Our Italian experience using lung ultrasound for identification,grading and serial follow-up of severity of lung involvement for management of patients with COVID-19

Lung ultrasound (LU) has rapidly become a tool for assessment of patients stricken by the novel coronavirus 2019 (COVID-19). Over the past two and a half months (January, February, and first half of March 2020) we have used this modality for identification of lung involvement along with pulmonary severity in patients with suspected or documented COVID-19 infection. Use of LU has helped us in clinical decision making and reduced the use of both chest x-rays and computed tomography (CT).     

Sporadic hereditary neuropathies misdiagnosed as chronic inflammatory demyelinating polyradiculoneuropathy: Pitfalls and red flags

Hereditary neuropathies may be misdiagnosed with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). A correct diagnosis is crucial for avoiding unnecessary therapies and access genetic counseling. We report on nine patients (seven men, mean age 49.2 ± 16.1) diagnosed with and treated as CIDP, in whom mutations or variants of unknown significance (VUS) in genes associated with hereditary neuropathies were reported. All underwent neurological and neurophysiological examination, eight also cerebrospinal fluid (CSF) analysis. In 4/9, nerve ultrasound and/or MR‐neurography were performed. All the patients complained of progressive upper or lower limbs sensory‐motor symptoms, with heterogeneous disease duration (1‐34 years, mean 8.6 ± 10.8). Neurophysiology showed demyelinating signs in seven patients, mixed findings with predominant axonal damage in two patients. Neuroimaging disclosed diffuse abnormalities at proximal and distal segments. Molecular screening showed PMP22 duplication in two patients, mutations in the MPZ, EGR2, and GJB1 genes were reported in each of the remaining patients. The two patients with mixed neurophysiological findings had p.Val30Met mutation in the transthyretin gene. Two patients had VUS in the MARS and HSPB1 genes. Four patients had partial response to immunomodulant therapies, and CSF and neurophysiological features suggesting an inflammatory condition concomitant with the hereditary neuropathy. Hereditary neuropathy may be misdiagnosed with CIDP. The most common pitfalls are CSF (high protein levels and oligoclonal bands), incorrect interpretation of neurophysiology, and transient benefit from therapies. Neuroimaging may be helpful in cases with atypical presentations or when severe axonal damage complicate the neurophysiological interpretation.     

AI-assisted superresolution cosmological simulations

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to 16 h−1Mpc and the HR halo mass function to within 10% down to 1011 M⊙. We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.

As telescopes and satellites become more powerful, observational data on galaxies, quasars, and the matter in intergalactic space becomes more detailed and covers a greater range of epochs and environments in the Universe. Our cosmological simulations (see, e.g., ref. 1) must also become more detailed and more wide-ranging in order to make predictions and test the effects of different physical processes and different dark-matter candidates. Even with supercomputers, we are forced to decide whether to maximize either resolution or volume, or else compromise on both. These limitations can be overcome through the development of methods that leverage techniques from the artificial intelligence (AI) revolution (see, e.g., ref. 2) and make superresolution (SR) simulations possible. In the present work, we begin to explore this possibility, combining knowledge and existing superscalable codes for petascale-plus cosmological simulations (3) with machine learning (ML) techniques to effectively create representative volumes of the Universe that incorporate information from higher-resolution models of galaxy formation. Our first attempts, presented here, involve simulations with dark matter and gravity only, and extensions to full hydrodynamics will follow. This hybrid approach, which will imply offloading simulations to neural networks (NNs) and other ML algorithms, has the promise to enable the prediction of quasar, supermassive black hole, and galaxy properties in a way that is statistically identical to full hydrodynamic models, but with a significant speed-up.Adding details to images below the resolution scale (SR image enhancement) has become possible with the latest advances in deep learning (DL; ML with NN; ref. 4), including generative adversarial networks (GANs; ref. 5). The technique has applications in many fields, from microscopy to law enforcement (6). It has been used for observational astronomical images by (7), to recover galaxy features from below the resolution scale in degraded Hubble Space Telescope images. Besides SR image enhancement, DL has started to find applications in cosmological simulations. For example, refs. 8 and 9 showed how NNs can predict the nonlinear formation of structures given simple linear theory predictions. NN models have also been trained to predict galaxies (10, 11) and 21-cm emission from neutral hydrogen (12) from simulations that only contain dark matter. GANs have been used in ref. 13 to generate image slices of cosmological models and to generate dark-matter halos from density fields (14). ML techniques other than DL find many applications, too. For example, Kamdar et al. (15) have applied extremely randomized trees to dark-matter simulations to predict hydrodynamic galaxy properties.Generating mocks for future sky surveys requires large volumes and high accuracy, a task that quickly becomes computationally prohibitive. To alleviate the cost, recently, Dai and Seljak (16) developed a Lagrangian-based parametric ML model to predict various hydrodynamical outputs from the dark-matter density field. In other work, Dai et al. (17, 18) sharpened the particle distribution using a potential gradient descent method starting from low-resolution (LR) simulations. Note, however, that these approaches did not aim to enhance the spatial or mass resolution of a simulation.On the DL side, recently, Ramanah et al. (19) explored using the SR technique to map density fields of LR cosmological simulations to that of the high-resolution (HR) ones. While the goal is similar, our work has the following three key differences. First, instead of focusing on the dark-matter density field, we aim to enhance the number of particles and predict their displacements, from which the density fields can be inferred. This approach allows us to preserve the particle nature of the N-body simulations and therefore to interpret the SR outputs as simulations themselves. Second, we test our technique at a higher SR ratio. Compared to ref. 19, which increased the number of Eulerian voxels by 8 times, we increase the number of particles and thus the mass resolution by a factor of 512. Finally, to facilitate future applications of SR on hydrodynamic simulations in representative volumes, we test our method at much smaller scales and in large simulations whose volume is much bigger than that of the training data.     

