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In this paper, we deal with the class of unconstrained multi-objective optimization problems. In this setting we introduce, for the first time in the literature, a Limited Memory Quasi-Newton type method, which is well suited especially in large scale scenarios. The proposed algorithm approximates, through a suitable positive definite matrix, the convex combination of the Hessian matrices of the objectives; the update formula for the approximation matrix can be seen as an extension of the one used in the popular L-BFGS method for scalar optimization. Equipped with a Wolfe type line search, the considered method is proved to be well defined even in the nonconvex case. Furthermore, for twice continuously differentiable strongly convex problems, we state global and R-linear convergence to Pareto optimality of the sequence of generated points. The performance of the new algorithm is empirically assessed by a thorough computational comparison with state-of-the-art Newton and Quasi-Newton approaches from the multi-objective optimization literature. The results of the experiments highlight that the proposed approach is generally efficient and effective, outperforming the competitors in most settings. Moreover, the use of the limited memory method results to be beneficial within a global optimization framework for Pareto front approximation.

Background and purpose: Real-world data on alemtuzumab are limited and do not provide evidence of its effectiveness after various disease-modifying therapies (DMTs). Our aim was to provide real-world data on the impact of clinical variables and previous DMTs on clinical response to alemtuzumab. Methods: Sixteen Italian multiple sclerosis centers retrospectively included patients who started alemtuzumab from January 2015 to December 2018, and recorded demographics, previous therapies, washout duration, relapses, Expanded Disability Status Scale (EDSS) score, and magnetic resonance imaging data. Negative binomial regression models were used to assess the effect of factors on annualized relapse (ARR) after alemtuzumab initiation. Results: We studied 322 patients (mean age 36.8years, median EDSS score 3, median follow-up 1.94years). Previous treatments were: fingolimod (106), natalizumab (80), first-line oral agents (56), first-line injectables (interferon/glatiramer acetate; 30), and other drugs (15). Thirty-five patients were treatment-naïve. The pre-alemtuzumab ARR was 0.99 and decreased to 0.13 during alemtuzumab treatment (p&nbsp

Background and purpose: Clinical outcomes of multiple sclerosis (MS) patients affected by coronavirus disease 2019 (COVID-19) have been thoroughly investigated, but a further analysis on main signs and symptoms and their risk factors still needs attention. The objective of this study was to group together and describe based on similarity the most common signs and symptoms of COVID-19 in MS patients and identify all factors associated with their manifestation. Method: Logistic and linear regression models were run to recognize factors associated with each pooled group of symptoms and their total number. Results: From March 2020 to November 2021, data were collected from 1354 MS patients with confirmed infection of COVID-19. Ageusia and anosmia was less frequent in older people (odds ratio [OR] 0.98; p=0.005) and more in smoker patients (OR 1.39; p=0.049). Smoke was also associated with an incremental number of symptoms (OR 1.24; p=0.031), substance abuse (drugs or alcohol), conjunctivitis and rash (OR 5.20; p=0.042) and the presence of at least one comorbidity with shortness of breath, tachycardia or chest pain (OR 1.24; p=0.008). Some disease-modifying therapies were associated with greater frequencies of certain COVID-19 symptoms (association between anti-CD20 therapies and increment in the number of concomitant symptoms: OR 1.29; p=0.05). Differences in frequencies between the three waves were found for flu-like symptoms (G1, p=0.024), joint or muscle pain (G2, p=0.013) and ageusia and anosmia (G5, p < 0.001). All cases should be referred to variants up to Delta. Conclusion: Several factors along with the choice of specific therapeutic approaches might have a different impact on the occurrence of some COVID-19 symptoms.

Background and purpose Some studies have shown that air pollution, often assessed by thin particulate matter with diameter below 2.5 µg/m3 (PM2.5), may contribute to severe COVID‐19 courses, as well as play a role in the onset and evolution of multiple sclerosis (MS). However, the impact of air pollution on COVID‐19 has never been explored specifically amongst patients with MS (PwMS). This retrospective observational study aims to explore associations between PM2.5 and COVID‐19 severity amongst PwMS. Methods Data were retrieved from an Italian web‐based platform (MuSC‐19) which includes PwMS with COVID‐19. PM2.5 2016–2018 average concentrations were provided by the Copernicus Atmospheric Monitoring Service. Italian patients inserted in the platform from 15 January 2020 to 9 April 2021 with a COVID‐19 positive test were included. Ordered logistic regression models were used to study associations between PM2.5 and COVID‐19 severity. Results In all, 1087 patients, of whom 13% required hospitalization and 2% were admitted to an intensive care unit or died, were included. Based on the multivariate analysis, higher concentrations of PM2.5 increased the risk of worse COVID‐19 course (odds ratio 1.90; p = 0.009). Conclusions Even if several other factors explain the unfavourable course of COVID‐19 in PwMS, the role of air pollutants must be considered and further investigated., Air pollution, often assessed by particulate matter with diameter below 2.5 µg/m3, may contribute to severe COVID‐19 courses. 1087 patients were included, of whom 13% required hospitalization and 2% were admitted to an intensive care unit or died. Even if several other factors explain the unfavourable course of COVID‐19 in patients with multiple sclerosis, the role of air pollutants must be considered and further investigated.

