Search

Pd supported on different anatase TiO2 nanocrystals predominantly exposing either {100}, {101}, or {001} facets were tested for CO2 hydrogenation into formate. Remarkable morphology-dependent catalysis was observed. Compared to 2%Pd/TiO2{101} and 2%Pd/TiO2{001} catalysts, 2%Pd/TiO2{100} is highly active and stable, affording an unprecedented turnover frequency of ca. 1369 h-1 and keeping stable after 6 cycles at 313 K. This can be associated with, on the one hand, higher density of moderate basic site and relatively more Pd(0) species over 2%Pd/TiO2{100} contribute to the activations of CO2 and H2, respectively, favorable for the activity; On the other hand, higher oxygen vacancy concentrations in the TiO2{100} promote the Pd-TiO2 interactions and result in the formation and stability of flat Pd particles over 2%Pd/TiO2{100}, beneficial to the stability. These results highlight the importance of oxide morphology in formate formation and open up possibilities of oxide morphology engineering for developing efficient Pd-based catalysts for CO2 hydrogenation.

ObjectivesTo develop and validate the QCancer2 (10-year risk) lung model for estimation of future risk of lung cancer and to compare the model performance against other prediction models for lung cancer screeningDesignopen cohort study using linked electronic health records (EHRs) from the QResearch database (1 January 2005 – 31 March 2020)SettingEnglish primary careParticipants12.99 million patients aged 25-84 years were in the derivation cohort to develop the models and 4.14 million patients were in the validation cohort. All patients were free of lung cancer at baseline.Main outcome measureIncident lung cancer casesMethodsThere were two stages in this study. First, Cox proportional hazards models were used in the derivation cohort to update the QCancer (10-year risk) lung model in men and women for a 10-year predictive horizon, including two new predictors (pneumonia and venous thromboembolism) and more recent data. Discrimination measures (Harrell’s C, D statistic, and ) and calibration plots were used to evaluate model performance in the validation cohort by sex. Secondly, seven prediction models for lung cancer screening (LLPv2, LLPv3, LCRAT, PLCOM2012, PLCOM2014, Pittsburgh, and Bach) were selected to compare the model performance with the QCancer2 (10-year risk) lung model in two subgroups: (1) smokers and non-smokers aged 40-84 years and (2) ever-smokers aged 55-74 years.Results73,380 incident lung cancer cases were identified in the derivation cohort and 22,838 in the validation cohort during follow-up. The updated models explained 65% of the variation in time to diagnosis of lung cancer in both sexes. Harrell’s C statistics were close to 0.9 (indicating excellent discrimination), and the D statistics were around 2.8. Compared with the original models, the discrimination measures in the updated models improved slightly in both sexes. Compared with other prediction models, the QCancer2 (10-year risk) lung model had the best model performance in discrimination, calibration, and net benefit across three predictive horizons (5, 6, and 10 years) in the two subgroups.ConclusionDeveloped and validated using large-scale EHRs, the QCancer2 (10-year risk) lung model can estimate the risk of an individual patient aged 25-84 years for up to 10 years. It has the best model performance among other prediction models. It has potential utility for risk stratification of the English primary care population and selection of eligible people at high risk for the targeted lung health check programme or lung cancer screening.What is already known on this topicUsing risk prediction models to stratify people at the population level and selecting those at the highest risks is an efficient and cost-effective strategy for screening programmes. It avoids waste of resources in screening patients at low risk.An ideal prediction model should have excellent discrimination and calibration in the target population.The Liverpool Lung Project (LLPv2) and the Prostate Lung Colorectal and Ovarian (PLCOM2012) models had only moderate discrimination and were not well-calibrated when externally validated using the Clinical Practice Research Datalink (CPRD) data for the English primary care population.What this study addsDeveloped and validated using robust statistical methodologies, the QCancer2 (10-year risk) lung model shows excellent discrimination and calibration in both sexes. It can estimate an individual adult patient’s risk for each year of follow-up, for up to 10 years.The QCancer2 (10-year risk) lung model has the best model performance in discrimination and calibration when compared with the other eight models (QCancer (10-year risk), LLPv2, LLPv3, LCRAT, PLCOM2012, PLCOM2014, Pittsburgh, and Bach) in three predictive horizons (5/6/10 years) and two sub-populations (smokers and non-smokers aged 40-84 years and ever-smokers aged 55-74 years).The QCancer2 (10-year risk) lung model can be applied to the English primary care population to select eligible patients for the Targeted Lung Health Check programme or lung cancer screening using low dose CT.