Peptides of human gingival crevicular fluid determined by HPLC-ESI-MS

The acidic-soluble protein content of human gingival crevicular fluid was analyzed by reverse-phase high-performance liquid chromatography (RP-HPLC), and the eluent deriving from the chromatography separation was directly introduced into an ion-trap mass spectrometer through electrospray ionization (ESI-IT MS). By this technique the molecular weight of peptides/proteins was determined with a precision of approximately 1/10,000 amu. On the basis of the chromatographic behavior and the knowledge of the molecular mass value, some peptides and proteins soluble in acidic solution were unambiguously recognized. Besides high quantities of human serum albumin, alpha-defensins 1-4 and minor amounts of cystatin A, statherin, basic PB salivary peptide and other unidentified components were detected. The presence of alpha-defensins in gingival crevicular fluid is in agreement with their relevant contribution to protein composition deriving from granulocyte secretions. Other peptides and proteins abundant in human saliva, such as proline-rich proteins (PRPs) and histatins, were not detected in gingival crevicular fluid. Further investigations will be necessary to establish the origin of statherin and PB salivary peptide in gingival crevicular fluid.     

Carbachol-induced in vitro secretion of certain human submandibular proteins investigated by mass-spectrometry

Objective

To investigate protein content of saliva produced in vitro by samples of human submandibular gland following stimulation with the muscarinic agent carbachol.

Design

Tissue samples, obtained at surgery from seven patients and showing normal morphological appearance, were tested for 30 min: in absence of carbachol and atropine; in presence of carbachol (10 μM); in presence of carbachol (10 μM) and atropine (20 μM); or in presence of just atropine (20 μM). Medium was analysed by high-performance liquid chromatography-mass-spectrometry. Neither before nor during surgery were the patients exposed to drug treatments that were likely to influence the in vitro secretion.

Results

Proline-rich proteins (PRP)-1 and -3, peptide PC and PB, statherin, cystatins SN, S1 and S2 were invariably found in control gland tissue medium. Mean concentrations of these proteins/peptides in the medium were non-proportionally elevated following carbachol exposure to the gland tissues. Difference between basal release and carbachol-induced secretion achieved statistical significance as to all the proteins/peptides under study but for statherin. Atropine alone or atropine plus carbachol caused no significant changes compared to the basal release of proteins/peptides.

Conclusions

In vitro studies on salivary glands make it possible to study protein secretion from individual glands and thus, to reveal the contribution of the various types of gland to protein/peptide content of whole saliva. The disproportional responses to carbachol may imply that the proteins/peptides are not confined to the same cells or to the same intracellular locations and are therefore not secreted as packages at parasympathetic cholinergic activity.