Double inversion recovery (DIR) imaging is used in multiple sclerosis (MS) clinical protocols to improve the detection of cortical and juxtacortical gray matter lesions by nulling confounding signals originating from the cerebrospinal fluid and white matter. Achieving a high isotropic spatial resolution, to depict the neocortex and its typically small lesions, is challenged by the reduced signal-to-noise ratio (SNR) determined by multiple tissue signal nulling. Here, we evaluate both conventional and optimized DIR implementations to improve tissue contrast (TC), SNR, and MS lesion conspicuity.DIR images were obtained from MS patients and healthy controls using both conventional and prototype implementations featuring a T2-preparation module (T2P), to improve SNR and TC, as well as an image reconstruction routine with iterative denoising (ID). We obtained quantitative measures of SNR and TC, and evaluated the visibility of MS cortical, cervical cord, and optic nerve lesions in the different DIR images.DIR implementations adopting T2P and ID enabled improving the SNR and TC of conventional DIR. In MS patients, 34% of cortical, optic nerve, and cervical cord lesions were visible only in DIR images acquired with T2P, and not in conventional DIR images. In the studied cases, image reconstruction with ID did not improve lesion conspicuity.DIR with T2P should be preferred to conventional DIR imaging in protocols studying MS patients, as it improves SNR and TC and determines an improvement in cortical, optic nerve, and cervical cord lesion conspicuity.

Background: Cortical multiple sclerosis lesions are clinically relevant but inconspicuous at conventional clinical MRI. Double inversion recovery (DIR) and phase-sensitive inversion recovery (PSIR) are more sensitive but often unavailable. In the past 2 years, artificial intelligence (AI) was used to generate DIR and PSIR from standard clinical sequences (eg, T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery sequences), but multicenter validation is crucial for further implementation. Purpose: To evaluate cortical and juxtacortical multiple sclerosis lesion detection for diagnostic and disease monitoring purposes on AI-generated DIR and PSIR images compared with MRI-acquired DIR and PSIR images in a multicenter setting. Materials and Methods: Generative adversarial networks were used to generate AI-based DIR (n = 50) and PSIR (n = 43) images. The number of detected lesions between AI-generated images and MRI-acquired (reference) images was compared by randomized blinded scoring by seven readers (all with >10 years of experience in lesion assessment). Reliability was expressed as the intraclass correlation coefficient (ICC). Differences in lesion subtype were determined using Wilcoxon signed-rank tests. Results: MRI scans of 202 patients with multiple sclerosis (mean age, 46 years ± 11 [SD]; 127 women) were retrospectively collected from seven centers (February 2020 to January 2021). In total, 1154 lesions were detected on AI-generated DIR images versus 855 on MRI-acquired DIR images (mean difference per reader, 35.0% ± 22.8; P < .001). On AI-generated PSIR images, 803 lesions were detected versus 814 on MRI-acquired PSIR images (98.9% ± 19.4; P = .87). Reliability was good for both DIR (ICC, 0.81) and PSIR (ICC, 0.75) across centers. Regionally, more juxtacortical lesions were detected on AI-generated DIR images than on MRI-acquired DIR images (495 [42.9%] vs 338 [39.5%]; P < .001). On AI-generated PSIR images, fewer juxtacortical lesions were detected than on MRI-acquired PSIR images (232 [28.9%] vs 282 [34.6%]; P = .02). Conclusion: Artificial intelligence–generated double inversion-recovery and phase-sensitive inversion-recovery images performed well compared with their MRI-acquired counterparts and can be considered reliable in a multicenter setting, with good between-reader and between-center interpretative agreement.

In this paper we address the problem of controlling chaos by using a selective filter inserted in a negative feedback loop. This has been achieved in driven double-well Duffing oscillator in a parameter region exhibiting both a boundary crisis and generalized bistability. The optimization of the filter response allows us to improve the reduction of the control signal when the periodic solution competing with the chaotic attractor is approached. The advantage in using a selective frequency approach with respect to the Pyragas's method mainly resides in the tunability of the maximum of its amplitude response and zero phase condition. Numerical simulations performed in the temporal domain match with the experiment and make us glimpse the potential of an automatic adaptive strategy adopting filter parameter variations in order to reach an optimal feedback signal reduction.

