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.

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.

Spinal cord injury(SCI)is a central nervous system disease with high morbidity and disability rates,bringing serious economic and psychological burdens to families and society worldwide.AMP-activated protein kinase(AMPK)is an important sensor in the energy metabolism process in living organisms,which plays a central role in maintaining energy balance.It is currently considered a key target for the prevention and treatment of multiple diseases.Studies have shown that AMPK signaling can regulate autophagy,neuroinflammation,oxidative stress,mitochondrial function and other processes after SCI,thus affecting the pathological process of SCI.This review summarizes the research progress on AMPK signaling pathway involved in the regulation of SCI,in order to provide new ideas for the treatment and drug development of SCI.

Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.

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

The chemical constituents of Commiphora myrrha was investigated by column chromatography on silica gel, ODS, Sephadex LH-20, and semi-preparative HPLC. Their structures were elucidated by comprehensive spectroscopic methods including UV, IR, MS, NMR, as well as ECD calculation. Seven compounds were isolated from the dichloromethane-soluble fraction of C. myrrha and their structures were identified as(1S,2R,4S,5R,8S)-guaiane-2-hydroxy-7(11),10(15)-dien-6-oxo-12,8-olide(1), commipholide E(2), myrrhterpenoid H(3), myrrhterpenoid I(4), myrrhterpenoid E(5), 2α-methoxy-8α-hydroxy-6-oxogermacra-1(10),7(11)-dien-8,12-olide(6), 8,12-epoxy-1α,9α-hydroxy-eudesma-7,11-diene-6-dione(7). Compound 1 was a new compound and named myrrhterpenoid P. Compound 7 was isolated from Commiphora genus for the first time. Compounds 2, 5, and 6 significantly inhibited nitric oxide(NO) production in LPS-stimulated RAW264.7 cells, with IC_(50) values of(49.67±4.16),(40.80±1.27),(47.22±0.87) μmol·L~(-1), respectively [indomethacin as the positive control, with IC_(50) value of(63.92±2.60) μmol·L~(-1)].
Commiphora/chemistry* ; Animals ; Mice ; Resins, Plant/chemistry* ; Sesquiterpenes/isolation & purification* ; Molecular Structure ; Nitric Oxide ; Macrophages/metabolism* ; RAW 264.7 Cells ; Drugs, Chinese Herbal/pharmacology*

This study aims to explore the medication rules and mechanisms of treating chronic renal failure(CRF) by Jinling medical school based on data mining, network pharmacology, and experimental validation systematically and deeply. Firstly, the study selected the papers published by the inherited clinicians in Jinling medical school in Chinese journals using the subject headings named "traditional Chinese medicine(TCM) + chronic renal failure", "TCM + chronic renal inefficiency", or "TCM + consumptive disease" in China National Knowledge Infrastructure, Wanfang, and VIP Chinese Science and Technology Periodical Database and screened TCM formulas for treating CRF according to inclusion and exclusion criteria. The study analyzed the frequency of use of single TCM and the four properties, five tastes, channel tropism, and efficacy of TCM used with high frequency and performed association rule and clustering analysis, respectively. As a result, a total of 215 TCM formulas and 235 different single TCM were screened, respectively. The TCM used with high frequency included Astragali Radix, Rhei Radix et Rhizoma, Salviae Miltiorrhizae Radix et Rhizoma, Poria, and Atractylodis Macrocephalae Rhizoma(top 5). The single TCM characterized by "cold properties, sweet flavor, and restoring spleen channel" and the TCM with the efficacy of tonifying deficiency had the highest frequency of use, respectively. Then, the TCM with the rules of "blood-activating and stasis-removing" and "diuretic and dampness-penetrating" appeared. In addition, the core combination of TCM [(Hexin Formula, HXF)] included "Astragali Radix, Rhei Radix et Rhizoma, Poria, Salviae Miltiorrhizae Radix, and Angelicae Sinensis Radix". The network pharmacology analysis showed that HXF had 91 active compounds and 250 corresponding protein targets including prostaglandin-endoperoxide synthase 2(PTGS2), PTGS1, sodium voltage-gated channel alpha subunit 5(SCN5A), cholinergic receptor muscarinic 1(CHRM1), and heat shock protein 90 alpha family class A member 1(HSP90AA1)(top 5). Gene Ontology(GO) function analysis revealed that the core targets of HXF predominantly affected biological processes, cellular components, and molecular functions such as positive regulation of transcription by ribonucleic acid polymerase Ⅱ and DNA template transcription, formation of cytosol, nucleus, and plasma membrane, and identical protein binding and enzyme binding. Kyoto Encyclopedia of Genes and Genomes(KEGG) analysis revealed that CRF-related genes were involved in a variety of signaling pathways and cellular metabolic pathways, primarily involving "phosphatidylinositol 3-kinase(PI3K)-protein kinase B(Akt) pathway" and "advanced glycation end products-receptor for advanced glycation end products". Molecular docking results showed that the active components in HXF such as isomucronulatol 7-O-glucoside, betulinic acid, sitosterol, and przewaquinone B might be crucial in the treatment of CRF. Finally, a modified rat model with renal failure induced by adenine was used, and the in vivo experimental confirmation was performed based on the above-mentioned predictions. The results verify that HXF can regulate mitochondrial autophagy in the kidneys and the PI3K-Akt-mammalian target of rapamycin(mTOR) signaling pathway activation at upstream, so as to alleviate renal tubulointerstitial fibrosis and then delay the progression of CRF.
Data Mining ; Drugs, Chinese Herbal/chemistry* ; Network Pharmacology ; Humans ; Kidney Failure, Chronic/metabolism* ; Medicine, Chinese Traditional ; China

