Cerebral infarction in diabetes: Clinical pattern, stroke subtypes, and predictors of in-hospital mortality

BACKGROUND: To compare the characteristics and prognostic features of ischemic stroke in patients with diabetes and without diabetes, and to determine the independent predictors of in-hospital mortality in people with diabetes and ischemic stroke. METHODS: Diabetes was diagnosed in 393 (21.3%) of 1,840 consecutive patients with cerebral infarction included in a prospective stroke registry over a 12-year period. Demographic characteristics, cardiovascular risk factors, clinical events, stroke subtypes, neuroimaging data, and outcome in ischemic stroke patients with and without diabetes were compared. Predictors of in-hospital mortality in diabetic patients with ischemic stroke were assessed by multivariate analysis. RESULTS: People with diabetes compared to people without diabetes presented more frequently atherothrombotic stroke (41.2% vs 27%) and lacunar infarction (35.1% vs 23.9%) (P < 0.01). The in-hospital mortality in ischemic stroke patients with diabetes was 12.5% and 14.6% in those without (P = NS). Ischemic heart disease, hyperlipidemia, subacute onset, 85 years old or more, atherothrombotic and lacunar infarcts, and thalamic topography were independently associated with ischemic stroke in patients with diabetes, whereas predictors of in-hospital mortality included the patient's age, decreased consciousness, chronic nephropathy, congestive heart failure and atrial fibrillation CONCLUSION: Ischemic stroke in people with diabetes showed a different clinical pattern from those without diabetes, with atherothrombotic stroke and lacunar infarcts being more frequent. Clinical factors indicative of the severity of ischemic stroke available at onset have a predominant influence upon in-hospital mortality and may help clinicians to assess prognosis more accurately

Cerebral infarction in diabetes: Clinical pattern, stroke subtypes, and predictors of in-hospital mortality

BACKGROUND: To compare the characteristics and prognostic features of ischemic stroke in patients with diabetes and without diabetes, and to determine the independent predictors of in-hospital mortality in people with diabetes and ischemic stroke. METHODS: Diabetes was diagnosed in 393 (21.3%) of 1,840 consecutive patients with cerebral infarction included in a prospective stroke registry over a 12-year period. Demographic characteristics, cardiovascular risk factors, clinical events, stroke subtypes, neuroimaging data, and outcome in ischemic stroke patients with and without diabetes were compared. Predictors of in-hospital mortality in diabetic patients with ischemic stroke were assessed by multivariate analysis. RESULTS: People with diabetes compared to people without diabetes presented more frequently atherothrombotic stroke (41.2% vs 27%) and lacunar infarction (35.1% vs 23.9%) (P < 0.01). The in-hospital mortality in ischemic stroke patients with diabetes was 12.5% and 14.6% in those without (P = NS). Ischemic heart disease, hyperlipidemia, subacute onset, 85 years old or more, atherothrombotic and lacunar infarcts, and thalamic topography were independently associated with ischemic stroke in patients with diabetes, whereas predictors of in-hospital mortality included the patient's age, decreased consciousness, chronic nephropathy, congestive heart failure and atrial fibrillation CONCLUSION: Ischemic stroke in people with diabetes showed a different clinical pattern from those without diabetes, with atherothrombotic stroke and lacunar infarcts being more frequent. Clinical factors indicative of the severity of ischemic stroke available at onset have a predominant influence upon in-hospital mortality and may help clinicians to assess prognosis more accurately

A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data

Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

BackgroundTargeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing.ResultsAll panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden.ConclusionThis comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.Peer reviewe

The SEQC2 epigenomics quality control (EpiQC) study

BACKGROUND: Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group. RESULTS: Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. CONCLUSIONS: The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research

Analysis of trapping effects on the forward current-voltage characteristics of al-implanted 4H-SiC p-i-n Diodes

The forward current-voltage characteristics (IF-VF) of aluminum (Al)-implanted 4H-SiC p-i-n diodes are investigated by means of a numerical simulation study that takes into account both intrinsic and doping-induced deep defects, namely, the Z1/2 and EH6/7 centers inside the drift region and an electrically active trap concentration inside the anode region due to the Al+ ion implantation process. From the experimental results, the fundamental electric parameters of several samples were extracted at different regions of diode operation and used for comparison. The modeling analysis reveals that Z1/2 and EH6/7 centers reduce the effective carrier lifetimes and increase the recombination rate in the drift region determining the slope of the IF curve in the recombination and diffusion regimes. In addition, a defect density that becomes comparable to the epilayer doping concentration introduces an apparent shunt resistance effect at low-medium biases and at the same time has a noticeable impact on the diode series resistance at voltages higher than 2.7 V. A detrimental effect on the series resistance is also observed in dependence of the trap concentration in the anode region that increases the diode's internal resistance as a consequence of the carrier mobility decrease. Above the IF curve knee, the diode current is largely dominated by the electron injection into the anode since the concentration of free holes for conduction is strongly limited in turn by the incomplete activation of the ion-implanted impurities and the trap activity

Analysis of the Forward I–V Characteristics of Al-Implanted 4H-SiC p-i-n Diodes with Modeling of Recombination and Trapping Effects Due to Intrinsic and Doping-Induced Defect States

In this paper, the impact of silicon carbide intrinsic defect states, such as Z1/2 and EH6/7 centers, on the forward current–voltage curves of aluminum (Al)-implanted 4H-SiC p-i-n diodes is investigated by means of a physics-based device simulator. During the simulations, an explicit carrier trap effect due to an electrically active defect concentration produced by the Al+ ion implantation process in the anode region was also taken into account. The obtained current–voltage characteristics are compared with those measured experimentally for several samples at different current levels. It is found that intrinsic defect densities as high as the epilayer doping may lead to undesirable device properties and instability of the forward bias behavior. The diode ideality factor and the series resistance increase with the increase of defects and could be controlled by using high-purity epi-wafers. Furthermore, due to their location in the bandgap and capture cross-sections, the impact of Z1/2 centers on the device electrical characteristics is more severe than that of EH6/7 centers

Numerical simulations of the electrical transport characteristics of a Pt/n-GaN Schottky diode

In this paper, using a numerical simulator, we investigated the current-voltage characteristics of a Pt/n-GaN thin Schottky diode on the basis of the thermionic emission (TE) theory in the 300 to 500 K temperature range. During the simulations, the effect of different defect states within the n-GaN bulk with different densities and spatial locations is considered. The results show that the diode ideality factor and the threshold voltage decrease with increasing temperature, while at the same time, the zero-bias Schottky barrier height (Fb0) extracted from the forward current density-voltage (J-V) characteristics increases. The observed behaviors of the ideality factor and zero-bias barrier height are analyzed on the basis of spatial barrier height inhomogeneities at the Pt/GaN interface by assuming a Gaussian distribution (GD). The plot of apparent barrier height (Fb,App) as a function of q/2kT gives a straight line, where the mean zero-bias barrier height (b0 ) and the standard deviation (s0) are 1.48 eV and 0.047 V, respectively. The plot of the modified activation energy against q/kT gives an almost the same value ofb0 and an effective Richardson constant A* of 28.22 A cm%2 K%2, which is very close to the theoretical value for n-type GaN/Pt contacts. As expected, the presence of defect states with different trap energy levels has a noticeable impact on the device electrical characteristics