Efficacy and Safety of Non-Vitamin K Antagonist Oral Anticoagulants Compared with Vitamin K Antagonists in Patients with Atrial Fibrillation and Type 2 Valvular Heart Disease: A Systematic Review and Meta-Analysis.

PURPOSE: This meta-analysis aimed to evaluate the efficacy and safety of non-vitamin K antagonist oral anticoagulants (NOACs) compared with vitamin K antagonists (VKAs) in patients with atrial fibrillation (AF) and type 2 valvular heart disease (VHD). METHODS: We searched the PubMed, LILACS, and MEDLINE databases to retrieve, randomized controlled trials (RCTs) comparing NOACs and VKAs in patients with AF and type 2 VHD, excluding mitral stenosis (moderate to severe, of rheumatic origin) or mechanical heart valves. The efficacy outcomes assessed were stroke and systemic embolism (SE), while safety outcomes included major bleeding and intracranial hemorrhage (ICH). RESULTS: Seven RCTs, including 16,070 patients with AF and type 2 VHD, were included. NOACs reduced the risk of stroke/SE (relative risk [RR], 0.75; 95% confidence interval [CI], 0.64-0.89; P = 0.0005), with no significant difference in major bleeding (RR, 0.88; 95% CI, 0.64-1.21; P = 0.43). The risk of ICH was reduced with NOACs (RR, 0.46; 95% CI, 0.27-0.77; P = 0.003). For patients with AF and bioprosthetic heart valve (five trials, 2805 patients), stroke/SE risks (RR, 0.65, 95% CI, 0.44-0.96) with NOACs were superior to VKAs. Major bleeding risks without ENVISAGE TAVI AF trial (RR, 0.53; 95% CI, 0.30-0.94; P = 0.03) with NOACs were superior to VKAs. The risks of ICH (RR, 0.61; 95% CI 0.34-1.09; P = 0.09) with NOACs were comparable to VKAs. CONCLUSIONS: NOACs demonstrate efficacy and safety in patients with AF and type 2 VHD and reduce the risk of stroke/SE and ICH when compared with those with VKAs.

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning.

INTRODUCTION: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features. METHODS: We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results. RESULTS: The model 3-B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively. CONCLUSIONS: DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.

Modulation of High-Frequency rTMS on Reward Circuitry in Individuals with Nicotine Dependence: A Preliminary fMRI Study.

Although previous studies have shown that repetitive transcranial magnetic stimulation (rTMS) can ameliorate addictive behaviors and cravings, the underlying neural mechanisms remain unclear. This study aimed to investigate the effect of high-frequency rTMS with the left dorsolateral prefrontal cortex (L-DLPFC) as a target region on smoking addiction in nicotine-dependent individuals by detecting the change of spontaneous brain activity in the reward circuitry. We recruited 17 nicotine-dependence participants, who completed 10 sessions of 10 Hz rTMS over a 2-week period and underwent evaluation of several dependence-related scales, and resting-state fMRI scan before and after the treatment. Functional connectivity (FC) analysis was conducted with reward-related brain regions as seeds, including ventral tegmental area, bilateral nucleus accumbens (NAc), bilateral DLPFC, and bilateral amygdala. We found that, after the treatment, individuals showed reduced nicotine dependence, alleviated tobacco withdrawal symptoms, and diminished smoking cravings. The right NAc showed increased FC with right fusiform gyrus, inferior temporal gyrus (ITG), calcarine fissure and surrounding cortex, superior occipital gyrus (SOG), lingual gyrus, and bilateral cuneus. No significant FC changes were observed in other seed regions. Moreover, the changes in FC between the right NAc and the right ITG as well as SOG before and after rTMS were negatively correlated with changes in smoking scale scores. Our findings suggest that high-frequency L-DLPFC-rTMS reduces nicotine dependence and improves tobacco withdrawal symptoms, and the dysfunctional connectivity in reward circuitry may be the underlying neural mechanism for nicotine addiction and its therapeutic target.

A Longitudinal Study of Sleep Habits and Leukemia Incidence Among Postmenopausal Women.

