Diastereoselective Synthesis of C-Glycosylated Amino Acids with Lactams as Peptide Building Blocks

Readily available by lipase-catalyzed kinetic resolution or from a chiral pool, beta-, gamma-, and delta-lactams can be used as peptide building blocks for the synthesis of C-glycosylated amino acids 1. By reaction with glycosyl dianions, metabolic stable glycosylated amino acids can be prepared diastereoselectively. Ac=acetyl; Bn=benzyl; Boc=tert-butoxycarbonyl; R=Et, Bn; R'=H, alkyl; n=1-3.     

Reactions of metal ions with dithizone in molten naphthalene

A comparative study has been made of the reactions of metal ions with dithizone in the two media, chloroform and molten naphthalene. The absorption spectra of several metal dithizonates, prepared in molten naphthalene, were recorded and were found to be the same as those of the dithizonates obtained by extraction of metal ions from aqueous solution with chloroform dithizone.     

Isotope fractionation of water during evaporation without condensation

The microscopic events engendering liquid water evaporation have received much attention over the last century, but remain incompletely understood. We present measurements of isotope fractionation occurring during free molecular evaporation from liquid microjets and show that the isotope ratios of evaporating molecules exhibit dramatic differences from equilibrium vapor values, strong variations with the solution deuterium mole fraction, and a clear temperature dependence. These results indicate the existence of an energetic barrier to evaporation and that the evaporation coefficient of water is less than unity. These new insights into water evaporation promise to advance our understanding of the processes that control the formation and lifetime of clouds in the atmosphere.     

On the Fourier transformation on spaces of (p,q)-multipliers

LetG denote a locally compact abelian topological group. The aim of the present paper is to prove an “intermediate” result between two well-known results ofL. Hörmander andG. I. Gaudry concerning the structure of the spaces ?_G?μ?t ^p,q (G).     

Low-Cost Potentiometric Sensor for Chloride Measurement in Continuous Industrial Process Control

Recently, the new updates in legislation about drinking water control and human health have increased the demand for novel electrochemical low-cost sensors, such as potentiometric ones. Nowadays, the determination of chloride ion in aqueous solutions has attracted great attention in several fields, from industrial processes to drinking water control. Indeed, chloride plays a crucial role in corrosion, also influencing the final taste of beverages, especially coffee. The main goal is to obtain devices suitable for continuous and real-time analysis. For these reasons, we investigated the possibility to develop an easy, low-cost potentiometric chloride sensor, able to perform analysis in aqueous mediums for long immersion time and reducing the need of periodic calibration. We realized a chloride ion selective electrode made of Ag/AgCl sintered pellet and we tested its response in model solutions compatible with drinking water. The sensor was able to produce a stable, reproducible, and accurate quantification of chloride in 900 s, without the need for a preliminary calibration test. This opens the route to potential applications of this sensor in continuous, in situ, and real time measurement of chloride ions in industrial processes, with a reduced need for periodic maintenance.     

K18- and CAG-hACE2 Transgenic Mouse Models and SARS-CoV-2: Implications for Neurodegeneration Research

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic that might lead to very serious consequences. Notably, mental status change, brain confusion, and smell and taste disorders along with neurological complaints have been reported in patients infected with SARS-CoV-2. Furthermore, human brain tissue autopsies from COVID-19 patients show the presence of SARS-CoV-2 neuroinvasion, which correlates with the manifestation of meningitis, encephalitis, leukocyte infiltration, and neuronal damage. The olfactory mucosa has been suggested as a way of entry into the brain. SARS-CoV-2 infection is also known to provoke a hyper-inflammatory reaction with an exponential increase in the production of pro-inflammatory cytokines leading to systemic responses, even in the absence of direct infection of brain cells. Angiotensin-converting enzyme 2 (ACE2), the entry receptor of SARS-CoV-2, has been extensively demonstrated to be present in the periphery, neurons, and glial cells in different brain regions. To dissect the details of neurological complications and develop therapies helping COVID-19 survivors regain pre-infection quality of life, the development of robust clinical models is highly warranted. Several human angiotensin-converting enzyme 2 (hACE2) transgenic mouse models have been developed and used for antiviral drug screening and vaccine development, as well as for better understanding of the molecular pathogenetic mechanisms of SARS-CoV-2 infection. In this review, we summarize recent results from the studies involving two such mouse models, namely K18- and CAG-hACE2 transgenics, to evaluate the direct and indirect impact of SARS-CoV-2 infection on the central nervous system.     

Ultrastructural Damages to H1N1 Influenza Virus Caused by Vapor Essential Oils

Influenza viruses are transmitted from human to human via airborne droplets and can be transferred through contaminated environmental surfaces. Some works have demonstrated the efficacy of essential oils (EOs) as antimicrobial and antiviral agents, but most of them examined the liquid phases, which are generally toxic for oral applications. In our study, we describe the antiviral activity of Citrus bergamia, Melaleuca alternifolia, Illicium verum and Eucalyptus globulus vapor EOs against influenza virus type A. In the vapor phase, C. bergamia and M. alternifolia strongly reduced viral cytopathic effect without exerting any cytotoxicity. The E. globulus vapor EO reduced viral infection by 78% with no cytotoxicity, while I. verum was not effective. Furthermore, we characterized the EOs and their vapor phase by the head-space gas chromatography–mass spectrometry technique, observing that the major component found in each liquid EO is the same one of the corresponding vapor phases, with the exception of M. alternifolia. To deepen the mechanism of action, the morphological integrity of virus particles was checked by negative staining transmission electron microscopy, showing that they interfere with the lipid bilayer of the viral envelope, leading to the decomposition of membranes. We speculated that the most abundant components of the vapor EOs might directly interfere with influenza virus envelope structures or mask viral structures important for early steps of viral infection.     

GlyNet: a multi-task neural network for predicting protein–glycan interactions

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan–protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet''s output of continuous values provides more detailed results than the standard binary classification models. After incorporating a simple threshold to transform such continuous outputs the resulting GlyNet classifier outperforms those standard classifiers. GlyNet is the first multi-output regression model for predicting protein–glycan interactions and serves as an important benchmark, facilitating development of quantitative computational glycobiology.

GlyNet, a neural net model of glycan-protein binding strengths. Given a glycan it outputs binding to each of several protein samples. Reproducing glycan array data, it extrapolates the binding of untested glycans against the protein samples.