Self-Powered Colorful Dynamic Electrowetting Display Systems Based on Triboelectricity.

Electrowetting displays (EWDs) based on microfluidics are highly sought after in the fields of electronic devices, smart homes, and information communication. However, the power supply of the EWD systems for visually engaging multi-color displays remains a big challenge. Herein, self-powered colorful dynamic display systems are developed by integrating the triboelectric nanogenerator (TENG) with the EWD device. The TENG is designed with a nanotube-patterned surface and can generate open-circuit voltages ranging from 30 to 295 V by controlling the contact area. The wetting property of the micro-droplet exhibits a response to the applied voltage, enabling the triboelectricity-triggered electrowetting-on-dielectric. Driven by the voltage of 160 V, the monochromatic EWD exhibits bright color switching from magenta to transparent with a pixel aperture ratio of 78%, and the recovery process can be rapidly completed. Furthermore, the self-powered colorful dynamic EWD system can be achieved. By selectively applying the voltage to the pixels in the three monochromatic layers that constitute the colorful EWD, the wetting properties of the fluids can be controlled, allowing for colorful dynamic display. This work contributes to the advancement of color display technology for portable and wearable electronic ink displays, indoor and outdoor sports equipment, and information communication.

Compound four-cavity single-longitudinal mode fiber laser.

A compound resonant cavity-type single longitudinal mode erbium-doped fiber laser is proposed and verified in this paper. We use a compound four-cavity to expand the longitudinal mode spacing of the laser and a homemade grating F-P filter and a saturable absorber to narrow the gain spectral bandwidth, enabling the laser to operate in a single-frequency state. Through theoretical analysis and experimental verification of the important roles of the main constructed fiber laser parts, the center wavelength of the laser output of 1550.06 nm is obtained, and the optical signal-to-noise ratio is 65 dB. In a 50 minute period, the wavelength fluctuation is exhibited along with the maximum power fluctuation of 0.28 dB. The value of the laser output linewidth is ∼250 Hz, measured by using the delayed self-heterodyne method.

Transcriptional level of inflammation markers associates with short-term brain structural changes in first-episode schizophrenia.

BACKGROUND: Inflammation has been implicated in the pathology of schizophrenia and may cause neuronal cell death and dendrite loss. Neuroimaging studies have highlighted longitudinal brain structural changes in patients with schizophrenia, yet it is unclear whether this is related to inflammation. We aim to address this question, by relating brain structural changes with the transcriptional profile of inflammation markers in the early stage of schizophrenia. METHODS: Thirty-eight patients with first-episode schizophrenia and 51 healthy controls were included. High-resolution T1-weighted magnetic resonance imaging (MRI) and clinical assessments were performed at baseline and 2 ~ 6 months follow-up for all subjects. Changes in the brain structure were analyzed using surface-based morphological analysis and correlated with the expression of immune cells-related gene sets of interest reported by previous reviews. Transcriptional data were retrieved from the Allen Human Brain Atlas. Furthermore, we examined the brain structural changes and peripheral inflammation markers in association with behavioral symptoms and cognitive functioning in patients. RESULTS: Patients exhibited accelerated cortical thickness decrease in the left frontal cortices, less decrease or an increase in the superior parietal lobule and right lateral occipital lobe, and increased volume in the bilateral pallidum, compared with controls. Changes in cortical thickness correlated with the transcriptional level of monocyte across cortical regions in patients (r = 0.54, p < 0.01), but not in controls (r = - 0.05, p = 0.76). In addition, cortical thickness change in the left superior parietal lobule positively correlated with changes in digital span-backward test scores in patients. CONCLUSIONS: Patients with schizophrenia exhibit regional-specific cortical thickness changes in the prefrontal and parietooccipital cortices, which is related to their cognitive impairment. Inflammation may be an important factor contributing to cortical thinning in first-episode schizophrenia. Our findings suggest that the immunity-brain-behavior association may play a crucial role in the pathogenesis of schizophrenia.

Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction.

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Differential expression of diacylglycerol kinase ζ is involved in inferior parietal lobule-related dysfunction in schizophrenia with cognitive impairments.

