Predictive molecular markers for EGFR-TKI in non-small cell lung cancer patients: new insights and critical aspects

In recent years, a number of novel agents have been investigated that target specific molecular pathways in non-small cell lung cancer (NSCLC). A great deal of effort has been focused on identifying specific markers that predict treatment response, given that a tailored approach would maximize both the therapeutic index and the cost-effectiveness. The epidermal growth factor receptor (EGFR) pathway has emerged as a key regulator of cancer cell proliferation and invasion, and several specific EGFR inhibitors have been examined. Gefitinib and erlotinib are selective EGFR tyrosine kinase inhibitors (EGFR-TKIs), demonstrating good results in selected cases both in terms of objective response rate and of overall survival. At present, EGFR gene mutations are the best positive predictive factors for TKI therapy, and a number of other potential biomarkers are being investigated as additional positive or negative predictors of response. The correct selection of patients that could benefit from these innovative therapies, based on an accurate molecular characterization, is mandatory to provide the best clinical management. Currently, the main factor limiting the characterization of metastatic NSCLC patients is the small quantity of tumor cells available for molecular analysis. In this paper we provide an overview of the most important molecular predictive markers for EGFR-TKIs therapy in NSCLC patients, and focus attention on biological samples suitable for analysis and alternative sampling approaches such as plasma- or serum-derived DNA

Detection of Spatial and Temporal Stress Changes During the 2016 Central Italy Seismic Sequence by Monitoring the Evolution of the Energy Index

We consider approximately 23,000 microearthquakes that occurred between 2005 and 2016 in central Italy to investigate the crustal strength before and after the three largest earthquakes of the 2016 seismic sequence (i.e., the Mw 6.2, 24 August 2016 Amatrice, the Mw 6.1, 26 October 2016 Visso, and the Mw 6.5, 30 October 2016 Norcia earthquakes). We monitor the spatiotemporal deviations of observed radiated energy, ES, with respect to theoretical values, ESt, derived from a scaling model between ES and M0 calibrated for background seismicity in central Italy. These deviations, defined here as Energy Index (EI), allow us to identify in the years following the Mw 6.1, 2009 L’Aquila earthquake a progressive evolution of the dynamic properties of microearthquakes and the existence of high EI patches close to the Amatrice earthquake hypocenter. We show the existence of a crustal volume with high EI even before the Mw 6.5 Norcia earthquake. Our results agree with the previously suggested hypothesis that the Norcia earthquake nucleated at the boundary of a large patch, highly stressed by the two previous mainshocks of the sequence. We highlight the mainshocks interaction both in terms of EI and of the mean loading shear stress associated to microearthquakes occurring within the crustal volumes comprising the mainshock hypocenters. Our study shows that the dynamic characteristics of microearthquakes can be exploited as beacons of stress change in the crust and thus be exploited to monitor the seismic hazard of a region and help to intercept the preparation phase of large earthquakes

Co-design of human-centered, explainable AI for clinical decision support

eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models, and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique, and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback, with a two-fold outcome: first, we obtain evidence that explanations increase users’ trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so that we can re-design a better, more human-centered explanation interface

The Candoglia Marble and the “Veneranda Fabbrica del Duomo di Milano”: A Renowned Georesource to Be Potentially Designed as Global Heritage Stone

Marbles from Alpine area have been widely employed to build and decorate masterpieces and buildings which often represent the cultural heritage of an area (statuary, historic buildings and sculptures). Candoglia marble, object of the present research, is one of the most famous and appreciated marbles from Alpine area; it has been quarried since Roman times in the Verbano-Cusio-Ossola (VCO; Piemonte—NW Italy) extractive area. Candoglia Marble outcrops are present as lenses within the high-grade paragneisses of the Ivrea Zone, a visible section of deep continental crust characterised by amphibolite- to granulite-facies metamorphism (Palaeozoic period). Candoglia calcitic marble (80–85% CaCO3 and the 15–20% other minerals) shows a characteristic pink to gray colour and a coarse-grained texture (>3 mm): frequent centimetre-thick dark-greenish silicate layers (mainly represented by diopside and tremolite) characterize the texture of the marble. It has been largely used in local rural constructions and historical buildings, but its most famous application has been (and still is) for the “Duomo di Milano” construction (fourteenth century). The Veneranda Fabbrica del Duomo di Milano carried out the anthropogenic activities dealing with the Candoglia marble exploitation; it has to be highlighted that the company have managed the Marble exploitation during the last seven centuries and that the quarry itself is a tangible sign of the development of extraction and heritage in the VCO area. Candoglia marble can be recognized as a significant example of a “Global Heritage Stone Resource”: its exploitation from quarry to building (the Duomo di Milano) well represents the close correlation between stone and cultural heritage, between georesources and humankind development

Momentum and Reversal in a Financial Market with Persistent Heterogeneity

This paper investigates whether short-term momentum and long-term reversal may emerge from the wealth reallocation process taking place in speculative markets. We assume that there are two classes of investors who trade long-lived assets by holding constantly rebalanced portfolios based on their beliefs. Provided beliefs, and thus portfolios, are sufficiently diversified, all investors survive in the long-run and, due to waves of mispricing, the resulting equilibrium returns exhibit long-term reversal. If, moreover, asset dividends are positively correlated, investors’ profitable trades become positively correlated too, thus generating short-term momentum in equilibrium returns. We use the model to replicate the performance of the Winners and Losers portfolios highlighted by the empirical literature and to provide insights on how to improve upon them. Finally, we show that dividend positive autocorrelation is positively related to momentum and negatively related to reversal while diversity of beliefs is positively related to both momentum and reversa

