One-year aerobic exercise increases cerebral blood flow in cognitively normal older adults.

The impact of aerobic exercise training (AET) on cerebral blood flow (CBF) regulation remains inconclusive. This study investigated the effects of one-year progressive, moderate-to-vigorous AET on CBF, central arterial stiffness, and cognitive performance in cognitively normal older adults. Seventy-three older adults were randomly assigned to AET or stretching-and-toning (SAT, active control) intervention. CBF was measured with 2D duplex ultrasonography. Central arterial stiffness, measured by carotid ß-stiffness index, was assessed with the ultrasonography and applanation tonometry. Cerebrovascular resistance (CVR) was calculated as mean arterial pressure divided by CBF. A cognitive battery was administered with a focus on memory and executive function. Cardiorespiratory fitness was measured by peak oxygen consumption (V˙O2peak). One-year AET increased V˙O2peak and CBF and decreased CVR and carotid ß-stiffness index. In the AET group, improved V˙O2peak was correlated with increased CBF (r = 0.621, p = 0.001) and decreased CVR (r = -0.412, p = 0.037) and carotid ß-stiffness index (r = -0.478, p = 0.011). Further, increased Woodcock-Johnson recall score was associated with decreased CVR (r = -0.483, p = 0.012) and carotid ß-stiffness index (r = -0.498, p = 0.008) in AET group (not in SAT group). In conclusion, one-year progressive, moderate-to-vigorous aerobic exercise training increased CBF and decreased carotid arterial stiffness and CVR which were associated with improved memory function in cognitively normal older adults.

Detecting COVID-19 from chest computed tomography scans using AI-driven android application.

The COVID-19 (coronavirus disease 2019) pandemic affected more than 186 million people with over 4 million deaths worldwide by June 2021. The magnitude of which has strained global healthcare systems. Chest Computed Tomography (CT) scans have a potential role in the diagnosis and prognostication of COVID-19. Designing a diagnostic system, which is cost-efficient and convenient to operate on resource-constrained devices like mobile phones would enhance the clinical usage of chest CT scans and provide swift, mobile, and accessible diagnostic capabilities. This work proposes developing a novel Android application that detects COVID-19 infection from chest CT scans using a highly efficient and accurate deep learning algorithm. It further creates an attention heatmap, augmented on the segmented lung parenchyma region in the chest CT scans which shows the regions of infection in the lungs through an algorithm developed as a part of this work, and verified through radiologists. We propose a novel selection approach combined with multi-threading for a faster generation of heatmaps on a Mobile Device, which reduces the processing time by about 93%. The neural network trained to detect COVID-19 in this work is tested with a F1 score and accuracy, both of 99.58% and sensitivity of 99.69%, which is better than most of the results in the domain of COVID diagnosis from CT scans. This work will be beneficial in high-volume practices and help doctors triage patients for the early diagnosis of COVID-19 quickly and efficiently.