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2%) had hypertension, 671 (24.7%) were obese, 53 (2.0%) had obstructive sleep apnoea, 110 (4.0%) had alcohol abuse and 340 (12.5%) were smokers.

In the BALKAN-AF cohort, the use of OAC for stroke prevention was poorly associated with patients stroke risk. The use of AADs in patients with permanent AF was low. The prescription of class IC AADs to patients with structural heart disease was infrequent. A large proportion of AF patients had their modifiable risk factors identified.
In the BALKAN-AF cohort, the use of OAC for stroke prevention was poorly associated with patients stroke risk. The use of AADs in patients with permanent AF was low. The prescription of class IC AADs to patients with structural heart disease was infrequent. A large proportion of AF patients had their modifiable risk factors identified.
Differentiation between exercise induced adaptive myocardial hypertrophy (athlete's heart) and hypertrophic cardiomyopathy (HCM) is currently based on echocardiographic and cardiac magnetic resonance (CMR) criteria, but these may be insufficient in patients with subtle phenotype expression. This study aimed to assess whether left ventricular (LV) fractal pattern could permit to differentiate athlete's heart from HCM.

We recruited retrospectively 61 elite marathon runners, 67 patients with HCM, and 33 healthy subjects. A CMR study was performed in all subjects and the LV trabeculae fractal dimension (FD) was measured in end-diastolic frames of each short-axis cine sequence. For group comparison, the ratio of maximal myocardial wall thickness (mMWT)/indexed LV end-diastolic volume (LVED) was determined.

As compared with athletes, patients with HCM had significantly (p<0.001) greater FD in the LV basal (1.30±0.07 vs. 1.23±0.05) and apical (1.38±0.06 vs. 1.30±0.07) regions and in the whole heart (1.34±0.05 vs. 1.27±0.05). FD increased with age, left atrial area and indexed left ventricular mass (p<0.05 for all) and correlated negatively with LV and RV end-diastolic volumes (p<0.05 each). The addition of whole heart FD to the ratio of maximal myocardial wall thickness/indexed LVEDV lead to an improvement in the ability to discriminate HCM with a net reclassification index (NRI) of 71%.

The FD regional distribution of the LV trabeculae differentiates patients with athlete's heart from patients with HCM. The addition of whole heart FD to the mMWT/indexed LVEDV ratio improves the predictive capacity of the model to differentiate both entities.
The FD regional distribution of the LV trabeculae differentiates patients with athlete's heart from patients with HCM. The addition of whole heart FD to the mMWT/indexed LVEDV ratio improves the predictive capacity of the model to differentiate both entities.
The newly emerged severe acute respiratory syndrome coronavirus (SARS-CoV-2) has caused a worldwide pandemic of human respiratory disease. Angiotensin-converting enzyme (ACE) 2 is the key receptor on lung epithelial cells to facilitate initial binding and infection of SARS-CoV-2. The binding to ACE2 is mediated via the spike glycoprotein present on the viral surface. Recent clinical data have demonstrated that patients with previous episodes of brain injuries are a high-risk group for SARS-CoV-2 infection. An explanation for this finding is currently lacking. Sterile tissue injuries including stroke induce the release of several inflammatory mediators that might modulate the expression levels of signaling proteins in distant organs. Whether systemic inflammation following brain injury can specifically modulate ACE2 expression in different vital tissues has not been investigated.

For the induction of brain stroke, mice were subjected to a surgical procedure for transient interruption of blood flow in the mS-CoV-2 in their lungs which might explain why stroke is a risk factor for higher susceptibility to develop COVID-19.Automatic interpretation biases (AIB) are theorized to be a risk factor for depression. However, documenting AIB in depressed persons has been challenging and the source (affective vs cognitive) of AIB remains unclear. We conducted a psychophysiological investigation of AIB in a sample of 25 clinical interview assessed individuals experiencing a current major depressive episode and 28 never-depressed control individuals. Participants completed the Word Sentence Association Paradigm for Depression while their pupil size was recorded. Repeated measures ANOVAs were used to examine behavioral response data and multilevel modeling was used to examine pupillary reactivity (change from trial baseline). Compared to controls, the depressed group was both more likely to endorse negative AIB (p = .001, d = 1.01) and less likely to endorse benign AIB (p = .011, d = 0.72). Further, the depressed group exhibited significantly increased pupil size while processing negative words when they endorsed a negative interpretation compared with controls (ps = .010-.037, ds = 0.69-0.87), but did not differ during other AIB trial types. SNX-5422 Within group comparisons revealed greater differentiation between interpretations in the healthy control group in both reaction time and pupillary reactivity AIB measures. This depression-related pupillary reactivity pattern fits with an emotional salience-based explanation better than a cognitive effort-based hypothesis of negative AIB, while pupillary reactivity pattern within the control group is consistent with a benign bias. People with depression lack benign AIB and may be more emotionally engaged during negative AIB than healthy controls.
As we vocalize, our brains generate predictions of the sounds we produce to enable suppression of neural responses when intentions match vocalizations and to make adjustments when they do not. This may be instantiated by efference copy and corollary discharge mechanisms, which are impaired in people with schizophrenia (SZ). Although innate, these mechanisms can be affected by intentions. We asked if attending to pitch during vocalizations would take these mechanisms "off-line" and reduce suppression.

Event-related potentials (ERP) were recorded from 96 SZ and 92 healthy controls (HC) as they vocalized triplets in monotone (Phrase) or sang triplets in ascending thirds (Pitch). Pre-vocalization activity (Bereitschaftspotential, BP), N1, and P2 ERP components to sounds were compared during vocalization and playback.

N1 was not as suppressed during Pitch as during Phrase. N1 suppression was not affected by SZ in either task when all data were collapsed across pitches (Pitch) and positions (Phrase). However, when binned according to vocalization performance, SZ showed less N1 suppression than HC at longer (>2s) inter-stimulus intervals (Phrase) and inconsistent suppression across pitches (Pitch).
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