A Deep Generative Model for Fragment-Based Molecule Generation

Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as strings of text, and learns their corresponding character-based language model. Another, more expressive, approach operates directly on the molecular graph. In this work, we address two limitations of the former: generation of invalid and duplicate molecules. To improve validity rates, we develop a language model for small molecular substructures called fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug Design. In other words, we generate molecules fragment by fragment, instead of atom by atom. To improve uniqueness rates, we present a frequency-based masking strategy that helps generate molecules with infrequent fragments. We show experimentally that our model largely outperforms other language model-based competitors, reaching state-of-the-art performances typical of graph-based approaches. Moreover, generated molecules display molecular properties similar to those in the training sample, even in absence of explicit task-specific supervision

Predicting mortality in low birth-weight infants: a machine learning perspective

Mortality predictions of low and very low birth-weight infants using logistic regression models are widely used in risk adjustment procedures for comparing different NICUs (Neonatal Intensive Care Units). We tackled this problem from a machine learning point of view by training state-of-the-art supervised models for the task. Furthermore, we used unsupervised techniques to provide clinicians with new insights on the matter that could ultimately lead to new improvements

A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor

Estimation of mortality risk of very preterm neonates is carried out in clinical and research settings. We aimed at elaborating a prediction tool using machine learning methods. We developed models on a cohort of 23747 neonates <30 weeks gestational age, or <1501 g birth weight, enrolled in the Italian Neonatal Network in 2008–2014 (development set), using 12 easily collected perinatal variables. We used a cohort from 2015–2016 (N = 5810) as a test set. Among several machine learning methods we chose artificial Neural Networks (NN). The resulting predictor was compared with logistic regression models. In the test cohort, NN had a slightly better discrimination than logistic regression (P < 0.002). The differences were greater in subgroups of neonates (at various gestational age or birth weight intervals, singletons). Using a cutoff of death probability of 0.5, logistic regression misclassified 67/5810 neonates (1.2 percent) more than NN. In conclusion our study – the largest published so far – shows that even in this very simplified scenario, using only limited information available up to 5 minutes after birth, a NN approach had a small but significant advantage over current approaches. The software implementing the predictor is made freely available to the community

Recent advances in classic heparin-induced thrombocytopenia (HIT), autoimmune HIT, spontaneous HIT, and vaccine-induced immune thrombotic thrombocytopenia

Anti-platelet factor 4 (PF4) disorders are a group of platelet-consumptive disorders characterized by platelet-activating antibodies against PF4, thrombocytopenia and an increased risk of thrombosis. PF4 is a chemokine released by platelet alpha granules upon activation, which can form immune complexes with negatively charged substances, such as heparin, cartilage components, nucleic acids, and viral and bacterial agents. Antibodies formed in response to PF4-polyanion complexes may display platelet-activating properties and cause pan-cellular activation, leading to the marked prothrombotic state of anti-PF4 disorders. In recent years, the landscape of anti-PF4 disorders has evolved to include classic heparin-induced thrombocytopenia (cHIT), autoimmune HIT (aHIT), spontaneous HIT (SpHIT), vaccine-induced immune thrombotic thrombocytopenia (VITT), and the newly recognized spontaneous VITT (SpVITT). These disorders have garnered increased attention due to their association with severe clinical outcomes. Recent discoveries have expanded the understanding of these conditions, highlighting the role of various triggers, such as upper respiratory tract infections and monoclonal gammopathy of undetermined significance, in their development. Compared to cHIT, the less common anti-PF4 disorders VITT, aHIT, SpHIT and SpVITT generally appear more severe, with aggressive disease courses, more severe thrombocytopenia and a higher frequency of bleeding, thrombosis at unusual sites, involvement of the central nervous system and of multiple vascular beds. Clinical suspicion and knowledge of the less well-known triggers of anti-PF4 disorders are pivotal to ordering the appropriate laboratory tests and initiating the necessary treatments. Herein, we will review cHIT, aHIT, SpHIT and VITT, focusing on their clinical presentation and therapeutic management

Localizzazione e caratterizzazione di frane all’interno dei crateri di impatto lunari