Background and research aimThe incidence and mortality of liver cancer have been increasing in recent years in the UK. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings.MethodsThis population-based study uses the QResearch® database (version 46) and includes patients aged 25-84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008 to 31 December 2020). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal-external validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated.DiscussionThe DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits.Medical Subject Headings (MeSH)Liver Neoplasms; Carcinoma, Hepatocellular; Cholangiocarcinoma; Clinical Decision Rules; Early Detection of Cancer; Early Diagnosis; Symptom Assessment; Comorbidity

Background and research aimLung cancer is a research priority in the UK. Early diagnosis of lung cancer can improve patients’ survival outcomes. The DART-QResearch project is part of a larger academic-industrial collaborative initiative, using big data and artificial intelligence to improve patient outcomes with thoracic diseases. There are two general research aims in the DART-QResearch project: (1) to understand the natural history of lung cancer, (2) to develop, validate, and evaluate risk prediction models to select patients at high risk for lung cancer screening.MethodsThis population-based cohort study uses the QResearch® database (version 45) and includes patients aged between 25 and 84 years old and without a diagnosis of lung cancer at cohort entry (study period: 1 January 2005 to 31 December 2020). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. The following statistical techniques will be used for different research objectives, including descriptive statistics, multi-level modelling, multiple imputation for missing data, fractional polynomials to explore non-linear relationships between continuous variables and the outcome, and Cox regression for the prediction model. We will update our QCancer (lung, 10-year risk) algorithm, and compare it with the other two mainstream models (LLP and PLCOM2012) for lung cancer screening using the same dataset. We will evaluate the discrimination, calibration, and clinical usefulness of the prediction models, and recommend the best one for lung cancer screening for the English primary care population.DiscussionThe DART-QResearch project focuses on both symptomatic presentation and asymptomatic patients in the lung cancer care pathway. A better understanding of the patterns, trajectories, and phenotypes of symptomatic presentation may help GPs consider lung cancer earlier. Screening asymptomatic patients at high risk is another route to achieve earlier diagnosis of lung cancer. The strengths of this study include using large-scale representative population-based clinical data, robust methodology, and a transparent research process. This project has great potential to contribute to the national cancer strategic plan and yields substantial public and societal benefits through earlier diagnosis of lung cancer.

