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Objectives. Schizophrenia (SCZ) is associated with disrupted functional brain connectivity, and antipsychotic medications are the primary and most commonly used treatment for schizophrenia. However, not all patients respond to antipsychotic medications. Methods. The study is aimed at investigating whether the graph-theory-based degree centrality (DC), derived from resting-state functional MRI (rs-fMRI), can predict the treatment outcomes. rs-fMRI data from 38 SCZ patients were collected and compared with findings from 38 age- and gender-matched healthy controls (HCs). The patients were treated with antipsychotic medications for 16 weeks before undergoing a second rs-fMRI scan. DC data were processed using DPABI and SPM12 software. Results. SCZ patients at baseline showed increased DC in the frontal and temporal gyrus, anterior cingulate cortex, and precuneus and reduced DC in bilateral subcortical gray matter structures. However, those abnormalities showed a clear renormalization after antipsychotic medication treatments. Support vector machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.2% (sensitivity 78.9%, specificity 89.5%, and area under the receiver operating characteristic curve (AUC) 0.925) for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the bilateral putamen, left inferior frontal gyrus, left middle occipital cortex, bilateral middle frontal gyrus, left cerebellum, left medial frontal gyrus, left inferior temporal gyrus, and left angular. Furthermore, the DC change within the bilateral putamen is negatively correlated with the symptom improvements after treatment. Conclusions. Our study confirmed that graph-theory-based measures, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of antipsychotic medications.

Biomass-derived porous carbon as a conductive framework in which the redox molecule Alizarin red S is anchored by strong interactions.

This paper focuses on the chemical and physical adsorption of 1-hexyl-2,3-dimethyl imidazolium bromide (HDMIMBr), 1-decyl-2,3-dimethyl imidazolium bromide (DDMIMBr), and 1-hexadecyl-2,3-dimethyl imidazolium bromide (C16DMIMBr) on the surface of mild steel at high temperature in order to explore the mechanism of a corrosion inhibitor in a complex environment.Gravimetric, scanning electron microscope, X-ray photoelectron spectroscopy, and electrochemical tests explored the corrosion inhibition performance from the experimental level. Quantum chemical calculations and molecular dynamics simulations reveal the corrosion inhibition mechanism from the molecular scale.The results show that the longer the alkyl chain of the three corrosion inhibitors studied, the better the corrosion inhibition performance. This is due to the hydrophobic effect of the long alkyl chain, which has its own synergistic effect and then self-assembles to form an adsorption film with a multilayer structure.This dense adsorption film makes corrosion inhibitors a good application prospect in complex corrosive environments.

In this paper, a ternary hybrid material carbon fiber/manganese dioxide/polyaniline (CF@γ-MnO2/PANI) is synthesized for its utility in supercapacitor application. γ-MnO2 nanoparticles are loaded on the surface of CF under hydrothermal conditions to prepare CF@γ-MnO2. Subsequently, PANI in situ polymerized on the surface of CF@γ-MnO2 to form CF@γ-MnO2/PANI ternary composite. The electrochemical performance of CF@γ-MnO2/PANI is investigated using cyclic voltammetry (CV), galvanostatic charge–discharge measurement (GCD), and electrochemical impedance spectroscopy (EIS). Compared with CF/PANI and PANI, the as-prepared ternary hybrid material exhibits the highest capacitance of 654.3 F g−1 at a current density of 1 A g−1, its rate performance is 78.1% (10 A g−1), and 75.94% of the initial capacitance after 4000 charge–discharge cycles. The asymmetric supercapacitor shows a specific capacitance of 260 F g −1 and high energy density 30.9 Wh kg−1 at a power density of 750 W kg−1, good cycling stability by maintaining 73.2% initial capacitance after 5000 cycles. The good capacitive behaviors demonstrated that the low-cost CF provides an excellent base for γ-MnO2 and PANI. The nanoparticles γ-MnO2 is supported on the surface of CF and coated by PANI, which effectively improves the utilization rate of MnO2 and PANI. It could be a promising material for supercapacitors applications.

