OBJECTIVE To investigate the correlation of component content with colorimetric values during the roasting process of Ziziphus jujuba ， and to provide criteria for discriminating the roasting degree of Z. jujuba . METHODS Samples were prepared by dry stir-frying for different roasting times. The eight main components in raw Z. jujuba and the samples stir-fried for different roasting times-namely adenosine， magnoflorine， jujuboside A， spinosin， 6-feruloylspinosin， betulinic acid， oleic acid， and linoleic acid-were quantitativel y analyzed using ultra-performance liquid chromatography. The chromaticity values were determined using a UV spectrophotometer. The correlation and differences between the chromaticity values of Z. jujuba at different roasting times and their components content were analyzed by Pearson correlation analysis， linear regression analysis， principal component analysis （PCA） ， cluster heatmap analysis （CHA）， and partial least squares discriminant analysis （PLS-DA） to clarify the processing endpoint. RESULTS As the roasting time increased， the contents of linoleic acid and oleic acid decreased， while the contents of other components exhibited an increasing trend. Concurrently， the colorimetric value L* and E*ab were observed to decline， whereas the a* value demonstrated a gradual increase. Pearson correlation analysis revealed that L* and E*ab exhibited a significant negative correlation with the contents of adenosine， spinosin， 6-feruloylspinosin， jujuboside A， betulinic acid and magnoflorine （ P ＜0.05）. The results of linear regression analysis indicate that the content of six components, including adenosine， in the medicinal material can be preliminarily predicted by analyzing the colorimetric values of Z. jujuba powder. PCA and CHA successfully classified raw and stir-fried samples. The PLS-DA results indicated that L*， E*ab， a*， linoleic acid content， and oleic acid content were the main parameters that differentiated the color and quality of Z. jujuba at different roasting times. After frying for 9 to 10 minutes， the colorimetric values L* and E*ab decreased to their minimum values and stabilized， while a* remained consistently high with little variation；simultaneously， the concentrations of the six major components， excluding linoleic acid and oleic acid， reached their peak levels. CONCLUSIONS A significant correlation between the colorimetric values of Z. jujuba and the contents of six components， including adenosine， is confirmed. The optimal roasting time range is determined to be 9-10 minutes. Furthermore， the colorimetric value-component content correlation analysis method established in this study proved to be practical and applicable for discriminating the roasting degree of Z. jujuba .

Objective:To investigate the role and underlying mechanism of parthanatos death in neonatal SD rats with bilirubin encephalopathy(BE).Methods:Eighty 3-day-old neonatal SD rats were selected and randomly divided into control group and BE group.The BE model was established by intraperitoneal injection of bilirubin solution,and the pathological changes in the hippocampus were observed by hematoxylin-eosin(HE)staining and Nissl staining.The protein expressions of the phosphorylation of the core histone protein H2AX(termed gamma H2AX),poly ADP-ribose polymerasw-1(PARP-1)and apoptosis-inducing factor(AIF)in hippocampus were detected by Western blot.Immuno-fluorescence staining was used to detect the expression and distribution of AIF in hippocampus.Results:Compared with the control group,neonatal SD rats developed jaundice 12 hours after bilirubin injection,accompanied by slow weight gain.HE staining and Nissl staining showed that the hippocampus in BE group were damaged and the content of Nissl bodies was decreased.Western blot results showed that the expression of γ-H2AX protein in hippocampus began to increase at 72 h after modeling(P＜0.05),and the levels of PARP-1 and AIF protein in hippocampus increased signif-icantly at 72 h after modeling(P＜0.05).Immunofluorescence staining showed increased AIF expression and nuclear translocation.Conclusion:Intraperitoneal injection of bilirubin can induce DNA damage in hippocampal neurons of neonatal SD rats and activate the PARP-1/AIF pathway to cause parthanatos death of hippocampal neurons.

Dendritic cells (DCs) serve as the primary antigen-presenting cells in autoimmune diseases, like rheumatoid arthritis (RA), and exhibit distinct signaling profiles due to antigenic diversity. Type II collagen (CII) has been recognized as an RA-specific antigen; however, little is known about CII-stimulated DCs, limiting the development of RA-specific therapeutic interventions. In this study, we show that CII-stimulated DCs display a preferential gene expression profile associated with migration, offering a new perspective for targeting DC migration in RA treatment. Then, saikosaponin D (SSD) was identified as a compound capable of blocking CII-induced DC migration and effectively ameliorating arthritis. Optineurin (OPTN) is further revealed as a potential SSD target, with Optn deletion impairing CII-pulsed DC migration without affecting maturation. Function analyses uncover that OPTN prevents the proteasomal transport and ubiquitin-dependent degradation of C-C chemokine receptor 7 (CCR7), a pivotal chemokine receptor in DC migration. Optn-deficient DCs exhibit reduced CCR7 expression, leading to slower migration in CII-surrounded environment, thus alleviating arthritis progression. Our findings underscore the significance of antigen-specific DC activation in RA and suggest OPTN is a crucial regulator of CII-specific DC migration. OPTN emerges as a promising drug target for RA, potentially offering significant value for the therapeutic management of RA.

