Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study

Aim: To assess the effect of the coronavirus disease 2019 (COVID-19) lockdown on glycaemic control in subjects with type 2 diabetes (T2D). Materials and Methods: In this observational, multicentre, retrospective study conducted in the Lazio region, Italy, we compared the differences in the HbA1c levels of 141 subjects with T2D exposed to lockdown with 123 matched controls with T2D who attended the study centres 1 year before. Basal data were collected from 9 December to 9 March and follow-up data from 3 June to 10 July in 2020 for the lockdown group, and during the same timeframes in 2019 for the control groups. Changes in HbA1c (ΔHbA1c) and body mass index (ΔBMI) during lockdown were compared among patients with different psychological well-being, as evaluated by tertiles of the Psychological General Well-Being Index (PGWBS). Results: No difference in ΔHbA1c was found between the lockdown and control groups (lockdown group −0.1% [−0.5%−0.3%] vs. control group −0.1% [−0.4%−0.2%]; p =.482). Also, no difference was found in ΔBMI (p =.316) or ΔGlucose (p =.538). In the lockdown group, subjects with worse PGWBS showed a worsening of HbA1c (p =.041 for the trend among PGWBS tertiles) and BMI (p =.022). Conclusions: The COVID-19 lockdown did not significantly impact glycaemic control in people with T2D. People with poor psychological well-being may experience a worsening a glycaemic control because of restrictions resulting from lockdown. These findings may aid healthcare providers in diabetes management once the second wave of COVID-19 has ended

The Italian open data meteorological portal: MISTRAL

At the national level, in Italy, observational and forecast data are collected by various public bodies and are often kept in various small, heterogeneous and non-interoperable repositories, released under different licenses, thus limiting the usability for external users. In this context, MISTRAL (the Meteo Italian SupercompuTing PoRtAL) was launched as the first Italian meteorological open data portal, with the aim of promoting the reuse of meteorological data sets available at national level coverage. The MISTRAL portal provides (and archives) meteorological data from various observation networks, both public and private, and forecast data that are generated and post-processed within the Consortium for Small-scale Modeling-Limited Area Model Italia (COSMO-LAMI) agreement using high performance computing (HPC) facilities. Also incorporated is the Italy Flash Flood use case, implemented with the collaboration of European Centre for Medium-Range Weather Forecasts (ECMWF), which exploits cutting edge advances in HPC-based post-processing of ensemble precipitation forecasts, for different model resolutions, and applies those to deliver novel blended-resolution forecasts specifically for Italy. Finally, in addition to providing architectures for the acquisition and display of observational data, MISTRAL also delivers an interactive system for visualizing forecast data of different resolutions as superimposed multi-layer maps

Evaluation of marine subareas of Europe using life history parameters and trophic levels of selected fish populations

European marine waters include four regional seas that provide valuable ecosystem services to humans, including fish and other seafood. However, these marine environments are threatened by pressures from multiple anthropogenic activities and climate change. The European Marine Strategy Framework Directive (MSFD) was adopted in 2008 to achieve good environmental status (GEnS) in European Seas by year 2020, using an Ecosystem Approach. GEnS is to be assessed using 11 descriptors and up to 56 indicators. In the present analysis two descriptors namely "commercially exploited fish and shellfish populations" and "food webs" were used to evaluate the status of subareas of FAO 27 area. Data on life history parameters, trophic levels and fisheries related data of cod, haddock, saithe, herring, plaice, whiting, hake and sprat were obtained from the FishBase online database and advisory reports of International Council for the Exploration of the Sea (ICES). Subareas inhabited by r and K strategists were identified using interrelationships of life history parameters of commercially important fish stocks. Mean trophic level (MTL) of fish community each subarea was calculated and subareas with species of high and low trophic level were identified. The Fish in Balance (FiB) index was computed for each subarea and recent trends of FiB indices were analysed. The overall environmental status of each subarea was evaluated considering life history trends, MTL and FiB Index. The analysis showed that subareas I, II, V, VIII and IX were assessed as "good" whereas subareas III, IV, VI and VII were assessed as "poor". The subareas assessed as "good" were subject to lower environmental pressures, (less fishing pressure, less eutrophication and more water circulation), while the areas with "poor" environment experienced excessive fishing pressure, eutrophication and disturbed seabed. The evaluation was based on two qualitative descriptors ("commercially exploited fish and shellfish populations" and "food webs") is therefore more robust (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Erasmus Mundus Joint Doctorate in Marine and Coastal Management (MACOMA); EC 7FP grant [308392]info:eu-repo/semantics/publishedVersio