Chen Wang,1,* Peng Fang,2,* Ya Li,1,* Lin Wu,2,* Tian Hu,3 Qi Yang,4 Aiping Han,5 Yingjuan Chang,1 Xing Tang,1 Xiuhua Lv,1 Ziliang Xu,1 Yongqiang Xu,1 Leilei Li,1 Minwen Zheng,1 Yuanqiang Zhu1 1Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, People’s Republic of China; 2Department of Military Medical Psychology, Air Force Medical University, Xi’an, People’s Republic of China; 3Department of Radiology, Yan’an University Affiliated Hospital, Yan’an, People’s Republic of China; 4Department of Radiology, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang, People’s Republic of China; 5Imaging Diagnosis and Treatment Center, Xi’an International Medical Center Hospital, Xi’an, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuanqiang Zhu, Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi’an, 710032, People’s Republic of China, Tel/Fax +86-29-84775415, Email Zhu_YQ_fmmu@163.com Minwen Zheng, Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi’an, 710032, People’s Republic of China, Tel/Fax +86-29-84771938, Email zhengmw2007@163.comBackground: Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level. Currently, a neuroimaging marker that can reliably predict this vulnerability at the individual level is lacking.Methods: Efficient transfer of information relies on the integrity of white matter (WM) tracts in the human brain, we therefore applied machine learning approach to investigate whether the WM diffusion metrics can predict vulnerability to SD. Forty-nine participants completed the psychomotor vigilance task (PVT) both after resting wakefulness (RW) and after 24 h of sleep deprivation (SD). The number of PVT lapse (reaction time > 500 ms) was calculated for both RW condition and SD condition and participants were categorized as vulnerable (24 participants) or resistant (25 participants) to SD according to the change in the number of PVT lapses between the two conditions. Diffusion tensor imaging were acquired to extract four multitype WM features at a regional level: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) learning approach using leave-one-out cross-validation (LOOCV) was performed to assess the discriminative power of WM features in SD-vulnerable and SD-resistant participants.Results: LSVM analysis achieved a correct classification rate of 83.67% (sensitivity: 87.50%; specificity: 80.00%; and area under the receiver operating characteristic curve: 0.85) for differentiating SD-vulnerable from SD-resistant participants. WM fiber tracts that contributed most to the classification model were primarily commissural pathways (superior longitudinal fasciculus), projection pathways (posterior corona radiata, anterior limb of internal capsule) and association pathways (body and genu of corpus callosum). Furthermore, we found a significantly negative correlation between changes in PVT lapses and the LSVM decision value.Conclusion: These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.Keywords: sleep deprivation, vulnerability, diffusion tensor imaging, machine learning, psychomotor vigilance task

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = -.42, p = 5.57 × 10

Background It is well known that circadian rhythms and sleep homeostasis contribute to a pronounced trough in sleepiness and behavioral performance at night. However, the underlying neuroimaging mechanisms remain unclear. How brain-function connectivity is modulated during sleep deprivation (SD) has been rarely examined. Methods By increasing the number of scanning sessions during SD, the current study used voxel-mirrored homotopic connectivity (VMHC) to investigate dynamic changes in interhemispheric communication during one night of SD. Every 2 hours from 10 pm to 06 am (session 1, 10 pm; session 2, 12 am; session 3, 2 am; session 4, 4 am; session 5, 6 am), functional magnetic resonance-imaging data and Stanford Sleepiness Scale (SSS) scores were collected from 36 healthy participants with intermediate chronotype. Dynamic changes in SSS scores and VMHC were determined using one-way repeated-measure ANOVA with the false discovery-rate method to correct for multiple comparisons. Results Significant time effects for VMHC were found mainly in the bilateral thalamus, bilateral superior temporal gyrus, and bilateral precentral gyrus. SSS scores and VMHC in these areas were both found to be monotonously increased during SD. Furthermore, significant positive associations were found between SSS valu and VMHC values in the left superior temporal and right superior gyri. Conclusion These findings might represent the dynamic modulation of circadian rhythm merely or the interaction effects of both circadian rhythm and sleep homeostasis on interhemispheric connectivity within the thalamus, default-mode network, and sensorimotor network. Our study provides more comprehensive information on how SD regulates brain connectivity between hemispheres and adds new evidence of neuroimaging correlates of increased sleepiness after SD.