BackgroundPancreatic cancer continues to have an extremely poor prognosis in part due to late diagnosis. 25% of pancreatic cancer patients have a prior diagnosis of diabetes, and hence identifying individuals at risk of pancreatic cancer in those with recently diagnosed type 2 diabetes may be a useful opportunity to identify candidates for screening and early detection. In this study, we will comparatively evaluate regression and machine learning-based clinical prediction models for estimating individual risk of developing pancreatic cancer two years after type 2 diabetes diagnosis.MethodsIn the development dataset, we will include adults aged 30-84 years with incident type-2 diabetes registered with QResearch primary care database. Patients will be followed up from type-2 diabetes diagnosis to first diagnosis of pancreatic cancer as recorded in any one of primary care records, hospital episode statistics, cancer registry data, or death records. Cox-proportional hazards models will be used to develop a risk prediction model for estimating individual risk of developing pancreatic cancer during up to 2 years of follow-up. We will perform variable selection using a combination of clinical and statistical significance approach i.e. HR 1.1 and pInternal-external cross-validation (IECV) framework across geographical regions within England will be used to assess performance and pooled using random effects meta-analysis using: (i) model fit in terms of variation explained by the model Royston & Sauerbrei’s R2D, (ii) calibration slope and calibration-in-the-large, and (iii) discrimination measured in terms of Harrell’s C and Royston & Sauerbrei’s D-statistic.Further, we will evaluate machine learning (ML) approaches for the clinical prediction model using neural networks (NN) and XGBoost. The model predictors and performance of these will be compared with the results of those derived from the regression-based strategy.DiscussionThe proposed study will develop and validate a novel risk prediction model to aid early diagnosis of pancreatic cancer in patients with new-onset diabetes in primary care. With an enhanced decision-risk tool for use at point-of care by general practitioners to assess pancreatic cancer risk, it may improve decision-making so that at-risk patients are rapidly prioritised to aid early diagnosis of pancreatic cancer in patients with newly diagnosed diabetes.

Background A more transmissible variant of SARS-CoV-2, the variant of concern 202012/01 or lineage B.1.1.7, has emerged in the UK. We aimed to estimate the risk of critical care admission, mortality in patients who are critically ill, and overall mortality associated with lineage B.1.1.7 compared with non-B.1.1.7. We also compared clinical outcomes between these two groups. Methods For this observational cohort study, we linked large primary care (QResearch), national critical care (Intensive Care National Audit & Research Centre Case Mix Programme), and national COVID-19 testing (Public Health England) databases. We used SARS-CoV-2 positive samples with S-gene molecular diagnostic assay failure (SGTF) as a proxy for the presence of lineage B.1.1.7. We extracted two cohorts from the data: the primary care cohort, comprising patients in primary care with a positive community COVID-19 test reported between Nov 1, 2020, and Jan 26, 2021, and known SGTF status; and the critical care cohort, comprising patients admitted for critical care with a positive community COVID-19 test reported between Nov 1, 2020, and Jan 27, 2021, and known SGTF status. We explored the associations between SARS-CoV-2 infection with and without lineage B.1.1.7 and admission to a critical care unit (CCU), 28-day mortality, and 28-day mortality following CCU admission. We used Royston-Parmar models adjusted for age, sex, geographical region, other sociodemographic factors (deprivation index, ethnicity, household housing category, and smoking status for the primary care cohort; and ethnicity, body-mass index, deprivation index, and dependency before admission to acute hospital for the CCU cohort), and comorbidities (asthma, chronic obstructive pulmonary disease, type 1 and 2 diabetes, and hypertension for the primary care cohort; and cardiovascular disease, respiratory disease, metastatic disease, and immunocompromised conditions for the CCU cohort). We reported information on types and duration of organ support for the B.1.1.7 and non-B.1.1.7 groups. Findings The primary care cohort included 198 420 patients with SARS-CoV-2 infection. Of these, 117 926 (59·4%) had lineage B.1.1.7, 836 (0·4%) were admitted to CCU, and 899 (0·4%) died within 28 days. The critical care cohort included 4272 patients admitted to CCU. Of these, 2685 (62·8%) had lineage B.1.1.7 and 662 (15·5%) died at the end of critical care. In the primary care cohort, we estimated adjusted hazard ratios (HRs) of 2·15 (95% CI 1·75–2·65) for CCU admission and 1·65 (1·36–2·01) for 28-day mortality for patients with lineage B.1.1.7 compared with the non-B.1.1.7 group. The adjusted HR for mortality in critical care, estimated with the critical care cohort, was 0·91 (0·76–1·09) for patients with lineage B.1.1.7 compared with those with non-B.1.1.7 infection. Interpretation Patients with lineage B.1.1.7 were at increased risk of CCU admission and 28-day mortality compared with patients with non-B.1.1.7 SARS-CoV-2. For patients receiving critical care, mortality appeared to be independent of virus strain. Our findings emphasise the importance of measures to control exposure to and infection with COVID-19. Funding Wellcome Trust, National Institute for Health Research Oxford Biomedical Research Centre, and the Medical Sciences Division of the University of Oxford.

