Notes

Emotional wellness psychosocial help in conflict: childrens security concerns and intervention results inside Syria.
Community adaptations to increase resilience and reduce vulnerability depend on adaptability, which is constituted by how power shapes collective mobilization of differential capacities across scales and levels of governance systems. A multidimensional power framework (MPF) to track the role of power in adaptability is presented and applied in a qualitative, comparative case study of two historical fishing communities in the United States. The MPF conceptualizes power as differential capacities at the individual and group levels, structural at the policy level, and systemic, reflective of generalized norms, strategies, and technologies of political economic imperatives. The first case, Two Rivers, North Carolina represents an example of collective action failure resulting in transformation to a new resilience regime consisting of fundamentally altered community functions, structures, and identity. The second case, Delcambre, Louisiana represents collective action success; community adaptations resulted in continuity in change for fisher livelihoods. Success here is defined as the ability to maintain a semblance of structure, function, or identity of the original resilience regime without connotations of positive or negative desirability. Selleck AG-120 The cases were similar in demographics, vulnerabilities, and differential capacities. Key differences existed in systemic political economic imperatives, structural power at the policy level, and the ways closure, the ability to enforce a common sense of place within the communities reflected systemic power. The utility of the MPF is mapping vulnerabilities and differential capacities against broader structures and systemic processes to inform effective mobilization for improved socio-ecological resilience and sustainability.Impaired coronary microvascular function (e.g., reduced dilation and coronary flow reserve) predicts cardiac mortality in obesity, yet underlying mechanisms and potential therapeutic strategies remain poorly understood. Mineralocorticoid receptor (MR) antagonism improves coronary microvascular function in obese humans and animals. Whether MR blockade improves in vivo regulation of coronary flow, a process involving voltage-dependent K+ (Kv) channel activation, or reduces coronary structural remodeling in obesity is unclear. Thus, the goals of this investigation were to determine the effects of obesity on coronary responsiveness to reductions in arterial PO2 and potential involvement of Kv channels and whether the benefit of MR blockade involves improved coronary Kv function or altered passive structural properties of the coronary microcirculation. Hypoxemia increased coronary blood flow similarly in lean and obese swine; however, baseline coronary vascular resistance was significantly higher in obese swine. Inhibition of Kv channels reduced coronary blood flow and augmented coronary resistance under baseline conditions in lean but not obese swine and had no impact on hypoxemic coronary vasodilation. Chronic MR inhibition in obese swine normalized baseline coronary resistance, did not influence hypoxemic coronary vasodilation, and did not restore coronary Kv function (assessed in vivo, ex vivo, and via patch clamping). Lastly, MR blockade prevented obesity-associated coronary arteriolar stiffening independent of cardiac capillary density and changes in cardiac function. These data indicate that chronic MR inhibition prevents increased coronary resistance in obesity independent of Kv channel function and is associated with mitigation of obesity-mediated coronary arteriolar stiffening.
To prevent cardiac collapse and to protect cerebral function, hypothermic cardiopulmonary bypass is established before resternotomy. However, ventricular fibrillation under hypothermia facilitates left ventricular distension, which causes irreversible myocardial damage when the patient has aortic regurgitation. We report a case of successful management in preventing ventricular fibrillation under hypothermia by using nifekalant.

A 56-year-old male, who had been performed a David operation, was scheduled for a Bentall operation for a pseudo aortic aneurysm with severe aortic regurgitation. After inducing anesthesia, we administered intravenous nifekalant and a vent tube was inserted into the left ventricle under one-lung ventilation. Extracorporeal circulation was established and resternotomy started after cooling to 27 °C. Although severe bradycardia and QT prolongation were observed, ventricular fibrillation did not occur until aortic cross-clamping.

Combining maintaining cerebral perfusion and avoiding left ventricle distension during hypothermia was successfully managed with nifekalant in our redo cardiac patient with aortic regurgitation.
Combining maintaining cerebral perfusion and avoiding left ventricle distension during hypothermia was successfully managed with nifekalant in our redo cardiac patient with aortic regurgitation.
We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML's accuracy with that of a breast radiologist, and verify if the radiologist's performance is improved by using ML.

Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar's test.

After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard devst • The radiologist's accuracy improved with machine learning, but not significantly.
Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning-based neuroanatomic biomarker to measure the changed feature of "brain age" in RE.

The study constructed a 3D-CNN brain age prediction model through 1155 cases of typically developing children's morphometric brain MRI from open-source datasets and further applied to a local dataset of 167 RE patients and 107 typically developing children. The brain-predicted age difference was measured to quantitatively estimate brain age changes in RE and further investigated the relevancies with cognitive and clinical variables.

The brain age estimation network model presented a good performance for brain age prediction in typically developing children. The children with RE showed a 0.45-year delay of brain age by contrast with typically developing children. Delayed brain age was associated with neuroanatomic changes in the Rolandic regions and also associated with cognitive dysfunction of attention.
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