Immune inflammatory response runs through the whole pathological process of liver injury， but its specific regulatory mechanism remains unclear. Recent studies have shown that the Notch signaling pathway plays an important role in liver injury by regulating macrophage polarization， activating neutrophil recruitment， and modulating the differentiation of regulatory immune cells. This article systematically reviews the molecular mechanisms of the Notch signaling pathway in mediating immune inflammatory response in liver injury， in order to provide new perspectives for clarifying the molecular mechanism of immune inflammatory damage in liver diseases， as well as a new reference for future research directions.

Objective To investigate how dietary supplementation with tetrahydrocurcumin(THC)improves kidney injury in type 2 dia-betic mellitus(T2DM)and its mechanism of action using transcriptome sequencing(RNA-seq).Methods C57BL/6J mice were randomly assigned to the control,T2DM,and T2DM+THC groups.After a high-fat meal and streptozotocin injection,the body weights and fasting blood glucose levels of each mouse with T2DM were measured.Hematoxylin and eosin staining,Oil red O staining,and RNA-seq were performed to examine kidney pathology,lipid deposition,and differentially expressed genes,respectively,in the mice.Results Mice in T2DM group exhibited significantly higher fasting blood glucose levels(P＜0.001),renal tubule degeneration,glomeruli enlargement,disordered epithelial cells,and increased kidney lipid deposition after 12 weeks compared with those of the control group.THC adminis-tration alleviated all these conditions(P＜0.001).RNA-seq analysis revealed significant gene expression variations among the control,T2DM,and T2DM+THC groups.THC may protect against T2DM-induced kidney injury and lipid deposition by regulating the cell cycle(apoptosis),P53 signaling pathway,and PPARγ signaling path way,as indicated by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses.In mice with T2DM,THC intervention may upregulate the expression of Cd36,Lpl,PPARγ,and Plin4 genes in renal tissues,while downregulating Ccnb1,Ccnb2,Cdk1,Bub1,and Cdc25c gene expressions.The proteins encoded by the four upregulated genes interact,as do those encoded by the five downregulated genes.Conclusion THC administration improves fasting blood glucose levels,reduces renal damage,and decreases fat deposition in mice with T2DM.The processes may involve decreasing apoptosis,blocking the P53 signaling pathway,and activating the PPARγ signaling pathway.

Objective This study aims to explore the efficacy of tetrahydrocurcumin(THC),the major active metabolite of curcumin,on high glucose(HG)-induced human platelet aggregation and activation as well as to clarify the underlying mechanisms in vitro.Methods Purified platelets prepared from healthy subjects were pre-incubated with various concentrations of THC(0.5 μmol/L,1 μmol/L or 10 μmol/L)or vehicle control(0.05％DMSO)for 40 min at 37℃,followed by the stimulation of normal glucose(NG,5 mmol/L)or HG(25 mmol/L)for additional 90 min.The maximal aggregation rate was determined by an aggregometer.Flow cytometry was used to measure platelet surface expression of CD62P(a typical marker of platelet activation)and generation of total intraplatelet reactive oxygen species(ROS).Meanwhile,the phosphorylation level of platelet p53 was detected by Western blot assay.Results Compared with NG group,HG intervention significantly increased platelet aggrega-tion(P<0.05)and CD62P expression(P<0.001),which were greatly inhibited by different concentrations of THC(P<0.05).Mechanistically,when compared with solvent control,THC significantly decreased the level of total ROS production(P<0.001)and p53 phosphorylation(P<0.05).In addition,HG-induced total intraplatelet ROS generation(P<0.001)and p53 phosphorylation(P<0.05)were greatly attenuated by adding a ROS scavenger N-acetyl-L-cysteine(NAC).The combination of NAC with THC(10 μmol/L)showed no additive inhibitory effects(P>0.05).Moreover,platelet aggregation and activation induced by HG were greatly decreased by NAC and a p53 specific inhibitor PFT-μ(P<0.05).The combination of THC(10 μmol/L)and NAC resulted no additive inhibitory effects on HG-increased platelet aggregation and activation(P>0.05).THC(10 μmol/L)exhibited additive inhibitory effects on platelet aggregation(P<0.05)but no additive inhibitory effects on platelet activation when combined with PFT-μ(P>0.05).Conclusions THC exerts a protective effect on HG-induced platelet aggregation and activation possibly through down-regulating ROS/p53 signaling pathway in human platelets in vitro.The current study may provide potential value for THC to improve thrombosis in diabetes mellitus and the related chronic metabolic diseases.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective:To explore the relationship between programmed death-1(PD-1)inhibitors and cancer-related fatigue(CRF)in patients with malignant melanoma,and to identify associated influencing factors.Methods:A total of 100 patients with malignant melanoma treated at Jinan People's Hospital between April 2019 and April 2024 were included as study subjects.The Chinese version of the Piper Fatigue Scale was used to evaluate patients'fatigue levels within three months before and after the initiation of PD-1 inhibitor therapy.Results:There was a significant difference in CRF score before and after PD-1 inhibitor treatment(P<0.001).Univariate analysis showed no significant association between fatigue severity and factors such as gender,smoking history,tumor site,or PD-1 inhibitor type(all P>0.05).However,age,tumor stage,anemia,leukopenia,secondary hypothyroidism,secondary adrenal insufficiency(AI),and secondary adrenocorticotropic hormone deficiency(P<0.05 or P<0.01 or P<0.001)were significantly associated with CRF.Multivariate regression analysis identified secondary hypothyroidism,secondary AI,anemia,and leukopenia as independent risk factors for severe CRF in patients with malignant melanoma(all P<0.05).Conclusion:Adverse reactions of PD-1 inhibitors,including secondary hypothyroidism,secondary AI,anemia,and leukopenia,are independent risk factors for CRF in patients with malignant melanoma.