We sought to assess the relationship between sleep duration, sleep disturbance, and leukemia incidence among postmenopausal women. This study included 130,343 postmenopausal women aged 50-79 years who were enrolled in the Women's Health Initiative (WHI) during 1993-1998. Information on self-reported typical sleep duration and sleep disturbance was obtained by questionnaire at baseline, and sleep disturbance level was defined according to the Women's Health Initiative Insomnia Rating Scale (WHIIRS). WHIIRS scores of 0-4, 5-8, and 9-20 comprised 37.0%, 32.6%, and 30.4% of all women, respectively. After an average of 16.4 years (2,135,109 cumulative person-years) of follow-up, 930 of the participants were identified as having incident leukemia. Compared with women with the lowest level of sleep disturbance (WHIIRS score 0-4), women with higher sleep disturbance levels (WHIIRS scores of 5-8 and 9-20) had 22% (95% confidence interval (CI): 1.04, 1.43) and 18% (95% CI: 1.00, 1.40) excess risks of leukemia, respectively, after multivariable adjustment. A significant dose-response trend was found for the association between sleep disturbance and leukemia risk (P for trend = 0.048). In addition, women with the highest level of sleep disturbance had a higher risk of myeloid leukemia (for WHIIRS score 9-20 vs. WHIIRS score 0-4, hazard ratio = 1.39, CI: 1.05, 1.83). Higher sleep disturbance level was associated with increased risk of leukemia, especially for myeloid leukemia among postmenopausal women.

T2 MRI at 3T of cartilage and menisci in patients with hyperuricemia: initial findings.

OBJECTIVE: To compare and evaluate T2 values of compartmental femorotibial cartilage and subregional menisci in patients with hyperuricemia at 3T. MATERIALS AND METHODS: Thirty-two subjects were included in this study and subdivided into two subgroups: 15 healthy controls (3 females, 12 males; mean age = 45.3 ± 10.9 years), 17 patients with hyperuricemia (2 females, 15 males; mean age = 44.4 ± 12.7 years). All subjects were assessed on a 3T MR scanner using an 8-channel phased-array knee coil (transmit-receive). Wilcoxon rank sum test and analysis of covariance (ANCOVA) were performed to determine whether there were any statistically significant differences in T2 values of compartmental femorotibial cartilage and subregional menisci between the two subgroups. RESULTS: Lateral tibial cartilage (48.6 ± 3.5 ms) in healthy subgroup had significantly lower (p < 0.05) T2 values than all subcompartments of femorotibial cartilage in hyperuricemia subgroup. Medial tibial cartilage (56.5 ± 4.3 ms) in hyperuricemia subgroup had significantly higher (p < 0.05) T2 values than all subcompartments of femorotibial cartilage except medial tibial cartilage in healthy subgroup. Medial anterior horn of meniscus (39.4 ± 2.9 ms) in healthy subgroup had significantly lower (p < 0.05) T2 values than all subregional menisci except both medial anterior horn and medial body segment of meniscus in hyperuricemia subgroup. CONCLUSION: T2 values in certain compartmental femorotibial cartilage and subregional menisci in patients with hyperuricemia are evidently and abnormally heightened compared with those in healthy subjects, to which special attention should be paid when diagnosing and treating the patients with hyperuricemia in the clinical setting. The LT cartilage had significantly lower T2 values (48.6 ± 3.5 ms) in healthy subgroup compared to all compartmental femorotibial cartilage in cohort with HU. MF cartilage had significantly lower T2 values (51.6 ± 2.9 ms) in healthy subgroup compared to both LF (54.4 ± 4.1 ms) and MT (56.5 ± 4.3 ms) in cohort with HU. MT cartilage had significantly higher T2 values (56.5 ± 4.3 ms) in cohort with HU subgroup compared to LF (52.5 ± 3.0 ms) in healthy subgroup. T2 mapping may be promising and potential sensitive discriminator of understanding and examining the early compositional and structural change in proteoglycan-collagen matrix of human femorotibial cartilage in patients with hyperuricemia.

Structural Connectivity Remote From Lesions Correlates With Somatosensory Outcome Poststroke.

