Objective To explore the risk factors of nonalcoholic fatty liver disease(NAFLD)in obese children,and evaluate the diagnostic value of each index for NAFLD and establish a Nomogram prediction model.Methods A total of 207 obese children admitted at Department of Pediatrics the Second Hospital of Jiaxing from January 2022 to January 2025 were selected.These children were divided into two groups based on NAFLD diagnosis:non-NAFLD group(n=99)and NAFLD group(n=108).Differences in gender,age,body mass index(BMI),and related metabolic indicators were compared between two groups.Logistic regression was employed to analyze potential risk factors for NAFLD development,while receiver operating characteristic(ROC)curve and Nomograms were used to evaluate the predictive value of different factors for NAFLD.Results ROC curve analysis demonstrated diagnostic value for NAFLD in triglyceride-glucose index(TyG),triglyceride-to-cholesterol ratio,TyG-waist circumference,and TyG-BMI.Among these,the area under the curve(AUC)of TyG showed the highest value of 0.713,with an optimal cutoff of 8.189,sensitivity of 50.5％and specificity of 83.3％.Univariate Logistic regression analysis revealed multiple insulin resistance indicators associated with NAFLD development.Multivariate analysis identified homeostatic model assessment of insulin resistance(HOMA-IR)and TyG as independent risk factors,with TyG showing the best predictive value(OR=3.038 95％CI:1.089-8.475,P＜0.05).The constructed Nomogram prediction model demonstrated strong comprehensive discriminant capability(AUC=0.742).Conclusion The Nomogram model based on HOMA-IR,TyG and its derived indexes has certain clinical application value in the screening of NAFLD in children.

The bioactivity-guided isolation of potentially active natural products has been widely utilized in pharmaceutical discovery. In this study, by screening fungal extracts against coxsackievirus B3 (CVB3), three new aspochalasins, templichalasins A‒C (1‒3), along with six known aspochalasins (4‒9) were isolated from an active extract derived from the endophytic fungus Aspergillus templicola LHWf045. Compound 1 features a unique 5/6/5/7/5 pentacyclic ring system, while compounds 2 and 3 possess unusual 5/6/6/7 tetracyclic skeletons. Their structures were characterized through extensive spectroscopic analyses, electronic circular dichroism (ECD) calculations, and single-crystal X-ray diffraction analysis. Additionally, we demonstrated that compound 4 can be readily converted into compounds 1‒3 under mild acidic conditions and proposed a plausible mechanism for this conversion. Bioactivity evaluation of compounds 1‒9 against CVB3 revealed the inhibitory effects of all compounds against the virus. Notably, compound 9 exhibited superior antiviral activity, surpassing the commercial drug ribavirin in selectivity index (SI) value.
Antiviral Agents/isolation & purification* ; Aspergillus/chemistry* ; Molecular Structure ; Enterovirus B, Human/drug effects* ; Endophytes/chemistry* ; Cytochalasins/isolation & purification* ; Drug Discovery ; Humans

Acute lung injury (ALI) is a severe disease caused by viral infection that triggers an uncontrolled inflammatory response. This study investigated the capacity of jasurolignoside (JO), a natural compound, to bind to Toll-like receptor 4 (TLR4) and treat ALI. The anti-inflammatory properties of JO were evaluated in vitro through Western blotting, enzyme-linked immunosorbent assay (ELISA), immunofluorescence staining, and co-immunoprecipitation. The investigation utilized a lipopolysaccharide (LPS)-induced ALI animal model to examine the therapeutic efficacy and mechanism of JO in vivo. JO attenuated inflammatory symptoms in infected cells and tissues by modulating the NOD-like receptor family pyrin domain containing protein 3 (NLRP3) inflammasome and the nuclear factor κB (NF-κB)/mitogen-activated protein kinase (MAPK) pathway. Molecular docking simulations revealed JO binding to TLR4 active sites, confirmed by cellular thermal shift assay. Surface plasmon resonance (SPR) demonstrated direct interaction between JO and TLR4 with a Kd value of 35.1 μmol·L^-1. Moreover, JO inhibited tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), and IL-6 secretion and reduced leukocyte, neutrophil, lymphocyte, and macrophage infiltration in ALI-affected mice. JO also enhanced lung function and reduced ALI-related mortality. Immunohistochemical staining demonstrated JO's ability to suppress TLR4 expression in ALI-affected mouse lung tissue. This study establishes that JO can bind to TLR4 and effectively treat ALI, indicating its potential as a therapeutic agent for clinical applications.
Toll-Like Receptor 4/chemistry* ; Animals ; Acute Lung Injury/chemically induced* ; Mice ; Humans ; Ilex/chemistry* ; Molecular Docking Simulation ; Male ; NF-kappa B/immunology* ; Mice, Inbred C57BL ; NLR Family, Pyrin Domain-Containing 3 Protein/immunology* ; Tumor Necrosis Factor-alpha/genetics* ; Interleukin-1beta/genetics* ; RAW 264.7 Cells ; Disease Models, Animal