Alpha-rhythm stimulation using brain entrainment enhances heart rate variability in subjects with reduced HRV

In the present research, we have used the brain en- trainment (BWE) treatment simultaneously record- ing time series data of R-R intervals of the ECG dur- ing rest condition. In detail, we have used alpha brain stimulation and we have found that it induces an en- hancement of HRV, particularly in Total Variability and Vagal Modulation activities. The experiment has been performed by us on ten subjects with age rang- ing from 20 to 70 years old. The risk induced from low HRV is by this time well known in literature. Therefore, the obtained result promises to be of valu- able interest not only in terms of the basic neurologi- cal investigation but also because it delineates new possibilities in terms of clinical application

Traditional and a new methodology for analysis of heart rate variability: a review by physiological and clinical experimental results.

The aim of the present contribution is to give a review on a new methodology that we may use when we are employed in the analyis of one of the most fundamental signals that we encounter in electrophysiology, the R-R intervals in analysis of the ECG. First of all the limits of the current FFT application are discussed. Soon after the basic foundations of the CZF method are exposed and we expose and discuss in detail a large number of physiological and clinical applications, based directly on experimental results .The results evidence the importance to use the CZF method as non invasive marker in analysis of HRV

The vicious circle of left ventricular dysfunction and diabetes: from pathophysiology to emerging treatments

Context: Diabetes and heart failure (HF) are 2 deadly and strictly related epidemic disorders. The aim of this review is to present an updated discussion of the epidemiology, pathophysiology, clinical presentation and treatment options for HF in diabetes. Evidence Acquisition: Relevant references published up to February 2020 were identified through searches in PubMed. Quality was graded using the Newcastle-Ottawa score in observational studies and the Cochrane Collaboration tool in randomized studies. Evidence Synthesis: Metabolic and neurohumoral derangements, oxidative stress, inflammation, micro- and macroangiopathy all contribute through complex molecular and cellular mechanisms to cardiac dysfunction in diabetes, which in turn, results as one the most frequent underlying conditions affecting up to 42% of patients with HF and causing a 34% increased risk of cardiovascular death. On top of traditional guideline-based HF medical and device therapies, equally effective in patients with and without diabetes, a new class of glucose-lowering agents acting through the sodium-glucose cotransporter 2 (SGLT2) inhibition showed impressive results in reducing HF outcomes in individuals with diabetes and represents an active area of investigation. Conclusions: Diabetes and HF are strictly linked in a bidirectional and deadly vicious circle difficult to break. Therefore, preventive strategies and a timely diagnosis are crucial to improve outcomes in such patients. SGLT2 inhibitors represent a major breakthrough with remarkably consistent findings. However, it is still not clear whether their benefits may be definitely extended to patients with HF with preserved ejection fraction, to those without diabetes and in the acute setting

Layered Meta-Learning Algorithm for Predicting Adverse Events in Type 1 Diabetes

Type 1 diabetes mellitus (T1D) is a chronic disease that, if not treated properly, can lead to serious complications. We propose a layered meta-learning approach based on multi-expert systems to predict adverse events in T1D. The base learner is composed of three deep neural networks and exploits only continuous glucose monitoring data as an input feature. Each network specializes in predicting whether the patient is about to experience hypoglycemia, hyperglycemia, or euglycemia. The output of the experts is passed to a meta-learner to provide the final model classification. In addition, we formally introduce a novel parameter, α , to evaluate the advance by which a prediction is performed. We evaluate the proposed approach on both a public and a private dataset and implement it on an edge device to test its feasibility in real life. On average, on the Ohio T1DM dataset, our system was able to predict hypoglycemia events with a time gain of 22.8 minutes, hyperglycemia ones with an advance of 24.0 minutes. Our model not only outperforms presented models in the literature in terms of events predicted with sufficient advance, but also with regard to the number of false positives, achieving on average 0.45 and 0.46 hypo- and hyperglycemic false alarms per day, respectively. Furthermore, the meta-learning approach effectively improves performance in a new cohort of patients by training only the meta-learner with a limited amount of data. We believe our approach would be an essential ally for the patients to control the glycemic fluctuations and adjust their insulin therapy and dietary intakes, enabling them to speed up decision-making and improve personal self-management, resulting in a reduced risk of acute and chronic complications. As our last contribution, we assessed the validity of the approach by exploiting only blood glucose variations as well as in combination with the information of the insulin boluses, the skin temperature, and the galvanic skin response. In general, we have observed that providing other information but CGM leads to slightly lower performances with respect to considering CGM alone