Background and Aim
Histological score systems may not fully capture the essential nonalcoholic steatohepatitis (NASH) features, which is one of the leading causes of screening failure in clinical ...trials. We assessed the NASH distribution and its components across the fibrosis stages and their impact on the prognosis and their relationship with the concept of metabolic‐associated fatty liver disease (MAFLD).
Methods
Spanish multicenter study including 1893 biopsy‐proven nonalcoholic fatty liver disease (NAFLD) patients from HEPAmet registry. NASH was diagnosed by NAS score ≥4 (including steatosis, ballooning and lobular inflammation) and fibrosis by Kleiner score. The presence of MAFLD was determined. Progression to cirrhosis, first episode of decompensated cirrhosis and death were collected during the follow‐up (4.7 ± 3.8 years).
Results
Fibrosis was F0 34.3% (649/1893), F1 27% (511/1893), F2 16.5% (312/1893), F3 15% (284/1893) and F4 7.2% (137/1893). NASH diagnosis 51.9% (982/1893), and its individual components (severe steatosis, ballooning and lobular inflammation), increased from F0 (33.6%) to F2 (68.6%), and decreased significantly in F4 patients (51.8%) (P = .0001). More than 70% of non‐NASH patients showed some inflammatory activity (ballooning or lobular inflammation), showing a similar MAFLD rate than NASH (96.2% 945/982 vs. 95.2% 535/562) and significantly higher than nonalcoholic fatty liver (NAFL) subjects (89.1% 311/349) (P < .0001). Progression to cirrhosis was similar between NASH (9.5% 51/539) and indeterminate NASH (7.9% 25/316), and higher than steatosis (5% 14/263) (logRank 8.417; P = .015). Death and decompensated cirrhosis were similar between these.
Conclusions
The prevalence of steatohepatitis decreased in advanced liver disease. However, most of these patients showed some inflammatory activity histologically and had metabolic disturbances. These findings should be considered in clinical trials whose main aim is to prevent cirrhosis progression and complications, liver transplant and death.
Our goal was to study whether influenza vaccination induced antibody mediated rejection in a large cohort of solid organ transplant recipients (SOTR).
Serum anti-Human Leukocyte Antigen (HLA) ...antibodies were determined using class I and class II antibody-coated latex beads (FlowPRA
Screening Test) by flow cytometry. Anti-HLA antibody specificity was determined using the single-antigen bead flow cytometry (SAFC) assay and assignation of donor specific antibodies (DSA) was performed by virtual-crossmatch.
We studied a cohort of 490 SOTR that received an influenza vaccination from 2009 to 2013: 110 (22.4%) received the pandemic adjuvanted vaccine, 59 (12%) within the first 6 months post-transplantation, 185 (37.7%) more than 6 months after transplantation and 136 (27.7%) received two vaccination doses. Overall, no differences of anti-HLA antibodies were found after immunization in patients that received the adjuvanted vaccine, within the first 6 months post-transplantation, or based on the type of organ transplanted. However, the second immunization dose increased the percentage of patients positive for anti-HLA class I significantly compared with patients with one dose (14.6% vs. 3.8%;
= 0.003). Patients with pre-existing antibodies before vaccination (15.7% for anti-HLA class I and 15.9% for class II) did not increase reactivity after immunization. A group of 75 (14.4%) patients developed
anti-HLA antibodies, however, only 5 (1.02%) of them were DSA, and none experienced allograft rejection. Only two (0.4%) patients were diagnosed with graft rejection with favorable outcomes and neither of them developed DSA.
Our results suggest that influenza vaccination is not associated with graft rejection in this cohort of SOTR.
