Changes in dementia treatment patterns associated with changes in the National Policy in South Korea among patients with newly diagnosed Alzheimer's disease between 2011 and 2017: results from the multicenter, retrospective CAPTAIN study.

BACKGROUND: The South Korean government has been actively involved in plans to combat dementia, implementing a series of national strategies and plans since 2008. In July 2014, eligibility for mandatory long-term care insurance (LTCI) was extended to people with dementia enabling access to appropriate long-term care including the cognitive function training program and home nursing service. This study aimed to investigate changes in treatment patterns for Alzheimer's disease (AD) between July 2011 and June 2017 which spanned the 2014 revision. METHODS: This multicenter, retrospective, observational study of patients with newly diagnosed AD analyzed electronic medical records from 17 general hospitals across South Korea. Based on their time of AD diagnosis, subjects were categorized into Cohort 1 (1 July 2011 to 30 June 2014) and Cohort 2 (1 July 2014 to 30 June 2017). RESULTS: Subjects (N=3,997) divided into Cohorts 1 (n=1,998) and 2 (n=1,999), were mostly female (66.4%) with a mean age of 84.4 years. Cohort 1 subjects were significantly older (P<0.0001) and had a lower number of comorbidities (P=0.002) compared with Cohort 2. Mean Mini-Mental State Examination (MMSE) scores in Cohorts 1 and 2 at the time of AD diagnosis or start of initial treatment were 16.9 and 17.1, respectively (P=0.2790). At 1 year, mean MMSE scores in Cohorts 1 and 2 increased to 17.9 and 17.4, respectively (P=0.1524). Donepezil was the most frequently administered medication overall (75.0%), with comparable rates between cohorts. Rates of medication persistence were ≥98% for acetylcholinesterase inhibitor or memantine therapy. Discontinuation and switch treatment rates were significantly lower (49.7% vs. 58.0%; P<0.0001), and mean duration of initial treatment significantly longer, in Cohort 2 vs. 1 (349.3 vs. 300.2 days; P<0.0001). CONCLUSIONS: Comparison of cohorts before and after revision of the national LTCI system for dementia patients found no significant difference in mean MMSE scores at the time of AD diagnosis or start of initial treatment. The reduction in the proportion of patients who discontinued or changed their initial treatment, and the significant increase in mean duration of treatment, were observed following revision of the LTCI policy which enabled increased patient access to long-term care.

Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification.

In drug-resistant temporal lobe epilepsy, precise predictions of drug response, surgical outcome and cognitive dysfunction at an individual level remain challenging. A possible explanation may lie in the dominant 'one-size-fits-all' group-level analytical approaches that does not allow parsing interindividual variations along the disease spectrum. Conversely, analysing inter-patient heterogeneity is increasingly recognized as a step towards person-centred care. Here, we used unsupervised machine learning to estimate latent relations (or disease factors) from 3 T multimodal MRI features [cortical thickness, hippocampal volume, fluid-attenuated inversion recovery (FLAIR), T1/FLAIR, diffusion parameters] representing whole-brain patterns of structural pathology in 82 patients with temporal lobe epilepsy. We assessed the specificity of our approach against age- and sex-matched healthy individuals and a cohort of frontal lobe epilepsy patients with histologically verified focal cortical dysplasia. We identified four latent disease factors variably co-expressed within each patient and characterized by ipsilateral hippocampal microstructural alterations, loss of myelin and atrophy (Factor 1), bilateral paralimbic and hippocampal gliosis (Factor 2), bilateral neocortical atrophy (Factor 3) and bilateral white matter microstructural alterations (Factor 4). Bootstrap analysis and parameter variations supported high stability and robustness of these factors. Moreover, they were not expressed in healthy controls and only negligibly in disease controls, supporting specificity. Supervised classifiers trained on latent disease factors could predict patient-specific drug response in 76 ± 3% and postsurgical seizure outcome in 88 ± 2%, outperforming classifiers that did not operate on latent factor information. Latent factor models predicted inter-patient variability in cognitive dysfunction (verbal IQ: r = 0.40 ± 0.03; memory: r = 0.35 ± 0.03; sequential motor tapping: r = 0.36 ± 0.04), again outperforming baseline learners. Data-driven analysis of disease factors provides a novel appraisal of the continuum of interindividual variability, which is probably determined by multiple interacting pathological processes. Incorporating interindividual variability is likely to improve clinical prognostics.