BackgroundA new, more transmissible variant of SARS-CoV-2, variant of concern (VOC) 202012/01 or lineage B.1.1.7, has emerged in the UK. We estimate the risk of critical care admission, mortality in critical ill patients, and overall mortality associated with VOC B.1.1.7 compared with the original variant. We also compare clinical outcomes between these variants ‘ groups.MethodsWe linked a large primary care (QResearch), the national critical care (ICNARC CMP) and the COVID-19 testing (PHE) database and extracted two cohorts. The first was used to explore the association between VOC B.1.1.7 and critical care admission and 28-day mortality. The second to determine the risk of mortality in critically ill patients with VOC B.1.1.7 compared to those without. We used Royston-Parmar models adjusted for age, sex, region, other socio-demographics and comorbidities (asthma, COPD, type I and II, hypertension). We reported information on types and duration of organ supports for the two variants ‘ groups.FindingsThe first cohort included 198,420 patients. Of these, 80,494 had VOC B.1.1.7, 712 were critically ill and 630 died by 28 days. The second cohort included 3432 critically ill patients. Of these, 2019 had VOC B.1.1.7 and 822 died at the end of critical care. Using the first cohort, we estimated adjusted hazard ratios for critical care admission and mortality to be 1.99 (95% CI: 1.59, 2.49) and 1.59 (95% CI: 1.25-2.03) for VOC B.1.1.7 compared with the original variant group, respectively. The adjusted hazard ratio for mortality in critical care, estimated using the second cohort, was 0.93 (95% CI 0.76-1.15) for patients with VOC B.1.1.7, compared to those without.InterpretationVOC B.1.1.7 appears to be more severe. Patients with VOC B.1.1.7 are at increased risk of critical care admission and mortality compared with patients without. For patients receiving critical care, mortality appears independent of virus strain.RESEARCH IN CONTEXTEvidence before this studyA new variant of the SARS-CoV-2 virus, variant of concern (VOC) 202012/01, or lineage B.1.1.7, was detected in England in September 2020. The characteristics and outcomes of patients infected with VOC B.1.1.7 are not yet known. VOC B.1.1.7 has been associated with increased transmissibility. Early analyses have suggested infection with VOC B.1.1.7 may be associated with a higher risk of mortality compared with infection with other virus variants, but these analyses had either limited ability to adjust for key confounding variables or did not consider critical care admission. The effects of VOC B.1.1.7 on severe COVID-19 outcomes remain unclear.Added value of this studyThis study found a 60% higher risk of 28-day mortality associated with infection with VOC B.1.1.7 in patients tested in the community in comparison with the original variant, when adjusted for key confounding variables. The risk of critical care admission for those with VOC B.1.1.7 is double the risk associated with the original variant. For patients receiving critical care, the infecting variant is not associated with the risk of mortality at the end of critical care.Implications of all the available evidenceThe higher mortality and rate of critical care admission associated with VOC B.1.1.7, combined with its known increased transmissibility, are likely to put health care systems under further stress. These effects may be mitigated by the ongoing vaccination programme.

