Objective To investigate the value of machine learning models based on contrast-enhanced CT radiomics in differentia-ting renal oncocytoma(RO)from chromophobe renal cell carcinoma(chRCC).Methods A total of 65 patients with RO and chRCC confirmed by pathology with complete clinical and imaging data were analyzed retrospectively.The patients were randomly divided into training set(n=45)and test set(n=20)according to a ratio of 7︰3.The tumor boundaries were delineated on the preoperative CT images using 3D Slicer software,and radiomics features were extracted using the Radiomics plugin.Univariate analysis,recursive fea-ture elimination(RFE),least absolute shrinkage and selection operator(LASSO)algorithms were used to select the best radiomics features.Three machine learning models were constructed on the training set and the grid search method was used to select the best combination of hyperparameters.The receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the performance of each machine learning model on the training set and test set.Results Random forest model,logistic regres-sion model and support vector machine model can better identify RO and chRCC.In the training set,the area under the curve(AUC)of random forest model and support vector machine model were 0.950[95%confidence interval(CI)0.901-0.998]and 0.955(95%CI 0.908-1.000),respectively,which were higher than the AUC of logistic regression model 0.882(95%CI 0.806-0.956).Statistical differences were found by DeLong test(P<0.05);In the test set,the AUC of random forest model,logistic regression model and support vector machine model were 0.876(95%CI 0.758-0.993),0.883(95%CI 0.768-0.997)and 0.883(95%CI 0.768-0.997),respectively.There was no significant statistical difference in the AUC of each model by DeLong test(P>0.05).The decision curve showed that all three models had significant net clinical benefits.Conclusion The machine learning model based on contrast-enhanced CT radiomics can effectively distinguish RO from chRCC.

Spinal cord injury(SCI)can result in varying degrees of spinal motor dysfunction,partial sensory loss and sphincter dysfunction.SCI is divided into two stages:primary injury and secondary injury.The spinal cord cell necrosis during the primary injury period and apoptosis,oxidative stress and autophagy during the secondary injury period lead a large number of cell injury and permanant neurodysfunction.The histone deacetylase(HDAC)plays a key role in regulating the cellular viability and gene transcription.Neuro-dysfunction induced by SCI is associated with transcriptional dysfunction associated with unbalanced levels of protein acetylation.Valproic acid(VPA)is a inhibitor of HDAC and is usually used as an antiepileptic drug in clinic.Studies show that VPA may have the potential to treat the central nervous system diseases.VPA inhib-its HDAC,and then regulates oxidative stress,cellular autophagy,ion imbalance,microglia differentiation and inflammatory response,and plays the neuroprotective effect.This paper reviewed the related molecular mecha-nism of VPA in treating SCI.

Objective To investigate the value of machine learning models based on contrast-enhanced CT radiomics in differentia-ting renal oncocytoma(RO)from chromophobe renal cell carcinoma(chRCC).Methods A total of 65 patients with RO and chRCC confirmed by pathology with complete clinical and imaging data were analyzed retrospectively.The patients were randomly divided into training set(n=45)and test set(n=20)according to a ratio of 7︰3.The tumor boundaries were delineated on the preoperative CT images using 3D Slicer software,and radiomics features were extracted using the Radiomics plugin.Univariate analysis,recursive fea-ture elimination(RFE),least absolute shrinkage and selection operator(LASSO)algorithms were used to select the best radiomics features.Three machine learning models were constructed on the training set and the grid search method was used to select the best combination of hyperparameters.The receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the performance of each machine learning model on the training set and test set.Results Random forest model,logistic regres-sion model and support vector machine model can better identify RO and chRCC.In the training set,the area under the curve(AUC)of random forest model and support vector machine model were 0.950[95%confidence interval(CI)0.901-0.998]and 0.955(95%CI 0.908-1.000),respectively,which were higher than the AUC of logistic regression model 0.882(95%CI 0.806-0.956).Statistical differences were found by DeLong test(P<0.05);In the test set,the AUC of random forest model,logistic regression model and support vector machine model were 0.876(95%CI 0.758-0.993),0.883(95%CI 0.768-0.997)and 0.883(95%CI 0.768-0.997),respectively.There was no significant statistical difference in the AUC of each model by DeLong test(P>0.05).The decision curve showed that all three models had significant net clinical benefits.Conclusion The machine learning model based on contrast-enhanced CT radiomics can effectively distinguish RO from chRCC.