Background Recent progress in neuroscience research has provided evidence that schizophrenia is a disease that involves dysconnectivity of brain networks. Widespread gray matter loss was commonly observed but how these gray matter abnormalities are characterized at the large-scale network-level in schizophrenia, especially patients with first-episode (FE-SCZ) remains unclear. Methods In this study, gray matter structural network aberrations were investigated by applying structural covariance network analysis to 193 first episode schizophrenia patients and 178 age and gender-matched healthy controls (HCs). The mean gray matter volume in seed regions relating to eight specific networks (visual, auditory, sensorimotor, speech, semantic, default-mode, executive control, and salience) were extracted, and voxel-wise analyses of covariance were conducted to compare the association between whole-brain gray matter volume and each seed region for FE-SCZ and HCs. Results The auditory network was less extended in FE-SCZ compared with HCs, with a significant decrease in the structural association between the Hesch's gyrus and the middle frontal gyrus and the superior frontal gyrus. Hyperconnectivity was observed in executive control network with a significant increase in the structural association between the dorsal lateral prefrontal cortex and the superior frontal gyrus and supplementary motor area. Conclusion Our research shows that seed based structural covariance analysis can well characterize multiple large-scale networks, the observed changes might underly the hallucinations and cognitive impairments observed in FE-SCZ. Given that these patients were experiencing their first episode of schizophrenia, our findings suggest that such structural network deficits are present at an early stage in this disorder.

The structure–activity relationship of ten mercaptoalcohol molecules as corrosion inhibitors for carbon steel was investigated through quantum chemistry calculation and molecular dynamics simulation (MD). Quantum chemical parameters including the energy of the highest occupied molecular orbital (EHOMO) and the lowest unoccupied molecular orbital (ELUMO), energy gap (ΔE), electronegativity (χ) have been calculated to investigate the relationship between the corrosion inhibition performance and the molecular structure. Quantum chemical parameters show the corrosion inhibition performance is strengthened with the lengthening of the carbon chain. The theoretical inhibition efficiency increases with the increasing of the length of carbon chain. As a result, the C_11 molecule presents the best corrosion inhibition performance among the studied molecules. At the same time, molecular dynamics simulation results indicate that both the –SH and –OH can be adsorbed onto the Fe (1 1 0) surface and the molecular plane is parallel to the Fe (1 1 0) surface. The absolute values of the adsorption energy increases with the lengthening of the carbon chain. According to the adsorption energy, the longer of the carbon chain is, the easier it is for molecule to be adsorbed onto the Fe (1 1 0) surface. As a result, the corrosion inhibition performance of the mercaptoalcohol molecules becomes better with the lengthening of the carbon chain, and the C_11 molecule is the best one.

In this paper, using a low-cost paper fiber (PF) as a substrate, eigenstate polyaniline–coated PFs with graphene (GO/PANI/PF) ternary materials were synthesized by using the chemical oxidative polymerization method. Electrochemical measurements showed that the GO/PANI/PF electrode exhibited an excellent electrochemical performance compared with the PANI electrode. By adjusting the ratio of PANI and PF, the as-prepared GO/PANI/PF–2.5% electrode exhibited the highest specific capacitance 937 F g−1 at current density 1 A g−1 in a 0.5 M H2SO4 aqueous solution, and 74.5% retention of initial capacitance after 2000 charge–discharge cycles at 5 A g−1. The symmetric capacitor on the basis of GO/PANI/PF nanocomposite delivers a superior energy density of 10.12 Wh kg−1 at 456 W kg−1 at a current density of 1 A g−1.