BackgroundPancreatic cancer has the worst survival rate among all cancers. Almost 70% of patients in the UK were diagnosed at Stage IV.AimThis study aimed to investigate the symptoms associated with the diagnoses of pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine neoplasms (PNEN), and comparatively characterise the symptomatology between the two tumour types to inform earlier diagnosis.Design and settingA nested case-control study in primary care was conducted using data from the QResearch® database. Patients aged ≥25 years and diagnosed with PDAC or PNEN during 2000 to 2019 were included as cases. Up to 10 controls from the same general practice were matched with each case by age, sex, and calendar year using incidence density sampling.MethodConditional logistic regression was used to investigate the association between the 42 shortlisted symptoms and the diagnoses of PDAC and (or) PNEN in different timeframes relative to the index date, adjusting for patients’ sociodemographic characteristics, lifestyle, and relevant comorbidities.ResultsA total of 23 640 patients were identified as diagnosed with PDAC and 596 with PNEN. Of the symptoms identified, 23 were significantly associated with PDAC, and nine symptoms with PNEN. The two alarm symptoms for both tumours were jaundice and gastrointestinal bleeding. The two newly identified symptoms for PDAC were thirst and dark urine. The risk of unintentional weight loss may be longer than 2 years before the diagnosis of PNEN.ConclusionPDAC and PNEN have overlapping symptom profiles. The QCancer® (pancreas) risk prediction model could be updated by including the newly identified symptoms and comorbidities, which could help GPs identify high-risk patients for timely investigation in primary care.

This article is the study protocol and statistical analysis plan for the project. This study is set up to identify and quantify “red flag” symptoms associated with the diagnosis of Pancreatic Neuroendocrine Neoplasm (PNEN) and compare these with the symptoms associated with Pancreatic Ductal Adenocarcinoma (PDAC). The results of this study will inform the evidence base for the refinement of the QCancer (Pancreas) tools, which have been integrated into the UK NHS primary care computer systems, to improve the early recognition of PNEN.

The cascaded H-bridge rectifier (CHBR) control consists of two parts, namely whole control and DC voltage balance control. Double closed-loop proportional integral (PI) control strategy with current feedforward decoupling and proportional pulse compensation strategy are typical whole control strategy and voltage balance strategy respectively, which can realize unity-power factor and DC voltage balance with good performance under special working condition. However, when the CHBR system is unstable or disturbed, the conventional double closed-loop PI control would cause some problems such as slow response speed and big overshoot. This paper proposes a sliding mode variable structure control (SMVSC) strategy for whole control through modeling analysis on the CHBR circuit. By choosing the appropriate switching surface to make the system state trajectory set on the sliding mode surface, the proposed SMVSC strategy can improve the response speed of DC voltage and the dynamic performance of the system. Furthermore, modeling analysis for proportional pulse compensation strategy is proposed. Simulation results verify the feasibility and effectiveness of the proposed strategy.

Mine Splitter is widely used in mining machinery, and wedge assembly components are very important parts of it. In this work, we studied wedge assembly’s element content by EDS, and through different heat treatments to improve the performance of wedge assembly components of Mine splitter. The heat treatment process of wedge assembly components is divided into quenching, quenching and tempering to find the changes on microstructure and mechanical properties. It is found that after quenching at 840°C and tempering at 450°C, the structure is tempered Sorbite with the highest strength and wear resistance and the best comprehensive mechanical properties.

BACKGROUND: Offering free glasses can be important to increase children's wear. We sought to assess whether "Upgrade glasses" could avoid reduced glasses sales when offering free glasses to children in China.METHODS: In this cluster-randomized, controlled trial, children with uncorrected visual acuity (VA)< = 6/12 in either eye correctable to >6/12 in both eyes at 138 randomly-selected primary schools in 9 counties in Guangdong and Yunnan provinces, China, were randomized by school to one of four groups: glasses prescription only (Control); Free Glasses; Free Glasses + offer of $15 Upgrade Glasses; Free Glasses + offer of $30 Upgrade Glasses. Spectacle purchase (main outcome) was assessed 6 months after randomization.RESULTS: Among 10,234 children screened, 882 (8.62%, mean age 10.6 years, 45.5% boys) were eligible and randomized: 257 (29.1%) at 37 schools to Control; 253 (28.7%) at 32 schools to Free Glasses; 187 (21.2%) at 31 schools to Free Glasses + $15 Upgrade; and 185 (21.0%) at 27 schools to Free Glasses +$30 Upgrade. Baseline ownership among these children needing glasses was 11.8% (104/882), and 867 (98.3%) children completed follow-up. Glasses purchase was significantly less likely when free glasses were given: Control: 59/250 = 23.6%; Free glasses: 32/252 = 12.7%, P = 0.010. Offering Upgrade Glasses eliminated this difference: Free + $15 Upgrade: 39/183 = 21.3%, multiple regression relative risk (RR) 0.90 (0.56-1.43), P = 0.65; Free + $30 Upgrade: 38/182 = 20.9%, RR 0.91 (0.59, 1.42), P = 0.69.CONCLUSIONS: Upgrade glasses can prevent reductions in glasses purchase when free spectacles are provided, providing important program income.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02231606. Registered on 31 August 2014.