In this paper, we consider multi-objective optimization problems with a sparsity constraint on the vector of variables. For this class of problems, inspired by the homonymous necessary optimality condition for sparse single-objective optimization, we define the concept of L-stationarity and we analyze its relationships with other existing conditions and Pareto optimality concepts. We then propose two novel algorithmic approaches: the first one is an Iterative Hard Thresholding method aiming to find a single L-stationary solution, while the second one is a two-stage algorithm designed to construct an approximation of the whole Pareto front. Both methods are characterized by theoretical properties of convergence to points satisfying necessary conditions for Pareto optimality. Moreover, we report numerical results establishing the practical effectiveness of the proposed methodologies., Comment: 20 pages, 7 figures, 1 table

The Classification Tree (CT) is one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in recent years, thanks to remarkable advances in Mixer-Integer Programming (MIP) solvers, several exact formulations of the learning problem have been developed. In this paper, we argue that some of the most relevant ones among these training models can be encapsulated within a general framework, whose instances are shaped by the specification of loss functions and regularizers. Next, we introduce a novel realization of this framework: specifically, we consider the logistic loss, handled in the MIP setting by a linear piece-wise approximation, and couple it with $\ell_1$-regularization terms. The resulting Optimal Logistic Tree model numerically proves to be able to induce trees with enhanced interpretability features and competitive generalization capabilities, compared to the state-of-the-art MIP-based approaches.

In this paper, we deal with the Front Steepest Descent algorithm for multi-objective optimization. We point out that the algorithm from the literature is often incapable, by design, of spanning large portions of the Pareto front. We thus introduce some modifications within the algorithm aimed to overcome this significant limitation. We prove that the asymptotic convergence properties of the algorithm are preserved and numerically show that the proposed method significantly outperforms the original one.

Defining immune mechanisms leading to multiple sclerosis (MS) is difficult, due to the great inter-individual difference in immune system responses. The anti-CD52 antibody alemtuzumab transiently abolishes differences in immune parameters among individuals, allowing analysis of subsequent immune cell repopulation patterns, and their possible role in MS.To evaluate the correlation between innate and adaptive immune cell subsets and disease activity in MS in the context of treatment with alemtuzumab.A two-center observational cohort of patients treated with alemtuzumab underwent immune profiling of T, B, and natural killer (NK) cells, biomarker, clinical and radiological follow-up.After treatment, the percentage of NK and B cells increased; NK, T- and B-cell populations underwent a profound rearrangement. Within the effector T-cell compartment, treatment led to a transient decrease, followed by an increase, of T-helper 1 cells, and to a transient decrease of T-helper 17 cells. Within the T-regulatory compartment, naïve T-regulatory cells increased. Within the B-cell compartment, memory B cells and mature B cells decreased, whereas transitional B cells increased. Within the NK cell compartment, CD56The results of this study provide novel evidence that NK cells influence reconstitution of adaptive immune cells upon alemtuzumab and that patients with a successful response to alemtuzumab have an early immune reconstitution dominated by NK cells.

The central vein sign (CVS) has been proposed as a biomarker of multiple sclerosis (MS). In adult-onset MS (AOMS), 40%-threshold of CVS positive (+) lesions demonstrated high accuracy for MS diagnosis. However, CVS+ lesions’ performance has not been characterized in paediatric-onset (POMS) yet. We compared the CVS contribution to MS diagnosis in 10 POMS and 12 disease-duration-matched AOMS patients. Three POMS patients did not meet the 40%-threshold, while all AOMS patients were correctly diagnosed as having MS. The high proportion of periventricular confluent lesions, excluded from the CVS assessment, seemed to impair CVS sensitivity in POMS diagnosis.

Treatment with fingolimod for multiple sclerosis (MS) reduces the efficacy of COVID-19 vaccination. The aim of this exploratory study was to evaluate whether main lymphocyte subsets and demographic features correlated to the subsequent increase in anti-SARS-CoV2 antibodies following the third dose of COVID-19 vaccination in fingolimod-treated MS patients.This was a prospective single-center observational exploratory study including a subgroup of adult patients with MS (pwMS) in treatment with fingolimod who underwent COVID-19 vaccination. The association of anti-SARS-CoV2 antibody levels (reported as the Log10 of the difference between the post and pre third dose levels) with the total number and percentage of CD3+ T and CD19+ B was assessed by a linear regression model adjusted for age and sex.We found that peripheral blood CD19+ B lymphocytes before the third dose of vaccination in pwMS treated with fingolimod predict the subsequent increase of anti-SARS-CoV2 antibodies.This work suggests that evaluating the percentage of CD19+ B cells may be important to identify patients at risk of not producing SARS-CoV-2 antibodies, with possible reduced protection from COVID-19.

