Decreased Gas6 and sAxl Plasma Levels Are Associated with Hair Loss in COVID-19 Survivors

: Post-acute conditions after coronavirus disease 2019 (COVID-19) are quite common, although the underlying pathogenetic mechanisms leading to these conditions are not yet completely understood. In this prospective observational study, we aimed to test the hypothesis that Growth Arrest-Specific 6 (Gas6) and its soluble receptors, Axl (sAxl) and MerTK (sMer), might be implicated. A total of 263 subjects underwent a structured clinical evaluation one year after their hospital discharge for COVID-19, and they consented to donate a blood sample to measure their circulating Gas6, sAxl, and sMer levels. A total of 98 (37.3%) post-COVID-19 subjects complained of at least one residual physical symptom one year after their hospital discharge. Univariate analysis revealed that sAxl was marginally associated with residual symptoms, but at the level of logistic regression analysis, only the diffusing capacity of the lungs for carbon monoxide (DLCO) (OR 0.98, CI 95%: 0.96-0.99; p = 0.007) and the female sex (OR 2.49, CI 95%: 1.45-4.28; p = 0.001) were independently associated with long-lasting symptoms. A total of 69 (26.2%) subjects had hair loss. At the level of univariate analysis, Gas6, sAxl, DLCO, and the female gender were associated with its development. In a logistic regression analysis model, Gas6 (OR 0.96, CI 95%: 0.92-0.99; p = 0.015) and sAxl (OR 0.98, CI 95%; 0.97-1.0; p = 0.014), along with the female sex (OR 6.58, CI 95%: 3.39-12.78; p = 0.0001), were independent predictors of hair loss. Decreased levels of Gas6 and sAxl were associated with a history of hair loss following COVID-19. This was resolved spontaneously in most patients, although 23.7% complained of persistent hair loss one year after hospital discharge

Change over time of COVID-19 hospital presentation in Northern Italy

none40After the first autochthonous case described on February 19, also in Italy the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV-2) infection rapidly circulated, mainly in the Northern regions of the country. The earliest reports on Coronavirus disease-19 (COVID-19) have described worldwide a high prevalence of severe respiratory illness [1]. A suggestive feature of COVID-19 has been a rapid progression of the respiratory impairment, leading to acute respiratory distress syndrome (ARDS) and often requiring ventilation support [2]. To date, whether clinical features at hospital presentation and outcome of COVID-19 have changed over the outbreak course is unknown. We explored this issue in a multicenter cohort of patients hospitalized for COVID-19 in Northern Italy.mixedPatti G.; Mennuni M.; Della Corte F.; Spinoni E.; Sainaghi P. P.; COVID-UPO Clinical Team; Azzolina D; Hayden E; Rognon A; Grisafi L; Colombo C; Lio V; Pirisi M; Vaschetto R; Aimaretti G; Krengli M; Avanzi GC; Balbo PE; Capponi A; Castello LM; Bellan M; Malerba M; Garavelli PL; Zeppegno P; Savoia P; Chichino G; Olivieri C; Re R; Maconi A; Comi C; Roveta A; Bertolotti M; Carriero A; Betti M; Mussa M; Borrè S; Cantaluppi V; Cantello R; Bobbio F; GavellI F.Patti, G.; Mennuni, M.; Della Corte, F.; Spinoni, E.; Sainaghi, P. P.; COVID-UPO Clinical, Team; Azzolina, D; Hayden, E; Rognon, A; Grisafi, L; Colombo, C; Lio, V; Pirisi, M; Vaschetto, R; Aimaretti, G; Krengli, M; Avanzi, Gc; Balbo, Pe; Capponi, A; Castello, Lm; Bellan, M; Malerba, M; Garavelli, Pl; Zeppegno, P; Savoia, P; Chichino, G; Olivieri, C; Re, R; Maconi, A; Comi, C; Roveta, A; Bertolotti, M; Carriero, A; Betti, M; Mussa, M; Borrè, S; Cantaluppi, V; Cantello, R; Bobbio, F; Gavelli, F

On computational approaches for size-and-shape distributions from sedimentation velocity analytical ultracentrifugation

Sedimentation velocity analytical ultracentrifugation has become a very popular technique to study size distributions and interactions of macromolecules. Recently, a method termed two-dimensional spectrum analysis (2DSA) for the determination of size-and-shape distributions was described by Demeler and colleagues (Eur Biophys J 2009). It is based on novel ideas conceived for fitting the integral equations of the size-and-shape distribution to experimental data, illustrated with an example but provided without proof of the principle of the algorithm. In the present work, we examine the 2DSA algorithm by comparison with the mathematical reference frame and simple well-known numerical concepts for solving Fredholm integral equations, and test the key assumptions underlying the 2DSA method in an example application. While the 2DSA appears computationally excessively wasteful, key elements also appear to be in conflict with mathematical results. This raises doubts about the correctness of the results from 2DSA analysis