Ziliang Xu,1,* Yuanqiang Zhu,1,* Hongliang Zhao,1,* Fan Guo,1 Huaning Wang,2 Minwen Zheng1 1Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 2Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China*These authors contributed equally to this workCorrespondence: Minwen Zheng, Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127# Changle West Road, Xi’an 710032, People’s Republic of China, Email zhengmw2007@163.com Huaning Wang, Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, 127# Changle West Road, Xi’an 710032, People’s Republic of China, Email xskzhu@fmmu.edu.cnPurpose: To investigate the general sleep stage classification performance of deep learning networks, three datasets, across different age groups, mental health conditions, and acquisition devices, comprising adults (SHHS) and children without mental health conditions (CCSHS), and subjects with mental health conditions (XJ), were included in this study.Methods: A long short-term memory (LSTM) network was used to evaluate the effect of different ages, mental health conditions, and acquisition devices on the sleep stage classification performance and the general performance.Results: Results showed that the age and different mental health conditions may affect the sleep stage classification performance of the network. The same acquisition device using different parameters may not have an obvious effect on the classification performance. When using a single dataset and two datasets for training, the network performed better only on the training dataset. When training was conducted with three datasets, the network performed well for all datasets with a Cohen’s Kappa of 0.8192 and 0.8472 for the SHHS and CCSHS, respectively, but performed relatively worse (0.6491) for the XJ, which indicated the complexity effect of different mental health conditions on the sleep stage classification task. Moreover, the performance of the network trained using three datasets was similar for each dataset to that of the network trained using a single dataset and tested on the same dataset.Conclusion: These results suggested that when more datasets across different age groups, mental health conditions, and acquisition devices (ie, more datasets with different feature distributions for each sleep stage) are used for training, the general performance of a deep learning network will be superior for sleep stage classification tasks with varied conditions.Keywords: sleep stage classification, deep learning network, electroencephalogram, time-frequency spectrum

Objectives Dynamic contrast-enhanced MRI (DCE-MRI) with Extended Tofts Linear (ETL) model has been used in tissue and tumor evaluation. However, its reliability and reproducibility in pancreatic evaluation has been unclear. It is also unclear whether pancreatic DCE-MRI pharmacokinetic parameters were consistent and stable among different pancreatic regions, ages and genders. Methods Pancreatic pharmacokinetic parameters of 54 volunteers were calculated using DCE-MRI with ETL model. Firstly, Intra- and inter-observer reproducibility was evaluated using intra-class correlation coefficient (ICC) and coefficient of variation (CoV). Secondly, subgroup evaluation of pancreatic DCE-MRI pharmacokinetic parameters was performed. 54 subjects were divided into three groups in virtue of pancreatic region, three groups according to age, two groups according to gender, which pharmacokinetic parameters among and between different groups were calculated and compared. Results There was excellent agreement and low variability of intra- and inter-observer to pancreatic DCE-MRI pharmacokinetic parameters. The intra- and inter-observer ICCs of Ktrans, kep, ve, vp were 0.971, 0.952, 0.959, 0.944 and 0.947, 0.911, 0.978, 0.917, respectively. The intra- and inter-observer CoVs of Ktrans, kep, ve, vp were 9.98%, 5.99%, 6.47%, 4.76% and 10.15%, 5.22%, 6.28%, 5.40%, respectively. There were no significant differences of Ktrans, kep among different pancreatic regions, among different age groups, between male and female groups (P all > 0.10). Only, pancreatic ve of old group was higher than that of young and middle-aged groups (P = 0.042, 0.001), and vp of pancreatic head was higher than that of pancreatic body and tail (P = 0.014, 0.043). Conclusions DCE-MRI with ETL model is reliable and reproducible for quantitative assessment of pancreatic pharmacokinetic parameters. ve varies with age and vp varies with pancreatic region, which can provide guidance for the selection of normal reference in the pharmacokinetics study of pancreatic diseases.