BACKGROUND: Cognitive impairment is the main factor in the poor prognosis of schizophrenia, but its mechanism remains unclear. The inferior parietal lobule (IPL) is related to various clinical symptoms and cognitive impairment in schizophrenia. We aimed to explore the relationship between IPL-related functions and cognitive impairment in schizophrenia. METHODS: 136 schizophrenia patients and 146 demographically matched healthy controls were enrolled for a cross-sectional study. High-spatial-resolution structural and resting-state functional images were acquired to demonstrate the alternations of brain structure and function. At the same time, the digit span and digit symbol coding tasks of the Chinese Wechsler Adult Intelligence Test Revised (WAIS-RC) were utilized in assessing the subjects' cognitive function. Patients were divided into cognitive impairment and normal cognitive groups according to their cognitive score and then compared whether there were differences between the three groups in fractional amplitude of low-frequency fluctuation (fALFF). In addition, we did a correlation analysis between cognitive function and the fALFF for the left IPL of patients and healthy controls. Based on the Allen Human Brain Atlas, we obtained genes expressed in the left IPL, which were then intersected with the transcriptome-wide association study results and differentially expressed genes in schizophrenia. RESULTS: Grouping of patients by the backward digit span task and the digit symbol coding task showed differences in fALFF values between healthy controls and cognitive impairment patients (P < 0.05). We found a negative correlation between the backward digit span task score and fALFF of the left IPL in healthy controls (r = - 0.388, P = 0.003), which was not seen in patients (r = 0.203, P = 0.020). In addition, none of the other analyses were statistically significant (P > 0.017). In addition, we found that diacylglycerol kinase ζ (DGKζ) is differentially expressed in the left IPL and associated with schizophrenia. CONCLUSION: Our study demonstrates that the left IPL plays a vital role in cognitive impairment in schizophrenia. DGKζ may act as an essential regulator in the left IPL of schizophrenia patients with cognitive impairment.

The correlation between dynamic functional architecture and response to electroconvulsive therapy combined with antipsychotics in schizophrenia.

Attempts to determine why some patients respond to electroconvulsive therapy (ECT) are valuable in schizophrenia. Schizophrenia is associated with aberrant dynamic functional architecture, which might impact the efficacy of ECT. We aimed to explore the relationship between pre-treatment temporal variability and ECT acute efficacy. Forty-eight patients with schizophrenia and 30 healthy controls underwent functional magnetic resonance imaging to examine whether patterns of temporary variability of functional architecture differ between high responders (HR) and low responders (LR) at baseline. Compared with LR, HR exhibited significantly abnormal temporal variability in right inferior front gyrus (IFGtriang.R), left temporal pole (TPOsup.L) and right middle temporal gyrus (MTG.R). In the pooled patient group, ∆PANSS was correlated with the temporal variability of these regions. Patients with schizophrenia with a distinct dynamic functional architecture appear to reveal differential response to ECT. Our findings provide not only an understanding of the neural functional architecture patterns that are found in schizophrenia but also the possibility of using these measures as moderators for ECT selection.

Magnetic Resonance Imaging throughout the Clinical Course of Schizophrenia: Neurobiological Underpinnings and Clinical Implications.

As a non-invasive detection method and an advanced imaging method, magnetic resonance imaging (MRI) has been widely used in the research of schizophrenia. Although a large number of neuroimaging studies have confirmed that MRI can display abnormal brain phenotypes in patients with schizophrenia, no valid uniform standard has been established for its clinical application. On the basis of previous evidence, we argue that MRI is an important tool throughout the whole clinical course of schizophrenia. The purpose of this commentary is to systematically describe the role of MRI in schizophrenia and to provide references for its clinical application.

An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells.

As 5G small cells gradually become the main force of 5G indoor deployment, it is necessary to study channel estimators for 5G small base stations, but there has been limited research on high-performance channel estimators in recent years. This study implemented a low-delay, low-overhead, relatively universal channel estimation module by dedicated instruction set acceleration including reference signal estimation, Wiener, 1st order, and 2nd order interpolations in frequency and time domains. The instruction level acceleration is on our vector processor, yet is suitable for other commercial and academic vector processors. Through instruction acceleration, compared with the existing general vector processing instruction sets, the processor performance of the LS estimation module and Wiener filter interpolation in the frequency domain is improved by 50% and 37.5%, respectively. The BER VS SNR measure of time-frequency Wiener filter interpolation achieves 4 db compared with linear interpolation, meaning our instruction level acceleration can be an optimum solution.

