Abstract
Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.

Abstract
Background and objective: Traumatic Brain Injury (TBI) is one of the leading causes of injury-related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences making more complex the medical interpretation and prognosis. Gathering clinical, demographic, and laboratory data to perform a prognosis requires time and skill in several clinical specialties. Artificial intelligence (AI) methods can take advantage of existing data by performing helpful predictions and guiding physicians toward a better prognosis and, consequently, better healthcare. The objective of this work was to develop learning models and evaluate their capability of predicting the mortality of TBI. The predictive model would allow the early assessment of the more serious cases and scarce medical resources can be pointed toward the patients who need them most. Methods: Long Short Term Memory (LSTM) and Transformer architectures were tested and compared in performance, coupled with data imbalance, missing data, and feature selection strategies. From the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, a cohort of TBI patients was selected and an analysis of the first 48 hours of multiple time series sequential variables was done to predict hospital mortality. Results: The best performance was obtained with the Transformer architecture, achieving an AUC of 0.907 with the larger group of features and trained with class proportion class weights and binary cross entropy loss. Conclusions: Using the time series sequential data, LSTM and Transformers proved to be both viable options for predicting TBI hospital mortality in 48 hours after admission. Overall, using sequential deep learning models with time series data to predict TBI mortality is viable and can be used as a helpful indicator of the well-being of patients.

Abstract
Emerging evidence of the relationship between the microbiome composition and the development of numerous diseases, including cancer, has led to an increasing interest in the study of the human microbiome. Technological breakthroughs regarding DNA sequencing methods propelled microbiome studies with a large number of samples, which called for the necessity of more sophisticated data-analytical tools to analyze this complex relationship. The aim of this work was to develop a machine learning-based approach to distinguish the type of cancer based on the analysis of the tissue-specific microbial information, assessing the human microbiome as valuable predictive information for cancer identification. For this purpose, Random Forest algorithms were trained for the classification of five types of cancer-head and neck, esophageal, stomach, colon, and rectum cancers-with samples provided by The Cancer Microbiome Atlas database. One versus all and multi-class classification studies were conducted to evaluate the discriminative capability of the microbial data across increasing levels of cancer site specificity, with results showing a progressive rise in difficulty for accurate sample classification. Random Forest models achieved promising performances when predicting head and neck, stomach, and colon cancer cases, with the latter returning accuracy scores above 90% across the different studies conducted. However, there was also an increased difficulty when discriminating esophageal and rectum cancers, failing to differentiate with adequate results rectum from colon cancer cases, and esophageal from head and neck and stomach cancers. These results point to the fact that anatomically adjacent cancers can be more complex to identify due to microbial similarities. Despite the limitations, microbiome data analysis using machine learning may advance novel strategies to improve cancer detection and prevention, and decrease disease burden.

Abstract
The clinical relevance of pulse wave velocity (PWV), as an indicator of cardiac risk associated to arterial stiffness, has gained clinical relevance over the last years. Optic sensors are an attractive instrumental solution for this type of measurement due to their truly non-contact operation capability, which has the potential of an interference free measurement. The nature of the optically originated signals, however, poses new challenges to the designer, either at the probe design level as at the signal processing required to extract the timing information that yields PWV. In this work we describe the construction of two prototype optical probes and discuss their evaluation using three algorithms for pulse transit time (PTT) evaluation. Results, obtained in a dedicated test bench, that is also described, demonstrate the possibility of measuring pulse transit times as short as 1ms with less than 1% error. © Springer-Verlag Berlin Heidelberg 2013.

Abstract
Sub-millimetre distension waveforms (0.7 mm, max) are assessed using two new optical probes. The probes differ on the type of photo-detector used: planar photodiodes (PPD), in one case, and avalanche photodiodes (APD), in the other. Performance of the probes is evaluated in an especially developed test setup and in vivo, at the carotid site of humans. In the latter case, distension (associated to the pressure wave generated by the left ventricle contraction that propagates through the arterial system) carries clinically relevant information that can be extracted if, as will be shown, the waveforms are accurate and have enough resolution. An ultrasound image system, Vivid" e, was used as source of reference data for comparison. Along with the probes, a set of software routines was also developed to extract artefact-free data and evaluate the error. Results from the test setup demonstrate the possibility of waveform distension measurements with less than 6% error for both optical probes in this study. In comparison with an ultrasound system, the optical sensors allow the reproduction of the arterial waveform with a higher resolution, adequate to feed feature extraction algorithms.

Abstract
The non-invasive assessment of hemodynamic parameters has been a permanent challenge posed to the scientific community. The literature shows many contributions to this quest expressed as algorithms dedicated to revealing some of its characteristics and as new probes or electronics, featuring some enhanced instrumental capability that can improve their insight. A test system capable of replicating some of the basic properties of the cardiovascular system, especially the ones related with the propagation of the arterial pressure wave (APW), is a powerful tool in the development of those probes and in the validation of the various algorithms that extract clinically relevant information from the data that they can collect. This work describes a test bench system, based on the combination of a new programmable pressure wave generator with a flexible tube, capable of emulating some of these properties. It discusses its main characterization issues and demonstrates the system in a relevant case study. Two versions of the system have been set up: one that generates a short duration pulse-like pressure wave from an actuator operated in a switched mode, appropriate to system characterization; a second one, using a long stroke actuator, linearly operated under program control, capable of generating complex, including cardiac-like, pressure waveforms. This configuration finds its main use in algorithm test and validation. Tests with a new piezoelectric probe, designed to collect the APW at the major artery sites are shown, demonstrating the possibility of non-invasive precise recovery of the pressure waveform.