Optimizing Transesophageal Atrial Pacing in Mice to Detect Atrial Fibrillation

Mice are routinely used to investigate molecular mechanisms underlying the atrial fibrillation (AF) substrate. We sought to optimize transesophageal rapid atrial pacing (RAP) protocols for the detection of AF susceptibility in mouse models. Hypertensive and control C57Bl/6J mice were subjected to burst RAP at a fixed stimulus amplitude. The role of parasympathetic involvement in pacing-related atrioventricular (AV) block and AF was examined using an intraperitoneal injection of atropine. In a crossover study, burst and decremental RAP at twice diastolic threshold were compared for induction of AV block during pacing. The efficacy of burst and decremental RAP to elicit an AF phenotype was subsequently investigated in mice deficient in the lymphocyte adaptor protein (Lnk-/-) resulting in systemic inflammation, or the paired-like homeodomain 2 transcription factor (Pitx2+/-) as a positive control. When pacing at a fixed stimulus intensity, pacing-induced AV block with AF induction occurred frequently, so that there was no difference in AF burden between hypertensive and control mice. These effects were prevented by atropine administration, implicating parasympathetic activation due to ganglionic stimulation as the etiology. When mice with AV block during pacing were eliminated from analysis, male Lnk-/- mice displayed an AF phenotype only during burst RAP compared to controls whereas male Pitx2+/- mice showed AF susceptibility during burst and decremental RAP. Notably, Lnk-/- and Pitx2+/- females exhibited no AF phenotype. Our data support the conclusion that multiple parameters should be used to ascertain AF inducibility and facilitate reproducibility across models and studies.

The coronavirus response: Boxed in by models

Science has a mixed record when it comes to predicting the future. Engineers build bridges based on foreknowledge of the forces that they are likely to encounter – and their constructions tend to withstand the test of time. Predicting the future course of epidemics and building intervention to contain them are much more precarious. And yet simulation models produced in prestigious centres for mathematical biology have played a significant role informing coronavirus policy in the United Kingdom and elsewhere. The predictive uncertainties include the inherent variability of the pathogen, considerable variation in host population immunity as well as the concern of this article, namely, the constantly adapting human judgements of those designing, implementing and experiencing the national response to an outbreak. Assumptions about how interventions are implemented and how people will react are, of course, built into modelling scenarios – but these estimates depict behavioural change in fixed, stimulus-response terms. Real reactions to the complex restrictions introduced to combat the virus unfold in scores of different pathways – people comply, they resist, they learn, they grow weary, they change their minds, they seek exceptions and so on. Model building is intrinsically speculative, and it is important that crisis management is not boxed in by its latent simplifications. A more pluralistic evidence base needs to be drawn on, to understand how complex interventions operate within complex societies.

Assessing the accumulated stickiness magnitude from fabric–skin friction: effect of wetness level of various fabrics

Increasing skin wetness tends to increase fabric–skin adhesion and friction, resulting in wear discomfort or skin injuries. Here, the magnitude estimation approach was used to assess the stickiness sensation perceived in fabrics. Seven fabric types were wetted by putting onto wet ‘skin’ surface and dried for different durations to achieve different wetness levels, simulating wearing conditions during the recovery period after sweating. Results showed that the relationship between magnitude estimates of stickiness and amount of water present in fabric demonstrated a power function. The exponents and constant from power regression show the growth rate of stickiness sensation with moisture intensity and the perceived stickiness under fixed stimulus intensity, respectively. A novel parameter, accumulated stickiness magnitude (ASM), describing how much discomfort a wetted fabric offered throughout the drying period, was developed. Thin cotton fabrics (fabric W01 and W03), having higher saturation level after contacting with wetted skin surface, arouse stronger stickiness feeling and their ASM is remarkably higher. The difference in stickiness estimates is due to the difference in chemical composition and surface geometry. This study suggests us the way to predict perceived stickiness in fabrics with different wetness levels which is useful for applications like sportswear, intimate apparel or healthcare products.