Objective The aim of this study was to investigate whether there are differences in the expression spectrums of RSK4 in different tumor cells and to predict the possible protein subtypes and their biological characteris-tics.Methods RNA was extracted from glioma cells GL261,ovarian cancer cells ID8,breast cancer cells 4T1 and 168FARN,colon cancer cells mc38 and CT26,gastric cancer cells MFC and lung cancer cells LLC1.The expres-sions and splicing isomers of RSK4 were detected by RT-PCR.The biological characteristics and functions of RSK4 were analyzed by bioinformatics.Results RT-PCR results showed that RSK4 was expressed in GL261,4T1,mc38,CT26,MFC and LLC1;and multiple splicing isomers were expressed in different tumor cells.Open reading frame analysis revealed RSK4 may encode at least 11 protein subtypes.Secondary structure analysis showed that these protein subtypes encoded by the splicing isomers were composed of α-helix,extended strand,β-turn and random coil,and had the same conserved domain.The results of protein interaction network enrichment analysis showed that RSK4 exerted kinase activity in the nucleus and cytoplasm mainly through the mTOR signaling pathway and long-term potentiation.Conclusions RSK4 has different expression spectrums in different tumor cells,and may produces a variety of protein isoforms with different nitrogen terminals,which can be involved in different biological processes through multiple signaling pathways.

Background and Purpose:The early prediction of local tumor progression-free survival(LTPFS)after radiofrequency ablation(RFA)for colorectal cancer(CRC)lung metastases has significant clinical importance.The application of radiomics in the prediction of tumor prognosis has been explored.This study aimed to construct a radiomics-based nomogram for predicting LTPFS after RFA in CRC patients with lung metastases.Methods:This study retrospectively analyzed 172 CRC patients with 401 lung metastases admitted to Department of Interventional Radiology,Fudan University Shanghai Cancer Center from August 2016 to January 2019.This study was reviewed by the medical ethics committee of Fudan University Shanghai Cancer Center(ethics number:2402291-24).After augmentation of pre-ablation and immediate post-ablation computed tomography(CT)images,the target metastases and ablation regions were segmented manually to extract the radiomic features.Maximum relevance and minimum redundancy algorithm(MRMRA)and least absolute shrinkage and selection operator(LASSO)regression models were applied for feature selection.The clinical model,the radiomics model,and the fusion model were constructed based on the selected radiomic features and clinical variables screened by the multivariate analysis.The Harrell concordance index(C-index)and area under receiver operating characteristic(ROC)curves(AUC)were calculated to evaluate the prediction performance.Finally,the corresponding nomogram of the best model was drawn.Results:Among all the lung metastases,102(25.4%)had final recurrence,and 299(74.6%)had complete response(CR).The median follow-up time was 21 months(95%CI:19.466-22.534),and the LTPFS rates at 1,2,and 3 years after RFA were 76.5%(95%CI:72.0-80.4),72.1%(95%CI:66.6-76.9)and 69.9%(95%CI:64.0-75.1).In both the training and test dataset,the fusion model based on the final 12 radiomic features through the LASSO regression and 4 clinical variables screened by multivariate analysis achieved the highest AUC values for LTPFS,with C-index values of 0.890(95%CI:0.854-0.927)and 0.843(95%CI:0.768-0.916),respectively.Conclusion:The fusion model based on radiomic features and clinical variables is feasible for predicting LTPFS after RFA of CRC patients with lung metastases,whose performance is superior to the single radiomic and clinical model.At the same time,the nomogram of the fusion model can intuitively predict the prognosis of CRC patients with lung metastases after RFA,thus assisting clinicians in developing individualized follow-up review plans for patients and adjusting treatment strategies flexibly.

Long non-coding RNAs (lncRNAs) play a significant role in maintaining tissue morphology and functions, and their precise regulatory effectiveness is closely related to expression patterns. However, the spatial expression patterns of lncRNAs in humans are poorly characterized. Here, we constructed five comprehensive transcriptomic atlases of human lncRNAs covering thousands of major tissue samples in normal and disease states. The lncRNA transcriptomes exhibited high consistency within the same tissues across resources, and even higher complexity in specialized tissues. Tissue-elevated (TE) lncRNAs were identified in each resource and robust TE lncRNAs were refined by integrative analysis. We detected 1 to 4684 robust TE lncRNAs across tissues; the highest number was in testis tissue, followed by brain tissue. Functional analyses of TE lncRNAs indicated important roles in corresponding tissue-related pathways. Moreover, we found that the expression features of robust TE lncRNAs made them be effective biomarkers to distinguish tissues; TE lncRNAs also tended to be associated with cancer, and exhibited differential expression or were correlated with patient survival. In summary, spatial classification of lncRNAs is the starting point for elucidating the function of lncRNAs in both maintenance of tissue morphology and progress of tissue-constricted diseases.
Humans ; Gene Expression Profiling ; Neoplasms/genetics* ; Organ Specificity ; RNA, Long Noncoding/genetics* ; Transcriptome

Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Evoked Potentials, Visual ; Photic Stimulation

Objective:To establish ultra performance liquid chromatography （UPLC） fingerprint of Shengyutang and quantitative analysis method of 11 index components in this famous classical formula. Method:UPLC-diode array detector/evaporative light scattering detector （UPLC-PDA/ELSD） was used， two chromatographic conditions were established by different detectors according to the polarity of chemical components. Conditions of fingerprint 1 were as follows：ACQUITY UPLC HSS T₃ column （2.1 mm×100 mm， 1.8 µm） with the mobile phase of acetonitrile （A）-0.6% formic acid solution （C） for gradient elution （0-4 min， 0-4%A； 4-8 min， 4%A； 8-9 min， 4%-8%A； 9-14 min， 8%-9%A； 14-21 min， 9%-15%A； 21-26 min， 15%-17%A； 26-30 min， 17%-20%A； 30-35 min， 20%-32%A； 35-40 min， 32%-40%A； 40-50 min， 40%-80%A； 50-55 min， 80%A）， the flow rate of 0.3 mL·min^-1， PDA with detection wavelengths of 280 nm and 321 nm， the column temperature at 30 ℃. Conditions of fingerprint 2 were as follows：the CORTECS C₁₈ column （3.0 mm×100 mm， 2.7 µm） with the mobile phase of acetonitrile （A）-water （D） for gradient elution （0-11 min， 19%A； 11-16 min， 19%-25%A； 16-34 min， 25%-28%A； 34-47 min， 28%-47%A； 47-60 min， 47%-80%A）， the flow rate of 0.4 mL·min^-1， ELSD with drift tube temperature of 95 ℃， the carrier gas （air） flow rate of 2.0 L·min^-1， and the column temperature at 30 ℃. UPLC-PDA/ELSD fingerprints of 15 batches of Shengyutang were established， and the similarity was evaluated by similarity evaluation system of chromatographic fingerprint of traditional Chinese medicine （2012 edition） issued by the Chinese Pharmacopoeia Commission， and the contents of eleven index components in this famous classical formula were determined. Result:The similarities of UPLC-PDA/ELSD fingerprints of 15 batches of Shengyutang were >0.98 by comparing with the control fingerprint， 27 and 16 common peaks were identified in fingerprint 1， 2， respectively. It was tested and verified that the precision， repeatability， stability， linear relationship and other results of this method all met the requirements of the 2020 edition of Chinese Pharmacopoeia. The contents of chlorogenic acid， ferulic acid， calycosin glucoside， verbascoside， senkyunolide I， senkyunolide H， senkyunolide A， ginsenoside Rg₁， ginsenoside Re， ginsenoside Rb₁ and astragaloside A in 15 batches of Shengyutang were 0.063-0.193， 0.509-0.638， 0.160-0.318， 0.012-0.056， 0.394-0.519， 0.110-0.143， 0.031-0.097， 0.382-0.595， 0.292-0.505， 0.590-0.803， 0.142-0.367 mg·g^-1， respectively. Conclusion:The established detection method meets the requirements of the 2020 edition of Chinese Pharmacopoeia， which can characterize the overall characteristics of chemical components in Shengyutang， and provide experimental basis for the quality standard research of this famous classical formula.

