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Abstract Background The efficacy of early treatment with convalescent plasma in patients with COVID-19 is debated. Nothing is known about the potential effect of other plasma components other than anti-SARS-CoV-2 antibodies. Methods To determine whether convalescent or standard plasma would improve outcomes for adults in early phase of Covid19 respiratory impairment we designed this randomized, three-arms, clinical trial (PLACO COVID) blinded on interventional arms that was conducted from June 2020 to August 2021. It was a multicentric trial at 19 Italian hospitals. We enrolled 180 hospitalized adult patients with COVID-19 pneumonia within 5 days from the onset of respiratory distress. Patients were randomly assigned in a 1:1:1 ratio to standard of care (n = 60) or standard of care + three units of standard plasma (n = 60) or standard of care + three units of high-titre convalescent plasma (n = 60) administered on days 1, 3, 5 after randomization. Primary outcome was 30-days mortality. Secondary outcomes were: incidence of mechanical ventilation or death at day 30, 6-month mortality, proportion of days with mechanical ventilation on total length of hospital stay, IgG anti-SARS-CoV-2 seroconversion, viral clearance from plasma and respiratory tract samples, and variations in Sequential Organ Failure Assessment score. The trial was analysed according to the intention-to-treat principle. Results 180 patients (133/180 [73.9%] males, mean age 66.6 years [IQR 57–73]) were enrolled a median of 8 days from onset of symptoms. At enrollment, 88.9% of patients showed moderate/severe respiratory failure. 30-days mortality was 20% in Control arm, 23% in Convalescent (risk ratio [RR] 1.13; 95% confidence interval [CI], 0.61–2.13, P = 0.694) and 25% in Standard plasma (RR 1.23; 95%CI, 0.63–2.37, P = 0.544). Time to viral clearance from respiratory tract was 21 days for Convalescent, 28 for Standard plasma and 23 in Control arm but differences were not statistically significant. No differences for other secondary endpoints were seen in the three arms. Serious adverse events were reported in 1.7%, 3.3% and 5% of patients in Control, Standard and Convalescent plasma arms respectively. Conclusions Neither high-titer Convalescent nor Standard plasma improve outcomes of COVID-19 patients with acute respiratory failure. Trial Registration Clinicaltrials.gov Identifier: NCT04428021. First posted: 11/06/2020

Current therapeutic approaches for chronic venous ulcers (CVUs) still require evidence of effectiveness. Diverse sources of extracellular vesicles (EVs) have been proposed for tissue regeneration, however the lack of potency tests, to predict in-vivo effectiveness, and a reliable scalability have delayed their clinical application. This study aimed to investigate whether autologous serum-derived EVs (s-EVs), recovered from patients with CVUs, may be a proper therapeutic approach to improve the healing process. A pilot case-control interventional study (CS2/1095/0090491) has been designed and s-EVs recovered from patients. Patient eligibility included two or more distinct chronic lesions in the same limb with 11 months as median persistence of active ulcer before enrollment. Patients were treated three times a week, for 2 weeks. Qualitative CVU analysis demonstrated that s-EVs-treated lesions displayed a higher percentage of granulation tissue compared to the control group (Sham) (s-EVs 3 out of 5: 75–100 % vs Sham: none), further confirmed at day 30. s-EVs-treated lesions also displayed higher sloughy tissue reduction at the end of treatment even increased at day 30. Additionally, s-EV treatment led to a median surface reduction of 151 mm2 compared to 84 mm2 in the Sham group, difference even more evident at day 30 (s-EVs 385 mm2 vs Sham 106 mm2 p = 0.004). Consistent with the enrichment of transforming growth factor-β1 in s-EVs, histological analyses showed a regenerative tissue with an increase in microvascular proliferation areas. This study first demonstrates the clinical effectiveness of autologous s-EVs in promoting the healing process of CVUs unresponsive to conventional treatments.

The progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.

We introduce a theoretical framework for estimating, comparing and interpreting mechanistic hypotheses on long term protein propagation across brain networks in neurodegenerative disorders (ND). The model is expressed within a Bayesian non-parametric regression setting, where mechanisms of protein dynamics are inferred by means of gradient matching on dynamical systems (DS). The Bayesian formalism, combined with stochastic variational inference, naturally allows for model comparison via assessment of model evidence, while providing uncertainty quantification of causal relationship underlying protein progressions. When applied to in–vivo AV45-PET brain imaging data measuring topographic amyloid deposition in Alzheimer’s disease (AD), our model identified the mechanisms of accumulation, clearance and propagation as the best suited DS for bio-mechanical description of amyloid dynamics in AD, enabling realistic and accurate personalized simulation of amyloidosis.

We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from collections of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparameterized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis.

The spatial distribution of atrophy in neurodegenerative diseases suggests that brain connectivity mediates disease propagation. Different descriptors of the connectivity graph potentially relate to different underlying mechanisms of propagation. Previous approaches for evaluating the influence of connectivity on neurodegeneration consider each descriptor in isolation and match predictions against late-stage atrophy patterns. We introduce the notion of a topological profile — a characteristic combination of topological descriptors that best describes the propagation of pathology in a particular disease. By drawing on recent advances in disease progression modeling, we estimate topological profiles from the full course of pathology accumulation, at both cohort and individual levels. Experimental results comparing topological profiles for Alzheimer’s disease, multiple sclerosis and normal ageing show that topological profiles explain the observed data better than single descriptors. Within each condition, most individual profiles cluster around the cohort-level profile, and individuals whose profiles align more closely with other cohort-level profiles show features of that cohort. The cohort-level profiles suggest new insights into the biological mechanisms underlying pathology propagation in each disease.

The last decade has seen a great proliferation of supervised learning pipelines for individual diagnosis and prognosis in Alzheimer's disease. As more pipelines are developed and evaluated in the search for greater performance, only those results that are relatively impressive will be selected for publication. We present an empirical study to evaluate the potential for optimistic bias in classification performance results as a result of this selection. This is achieved using a novel, resampling-based experiment design that effectively simulates the optimisation of pipeline specifications by individuals or collectives of researchers using cross validation with limited data. Our findings indicate that bias can plausibly account for an appreciable fraction (often greater than half) of the apparent performance improvement associated with the pipeline optimisation, particularly in small samples. We discuss the consistency of our findings with patterns observed in the literature and consider strategies for bias reduction and mitigation.