Acetylation changes tau interactome to degrade tau in Alzheimer’s disease animal and organoid models

© 2019 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.Alzheimer's disease (AD) is an age-related neurodegenerative disease. The most common pathological hallmarks are amyloid plaques and neurofibrillary tangles in the brain. In the brains of patients with AD, pathological tau is abnormally accumulated causing neuronal loss, synaptic dysfunction, and cognitive decline. We found a histone deacetylase 6 (HDAC6) inhibitor, CKD-504, changed the tau interactome dramatically to degrade pathological tau not only in AD animal model (ADLPAPT) brains containing both amyloid plaques and neurofibrillary tangles but also in AD patient-derived brain organoids. Acetylated tau recruited chaperone proteins such as Hsp40, Hsp70, and Hsp110, and this complex bound to novel tau E3 ligases including UBE2O and RNF14. This complex degraded pathological tau through proteasomal pathway. We also identified the responsible acetylation sites on tau. These dramatic tau-interactome changes may result in tau degradation, leading to the recovery of synaptic pathology and cognitive decline in the ADLPAPT mice11Nsciescopu

Mediterranean winter rainfall in phase with African monsoons during the past 1.36 million years

Mediterranean climates are characterized by strong seasonal contrasts between dry summers and wet winters. Changes in winter rainfall are critical for regional socioeconomic development, but are difficult to simulate accurately1 and reconstruct on Quaternary timescales. This is partly because regional hydroclimate records that cover multiple glacial–interglacial cycles2,3 with different orbital geometries, global ice volume and atmospheric greenhouse gas concentrations are scarce. Moreover, the underlying mechanisms of change and their persistence remain unexplored. Here we show that, over the past 1.36 million years, wet winters in the northcentral Mediterranean tend to occur with high contrasts in local, seasonal insolation and a vigorous African summer monsoon. Our proxy time series from Lake Ohrid on the Balkan Peninsula, together with a 784,000-year transient climate model hindcast, suggest that increased sea surface temperatures amplify local cyclone development and refuel North Atlantic low-pressure systems that enter the Mediterranean during phases of low continental ice volume and high concentrations of atmospheric greenhouse gases. A comparison with modern reanalysis data shows that current drivers of the amount of rainfall in the Mediterranean share some similarities to those that drive the reconstructed increases in precipitation. Our data cover multiple insolation maxima and are therefore an important benchmark for testing climate model performance

A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

Abstract Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR

Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity

Abstract Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male) who underwent ECGs at our emergency department before severity classification. The AI-ECG algorithm was evaluated for severity assessment during admission, compared to the Early Warning Scores (EWSs) using the area under the curve (AUC) of the receiver operating characteristic curve, precision, recall, and F1 score. During the internal and external validation, the AI algorithm demonstrated reasonable outcomes in predicting COVID-19 severity with AUCs of 0.735 (95% CI: 0.662–0.807) and 0.734 (95% CI: 0.688–0.781). Combined with EWSs, it showed reliable performance with an AUC of 0.833 (95% CI: 0.830–0.835), precision of 0.764 (95% CI: 0.757–0.771), recall of 0.747 (95% CI: 0.741–0.753), and F1 score of 0.747 (95% CI: 0.741–0.753). In Cox proportional hazards models, the AI-ECG revealed a significantly higher hazard ratio (HR, 2.019; 95% CI: 1.156–3.525, p = 0.014) for mortality, even after adjusting for relevant parameters. Therefore, application of AI-ECG has the potential to assist in early COVID-19 severity prediction, leading to improved patient management

Empowering eaters to make climate-friendly choices : a public education initiative

There are few things that so clearly connect us to the environment as food, or illustrate how overtly dependant we are on the plant for our survival. Examining how our food is produced is at the core uniting many of the global issues relating to our misuse of land, energy and other resources – and with a modest amount of thought and effort about the food selections we make, we have the potential to make strides towards resolving climate-related problems each time we pick up a fork. Initially, it may seem like changing one’s eating habits is a strange way to approach preventing climate change, however, it is known that our food system is a significant contributor of greenhouse gas (GHG) emissions. Therefore, educating ourselves about climate-friendly food choices is an essential step towards becoming empowered eaters who are able to effect change. As a first attempt to increasing public awareness about the relationship that exists between food choices and climate change, our group was assigned the task of developing an educational campaign to be used at The UBC Farm. The main purpose of this initiative is to encourage patrons at the Saturday Farmer’s Market to adopt a more sustainable approach to food-shopping and eating by exposing them to one or more of three complementary marketing tools: 1. a “Carbon Smart Food Guide” containing general information and guiding principles on how to become a carbon smart consumer 2. an on-line publication aimed at providing those interested with more detailed information than that presented in the food guide 3. a carbon smart logo to be used as a part of a visual display at food stands so that shoppers are able to quickly and clearly identify carbon smart items It is our goal that, collectively, this campaign will help climate-concerned consumers be more successful at navigating the food system, thereby having a positive impact on our community, and our environment. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Land and Food Systems, Faculty ofUnreviewedUndergraduat