Effect of Lactoferrin on Clinical Outcomes of Hospitalized Patients with COVID-19: The LAC Randomized Clinical Trial

As lactoferrin is a nutritional supplement with proven antiviral and immunomodulatory abilities, it may be used to improve the clinical course of COVID-19. The clinical efficacy and safety of bovine lactoferrin were evaluated in the LAC randomized double-blind placebo-controlled trial. A total of 218 hospitalized adult patients with moderate-to-severe COVID-19 were randomized to receive 800 mg/die oral bovine lactoferrin (n = 113) or placebo (n = 105), both given in combination with standard COVID-19 therapy. No differences in lactoferrin vs. placebo were observed in the primary outcomes: the proportion of death or intensive care unit admission (risk ratio of 1.06 (95% CI 0.63–1.79)) or proportion of discharge or National Early Warning Score 2 (NEWS2) ≤ 2 within 14 days from enrollment (RR of 0.85 (95% CI 0.70–1.04)). Lactoferrin showed an excellent safety and tolerability profile. Even though bovine lactoferrin is safe and tolerable, our results do not support its use in hospitalized patients with moderate-to-severe COVID-19

Respective roles of the DRL receptor and its ligand WNT5 in Drosophila mushroom body development.

International audienceIn recent decades, Drosophilamushroom bodies (MBs) have become a powerful model for elucidating the molecular mechanismsunderlying brain development and function. We have previously characterized the derailed(drl; also known as linotte) receptortyrosine kinase as an essential component of adult MB development. Here we show, using MARCM clones, a non-cell-autonomousrequirement for the DRL receptor in MB development. This result is in accordance with the pattern of DRL expression, which occursthroughout development close to, but not inside, MB cells. While DRL expression can be detected within both interhemispheric glialand commissural neuronal cells, rescue of the drlMB defects appears to involve the latter cellular type. The WNT5 protein has beenshown to act as a repulsive ligand for the DRL receptor in the embryonic central nervous system. We show here that WNT5 isrequired intrinsically within MB neurons for proper MB axonal growth and probably interacts with the extrinsic DRL receptor inorder to stop axonal growth. We therefore propose that the neuronal requirement for both proteins defines an interactingnetwork acting during MB development

Control of Central Synaptic Specificity in Insect Sensory Neurons

Synaptic specificity is the culmination of several processes, beginning with the establishment of neuronal subtype identity, followed by navigation of the axon to the correct subdivision of neuropil, and finally, the cell-cell recognition of appropriate synaptic partners. In this review we summarize the work on sensory neurons in crickets, cockroaches, moths, and fruit flies that establishes some of the principles and molecular mechanisms involved in the control of synaptic specificity. The identity of a sensory neuron is controlled by combinatorial expression of transcription factors, the products of patterning and proneural genes. In the nervous system, sensory axon projections are anatomically segregated according to modality, stimulus quality, and cell-body position. A variety of cell-surface and intracellular signaling molecules are used to achieve this. Synaptic target recognition is also controlled by transcription factors such as Engrailed and may be, in part, mediated by cadherin-like molecules

Asymptotic properties of quasi-maximum likelihood estimators for ARMA models with time-dependent coefficients

For about thirty years, time series models with time-dependent coefficients have sometimes been considered as an alternative to models with constant coefficients or non-linear models. Analysis based on models with time-dependent models has long suffered from the absence of an asymptotic theory except in very special cases. The purpose of this paper is to provide such a theory without using a locally stationary spectral representation and time rescaling. We consider autoregressive-moving average (ARMA) models with time-dependent coefficients and a heteroscedastic innovation process. The coefficients and the innovation variance are deterministic functions of time which depend on a finite number of parameters. These parameters are estimated by maximising the Gaussian likelihood function. Deriving conditions for consistency and asymptotic normality and obtaining the asymptotic covariance matrix are done using some assumptions on the functions of time in order to attenuate non-stationarity, mild assumptions for the distribution of the innovations, and also a kind of mixing condition. Theorems from the theory of martingales and mixtingales are used. Some simulation results are given and both theoretical and practical examples are treated. © Springer 2006.SCOPUS: ar.jinfo:eu-repo/semantics/publishe