Sleep deprivation (SD) has become very common in contemporary society, where people work around the clock. SD-induced cognitive deficits show large inter-individual differences and are trait-like with known neural correlates. However, few studies have used neuroimaging to predict vulnerability to SD. Here, resting state functional magnetic resonance imaging (fMRI) data and psychomotor vigilance task (PVT) data were collected from 60 healthy subjects after resting wakefulness and after one night of SD. The number of PVT lapses was then used to classify participants on the basis of whether they were vulnerable or resilient to SD. We explored the viability of graph-theory-based degree centrality to accurately classify vulnerability to SD. Compared with during resting wakefulness, widespread changes in degree centrality (DC) were found after SD, indicating significant reorganization of sleep homeostasis with respect to activity in resting state brain network architecture. Support vector machine (SVM) analysis using leave-one-out cross-validation achieved a correct classification rate of 84.75% [sensitivity 82.76%, specificity 86.67%, and area under the receiver operating characteristic curve (AUC) 0.94] for differentiating vulnerable subjects from resilient subjects. Brain areas that contributed most to the classification model were mainly located within the sensorimotor network, default mode network, and thalamus. Furthermore, we found a significantly negative correlation between changes in PVT lapses and DC in the thalamus after SD. These findings suggest that resting-state network measures combined with a machine learning algorithm could have broad potential applications in screening vulnerability to SD.

Despite evidence of structural brain abnormalities in schizophrenia, the current study aimed to explore the effects of antipsychotic treatment on gray matter (GM) volume using structural magnetic resonance imaging (MRI) and investigate the relationship between brain structure and treatment response. The GM volumes of 33 patients with first-episode schizophrenia were calculated with voxel-based morphometry (VBM), with 33 matched healthy controls. Longitudinal volume changes within subjects after 4-month antipsychotic treatment were also evaluated. Correlation between volumetric changes and clinical symptoms derived from the Positive and Negative Syndrome Scale (PANSS) were further investigated. Compared with healthy controls, decreased GM volumes in the frontal gyrus were observed in schizophrenia patients. After 4-month treatment, patients showed significantly decreased GM volume primarily in the bilateral frontal, temporal and left parietal brain regions. In addition, the GM volume changes of the left postcentral gyrus was positively correlated with negative symptoms improvement, and the correlation analysis revealed the total PANSS scores changes were associated with GM volume changes in the right inferior frontal gyrus and the right superior temporal gyrus. Besides, non-responders had reduced GM volume in the bilateral middle frontal gyrus and the right superior frontal gyrus compared with responders and healthy controls. Our results suggest that the abnormality in the right frontal gyrus exists in the early stage of schizophrenia. Moreover, the relationship between antipsychotics and structural changes was identified. The GM volume might have the potential to reflect the symptom improvement in schizophrenia patients. And MRI may assist in predicting the antipsychotic treatment response in first-episode schizophrenia patients.

Objective We aimed to evaluate the functional network properties in first-episode schizophrenia (SZ) patients at baseline and after 4-months treatment with second-generation antipsychotic drugs. Methods Resting-state functional magnetic resonance imaging and graph theory approaches were utilized to evaluate the functional integration and segregation of brain networks in 36 first-episode patients (20 male/16 female) with SZ and 36 age and sex matched healthy controls (20 male/16 female). Results Compared with healthy controls, SZ at baseline showed lower clustering coefficient (Cp) and local network efficiency (Eloc), and this abnormal pattern was modulated with treatment of antipsychotic drugs at follow-up. Longitudinally, the increase of Cp was associated with the improvement of negative symptom. We found that the strength of functional connectivity between brain regions were significantly increased in three connections after treatment, mainly involving the frontal, parietal and occipital lobes. Conclusion The current study suggested that antipsychotic drugs could modulate the faulty local clustering of the functional connectome in SZ. Furthermore, Cp, the parameter that reflects local clustering of topological organization, demonstrated the potential to be a connectome-based biomarker of treatment response to second-generation antipsychotics in patients with SZ.