Dense Hebbian neural networks: a replica symmetric picture of supervised learning

We consider dense, associative neural-networks trained by a teacher (i.e., with supervision) and we investigate their computational capabilities analytically, via statistical-mechanics of spin glasses, and numerically, via Monte Carlo simulations. In particular, we obtain a phase diagram summarizing their performance as a function of the control parameters such as quality and quantity of the training dataset, network storage and noise, that is valid in the limit of large network size and structureless datasets: these networks may work in a ultra-storage regime (where they can handle a huge amount of patterns, if compared with shallow neural networks) or in a ultra-detection regime (where they can perform pattern recognition at prohibitive signal-to-noise ratios, if compared with shallow neural networks). Guided by the random theory as a reference framework, we also test numerically learning, storing and retrieval capabilities shown by these networks on structured datasets as MNist and Fashion MNist. As technical remarks, from the analytic side, we implement large deviations and stability analysis within Guerra's interpolation to tackle the not-Gaussian distributions involved in the post-synaptic potentials while, from the computational counterpart, we insert Plefka approximation in the Monte Carlo scheme, to speed up the evaluation of the synaptic tensors, overall obtaining a novel and broad approach to investigate supervised learning in neural networks, beyond the shallow limit, in general.Comment: arXiv admin note: text overlap with arXiv:2211.1406

Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning

We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, and numerically, via Monte Carlo simulations. In particular, we obtain a phase diagram summarizing their performance as a function of the control parameters such as the quality and quantity of the training dataset and the network storage, valid in the limit of large network size and structureless datasets. Moreover, we establish a bridge between macroscopic observables standardly used in statistical mechanics and loss functions typically used in the machine learning. As technical remarks, from the analytic side, we implement large deviations and stability analysis within Guerra's interpolation to tackle the not-Gaussian distributions involved in the post-synaptic potentials while, from the computational counterpart, we insert Plefka approximation in the Monte Carlo scheme, to speed up the evaluation of the synaptic tensors, overall obtaining a novel and broad approach to investigate neural networks in general

Models for the Type Ic Hypernova SN 2003lw associated with GRB 031203

The Gamma-Ray Burst 031203 at a redshift z=0.1055 revealed a highly reddened Type Ic Supernova, SN 2003lw, in its afterglow light. This is the third well established case of a link between a long-duration GRB and a type Ic SN. The SN light curve is obtained subtracting the galaxy contribution and is modelled together with two spectra at near-maximum epochs. A red VLT grism 150I spectrum of the SN near peak is used to extend the spectral coverage, and in particular to constrain the uncertain reddening, the most likely value for which is E_{G+H}(B-V) about 1.07 +/- 0.05. Accounting for reddening, SN 2003lw is about 0.3 mag brighter than the prototypical GRB-SN 1998bw. Light curve models yield a 56Ni mass of about 0.55 solar mass. The optimal explosion model is somewhat more massive (ejecta mass about 13 solar mass) and energetic (kinetic energy about 6 times 10^52 erg) than the model for SN 1998bw, implying a massive progenitor (40 - 50 solar mass). The mass at high velocity is not very large (1.4 solar mass above 30000 km/s, but only 0.1 solar mass above 60000 km/s), but is sufficient to cause the observed broad lines. The similarity of SNe 2003lw and 1998bw and the weakness of their related GRBs, GRB031203 and GRB980425, suggest that both GRBs may be normal events viewed slightly off-axis or a weaker but possibly more frequent type of GRB.Comment: 19 pages, 8 figures, accepted for publication in Ap

Genomic alterations in rectal tumors and response to neoadjuvant chemoradiotherapy: an exploratory study

<p>Abstract</p> <p>Background</p> <p>Neoadjuvant chemoradiotherapy is the treatment of choice in advanced rectal cancer, even though there are many patients who will not benefit from it. There are still no effective methods for predicting which patients will respond or not. The present study aimed to define the genomic profile of rectal tumors and to identify alterations that are predictive of response in order to optimize therapeutic strategies.</p> <p>Methods</p> <p>Forty-eight candidates for neoadjuvant chemoradiotherapy were recruited and their pretherapy biopsies analyzed by array Comparative Genomic Hybridization (aCGH). Pathologic response was evaluated by tumor regression grade.</p> <p>Results</p> <p>Both Hidden Markov Model and Smoothing approaches identified similar alterations, with a prevalence of DNA gains. Non responsive patients had a different alteration profile from responsive ones, with a higher number of genome changes mainly located on 2q21, 3q29, 7p22-21, 7q21, 7q36, 8q23-24, 10p14-13, 13q12, 13q31-34, 16p13, 17p13-12 and 18q23 chromosomal regions.</p> <p>Conclusions</p> <p>This exploratory study suggests that an in depth characterization of chromosomal alterations by aCGH would provide useful predictive information on response to neoadjuvant chemoradiotherapy and could help to optimize therapy in rectal cancer patients.</p> <p>The data discussed in this study are available on the NCBI Gene Expression Omnibus [GEO: GSE25885].</p