Bladder cancer is the 10th most common cancer type worldwide. Cystoscopy represents the gold standard for bladder cancer diagnosis, but this procedure is invasive and painful, hence the need to identify new biomarkers through noninvasive procedures. microRNAs (miRNAs) are considered to be promising diagnostic molecules, because they are very stable in biological fluids (including urine) and easily detectable. This systematic review analyses the power of urine miRNAs as bladder cancer diagnostic markers. We conducted this systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 293 records related to miRNAs and their diagnostic significance in BC were retrieved from the PubMed and Embase databases. A systematic search of the literature was performed, and a total of 25 articles (N = 4054 participants) were identified and reviewed. Although many of the selected studies were of high scientific quality, the results proved to be quite heterogeneous, because we did not identify a univocal consensus for a specific miRNA signature but only isolated the signatures. We did not identify a univocal consensus for a specific diagnostic miRNA signature but only isolated the signatures, some of them with better diagnostic power compared to the others.

Colorectal cancer is one of the most common causes of cancer-related deaths worldwide. Despite the advances in the knowledge of pathogenetic molecular mechanisms and the implementation of more effective drug treatments in recent years, the overall survival rate of patients remains unsatisfactory. The high death rate is mainly due to metastasis of cancer in about half of the cancer patients and the emergence of drug-resistant populations of cancer cells. Improved understanding of cancer molecular biology has highlighted the role of non-coding RNAs (ncRNAs) in colorectal cancer development and evolution. ncRNAs regulate gene expression through various mechanisms, including epigenetic modifications and interactions of long non-coding RNAs (lncRNAs) with both microRNAs (miRNAs) and proteins, and through the action of lncRNAs as miRNA precursors or pseudogenes. LncRNAs can also be detected in the blood and circulating ncRNAs have become a new source of non-invasive cancer biomarkers for the diagnosis and prognosis of colorectal cancer, as well as for predicting the response to drug therapy. In this review, we focus on the role of lncRNAs in colorectal cancer development, progression, and chemoresistance, and as possible therapeutic targets.

Background: In this study we aimed to monitor the risk of breakthrough SARS-CoV-2 infection in patients with MS (pwMS) under different DMTs and to identify correlates of reduced protection. Methods: This is a prospective Italian multicenter cohort study, long-term clinical follow-up of the CovaXiMS (Covid-19 vaccine in Multiple Sclerosis) study. 1855 pwMS scheduled for SARS-CoV-2 mRNA vaccination were enrolled and followed up to a mean time of 10 months. The cumulative incidence of breakthrough Covid-19 cases in pwMS was calculated before and after December 2021, to separate the Delta from the Omicron waves and to account for the advent of the third vaccine dose. Findings: 1705 pwMS received 2 m-RNA vaccine doses, 21/28 days apart. Of them, 1508 (88.5%) had blood assessment 4 weeks after the second vaccine dose and 1154/1266 (92%) received the third dose after a mean interval of 210 days (range 90-342 days) after the second dose. During follow-up, 131 breakthrough Covid-19 infections (33 during the Delta and 98 during the Omicron wave) were observed. The probability to be infected during the Delta wave was associated with SARS-CoV-2 antibody levels measured after 4 weeks from the second vaccine dose (HR=0.57, p&nbsp

In this paper, the problem of best subset selection in logistic regression is addressed. In particular, we take into account formulations of the problem resulting from the adoption of information criteria, such as AIC or BIC, as goodness-of-fit measures. There exist various methods to tackle this problem. Heuristic methods are computationally cheap, but are usually only able to find low quality solutions. Methods based on local optimization suffer from similar limitations as heuristic ones. On the other hand, methods based on mixed integer reformulations of the problem are much more effective, at the cost of higher computational requirements, that become unsustainable when the problem size grows. We thus propose a new approach, which combines mixed-integer programming and decomposition techniques in order to overcome the aforementioned scalability issues. We provide a theoretical characterization of the proposed algorithm properties. The results of a vast numerical experiment, performed on widely available datasets, show that the proposed method achieves the goal of outperforming state-of-the-art techniques.

The present pilot study investigates whether an abnormal miRNA profile in NIPT plasma samples can explain the finding of a low cell-free DNA (cfDNA) fetal fraction (cfDNAff) in euploid fetuses and non-obese women. Twelve women who underwent neoBona® NIPT with a normal fetal karyotype were studied. Six with a cfDNAff < 4% were matched with a control group with normal levels of cfDNAff > 4%. Samples were processed using the nanostring nCounter® platform with a panel of 800 miRNAs. Four of the maternal miRNAs, miR-579, miR-612, miR-3144 and miR-6721, had a significant abnormal expression in patients. A data filtering analysis showed that miR-579, miR-612, miR-3144 and miR-6721 targeted 169, 1, 48 and 136 placenta-specific genes, respectively. miR-579, miR-3144 and miR-6721 shared placenta-specific targeted genes involved in trophoblast invasion and migration pathways (IGF2R, PTCD2, SATB2, PLAC8). Moreover, the miRNA target genes encoded proteins localized in the placenta and involved in the pathogenesis of pre-eclampsia, including chorion-specific transcription factor GCMa, PRG2, Lin-28 Homolog B and IGFBP1. In conclusion, aberrant maternal miRNA expression in circulating plasma could be a source of dysregulating trophoblast invasion and migration and could represent a novel cause of a low cfDNAff in the sera of pregnant women at the time of NIPT analysis.