Background::Mild traumatic brain injury (mTBI) is a common neurological trauma that can lead to cognitive impairment. The sirtuin-1 (SIRT-1)/peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) pathway has been reported to have neuroprotective effects in rats with craniocerebral injury. We evaluated potential mechanisms underlying electroacupuncture-mediated recovery of cognitive function after mTBI, focusing on the SIRT-1/PGC-1α/mitochondrial pathway.Methods::We included forty 6-week-old male Sprague-Dawley rats in this study. Rats were randomly divided into four groups: controlled cortical impactor (CCI, n = 10), sham operation (sham, n = 10), electroacupuncture-treated CCI (CCI+EA, n = 10), and electroacupuncture-treated sham (sham+EA, n = 10) group. Randomization was performed by assigning a random number to each rat and using a random number table. The mTBI rat model was established using a controllable cortical impactor. Electroacupuncture therapy was performed on the back of rats, by inserting acupuncture needles to the specific acupoints and setting appropriate parameters for treatment. We evaluated spatial learning and memory functions with the Morris water maze test. We performed quantitative real-time polymerase chain reaction (qRT-PCR), western blotting, adenosine triphosphate (ATP) determination, and mitochondrial respiratory chain complex I (MRCC I) determination on rat hippocampal tissue. We analyzed SIRT-1/PGC-1α expression levels and the results of mitochondrial function assays, and compared differences between groups using bilateral Student’s t-tests. Results::Compared with the sham group, SIRT-1/PGC-1α expression was downregulated in the hippocampus of CCI group ( P <0.01). Although this expression was upregulated following electroacupuncture, it did not reach the levels observed in the sham group ( P <0.05). Compared with the sham group, MRCC I and ATP levels in the CCI group were significantly reduced, and increased after electroacupuncture ( P <0.01). In the Morris water maze, electroacupuncture reduced the incubation period of rats and increased average speed and number of crossing platforms ( P <0.05). Conclusion::Electroacupuncture may improve cognitive function in the mTBI rat model by regulating the SIRT-1/PGC-1α/mitochondrial pathway.

Intracranial hypertension crisis is a critical condition of intracranial hypertension in children.It often occurs in acute intracranial hypertension and is a precursor to cerebral hernia.It needs to be identified and treated urgently.The treatment and prognosis of acute intracranial hypertension and intracranial hypertension crisis are important to the long-term development of children.In this review, we reviewed the etiology, pathogenesis, early identification, monitoring and intervention of acute intracranial hypertension/intracranial hypertension crisis, hoping to be helpful to clinicians.

Objective To investigate the expression of beclin1 and LC3 in non small cell lung cancer and explore their clinical significance. Methods Immunohistochemistry was applied to confirm expression of Beclin1 and LC3 in 66 samples of NSCC and 52 samples of adjacent tissues. Clinical pathological features were analyzed. Results Beclin1 and LC3 expression were lower in cancer tissues than that in adjacent cancer tissues ,and the dif-ference was statistically significant(46.9%vs 75%,53%vs 80.7%,P<0.05). The expression of Beclin1 and LC3 was down-regulated with TNM staging rising and tissue differentiation decreasing. With tumor enlarging ,Beclin1 down-regulated,and the difference is statistically significant(P<0.05). Expression of Beclin1 was positively cor-related with LC3 expression(r=0.31,P<0.05). Conclusion The down-regulation of autophagy-related protein Beclinl and LC3 might be involved in the genesis and development of non small lung cancer.

Background and purpose:Bladder cancer is the most common urological tumor, and its pathogen-esis is still not fully understood. The study was aimed to observe the expressions of key genes in many tumor-associated signaling pathways in normal bladder tissue and bladder carcinoma, and to provide further evidence for the subsequent study of bladder cancer recurrence and metastasis.Methods:Twenty-seven cases of bladder cancer specimens were col-lected, and normal bladder tissues and bladder cancer tissues were distinguished by frozen section. Then, the expressions of 84 genes of cancer-related signaling pathways in bladder cancer tissues and normal bladder tissues were screened by Cancer Pathway Finder PCR Array produced by QIAGEN company.Results:Compared with the normal bladder tissues, the bladder carcinoma tissues had 8 up-regulated genes and 19 down-regulated genes. In this study, the impact of epithe-lial-mesenchymal transition (EMT) signaling pathway was selected as a research direction in which theGSC,KRT14,DSP were up-regulated,SNAI2,SNAI3 were down-regulated. ThereforeGSC,KRT14,DSP,SNAI2 andSNAI3 were chosen as target genes, and verified by qRT-PCR in many examples. The result showed that the expressions ofGSC gene in bladder cancer tissues were up-regulated, but with no statistical significance;KRT14,DSP expressions in bladder cancer were higher than those in normal bladder tissues (P<0.05);SNAI2,SNAI3 expressions in bladder cancer were lower than those in normal bladder tissues (P<0.05), andSNAI3 showed the most obvious expression differences.Conclusion:KRT14,DSP andSNAI3 may play an important role in bladder cancer’s occurrence, development and metastasis.