Geological slope failure processes have been observed on the Moon surface for decades. However a detailed and exhaustive lunar landslide inventory has not been produced yet. As a part of the “Moon Mapping” cooperative project between Italy and China, an algorithm for lunar landslide detection in impact craters has been proposed. The simple type of impact craters sizing between 5-12 km has been analysed. The Chebyshev polynomials have been used for estimating crater’s cross-sectional profiles on the basis of a 100 m x 100 m resolution digital elevation model (WACGDL100 DEM) derived from LROC NASA mission. The presence of landslides in lunar craters is then investigated by analysing the contribution of odd coefficients of the estimated polynomials, since they are representing the asymmetric component of a transversal profile. After the analysis of four orthogonal profiles per crater, we correctly classified 87.7% of cross-sectional profiles really affected by slope failures. On the other side, we obtained a correct classification of 83.3% of cross- sectional profiles without slope failures. Even though a complete successful rate could not be achieved, these results are quite encouraging since the proposed automated procedure would allow to a first scrutiny of the presence of landslides in craters, to be refined afterwards with visual recognition and the analysis of other types of data

CyTest – An Innovative Open-source Platform for Training and Testing in Cythopathology

Abstract This paper describes an e-learning platform developed in the context of the European Project CyTest (2014-1-IT01-KA202-002607), dedicated to Cytological Training at European Standard through Telepathology. The main, and novel, feature of our system is the deep integration between virtual microscopy and the training system: images are not simply there to be seen but they are active parts of testing, supporting quantitative measurement of image comprehension, for instance by evaluating the identification of relevant cellular structures by the position of markers put by the student on the image. The solution we developed offers a complete tool for easy creation and interactive access to questions related to images and fully integrates the components of virtual microscopy and teaching, based on state-of-the-art instruments for digital pathology images management, as OMERO, and for training course distribution, as Moodle. The system can be easily extended to support histopathological diagnosis. The software is distributed as Open Source and available on GitHub

Motor imagery has a priming effect on motor execution in people with multiple sclerosis

Priming is a learning process that refers to behavioral changes caused by previous exposure to a similar stimulus. Motor imagery (MI), which involves the mental rehearsal of action representations in working memory without engaging in actual execution, could be a strategy for priming the motor system. This study investigates whether MI primes action execution in Multiple Sclerosis (MS). Here, 17 people with MS (PwMS) and 19 healthy subjects (HS), all right-handed and good imaginers, performed as accurately and quickly as possible, with a pencil, actual or mental pointing movements between targets of small (1.0 x 1.0 cm) or large (1.5 x 1.5 cm) size. In actual trials, they completed five pointing cycles between the left and right targets, whereas in mental trials, the first 4 cycles were imagined while the fifth was actually executed. The fifth cycle was introduced to assess the MI priming effect on actual execution. All conditions, presented randomly, were performed with both dominant (i.e., right) and non-dominant arms. Analysis of the duration of the first 4 cycles in both actual and mental trials confirmed previous findings, showing isochrony in HS with both arms and significantly faster mental than actual movements (anisochrony) in PwMS (p < 0.01) [time (s); HS right: actual: 4.23 +/- 0.15, mental: 4.36 +/- 0.16; left: actual: 4.32 +/- 0.15, mental: 4.43 +/- 0.18; PwMS right: actual: 5.85 +/- 0.16, mental: 5.99 +/- 0.21; left: actual: 6.68 +/- 0.20, mental: 5.94 +/- 0.23]; anisochrony in PwMS was present when the task was performed with the non-dominant arm. Of note, temporal analysis of the fifth actual cycle showed no differences between actual and mental trials for HS with both arms, whereas in PwMS the fifth actual cycle was significantly faster after the four actual cycles for the non-dominant arm (p < 0.05) [time (s); HS right: actual: 1.03 +/- 0.04, mental: 1.03 +/- 0.03; left: actual: 1.08 +/- 0.04, mental: 1.05 +/- 0.03; PwMS right: actual: 1.48 +/- 0.04, mental: 1.48 +/- 0.06; left: actual: 1.66 +/- 0.05, mental: 1.48 +/- 0.06]. These results seem to suggest that a few mental repetitions of an action might be sufficient to exert a priming effect on the actual execution of the same action in PwMS. This would indicate further investigation of the potential use of MI as a new motor-cognitive tool for MS neurorehabilitation