OBJECTIVE: This study aims to elucidate the therapeutic potential of osthole for the treatment of atopic dermatitis (AD), focusing on its ability to alleviate chronic pruritus (CP) and the underlying molecular mechanisms. METHODS: In this study, we investigated the anti-inflammatory effects of osthole in both a 2,4-dichloronitrobenzene (DNCB)-induced AD mouse model and tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) stimulated huma immortalized epidermal (HaCaT) cells. The anti-itch effect of osthole was specifically assessed in the AD mouse model. Using methods such as hematoxylin and eosin (HE) staining, enzyme-linked immunosorbent assay (ELISA), western blot (WB), quantitative real-time PCR (qRT-PCR), and immunofluorescence staining. RESULTS: Osthole improved skin damage and clinical dermatitis scores, reduced scratching bouts, and decreased epidermal thickness AD-like mice. It also reduced the levels of interleukin (IL)-31 and IL-31 receptor A (IL-31 RA) in both skin tissues and HaCaT cells. Furthermore, Osthole suppressed the protein expression levels of phosphor-p65 (p-p65) and phosphor-inhibitor of nuclear factor kappa-Bα (p-IκBα). Meanwhile, it increased the protein expression levels of peroxisome proliferator-activated receptor α (PPARα) and PPARγ in HaCaT cells. CONCLUSION These findings indicated that osthole effectively inhibited CP in AD by activating PPARα, PPARγ, repressing the NF-κB signaling pathway, as well as the expression of IL-31 and IL-31 RA.

Objective To explore the value of diffusion tensor imaging analysis index along the perivascular space(DTI-ALPS)for assessing age-related functional changes in glymphatic system(GS).Methods Totally 27 healthy subjects from Meizhou People's Hospital and 100 healthy subjects from neuroimaging informatics tools and resources collaborator database who underwent T1-weighted magnetization-prepared rapid gradient echo(T1-MPRAGE)and DTI scanning were retrospectively enrolled and divided into youth group(n=38),middle-aged group(n=57)and elderly group(n=32).Automated DTI-ALPS index analysis procedure was used to minimize manual errors and derive DTI-ALPS index.The general data,neuropsychological assessment results and DTI-ALPS indices were compared among groups.Spearman correlation analysis was performed to observe the relationships of DTI-ALPS index and age,gender,as well as neuropsychological scores.Results The average age in youth group,middle-aged group and elderly group was(28.5±5.8),(53.7±6.8)and(73.8±2.3)years,respectively.No significant difference of DTI-ALPS index was found between middle-aged group and elderly group(P＞0.05),which were both lower than that in youth group(both P＜0.05).DTI-ALPS index was weakly negatively correlated with age(rs=-0.340,P＜0.001),but not significantly correlated with gender nor neuropsychological assessment results(both P＞0.05).Conclusion DTI-ALPS index was negatively correlated with age in healthy individuals,hence having potential utility for assessing age-related functional changes in GS.

Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

Objective To explore the value of diffusion tensor imaging analysis index along the perivascular space(DTI-ALPS)for assessing age-related functional changes in glymphatic system(GS).Methods Totally 27 healthy subjects from Meizhou People's Hospital and 100 healthy subjects from neuroimaging informatics tools and resources collaborator database who underwent T1-weighted magnetization-prepared rapid gradient echo(T1-MPRAGE)and DTI scanning were retrospectively enrolled and divided into youth group(n=38),middle-aged group(n=57)and elderly group(n=32).Automated DTI-ALPS index analysis procedure was used to minimize manual errors and derive DTI-ALPS index.The general data,neuropsychological assessment results and DTI-ALPS indices were compared among groups.Spearman correlation analysis was performed to observe the relationships of DTI-ALPS index and age,gender,as well as neuropsychological scores.Results The average age in youth group,middle-aged group and elderly group was(28.5±5.8),(53.7±6.8)and(73.8±2.3)years,respectively.No significant difference of DTI-ALPS index was found between middle-aged group and elderly group(P＞0.05),which were both lower than that in youth group(both P＜0.05).DTI-ALPS index was weakly negatively correlated with age(rs=-0.340,P＜0.001),but not significantly correlated with gender nor neuropsychological assessment results(both P＞0.05).Conclusion DTI-ALPS index was negatively correlated with age in healthy individuals,hence having potential utility for assessing age-related functional changes in GS.