Treatment with fingolimod for multiple sclerosis (MS) reduces the efficacy of COVID-19 vaccination. We evaluated by a multivariate linear regression model whether main lymphocyte subsets and demographic feature correlated to the subsequent increase in anti-SARS-CoV2 antibodies following the third dose of COVID-19 vaccination in fingolimod-treated MS patients. We found that number and proportion of peripheral blood CD19+ B lymphocytes before the third dose of vaccination in MS patients treated with fingolimod, predict the subsequent increase of anti-SARS-CoV2 antibodies (respectively p = 0.013; p = 0.015). This work suggests that evaluating the numbers of CD19+ B cells may be important to identify patients at risk of not producing SARS-CoV-2 antibodies, with possible reduced protection from COVID-19.

Coronavirus disease 2019 (COVID-19) resulted in several psychological consequences. Past epidemiological experiences already showed the deep albeit heterogeneous psychological repercussions of pandemics. Nevertheless, little is known about COVID-19 outbreak and the possible strategies for boosting resilience in patients with chronic diseases such as Multiple Sclerosis (MS). Therefore, we designed a study aiming to assess the changes in mental distress during COVID-19 outbreak in patients with MS and to identifyfactors contributing to resilience's development.We enrolled 106 patients (69 relapsing-remitting, 20 secondary-progressive, and 17 primary-progressive) whose neuropsychological assessment before the COVID-19 pandemic (1 January 2019-1 March 2020) was available. It consisted of Brief International Cognitive Assessment for MS (BICAMS), Hospital Anxiety and Depression Scale (HADS) and patient-reported MS Neuropsychological Screening Questionnaire (MSNQ-P). All patients were re-tested during Italian lockdown through an online survey, comprehensive of sociodemographic information, HADS self-rating Scale, MSNQ-P Questionnaire and finally Connor-Davidson Resilience self-rating Scale (CD-RISC 25), in order to evaluate resilience.No significant changes in HADS and MSNQ-P scores were detected during COVID-19 pandemic in our population. Though, pre-existing lower HADS and MSNQ-P scores but not demographic, disease- and treatment-related elements were found significantly (p

Invasive ductal carcinoma (IDC) constitutes the most frequent malignant cancer endangering women’s health. In this study, a new spontaneously immortalized breast cancer cell line, DHSF-BR16 cells, was isolated from the primary IDC of a 74-years old female patient, treated with neoadjuvant chemotherapy and disease-free 5-years after adjuvant chemotherapy. Primary breast cancer tissue surgically removed was classified as ER−/PR−/HER2+, and the same phenotype was maintained by DHSF-BR16 cells. We examined DHSF-BR16 cell morphology and relevant biological and molecular markers, as well as their response to anticancer drugs commonly used for breast cancer treatment. MCF-7 cells were used for comparison purposes. The DHSF-BR16 cells showed the ability to form spheroids and migrate. Furthermore, DHSF-BR16 cells showed a mixed stemness phenotype (i.e. CD44+/CD24−/low), high levels of cytokeratin 7, moderate levels of cytokeratin 8 and 18, EpCAM and E-Cadh. Transcriptome analysis showed 2071 differentially expressed genes between DHSF-BR16 and MCF-7 cells (logFC > 2, p-adj

In this manuscript, we consider the problem of minimizing a smooth function with cardinality constraint, i.e., the constraint requiring that the "Equation missing"-norm of the vector of variables cannot exceed a given threshold value. A well-known approach of the literature is represented by the class of penalty decomposition methods, where a sequence of penalty subproblems, depending on the original variables and new variables, are inexactly solved by a two-block decomposition method. The inner iterates of the decomposition method require to perform exact minimizations with respect to the two blocks of variables. The computation of the global minimum with respect to the original variables may be prohibitive in the case of nonconvex objective function. In order to overcome this nontrivial issue, we propose a modified penalty decomposition method, where the exact minimizations with respect to the original variables are replaced by suitable line searches along gradient-related directions. We also present a derivative-free penalty decomposition algorithm for black-box optimization. We state convergence results of the proposed methods, and we report the results of preliminary computational experiments.

