Selezione di funghi endofiti antagonisti di patogeni forestali

This research has been aimed to isolate endophytic fungi from Sardinian chestnut, cork oak and yew and to select those which have higher competitive capacities towards important pathogens (Biscogniauxia mediterranea, Diplodia corticola, Discula quercina and Cryphonectria parasitica) of forest trees. According to the obtained results, 5 antagonistic endophytes have been selected and identified on a morphological and biomolecular base: Bionectria ochroleuca, Dictyochaeta parva, Trichoderma viridescens, Trichoderma sp. and Penicillium sp. They all have shown to have a high competitive activity, which differs depending on their biological properties and metabolic versatility. The 2 Trichoderma species have contrasted the 4 pathogens mycelial growth with different action mechanisms; B. Ochroleuca has highlighted a marked micoparasitic aptitude and Penicillium sp. and D. Parva have been able to inhibit at a distance the pathogens growth. Further investigations have been aimed to evaluate the antifungine and zootoxic activity of the selected endophytes organic extracts. D. Parva, being the most active, apart from representing a potential biocontrol agent of the considered pathogens, can also be a good source of bioactive secondary metabolites to be used in various sectors of industrial biotechnologies and medical-pharmaceutical fields

Longitudinal assessment of brain-derived neurotrophic factor in Sardinian psychotic patients (LABSP): a protocol for a prospective observational study

Brain-derived neurotrophic factor (BDNF) plays a crucial role in neurodevelopment, synaptic plasticity and neuronal function and survival. Serum and plasma BDNF levels are moderately, but consistently, decreased in patients with schizophrenia (SCZ) compared with healthy controls. There is a lack of knowledge, however, on the temporal manifestation of this decline. Clinical, illness course and treatment factors might influence the variation of BDNF serum levels in patients with psychosis. In this context, we propose a longitudinal study of a cohort of SCZ and schizophrenic and schizoaffective disorder (SAD) Sardinian patients with the aim of disentangling the relationship between peripheral BDNF serum levels and changes of psychopathology, cognition and drug treatments

Numerical and experimental characterization of a novel modular passive micromixer

This paper reports a new low-cost passive microfluidic mixer design, based on a replication of identical mixing units composed of microchannels with variable curvature (clothoid) geometry. The micromixer presents a compact and modular architecture that can be easily fabricated using a simple and reliable fabrication process. The particular clothoid-based geometry enhances the mixing by inducing transversal secondary flows and recirculation effects. The role of the relevant fluid mechanics mechanisms promoting the mixing in this geometry were analysed using computational fluid dynamics (CFD) for Reynolds numbers ranging from 1 to 110. A measure of mixing potency was quantitatively evaluated by calculating mixing efficiency, while a measure of particle dispersion was assessed through the lacunarity index. The results show that the secondary flow arrangement and recirculation effects are able to provide a mixing efficiency equal to 80 % at Reynolds number above 70. In addition, the analysis of particles distribution promotes the lacunarity as powerful tool to quantify the dispersion of fluid particles and, in turn, the overall mixing. On fabricated micromixer prototypes the microscopic-Laser-Induced-Fluorescence (μLIF) technique was applied to characterize mixing. The experimental results confirmed the mixing potency of the microdevice

SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

In this paper, we propose a classifier for predicting message-level sentiments of English micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Severyn and Moschitti, 2015a; Severyn and Moschitti, 2015b). We leverage large amounts of data with distant supervision to train an ensemble of 2-layer convolutional neural networks whose predictions are combined using a random forest classifier. Our approach was evaluated on the datasets of the SemEval-2016 competition (Task 4) outperforming all other approaches for the Message Polarity Classification task

Segmental Mandibulectomy and Mandibular Reconstruction with Fibula-Free Flap Using a 3D Template

Introduction: The present study evaluates the influence of virtual surgical planning with a preoperative 3D resin model on aesthetic and functional outcomes in patients treated by segmental mandibulectomy and reconstruction with fibula-free flap for oral cancer. Methods: All consecutive patients who underwent segmental mandibulectomy and mandibular reconstruction with a fibula-free flap using a 3D template at our department from January 2021 to January 2023 were included in the study. "Patients control" were patients treated by reconstruction with a fibula-free flap without using a 3D template. Three-dimensional modeling was performed by converting from preoperative computed tomography to a stereolithography format to obtain the resin 3D models. Qualitative analysis of anatomical and aesthetic results consisted of the evaluation of the patients' aesthetic and functional satisfaction and the symmetry of the mandibular contour observed at clinical examination. Quantitative analysis was based on the assessment of the accuracy and precision of the reconstruction by comparing preoperative and postoperative computed tomograms as objective indicators. Results: Seven patients (five males and two females, mean age of 65.1 years) were included in the study. All patients showed a symmetric mandibular contour based on the clinical examination. After recovery, six patients (85.7%) considered themselves aesthetically satisfied. The quantitative analysis (assessed in six/seven patients) showed that the mean difference between preoperative and postoperative intercondylar distance, intergonial angle distance, anteroposterior dimension, and gonial angle improved in the 3D template-assisted group. Conclusion: The 3D-printed template for mandibular reconstruction with microvascular fibula-free flap can improve aesthetic outcomes in comparison with standard approaches

