Deep and cognitive learning applied to Precision Medicine: the initial experiments linking (epi)genome to phenotypes-disease characteristics

Present-day era of Big Data provides the unique opportunity to develop innovative approaches for data analysis to find new insights into specialized fields of biomedical research such as Precision Medicine [1]. Precision Medicine is defined as the integration of molecular research with clinical data in order to deliver better diagnoses and treatments tailored to the individual characteristics of each patient. Advanced analysis of health related data that is specific to a given individual must focus on both clinical information (e.g. clinical reports, medical images, patient histories) and biological data (e.g. gene and protein sequences, functions and pathways). This wealth of information has the potential to inspire systematic ways of making sense from the massive and heterogeneous stream of data and providing a unified view. In the regards, Deep Learning (DL) [2] and Cognitive Computing (CC) [3] are two branches of Artificial Intelligence (AI) representing convenient choices to tackle the problem of Big Data integration for Precision Medicine. DL comprises several machine learning techniques modeling multiple representations of data through many layers of nonlinear processing units. CC is a cross-disciplinary technology for adaptive and contextual knowledge representation and reasoning through sophisticated analytics aiming to mimic human learning mechanisms

A Scalable High-Yielding and Selective Oxidative Heck Cross-Coupling – A Key Step for the Synthesis of trans- Stilbenes

A selective oxidative Heck cross-coupling method was developed and optimized as a pivotal step for a synthetic route leading to the trans-stilbene framework. The developed method and synthesis were needed in a SAR study in progress that concerned design and development of an inhibitor for the human cell xCT antiporter system. The developed oxidative Heck cross-coupling method was examined with a variety of substrates and reagents to produce a library of different substituted trans-stilbenes, which revealed the method to hold a very good tolerance for an assortment of functional groups. The final synthetic route was successfully scaled-up (from mg scale) and performed in a 150 g (>1000×up-scaled) batch run to obtain an overall yield of 73 % (over three steps), which corresponds to a mean step yield of 90 %. The inhibitor candidate DC10 [(E)-5-(2-([1,1′-biphenyl]-4-yl)vinyl)-2-hydroxy-benzoic acid] was produced in multi-gram quantities (≈33 g) that subsequently was forwarded for animal efficacy and toxicology studies. The scaled-up process constitutes the first example of an oxidative Heck cross-coupling on >100-gram scale.publishedVersio

Fick and Fokker--Planck diffusion law in inhomogeneous media

We discuss diffusion of particles in a spatially inhomogeneous medium. From the microscopic viewpoint we consider independent particles randomly evolving on a lattice. We show that the reversibility condition has a discrete geometric interpretation in terms of weights associated to un--oriented edges and vertices. We consider the hydrodynamic diffusive scaling that gives, as a macroscopic evolution equation, the Fokker--Planck equation corresponding to the evolution of the probability distribution of a reversible spatially inhomogeneous diffusion process. The geometric macroscopic counterpart of reversibility is encoded into a tensor metrics and a positive function. The Fick's law with inhomogeneous diffusion matrix is obtained in the case when the spatial inhomogeneity is associated exclusively with the edge weights. We discuss also some related properties of the systems like a non-homogeneous Einstein relation and the possibility of uphill diffusion

Experimental Investigation on Fluid Dynamic and Thermal Behavior in Confined Impinging Round Jets in Aluminum Foam

In this paper an experimental investigation is carried out on impinging jets in porous media with the wall heated from below with a uniform heat flux. The fluid is air. The experimental apparatus is made up of a fun systems, a test section, a tube, to reduce the section in a circular section. The tube is long 1.0 m and diameter of 0.012 m. The test section has a diameter of 0.10 m and it has the thickness of 10, 20 and 40 mm. In the test section the lower plate is in aluminum and is heated by an electrical resistances whereas the upper plate is in Plexiglas. The experiments are carried out employing aluminum foams with 5, 10 and 40 PPI and three thickness over the heated circular plate. Results are obtained in a Reynolds number range from 500 to 1500 and wall heat flux from 500 W/m2 to 1400 W/m2. Results are given in terms of wall temperature profiles, local and average Nusselt numbers, pressure drops, friction factor and Richardson number. Moreover, to evaluate the improvement due to the presence of the metal foam, it is necessary a quantitative methodology. In this work an energy performance ratio is employed to compare the performances of surface with and without foams in terms of heat transfer coefficients and pressure drops. Preliminarily experimental results has confirmed that the use of the porous medium improves the heat transfer promoting the heat dissipation of the surface with high efficacy but determines an increase in pressure drops