Background and purpose Conventional magnetic resonance imaging (MRI) is sometimes difficult to distinguish primary central nervous system lymphoma (PCNSL) from other malignant brain tumors effectively. The study aimed to evaluate the diagnostic performance of arterial spin labeling (ASL) and dynamic contrast-enhanced (DCE)-derived permeability parameters to differentiate PCNSL from high-grade glioma (HGG) and brain metastasis. Materials and methods Eight patients with PCNSL, twenty one patients with HGG and six brain metastasis underwent preoperative 3.0-T MR imaging including conventional, ASL and DCE. Quantitative parameters including relative cerebral blood flow (rCBF), extravascular extracellular volume fraction (Ve) and the volume transfer constant (Ktrans) among PCNSL, HGG and metastasis were compared with a one-way analysis of variance. In addition, the area under the receiver-operating characteristic (ROC) curve (AUC) was constructed to evaluate the differentiation diagnostic performance of each parameter and the combination. Results The PCNSL demonstrated significantly lower rCBF, higher Ktrans and Ve compared with HGG and metastasis. For the ROC analyses, both Ktrans and rCBF had good diagnostic performance for discriminating PCNSL from HGG and metastasis, with the AUC of 0.880 and 0.889. With the combination of rCBF and Ktrans, the diagnostic ability for PCNSL was improved with AUC of 0.986. Conclusion rCBF and Ktrans are useful parameters for differentiating PCNSL from HGG and brain metastasis. The combination of rCBF and Ktrans further helps to improve the diagnostic performance of PCNSL.

Previous studies have revealed that sleep deprivation (SD) alters hippocampal functional connectivity (FC). However, the effects of SD on the FC of hippocampal subregions are still unknown. In this study, we used a masked independent component analysis (mICA) to partition the hippocampus into several small regions and investigated the changes in the FC of each small region within the whole brain after 24 h of SD in 40 normal young subjects. First, we determined the optimal number of hippocampal subregions in a data-driven manner using a reproducibility analysis and chose 17 as the optimal number of hippocampal subregions. Second, we compared the FC of each subregion between rested wakefulness and SD states using mCIA. Reduced FC was found between the left junction of anterior and anterolateral hippocampal region and the default mode network and bilateral thalamus after SD (p 0.05/17, Threshold-Free Cluster Enhancement correction, Bonferroni's corrected for the number of subregions). The FC between the left posterior of the anterolateral and the left lateral posterior of the anterior hippocampal regions and somatomotor network changed more negative after SD. However, increased FC was identified between the left middle hippocampal region and vision-related regions after SD. Our results reflect differential effects of SD on the FC in specific hippocampal regions and provide new insights into the impact of SD on the resting-state functional organization in the human brain.

Hepatic myelopathy is a complication seen in patients with chronic liver failure with physiologic or iatrogenic portosystemic shunting. The main symptom is progressive lower limb dyskinesia. The role of the brain motor control center in hepatic myelopathy is unknown. This study aimed to investigate the gray matter changes in patients with hepatic myelopathy secondary to transjugular intrahepatic portosystemic shunt and to examine their clinical relevance. This was a cross-sectional study. Twenty-three liver failure patients with hepatic myelopathy (hepatic myelopathy group), 23 liver failure patients without hepatic myelopathy (non-hepatic myelopathy group) after transjugular intrahepatic portosystemic shunt, and 23 demographically matched healthy volunteers were enrolled from March 2014 to November 2016 at Xijing Hospital, Air Force Military Medical University (Fourth Military Medical University), China. High-resolution magnetization-prepared rapid gradient-echo brain imaging was acquired. Group differences in regional gray matter were assessed using voxel-based morphometry analysis. The relationship between aberrant gray matter and motor characteristics was investigated. Results demonstrated that compared with the non-hepatic myelopathy group, gray matter volume abnormalities were asymmetric, with decreased volume in the left insula (P = 0.003), left thalamus (P = 0.029), left superior frontal gyrus (P = 0.006), and right middle cingulate cortex (P = 0.021), and increased volume in the right caudate nucleus (P = 0.017), corrected with open-source software. The volume of the right caudate nucleus in the hepatic myelopathy group negatively correlated with the lower limb clinical rating of the Fugl-Meyer Assessment (r = -0.53, P = 0.01). Compared with healthy controls, patients with and without hepatic myelopathy exhibited overall increased gray matter volume in both thalami, and decreased gray matter volume in both putamen, as well as in the globus pallidus, cerebellum, and vermis. The gray matter abnormalities we found predominantly involved motor-related regions, and may be associated with motor dysfunction. An enlarged right caudate nucleus might help to predict weak lower limb motor performance in patients with preclinical hepatic myelopathy after transjugular intrahepatic portosystemic shunt. This study was approved by the Ethics Committee of Xijing Hospital, Air Force Military Medical University (Fourth Military Medical University), China (approval No. 20140227-6) on February 27, 2014.