A Machine Learning-Based Risk Prediction Model for Post-Traumatic Stress Disorder during the COVID-19 Pandemic.

Background and Objectives: The COVID-19 pandemic has caused global public panic, leading to severe mental illnesses, such as post-traumatic stress disorder (PTSD). This study aimed to establish a risk prediction model of PTSD based on a machine learning algorithm to provide a basis for the extensive assessment and prediction of the PTSD risk status in adults during a pandemic. Materials and Methods: Model indexes were screened based on the cognitive-phenomenological-transactional (CPT) theoretical model. During the study period (1 March to 15 March 2020), 2067 Chinese residents were recruited using Research Electronic Data Capture (REDCap). Socio-demographic characteristics, PTSD, depression, anxiety, social support, general self-efficacy, coping style, and other indicators were collected in order to establish a neural network model to predict and evaluate the risk of PTSD. Results: The research findings showed that 368 of the 2067 participants (17.8%) developed PTSD. The model correctly predicted 90.0% (262) of the outcomes. Receiver operating characteristic (ROC) curves and their associated area under the ROC curve (AUC) values suggested that the prediction model possessed an accurate discrimination ability. In addition, depression, anxiety, age, coping style, whether the participants had seen a doctor during the COVID-19 quarantine period, and self-efficacy were important indexes. Conclusions: The high prediction accuracy of the model, constructed based on a machine learning algorithm, indicates its applicability in screening the public mental health status during the COVID-19 pandemic quickly and effectively. This model could also predict and identify high-risk groups early to prevent the worsening of PTSD symptoms.

Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy.

Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N = 85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non-responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter-hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non-responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI-derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies.

Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication.

BACKGROUND: Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. AIMS: To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. METHOD: The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. RESULTS: The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = -0.326, P = 0.014). CONCLUSIONS: The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Network structure of depression and anxiety symptoms in Chinese female nursing students.

BACKGROUND: Comorbidity between depressive and anxiety disorders is common. From network perspective, mental disorders arise from direct interactions between symptoms and comorbidity is due to direct interactions between depression and anxiety symptoms. The current study investigates the network structure of depression and anxiety symptoms in Chinese female nursing students and identifies the central and bridge symptoms as well as how other symptoms in present network are related to depression symptom "thoughts of death". METHODS: To understand the full spectrum of depression and anxiety, we recruited 776 Chinese female nursing students with symptoms of depression and anxiety that span the full range of normal to abnormal. Depression symptoms were measured by Patient Health Questionnaire-9 while anxiety symptoms were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Network analysis was used to construct networks. Specifically, we computed the predictability, expected influence and bridge expected influence for each symptom and showed a flow network of "thoughts of death". RESULTS: Nine strongest edges existed in network were from the same disorder. Four were between depression symptoms, like "sleep difficulties" and "fatigue", and "anhedonia" and "fatigue". Five were between anxiety symptoms, like "nervousness or anxiety" and "worry too much", and "restlessness" and "afraid something will happen". The symptom "fatigue", "feeling of worthlessness" and "irritable" had the highest expected influence centrality. Results also revealed two bridge symptoms: "depressed or sad mood" and "irritable". As to "thoughts of death", the direct relations between it and "psychomotor agitation/retardation" and "feeling of worthlessness" were the strongest direct relations. CONCLUSIONS: The current study highlighted critical central symptoms "fatigue", "feeling of worthlessness" and "irritable" and critical bridge symptoms "depressed or sad mood" and "irritable". Particularly, "psychomotor agitation/retardation" and "feeling of worthlessness" were identified as key priorities due to their strongest associations with suicide ideation. Implications for clinical prevention and intervention based on these symptoms are discussed.

The relations between different components of intolerance of uncertainty and symptoms of generalized anxiety disorder: a network analysis.

