for patients being managed in Priority 1 (57.6%) than in the other triage categories (40.1% for Priority 2, 23.2% for Priority 3 and 12.0% for Priority 4). Treatment procedures carried out were mainly symptomatic (analgesics, antiemetics, proton pump inhibitors) for 79.8% of the patients. There were no significant differences in the proportion of patients requiring various treatment modalities among the triage categories. CONCLUSION Selected oncological patients may potentially be managed in an ambulatory setting.
Humans ; Emergency Service, Hospital/statistics & numerical data* ; Retrospective Studies ; Female ; Neoplasms/diagnosis* ; Male ; Singapore ; Patient Discharge/statistics & numerical data* ; Middle Aged ; Aged ; Triage ; Adult ; Emergencies ; Aged, 80 and over

Objective To compare the differences in dosimetry and treatment efficiency of three radiotherapy plans after left-sided breast-conserving surgery,including modified intensity-modulated radiation therapy(IMRT),cross-field volume-modulated arc therapy(VMAT)or improved VMAT,so as to provide references for clinical practice.Methods Three radiotherapy plans of modified IMRT,cross-field VMAT and improved VMAT were designed for 12 patients after left-sided-breast-conserving surgery.The modified IMRT with five irradiation fields and the improved VMAT with two arcs were modified by not setting cross-fields while determining the start and end angles with the rays passing through the least lung area.The cross-field VMAT had its start and end angles set based on the cross-fields.The doses to the target areas,peripheral organs at risk,heart and its substructures were evaluated,and dose verification was carried out.The three plans were compared in terms of treatment efficiency and gamma pass rate.SPSS 22.0 was used for statistical analysis.Results All the three plans behaved well in dose distribution.In terms of planning gross tumor volume dosimetry dosimetry,the improved VMAT and modified IMRT gained advantages than others in CI and D50,respectively,with the differences being significant(all P<0.05).In terms of planning target volume dosimetry,the modified IMRT had the V107 and D50 lower than those of the others,with the differences being significant(P<0.05).In terms of the protection of peripheral organs at risk,V5 of the left lung,Dmean of the right lung and Dmean of the healthy breast were lower in the modified IMRT plan than in the other 2 plans,with statistically significant differences(P<0.05);V20,V30,V35 and V40 of the left lung were lower in the modified VMAT plan than in the other 2 plans,with statistically significant differences(P<0.05).In terms of protection of heart and its substructures,the left ventricle V20,V30 and Dmean of the improved VMAT plan behaved better than those of other 2 plans,and the difference was statistically significant(all P<0.05).In terms of treatment efficiency,the cross-field VMAT plan had the lowest MU while highest treatment efficiency;the improved VMAT plan had the MU higher while the treatment efficiency lower than the cross-field VMAT plan;the modified IMRT plan had the highest MU while the lowest gamma pass rate,and the differences in the MUs and gamma pass rates among the three plans were statistically significant(P<0.05).Conclusion Under the same standard conditions,the cross-field VMAT and improved VMAT plans show technical advantages.Though the improved VMAT plan has the treatment efficiency lower than the cross-field VMAT,it decreases the possibility of radiocardiac injury in terms of dosimetry and thus can be used for radiotherapy after left-sided breast-conserving surgery.[Chinese Medical Equipment Journal,2025,46(4):45-51]