A whole-brain 3D myeloarchitectonic atlas: Mapping the Vogt-Vogt legacy to the cortical surface.

Building precise and detailed parcellations of anatomically and functionally distinct brain areas has been a major focus in Neuroscience. Pioneer anatomists parcellated the cortical manifold based on extensive histological studies of post-mortem brain, harnessing local variations in cortical cyto- and myeloarchitecture to define areal boundaries. Compared to the cytoarchitectonic field, where multiple neuroimaging studies have recently translated this old legacy data into useful analytical resources, myeloarchitectonics, which parcellate the cortex based on the organization of myelinated fibers, has received less attention. Here, we present the neocortical surface-based myeloarchitectonic atlas based on the histology-derived maps of the Vogt-Vogt school and its 2D translation by Nieuwenhuys. In addition to a myeloarchitectonic parcellation, our package includes intracortical laminar profiles of myelin content based on Vogt-Vogt-Hopf original publications. Histology-derived myelin density mapped on our atlas demonstrated a close overlap with in vivo quantitative MRI markers for myelin and relates to cytoarchitectural features. Complementing the existing battery of approaches for digital cartography, the whole-brain myeloarchitectonic atlas offers an opportunity to validate imaging surrogate markers of myelin in both health and disease.

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning.

Chest radiography is one of the most widely used diagnostic methods in hospitals, but it is difficult to read clearly because several human organ tissues and bones overlap. Therefore, various image processing and rib segmentation methods have been proposed to focus on the desired target. However, it is challenging to segment ribs elaborately using deep learning because they cannot reflect the characteristics of each region. Identifying which region has specific characteristics vulnerable to deep learning is an essential indicator of developing segmentation methods in medical imaging. Therefore, it is necessary to compare the deep learning performance differences based on regional characteristics. This study compares the differences in deep learning performance based on the rib region to verify whether deep learning reflects the characteristics of each part and to demonstrate why this regional performance difference has occurred. We utilized 195 normal chest X-ray datasets with data augmentation for learning and 5-fold cross-validation. To compare segmentation performance, the rib image was divided vertically and horizontally based on the spine, clavicle, heart, and lower organs, which are characteristic indicators of the baseline chest X-ray. Resultingly, we found that the deep learning model showed a 6-7% difference in the segmentation performance depending on the regional characteristics of the rib. We verified that the performance differences in each region cannot be ignored. This study will enable a more precise segmentation of the ribs and the development of practical deep learning algorithms.

Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography.

Analyzing spinal curvatures manually is time-consuming and tedious for clinicians, and intra-observer and inter-observer variability can affect manual measurements. In this study, we developed and evaluated the performance of an automated deep learning-based computer-aided diagnosis (CAD) tool for measuring the sagittal alignment of the spine from X-ray images. The CAD system proposed here performs two functions: deep learning-based lateral spine segmentation and automatic analysis of thoracic kyphosis and lumbar lordosis angles. We utilized 322 datasets with data augmentation for learning and fivefold cross-validation. The segmentation model was based on U-Net, which has multiple applications in medical image processing. Here, we utilized parameter equations and trigonometric functions to design spinal angle measurement algorithms. The kyphosis (T4-T12) and lordosis angle (L1-S1, L1-L5) were automatically measured to help diagnose kyphosis and lordosis. The segmentation model had precision, sensitivity, and dice similarity coefficient values of 90.53 ± 4.61%, 89.53 ± 1.8%, and 90.22 ± 0.62%, respectively. The performance of the CAD algorithm was also verified with the Pearson correlation, Bland-Altman, and intra-class correlation coefficient (ICC) analysis. The proposed angle measurement algorithm exhibited high similarity and reliability during verification. Therefore, CAD can help clinicians in reaching a diagnosis by analyzing the sagittal spinal curvatures while reducing observer-based variability and the required time or effort.

Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy.