BACKGROUND: Intolerance of uncertainty (IU) is considered as a specific risk factor in the development and maintenance of generalized anxiety disorder (GAD). Yet, researches have investigated the relations between IU and GAD (or worry) using total scores on self-report measures. This ignores that there are different components exist in IU and the heterogeneity of GAD symptoms. In the present study, we explored the relations among different components of IU and symptoms of GAD. METHODS: A dimensional approach which take individual differences into consideration in different components of IU along a full range of normal to abnormal symptom severity levels of GAD were used in this study. Components of IU were measured by 12-item Intolerance of Uncertainty Scale and symptoms of GAD were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Regularized partial-correlation network was estimated using cross-sectional data from 624 university students. RESULTS: Four strongest edges are between components of IU, like "Unforeseen events upset me greatly" and "It frustrates me not having all the information I need". Two strongest edges are between symptoms of GAD, like "Being so restless that it is hard to sit still" and "Feeling afraid as if something awful might happen". Symptom "Worrying too much about different things" and component "It frustrates me not having all the information I need" have the highest expected influences in the present network. In the community of IU, component "It frustrates me not having all the information I need" has the highest bridge expected influence. And in the community of GAD, symptoms "Worrying too much about different things" and "Not being able to stop or control worrying" have the highest bridge expected influence. CONCLUSIONS: This study reveals potential pathways between different components of IU and various symptoms of GAD. Understanding how putative risk factors such as different components of IU are related to symptoms of GAD may provide some references for related preventions and interventions, such as targeting component "It frustrates me not having all the information I need" may be more effective at reducing symptoms of GAD than targeting other components of IU.

Intranasal Insulin Treatment Attenuates Metabolic Distress and Early Brain Injury After Subarachnoid Hemorrhage in Mice.

BACKGROUND: Intranasal administration of insulin to the brain bypasses the blood brain barrier (BBB) and can increase cerebral glucose uptake and prevent energy failure. Intranasal insulin treatment has shown neuroprotective effects in multiple central nervous system (CNS) lesions, but the effects of intranasal insulin on the metabolic and pathological process of subarachnoid hemorrhage (SAH) are not clear. This study is designed to explore the effects of intranasal insulin treatment on metabolic distress and early brain injury (EBI) after experimental SAH. METHODS: SAH model was built by endovascular filament perforation method in adult male C57BL/6J mice, and then, insulin was administrated via intranasal route at 0, 24, and 48 h post-SAH. EBI was assessed according to the neurological performance, BBB damage, brain edema, neuroinflammatory reaction, and neuronal apoptosis at each time point. To evaluate metabolic conditions, microdialysis was used to continuously monitor the real-time levels of glucose, pyruvate, and lactate in interstitial fluid (ISF) in living animals. The mRNA and protein expression of glucose transporter-1 and 3 (GLUT-1 and -3) were also tested by RT-PCR and Western blot in brain after SAH. RESULTS: Compared to vehicle, intranasal insulin treatment promoted the relative mRNA and protein levels of GLUT-1 in SAH brain (0.98 ± 0.020 vs 0.33 ± 0.016 at 24 h, 0.91 ± 0.25 vs 0.21 ± 0.013 at 48 h and 0.94 ± 0.025 vs 0.28 ± 0.015 at 72 h in mRNA/0.96 ± 0.023 vs 0.36 ± 0.015 at 24 h, 0.91 ± 0.022 vs 0.22 ± 0.011 at 48 h and 0.95 ± 0.024 vs 0.27 ± 0.014 at 72 h in protein, n = 8/Group, p < 0.001). Similar results were also observed in GLUT-3. Intranasal insulin reduced the lactate/pyruvate ratio (LPR) and increased ISF glucose level. It also improved neurological dysfunction, BBB damage, and brain edema and attenuated the levels of pro-inflammatory cytokines as well as neuronal apoptosis after SAH. CONCLUSIONS: The intranasal insulin treatment protects brain from EBI possibly via improving metabolic distress after SAH.

The Prevalence of Cavum Septum Pellucidum in Mental Disorders Revealed by MRI: A Meta-Analysis.