Rationale and Objectives Noncontrast magnetic resonance angiography (NC-MRA) of pedal artery remains challenging because of the global and regional disease load, tissue integrity, and altered microcirculation. This study aims to investigate the feasibility of the NC-MRA of pedal arteries with flow-sensitive dephasing–prepared steady-state free precession (FSD-SSFP) and to explore the effect of disease load of type II diabetes on the vessel depiction. Materials and Methods FSD-SSFP was performed on a 1.5-T magnetic resonance system before the contrast-enhanced MRA (CE-MRA) as a reference standard in 39 consecutive diabetic subjects (29 men and 16 women, aged 57.9 ± 11.4 years). Two experienced radiologists evaluated the overall artery visibility (VA) and the contamination from soft tissue (SC) and veins (VC) with a four-point scale. Chronic complications and measures including random blood glucose (RBG), lipid panel, body mass index, risk of diabetic foot ulcers (RDF), and glycated hemoglobin (HbA1c) by the imaging were recorded as disease load indicators. Spearman rank correlation and ordinal regression were performed to investigate the effect of disease load on the depiction of pedal arteries. Results The measurement of RBG and RDF were significantly correlated with the VC in CE-MRA and with the overall visibility of pedal arteries in NC-MRA ( P P P P P 1 value that ranged from 70 to 160 mT⋅ms 2 /m (mean, 125 ± 18 mT⋅ms 2 /m) in this study. Conclusions FSD-SSFP proved to be a useful modality of NC-MRA for pedal artery imaging in diabetic patients. The vessel depiction is subject to the local and systemic disease load of type II diabetes. Technical optimization of the flow-sensitive dephasing gradient moment and properly choosing candidate would help augment the potential of this technique in patient care of peripheral artery disease.

Rationale and Objectives Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses. Materials and Methods T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection. Results Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs. Conclusions Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD.

Nonmonoexponential diffusion behavior has been previously reported to exist in some biological tissues, making quantification of diffusion tensor imaging (DTI) indices dependent on diffusion sensitivity of b-value. This study aims to investigate the effect of b-value in revealing postinfarct myocardial microstructural remodeling in ex vivo hearts. DTI scans were performed on heart samples 1, 3, 5, and 7 days after infarction induction as well as intact controls with b-values of 500 to 2500s/mm(2). DTI indices, including fractional anisotropy (FA), and mean and directional diffusivities, were measured in infarct, adjacent and remote regions with zero and each non-zero b-values respectively using conventional DTI analysis. Experimental results showed that these DTI indices decreased gradually with b-values in all regions and groups. Optimal b-values were found to vary with targeted DTI indices, and could strengthen DTI ability in revealing myocardium degradation with using conventional DTI approach. Specifically, FA showed the most sensitive detection of fiber integrity degradation at moderate b-values (≈1500 to 2000s/mm(2)), and the greatest ability of mean and directional diffusivities in monitoring diffusivity alteration occurred at relatively small b-values (≤1500s/mm(2)) during the necrotic and fibrotic phases. These findings may provide useful information for DTI protocol parameter optimization in assessing heart microstructures at other pathological or in vivo states in the future.