OBJECTIVE: Although temporal lobe epilepsy (TLE) is recognized as a system-level disorder, little work has investigated pathoconnectomics from a dynamic perspective. By leveraging computational simulations that quantify patterns of information flow across the connectome, we tested the hypothesis that network communication is abnormal in this condition, studied the interplay between hippocampal- and network-level disease effects, and assessed associations with cognition. METHODS: We simulated signal spreading via a linear threshold model that temporally evolves on a structural graph derived from diffusion-weighted magnetic resonance imaging (MRI), comparing a homogeneous group of 31 patients with histologically proven hippocampal sclerosis to 31 age- and sex-matched healthy controls. We evaluated the modulatory effects of structural alterations of the neocortex and hippocampus on network dynamics. Furthermore, multivariate statistics addressed the relationship with cognitive parameters. RESULTS: We observed a slowing of in- and out-spreading times across multiple areas bilaterally, indexing delayed information flow, with the strongest effects in ipsilateral frontotemporal regions, thalamus, and hippocampus. Effects were markedly reduced when controlling for hippocampal volume but not cortical thickness, underscoring the central role of the hippocampus in whole-brain disease expression. Multivariate analysis associated slower spreading time in frontoparietal, limbic, default mode, and subcortical networks with impairment across tasks tapping into sensorimotor, executive, memory, and verbal abilities. SIGNIFICANCE: Moving beyond descriptions of static topology toward the formulation of brain dynamics, our work provides novel insight into structurally mediated network dysfunction and demonstrates that altered whole-brain communication dynamics contribute to common cognitive difficulties in TLE.

Long-T2 -suppressed zero echo time imaging with weighted echo subtraction and gradient error correction.

PURPOSE: To perform direct, selective MRI of short-T2 tissues using zero echo time (ZTE) imaging with weighted echo subtraction (WSUB). METHODS: Radial imaging was performed at 7T, acquiring both ZTE and gradient echo (GRE) signals created by bipolar gradients. Long-T2 suppression was achieved by weighted subtraction of ZTE and GRE images. Special attention was given to imperfections of gradient dynamics, to which radial GRE imaging is particularly susceptible. To compensate for gradient errors, matching of gradient history was combined with data correction based on trajectory measurement. The proposed approach was first validated in phantom experiments and then demonstrated in musculoskeletal (MSK) imaging. RESULTS: Trajectory analysis and phantom imaging demonstrated that gradient imperfections were successfully addressed. Gradient history matching enabled consistency between antiparallel projections as required for deriving zeroth-order eddy current dynamics. Trajectory measurement provided individual echo times per projection that showed considerable variation between gradient directions. In in vivo imaging of knee, ankle, and tibia, the proposed approach enabled high-resolution 3D depiction of bone, tendons, and ligaments. Distinct contrast of these structures indicates excellent selectivity of long-T2 suppression. Clarity of depiction also confirmed sufficient SNR of short-T2 tissues, achieved by high baseline sensitivity at 7T combined with high SNR efficiency of ZTE acquisition. CONCLUSION: Weighted subtraction of ZTE and GRE data reconciles robust long-T2 suppression with fastest k-space coverage and high SNR efficiency. This approach enables high-resolution imaging with excellent selectivity to short-T2 tissues, which are of major interest in MSK and neuroimaging applications.

A connectome-based mechanistic model of focal cortical dysplasia.

Neuroimaging studies have consistently shown distributed brain anomalies in epilepsy syndromes associated with a focal structural lesion, particularly mesiotemporal sclerosis. Conversely, a system-level approach to focal cortical dysplasia has been rarely considered, likely due to methodological difficulties in addressing variable location and topography. Given the known heterogeneity in focal cortical dysplasia histopathology, we hypothesized that lesional connectivity consists of subtypes with distinct structural signatures. Furthermore, in light of mounting evidence for focal anomalies impacting whole-brain systems, we postulated that patterns of focal cortical dysplasia connectivity may exert differential downstream effects on global network topology. We studied a cohort of patients with histologically verified focal cortical dysplasia type II (n = 27), and age- and sex-matched healthy controls (n = 34). We subdivided each lesion into similarly sized parcels and computed their connectivity to large-scale canonical functional networks (or communities). We then dichotomized connectivity profiles of lesional parcels into those belonging to the same functional community as the focal cortical dysplasia (intra-community) and those adhering to other communities (inter-community). Applying hierarchical clustering to community-reconfigured connectome profiles identified three lesional classes with distinct patterns of functional connectivity: decreased intra- and inter-community connectivity, a selective decrease in intra-community connectivity, and increased intra- as well as inter-community connectivity. Hypo-connectivity classes were mainly composed of focal cortical dysplasia type IIB, while the hyperconnected lesions were type IIA. With respect to whole-brain networks, patients with hypoconnected focal cortical dysplasia and marked structural damage showed only mild imbalances, while those with hyperconnected subtle lesions had more pronounced topological alterations. Correcting for interictal epileptic discharges did not impact connectivity patterns. Multivariate structural equation analysis provided a mechanistic model of such complex, diverging interactions, whereby the focal cortical dysplasia structural makeup shapes its functional connectivity, which in turn modulates whole-brain network topology.