OBJECTIVE: The prevalence of cavum septum pellucidum (CSP) in mental disorders, particularly schizophrenia spectrum disorders and mood disorders, remains uncertain. The authors used a meta-analytical approach to determine the prevalence of CSP in mental disorders and to compare these with the prevalence of CSP in psychiatrically healthy comparison subjects. METHODS: PubMed and Embase were systematically searched for relevant articles published as of January 9, 2018. After a quality assessment of individual studies using the Newcastle-Ottawa Scale, a random-effects model within Stata statistical software was used to synthesize 25 eligible studies that included 2,392 patients with mental disorders and 1,445 psychiatrically healthy comparison subjects. RESULTS: The prevalence of CSP of any size and large CSP was found to be significantly higher in individuals with mental disorders compared with healthy comparison subjects, and the prevalence of CSP in schizophrenia spectrum and mood disorders did not differ between the groups. CONCLUSIONS: The meta-regression with predefined covariance indicated that imaging parameters were not associated with the heterogeneity among original studies; however, the mean age of enrolled subjects was identified as a possible source of heterogeneity. No publication bias was found.

The relations among worry, meta-worry, intolerance of uncertainty and attentional bias for threat in men at high risk for generalized anxiety disorder: a network analysis.

BACKGROUND: Improving the psychotherapies for generalized anxiety disorder (GAD) is dependent on a deeper understanding of the relations between GAD and its associated cognitive factors. In the present study, we investigate how the core feature of GAD (i.e., worry) and its associated cognitive factors, such as meta-worry, intolerance of uncertainty, and attention bias towards threat, relate to each other in men at high risk for GAD. METHODS: We used network analysis to explore the relations among these variables in a cross-sectional sample of 122 men at high risk for generalized anxiety disorder. Specifically, we computed the expected influence and predictability of each variable. RESULTS: In the final network, we found that worry and meta-worry had the highest expected influence and predictability. In contrast, attention bias towards threat showed the lowest expected influence and predictability. The estimates of the expected influence of the nodes were stable (correlation stability coefficient = 0.52). CONCLUSIONS: The present study is the first to investigate the relations among worry, meta-worry, intolerance of uncertainty, and attention bias towards threat in men at high risk for generalized anxiety disorder. These findings indicate that worry and meta-worry may play important roles in the present network. The implications for clinical interventions and future studies are discussed.

Natural variation in the promoter of OsHMA3 contributes to differential grain cadmium accumulation between Indica and Japonica rice.

Rice is a major source of cadmium (Cd) intake for Asian people. Indica rice usually accumulates more Cd in shoots and grains than Japonica rice. However, underlying genetic bases for differential Cd accumulation between Indica and Japonica rice are still unknown. In this study, we cloned a quantitative trait locus (QTL) grain Cd concentration on chromosome 7 (GCC7) responsible for differential grain Cd accumulation between two rice varieties by performing QTL analysis and map-based cloning. We found that the two GCC7 alleles, GCC7PA64s and GCC793-11 , had different promoter activity of OsHMA3, leading to different OsHMA3 expression and different shoot and grain Cd concentrations. By analyzing the distribution of different haplotypes of GCC7 among diverse rice accessions, we discovered that the high and low Cd accumulation alleles, namely GCC793-11 and GCC7PA64s , were preferentially distributed in Indica and Japonica rice, respectively. We further showed that the GCC7PA64s allele can be used to replace the GCC793-11 allele in the super cultivar 93-11 to reduce grain Cd concentration without adverse effect on agronomic traits. Our results thus reveal that the QTL GCC7 with sequence variation in the OsHMA3 promoter is an important determinant controlling differential grain Cd accumulation between Indica and Japonica rice.

Quantitative Identification of Nonmuscle-Invasive and Muscle-Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis.

BACKGROUND: Preoperative discrimination between nonmuscle-invasive bladder carcinomas (NMIBC) and the muscle-invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). PURPOSE: To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. STUDY TYPE: Retrospective, radiomics. POPULATION: Fifty-four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. FIELD STRENGTH/SEQUENCE: 3.0T MRI/T2 -weighted (T2 W) and multi-b-value diffusion-weighted (DW) sequences. ASSESSMENT: A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T2 W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. STATISTICAL TESTS: Chi-square test and Student's t-test were applied on clinical characteristics to analyze the significant differences between patient groups. RESULTS: Of the 1104 features, an optimal subset involving 19 features was selected from T2 W and DW sequences, which outperformed the other two subsets selected from T2 W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM-RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. DATA CONCLUSION: The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T2 W sequence or DW sequence only. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489-1498.