Laterality of human brain varies under healthy aging and diseased conditions. The alterations in hemispheric asymmetry may embed distinct biomarkers linked to the disease dynamics. Statistical parametric mapping based on high-resolution magnetic resonance imaging (MRI) and image processing techniques have allowed automated characterization of morphological features across the entire brain. In this study, 149 subjects grouped in healthy young, healthy elderly, mild cognitive impairment (MCI), and Alzheimer's disease (AD) were investigated using multivariate analysis for regional cerebral laterality indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume measured on high-resolution MR images. Asymmetry alteration of MCI and AD were characterized by marked region-specific reduction, while healthy elderly featured a distinct laterality shift in the limbic system in addition to regional asymmetry loss. Lack of the laterality shift in limbic system and early loss of asymmetry in entorhinal cortex may be biomarkers to identify preclinical AD among other dementia. Multivariate analysis of hemispheric asymmetry may provide information helpful for monitoring the disease progress and improving the management of MCI and AD.

Rationale and Objectives Morphologic changes of the human brain during healthy aging provide useful reference knowledge for age-related brain disorders. The aim of this study was to explore age-related global and regional morphological changes of healthy adult brains. Materials and Methods T1-weighted magnetic resonance images covering the entire brain were acquired for 314 subjects. Image processing of registration, segmentation, and surface construction were performed to calculate the volumes of the cerebrum, cerebellum, brain stem, lateral ventricle, and subcortical nuclei, as well as the surface area, mean curvature index, cortical thickness of the cerebral cortex, and subjacent white matter volume using FreeSurfer software. Mean values of each morphologic index were calculated and plotted against age group for sectional analysis. Regression analysis was conducted using SPSS to investigate the age effects on global and regional volumes of human brain. Results Overall global and regional volume loss was observed for the entire brain during healthy aging. Moderate atrophy was observed in subcortical gray matter structures, including the thalamus (R2 = 0.476, P Conclusions Morphologic alterations of human brain manifested regional heterogeneity in the scenario of general volume loss during healthy aging. The age effect on the hippocampus demonstrated a unique evolution. These findings provide informative reference knowledge that may help in identifying and differentiating pathologic aging and other neurologic disorders.

Noncontrast magnetic resonance angiography (NC-MRA) of pedal artery remains challenging because of the global and regional disease load, tissue integrity, and altered microcirculation. This study aims to investigate the feasibility of the NC-MRA of pedal arteries with flow-sensitive dephasing-prepared steady-state free precession (FSD-SSFP) and to explore the effect of disease load of type II diabetes on the vessel depiction.FSD-SSFP was performed on a 1.5-T magnetic resonance system before the contrast-enhanced MRA (CE-MRA) as a reference standard in 39 consecutive diabetic subjects (29 men and 16 women, aged 57.9 ± 11.4 years). Two experienced radiologists evaluated the overall artery visibility (VA) and the contamination from soft tissue (SC) and veins (VC) with a four-point scale. Chronic complications and measures including random blood glucose (RBG), lipid panel, body mass index, risk of diabetic foot ulcers (RDF), and glycated hemoglobin (HbA1c) by the imaging were recorded as disease load indicators. Spearman rank correlation and ordinal regression were performed to investigate the effect of disease load on the depiction of pedal arteries.The measurement of RBG and RDF were significantly correlated with the VC in CE-MRA and with the overall visibility of pedal arteries in NC-MRA (P.025 and P.001, respectively). Blood pressure was the only parameter that was significantly associated with SC in NC-MRA with FSD-SSFP (P.025). For CE-MRA the effect of RDF on the overall VA manifested a significant linear trend (P.001), and the level of RBG was substantially associated with the VC (P.025) without significantly impacting VA and SC. Hypertension only correlated with SC in NC-MRA. VA was found independent of the presence of diabetic nephropathy, coronary artery disease, abnormal lipid panel, HbA1c (75.0%), or optimized m1 value that ranged from 70 to 160 mT⋅ms(2)/m (mean, 125 ± 18 mT⋅ms(2)/m) in this study.FSD-SSFP proved to be a useful modality of NC-MRA for pedal artery imaging in diabetic patients. The vessel depiction is subject to the local and systemic disease load of type II diabetes. Technical optimization of the flow-sensitive dephasing gradient moment and properly choosing candidate would help augment the potential of this technique in patient care of peripheral artery disease.

Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses.T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection.Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs.Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD.

Morphologic changes of the human brain during healthy aging provide useful reference knowledge for age-related brain disorders. The aim of this study was to explore age-related global and regional morphological changes of healthy adult brains.T1-weighted magnetic resonance images covering the entire brain were acquired for 314 subjects. Image processing of registration, segmentation, and surface construction were performed to calculate the volumes of the cerebrum, cerebellum, brain stem, lateral ventricle, and subcortical nuclei, as well as the surface area, mean curvature index, cortical thickness of the cerebral cortex, and subjacent white matter volume using FreeSurfer software. Mean values of each morphologic index were calculated and plotted against age group for sectional analysis. Regression analysis was conducted using SPSS to investigate the age effects on global and regional volumes of human brain.Overall global and regional volume loss was observed for the entire brain during healthy aging. Moderate atrophy was observed in subcortical gray matter structures, including the thalamus (R(2) = 0.476, P.001), nucleus accumbens (R(2) = 0.525, P.001), pallidum (R(2) = 0.461, P.001), and putamen (R(2) = 0.533, P.001). The volume of hippocampus showed a slight increase by 40 years of age, followed by a relatively faster decline after the age of 50 years (R(2) = 0.486, P.001). Surface area and mean curvature were less affected by aging relative to cortical thickness and subjacent white matter volume. Significant cortical thinning was mainly found in the parietal (R(2) = 0.553, P.001) and insula regions (R(2) = 0.405, P.001).Morphologic alterations of human brain manifested regional heterogeneity in the scenario of general volume loss during healthy aging. The age effect on the hippocampus demonstrated a unique evolution. These findings provide informative reference knowledge that may help in identifying and differentiating pathologic aging and other neurologic disorders.

BackgroundHeterogeneous disease trajectories of mild cognitive impairment (MCI) and dementia are frequently encountered in clinical practice, but there is still insufficient knowledge to understand the reasons and mechanisms causing this heterogeneity. In addition to correlates of the disorder, patient characteristics such as their health status, social environment, comorbidities and frailty may contribute to variability in trajectories over time. The current paper outlines the study design and the study population of and provides an overview of the data collected in the Clinical Course of Cognition and Comorbidity in Mild Cognitive Impairment (4C-MCI cohort, n = 315) and Dementia (4C-Dementia cohort, n = 331) Study.MethodsThe two complementary longitudinal cohorts part of the 4C study began enrolment in March 2010. Participants were prospectively recruited from three collaborating Dutch Alzheimer Centers, with three annual follow-up assessments after baseline. Extensive neuropsychological assessments, and detailed profiling of comorbidities, health and frailty at each follow up were the key features of the 4C study. As such, the 4C study was designed to study if and how patients’ comorbidities and frailty are associated with the course of MCI and dementia measured with a comprehensive and multidimensional set of outcomes including cognition, daily functioning, quality of life, behavioral disturbances, caregiver burden, institutionalization and death and whether the effects of medical health and frailty differ between MCI and dementia stages of cognitive disorders.ConclusionSampled in a clinical setting, the 4C study complements population-based studies on neurodegenerative disorders in terms of the type of assessment (e.g. comorbidity, frailty, and functional status were repeatedly assessed). The 4C study complements available clinical cohorts of MCI and dementia patients, because the exclusion criteria were kept to a minimum, to obtain a sample that is representative for the average patient visiting a memory clinic.