Objective To investigate how dietary supplementation with tetrahydrocurcumin(THC)improves kidney injury in type 2 dia-betic mellitus(T2DM)and its mechanism of action using transcriptome sequencing(RNA-seq).Methods C57BL/6J mice were randomly assigned to the control,T2DM,and T2DM+THC groups.After a high-fat meal and streptozotocin injection,the body weights and fasting blood glucose levels of each mouse with T2DM were measured.Hematoxylin and eosin staining,Oil red O staining,and RNA-seq were performed to examine kidney pathology,lipid deposition,and differentially expressed genes,respectively,in the mice.Results Mice in T2DM group exhibited significantly higher fasting blood glucose levels(P＜0.001),renal tubule degeneration,glomeruli enlargement,disordered epithelial cells,and increased kidney lipid deposition after 12 weeks compared with those of the control group.THC adminis-tration alleviated all these conditions(P＜0.001).RNA-seq analysis revealed significant gene expression variations among the control,T2DM,and T2DM+THC groups.THC may protect against T2DM-induced kidney injury and lipid deposition by regulating the cell cycle(apoptosis),P53 signaling pathway,and PPARγ signaling path way,as indicated by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses.In mice with T2DM,THC intervention may upregulate the expression of Cd36,Lpl,PPARγ,and Plin4 genes in renal tissues,while downregulating Ccnb1,Ccnb2,Cdk1,Bub1,and Cdc25c gene expressions.The proteins encoded by the four upregulated genes interact,as do those encoded by the five downregulated genes.Conclusion THC administration improves fasting blood glucose levels,reduces renal damage,and decreases fat deposition in mice with T2DM.The processes may involve decreasing apoptosis,blocking the P53 signaling pathway,and activating the PPARγ signaling pathway.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective This study aims to explore the efficacy of tetrahydrocurcumin(THC),the major active metabolite of curcumin,on high glucose(HG)-induced human platelet aggregation and activation as well as to clarify the underlying mechanisms in vitro.Methods Purified platelets prepared from healthy subjects were pre-incubated with various concentrations of THC(0.5 μmol/L,1 μmol/L or 10 μmol/L)or vehicle control(0.05％DMSO)for 40 min at 37℃,followed by the stimulation of normal glucose(NG,5 mmol/L)or HG(25 mmol/L)for additional 90 min.The maximal aggregation rate was determined by an aggregometer.Flow cytometry was used to measure platelet surface expression of CD62P(a typical marker of platelet activation)and generation of total intraplatelet reactive oxygen species(ROS).Meanwhile,the phosphorylation level of platelet p53 was detected by Western blot assay.Results Compared with NG group,HG intervention significantly increased platelet aggrega-tion(P<0.05)and CD62P expression(P<0.001),which were greatly inhibited by different concentrations of THC(P<0.05).Mechanistically,when compared with solvent control,THC significantly decreased the level of total ROS production(P<0.001)and p53 phosphorylation(P<0.05).In addition,HG-induced total intraplatelet ROS generation(P<0.001)and p53 phosphorylation(P<0.05)were greatly attenuated by adding a ROS scavenger N-acetyl-L-cysteine(NAC).The combination of NAC with THC(10 μmol/L)showed no additive inhibitory effects(P>0.05).Moreover,platelet aggregation and activation induced by HG were greatly decreased by NAC and a p53 specific inhibitor PFT-μ(P<0.05).The combination of THC(10 μmol/L)and NAC resulted no additive inhibitory effects on HG-increased platelet aggregation and activation(P>0.05).THC(10 μmol/L)exhibited additive inhibitory effects on platelet aggregation(P<0.05)but no additive inhibitory effects on platelet activation when combined with PFT-μ(P>0.05).Conclusions THC exerts a protective effect on HG-induced platelet aggregation and activation possibly through down-regulating ROS/p53 signaling pathway in human platelets in vitro.The current study may provide potential value for THC to improve thrombosis in diabetes mellitus and the related chronic metabolic diseases.

