Cardiorespiratory Fitness Is an Indicator of Arterial Stiffness and Aortic Blood Pressure in Healthy Adolescents.

We aimed to investigate the influence of cardiorespiratory fitness (CRF) and body composition on arterial stiffness. Carotid-to-femoral pulse wave velocity (cfPWV) and aortic systolic (ASBP) and diastolic (ADBP) blood pressure were compared between upper and lower tertiles of oxygen consumption at the aerobic threshold (VO), peak oxygen consumption (VO), percentage of fat mass (FM%), and body mass index (BMI) in sixty adolescents (30 males and 30 females, 14.9 ± 2.

Optic Nerve Microvascular Decompression for Carotid Dolichoectasia.

Vascular compression of the optic nerve in a patient with rapid monocular vision loss with otherwise negative diagnostic workup is a rare, but controversial dilemma. The literature is conflicted, advocating for either timely surgical decompression to preserve vision or observation only given the prevalence of asymptomatic vascular compression and observed arrest of visual decline. The most frequently reported sources of symptomatic compression are unruptured aneurysms and dolichoectatic vasculature, with recent consensus reached over a need for extensive perioperative ophthalmologic evaluations and follow-up.

Far-red and sensitive sensor for monitoring real time HO dynamics with subcellular resolution and in multi-parametric imaging applications.

HO is a key oxidant in mammalian biology and a pleiotropic signaling molecule at the physiological level, and its excessive accumulation in conjunction with decreased cellular reduction capacity is often found to be a common pathological marker. Here, we present a red fluorescent Genetically Encoded HO Indicator (GEHI) allowing versatile optogenetic dissection of redox biology. Our new GEHI, oROS-HT, is a chemigenetic sensor utilizing a HaloTag and Janelia Fluor (JF) rhodamine dye as fluorescent reporters.

Machine learning-guided engineering of genetically encoded fluorescent calcium indicators.

Here we used machine learning to engineer genetically encoded fluorescent indicators, protein-based sensors critical for real-time monitoring of biological activity. We used machine learning to predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions. Using the GCaMP calcium indicator as a scaffold, we developed an ensemble of three regression models trained on experimentally derived GCaMP mutation libraries.