Objective:To analyze the effectiveness and safety of robot-assisted percutaneous endoscopic posterior/transforaminal lumbar interbody fusion (RPE-P/TLIF) in the treatment of lumbar spinal stenosis with instability.Methods:From September 2018 to April 2022, 26 patients with lumbar spinal stenosis combined with lumbar segmental instability were treated with RPE-P/TLIF at the Department of Minimally Invasive Spine Surgery, Guangdong Provincial Hospital of Chinese Medicine. There were 13 males and 13 females, with a mean age of 57.92±7.09 years (range, 44 to 75 years) and a mean body mass index of 24.05±2.64 kg/m 2 (range, 19.38 to 29.06 kg/m 2). A total of 31 segments were included, including 21 cases of single-segment surgery (L 3, 4 in 2 cases, L 4, 5 in 18 cases, and L 5S 1 in 1 case) and 5 cases of two-segment surgery (all L 3-L 5). Before surgery and at 1, 6, and 12 months after surgery, as well as at the final follow-up, the visual analogue scale (VAS) was used to assess back pain and lower limb pain, and the Oswestry disability index (ODI) was used to evaluate spinal nerve function. Clinical efficacy was evaluated using the MacNab criteria at the final follow-up. Imaging evaluation indicators included pre- and post-operative intervertebral space height, lumbar lordosis angle, fusion rate, and accuracy of pedicle screw placement. Results:The follow-up time for the 26 patients was 42.88±10.09 months (range, 12 to 55 months). The mean operation time was 156.54±33.50 min, and the mean postoperative drainage volume was 27.23±20.20 ml. The VAS scores for back pain and lower limb pain before surgery were 4.35±1.23 and 6.08±0.63, respectively. These scores decreased to 2.08±0.69 and 1.85±0.54 at 1 month postoperatively, 1.85±0.54 and 0.77±0.59 at 6 months, 0.96±0.53 and 0.62±0.57 at 1 year, and 0.88±0.52 and 0.58±0.50 at the final follow-up, respectively. The differences were statistically significant ( F=85.943, P=0.001; F=547.946, P=0.014). The ODI scores before surgery and at 1, 6, 12 months after surgery, and at the final follow-up were 55.38%±5.89%, 28.38%±3.849%, 17.77%±2.67%, 12.58%±1.88% and 12.12%±2.27% respectively, with statistically significant differences ( F=783.289, P=0.010). According to the MacNab criteria, at the final follow-up, there were 18 cases of excellent, 6 cases of good, and 2 cases of fair, with an excellent and good rate of 92.3%. A total of 114 percutaneous pedicle screws were implanted with grades A, B and C being 109, 4 and 1, respectively. The preoperative, postoperative, and final follow-up intervertebral space heights were 10.55±1.96, 13.53±1.37, and 12.54±1.42 mm respectively, with statistically significant differences ( F=42.190, P<0.001). And the lumbar lordosis angles were 35.81°±10.80°, 35.69°±11.07°, and 36.08°±11.29° respectively, with no statistically significant differences ( P>0.05). At 12 months postoperatively, bone fusion was achieved in 25 cases, with a fusion rate of 96% (25/26). Conclusion:RPE-P/TLIF for lumbar spinal stenosis combined with lumbar instability were favorable. Being a safe and effective minimally invasive surgical option, it effectively enhanced the intervertebral space height in the surgical segment with fewer complications.

Objective To explore the comparative study of constructing prediction models for muscle invasive of bladder cancer based on different machine learning algorithms combined with MRI radiomic.Methods A total of 187 bladder cancer patients who underwent MRI examination and were confirmed by pathology were retrospectively selected.Patients were randomly divided into a training set and a test set in a 7∶3 ratio.The patients were divided into muscle invasive bladder cancer(MIBC)group and non-muscle invasive bladder cancer(NMIBC)group according to the surgical pathology results.Tumor volume of interest(VOI)was outlined on the images of T2 WI,diffusion weighted imaging(DWI),and apparent diffusion coefficient(ADC),and the radiomic features were extracted by A.K software,and dimensionality reduction was performed using the maximum relevance minimum redundancy(mRMR)algorithm combined with least absolute shrinkage and selection operator(LASSO).Six machine learning algorithms,including K-nearest neighbor(KNN),decision tree(DT),support vector machine(SVM),logistic regression(LR),random forest(RF),and explainable boosting machine(EBM)were used to construct the radiomic model and calculate the corresponding area under the curve(AUC),accuracy,sensitivity,and specificity,respectively.Results Six machine learning algorithms,including KNN,DT,SVM,LR,RF,and EBM were used to construct the radiomic model,and the AUC values for predicting MIBC in the training set were 0.863,0.838,0.853,0.866,0.977,0.997,and in the test set were 0.748,0.833,0.860,0.868,0.870,0.900.Among them,the MRI radiomic model constructed based on EBM had the highest predictive efficacy for MIBC,with AUC values,accuracy,sensitivity and specificity of 0.997,0.977,0.957 and 0.981 in the training set,and 0.900,0.877,0.800,and 0.894 in the test set,respectively.Conclusion Multiple machine learning algorithms combined with MRI radiomic to construct models have good predictive efficacy for MIBC,and the model constructed based on EBM shows the highest predictive value.