Objective:To apply deep neural networks to predict high-precision dose distribution in volume modulated arc therapy (VMAT) plans for non-small cell lung cancer (NSCLC) patients.Methods:This study developed a U-shaped network called MHA-resunet, which incorporated a large kernel dilated convolution module and a multi-head attention module. The model was trained from 151 VMAT plans of NSCLC patients. CT images, planning target volume (PTV) and organs at risk (OARs) were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks′performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics.Results:The MAE between the dose distribution predicted by MHA-resunet network and the manually planned dose distribution within the PTV area was 1.51 Gy, and the D98 and D95 errors in the PTV area were both < 1 Gy. Compared with the other three commonly used networks, the dose error of the MHA-resunet was the smallest in the PTV area and in OARs except for the heart. Conclusions:The proposed MHA-resunet network improves the receptive field to learn the relative spatial relationship between the PTV area and the OARs, enabling accurate prediction of dose distribution in NSCLC patients undergoing VMAT radiotherapy.

Objective To propose a deep learning neural network approach for transforming megavoltage computed tomography(MVCT)images of cervical cancer into pseudo kilovoltage computed tomography(kVCT)images with high signal-to-noise ratio and contrast-to-noise ratio,thus providing three-dimensional anatomical images and localization information required for adaptive radiotherapy of cervical cancer,and guiding the accelerator to achieve precise treatment.Methods The MVCT and kVCT images of 54 patients treated with cervical cancer radiotherapy were collected,with 44 cases randomly selected as the training set,and the remaining 10 cases as the test set.A cyclic generative adversarial network with gating mechanism and multi-channel data input was used to synthesize pseudo-kVCT images from MVCT images.The network training results were evaluated with imaging quality evaluation parameters,such as mean absolute error(MAE),peak signal-to-noise ratio(PSNR),and structural similarity index(SSIM).Results The MAE,PSNR,and SSIM of MVCT imagesvspseudo-kVCT(5:5)images were(24.9±0.7)HUvs(17.8±0.3)HU,(29.8±0.2)dBvs(30.7±0.2)dB,and 0.841±0.007 vs 0.898±0.003,respectively.Conclusion The generated pseudo-kVCT images have advantages in noise reduction and contrast enhancement,and can reduce the need for additional MV-kVCT electron density calibration in dose calculations.The dose calculation ability of pseudo-kVCT is comparable to that of MVCT,providing a possibility for the application of pseudo-kVCT images in image-guided adaptive radiotherapy.