Endovascular Management of Ruptured Middle Colic Artery Aneurysm and Review of the Literature.

A ruptured aneurysm of the middle colic artery (MCA) is extremely rare, and its etiology and optimal treatment are unclear. Previously, surgical intervention was the treatment of choice for ruptured visceral artery aneurysms. However, owing to recent advances in endovascular techniques, transcatheter arterial embolization has been recommended for the management of even ruptured splanchnic aneurysms in clinically stable patients without evidence of bowel ischemia. We report a case of a ruptured MCA aneurysm in a 52-year-old previously healthy man. Coil embolization was attempted successfully. In addition, 7 previously published cases of ruptured MCA aneurysms successfully treated by endovascular embolization were reviewed.

Effect of low-intensity resistance training with heat stress on the HSP72, anabolic hormones, muscle size, and strength in elderly women.

BACKGROUND: Several recent studies have reported that heat stress stimulates the activation of heat shock protein 72 (HSP72), leading to an increase in muscle synthesis. Some studies suggested that low-intensity resistance training combined with heat stress could improve muscle size and strength. AIM: This study aimed to identify the effect of low-intensity resistance training with heat stress over 12 weeks on the HSP72, anabolic hormones, muscle size, and strength in elderly women. METHODS: The subjects were physically healthy women of 65-75 years, who were randomly assigned to one of three groups: a low-intensity resistance training with heating sheet group (HRT group, n = 8), a moderate-intensity resistance training (RT group, n = 6), and a heating sheet group (HEAT group, n = 7). Computed tomography scans, 1-repetition maximum (1RM), and blood samples were taken pre- and post-training. RESULTS: The HSP72 did not vary significantly between the different groups and times. The IGF-1 and 1RM had significantly increased in all three groups after the training (respectively, p < 0.05). Moreover, the cross-sectional area (CSA) of the quadriceps showed a significantly greater increase in the HRT group than in the HEAT group (p < 0.05). CONCLUSIONS: We found that low-intensity training with heat stress stimulated the anabolic hormones of elderly women, improving their muscle strength and hypertrophy. We believe that low-intensity training with heat stress is an effective way to prevent muscle atrophy and to improve muscle strength in elderly women.

Probing signal phase in direct visualization of short transverse relaxation time component (ViSTa).

PURPOSE: To demonstrate the phase evolutions of direct visualization of short transverse relaxation time component (ViSTa) matches with those of myelin water. METHOD: Myelin water imaging (MWI) measures short transverse signals and has been suggested as a biomarker for myelin. Recently, a new approach, ViSTa, has been proposed to acquire short T2* signals by suppressing long T1 signals. This method does not require any ill-conditioned data processing and therefore provides high-quality images. In this study, the phase of the ViSTa signal was compared with the phase of myelin water simulated by the magnetic susceptibility model of hollow cylinder. RESULTS: The phase evolutions of the ViSTa signal were similar to the simulated myelin water phase evolutions. When fiber orientation was perpendicular relative to the main magnetic field, both the ViSTa and the simulated myelin water phase showed large positive frequency shifts, whereas the gradient echo phase showed a slightly negative frequency shift. Additionally, the myelin water phase map generated using diffusion tensor imaging (DTI) information revealed a good match with the ViSTa phase image. CONCLUSION: The results of this study support the origin of ViSTa signal as myelin water. ViSTa phase may potentially provide sensitivity to demyelination.

Mepivacaine-induced contraction involves phosphorylation of extracellular signal-regulated kinase through activation of the lipoxygenase pathway in isolated rat aortic smooth muscle.

