EPSILoN score: Validation cohort of a prognostic score in advanced non-small cell lung cancer (aNSCLC) patients treated with immunotherapy

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Participation of heparin binding proteins from the surface of Leishmania (Viannia) braziliensis promastigotes in the adhesion of parasites to Lutzomyia longipalpis cells (Lulo) in vitro

Background: Leishmania (V.) braziliensis is a causative agent of cutaneous leishmaniasis in Brazil. During the parasite life cycle, the promastigotes adhere to the gut of sandflies, to avoid being eliminated with the dejection. the Lulo cell line, derived from Lutzomyia longipalpis (Diptera: Psychodidae), is a suitable in vitro study model to understand the features of parasite adhesion. Here, we analyze the role of glycosaminoglycans (GAGs) from Lulo cells and proteins from the parasites in this event.Methods: Flagellar (F-f) and membrane (M-f) fractions from promastigotes were obtained by differential centrifugation and the purity of fractions confirmed by western blot assays, using specific antibodies for cellular compartments. Heparin-binding proteins (HBP) were isolated from both fractions using a HiTrap-Heparin column. in addition, binding of promastigotes to Lulo cells or to a heparin-coated surface was assessed by inhibition assays or surface plasmon resonance (SPR) analysis.Results: the success of promastigotes subcellular fractionation led to the obtainment of F-f and M-f proteins, both of which presented two main protein bands (65.0 and 55.0kDa) with affinity to heparin. the contribution of HBPs in the adherence of promastigotes to Lulo cells was assessed through competition assays, using HS or the purified HBPs fractions. All tested samples presented a measurable inhibition rate when compared to control adhesion rate (17 +/- 2.0% of culture cells with adhered parasites): 30% (for HS 20 mu g/ml) and 16% (for HS 10 mu g/ml); HBP M-f (35.2% for 10 mu g/ml and 25.4% for 20 mu g/ml) and HBP F-f (10.0% for 10 mu g/ml and 31.4% for 20 mu g/ml). Additionally, to verify the presence of sulfated GAGs in Lulo cells surface and intracellular compartment, metabolic labeling with radioactive sulfate was performed, indicating the presence of an HS and chondroitin sulfate in both cell sections. the SPR analysis performed further confirmed the presence of GAGs ligands on L. (V.) braziliensis promastigote surfaces.Conclusions: the data presented here point to evidences that HBPs present on the surface of L. (V.) braziliensis promastigotes participate in adhesion of these parasites to Lulo cells through HS participation.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)Lab Biol Mol & Doencas Endem, BR-21040360 Rio de Janeiro, BrazilLab Ultraestrutura Celular, BR-21040360 Rio de Janeiro, BrazilFiocruz MS, IOC, Lab Bioquim & Fisiol Insetos, BR-21040360 Rio de Janeiro, BrazilFiocruz MS, IPEC, Lab Vigilancia Leishmanioses, BR-21040360 Rio de Janeiro, BrazilUniversidade Federal de São Paulo, UNIFESP, Dept Bioquim, São Paulo, BrazilUniv Rosario, Escuela Med, Bogota, DC, ColombiaUniversidade Federal de São Paulo, UNIFESP, Dept Bioquim, São Paulo, BrazilCNPq: 300731/2010-8CNPq: 509737/2010-2CAPES: EDITAL - 11/2009FAPERJ: E-26/103.060/2008FAPERJ: E-26/110.257/2010Web of Scienc

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Secondary Autochthonous Outbreak of Chikungunya, Southern Italy, 2017

In 2017, a chikungunya outbreak in central Italy later evolved into a secondary cluster in southern Italy, providing evidence of disease emergence in new areas. Officials have taken action to raise awareness among clinicians and the general population, increase timely case detection, reduce mosquito breeding sites, and promote mosquito bite prevention

Emotional, hyperactivity and inattention problems in adolescents with immunocompromising chronic diseases during the COVID-19 pandemic