Objective To explore the risk factors of nonalcoholic fatty liver disease(NAFLD)in obese children,and evaluate the diagnostic value of each index for NAFLD and establish a Nomogram prediction model.Methods A total of 207 obese children admitted at Department of Pediatrics the Second Hospital of Jiaxing from January 2022 to January 2025 were selected.These children were divided into two groups based on NAFLD diagnosis:non-NAFLD group(n=99)and NAFLD group(n=108).Differences in gender,age,body mass index(BMI),and related metabolic indicators were compared between two groups.Logistic regression was employed to analyze potential risk factors for NAFLD development,while receiver operating characteristic(ROC)curve and Nomograms were used to evaluate the predictive value of different factors for NAFLD.Results ROC curve analysis demonstrated diagnostic value for NAFLD in triglyceride-glucose index(TyG),triglyceride-to-cholesterol ratio,TyG-waist circumference,and TyG-BMI.Among these,the area under the curve(AUC)of TyG showed the highest value of 0.713,with an optimal cutoff of 8.189,sensitivity of 50.5％and specificity of 83.3％.Univariate Logistic regression analysis revealed multiple insulin resistance indicators associated with NAFLD development.Multivariate analysis identified homeostatic model assessment of insulin resistance(HOMA-IR)and TyG as independent risk factors,with TyG showing the best predictive value(OR=3.038 95％CI:1.089-8.475,P＜0.05).The constructed Nomogram prediction model demonstrated strong comprehensive discriminant capability(AUC=0.742).Conclusion The Nomogram model based on HOMA-IR,TyG and its derived indexes has certain clinical application value in the screening of NAFLD in children.

Transarterial radioembolization (TARE) is a widely utilized therapeutic approach for hepatocellular carcinoma (HCC), however, the clinical implementation is constrained by the stringent preparation conditions of radioembolization agents. Herein, we incorporated the superstable homogeneous iodinated formulation technology (SHIFT), simultaneously utilizing an enhanced solvent form in a carbon dioxide supercritical fluid environment, to encapsulate radionuclides (such as ¹³¹I,¹⁷⁷Lu, or ¹⁸F) with lipiodol for the preparation of radiolipiodol. The resulting radiolipiodol exhibited exceptional stability and ultra-high labeling efficiency (≥99%) and displayed notable intratumoral radionuclide retention and in vivo stability more than 2 weeks following locoregional injection in subcutaneous tumors in mice and orthotopic liver tumors in rats and rabbits. Given these encouraging findings, ¹⁸F was authorized as a radiotracer in radiolipiodol for clinical trials in HCC patients, and showed a favorable tumor accumulation, with a tumor-to-liver uptake ratio of ≥50 and minimal radionuclide leakage, confirming the feasibility of SHIFT for TARE applications. In the context of transforming from preclinical to clinical screening, the preparation of radiolipiodol by SHIFT represents an innovative physical strategy for radionuclide encapsulation. Hence, this work offers a reliable and efficient approach for TARE in HCC, showing considerable promise for clinical application (ChiCTR2400087731).