The relapsed acute lymphoblastic leukemia network (ReALLNet): a multidisciplinary project from the spanish society of pediatric hematology and oncology (SEHOP)

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, with survival rates exceeding 85%. However, 15% of patients will relapse; consequently, their survival rates decrease to below 50%. Therefore, several research and innovation studies are focusing on pediatric relapsed or refractory ALL (R/R ALL). Driven by this context and following the European strategic plan to implement precision medicine equitably, the Relapsed ALL Network (ReALLNet) was launched under the umbrella of SEHOP in 2021, aiming to connect bedside patient care with expert groups in R/R ALL in an interdisciplinary and multicentric network. To achieve this objective, a board consisting of experts in diagnosis, management, preclinical research, and clinical trials has been established. The requirements of treatment centers have been evaluated, and the available oncogenomic and functional study resources have been assessed and organized. A shipping platform has been developed to process samples requiring study derivation, and an integrated diagnostic committee has been established to report results. These biological data, as well as patient outcomes, are collected in a national registry. Additionally, samples from all patients are stored in a biobank. This comprehensive repository of data and samples is expected to foster an environment where preclinical researchers and data scientists can seek to meet the complex needs of this challenging population. This proof of concept aims to demonstrate that a network-based organization, such as that embodied by ReALLNet, provides the ideal niche for the equitable and efficient implementation of “what's next” in the management of children with R/R ALL.Peer ReviewedPostprint (published version

Quantum Coherence in Loopless Superconductive Networks

Measurements indicating that planar networks of superconductive islands connected by Josephson junctions display long-range quantum coherence are reported. The networks consist of superconducting islands connected by Josephson junctions and have a tree-like topological structure containing no loops. Enhancements of superconductive gaps over specific branches of the networks and sharp increases in pair currents are the main signatures of the coherent states. In order to unambiguously attribute the observed effects to branches being embedded in the networks, comparisons with geometrically equivalent-but isolated-counterparts are reported. Tuning the Josephson coupling energy by an external magnetic field generates increases in the Josephson currents, along the above-mentioned specific branches, which follow a functional dependence typical of phase transitions. Results are presented for double comb and star geometry networks, and in both cases, the observed effects provide positive quantitative evidence of the predictions of existing theoretical models

ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli

SUMMARY: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis.RESULTS: Using coil/disorder, hydrophobicity, hydrophilicity, β-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity.AVAILABILITY AND IMPLEMENTATION: ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html.CONTACT: [email protected] INFORMATION: Supplementary data are available at Bioinformatics online

Digital biomarkers and sex impacts in Alzheimer’s disease management — potential utility for innovative 3P medicine approach

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer’s, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida’s digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida’s application, achieved a 75% ROC-AUC (receiver operating characteristic — area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline.RLH and IT were supported by Altoida Inc. JM was supported in this work by the Charles University Grant Agency (GA UK) project no. 436119 at Charles University, Second Faculty of Medicine, Prague, Czech Republic.Peer Reviewed"Article signat per 16 autors/es: Robbert L. Harms, Alberto Ferrari, Irene B. Meier, Julie Martinkova, Enrico Santus, Nicola Marino, Davide Cirillo, Simona Mellino, Silvina Catuara Solarz, Ioannis Tarnanas, Cassandra Szoeke, Jakub Hort, Alfonso Valencia, Maria Teresa Ferretti, Azizi Seixas & Antonella Santuccione Chadha "Postprint (published version