Toward Autonomous Guidance and Control: A Robust AI-Based Solution for Low-Thrust Orbit Transfers

The focus of our initial application scenario centers around a low-thrust orbit transfer in Low-Earth Orbit (LEO). This specific use-case has been chosen due to its inherent challenges, including the requirements for robustness and real-time computation. We propose an AI-based solution capable of autonomous and robust on-board G&C. The core of our approach leverages a Deep Neural Network (DNN) trained through Reinforcement Learning (RL) techniques. Our method aims at enhancing a traditional guidance approach by managing environmental perturbations, it processes the on-board navigation coordinates and provides the thrust to be imposed by the propulsion subsystem. Our approach demonstrates effectiveness in performing maneuvers changing semi-major axis (SMA), eccentricity (ECC), and inclination (INC), operating continuously with a control horizon of several days. Robustness is tested by using physical model uncertainties, introducing disturbances in the mission coordinates, and injecting perturbations in subsystems

Longitudinal Analysis of Sympton Expression in Grapevines Affected by Esca

An analysis of symptom expression in esca infected grapevines was performed by focusing on the dynamics of each plant. A parametric statistical model was proposed to evaluate the probability that a plant would show esca symptoms at given values for a relevant set of factors (year, presence of symptoms in the previous year, presence of plants with symptoms in the close neighborhood). The statistical tests of the hypotheses revealed that the considered factors explained a large amount of the observed variability. In particular, the state of plants in the close vicinity is one of those factors. Thus we found evidence that there was an association between plant vicinity and esca symptoms. Future developments of our model will include the factors field column and weather

Machine-Learning for Prescription Patterns: Random Forest in the Prediction of Dose and Number of Antipsychotics Prescribed to People with Schizophrenia

Objective: We aimed to predict antipsychotic prescription patterns for people with schizophrenia using machine learning (ML) algorithms.Methods: In a cross-sectional design, a sample of community mental health service users (SUs; n = 368) with a primary diagnosis of schizophrenia was randomly selected. Socio-demographic and clinical features, including the number, total dose, and route of administration of the antipsychotic treatment were recorded. Information about the number and the length of psychiatric hospitalization was retrieved. Ordinary Least Square (OLS) regression and ML algorithms (i.e., random forest [RF], supported vector machine, K-nearest neighborhood, and Naive Bayes) were used to estimate the predictors of total antipsychotic dosage and prescription of antipsychotic polytherapy (APP).Results: The strongest predictor of the total dose was APP. The number of Community Mental Health Centers (CMHC) contacts was the most important predictor of APP and, with APP omitted, of dosage. Treatment with anticholinergics predicted APP, emphasizing the strong correlation between APP and higher antipsychotic dose. RF performed better than OLS regression and the other ML algorithms in predicting both antipsychotic dose (root square mean error = 0.70, R-2 = 0.31) and APP (area under the receiving operator curve = 0.66, true positive rate = 0.41, and true negative rate = 0.78).Conclusion: APP is associated with the prescription of higher total doses of antipsychotics. Frequent attenders at CMHCs, and SUs recently hospitalized are often treated with APP and higher doses of antipsychotics. Future prospective studies incorporating standardized clinical assessments for both psychopathological severity and treatment efficacy are needed to confirm these findings

TCT-737 MONITORING THE LEARNING CURVE AND QUALITY OF CARE IN TAVI PROCEDURES BY CUSUM ANALYSIS

n/

Post-traumatic stress disorder among LGBTQ people: a systematic review and meta-analysis

