Correlation between IPSET-t risk at diagnosis and subsequent hemorrhage in patients with essential thrombocythemia; a single institution experience.

Essential thrombocythemia (ET) is a myeloproliferative neoplasm characterized by an increased risk of thrombotic and hemorrhagic events, that represent the leading causes of mortality and morbidity. Currently, while thrombotic risk is assessed through the IPSET-t and r-IPSET scores, there is no specific prognostic tool used to predict hemorrhagic risk in ET. The aim of the study was to define incidence and risk factors connected to hemorrhagic events by retrospectively analyzing 308 ET patients diagnosed between 1996 and 2022 at the Division of Hematology of Udine and treated according to the current international guidelines. According to molecular status, 193 patients (62.7%) were JAK2 mutated, 66 (21.4%) had a CALR mutation, 14 (4.5%) had a MPL mutation, 21 patients (6.8%) were "triple negative," and 14 patients (4.5%) were not evaluable. According to IPSET-t score, 49.7% patients were at high, 24.3% at intermediate, and 26.0% at low-risk, respectively. Twelve (3.9%) patients experienced bleeding at ET diagnosis, while 24 (7.8%) had at least one hemorrhagic event during follow-up at a median time of 103 months (range: 1-309). Forty hemorrhagic events were totally recorded and defined as minor in 22 cases, moderate in 11 cases, and severe in 7 cases. Cumulative incidence (CI) of hemorrhage at 10 and 20 years was 6.0% and 12.0%, respectively. A statistically significant correlation between hemorrhagic risk and IPSET-t score emerged: 10 years hemorrhage CI was 3.2% for low-risk, 2.9% for intermediate-risk, and 9.8% for high-risk patients, respectively (p=0.002). We found no correlation between hemorrhagic risk and gender or mutational status. Results of our study highlight the validity of IPSET-t score in predicting individual hemorrhagic risk among ET patients, suggesting a possible role of IPSET-t scoring system as a global evaluator for vascular events in ET patients.

A synergism of in silico and statistical approaches to discover new potential endocrine disruptor mycotoxins.

Mycotoxins are secondary metabolites produced by pathogenic fungi. They are found in a variety of different products, such as spices, cocoa, and cereals, and they can contaminate fields before and/or after harvest and during storage. Mycotoxins negatively impact human and animal health, causing a variety of adverse effects, ranging from acute poisoning to long-term effects. Given a large number of mycotoxins (currently more than 300 are known), it is impossible to use in vitro/in vivo methods to detect the potentially harmful effects to human health of all of these. To overcome this problem, this work aims to present a new robust computational approach, based on a combination of in silico and statistical methods, in order to screen a large number of molecules against the nuclear receptor family in a cost and time-effective manner and to discover the potential endocrine disruptor activity of mycotoxins. The results show that a high number of mycotoxins is predicted as a potential binder of nuclear receptors. In particular, ochratoxin A, zearalenone, α- and ß-zearalenol, aflatoxin B1, and alternariol have been shown to be putative endocrine disruptors chemicals for nuclear receptors.

Semiautomatic robust regression clustering of international trade data.

The purpose of this paper is to show in regression clustering how to choose the most relevant solutions, analyze their stability, and provide information about best combinations of optimal number of groups, restriction factor among the error variance across groups and level of trimming. The procedure is based on two steps. First we generalize the information criteria of constrained robust multivariate clustering to the case of clustering weighted models. Differently from the traditional approaches which are based on the choice of the best solution found minimizing an information criterion (i.e. BIC), we concentrate our attention on the so called optimal stable solutions. In the second step, using the monitoring approach, we select the best value of the trimming factor. Finally, we validate the solution using a confirmatory forward search approach. A motivating example based on a novel dataset concerning the European Union trade of face masks shows the limitations of the current existing procedures. The suggested approach is initially applied to a set of well known datasets in the literature of robust regression clustering. Then, we focus our attention on a set of international trade datasets and we provide a novel informative way of updating the subset in the random start approach. The Supplementary material, in the spirit of the Special Issue, deepens the analysis of trade data and compares the suggested approach with the existing ones available in the literature.

