Evidence for Shear Stress-Mediated Dilation of the Internal Carotid Artery in Humans.

Increases in arterial carbon dioxide tension (hypercapnia) elicit potent vasodilation of cerebral arterioles. Recent studies have also reported vasodilation of the internal carotid artery during hypercapnia, but the mechanism(s) mediating this extracranial vasoreactivity are unknown. Hypercapnia increases carotid shear stress, a known stimulus to vasodilation in other conduit arteries. To explore the hypothesis that shear stress contributes to hypercapnic internal carotid dilation in humans, temporal changes in internal and common carotid shear rate and diameter, along with changes in middle cerebral artery velocity, were simultaneously assessed in 18 subjects at rest and during hypercapnia (6% carbon dioxide). Middle cerebral artery velocity increased significantly (69±10-103±17 cm/s; P<0.01) along with shear in both the internal (316±52-518±105 1/s; P<0.01) and common (188±40-275±61 1/s; P<0.01) carotids. Diameter also increased (P<0.01) in both carotid arteries (internal: +6.3±2.9%; common: +5.8±3.0%). Following hypercapnia onset, there was a significant delay between the onset of internal carotid shear (22±12 seconds) and diameter change (85±51 seconds). This time course is associated with shear-mediated dilation of larger conduit arteries in humans. There was a strong association between change in shear and diameter of the internal carotid (r=0.68; P<0.01). These data indicate, for the first time in humans, that shear stress is an important stimulus for hypercapnic vasodilation of the internal carotid artery. The combination of a hypercapnic stimulus and continuous noninvasive, high-resolution assessment of internal carotid shear and dilation may provide novel insights into the function and health of the clinically important extracranial arteries in humans

Editorial Peer Reviewers' Recommendations at a General Medical Journal: Are They Reliable and Do Editors Care?

BACKGROUND: Editorial peer review is universally used but little studied. We examined the relationship between external reviewers' recommendations and the editorial outcome of manuscripts undergoing external peer-review at the Journal of General Internal Medicine (JGIM). METHODOLOGY/PRINCIPAL FINDINGS: We examined reviewer recommendations and editors' decisions at JGIM between 2004 and 2008. For manuscripts undergoing peer review, we calculated chance-corrected agreement among reviewers on recommendations to reject versus accept or revise. Using mixed effects logistic regression models, we estimated intra-class correlation coefficients (ICC) at the reviewer and manuscript level. Finally, we examined the probability of rejection in relation to reviewer agreement and disagreement. The 2264 manuscripts sent for external review during the study period received 5881 reviews provided by 2916 reviewers; 28% of reviews recommended rejection. Chance corrected agreement (kappa statistic) on rejection among reviewers was 0.11 (p<.01). In mixed effects models adjusting for study year and manuscript type, the reviewer-level ICC was 0.23 (95% confidence interval [CI], 0.19-0.29) and the manuscript-level ICC was 0.17 (95% CI, 0.12-0.22). The editors' overall rejection rate was 48%: 88% when all reviewers for a manuscript agreed on rejection (7% of manuscripts) and 20% when all reviewers agreed that the manuscript should not be rejected (48% of manuscripts) (p<0.01). CONCLUSIONS/SIGNIFICANCE: Reviewers at JGIM agreed on recommendations to reject vs. accept/revise at levels barely beyond chance, yet editors placed considerable weight on reviewers' recommendations. Efforts are needed to improve the reliability of the peer-review process while helping editors understand the limitations of reviewers' recommendations

Optimized EPI for fMRI studies of the orbitofrontal cortex: compensation of susceptibility-induced gradients in the readout direction

Object Most functional magnetic resonance imaging (fMRI) studies record the blood oxygen leveldependent (BOLD) signal using gradient-echo echo-planar imaging (GE EPI). EPI can suffer from substantial BOLD sensitivity loss caused by magnetic field inhomogeneities. Here, BOLD sensitivity losses due to susceptibility- induced gradients in the readout (RO) direction are characterized and a compensation approach is developed

