Pulmonary long-term consequences of COVID-19 infections after hospital discharge

Objectives: COVID-19 survivors are reporting residual abnormalities after discharge from the hospital. Limited information is available about this stage of recovery or the lingering effects of the virus on pulmonary function and inflammation. The aim of this study was to describe lung function and to identify biomarkers in serum and induced sputum samples from patients recovering from COVID-19 hospitalisation. Methods: Patients admitted to Spanish hospitals with laboratory-confirmed COVID-19 infection by a real-time PCR (RT-PCR) assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were recruited for this study. Each hospital screened their lists of discharged patients at least 45 days after symptom onset. SARS-CoV-2-infected patients were divided into mild/moderate and severe disease groups according to the severity of their symptoms during hospitalisation. Patients’ epidemiological and medical histories, comorbidities, chronic treatments, and laboratory parameters were evaluated. Pulmonary function tests, the standardised 6-minute walk test (6 MWT) and chest computed tomography (CT) were also performed. The levels of proteases, their inhibitors, and shed receptors were measured in serum and induced sputum samples. Results: A total of 100 patients with respiratory function tests were included in this study. The median number of days after the onset of symptoms was 104 (IQR 89.25, 126.75). COVID-19 was severe in 47% (47/100) of patients. CT was normal in 48% (48/100) of patients. Lung function was normal (FEV1 ≥80%, FVC ≥80%, FEV1/FVC ≥0.7, and diffusing capacity for carbon monoxide [DLCO] ≥80%) in 92% (92/100), 94% (94/100), 100% (100/100) and 48% (48/100) of patients, respectively. Multivariate analysis showed that a DLCO <80% (OR 5.92; 95%CI 2.28-15.37; p <0.0001) and a lower serum LDH level (OR 0.98; 95%CI 0.97-0.99) were associated with the severe disease group of SARS-CoV-2 during hospital stay. Conclusions: A diffusion deficit (DLCO <80%) was still present after hospital discharge and was associated with the most severe SARS-CoV-2 cases

Combining deep learning and physical models for solar nowcasting

Sudden changes in solar irradiance on a local scale can significantly influence solar power generation. This intermittent characteristic of the solar resource is mainly caused by passing clouds and represents a challenge when solar energy is integrated into the power system. By making use of intra hour nowcasts (very short-term forecasts), changing conditions on solar irradiance can be anticipated, allowing optimized power plant operation and grid integration. All-sky imagers, capturing sky conditions at high spatial and temporal resolution, can be the basis of such nowcasting systems. However, the benefit of these nowcasting systems heavily depends on the accuracy of the predictions. In a previous work, a hybrid model combining physics-based and persistence nowcasts has proven to be advantageous. In this work, we present a novel deep learning (DL) model based on the transformer architecture for solar irradiance nowcasts and show that integrating this model into the hybrid model further improves the nowcast quality significantly. While the physics-based nowcasts are derived from a pipeline of processing steps to model clouds and anticipating their impact on solar irradiance, the DL model is completely data-driven and trained end-to-end using sequences of sky images and groundbased irradiance measurements as input. For comparison to the literature, evaluation is carried out on a benchmark dataset of 2019 from the same site. First, the nowcast quality of the DL model is analyzed independently on standard forecasting error metrics like root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and forecast skill. For computing the forecast skill, we used the so-called smart persistence (SP) as reference model. Reaching scores of over 28%, the DL model itself already outperforms the previous hybrid model in terms of RMSE. Next, the hybrid model, combining physics-based, DL and SP nowcasts, is evaluated on the same dataset using the same metrics. Compared to the previous hybrid model, the new hybrid model shows significant improvement over all metrics

Inhibitory transmission in locus coeruleus neurons expressing GABAA receptor epsilon subunit has a number of unique properties.