Background and Purpose: Changes in connectivity of white matter fibers remote to a stroke lesion, suggestive of structural connectional diaschisis, may impact on clinical impairment and recovery after stroke. However, until recently, we have not had tract-specific techniques to map changes in white matter tracts in vivo in humans to enable investigation of potential mechanisms and clinical impact of such remote changes. Our aim was to identify and quantify white matter tracts that are affected remote from a stroke lesion and to investigate the associations between reductions in tract-specific connectivity and impaired touch discrimination function after stroke. Methods: We applied fixel-based analysis to diffusion magnetic resonance imaging data from 37 patients with stroke (right lesion =16; left lesion =21) and 26 age-matched healthy adults. Three quantitative metrics were compared between groups: fiber density; fiber-bundle cross-section; and a combined measure of both (fiber-bundle cross-section) that reflects axonal structural connectivity. Results: Compared with healthy adults, patients with stroke showed significant common fiber-bundle cross-section and fiber density reductions in 4 regions remote from focal lesions that play roles in somatosensory and spatial information processing. Structural connectivity along the somatosensory fibers of the lesioned hemisphere was correlated with contralesional hand touch function. Touch function of the ipsilesional hand was associated with connectivity of the superior longitudinal fasciculus, and, for the right-lesion group, the corpus callosum. Conclusions: Remote tract-specific reductions in axonal connectivity indicated by diffusion imaging measures are observed in the somatosensory network after stroke. These remote white matter connectivity reductions, indicative of structural connectional diaschisis, are associated with touch impairment in patients with stroke.

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities.

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities.

Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1.

Association between selenium intake and breast cancer risk: results from the Women's Health Initiative.

PURPOSE: It has been hypothesized that selenium (Se) can prevent cancer, and that Se deficiency may be associated with an increased risk of breast cancer. However, findings from epidemiological studies have been inconsistent. The objective of this study was to assess the association between Se intake and risk of breast cancer in the Women's Health Initiative (WHI). METHODS: This study included 145,033 postmenopausal women 50-79 years who completed baseline questionnaires between October 1993 and December 1998, which addressed dietary and supplemental Se intake and breast cancer risk factors. The association between baseline Se intake and incident breast cancer was examined in Cox proportional hazards analysis. RESULTS: During a mean follow-up of 15.5 years, 9487 cases of invasive breast cancer were identified. Total Se (highest versus lowest quartile: HR 1.00, 95% CI 0.92-1.09, Ptrend = 0.66), dietary Se (highest versus lowest quartile: HR 0.99, 95% CI 0.89-1.08, Ptrend = 0.61), and supplemental Se (yes versus no: HR 0.99, 95% CI 0.95-1.03) were not associated with breast cancer incidence. CONCLUSIONS: This study indicates that Se intake is not associated with incident breast cancer among postmenopausal women in the United States. Further studies are needed to confirm our findings by using biomarkers such as toenail Se to reduce the potential for misclassification of Se status.

Increased cerebral blood flow with increased amyloid burden in the preclinical phase of alzheimer's disease.

BACKGROUND: Arterial spin labeling (ASL) is an emerging MRI technique for noninvasive measurement of cerebral blood flow (CBF) that has been used to show hemodynamic changes in the brains of people with Alzheimer's disease (AD). CBF changes have been measured using positron emission tomography (PET) across the AD spectrum, but ASL showed limited success in measuring CBF variations in the preclinical phase of AD, where amyloid ß (Aß) plaques accumulate in the decades prior to symptom onset. PURPOSE: To investigate the relationship between CBF measured by multiphase-pseudocontinuous-ASL (MP-PCASL) and Aß burden as measured by 11 C-PiB PET imaging in a study of cognitively normal (CN) subjects age over 65. STUDY TYPE: Cross-sectional. POPULATION: Forty-six CN subjects including 33 with low levels of Aß burden and 13 with high levels of Aß. FIELD STRENGTH/SEQUENCE: 3T/3D MP-PCASL. ASSESSMENT: The MP-PCASL method was chosen because it has a high signal-to-noise ratio. Furthermore, the data were analyzed using an efficient processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, and partial volume effect correction. STATISTICAL TESTS: General Linear Model. RESULTS: In CN subjects positive for Aß burden (n = 13), we observed a positive correlation between CBF and Aß burden in the hippocampus, amygdala, caudate (P < 0.01), frontal, temporal, and insula (P < 0.05). DATA CONCLUSION: To the best of our knowledge, this is the first study using MP-PCASL in the study of AD, and the results suggest a potential compensatory hemodynamic mechanism that protects against pathology in the early stages of AD. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:505-513.