Objective:To establish the ultraperformance liquid chromatography （UPLC） fingerprint of Pipa Qingfeiyin substance benchmark， and to establish a quantitative analysis method for simultaneous determination of the contents of five index components， so as to provide reference for the quality control and evaluation of this famous classical formula. Method:ACQUITY UPLC^® CSH^TM C₁₈ column （2.1 mm×100 mm， 1.7 μm） was used with mobile phase of acetonitrile （A）-0.1% formic acid aqueous solution （B） for gradient elution （0-7 min， 5%-7%A； 7-11 min， 7%-8%A； 11-22 min， 8%-14%A； 22-30 min， 14%-15%A； 30-35 min， 15%-25%A； 35-42 min， 25%-40%A； 42-45 min， 40%-50%A； 45-50 min， 50%-60%A）， the flow rate was 0.35 mL·min^-1， the column temperature was 25 ℃， the detection wavelengths were 278 nm and 248 nm. UPLC fingerprints of 15 batches of Pipa Qingfeiyin substance benchmark were established， and the "Similarity Evaluation System of Chromatographic Fingerprint of Traditional Chinese Medicine" software （2012 edition） was used for similarity analysis， and the common peaks were assigned. Cluster analysis （CA）， principal component analysis （PCA） and orthogonal partial least squares discriminant analysis （OPLS-DA） were used to evaluate the fingerprint data. UPLC fingerprint method was used to simultaneously determine the contents of five components in the substance benchmark. Result:The method validation of fingerprint and determination method was good， the similarities between 15 batches of Pipa Qingfeiyin substance benchmark and their control fingerprint were ≥0.997， 23 common peaks were identified and 11 chromatographic peaks were identified. CA， PCA and OPLS-DA divided 15 batches of the substance benchmark into two groups. The linear relationship of phellodendrine hydrochloride， chlorogenic acid， berberine hydrochloride， palmatine hydrochloride and ammonium glycyrrhizinate was good in a certain range of concentration （R²>0.999）， their average recovery was 96.47%-101.16%， and the contents of these five components in the substance benchmark were 0.87-2.00， 1.53-5.95， 18.45-33.97， 3.87-6.29， 1.02-4.12 mg·g^-1， respectively. Conclusion:The established UPLC fingerprint and multi-index component content determination methods have strong specificity， good resolution and high sensitivity， it can be characterized except for the Ginseng Radix et Rhizoma flavor， which can provide reference for the quality control and evaluation of Pipa Qingfeiyin compound preparation.

Adolescent ; Decision Making ; Emotions ; Humans

Objective To investigate the role of AT1 receptor autoantibody (AT1-AA) in the inhibitory action of irbesartan on endoplasmic reticulum stress (ERS)-related apoptotic signals in rat kidney with diabetic nephropathy (DN).Methods DN model rats were induced by high-sugar and high-fat diet plus intraperitoneal injection of streptozotocin,and the serum level of AT1-AA was detected by ELISA.These DN rats with positive or negative AT1-AA were divided into DN group and irbesartan treated group.After 4 weeks of irbesartan treatment,TUNEL staining was used to detect renal cell apoptosis.The protein and mRNA expressions of ERS chaperone protein glucose-regulated protein 78 (GRP78) and ERS-associated apoptosis proteins were determined by Western blot and RT-PCR.Results Compared with NC group,the apoptosis rate of renal cells in DN group was obviously increased,along with the increased expressions of GRP78,C/EBP homology protein (CHOP),phosphorylated c-Jun N-terminal kinase (JNK),and Caspase12 protein and mRNA (all P＜0.01).The cell apoptosis and protein and mRNA levels of these genes were significantly decreased after irbesartan treatment (all P＜ 0.01),especially in AT1-AA positive DN rats(all P＜0.05).The renal cell apoptosis rate,and protein and mRNA levels of these four genes in AT1-AA positive DN group were much greater than those in AT1-AA negative DN group (all P＜0.05).Conclusions AT1-AA may be involved in ERS-related cell apoptosis in the kidney of DN rats,and play a role in irbesartan-improved renal function via inhibiting ERS-associated CHOP-JNK-Caspase12 apoptotic signals and renal cell apoptosis.

Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.
Brain ; physiopathology ; Electroencephalography ; Epilepsy ; Humans ; Seizures ; diagnosis

Neurofeedback, as an alternative treatment method of behavioral medicine, is a technique which translates the electroencephalogram (EEG) signals to styles as sounds or animation to help people understand their own physical status and learn to enhance or suppress certain EEG signals to regulate their own brain functions after several repeated trainings. This paper develops a neurofeedback system on the foundation of brain-computer interface technique. The EEG features are extracted through real-time signal process and then translated to feedback information. Two feedback screens are designed for relaxation training and attention training individually. The veracity and feasibility of the neurofeedback system are validated through system simulation and preliminary experiment.
Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Neurofeedback