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia.Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naive Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO(2) ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naive Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naive Bayes algorithm with 14 features chosen a priori.Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naive Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia

Symptomatic remission and recovery in major psychosis: Is there a role for BDNF? A secondary analysis of the LABSP cohort data

Remission, relapse prevention, and clinical recovery are crucial areas of interest in schizophrenia (SCZ) research. Although SCZ is a chronic disorder with poor overall outcomes, years of research demonstrated that recovery is possible. There are considerable data linking brain-derived neurotrophic factor (BDNF) to SCZ, however, evidence on the role of BDNF in remission in SCZ is scarce. This secondary analysis of the Longitudinal Assessment of BDNF in Sardinian patients (LABSP) data aimed to investigate the relationship between serum BDNF levels and symptomatic remission, simultaneous clinical and functional remission, and recovery in patients with SCZ. A total of 105 patients with SCZ or schizoaffective disorder were recruited for a longitudinal assessment of BDNF levels over 24 months. Longitudinal data were analyzed using mixed-effects linear regression models. The study found significant associations between use of long acting injectables (chi 2 = 7.075, df = 1, p = 0.008), baseline serum BDNF levels (U = 701, z = -2.543, p = 0.011), and "childhood" (U = 475, z = -2.124, p = 0.034) and "general" (U = 55, z = -2.014, p = 0.044) subscales of the Premorbid Adjustment Scale (PAS) with patients maintaining remission and recovery. The diagnosis of SCZ was significantly associated with lower BDNF levels for patients with simultaneous clinical and functional remission (Z = 2.035, p = 0.0419) and recovery (Z = 2.009, p = 0.0445) compared to those without. There were no significant associations between remission in the entire sample and longitudinal serum BDNF levels or genetic variants within the BDNF gene. These findings provide further insight into the complex relationship between BDNF and SCZ

A Secondary Analysis of the Complex Interplay between Psychopathology, Cognitive Functions, Brain Derived Neurotrophic Factor Levels, and Suicide in Psychotic Disorders: Data from a 2-Year Longitudinal Study

Identifying phenotypes at high risk of suicidal behaviour is a relevant objective of clinical and translational research and can facilitate the identification of possible candidate biomarkers. We probed the potential association and eventual stability of neuropsychological profiles and serum BDNF concentrations with lifetime suicide ideation and attempts (LSI and LSA, respectively) in individuals with schizophrenia (SCZ) and schizoaffective disorder (SCA) in a 2-year follow-up study. A secondary analysis was conducted on a convenience sample of previously recruited subjects from a single outpatient clinic. Retrospectively assessed LSI and LSA were recorded by analysing the available longitudinal clinical health records. LSI + LSA subjects consistently exhibited lower PANSS-defined negative symptoms and better performance in the BACS-letter fluency subtask. There was no significant association between BDNF levels and either LSI or LSA. We found a relatively stable pattern of lower negative symptoms over two years among patients with LSI and LSA. No significant difference in serum BDNF concentrations was detected. The translational viability of using neuropsychological profiles as a possible avenue for the identification of populations at risk for suicide behaviours rather than the categorical diagnosis represents a promising option but requires further confirmation

Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment

Abstract Introduction The performance of seven cardiovascular (CV) risk algorithms is evaluated in a multicentric cohort of ankylosing spondylitis (AS) patients. Performance and calibration of traditional CV predictors have been compared with the novel paradigm of machine learning (ML). Methods A retrospective analysis of prospectively collected data from an AS cohort has been performed. The primary outcome was the first CV event. The discriminatory ability of the algorithms was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), which is like the concordance-statistic (c-statistic). Three ML techniques were considered to calculate the CV risk: support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN). Results Of 133 AS patients enrolled, 18 had a CV event. c-statistic scores of 0.71, 0.61, 0.66, 0.68, 0.66, 0.72, and 0.67 were found, respectively, for SCORE, CUORE, FRS, QRISK2, QRISK3, RRS, and ASSIGN. AUC values for the ML algorithms were: 0.70 for SVM, 0.73 for RF, and 0.64 for KNN. Feature analysis showed that C-reactive protein (CRP) has the highest importance, while SBP and hypertension treatment have lower importance. Conclusions All of the evaluated CV risk algorithms exhibit a poor discriminative ability, except for RRS and SCORE, which showed a fair performance. For the first time, we demonstrated that AS patients do not show the traditional ones used by CV scores and that the most important variable is CRP. The present study contributes to a deeper understanding of CV risk in AS, allowing the development of innovative CV risk patient-specific models