The implicit beliefs and implicit behavioral tendencies towards smoking-related cues among Chinese male smokers and non-smokers.

BACKGROUND: The dual-process theory is central to several models of addiction, implying the importance of automatic processes in the maintenance and development of addiction. Implicit beliefs are traces of previous experience which relate to the representation in cognition. Implicit behavioral tendencies are traces of previous experience which relate to the representation in behavioral tendencies. In this study, we aim to provide behavioral evidence for implicit beliefs and implicit behavioral tendencies towards smoking-related cues among Chinese male smokers and non-smokers. We also examine the relationships among implicit beliefs, implicit behavioral tendencies and smoking behaviors of smokers. METHODS: In order to achieve these goals, we used an implicit association test (IAT) to measure implicit beliefs and implicit behavioral tendencies simultaneously. Thirty-nine smokers and twenty-five non-smokers were tested, using smoking-related words and images, as well as neutral words and images as stimuli. RESULTS: Our analysis shows significant differences in smokers' and non-smokers' implicit beliefs and behavioral tendencies (t62 = 3.494, p < 0.001; t62 = 5.034, p < 0.001). In the group of smokers, implicit beliefs and implicit behavioral tendencies were positively correlated with each other (r = 0.460, p < 0.01). In addition, smokers' scores for implicit behavioral tendencies are negatively correlated with the number of cigarettes smoked per day (r = - 0.51, p < 0.001). CONCLUSIONS: This study suggests that implicit beliefs and behavioral tendencies toward smoking-related cues vary significantly between Chinese male smokers and non-smokers. In addition, there is a positive correlation between implicit beliefs and behavioral tendencies within smokers. It also shows for the first time that the implicit behavioral tendencies are related to smoking behaviors. Our results may be considered as references for smoking cessation interventions focused on changes at the implicit level, and they provide a new perspective for measuring different dimensions of implicit attitudes by an IAT. This finding might promote the development of the network theory of implicit attitudes.

Glycosylated CD147 reduces myocardial collagen cross-linking in cardiac hypertrophy.

The mechanism of transition from chronic pressure overload-induced cardiac hypertrophy to heart failure is still unclear. Angiotensin II (Ang II) may be an important factor that mediates the transition in the end-stage of cardiac hypertrophy. In the present study, Goldblatt two-kidney one-clip (2K1C) rat model was used to simulate Ang II-induced hypertension. The elevated Ang II not only induced the concentric hypertrophy of left ventricle and cardiac fibrosis, but also increased the expression and glycosylation of CD147 in 2K1C rats. The left ventricular structure and function detected by echocardiogram showed a sign of the transition from cardiac hypertrophy to heart failure in 16 weeks of 2K1C rats. Ang II can activate N-acetylglucosamine transferase V (GnT-V), a key enzyme for CD147 glycosylation. Retinoic acid, an agonist of GnT-V, further increased glycosylated CD147, and activated matrix metalloproteinase-2/-9 (MMP-2 and MMP-9) in the hypertrophied left ventricle of 2K1C rat. Meanwhile, collagen cross-linking in the hypertrophied left ventricle significantly reduced in 2K1C rats. On the contrary, tunicamycin, an inhibitor of N-glycan biosynthesis, inhibited glycosylation of CD147 and activity of MMP-2 and MMP-9, and then maintained a stable of collagen cross-linking in the 2K1C rat hearts. The above results suggested that Ang II increased glycosylated CD147 which activated MMP-2 and MMP-9. Collagens were degraded by the activated MMPs and then reduced collagen cross-linking. Finally, the hypertrophied left ventricle was progressively dilated in chronic pressure overload due to losing the limitation of collagen cross-linking. Therefore, the compensated hypertrophy of left ventricle gradually transited to congestive heart failure.