Hemodynamic Traveling Waves in Human Visual Cortex

Functional MRI (fMRI) experiments rely on precise characterization of the blood oxygen level dependent (BOLD) signal. As the spatial resolution of fMRI reaches the sub-millimeter range, the need for quantitative modelling of spatiotemporal properties of this hemodynamic signal has become pressing. Here, we find that a detailed physiologically-based model of spatiotemporal BOLD responses predicts traveling waves with velocities and spatial ranges in empirically observable ranges. Two measurable parameters, related to physiology, characterize these waves: wave velocity and damping rate. To test these predictions, high-resolution fMRI data are acquired from subjects viewing discrete visual stimuli. Predictions and experiment show strong agreement, in particular confirming BOLD waves propagating for at least 5–10 mm across the cortical surface at speeds of 2–12 mm s-1. These observations enable fundamentally new approaches to fMRI analysis, crucial for fMRI data acquired at high spatial resolution

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies

What Does Brain Response to Neutral Faces Tell Us about Major Depression? Evidence from Machine Learning and fMRI

Introduction: A considerable number of previous studies have shown abnormalities in the processing of emotional faces in major depression. Fewer studies, however, have focused specifically on abnormal processing of neutral faces despite evidence that depressed patients are slow and less accurate at recognizing neutral expressions in comparison with healthy controls. The current study aimed to investigate whether this misclassification described behaviourally for neutral faces also occurred when classifying patterns of brain activation to neutral faces for these patients. Methods: Two independent depressed samples: (1) Nineteen medication-free patients with depression and 19 healthy volunteers and (2) Eighteen depressed individuals and 18 age and gender-ratio-matched healthy volunteers viewed emotional faces (sad/neutral; happy/neutral) during an fMRI experiment. We used a new pattern recognition framework: first, we trained the classifier to discriminate between two brain states (e.g. viewing happy faces vs. viewing neutral faces) using data only from healthy controls (HC). Second, we tested the classifier using patterns of brain activation of a patient and a healthy control for the same stimuli. Finally, we tested if the classifier's predictions (predictive probabilities) for emotional and neutral face classification were different for healthy controls and depressed patients. Results: Predictive probabilities to patterns of brain activation to neutral faces in both groups of patients were significantly lower in comparison to the healthy controls. This difference was specific to neutral faces. There were no significant differences in predictive probabilities to patterns of brain activation to sad faces (sample 1) and happy faces (samples 2) between depressed patients and healthy controls. Conclusions: Our results suggest that the pattern of brain activation to neutral faces in depressed patients is not consistent with the pattern observed in healthy controls subject to the same stimuli. This difference in brain activation might underlie the behavioural misinterpretation of the neutral faces content by the depressed patients. © 2013 Oliveira et al

Impact of diabetes on the effects of sodium glucose co-transporter-2 inhibitors on kidney outcomes: collaborative meta-analysis of large placebo-controlled trials

Background: Large trials have shown that sodium glucose co-transporter-2 (SGLT2) inhibitors reduce the risk of adverse kidney and cardiovascular outcomes in patients with heart failure or chronic kidney disease, or with type 2 diabetes and high risk of atherosclerotic cardiovascular disease. None of the trials recruiting patients with and without diabetes were designed to assess outcomes separately in patients without diabetes. Methods: We did a systematic review and meta-analysis of SGLT2 inhibitor trials. We searched the MEDLINE and Embase databases for trials published from database inception to Sept 5, 2022. SGLT2 inhibitor trials that were double-blind, placebo-controlled, performed in adults (age ≥18 years), large (≥500 participants per group), and at least 6 months in duration were included. Summary-level data used for analysis were extracted from published reports or provided by trial investigators, and inverse-variance-weighted meta-analyses were conducted to estimate treatment effects. The main efficacy outcomes were kidney disease progression (standardised to a definition of a sustained ≥50% decrease in estimated glomerular filtration rate [eGFR] from randomisation, a sustained low eGFR, end-stage kidney disease, or death from kidney failure), acute kidney injury, and a composite of cardiovascular death or hospitalisation for heart failure. Other outcomes were death from cardiovascular and non-cardiovascular disease considered separately, and the main safety outcomes were ketoacidosis and lower limb amputation. This study is registered with PROSPERO, CRD42022351618. Findings: We identified 13 trials involving 90 413 participants. After exclusion of four participants with uncertain diabetes status, we analysed 90 409 participants (74 804 [82·7%] participants with diabetes [>99% with type 2 diabetes] and 15 605 [17·3%] without diabetes; trial-level mean baseline eGFR range 37–85 mL/min per 1·73 m2). Compared with placebo, allocation to an SGLT2 inhibitor reduced the risk of kidney disease progression by 37% (relative risk [RR] 0·63, 95% CI 0·58–0·69) with similar RRs in patients with and without diabetes. In the four chronic kidney disease trials, RRs were similar irrespective of primary kidney diagnosis. SGLT2 inhibitors reduced the risk of acute kidney injury by 23% (0·77, 0·70–0·84) and the risk of cardiovascular death or hospitalisation for heart failure by 23% (0·77, 0·74–0·81), again with similar effects in those with and without diabetes. SGLT2 inhibitors also reduced the risk of cardiovascular death (0·86, 0·81–0·92) but did not significantly reduce the risk of non-cardiovascular death (0·94, 0·88–1·02). For these mortality outcomes, RRs were similar in patients with and without diabetes. For all outcomes, results were broadly similar irrespective of trial mean baseline eGFR. Based on estimates of absolute effects, the absolute benefits of SGLT2 inhibition outweighed any serious hazards of ketoacidosis or amputation. Interpretation: In addition to the established cardiovascular benefits of SGLT2 inhibitors, the randomised data support their use for modifying risk of kidney disease progression and acute kidney injury, not only in patients with type 2 diabetes at high cardiovascular risk, but also in patients with chronic kidney disease or heart failure irrespective of diabetes status, primary kidney disease, or kidney function. Funding: UK Medical Research Council and Kidney Research UK

