The adaptive response of humans to exercise: Impact of exercise intensity, fibre-type specific responses and molecular patterns of response

In an attempt to improve the current understanding of the adaptive response to exercise in humans, this dissertation performed a series of studies designed to examine the impact of training intensity and mode on aerobic capacity and performance, fibre-type specific adaptations to training, and individual patterns of response across molecular, morphological and genetic factors. Project #1 determined that training intensity, session dose, baseline VO2max and total training volume do not influence the magnitude of change in VO2max by performing a meta-regression, and meta-analysis of 28 different studies. The intensity of training had no effect on the magnitude of increase in maximal oxygen uptake in young healthy participants, but similar adaptations were achieved with lower training doses following high intensity training. Project # 2 determined the acute molecular response, and training-induced adaptations in aerobic performance, aerobic capacity and muscle phenotype following high-intensity interval training (HIT) or endurance exercise (END). The acute molecular response (fibre recruitment and signal activation) and training-induced adaptations in aerobic capacity, aerobic performance, and muscle phenotype were similar following HIT and END. Project # 3 examined the impact of baseline muscle morphology and molecular characteristics on the training response, and if muscle adaptations are coordinated. The muscle phenotype of individuals who experience the largest improvements (high responders) were lower before training for some muscle characteristics and molecular adaptations were coordinated within individual participants. Project # 4 examined the impact of 2 different intensities of HIT on the expression of nuclear and mitochondrial encoded genes targeted by PGC-1α. A systematic upregulation of nuclear and mitochondrial encoded genes was not present in the early recovery period following acute HIT, but the expression of mitochondrial genes were coordinated at an individual level. Collectively, results from the current dissertation contribute to our understanding of the molecular mechanisms influencing skeletal muscle and whole-body adaptive responses to acute exercise and training in humans.

Detection and Grading of Prostate Cancer in Temporal Enhanced Ultrasound using Convolutional Neural Networks

Prostate cancer is the most common non-dermatological cancer amongst men in the developed world. The current definitive diagnosis is core needle biopsy guided by transrectal ultrasound. However, this method suffers from low sensitivity and specificity in detecting cancer. Recently, a new ultrasound based tissue typing approach has been proposed, known as temporal enhanced ultrasound (TeUS). In this approach, a set of temporal ultrasound frames is collected from a stationary tissue location without any intentional mechanical excitation. The main aim of this thesis is to implement a deep learning-based solution for prostate cancer detection and grading using TeUS data. In the proposed solution, convolutional neural networks are trained to extract high-level features from time domain TeUS data in temporally and spatially adjacent frames in nine in vivo prostatectomy cases. This approach avoids information loss due to feature extraction and also improves cancer detection rate. The output likelihoods of two TeUS arrangements are then combined to form our novel decision support system. This deep learning-based approach results in the area under the receiver operating characteristic curve (AUC) of 0.80 and 0.73 for prostate cancer detection and grading, respectively, in leave-one-patient-out cross-validation. Recently, multi-parametric magnetic resonance imaging (mp-MRI) has been utilized to improve detection rate of aggressive prostate cancer. In this thesis, for the first time, we present the fusion of mp-MRI and TeUS for characterization of prostate cancer to compensates the deficiencies of each image modalities and improve cancer detection rate. The results obtained using TeUS are fused with those attained using consolidated mp-MRI maps from multiple MR modalities and cancer delineations on those by multiple clinicians. The proposed fusion approach yields the AUC of 0.86 in prostate cancer detection. The outcomes of this thesis emphasize the viable potential of TeUS as a tissue typing method. Employing this ultrasound-based intervention, which is non-invasive and inexpensive, can be a valuable and practical addition to enhance the current prostate cancer detection.