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.

Humans ; Vascular Stiffness ; Coal Mining ; Occupational Diseases/epidemiology* ; Risk Factors ; Coal ; Miners

Aim To establish a stable hepatic stellate cell ( HSC ) -specific G protein-coupled receptor kinase 2 ( GRK2 ) knockout mice and provide the important animal model for further studying the biological function of GRK2 in HSC. Methods The loxP-labeled Grk2 gene mouse (Grk2

Based on the strategy of metabolomics combined with bioinformatics, this study analyzed the potential allergens and mechanism of pseudo-allergic reactions (PARs) induced by the combined use of Reduning injection and penicillin G injection. All animal experiments and welfare are in accordance with the requirements of the First Affiliated Experimental Animal Ethics and Animal Welfare Committee of Henan University of Chinese Medicine (approval number: YFYDW2020002). Based on UPLC-Q-TOF/MS technology combined with UNIFI software, a total of 21 compounds were identified in Reduning and penicillin G mixed injection. Based on molecular docking technology, 10 potential allergens with strong binding activity to MrgprX2 agonist sites were further screened. Metabolomics analysis using UPLC-Q-TOF/MS technology revealed that 34 differential metabolites such as arachidonic acid, phosphatidylcholine, phosphatidylserine, prostaglandins, and leukotrienes were endogenous differential metabolites of PARs caused by combined use of Reduning injection and penicillin G injection. Through the analysis of the "potential allergen-target-endogenous differential metabolite" interaction network, the chlorogenic acids (such as chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, and isochlorogenic acid A) and β-lactam allergens in the combination of the two may be mainly regulated by PLD1, PLA2G12A and CYP1A1. The three upstream signal target proteins mainly activate the arachidonic acid metabolic pathway, promote the degranulation of mast cells, release downstream endogenous inflammatory mediators, and induce PARs.

Projection micro stereolithography three-dimensional(3D)printing method was proposed in this study to fabricate complex microchannels of combined cross-sections.By using 3D printing and polydimethylsiloxane(PDMS)replication methods,two inertial microfluidic chips of three-step and five-step cross-sections were fabricated,and the dimension precisions of the microchannels were controlled within 20 μm.Using the microfluidic chips,the movements of two fluorescent polystyrene particles with diameters of 10 and 6 μm in the stepped channels were investigated.In addition,numerical simulations were applied to demonstrate the inertial focusing mechanisms of particles in the channels.It was found that 10-μm particles had three equilibrium positions in the three-step channel,which located at the inner walls of the three steps,respectively,and most particles focused at the inner step.The 6-μm particles also had three equilibrium positions in the three-step channel.However,the particles migrated to the middle and the outer steps under high flow rates.In the five-step channel,when the flow rate was increased gradually,10-μm particles had a single and two equilibrium positions,respectively,and the particles migrated towards the inner channel wall under high flow rates.In comparison to 10-μm particles,6-μm particles had two stable equilibrium positions in the five-step channel at all flow rate range.It could be concluded that the quantity,shape and strength of the secondary flow vortex could be altered by changing structure of the combined cross-section,thus the equilibrium positions and quantities of the focusing particles could be also regulated.The research outcome might provide new insights for precise cell inertial manipulation and promote the application and development of inertial microfluidic technology in biomedical and other fields.

Superior vena cava syndrome(SVCS)is a group of clinical syndromes caused by obstruction of the superior vena cava and its major branches from various causes.Pulmonary artery stenosis(PS)is a complication of lung cancer or mediastinal tumours.SVCS combined with PS due to pulmonary metastases from bladder cancer is extremely rare and has not been reported in the literature.Here we reported an old male patient with pulmonary metastases from bladder cancer presenting with swelling of the head,neck and both upper limbs.SVCS combined with PS was clarified by pulmonary artery computed tomography angiography(CTA)and digital subtraction angiography(DSA).Endovascular stenting was used to treat SVCS.Angiography also showed that PS had not caused pulmonary hypertension and did not need to be treated.The swelling of the patient's head,neck and upper limbs was gradually reduced after the procedure.

Four pyrazines were isolated from the n-butanol fraction of Hypecoum erectum L. by using various chromatographic methods, including MCI gel, ODS, silica gel and semi-preparative HPLC. The structures of the isolated compounds were identified as hyperectpyrazin A (1), 1′S-(6-methylpyrazin-2-yl)-ethane-1′,2′-diol (2), 2-hydroxymethyl-6-methylpyrazin (3) and pyrazine-2-carboxylic acid (4) by spectroscopy methods (1D NMR, 2D NMR, UV, IR, MS, etc.). The absolute configuration of compound 2 was determined by using the Mo₂(OAc)₄ induced CD analysis for the first time. Compound 1 was a new compound, compounds 2-4 were isolated from H. erectum for the first time. Compounds 1-4 were evaluated for their inhibition against acetylcholinesterase and nitric oxide generation induced by lipopolysaccharide-RAW264.7 macrophage cells. At a concentration of 50 μmol·L^-1, compounds 2 and 4 displayed inhibitory effects on acetylcholinesterase with the inhibition rates of 44.40% and 43.99%, respectively.