Late onset spinal cord ischemia after thoracoabdominal aortic aneurysm open repair

The aim of this study is to evaluate if spinal cord ischemia (SCI), especially its late presentation, and can be correlated to the results of intraoperative evoked potential monitoring (IOM). Methods. This study is a physician-initiated, retrospective, single-center, non-randomized study. Data from all patients undergoing a thoracoabdominal aortic aneurysm surgical repair (TAAA SR) between January 2016 and March 2020 IOM was collected and analyzed. Results. During the study period, 261 patients underwent TAAA SR with MEP/SSEPs monitoring [190 males, 73%; median age 65 (57-71)]. Thirty-seven patients suffered from SCI, for an overall rate of 14% (permanent 9%). When stratifying patients according to the SCI onset, 18 patients presented with an early (11 permanent) and 19 with a late SCI (<24h) (11 permanent). Of 261 patients undergoing TAAA SR with IOM, 15 were excluded due to changes in the upper extremity motor evoked potentials. For the remaining 246, the association between SCI and IOM was investigated: only irreversible IOM loss without peripheral changes have been found to be a risk factor for late onset SCI (p=.006). Furthermore, given that no statistical differences were found between the two groups when no IOM changes were recorded (p=.679), this situation cannot reliably rule out any SCI in our cohort. Independent risk factors for late spinal cord ischemia onset found at multivariate analysis were smoking history (p=.008), BMI>28 (p=.048) and TAAA extent II (p=.009). The irreversible MEP change without peripheral showed a trend of significance (p=.052). Conclusions. Evoked potential intraoperative monitoring is an important adjunct during thoracoabdominal aortic open repair to predict and possibly prevent spinal cord ischemia. Irreversible IOM loss without peripheral changes was predictive of late SCI, therefore more attention should be paid to the postoperative management of this subgroup of patients.

Oscillatory mechanisms of conscious perception and attention

Although the prominent role of neural oscillations in perception and cognition has been continuously investigated, some critical questions remain unanswered. My PhD thesis was aimed at addressing some of them. First, can we dissociate oscillatory underpinnings of perceptual accuracy and subjective awareness? Current work would strongly suggest that this dissociation can be drawn. While the fluctuations in alpha-amplitude decide perceptual bias and metacognitive abilities, the speed of alpha activity (i.e., alpha-frequency) dictates sensory sampling, shaping perceptual accuracy. Second, how are these oscillatory mechanisms integrated during attention? The obtained results indicate that a top-down visuospatial mechanism modulates neural assemblies in visual areas via oscillatory re-alignment and coherence in the alpha/beta range within the fronto-parietal brain network. These perceptual predictions are reflected in the retinotopically distributed posterior alpha-amplitude, while perceptual accuracy is explained by the higher alpha-frequency at the to-be-attended location. Finally, sensory input, elaborated via fast gamma oscillations, is linked to specific phases of this slower activity via oscillatory nesting, enabling integration of the feedback-modulated oscillatory activity with sensory information. Third, how can we relate this oscillatory activity to other neural markers of behaviour (i.e., event-related potentials)? The obtained results favour the oscillatory model of ERP genesis, where alpha-frequency shapes the latency of early evoked-potentials, namely P1, with both neural indices being related to perceptual accuracy. On the other hand, alpha-amplitude dictates the amplitude of later P3 evoked-response, whereas both indices shape subjective awareness. Crucially, by combining different methodological approaches, including neurostimulation (TMS) and neuroimaging (EEG), current work identified these oscillatory-behavior links as causal and not just as co-occurring events. Current work aimed at ameliorating the use of the TMS-EEG approach by explaining inter-individual differences in the stimulation outcomes, which could be proven crucial in the way we design entrainment experiments and interpret the results in both research and clinical settings.

Computational modelling of human stem cell-derived cardiomyocytes - Texture descriptors for biological image processing

This thesis investigates two distinct research topics. The main topic (Part I) is the computational modelling of cardiomyocytes derived from human stem cells, both embryonic (hESC-CM) and induced-pluripotent (hiPSC-CM). The aim of this research line lies in developing models of the electrophysiology of hESC-CM and hiPSC-CM in order to integrate the available experimental data and getting in-silico models to be used for studying/making new hypotheses/planning experiments on aspects not fully understood yet, such as the maturation process, the functionality of the Ca2+ hangling or why the hESC-CM/hiPSC-CM action potentials (APs) show some differences with respect to APs from adult cardiomyocytes. Chapter I.1 introduces the main concepts about hESC-CMs/hiPSC-CMs, the cardiac AP, and computational modelling. Chapter I.2 presents the hESC-CM AP model, able to simulate the maturation process through two developmental stages, Early and Late, based on experimental and literature data. Chapter I.3 describes the hiPSC-CM AP model, able to simulate the ventricular-like and atrial-like phenotypes. This model was used to assess which currents are responsible for the differences between the ventricular-like AP and the adult ventricular AP. The secondary topic (Part II) consists in the study of texture descriptors for biological image processing. Chapter II.1 provides an overview on important texture descriptors such as Local Binary Pattern or Local Phase Quantization. Moreover the non-binary coding and the multi-threshold approach are here introduced. Chapter II.2 shows that the non-binary coding and the multi-threshold approach improve the classification performance of cellular/sub-cellular part images, taken from six datasets. Chapter II.3 describes the case study of the classification of indirect immunofluorescence images of HEp2 cells, used for the antinuclear antibody clinical test. Finally the general conclusions are reported.