Objective:To improve the isolation and culture method of seasonal influenza virus in embryonated chicken eggs (ECEs), and evaluate their isolation efficiency.Methods:We randomly selected 80 positive samples of H1N1 (H1N1pdm09) and seasonal H3N2 (H3N2snl) influenza virus nucleic acid, and inoculated them into the amniotic and urinary sac cavities of 10-day-old (traditional method) and 14-day-old (improved method) ECEs respectively to adapt the virus to the ECEs (E1-E2). Both method were used to inoculate 10-day-old urinary sac amplification virus (E2-E3), and the final virus isolation positive rates of the two method were compared; using fluorescence quantitative PCR method to detect viral nucleic acids in the improved amniotic and urinary sac cultures, and evaluate the viral proliferation at different inoculation sites; we analyzed the correlation between virus content and isolation positivity rate in the original specimen based on the CT value of nucleic acid testing and the final virus isolation positivity rate using the improved method.Results:The improved method obtained 42 strains of H1N1pdm09 strain, with a positive rate of 52.5% ( χ2=38.571, P<0.01); obtained 54 strains of H3N2snl strain, with a positive rate of 67.5% ( χ2=40.921, P<0.01). Significant differences were observed in the isolation efficiency of H1N1pdm09 samples when the improved method was applied to different inoculation sites of chicken embryos ( χ2=30.476, P<0.01), and similar differences were noted for H3N2snl samples ( χ2=4.928, P=0.026). There was no significant difference in the isolation rate of different CT value intervals of the original samples ( χH1N1pdm092=10.226, χH3N2snl2=3.764, P>0.05). Conclusions:The improved method of inoculating 14-day old ECEs adapted the virus, and the final number of strains obtained was significantly higher than the traditional method of inoculating 10 day old ECEs, which can significantly improve the positive isolation rate of H1N1pdm09 and H3N2snl influenza virus in ECEs. The amniotic cavity is more sensitive to H1N1pdm09 and H3N2snl influenza viruses, which helps the virus adapt in ECEs. There was no significant difference in the sample isolation rate and total positive rate of virus isolation among different CT value ranges, and further verification is needed.

Objective:To evaluate whether the reconstructed image on the basis of deep learning(DL)can improve the success rate and display quality of automatic segmentation of computed tomography(CT)with ultralow dose for chest of children on airway.Methods:The clinical data of 41 consecutive cases who adopted ultralow dose CT to underwent reexamination on chest at Beijing Children's Hospital,Capital Medical University from February 2020 to September 2020 were selected,whose average age was(4.43±1.61 years).The scan protocol of ultralow dose CT was(0.05 mGy).The reconstructed images included 6 groups,which were respectively filtered reflection projection(FBP)image with 0.625 mm thickness,50%adaptive iterative recombination(ASIR-V)images,100%ASIR-V images,low energy DL(DL-L),medium energy DL(DL-M),and high energy DL(DL-H).The automatically segmentation software was used to conduct automatically segmentation for airway,and the success rate of automatic segmentation was recorded.For images that were successful segmented,a 5-point scale was adopted to subjectively evaluate the displayed quality for airway(5 point is the best).In addition,the CT values and noise values of the images of 6 groups for airway were objectively measured.Results:The success rate of automatic segmentation of DL-H image was the highest(60.98%),and that of the 100%ASIR-V was the lowest(39.02%).The subjective score of DL-H image of the automatic segmentation was the highest(4.06±0.55)point,and that of 100%ASIR-V was the lowest(2.44±0.76)point.DL-H can display more fine and small airways.The noise values of objective measurement showed that both of DL-H and 100%ASIR-V had the lowest noise value,and there was no statistical difference in that between them.Conclusion:The use of high energy deep learning iterative reconstruction(DLIR)algorithm can improve the success rate and display effect of automatic segmentation of ultralow dose CT for chest of children on airway,and DLIR is contribute to improve the accuracy of automatic segmentation algorithm of artificial intelligence.