Objective To construct a model for predicting ki-67 expression in hepatocellular carcinoma using the intratumoral and peritumoral radiomic features of contrast enhanced magnetic resonance imaging(CEMRI)in the arterial phase as well as clinical imaging features.Methods A total of 120 patients pathologically diagnosed with hepatocellular carcinoma(HCC)from January 2016 to December 2024 in No.910 Hospital of the Joint Logis-tics Support Force of the Chinese People's Liberation Army were retrospectively enrolled and randomly divided into a training set(84 cases)and a test set(36 cases)in a ratio of 7∶3.ITK-SNAP software was used to delineate the global region of interest(ROI)of HCC on the arterial phase MR images.The ROIs of all patients were automatically expanded outward by 2 mm,and then the intratumoral ROI areas were eliminated to obtain the peritumoral ROI.With the help of PyRadiomics software,1 198 intratumoral and peritumoral radiomic features were extracted.Spearman correlation analysis,maximum relevance-minimum redundancy(mRMR),and least absolute shrinkage and selection operator(LASSO)regression were used to reduce the data dimension and select the best features.Then,a radiomics model of the logistic regression(LR)machine learning algorithm was constructed.A combined model including clinical imaging features and radiomics features was established.The area under the curve(AUC),accuracy,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),calibration curve and decision curve analysis(DCA)were used to evaluate the efficacy of the intratumoral and peritumoral radiomics features combined with clinical imaging features model in predicting ki-67 expression in hepatocellular car-cinoma.Results The intratumor model exhibited an efficacy in predicting the expression of ki-67 in hepatocellular carcinoma with AUC values of 0.817 and 0.787 in the training set and test set,respectively.The peritumoral model showed an efficacy with AUC values of 0.805 and 0.633 in the training set and test set,respectively.The intratumoral and peritumoral model demonstrated AUC values of 0.874 and 0.836 in the training set and test set,respectively.The combined model constructed by integrating the intratumoral and peritumoral model with clinical imaging features yielded AUC values of 0.877 and 0.849 in the training set and test set,respectively,indicating clinical imaging features improved the performance of the model.DCA showed that the combined models all had good clinical benefits,with the intratumoral and peritumoral model performing the best.Conclusion The intratumoral and peritumoral radiomics model based on CEMRI arterial phase combined with clinical imaging data can accurately predict the expression of ki-67 in hepatocellular carcinoma.This combined model yields the best clinical benefit.

Brain age prediction is of great significance to the in-depth understanding of individual neurodevelopment,early diagnosis of neuropsychiatric disorders,and formulation of personalized treatment plans. With the continuous advancement of deep learning,more and more researches focus on using such algorithms to predict brain age. Compared with traditional regression algorithms,deep learning which has the advantages of complex pattern learning,end-to-end learning and high adaptability can more accurately reveal the neuropathological mechanisms of neuropsychiatric disorders,and provide more precise tools for clinical assessment,assisted diagnosis and prognosis prediction. Herein the study reviews the recent advances in the application of deep learning algorithms in brain age prediction,introduces the achievements in deep learning model optimization,multimodal data inputs and interpretability studies for brain age prediction,discusses the methods for the establishment of integrated deep learning architectures and the future challenges of developing unified benchmarking,and provides an outlook on the application of deep learning in brain age prediction.

The increasing prevalence of aging has led to a rising incidence of comorbidity of sarcopenia and obesity, posing significant burdens on socioeconomic and public health. Current research has systematically explored the pathogenesis of each condition; however, the mechanisms underlying their comorbidity remain unclear. This study reviews the current literature on sarcopenia and obesity in the aging population, focusing on their shared biological mechanisms, which include loss of autophagy, abnormal macrophage function, mitochondrial dysfunction, and reduced sex hormone secretion. It also identifies metabolic mechanisms such as insulin resistance, vitamin D metabolism abnormalities, dysregulation of iron metabolism, decreased levels of nicotinamide adenine dinucleotide, and gut microbiota imbalances. Additionally, this study also explores the important role of genetic factors, such as alleles and microRNAs, in the co-occurrence of sarcopenia and obesity. A better understanding of these mechanisms is vital for developing clinical interventions and preventive strategies.
Humans ; Sarcopenia/physiopathology* ; Obesity/physiopathology* ; Aging/physiology* ; Autophagy/physiology* ; Insulin Resistance ; Comorbidity ; Vitamin D/metabolism* ; Gonadal Steroid Hormones/metabolism* ; Gastrointestinal Microbiome ; Mitochondria ; MicroRNAs