Objective To investigate the diagnostic efficiency of patients with human epidermal growth factor receptor-2(HER-2)over expressed breast cancer via combining the dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)perfusion parameters,apparent diffusion coefficient(ADC)value and clinical feature model.Methods A total of 197 breast cancer patients who underwent DCE-MRI and diffusion weighted imaging(DWI)scans were analyzed retrospectively,including 47 breast cancer patients with HER-2 over expressed and 150 breast cancer patients with non-HER-2 over expressed.The t-test or chi-square test was used to compare the DCE-MRI perfusion parameters[Ktrans,Kep,Ve,W-in,W-out,and time to peak(TTP)],ADC value,and clinical feature between the two groups.The diagnostic efficiency of the models were analyzed via receiver operating characteristic(ROC)curves.Results There were significant difference in the maximum tumor diameter,minimum tumor diameter,T stage,N stage,Kep,W-in,and ADC value between HER-2 over expressed breast cancer and non-HER-2 over expressed breast cancer groups(P＜0.05).The proposed combined model,which included the combined maximum tumor diameter,minimum tumor diameter,T stage,N stage,Kep,W-in,and ADC value,showed a better diagnostic efficiency with area under the curve(AUC)(AUC=0.763)than the clinical model(AUC=0.634)based on the combined maximum tumor diameter,minimum tumor diameter,T stage,and N stage,and the imaging model(AUC=0.715)based on the combined Kep,W-in and ADC value.Conclusion The maximum tumor diameter,minimum tumor diameter,T stage,N stage,Kep,W-in,and ADC value may be associated with HER-2 over expressed breast cancer.Combining all above parameters can improve the diagnostic ability of breast cancer patients with HER-2 over expressed.

Objective To examine the precise function of influenza A virus target genes(IATGs)in malignancy. Methods Using multi-omics data from the TCGA and TCPA datasets,33 tumor types were evaluated for IATGs.IATG expression in cancer cells was analyzed using transcriptome analysis.Copy number variation(CNV)was assessed using GISTICS 2.0.Spearman's analysis was used to correlate mRNA expression with methylation levels.GSEA was used for the enrichment analysis.Pearson's correlation analysis was used to examine the association between IATG mRNA expression and IC50.The ImmuCellAI algorithm was used to calculate the infiltration scores of 24 immune cell types. Results In 13 solid tumors,IATG mRNA levels were atypically expressed.Except for UCS,UVM,KICH,PCPG,THCA,CHOL,LAMI,and MESO,most cancers contained somatic IATG mutations.The main types of CNVs in IATGs are heterozygous amplifications and deletions.In most tumors,IATG mRNA expression is adversely associated with methylation.RT-PCR demonstrated that EGFR,ANXA5,CACNA1C,CD209,UVRAG were upregulated and CLEC4M was downregulated in KIRC cell lines,consistent with the TCGA and GTEx data. Conclusion Genomic changes and clinical characteristics of IATGs were identified,which may offer fresh perspectives linking the influenza A virus to cancer.

Objective To investigate the value of multimodal MRI radiomics in predicting muscle-invasive bladder cancer.Methods A total of 178 patients with pathology diagnosis of bladder cancer were retrospectively collected,including 31 cases of muscle invasive bladder cancer(MIBC)and 147 cases of non-muscle invasive bladder cancer(NMIBC).Patients were randomly divided into training group and testing group at a ratio of 7︰3.The range of bladder tumors in T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images were segmented as volume of interest(VOI)by using ITK-SNAP software.Radiomics features were extracted through A.K software.The optimal radiomics features were obtained through radiomics algorithm and least absolute shrinkage and selection operator(LASSO)method.Finally,the logistic regression analysis method and random forest model method were used to construct prediction models.The performance of prediction models was evaluated by the receiver operating characteristic(ROC)curve.Results This study constructed four groups of models containing T2WI prediction model,DWI prediction model,ADC prediction model,and T2WI+DWI+ADC prediction model.The area under the curve(AUC)of T2WI,DWI,and ADC prediction models for identifying MIBC and NMIBC were separately 0.920,0.914,and 0.954 in the training group while those were respectively 0.881,0.773,and 0.871 in the testing group.There was no statistical significance between T2WI,DWI,and ADC prediction models.In training and testing groups,the AUC of T2WI+DWI+ADC prediction model were respectively 0.959 and 0.909,which were higher than the single sequence prediction model.The sensitivity and specificity of the training group were 0.905 and 0.853 and the sensitivity and specificity of the testing group were 0.778 and 0.795.Conclusion MRI radiomics prediction model can effectively differentiate MIBC and NMIBC.The T2WI+DWI+ADC prediction model shows better prediction efficiency.