Mepivacaine is an aminoamide local anesthetic with an intermediate duration that intrinsically produces vasoconstriction both in vivo and in vitro. This study investigated the arachidonic acid metabolic pathways involved in mepivacaine-induced contraction, and elucidated the associated cellular mechanism with a particular focus on extracellular signal-regulated kinase (ERK) in endothelium-denuded rat aorta. Isolated rat thoracic aortic rings were suspended for isometric tension recording. Cumulative mepivacaine concentration-response curves were generated in the presence or absence of the following inhibitors: quinacrine dihydrochloride, nordihydroguaiaretic acid, phenidone, AA-861, indomethacin, NS-398, SC-560, fluconazole, PD 98059, and verapamil. Mepivacaine-induced ERK phosphorylation, 5-lipoxygenase (5-LOX) expression, and cyclooxygenase (COX)-2 expression in rat aortic smooth muscle cells were detected by Western blot analysis in the presence or absence of inhibitors. Mepivacaine produced tonic contraction in isolated endothelium-denuded rat aorta. Quinacrine dihydrochloride, nordihydroguaiaretic acid, phenidone, AA-861, NS-398, PD 98059, and verapamil attenuated mepivacaine-induced contraction in a concentration-dependent manner. However, fluconazole had no effect on mepivacaine-induced contraction. PD 98059, quinacrine dihydrochloride, nordihydroguaiaretic acid, AA-861, phenidone, and indomethacin attenuated mepivacaine-induced ERK phosphorylation. Mepivacaine upregulated 5-LOX and COX-2 expression. These results suggest that mepivacaine-induced contraction involves ERK activation, which is primarily mediated by the 5-LOX pathway and in part by the COX-2 pathway.

Myeloarchitectonic cortical parcellation data for contemporary neuroimaging - the Vogt-Vogt legacy in the 21st century.

Obtaining precise and detailed parcellations of the human brain has been a major focus of neuroscience research. Here, we present a multimodal dataset, MYATLAS, based on histology-derived myeloarchitectonic parcellations for use with contemporary neuroimaging analyses software. The core of MYATLAS is a novel 3D neocortical, surface-based atlas derived from legacy myeloarchitectonic histology studies. Additionally, we provide digitized quantitative laminar profiles of intracortical myelin content derived from postmortem photometric data, cross-correlated with in vivo myeloarchitectonic features obtained by quantitative MRI mapping. Moreover, congregated, digitized and quality-improved Vogt-Vogt legacy histology data is made available. Finally, to allow for cross-modality correlations, maps of quantitative myelin estimates and corresponding von Economo-Koskinas' cytoarchitectonic features are also included. We share all necessary surface and volume-based registration files as well as shell scripts to facilitate applications of MYATLAS to future in vivo MRI studies.

MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy.

BACKGROUND AND OBJECTIVES: MRI fails to reveal hippocampal pathology in 30% to 50% of temporal lobe epilepsy (TLE) surgical candidates. To address this clinical challenge, we developed an automated MRI-based classifier that lateralizes the side of covert hippocampal pathology in TLE. METHODS: We trained a surface-based linear discriminant classifier that uses T1-weighted (morphology) and T2-weighted and fluid-attenuated inversion recovery (FLAIR)/T1 (intensity) features. The classifier was trained on 60 patients with TLE (mean age 35.6 years, 58% female) with histologically verified hippocampal sclerosis (HS). Images were deemed to be MRI negative in 42% of cases on the basis of neuroradiologic reading (40% based on hippocampal volumetry). The predictive model automatically labeled patients as having left or right TLE. Lateralization accuracy was compared to electroclinical data, including side of surgery. Accuracy of the classifier was further assessed in 2 independent TLE cohorts with similar demographics and electroclinical characteristics (n = 57, 58% MRI negative). RESULTS: The overall lateralization accuracy was 93% (95% confidence interval 92%-94%), regardless of HS visibility. In MRI-negative TLE, the combination of T2 and FLAIR/T1 intensities provided the highest accuracy in both the training (84%, area under the curve [AUC] 0.95 ± 0.02) and validation (cohort 1 90%, AUC 0.99; cohort 2 76%, AUC 0.94) cohorts. DISCUSSION: This prediction model for TLE lateralization operates on readily available conventional MRI contrasts and offers gain in accuracy over visual radiologic assessment. The combined contribution of decreased T1- and increased T2-weighted intensities makes the synthetic FLAIR/T1 contrast particularly effective in MRI-negative HS, setting the basis for broad clinical translation. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in people with TLE and MRI-negative HS, an automated MRI-based classifier accurately determines the side of pathology.