To explore the variation laws of volatile oil during the extraction process of Artemisiae Argyi Folium and its impact on the quality of the medicinal solution, as well as to achieve precise control of the extraction process, this study employed headspace solid phase microextraction gas chromatography-mass spectrometry(HS-SPME-GC-MS) in combination with multiple light scattering techniques to conduct a comprehensive analysis, identification, and characterization of the changes in volatile components and the physical properties of the medicinal solution during the extraction process. A total of 82 volatile compounds were identified using the HS-SPME-GC-MS technique, including 21 alcohols, 15 alkenes, 14 ketones, 9 acids, 6 aldehydes, 5 phenols, 3 esters, and 9 other types of compounds. At different extraction time points(15, 30, 45, and 60 min), 71, 72, 64, and 44 compounds were identified in the medicinal solution, respectively. It was observed that the content of volatile components gradually decreased with the extension of extraction time. Through multivariate statistical analysis, four compounds with significant differences during different extraction time intervals were identified, namely 1,8-cineole, terpinen-4-ol, 3-octanone, and camphor. RESULTS:: from multiple light scattering techniques indicated that at 15 minutes of extraction, the transmittance of the medicinal solution was the lowest(25%), the particle size was the largest(0.325-0.350 nm), and the stability index(turbiscan stability index, TSI) was the highest(0-2.5). With the extension of extraction time, the light transmittance of the medicinal solution improved, stability was enhanced, and the particle size decreased. These laws of physicochemical property changes provide important basis for the control of Artemisiae Argyi Folium extraction process. In addition, the changes in the bioactivity of Artemisiae Argyi Folium extracts during the extraction process were investigated through mouse writhing tests and antimicrobial assays. The results indicated that the analgesic and antimicrobial effects of the medicinal solution were strongest at the 15-minute extracting point. In summary, the findings of this study demonstrate that the content of volatile oil in Artemisiae Argyi Folium extracts gradually decreases with the extension of extraction time, and the variation in volatile oil content directly influences the physicochemical properties and pharmacological efficacy of the medicinal solution. This discovery provides important scientific reference for the optimization of Artemisiae Argyi Folium extraction processes and the development and application of process analytical technologies.
Oils, Volatile/pharmacology* ; Artemisia/chemistry* ; Gas Chromatography-Mass Spectrometry ; Drugs, Chinese Herbal/pharmacology* ; Chlorogenic Acid/pharmacology* ; Solid Phase Microextraction ; Quality Control

Objective:To analyze the incidence of abnormal hemoglobin(Hb)in neonates in Guangzhou area,as well as the results of quantitative analysis of Hb in neonatal umbilical cord blood and genetic diagnosis of thalassemia in neonates with abnormal Hb;And to explore the hematological phenotypes and clinical characteristics of neonates with abnormal Hb and their parents,providing a reference for eugenics and childcare.Methods:650 neonates born at Guangdong Provincial People's Hospital who underwent Hb electrophoresis were included in this study.The results of routine blood test of umbilical cord blood,Hb electrophoresis and α-,β-thalassemia gene detection of the neonates were collected.The genotype distribution of thalassemia in the neonates was analyzed.Additionally,the abnormal Hb content of α and β variants was studied.Furthermore,the differences in hematological parameters between abnormal Hb neonates and normal neonates and α-thalassemia neonates,as well as between the parents of abnormal Hb neonates and normal adults were compared.Results:Among the 650 neonates,332(51.08％)were diagnosed with thalassemia,including 235 cases of α-thalassemia(36.15％),79 cases of β-thalassemia(12.15％),and 18 cases of compound α β-thalassemia(2.77％).Among all the α-thalassemia genotypes,the most prevalent one was--SEA/α α(48.94％),followed by-α3.7/α α(20.00％),-α42/α α(11.06％),and α α CS/α α(8.94％).The four most common genotypes of β-thalassemia were β CD41-42(32.91％),βIVS-Ⅱ-654(26.58％),β-28(21.52％),and β E(10.13％),respectively.275 cases of abnormal bands were found in Hb electrophoresis of umbilical cord blood,with a detection rate of 42.31％.The abnormal Hb content ofα-variant in the neonates was significantly higher than that of β-variant(P＜0.001).The levels of Hb,MCV,MCH,Hb A,and Hb F in neonates with abnormal Hb were lower than those in normal neonates,while the RDW-CV was higher than that in normal neonates,with statistical significantce(P＜0.05).The levels of RBC and Hb A in neonates with abnormal Hb were lower than those in neonates with α-thalassemia,while the level of MCH was higher than that in neonats withα-thalassemia,with statistical significance(P＜0.05).The levels of Hb,MCV,MCH,and Hb A in parents of neonates with abnormal Hb were lower than those in normal adults,while the RDW-CV was higher than that in normal adults,and the differences were statistically significant(P＜0.05).Conclusion:The abnormal Hb content of α-variant in the neonates is significantly higher than that of β-variant in the neonates in Guangzhou,which can help to presume whether it isα chain or β chain based on the abnormal Hb content,providing a reference for globin gene sequencing.Meanwhile,analysis of various hematological screening-related indicators in neonates in the early stage is beneficial for early warning of the occurrence of abnormal Hb combined with thalassemia,reducing missed diagnoses to a certain extent.