Soma and Neurite Density Image (SANDI) is an advanced diffusion magnetic resonance imaging (dMRI) signal model devised to probe in vivo microstructural information from both grey and white matter. However, this model requires multi-shell acquisitions that include b-values that are at least 6 times higher than those used in clinical practice. Here we present a 10-minute acquisition protocol that enables to acquire the necessary images for SANDI modelling on a clinical 3T scanner. We demonstrate the feasibility and assess the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI and other state-of-the-art models on five healthy subjects and we present its potential clinical impact on five subjects affected by multiple sclerosis with relapsing-remitting course. Our results suggest that SANDI is a repeatable, reproducible, feasible, and practical method to characterize both white and grey matter tissues in both the healthy brain and in neurological diseases.

In addition to its well-established immunosuppressive actions, tryptophan 2,3-dioxygenase (TDO) appears to elicit direct effects on tumor cell function. Although TDO has been associated with cancer stemness, its involvement in melanoma stem cell biology remains largely unknown. Since we showed that by upregulating TDO, dexamethasone (dex) promotes proliferation and migration of SK-Mel-28 human melanoma cells, we sought to investigate dex effects on melanoma spherogenesis and stemness, and whether these events are mediated by TDO. We demonstrate here that dex significantly upregulates TDO in A375, a more aggressive melanoma cell line, confirming that dex effects are not limited to SK-Mel-28 cells. Moreover, dex stimulates spherogenesis of both cell lines, which is mediated by TDO, evident by its suppression with 680C91, a TDO inhibitor. The formed melanospheres appear to be enriched with embryonic stem cell marker mRNAs, the expression of which is potentiated by dex. Expression of cancer stem cell markers (CD133, CD44, ganglioside GD2) was significantly increased in A375 spheres, as detected by flow cytometry. Taken together, our results suggest that TDO could represent a promising target in the management of melanoma and that dex, routinely used as a co-medication also in advanced melanoma, may stimulate melanoma cell function/tumor-supporting properties, a rather debilitating and undesired side effect.

to investigate, for the first time, the incidence of cancer (years 2009-2015) and geographical distribution among children and adolescents with cancer diagnosis in Lazio Region (Central Italy).to compute incidence rates of childhood cancers from Lazio Region Childhood Cancer Registry (LRCCR) database, established in 2015, and to compare results with national figures for 2012 provided by the Italian cancer registries network (AIRTUM).all new cases of malignant tumours (behaviour: /3 of ICD-O-3 classification) and all central nervous system tumours were selected, regardless of behaviour (/0, /1, /3) in children and adolescents (0-19 years) registered in the LRCCR data base.it was computed: • the raw and the direct standardised rates for the 0-14-year and the 15-19-year age groups for total malignant tumours of the ICCC-3 classification by area (province level and municipality of Rome); • Relative Risks (RR) for area-specific rate compared with that of the Lazio Region and 95% Confidence Intervals (95%CI).a total of 1,782 incident cases were recorded in 2009-2015; of these, 91.4% were confirmed by a pathology report. Standardized Incidence Rate for all malignant tumours is 207.2×1,000,000 (95%CI 195.5-219.5) in children and 335.1×1,000,000 (95%CI 308.9-361.2) in adolescents. Compared to the Lazio Region, a higher incidence of tumours is observed in Rome municipality (RR 1.09; 95%CI 0.98-1.20) and in the Frosinone province (RR 1.07; 95%CI 0.91-1.25) for the whole 0-19-year age group.compared to the pooled AIRTUM figures for 2003-2008, Lazio Region showed a higher incidence for all cancers, both in children and adolescents, and for specific tumours, such as leukaemia in children and thyroid carcinoma in adolescents. Apart from the diverse observation period, these differences may be due to a higher registry sensitivity of the childhood specialized registry compared to general population registries. The observed incidence excesses for specific geographical areas and tumours deserve further investigations. Overall, in its first seven years of activity, the Lazio childhood cancer registry was able to provide reliable epidemiological figures of cancer incidence in children and adolescents in the Italian context.

Local and global anthropogenic pressures due to climate change and to local uses and activities are exerting significant cumulative impacts to greater extents of the oceans and seas. Coastal ecosystems are particularly threatened by the intensity and coexistence of several marine uses and pressures, including sewage and urban constructions, tourism, ship traffic, fisheries and aquaculture. Assessment of pressures and the identification of mitigation measures are key urgent actions, as already highlighted by the EU Marine Strategy Framework Directive and the United Nations Sustainable Development Goal 14. The aim of this work, developed within the Interreg-Med project SHAREMED, is to systematize existing knowledge on threats and pollution, including those of transboundary origin, for long term strategies and common action marine spatial planning, jointly developed with stakeholders. The quest is to assess coexisting environmental threats, and their propagation in space and time, at proper spatial and temporal scales, according to the type and action of each stressor (i.e. global vs. local). Cumulative pressures are tackled within a dedicated Atlas comprising three sub-basinsins of the Mediterranean Sea: the North Adriatic Sea, the Sicilian Channel and the North-Western region. The Atlas integrates information generated at the best available resolutions by 1) in-situ sampling, 2) remote observations, 3) numerical models, and 4) focusing on target ecosystems and habitat forming species. These sub-basins are subjected to multiple local and larger scale (e.g. climate) pressures that propagate in space and time, and across political boundaries, that need to be addressed through coordinated actions, based on evidence-rooted common understanding. Interactions with relevant Stakeholders, solicited through an online survey, and meetings, were used to select target ecosystems and to identify the key relevant pressures. The Atlas is based on open-access databases and portals, literature reviews and from ad-hoc model simulations concerning marine heatwaves, ship traffic, oil pollution, marine litter and fishing efforts. We will present the main preliminary results and needs and gaps in observations related to marine ecosystems threats.