ObjectiveTo investigate the relationship between Jiaotai pill (JTP), its main component berberine (BBR), and the serotonin (5-HT) system in regulating islet hormone secretion and alleviating pancreatic β-cell dysfunction during type 2 diabetes mellitus (T2DM) progression.MethodsT2DM rat model was established using a high-fat diet and streptozotocin injection. JTP, BBR, and Metformin were intragastrically administered for 35 days. The analyzed indices included blood glucose, blood lipids, islet hormones, and proteins related to 5-HT synthesis, secretion, and transport. Additionally, an in vitro model of glucose injury in islet cells was established to study the effects of JTP and BBR on islet hormone secretion following tryptophan hydroxylase 1 (TPH1) inhibition.ResultsJTP and BBR significantly improved blood glucose and lipid levels and islet morphology in T2DM rats. Both models exhibited reduced islet 5-HT levels and impaired islet hormone secretion. However, the administration of JTP and BBR reversed these effects. Furthermore, JTP and BBR upregulated the expression of TPH1(P = .0194, P = .0413) transglutaminase 2 (TGase2; P = .0492, P = .0349), serotonin transporter (SERT, P = .0090), and 5- hydroxytryptamine 1F receptor (5-HT1FR) in the islet 5-HT pathway (P = .0194). In the cell model, the regulatory effects of JTP and BBR on islet hormone levels were significantly weakened after TPH1 inhibition (P = .001), suggesting that JTP and BBR influence islet hormone secretion through the pancreatic 5-HT system.ConclusionThe islet 5-HT system is correlated with islet hormone secretion dysfunction in T2DM. JTP and BBR can improve islet hormone secretion by activating the TPH1/TGase2/SERT/5-HT1FR pathway in the islet 5-HT system in T2DM rats.

Objective To construct a model for predicting ki-67 expression in hepatocellular carcinoma using the intratumoral and peritumoral radiomic features of contrast enhanced magnetic resonance imaging(CEMRI)in the arterial phase as well as clinical imaging features.Methods A total of 120 patients pathologically diagnosed with hepatocellular carcinoma(HCC)from January 2016 to December 2024 in No.910 Hospital of the Joint Logis-tics Support Force of the Chinese People's Liberation Army were retrospectively enrolled and randomly divided into a training set(84 cases)and a test set(36 cases)in a ratio of 7∶3.ITK-SNAP software was used to delineate the global region of interest(ROI)of HCC on the arterial phase MR images.The ROIs of all patients were automatically expanded outward by 2 mm,and then the intratumoral ROI areas were eliminated to obtain the peritumoral ROI.With the help of PyRadiomics software,1 198 intratumoral and peritumoral radiomic features were extracted.Spearman correlation analysis,maximum relevance-minimum redundancy(mRMR),and least absolute shrinkage and selection operator(LASSO)regression were used to reduce the data dimension and select the best features.Then,a radiomics model of the logistic regression(LR)machine learning algorithm was constructed.A combined model including clinical imaging features and radiomics features was established.The area under the curve(AUC),accuracy,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),calibration curve and decision curve analysis(DCA)were used to evaluate the efficacy of the intratumoral and peritumoral radiomics features combined with clinical imaging features model in predicting ki-67 expression in hepatocellular car-cinoma.Results The intratumor model exhibited an efficacy in predicting the expression of ki-67 in hepatocellular carcinoma with AUC values of 0.817 and 0.787 in the training set and test set,respectively.The peritumoral model showed an efficacy with AUC values of 0.805 and 0.633 in the training set and test set,respectively.The intratumoral and peritumoral model demonstrated AUC values of 0.874 and 0.836 in the training set and test set,respectively.The combined model constructed by integrating the intratumoral and peritumoral model with clinical imaging features yielded AUC values of 0.877 and 0.849 in the training set and test set,respectively,indicating clinical imaging features improved the performance of the model.DCA showed that the combined models all had good clinical benefits,with the intratumoral and peritumoral model performing the best.Conclusion The intratumoral and peritumoral radiomics model based on CEMRI arterial phase combined with clinical imaging data can accurately predict the expression of ki-67 in hepatocellular carcinoma.This combined model yields the best clinical benefit.