The objective of this study is to determine the prevalence of cognitive impairment (CogI) in patients hospitalized for congestive heart failure, and the influence of CogI on mortality and hospital ...readmission. This is a multicenter cohort study of patients hospitalized for congestive heart failure enrolled in the RICA registry. The patients were divided into 3 groups according to their Short Portable Mental Status Questionnaire score: 0–3 errors (no CogI or mild CogI), 4–7 (moderate CogI) and 8–10 (severe CogI). A total of 3845 patients with a mean (SD) age of 79 (8.6) years were included; 2038 (53%) were women. A total of 550 (14%) patients had moderate CogI and 76 (2%) had severe CogI. Factors independently associated with severe CogI were age (OR 1.09, 95% CI 1.05–1.14
p
< 0.001), male sex (OR 0.57, 95% CI 0.34–0.95,
p
= 0.031), heart rate (OR 1.01, 95% CI 1.00–1.02,
p
= 0.004), Charlson index (OR 1.16, 95% CI 1.06–1.27,
p
= 0.002), and history of stroke (OR 2.67, 95% CI 1.60–4.44,
p
< 0.001). Severe CogI was associated with higher mortality after one year (HR 3.05, 95% CI 2.25–4.14,
p
< 0.001). The composite variable of death/hospital readmission was higher in patients with CogI (log rank
p
< 0.001). Patients with heart failure and severe CogI are older and have a higher comorbidity burden, lower survival, and a higher rate of death or readmission at 1 year, compared to patients with no CogI.
The biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of ...specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this paper was to assess stress using EEG-based variables obtained from univariate analysis and functional connectivity evaluation. Two different stressors, the Stroop test and sleep deprivation, were applied to 30 volunteers to find common EEG patterns related to stress effects. Results showed a decrease of the high alpha power (11 to 12 Hz), an increase in the high beta band (23 to 36 Hz, considered a busy brain indicator), and a decrease in the approximate entropy. Moreover, connectivity showed that the high beta coherence and the interhemispheric nonlinear couplings, measured by the cross mutual information function, increased significantly for both stressors, suggesting that useful stress indexes may be obtained from EEG-based features.
Background:
Psychedelics induce intense modifications in the sensorium, the sense of “self,” and the experience of reality. Despite advances in our understanding of the molecular and cellular level ...mechanisms of these drugs, knowledge of their actions on global brain dynamics is still incomplete. Recent imaging studies have found changes in functional coupling between frontal and parietal brain structures, suggesting a modification in information flow between brain regions during acute effects.
Methods:
Here we assessed the psychedelic-induced changes in directionality of information flow during the acute effects of a psychedelic in humans. We measured modifications in connectivity of brain oscillations using transfer entropy, a nonlinear measure of directed functional connectivity based on information theory. Ten healthy male volunteers with prior experience with psychedelics participated in 2 experimental sessions. They received a placebo or a dose of ayahuasca, a psychedelic preparation containing the serotonergic 5-HT2A agonist N,N-dimethyltryptamine.
Results:
The analysis showed significant changes in the coupling of brain oscillations between anterior and posterior recording sites. Transfer entropy analysis showed that frontal sources decreased their influence over central, parietal, and occipital sites. Conversely, sources in posterior locations increased their influence over signals measured at anterior locations. Exploratory correlations found that anterior-to-posterior transfer entropy decreases were correlated with the intensity of subjective effects, while the imbalance between anterior-to-posterior and posterior-to-anterior transfer entropy correlated with the degree of incapacitation experienced.
Conclusions:
These results suggest that psychedelics induce a temporary disruption of neural hierarchies by reducing top-down control and increasing bottom-up information transfer in the human brain.
Background
The study of cerebral underpinnings of schizophrenia may benefit from the high temporal resolution of electromagnetic techniques, but its spatial resolution is low. However, source imaging ...approaches such as low-resolution brain electromagnetic tomography (LORETA) allow for an acceptable compromise between spatial and temporal resolutions.
Methods
We combined LORETA with 32 channels and 3-Tesla diffusion magnetic resonance (Dmr) to study cerebral dysfunction in 38 schizophrenia patients (17 first episodes, FE), compared to 53 healthy controls. The EEG was acquired with subjects performing an odd-ball task. Analyses included an adaptive window of interest to take into account the interindividual variability of P300 latency. We compared source activation patters to distractor (P3a) and target (P3b) tones within- and between-groups.
Results
Patients showed a reduced activation in anterior cingulate and lateral and medial prefrontal cortices, as well as inferior/orbital frontal regions. This was also found in the FE patients alone. The activation was directly related to IQ in the patients and controls and to working memory performance in controls. Symptoms were unrelated to source activation. Fractional anisotropy in the tracts connecting lateral prefrontal and anterior cingulate regions predicted source activation in these regions in the patients.
Conclusions
These results replicate the source activation deficit found in a previous study with smaller sample size and a lower number of sensors and suggest an association between structural connectivity deficits and functional alterations.
Objective. We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different ...families of previously published detectors. Approach. This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments. Main results. We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM. Significance. The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.
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