Marine plastic litter is one of the most significant signal of the impact of human activities on the marine environment. Therefore, improved methods and models are needed to better understand the distribution pattern of plastics (mainly microplastics) on the sea surface, along the water column, and on the seabed. So far, most plastics sampling campaigns have collected sea surface data, but these data were very scattered and mostly unrepresentative of the seasonal variability of their distribution. A comprehensive overview of the presence of plastic litter in marine enviroment must rely on models having the skill to better represent spatial patterns, interactions with marine ecosystems, and even predict the possible presence of plastic clusters at a specific time and position. Numerous studies adopted models of plastic transport which consider some sources of pollution (rivers, ports, ship routes) to determine plastic distribution in the open sea due to meteo-marine forcing. However, most of these models have not been validated against field data.In this presentation we show the results of a validation procedure of the modelled marine debris distributions expected in the North-Western Mediterranean between May and September 2019, through the comparison with field observations on the sea surface from campaigns carried out within the Interreg Med project Plastic Busters MPA. Marine debris observations show a significant variability, especially along the coasts, highlighting the need to employ a hydrodynamic model with a resolution much higher than that of basin-scale models. In the comparison between the observed and modelled surface plastic concentrations, the effect of model resolution will be specifically addressed.

Introduction In the last years, many new drugs have been developed targeting different oncology pathways, overall improving both quality of life and survival in several malignancies. However, the increase of those therapies is associated with novel toxicities, mainly immune-related adverse events (irAEs), never observed before. Different irAEs are now well characterized, and, among them, neuromuscular complications, following immune checkpoint inhibitor (ICPi) therapy, are increasingly studied and described. However, there are also neurological complications related to the use of other targeted therapies, less known and probably underestimated. Herein we describe two oncological patients who developed neuromuscular diseases after administration of targeted therapies, different from ICPi. Case reports The first patient was treated with the combination of Vemurafenib and Cobimetinib, BRAF and MEK inhibitors, respectively, for a cutaneous melanoma. One year after the beginning of the combined treatment, she developed a sub-acute motor neuropathy with predominant cranial nerve involvement. She was successfully treated with methylprednisolone. The second patient received therapy with Imatinib, tyrosine kinase inhibitor and precursor of the targeted therapy, for a gastrointestinal stromal tumour. Few days after the first administration, he developed generalized myasthenia gravis with respiratory failure. Clinical remission was obtained with plasma-exchange, intravenous immunoglobulins and steroids. Discussion and Conclusion We strengthen the relevance of neuromuscular complications which may occur long after treatment start or in patients receiving not only the latest ICPi but also “older” and apparently better-known targeted therapies. Also in the latter cases, an immune-mediated “off-target” pathogenic mechanism can be hypothesized, and consequences can be life threatening, if not promptly diagnosed and appropriately managed.

In this paper, we propose an adaptation of the classical augmented Lagrangian method for dealing with multi-objective optimization problems. Specifically, after a brief review of the literature, we give a suitable definition of Augmented Lagrangian for equality and inequality constrained multi-objective problems. We exploit this object in a general computational scheme that is proved to converge, under mild assumptions, to weak Pareto points of such problems. We then provide a modified version of the algorithm which is more suited for practical implementations, proving again convergence properties under reasonable hypotheses. Finally, computational experiments show that the proposed methods not only do work in practice, but are also competitive with respect to state-of-the-art methods.