Objective:To analyze the current policy design of outpatient benefit policies for patients with hypertension and diabetes(H&D)in urban and rural residents in China,and to provide references for optimizing the outpatient benefit mechanism for patients with H&D.Methods:A questionnaire survey was conducted to collect data on the basic features of General Outpatient Benefit(GOB),Outpatient Medication Guarantee Mechanisms for Hypertension and Diabetes(OMGM-H&D)and Outpatient Benefit for Patient with Chronic and Special Diseases(OB-C&S),focusing on deductibles,policy reimbursement ratios,and maximum payment limits.Descriptive statistical analysis was performed on the data.Results:A total of 334 regions were surveyed,of which 253 regions(75.34％)had implemented all three policies.Regional analysis revealed significant differences(P<0.001)in reimbursement ratios for GOB and the OMGM-H&D between the eastern,central,and western regions,with the western region having notably higher ratios than the eastern and central regions.However,no significant difference (P>0.05) was observed in the reimbursement ratios for OB-C&S across regions. Regarding deductibles,no significant regional difference (P>0.05) was found for GOB,but significant differences (P<0.05) existed for the OMGM-H&D and OB-C&S. Additionally,the annual maximum payment limits for all three policies showed significant regional variations (P<0.001). Conclusions:The outpatient benefits policy for patients with H&D have achieved full coverage nationwide among urban and rural residents in China,but regional equity in benefit levels requires improvement. It is recommended to strengthen data feedback mechanisms and promote provincial-level pooling of medical insurance.

Aim To investigate the effect of tetrameth-ylpyrazine(TMP)on neuroinflammation after cerebral ischemia and hypoxia via mannose-binding lectin(MBL).Methods Patients diagnosed with ischaemic stroke at Shanxi Provincial People's Hospital were in-cluded in the study,and their clinicopathological data,as well as blood and urine samples,were collected with the consent of the patients and their families.Using these biological samples,differential proteins and tar-gets were identified by proteomic analysis and subse-quently verified with animal experiments.The mice were divided into the sham,dMCAO,and TMP(10,20,40 mg·kg-1)treatment groups.After seven days of drug administration,the modified neurological sever-ity score(mNSS)was used to assess the neurological function.TTC staining was used to detect the volume of cerebral infarction.Motor function was evaluated be-haviourally,and ELISA was used to detect MASP1,sC5b-9,TNF-α,IL-6,and IL-1β.Western blot was used to determine the expression of relevant proteins,such as MBL2,MASP2,and C3.Results Compared with the sham group,the dMCAO group exhibited in-creased neurological impairment,which was signifi-cantly ameliorated by TMP treatment.The expression levels of MBL2,C3 and MASP2 were elevated in the dMCAO group and were reduced following TMP treat-ment.Additionally,the dMCAO group showed elevat-ed expression of inflammatory factors IL-1 β,IL-6 and TNF-α,which were then suppressed by TMP treat-ment.Conclusion TMP inhibits the inflammatory re-sponse after ischemia and hypoxia by regulating MBL,thus attenuating brain injury.

AIM To study the chemical constituents from the branches and leaves of Michelia yunnanensis Franch.ex Finet & Gagnep.and their anti-inflammatory activities.METHODS The methanol extract was isolated and purified by silica gel,MCI,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their anti-inflammatory activities were evaluated by RAW264.7 model.RESULTS Twenty compounds were isolated and identified as dihydrodehydrodiconifenyl alcohol(1),8-hydroxypinoresinol(2),lariciresinol(3),isolariciresinol(4),(7S,8R)-4-hydroxy-3,3',5'-trimethoxy-8',9'-dinor-8,4'-oxyneoligna-7,9-diol-7'-aldehyde(5),thero-2,3-bis-(4-hydroxy-3-methoxypheyl)-3-methoxy-propanol(6),evofolin B(7),(E)-p-coumaryl alcohol γ-O-methyl ether(8),ω-hydroxypropioguaiacone(9),sinapaldehyde(10),isoscopoletin(11),6-hydroxy-5,7-dimethoxycoumarin(12),2α,3α-dihydroxy-2-methylbutyrolactone(13),6-hydroxy-3(1-hydroxy-1-methylethyl)-6-methyl-2-cyclohexen-1-one(14),benzofuran-2-carboxaldehyde(15),3,4-dihydroxy-5-methoxybenzaldehyde(16),3,5-dimethoxy-4-hydroxybenzaldehyde(17),3,4-dihydroxybenzaldehyde(18),3,4-dihydroxybenzoic methyl ester(19),vanillic acid(20).The inhibition rate of compound 1 on NO was 45.39％±0.32％.CONCLUSION Compounds 1-16,18-20 are first isolated from this plant.Compound 1 has anti-inflammatory activity.