PROJECT PUBLICATIONS
Abstract
We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-scale, heterogeneous, graph-structured data are envisioned, harnessing recent evidence for relativistic quantum neural coding of (co-)behavioral states in the self-organizing brain, under competitive, multidimensional dynamics. The designs draw on a self-dual classical description – via special Clifford-Lipschitz operations – of spinorial quantum states within registers of at most 16 qubits for efficient encoding of exponentially large neural structures. Formally ‘trained’, Lorentz neural architectures with precisely one lateral layer of exclusively inhibitory interneurons accounting for anti-modalities, as well as their co-architectures with intra-layer connections are highlighted. The approach accommodates the fusion of up to 16 timeinvariant interconnected (anti-)modalities and the crystallization of latent multidimensional patterns. Comprehensive insights are expected to be gained through applications to Multimodal Big Data, under diverse real-world scenarios.
Abstract
Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.
Abstract
Background and Objective: Only about 14% of eligible EU citizens finally participate in colorectal cancer (CRC) screening programs despite it being the third most common type of cancer worldwide. The development of CRC risk models can enable predictions to be embedded in decision-support tools facilitating CRC screening and treatment recommendations. This paper develops a predictive model that aids in characterizing CRC risk groups and assessing the influence of a variety of risk factors on the population.
Methods: A CRC Bayesian Network is learnt by aggregating extensive expert knowledge and data from a observational study and making use of structure learning algorithms to model the relations between variables. The network is then parametrised to characterize these relations in terms of local probability distributions at each of the nodes. It is finally used to predict the risks of developing CRC together with the uncertainty around such predictions.
Results: A graphical CRC risk mapping tool is developed from the model and used to segment the population into risk subgroups according to variables of interest. Furthermore, the network provides insights on the predictive influence of modifiable risk factors such as alcohol consumption and smoking, and medical conditions such as diabetes or hypertension linked to lifestyles that potentially have an impact on an increased risk of developing CRC.
Conclusion: CRC is most commonly developed in older individuals. However, some modifiable behavioral factors seem to have a strong predictive influence on its potential risk of development. Modeling these effects facilitates identifying risk groups and targeting influential variables which are subsequently helpful in the design of screening and treatment programs.
Abstract
Integrating biological material within soft microfluidic systems made of hydrogels offers countless possibilities in biomedical research to overcome the intrinsic limitations of traditional microfluidics based on solid, non-biodegradable, and non biocompatible materials. Hydrogel-based microfluidic technologies have the potential to transform in vitro cell/tissue culture and modeling. However, most hydrogel-based microfluidic platforms are associated with device deformation, poor structural definition, reduced stability/reproducibility due to swelling, and a limited range in rigidity, which threatens their applicability. Herein, we describe a new methodological approach for developing a soft cell-laden microfluidic device based on enzymatically-crosslinked silk fibroin (SF) hydrogels. Its unique mechano chemical properties and high structural fidelity, make this platform especially suited for in vitro disease modelling, as demonstrated by reproducing the native dynamic 3D microenvironment of colorectal cancer and its response to chemotherapeutics in a simplistic way. Results show that from all the tested concentrations, 14 wt% enzymatically-crosslinked SF microfluidic platform has outstanding structural stability and the ability to perfuse fluid while displaying in vivo-like biological responses. Overall, this work shows a novel technique to obtain an enzymatically-crosslinked SF microfluidic platform that can be employed for developing soft lab-on-a-chip in vitro models.