BACKGROUND AND PURPOSE The best therapeutic approach for aggressive relapsing-remitting multiple sclerosis remains unknown. The objective was to compare the efficacy and safety of autologous haematopoietic stem cell transplantation (aHSCT) and alemtuzumab in aggressive relapsing-remitting multiple sclerosis. METHODS The time to first relapse, time to confirmed disability worsening, time to first evidence of magnetic resonance imaging (MRI) activity and time to first evidence of disease activity were compared between the two treatment groups. Secondary outcomes included the 12, 24 and 36 month annualized relapse rate (ARR) and the 6-month confirmed Expanded Disability Status Scale (EDSS) changes at months 12 and 24. RESULTS Fifty-seven patients treated with aHSCT (n = 25) or alemtuzumab (n = 32) were included. At baseline, aHSCT patients had a higher EDSS (median score 6 vs. 3; P

The benefit of adjuvant chemotherapy in the early stages of colorectal cancer (CRC) is still disappointing and the prediction of treatment outcome quite difficult. Recently, through a transcriptomic approach, we evidenced a role of PNN and KCNQ1OT1 gene expression in predicting response to fluoropyrimidine-based adjuvant chemotherapy in stage III CRC patients. Thus, the aim of this study was to validate in an independent cohort of stages IIIII CRC patients our previous findings. PNN and KCNQ1OT1 mRNA expression levels were evaluated in 74 formalin-fixed paraffin-embedded tumor and matched normal mucosa samples obtained by stages IIIII CRC patients treated with fluoropyrimidine-based adjuvant chemotherapy. PININ, the protein encoded by PNN, was immunohistochemically evaluated in 15 tumor and corresponding normal mucosa samples, selected on the basis of a low, medium, or high mRNA expression tumor/mucosa ratio. PNN and KCNQ1OT1 mRNA mean expression levels were significantly higher in tumor compared with normal tissues. Patients with high PNN or KCNQ1OT1 tumor mRNA levels according to ROC-based cutoffs showed a shorter disease-free survival (DFS) compared with patients with low tumor mRNA gene expression. Also, patients with tumor mRNA expression values of both genes below the identified cutoffs had a significantly longer DFS compared with patients with the expression of one or both genes above the cutoffs. In a representative large cohort of stages IIIII CRC untreated patients retrieved from GEO datasets, no difference in DFS was observed between patients with high and low PNN or KCNQ1OT1 gene expression levels. These data confirm our previous findings and underscore the relevance of PNN and KCNQ1OT1 expression in predicting DFS in early stages of CRC treated with fluoropyrimidine-based adjuvant chemotherapy. If further validated in a prospective case series, both biomarkers could be used to identify patients who benefit from this treatment and to offer alternative chemotherapy regimens to potential unresponsive patients. In relation to the suggested biological role of PNN and KCNQ1OT1 in CRC, they might also be exploited as potential therapeutic targets.

To retrospectively evaluate the different performances of T1-SE and T1-GE sequences in detecting hypointense lesions in multiple sclerosis (MS), to quantify the degree of microstructural damage within lesions and to correlate them with patient clinical status. Sixty clinically isolated syndrome (CIS) and MS patients underwent brain magnetic resonance imaging (MRI) on 1.5-T and 3-T scanners. We identified T2 fluid-attenuated inversion recovery hyperintense lesions with no hypointense signal on T1-SE/T1-GE (a), hypointense lesions only on T1-GE (b), and hypointense lesions on both T1-SE and T1-GE sequences (c). We compared mean lesion number (LN) and volume (LV) identified on T1-SE and T1-GE sequences, correlating them with Expanded Disability Status Scale (EDSS); fractional anisotropy (FA) and mean diffusivity (MD) values inside each lesion type were extracted and normal-appearing white matter (NAWM). Thirty-five patients were female. Mean age was 39.2 (± 7.8); median EDSS was 3 (± 2). There were 23 CIS, 21 relapsing–remitting (RR), and 16 progressive MS. T1-GE and T1-SE LN and LV were significantly different (p

ATP Binding Cassette (ABC) transporters, widely studied in cancer for their role in drug resistance, have been more recently also considered for their contribution to cancer cell biology. To date, many data provide evidences for their potential role in all the phases of cancer development from cancer susceptibility, tumor initiation, tumor progression and metastasis. Although many evidences are based on correlative analyses, data describing a direct or indirect role of ABC transporters in cancer biology are increasing. Overall, current available information suggests a relevant molecular effector role of some ABC transporters in cancer invasion and metastasis as reported in experimental tumor models. From a therapeutic point of view, due to the physiological relevant roles that ABC transporters play in the organism, the capability to selectively inhibit the function or the expression of ABC transporters in cancer stem cells or other tumor cells, represents the main challenge for researcher scientists. A detailed and updated description of the current knowledge on the role of ABC transporters in cancer biology is provided.

In this paper we propose a new optimization model for maximum likelihood estimation of causal and invertible ARMA models. Through a set of numerical experiments we show how our proposed model outperforms, both in terms of quality of the fitted model as well as in the computational time, the classical estimation procedure based on Jones reparametrization. We also propose a regularization term in the model and we show how this addition improves the out of sample quality of the fitted model. This improvement is achieved thanks to an increased penalty on models close to the non causality or non invertibility boundary., Comment: 11 pages