Advances in pharmacotherapy for myelofibrosis: what is the current state of play?

INTRODUCTION: The introduction of the first JAK inhibitor (JAKi) ruxolitinib 10 years ago represented a pivotal advancement in myelofibrosis (MF) treatment, mostly in terms of spleen and symptoms response. Nowadays three more JAKi, fedratinib, pacritinib, and momelotinib, are available for both ruxolitinib-resistant and naïve patients. Moreover, many drugs are currently being investigated, both alone and in combination with JAKi. AREAS COVERED: In this review we discuss the long-term data of ruxolitinib and more recent evidence coming from clinical trials of fedratinib, pacritinib, and momelotinib, used as first- or second-line MF therapy. More, focus is set on data from non-JAKi drugs, such as the quite extensively studied BET-inhibitors (pelabresib) and BCL-inhibitors (navitoclax), novel target therapies, and drugs aimed to improve anemia, still representing a major determinant of reduced survival in MF. EXPERT OPINION: It's now evident that JAKi monotherapy, though clinically effective, is rarely able to change MF natural history; novel drugs are promising but long-term data are inevitably lacking. We feel that soon MF treatment will require clinicians to select the most appropriate JAKi inhibitor, based on patient characteristics, associating either front-line or in case of early suboptimal response, non-JAKi drugs with the aim to pursue disease modification.

Computational methods on food contact chemicals: Big data and in silico screening on nuclear receptors family.

According to Eurostat, the EU production of chemicals hazardous to health reached 211 million tonnes in 2019. Thus, the possibility that some of these chemical compounds interact negatively with the human endocrine system has received, especially in the last decade, considerable attention from the scientific community. It is obvious that given the large number of chemical compounds it is impossible to use in vitro/in vivo tests for identifying all the possible toxic interactions of these chemicals and their metabolites. In addition, the poor availability of highly curated databases from which to retrieve and download the chemical, structure, and regulative information about all food contact chemicals has delayed the application of in silico methods. To overcome these problems, in this study we use robust computational approaches, based on a combination of highly curated databases and molecular docking, in order to screen all food contact chemicals against the nuclear receptor family in a cost and time-effective manner.

Dynamical phase transitions in long-range Hamiltonian systems and Tsallis distributions with a time-dependent index.

We study dynamical phase transitions in systems with long-range interactions, using the Hamiltonian mean field model as a simple example. These systems generically undergo a violent relaxation to a quasistationary state (QSS) before relaxing towards Boltzmann equilibrium. In the collisional regime, the out-of-equilibrium one-particle distribution function (DF) is a quasistationary solution of the Vlasov equation, slowly evolving in time due to finite- N effects. For subcritical energy densities, we exhibit cases where the DF is well fitted by a Tsallis q distribution with an index q(t) slowly decreasing in time from q approximately = 3 (semiellipse) to q=1 (Boltzmann). When the index q(t) reaches an energy-dependent critical value q_(crit) , the nonmagnetized (homogeneous) phase becomes Vlasov unstable and a dynamical phase transition is triggered, leading to a magnetized (inhomogeneous) state. While Tsallis distributions play an important role in our study, we explain this dynamical phase transition by using only conventional statistical mechanics. For supercritical energy densities, we report the existence of a magnetized QSS with a very long lifetime.

Long-time behavior of quasistationary states of the Hamiltonian mean-field model.

The Hamiltonian mean-field model has been investigated, since its introduction about a decade ago, to study the equilibrium and dynamical properties of long-range interacting systems. Here we study the long-time behavior of long-lived, out-of-equilibrium, quasistationary dynamical states, whose lifetime diverges in the thermodynamic limit. The nature of these states has been the object of a lively debate in the recent past. We introduce a numerical tool, based on the fluctuations of the phase of the instantaneous magnetization of the system. Using this tool, we study the quasistationary states that arise when the system is started from different classes of initial conditions, showing that the new observable can be exploited to compute the lifetime of these states. We also show that quasistationary states are present not only below, but also above the critical temperature of the second-order magnetic phase transition of the model. We find that at supercritical temperatures the lifetime is much larger than at subcritical temperatures.