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia.

BACKGROUND AND OBJECTIVE: To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD). METHODS: We used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2-55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 cases with FCD (13 ± 10 years). Applying the algorithm to 42 healthy controls and 89 controls with temporal lobe epilepsy disease tested specificity. RESULTS: Overall sensitivity was 93% (137 of 148 FCD detected) using a leave-one-site-out cross-validation, with an average of 6 false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half, it ranked the highest. Sensitivity in the independent cohort was 83% (19 of 23; average of 5 false positives per patient). Specificity was 89% in healthy and disease controls. DISCUSSION: This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification, this classifier may assist clinicians in adjusting hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for presurgical evaluation of MRI-negative epilepsy. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in patients with epilepsy initially diagnosed as MRI negative.

Effects of repeated seafood consumption on urinary excretion of arsenic species by volunteers.

Arsenic (As) is a known human carcinogen and widely distributed in the environment. The main route of As exposure in the general population is through food and drinking water. Seafood harvested in Korea contains high-level organoarsenics such as arsenobetaine, arsenocholine, and arsenosugars, which are much less harmful than inorganic arsenics. However, for those who eat large amounts of seafood it is important to understand whether seafood consumption affects urinary levels of inorganic As metabolites such as arsenite, arsenate, monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA). In this study we investigated urinary As metabolites (inorganic As, MMA[V], DMA[V]) and some biological indexes such as AST, GSH, GPX, lipid peroxidation, and uric acid in volunteer study subjects (seven males and nine females). Total urinary As metabolites were analyzed by the hydride generation method, followed by arsenic speciation using HPLC with ICP-mass spectrometry. Study subjects refrained from eating seafood for 3 days prior to the first urine collection and then ingested seafood daily for 6 consecutive days. The first voided urine of the morning was collected from each subject the first day of the consecutive 6 days of seafood ingestion but prior to the first seafood meal. The first voided urine of the morning was also collected on days 1, 2, 3, 4, 5, 6, 7, 10, and 14 after seafood ingestion. The daily mean intake of total As was 6.98 mg, comprised of 4.71 mg of seaweed (67%), 1.74 mg of flat fish (25%), and 0.53 mg of conch (8%). We observed a substantial increase in total urinary As metabolites for subjects consuming seafood from day 1, which recovered to control level at day 10. The increase in total urinary As metabolites was attributed to the increase in DMA, which is a more harmful metabolite than organoarsenics. However, no significant changes in response biological indexes were observed. These results suggest that it is necessary to evaluate As metabolism when assessing the exposure to inorganic As and potential chronic health effects of seafood consumption in Korea.

Plasmid composition and the chpG gene determine the virulence level of Clavibacter capsici natural isolates in pepper.

The gram-positive bacterial species Clavibacter capsici causes necrosis and canker in pepper plants. Genomic and functional analyses of C. capsici type strain PF008 have shown that multiple virulence genes exist in its two plasmids. We aimed to identify the key determinants that control the virulence of C. capsici. Pepper leaves inoculated with 54 natural isolates exhibited significant variation in the necrosis. Six isolates showed very low virulence, but their population titres in plants were not significantly different from those of the highly virulent isolates. All six isolates lacked the pCM1Cc plasmid that carries chpG, which has been shown to be required for virulence and encodes a putative serine protease, but two of them, isolates 1,106 and 1,207, had the intact chpG elsewhere in the genome. Genomic analysis of these two isolates revealed that chpG was located in the pCM2Cc plasmid, and two highly homologous regions were present next to the chpG locus. The chpG expression in isolate 1,106 was not induced in plants. Introduction of chpG of the PF008 strain into the six low-virulence isolates restored their virulence to that of PF008. Our findings indicate that there are at least three different variant groups of C. capsici and that the plasmid composition and the chpG gene are critical for determining the virulence level. Moreover, our findings also indicate that the virulence level of C. capsici does not directly correlate with bacterial titres in plants.