Aims: Lesbian, gay, bisexual, transgender and queer people (LGBTQ) are at increased risk of traumatization. This systematic review aimed to summarize data regarding the risk of post-traumatic stress disorder (PTSD) for LGBTQ people and their subgroups. Methods: Medline, Scopus, PsycINFO and EMBASE were searched until September 2022. Studies reporting a comparative estimation of PTSD among LGBTQ population and the general population (i.e., heterosexual/cisgender), without restrictions on participants' age and setting for the enrolment, were identified. Meta-analyses were based on odds ratio (OR and 95% confidence intervals [CI]), estimated through inverse variance models with random effects. Results: The review process led to the selection of 27 studies, involving a total of 31,903 LGBTQ people and 273,842 controls, which were included in the quantitative synthesis. Overall, LGBTQ people showed an increased risk of PTSD (OR: 2.20 [95% CI: 1.85; 2.60]), although there was evidence of marked heterogeneity in the estimate (I2 = 91%). Among LGBTQ subgroups, transgender people showed the highest risk of PTSD (OR: 2.52 [95% CI: 2.22; 2.87]) followed by bisexual people (OR: 2.44 [95% CI: 1.05; 5.66]), although these comparisons are limited by the lack of data for other sexual and gender minorities, such as intersex people. Interestingly, the risk of PTSD for bisexual people was confirmed also considering lesbian and gay as control group (OR: 1.44 [95% CI: 1.07; 1.93]). The quality of the evidence was low. Conclusions: LGBTQ people are at higher risk of PTSD compared with their cisgender/heterosexual peers. This evidence may contribute to the public awareness on LGBTQ mental health needs and suggest supportive strategies as well as preventive interventions (e.g., supportive programs, counselling, and destigmatizing efforts) as parts of a tailored health-care planning aimed to reduce psychiatric morbidity in this at-risk population

Minimally invasive aortic valve replacement with a sutureless valve through a right anterior mini-thoracotomy versus transcatheter aortic valve implantation in high-risk patients

n/

Uma revisão sistemática de estudos qualitativos

Objective: To gain a better understanding of how long-acting injectable antipsychotic (LAI) therapy is perceived by patients. Methods: A search for qualitative studies has been carried out on PubMed, Google Scholar, PsycINFO and PsycArticles, yielding 11 studies suitable for a review of qualitative studies. The reporting approach chosen was meta-ethnography, following the ENTREQ statement recommendations. Key concepts common to the different studies were extrapolated and then analysed in a systematic and comparative way. Results: Some recurrent issues were associated with LAIs, such as fear of coercion, fear of needles and lack of knowledge about depot therapy. These topics are linked to each other and the patients most concerned about the disadvantages of LAIs are those who are less informed about them, or who have experienced coercion and trauma during hospitalisation. On the other hand, patients who had already received LAIs, and those who had a good therapeutic relationship with their healthcare providers expressed satisfaction with this form of treatment and its continuation. Conclusion: Long-acting injectable antipsychotics are a tool in the management of mental disorders, and a viable alternative to oral medication. Patients show curiosity towards this method of administration, but lack of knowledge is a common finding. Shared decision making about the use of LAIs antipsychotics requires that patients receive accurate information and support for their decision regarding medication.publishersversionpublishe

Genome-Based Approach Delivers Vaccine Candidates Against Pseudomonas aeruginosa

High incidence, severity and increasing antibiotic resistance characterize Pseudomonas aeruginosa infections, highlighting the need for new therapeutic options. Vaccination strategies to prevent or limit P. aeruginosa infections represent a rational approach to positively impact the clinical outcome of risk patients; nevertheless this bacterium remains a challenging vaccine target. To identify novel vaccine candidates, we started from the genome sequence analysis of the P. aeruginosa reference strain PAO1 exploring the reverse vaccinology approach integrated with additional bioinformatic tools. The bioinformatic approaches resulted in the selection of 52 potential antigens. These vaccine candidates were conserved in P. aeruginosa genomes from different origin and among strains isolated longitudinally from cystic fibrosis patients. To assess the immune-protection of single or antigens combination against P. aeruginosa infection, a vaccination protocol was established in murine model of acute respiratory infection. Combinations of selected candidates, rather than single antigens, effectively controlled P. aeruginosa infection in the in vivo model of murine pneumonia. Five combinations were capable of significantly increase survival rate among challenged mice and all included PA5340, a hypothetical protein exclusively present in P. aeruginosa. PA5340 combined with PA3526-MotY gave the maximum protection. Both proteins were surface exposed by immunofluorescence and triggered a specific immune response. Combination of these two protein antigens could represent a potential vaccine to prevent P. aeruginosa infection