P2RY12:rs7637803 TT variant genotype increases coronary artery aneurysm risk in Kawasaki disease in a southern Chinese population.

BACKGROUND: Activated-platelet increases the risk of thrombosis in Kawasaki disease (KD) patients with a coronary artery aneurysm (CAA). The ADP pathway is one of the platelet activation and aggregation pathways. The P2RY12 gene encodes the ADP receptor that is highly concentrated on platelets. However, few studies have reported on P2RY12 in relation to KD susceptibility with or without CAA. METHODS: We recruited 1335 healthy controls and 776 KD patients, including 103 with CAA, and selected five P2RY12 polymorphisms: rs9859538, rs1491974, rs7637803, rs6809699 and rs2046934. The present study focused on the relationship between the P2RY12 polymorphisms and KD with or without CAA. RESULTS: Among all of the selected polymorphisms, single-locus analysis showed no significant association between the P2RY12 polymorphism and KD susceptibility. However, we found a significant relationship between rs7637803 and CAA risk in KD patients [CT versus CC: odds ratio (OR) = 0.41, 95% confidence interval (CI) = 0.22-0.75; p = 0.0041; TT versus CC: OR = 2.90, 95% CI = 1.12-7.46; p = 0.0276]. Stratification analysis by age in KD patients indicated that the rs7637803 TT genotype increased CAA formation risk among children aged (OR = 3.90, 95% CI = 1.42-10.69; p = 0.0081) and increased the onset risk of CAA in males (OR = 6.28, 95% CI = 2.01-19.65; p = 0.0016). The combined effect of the five selected P2RY12 risk genotypes with the KD patients compared to non-mutated P2RY12 genotypes (score: 0) showed that patients with P2RY12 genotype polymorphisms (score: 1-5) had a significantly increased CAA risk (p = 0.0086). Stratification analysis for the severity of CAA found that the rs7637803 TT genotype reduced giant CAA (GCAA) risk (OR = 4.60, 95% CI = 1.70-12.41; p = 0.0026). CONCLUSIONS: The results of the present study indicate that the P2RY12 rs7637803 genotype might be used as a biomarker to predict the occurrence of GCAA.

Robust Identification of Rich-Club Organization in Weighted and Dense Structural Connectomes.

The human brain is a complex network, in which some brain regions, denoted as 'hub' regions, play critically important roles. Some of these hubs are highly interconnected forming a rich-club organization, which has been identified based on the degree metric from structural connectomes constructed using diffusion tensor imaging (DTI)-based fiber tractography. However, given the limitations of DTI, the yielded structural connectomes are largely compromised, possibly affecting the characterization of rich-club organizations. Recent progress in diffusion MRI and fiber tractography now enable more reliable but also very dense structural connectomes to be achieved. However, while the existing rich-club analysis method is based on weighted networks, it is essentially built upon degree metric and, therefore, not suitable for identifying rich-club organizations from such dense networks, as it yields nodes with indistinguishably high degrees. Therefore, we propose a novel method, i.e. Rich-club organization Identification using Combined H-degree and Effective strength to h-degree Ratio (RICHER), to identify rich-club organizations from dense weighted networks. Overall, it is shown that more robust rich-club organizations can be achieved using our proposed framework (i.e., state-of-the-art fiber tractography approaches and our proposed RICHER method) in comparison to the previous method focusing on weighted networks based on degree, i.e., RC-degree. Furthermore, by simulating network attacks in 3 ways, i.e., attack to non-rich-club/non-rich-club edges (NRC2NRC), rich-club/non-rich-club edges (RC2NRC), and rich-club/rich-club edges (RC2RC), brain network damage consequences have been evaluated in terms of global efficiency (GE) reductions. As expected, significant GE reductions have been detected using our proposed framework among conditions, i.e., NRC2NRC < RC2NRC, NRC2NRC < RC2RC and RC2NRC < RC2RC, which however have not been detected otherwise.

T2 mapping of cartilage and menisci at 3T in healthy subjects with knee malalignment: initial experience.