Envelope Deglycosylation Enhances Antigenicity of HIV-1 gp41 Epitopes for Both Broad Neutralizing Antibodies and Their Unmutated Ancestor Antibodies

The HIV-1 gp41 envelope (Env) membrane proximal external region (MPER) is an important vaccine target that in rare subjects can elicit neutralizing antibodies. One mechanism proposed for rarity of MPER neutralizing antibody generation is lack of reverted unmutated ancestor (putative naive B cell receptor) antibody reactivity with HIV-1 envelope. We have studied the effect of partial deglycosylation under non-denaturing (native) conditions on gp140 Env antigenicity for MPER neutralizing antibodies and their reverted unmutated ancestor antibodies. We found that native deglycosylation of clade B JRFL gp140 as well as group M consensus gp140 Env CON-S selectively increased the reactivity of Env with the broad neutralizing human mAbs, 2F5 and 4E10. Whereas fully glycosylated gp140 Env either did not bind (JRFL), or weakly bound (CON-S), 2F5 and 4E10 reverted unmutated ancestors, natively deglycosylated JRFL and CON-S gp140 Envs did bind well to these putative mimics of naive B cell receptors. These data predict that partially deglycoslated Env would bind better than fully glycosylated Env to gp41-specific naïve B cells with improved immunogenicity. In this regard, immunization of rhesus macaques demonstrated enhanced immunogenicity of the 2F5 MPER epitope on deglyosylated JRFL gp140 compared to glycosylated JRFL gp140. Thus, the lack of 2F5 and 4E10 reverted unmutated ancestor binding to gp140 Env may not always be due to lack of unmutated ancestor antibody reactivity with gp41 peptide epitopes, but rather, may be due to glycan interference of binding of unmutated ancestor antibodies of broad neutralizing mAb to Env gp41

Mate-Finding as an Overlooked Critical Determinant of Dispersal Variation in Sexually-Reproducing Animals

Dispersal is a critically important process in ecology, but robust predictive models of animal dispersal remain elusive. We identify a potentially ubiquitous component of variation in animal dispersal that has been largely overlooked until now: the influence of mate encounters on settlement probability. We use an individual-based model to simulate dispersal in sexually-reproducing organisms that follow a simple set of movement rules based on conspecific encounters, within an environment lacking spatial habitat heterogeneity. We show that dispersal distances vary dramatically with fluctuations in population density in such a model, even in the absence of variation in dispersive traits between individuals. In a simple random-walk model with promiscuous mating, dispersal distributions become increasingly ‘fat-tailed’ at low population densities due to the increasing scarcity of mates. Similar variation arises in models incorporating territoriality. In a model with polygynous mating, we show that patterns of sex-biased dispersal can even be reversed across a gradient of population density, despite underlying dispersal mechanisms remaining unchanged. We show that some widespread dispersal patterns found in nature (e.g. fat tailed distributions) can arise as a result of demographic variability in the absence of heterogeneity in dispersive traits across the population. This implies that models in which individual dispersal distances are considered to be fixed traits might be unrealistic, as dispersal distances vary widely under a single dispersal mechanism when settlement is influenced by mate encounters. Mechanistic models offer a promising means of advancing our understanding of dispersal in sexually-reproducing organisms