Objective:To evaluate whether the reconstructed image on the basis of deep learning(DL)can improve the success rate and display quality of automatic segmentation of computed tomography(CT)with ultralow dose for chest of children on airway.Methods:The clinical data of 41 consecutive cases who adopted ultralow dose CT to underwent reexamination on chest at Beijing Children's Hospital,Capital Medical University from February 2020 to September 2020 were selected,whose average age was(4.43±1.61 years).The scan protocol of ultralow dose CT was(0.05 mGy).The reconstructed images included 6 groups,which were respectively filtered reflection projection(FBP)image with 0.625 mm thickness,50%adaptive iterative recombination(ASIR-V)images,100%ASIR-V images,low energy DL(DL-L),medium energy DL(DL-M),and high energy DL(DL-H).The automatically segmentation software was used to conduct automatically segmentation for airway,and the success rate of automatic segmentation was recorded.For images that were successful segmented,a 5-point scale was adopted to subjectively evaluate the displayed quality for airway(5 point is the best).In addition,the CT values and noise values of the images of 6 groups for airway were objectively measured.Results:The success rate of automatic segmentation of DL-H image was the highest(60.98%),and that of the 100%ASIR-V was the lowest(39.02%).The subjective score of DL-H image of the automatic segmentation was the highest(4.06±0.55)point,and that of 100%ASIR-V was the lowest(2.44±0.76)point.DL-H can display more fine and small airways.The noise values of objective measurement showed that both of DL-H and 100%ASIR-V had the lowest noise value,and there was no statistical difference in that between them.Conclusion:The use of high energy deep learning iterative reconstruction(DLIR)algorithm can improve the success rate and display effect of automatic segmentation of ultralow dose CT for chest of children on airway,and DLIR is contribute to improve the accuracy of automatic segmentation algorithm of artificial intelligence.

Objective:To improve the isolation and culture method of seasonal influenza virus in embryonated chicken eggs (ECEs), and evaluate their isolation efficiency.Methods:We randomly selected 80 positive samples of H1N1 (H1N1pdm09) and seasonal H3N2 (H3N2snl) influenza virus nucleic acid, and inoculated them into the amniotic and urinary sac cavities of 10-day-old (traditional method) and 14-day-old (improved method) ECEs respectively to adapt the virus to the ECEs (E1-E2). Both method were used to inoculate 10-day-old urinary sac amplification virus (E2-E3), and the final virus isolation positive rates of the two method were compared; using fluorescence quantitative PCR method to detect viral nucleic acids in the improved amniotic and urinary sac cultures, and evaluate the viral proliferation at different inoculation sites; we analyzed the correlation between virus content and isolation positivity rate in the original specimen based on the CT value of nucleic acid testing and the final virus isolation positivity rate using the improved method.Results:The improved method obtained 42 strains of H1N1pdm09 strain, with a positive rate of 52.5% ( χ2=38.571, P<0.01); obtained 54 strains of H3N2snl strain, with a positive rate of 67.5% ( χ2=40.921, P<0.01). Significant differences were observed in the isolation efficiency of H1N1pdm09 samples when the improved method was applied to different inoculation sites of chicken embryos ( χ2=30.476, P<0.01), and similar differences were noted for H3N2snl samples ( χ2=4.928, P=0.026). There was no significant difference in the isolation rate of different CT value intervals of the original samples ( χH1N1pdm092=10.226, χH3N2snl2=3.764, P>0.05). Conclusions:The improved method of inoculating 14-day old ECEs adapted the virus, and the final number of strains obtained was significantly higher than the traditional method of inoculating 10 day old ECEs, which can significantly improve the positive isolation rate of H1N1pdm09 and H3N2snl influenza virus in ECEs. The amniotic cavity is more sensitive to H1N1pdm09 and H3N2snl influenza viruses, which helps the virus adapt in ECEs. There was no significant difference in the sample isolation rate and total positive rate of virus isolation among different CT value ranges, and further verification is needed.

Objective To explore the feasibility and accuracy of neutrophil-to-lymphocyte ratio(NLR)in the prediction of testicular torsion(TT)in children with acute scrotal pain.Methods A retrospective case-control study was performed on 158 pediatric patients with ultrasound suspicion of TT who underwent surgical testicular examination during Jan.2017 and Jan.2024.The patients were divided into TT group and non-TT group.Clinical data and laboratory data at admission were analyzed.Sensitivity and specificity of NLR to TT were determined with the area under the curve(AUC)represented on the receiver operating characteristic(ROC)curves.Results There were with no statistically significant differences in clinical data between the two groups(P＞0.05).The NLR was significantly higher in the TT group than in the non-TT group[(4.82±2.37)vs.(2.85±0.75),P＜0.05].The optimal cut-off value of TT predicted by NLR was 2.07,the AUC was 0.809(95％CI:0.709-0.909),and the sensitivity and specificity were 97.9％and 93.3％,respectively,which were significantly higher than other factors.Conclusion For suspicious ultrasound diagnosis of pediatric acute scrotal pain cases,NLR can be used to predict the possibility of TT and may help to evaluate the urgent surgical treatment in these patients.