N-Doped carbon dots (NCDs) were facilely synthesized by a hydrothermal method using an ethanolamine-ionic liquid (1-carboxyethyl-3-methyl imidazole chloride) gel as a precursor. The NCDs had an average particle size of 3.4 nm and were partially crystalline and abundant in amide and hydroxyl groups and pyridinic/pyrrolic nitrogen atoms on their surfaces. In water, the NCDs showed excitation- and concentration-dependent photoluminescence (PL) properties with a high salt tolerance and a quantum yield of 24.7% under 350 nm excitation. The PL of the aqueous suspension of NCDs remained stable in a pH range of 4-11 and was significantly quenched by Hg2+ and Cu2+ owing to the strong interactions between the metal ions and the surface groups and/or nitrogen atoms of the NCDs. The possible quenching mechanism was determined to be photo-induced electron transfer. By introducing the masking agents of P2O74- and S2O32- into the aqueous suspension of NCDs, two label-free "turn-off" fluorescent probes for Hg2+ and Cu2+ with a high sensitivity and selectivity were built, respectively. Moreover, the quenched NCDs-Hg2+ system could be used as a selective "turn-on" fluorescent probe for S2O32- due to the strong affinity of S2O32- to Hg2+. The above described three probes all illustrated two good linear detection ranges for Hg2+ (0-10 and 10-50 μM), Cu2+ (0-2.5 and 2.5-40 μM) and S2O32- (0-20 and 20-80 μM), and their limits of detection were calculated to be 0.076, 0.125 and 1.17 μM. The recovery tests demonstrated that the above described probes were reliable and capable of detecting corresponding ions in tap water samples with satisfactory results.

Background: Non-small cell lung cancer (NSCLC) is treatable when caught early, yet limited non-invasive methods exist for grading NSCLC patients. In the present study, we aimed to examine the diagnostic utility of multi-sequence magnetic resonance imaging (MRI) radiomics and clinical features for grading NSCLC. Methods: In this retrospective study, 148 patients with postoperative pathologically-confirmed NSCLC were recruited. Both preoperative T2-weighted imaging (T2WI) and multi-b-value diffusion-weighted imaging (DWI) were performed on a 1.5 T MRI scanner. A total of 2775 radiomics features were extracted from the T2WI, DWI, and the corresponding apparent diffusion coefficient (ADC) maps of patients. The least absolute shrinkage and selection operator (LASSO) and stepwise regression method were used for feature selection using the training cohort (n=110). Next, these features were further evaluated assessed in the two cohorts using a non-linear support vector machine (SVM) classifier. Lastly, a Radscore model was used to develop the radiomics-clinical nomogram.Results: Favorable discrimination performance was obtained for five of the optimal features using both cohorts, as demonstrated by the area under the curves (AUC) of 0.761 and 0.753. In addition, the radiomics-clinical nomogram, which integrated the Radscore with four independent clinical predictors, showed higher discriminative power, with AUCs of 0.814 and 0.767 for the X.T cohort and H.Y cohort, respectively. The nomogram showed excellent predictive performance and potential clinical utility for grading NSCLC.Conclusions: Multi-sequence MRI radiomics features can stratify NSCLC tumor grades noninvasively. The radiomics features can be integrated with the clinical features to improve its predictive performance.