PURPOSE: To assess the relationship between knee alignment and T2 values of femorotibial cartilage and menisci in healthy subjects at 3 T. MATERIALS AND METHODS: Thirty-six healthy subjects divided into three subgroups of 12 neutral, 12 varus, and 12 valgus alignment of the femorotibial joint were investigated on 3-T MR scanner using a 2D multi-echo turbo spin-echo (TSE) sequence for T2 mapping. Wilcoxon signed-rank test and analysis of covariance (ANCOVA) were performed to determine any statistically significant differences in subregional T2 values of femorotibial cartilage and menisci among the three subgroups of healthy subjects. RESULTS: Lateral femoral anterior cartilage subregion (52 ± 3 ms, mean ± standard deviation; 53 ± 2 ms) had significantly higher T2 values (p < 0.05) than medial femoral anterior cartilage subregion (51 ± 2 ms; 51 ± 2 ms) in varus and valgus groups, respectively. There were statistically significant differences (p < 0.05) in T2 values of tibial central cartilage subregion between lateral and medical compartment among varus, valgus, and neutral subgroups. Lateral body segment of meniscus (41 ± 3 ms) had significantly higher (p < 0.05) T2 values than medial body segment (40 ± 2 ms) in the varus subgroup. CONCLUSIONS: Some degree of correlation between knee alignment and subregional T2 values of femorotibial cartilage and menisci exists in healthy subjects. These findings indicate that T2 mapping may be sensitive in assessing the load distribution pattern of human cartilage and menisci with knee alignment abnormality, which may be used as reference baseline when understanding the occurrence and progression of knee osteoarthritis.

A Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies.

The conventional way to estimate functional networks is primarily based on Pearson correlation along with classic Fisher Z test. In general, networks are usually calculated at the individual-level and subsequently aggregated to obtain group-level networks. However, such estimated networks are inevitably affected by the inherent large inter-subject variability. A joint graphical model with Stability Selection (JGMSS) method was recently shown to effectively reduce inter-subject variability, mainly caused by confounding variations, by simultaneously estimating individual-level networks from a group. However, its benefits might be compromised when two groups are being compared, given that JGMSS is blinded to other groups when it is applied to estimate networks from a given group. We propose a novel method for robustly estimating networks from two groups by using group-fused multiple graphical-lasso combined with stability selection, named GMGLASS. Specifically, by simultaneously estimating similar within-group networks and between-group difference, it is possible to address inter-subject variability of estimated individual networks inherently related with existing methods such as Fisher Z test, and issues related to JGMSS ignoring between-group information in group comparisons. To evaluate the performance of GMGLASS in terms of a few key network metrics, as well as to compare with JGMSS and Fisher Z test, they are applied to both simulated and in vivo data. As a method aiming for group comparison studies, our study involves two groups for each case, i.e., normal control and patient groups; for in vivo data, we focus on a group of patients with right mesial temporal lobe epilepsy.

Effect of depression before breast cancer diagnosis on mortality among postmenopausal women.

BACKGROUND: Few previous studies investigating depression before the diagnosis of breast cancer and breast cancer-specific mortality have examined depression measured at more than 1 time point. This study investigated the effect of depression (combining depressive symptoms alone with antidepressant use) measured at 2 time points before the diagnosis of breast cancer on all-cause mortality and breast cancer-specific mortality among older postmenopausal women. METHODS: A large prospective cohort, the Women's Health Initiative, was used. The study included 3095 women with incident breast cancer who had measures of depressive symptoms and antidepressant use before their diagnosis at the baseline and at year 3. Multivariate Cox proportional hazards regression was used to estimate adjusted hazard ratios (HRs) between depression at the baseline, depression at year 3, and combinations of depression at these time points and all-cause mortality and breast cancer-specific mortality. RESULTS: Depression at year 3 before a breast cancer diagnosis was associated with higher all-cause mortality after adjustments for multiple covariates (HR, 1.35; 95% confidence interval [CI], 1.02-1.78). There was no statistically significant association of baseline depression and all-cause mortality or breast cancer-specific mortality whether or not depression was also present at year 3. In women with late-stage (regional- or distant-stage) breast cancer, newly developed depression at year 3 was significantly associated with both all-cause mortality (HR, 2.00; 95% CI, 1.13-3.56) and breast cancer-specific mortality (HR, 2.42; 95% CI, 1.24-4.70). CONCLUSIONS: Women with newly developed depression before the diagnosis of breast cancer had a modestly but significantly increased risk for death from any cause and for death from breast cancer at a late stage. Cancer 2017;123:3107-15. © 2017 American Cancer Society.