Objective: To assess factors associated with emotional changes and Hyperactivity/Inattention (HI) motivated by COVID-19 quarantine in adolescents with immunocompromising diseases. Methods: A cross-sectional study included 343 adolescents with immunocompromising diseases and 108 healthy adolescents. Online questionnaires were answered including socio-demographic data and self-rated healthcare routine during COVID-19 quarantine and validated surveys: Strengths and Difficulties Questionnaire (SDQ), Pittsburgh Sleep Quality Index (PSQI), Pediatric Quality of Life Inventory 4.0 (PedsQL4.0). Results: The frequencies of abnormal emotional SDQ scores from adolescents with chronic diseases were similar to those of healthy subjects (110/343 [32%] vs. 38/108 [35%], p = 0.548), as well as abnormal hyperactivity/inattention SDQ scores (79/343 [23%] vs. 29/108 [27%], p = 0.417). Logistic regression analysis of independent variables associated with abnormal emotional scores from adolescents with chronic diseases showed: female sex (Odds Ratio [OR = 3.76]; 95% Confidence Interval (95% CI) 2.00‒7.05; p < 0.001), poor sleep quality (OR = 2.05; 95% CI 1.08‒3.88; p = 0.028) and intrafamilial violence during pandemic (OR = 2.17; 95% CI 1.12‒4.19; p = 0.021) as independently associated with abnormal emotional scores, whereas total PedsQL score was inversely associated with abnormal emotional scores (OR = 0.95; 95% CI 0.93‒0.96; p < 0.0001). Logistic regression analysis associated with abnormal HI scores from patients evidenced that total PedsQL score (OR = 0.97; 95% CI 0.95‒0.99; p = 0.010], changes in medical appointments during the pandemic (OR = 0.39; 95% CI 0.19-0.79; p = 0.021), and reliable COVID-19 information (OR = 0.35; 95% CI 0.16‒0.77; p = 0.026) remained inversely associated with abnormal HI scores. Conclusion: The present study showed emotional and HI disturbances in adolescents with chronic immunosuppressive diseases during the COVID-19 pandemic. It reinforces the need to promptly implement a longitudinal program to protect the mental health of adolescents with and without chronic illnesses during future pandemics

Shedding light on typical species : implications for habitat monitoring

Habitat monitoring in Europe is regulated by Article 17 of the Habitats Directive, which suggests the use of typical species to assess habitat conservation status. Yet, the Directive uses the term “typical” species but does not provide a definition, either for its use in reporting or for its use in impact assessments. To address the issue, an online workshop was organized by the Italian Society for Vegetation Science (SISV) to shed light on the diversity of perspectives regarding the different concepts of typical species, and to discuss the possible implications for habitat monitoring. To this aim, we inquired 73 people with a very different degree of expertise in the field of vegetation science by means of a tailored survey composed of six questions. We analysed the data using Pearson's Chi-squared test to verify that the answers diverged from a random distribution and checked the effect of the degree of experience of the surveyees on the results. We found that most of the surveyees agreed on the use of the phytosociological method for habitat monitoring and of the diagnostic and characteristic species to evaluate the structural and functional conservation status of habitats. With this contribution, we shed light on the meaning of “typical” species in the context of habitat monitoring

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Adhesion to carbon nanotube conductive scaffolds forces action-potential appearance in immature rat spinal neurons

In the last decade, carbon nanotube growth substrates have been used to investigate neurons and neuronal networks formation in vitro when guided by artificial nano-scaled cues. Besides, nanotube-based interfaces are being developed, such as prosthesis for monitoring brain activity. We recently described how carbon nanotube substrates alter the electrophysiological and synaptic responses of hippocampal neurons in culture. This observation highlighted the exceptional ability of this material in interfering with nerve tissue growth. Here we test the hypothesis that carbon nanotube scaffolds promote the development of immature neurons isolated from the neonatal rat spinal cord, and maintained in vitro. To address this issue we performed electrophysiological studies associated to gene expression analysis. Our results indicate that spinal neurons plated on electro-conductive carbon nanotubes show a facilitated development. Spinal neurons anticipate the expression of functional markers of maturation, such as the generation of voltage dependent currents or action potentials. These changes are accompanied by a selective modulation of gene expression, involving neuronal and non-neuronal components. Our microarray experiments suggest that carbon nanotube platforms trigger reparative activities involving microglia, in the absence of reactive gliosis. Hence, future tissue scaffolds blended with conductive nanotubes may be exploited to promote cell differentiation and reparative pathways in neural regeneration strategies