International audienceFast inhibitory synaptic transmission in the brain relies on ionotropic GABA(A) receptors (GABA(A)R). Eighteen genes code for GABA(A)R subunits, but little is known about the epsilon subunit. Our aim was to identify the synaptic transmission properties displayed by native receptors incorporating epsilon. Immunogold localization detected epsilon at synaptic sites on locus coeruleus (LC) neurons. In situ hybridization revealed prominent signals from epsilon, and mRNAs, some low beta1 and beta3 signals, and no gamma signal. Using in vivo extracellular and in vitro patch-clamp recordings in LC, we established that neuron firing rates, GABA-activated currents, and mIPSC charge were insensitive to the benzodiazepine flunitrazepam (FLU), in agreement with the characteristics of recombinant receptors including an epsilon subunit. Surprisingly, LC provided binding sites for benzodiazepines, and GABA-induced currents were potentiated by diazepam (DZP) in the micromolar range. A number of GABA(A)R ligands significantly potentiated GABA-induced currents, and zinc ions were only active at concentrations above 1 muM, further indicating that receptors were not composed of only alpha and beta subunits, but included an epsilon subunit. In contrast to recombinant receptors including an epsilon subunit, GABA(A)R in LC showed no agonist-independent opening. Finally, we determined that mIPSCs, as well as ensemble currents induced by ultra-fast GABA application, exhibited surprisingly slow rise times. Our work thus defines the signature of native GABA(A)R with a subunit composition including epsilon: differential sensitivity to FLU and DZP and slow rise time of currents. We further propose that alpha(3,) beta(1/3,) and epsilon subunits compose GABA(A)R in LC

Increased antiparkinson efficacy of the combined administration of VEGF- and GDNF-loaded nanospheres in a partial lesion model of Parkinson&rsquo;s disease

Enara Herr&aacute;n,1,2 Catalina Requejo,3 Jose Angel Ruiz-Ortega,4 Asier Aristieta,4 Manoli Igartua,1,2 Harkaitz Bengoetxea,3 Luisa Ugedo,4 Jose Luis Pedraz,1,2 Jose Vicente Lafuente,3 Rosa Maria Hern&aacute;ndez1,2 1NanoBioCel Group, Laboratory of Pharmaceutics, University of the Basque Country (UPV/EHU), School of Pharmacy, Vitoria, Spain; 2Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Vitoria, Spain; 3LaNCE, Department of Neurosciences, University of the Basque Country (UPV/EHU), Leioa, Spain; 4Department of Pharmacology, University of the Basque Country (UPV/EHU), Leioa, Spain Abstract: Current research efforts are focused on the application of growth factors, such as glial cell line-derived neurotrophic factor (GDNF) and vascular endothelial growth factor (VEGF), as neuroregenerative approaches that will prevent the neurodegenerative process in Parkinson&rsquo;s disease. Continuing a previous work published by our research group, and with the aim to overcome different limitations related to growth factor administration, VEGF and GDNF were encapsulated in poly(lactic-co-glycolic acid) nanospheres (NS). This strategy facilitates the combined administration of the VEGF and GDNF into the brain of 6-hydroxydopamine (6-OHDA) partially lesioned rats, resulting in a continuous and simultaneous drug release. The NS particle size was about 200&nbsp;nm and the simultaneous addition of VEGF NS and GDNF NS resulted in significant protection of the PC-12&nbsp;cell line against 6-OHDA in vitro. Once the poly(lactic-co-glycolic acid) NS were implanted into the striatum of 6-OHDA partially lesioned rats, the amphetamine rotation behavior test was carried out over 10&nbsp;weeks, in order to check for&nbsp;in vivo efficacy. The results showed that VEGF NS and GDNF NS significantly decreased the &shy;number of amphetamine-induced rotations at the end of the study. In addition, tyrosine hydroxylase immunohistochemical analysis in the striatum and the external substantia nigra confirmed a significant enhancement of neurons in the VEGF NS and GDNF NS treatment group. The synergistic effect of VEGF NS and GDNF NS allows for a reduction of the dose by half, and may be a valuable neurogenerative/neuroreparative approach for treating Parkinson&rsquo;s disease. Keywords: nanoparticles, PLGA, 6-OHDA, neuroregeneration, neurotrophic factors, tyrosine hydroxylas