Risk factors of the stigma towards psychiatric patients among primary healthcare workers in China: a county study.

BACKGROUND: Attitude towards psychiatric patients among healthcare workers has an impact on quality of medical care and rehabilitation of patients. In China, primary healthcare workers play an important role in mental health care, but little is known about the attitude of them towards psychiatric patients. This study aims to examine the risk factors associated with stigma among primary healthcare workers in West China. METHODS: This cross-sectional study randomly recruited 395 primary healthcare workers in Mianzhu County, China. Data were collected via self-reported questionnaires. Descriptive analyses, bivariate analyses, and hierarchical linear regressions were performed by SPSS 17.0 to test the factors that accounted for the variation of stigma towards psychiatric patients. RESULTS: Several risk factors were confirmed, including the satisfaction of income, work experience in psychiatric/ psychological departments, rehabilitation of patients, contact quality, and the attitude of mass media. However, demographic factors, the rest of work-related factors, and contact frequency might not be related with primary healthcare workers' attitude towards psychiatric patients. CONCLUSIONS: The findings suggested that the quality of contact between primary healthcare workers and psychiatric patients be enhanced in order to decrease the stigma of healthcare staff. On-the-job training and institutional medical education needs a further exploration and development.

Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics.

Diffusion MRI streamlines tractography has become a major technique for inferring structural networks through reconstruction of brain connectome. However, quantification of structural connectivity based on the number of streamlines interconnecting brain grey matter regions is known to be problematic in a number of aspects, such as the ill-posed nature of streamlines terminations and the non-quantitative nature of streamline counts. This study investigates the effects of state-of-the-art connectome construction methods on the subsequent analyses of structural brain networks using graph theoretical approaches. Our results demonstrate that the characteristics of structural connectivity, including connectome variability, global network metrics, small-world attributes and network hubs, alter significantly following the improvement in biological accuracy of streamlines tractograms provided by anatomically-constrained tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT). Importantly, the commonly-used correction for connection density based on scaling the contribution of each streamline to the connectome by its inverse length is shown to provide incomplete correction, highlighting the necessity for the use of advanced tractogram reconstruction techniques in structural connectomics research.

A novel joint sparse partial correlation method for estimating group functional networks.

Advances in graph theory have provided a powerful tool to characterize brain networks. In particular, functional networks at group-level have great appeal to gain further insight into complex brain function, and to assess changes across disease conditions. These group networks, however, often have two main limitations. First, they are popularly estimated by directly averaging individual networks that are compromised by confounding variations. Secondly, functional networks have been estimated mainly through Pearson cross-correlation, without taking into account the influence of other regions. In this study, we propose a sparse group partial correlation method for robust estimation of functional networks based on a joint graphical models approach. To circumvent the issue of choosing the optimal regularization parameters, a stability selection method is employed to extract networks. The proposed method is, therefore, denoted as JGMSS. By applying JGMSS across simulated datasets, the resulting networks show consistently higher accuracy and sensitivity than those estimated using an alternative approach (the elastic-net regularization with stability selection, ENSS). The robustness of the JGMSS is evidenced by the independence of the estimated networks to choices of the initial set of regularization parameters. The performance of JGMSS in estimating group networks is further demonstrated with in vivo fMRI data (ASL and BOLD), which show that JGMSS can more robustly estimate brain hub regions at group-level and can better control intersubject variability than it is achieved using ENSS.

Addressing antimicrobial resistance in China: policy implementation in a complex context.

The effectiveness of antibiotics in treating bacterial infections is decreasing in China because of the widespread development of resistant organisms. Although China has enacted a number of regulations to address this problem, but the impact is very limited. This paper investigates the implementation of these regulations through the lens of complex adaptive systems (CAS). It presents the findings from reviews of relevant policy documents and published papers. The paper identifies different types of agent and explores their interaction with regard to the use of antibiotics and their responses to changes of the regulations. It focuses particularly on the impact of perverse financial incentives on overall patterns of use of antibiotics. Implications for the possibilities of nonlinear results, interactive relationships, and new pathways of policy implementation are discussed. The paper concludes that policy-makers need to better understand the objectives, incentives and potential adaptive behaviors of the agents when they implement interventions to improve antibiotic use and reduce the risk of emergence of resistant organisms.