Variability in the Analgesic Response to Ibuprofen Is Associated With Cyclooxygenase Activation in Inflammatory Pain.

The mechanisms underlying interindividual variability in analgesic efficacy of nonsteroidal anti-inflammatory drugs (NSAIDs) are not well understood. Therefore, we performed pain phenotyping, functional neuroimaging, pharmacokinetic/pharmacodynamic assessments, inflammation biomarkers, and gene expression profiling in healthy subjects who underwent surgical extraction of bony impacted third molars and were treated with ibuprofen (400 mg; N = 19) or placebo (N = 10). Analgesic efficacy was not associated with demographic or clinical characteristics, ibuprofen pharmacokinetics, or the degree of cyclooxygenase inhibition by ibuprofen. Compared with partial responders to ibuprofen (N = 9, required rescue medication within the dosing interval), complete responders (N = 10, no rescue medication) exhibited greater induction of urinary prostaglandin metabolites and serum tumor necrosis factor-α and interleukin 8. Differentially expressed genes in peripheral blood mononuclear cells were enriched for inflammation-related pathways. These findings suggest that a less pronounced activation of the inflammatory prostanoid system is associated with insufficient pain relief on ibuprofen alone and the need for additional therapeutic intervention.

Intravascular injection in lumbar medial branch block: a prospective evaluation of 1433 injections.

BACKGROUND: The successful outcome and validity of lumbar medial branch block (MBB) are largely dependent on technique accuracy. Intravascular uptake of injectate causes a false-negative response to block and is associated with various possible complications. In the current study, we prospectively evaluated the incidence of, and the factors associated with, intravascular injection during block. In addition, we assessed the efficiencies of generally accepted safety measures, such as preinjection aspiration and intermittent fluoroscopy to avoid intravascular injections. METHODS: We prospectively examined 1433 lumbar MBBs in 456 patients performed by seven physicians. Aspiration test, spot radiography, and contrast injection under real-time fluoroscopic visualization were performed sequentially without repositioning the needle. Incidences of blood flashback and the presence of intravascular contrast spread on spot radiographic images and during real-time fluoroscopy were checked. The factors studied for intravascular injection included physician experience, patient age and sex, needle gauge, and block spinal level. RESULTS: No specific factors were found to affect the incidence of intravascular uptake during block. The overall incidence of intravascular uptake was 6.1% per nerve block (88/1433), and 30 of these were predicted by preinjection aspiration (sensitivity = 34.1%) and 52 by spot radiography (sensitivity = 59.1%). CONCLUSIONS: The aspiration test with or without spot radiography frequently missed the intravascular uptake of contrast during lumbar MBBs. We strongly advocate the use of real-time fluoroscopy during contrast injection to increase diagnostic and therapeutic value and to avoid possible complications.

Identification of patients with childhood moyamoya diseases showing temporary hypertension after anesthesia by preoperative multifractal Hurst analysis of heart rate variability.

OBJECTIVE: This study was performed to determine whether the preoperative multifractal Hurst analysis of heart rate variability might identify and characterize childhood patients with moyamoya disease (MMD) who showed temporary postoperative hypertension. METHODS: We studied 59 childhood patients with MMD. Thirty were classified as hypertensive group when the mean arterial pressure in the postoperative recovery room was 120% or greater than that during the preoperative period and 29 were classified as normotensive group. The 2 groups were compared with respect to preoperative indices of heart rate variability including frequency-domain measures, approximate entropy, and very short-term multifractal Hurst exponents of RR intervals (RRI). Using preoperative indices that showed significant differences, discriminant analysis was performed to identify postoperative hypertensive patients. RESULTS: Only exponents of the order > or =3 (H3alpha, H4alpha, and H5alpha) were significantly lower in the hypertensive group than in the normotensive group. Frequency-domain measures, approximate entropy, and the exponents of the order < or =2 were not significantly different in the 2 groups. Discriminant analysis using all of the three exponents correctly identified 27/30 (90%) of the postoperative hypertensive patients. CONCLUSIONS: Preoperative very short-term multifractal Hurst analysis of RRI variability identified 90% of childhood MMD patients who developed postoperative hypertension. The preoperative characteristic of RRI variability was the reduced smoothness at the 8-second-long, local RRI regions within which a very large change of RRI occurs.