Real impact and reasons for anti-VEGF treatment delay during Covid-19 pandemic lockdown in neovascular age-related macular degeneration: A national study

Purpose : To assess the visual impact and reasons for treatment delay during the COVID-19 pandemic lockdown in neovascular age-related macular degeneration (nAMD) patients in ongoing anti-VEGF therapy. Methods : Retrospective, national, multicentre, observational study in nAMD patients treated with anti-VEGF therapy and registered in the Fight Retinal Blindness (FRB) Spain platform prior to lockdown. Study cohort was divided in timely treated patients (TTP) and delayed treatment patients (DTP). Mean change in best corrected visual acuity (BCVA, in ETDRS letters) from the last follow-up visit (FUV) before lockdown (BLD) (baseline [BL] visit) to the first FUV after lockdown (ALD) was assessed. A specific questionnaire was distributed to the participant centers to investigate further the reasons for treatment delay in all individual cases. Results : A total of 245 eyes fulfilled the eligibility criteria, from which 39.6% were TTP (n=97) and 60.4% were DTP (n=148). TTP presented greater baseline and final BCVA compared to DTP (64.1 vs 58.7 letters, p=0.023, and 63.6 vs 57.1, p=0.004). BCVA loss was significantly greater for DTP vs TTP (-2.0 vs -0.6 letters, p=0.016). For DTP cohort, the primary reason for visit delay was patient decision (48.2%) followed by limited hospital clinic capacity (42.7%). When patients decided not to attend scheduled visits, the main reason was fear to Covid-19 infection (49.4%). Conclusions : This study provides relevant data about the impact on visual outcomes of Covid-19 pandemic lockdown on nAMD patients and specifically provides new additional information regarding the main reasons for treatment and visits delay from both patients and healthcare service delivery perspectives.

Uncertainty Propagation and Salient Features Maps in Deep Learning Architectures for Supporting Covid-19 Diagnosis

Doubt and the ability to point out details that identify an object or categories of objects are peculiarities of human intelligence. Roughly speaking, artificial intelligence aims to mimic the behavior of human intelligence. This work is a first attempt at joint use of previously existing technologies to mimic these characteristics of human intelligence. This work aims to help in the diagnosis of X-ray chest images with pneumonia, and covid-19;and images of healthy individuals by applying deep neural networks. These deep neural networks are modified so that they can generate predictions with uncertainty. Subsequently, on the previously generated predictions, the salient feature maps are generated to identify on which parts of the image the forecast decision is based. As a result of the work, examples of X-ray chest images will be shown where independent executions predict different labels, focusing attention on different areas of the radiography. So that the different areas indicated by the independent runs might help in the diagnosis of the different pathologies. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A granular tabu search for the refrigerated vehicle routing problem with homogeneous fleet

The Refrigerated Capacitated Vehicle Routing Problem (RCVRP) considers a homogeneous fleet with a refrigerated system to decide the selection of routes to be performed according to customers' requirements. The aim is to keep the energy consumption of the routes as low as possible. We use a thermodynamic model to understand the unloading of products from trucks and the variables' efficiency, such as the temperature during the day influencing energy consumption. By considering various neighborhoods and a shaking procedure, this paper proposes a Granular Tabu Search scheme to solve the RCVRP. Computational tests using adapted benchmark instances from the literature demonstrate that the suggested method delivers high-quality solutions within short computing times, illustrating the refrigeration system's effect on routing decisions. (c) 2022 by the authors;licensee Growing Science, Canada

Experience of transforming a clinical surgical-anesthetic unit into a COVID-19 critical patient one in a teaching hospital

Introduction: The experience of restructuring a clinical surgical-anesthetic unit into a critical patient unit in charge of surgical-anesthetic personnel is presented during the period from May to July 2020 in the context of a SARS-CoV-2 pandemic. Objectives: Describe the unit’s restructuring process, considering technical aspects, changes in staff functions, clinical outcomes of the patients, quality indicators obtained and the psychological impact on the healthcare team. Matherial and Methods: The strategies implemented by the responsible experts were described (ie: engineering). Clinical data were obtained from an institutional database and electronical medical records. The management of human resources was described using administrative records of the services of anesthesiology, OR and critical patient unit. The psychological impact on the unit staff was evaluated by applying the Maslach questionnaire. The quality of the clinical management of the unit was obtained from the compilation of standardized quality indicators for the critical patient units of the institution. Results: 25 patients were admitted in the unit. The mean age was 62 ± 12 years. About the complications, 52% had pulmonary embolism, 36% had acute kidney injury, and 1 patient died. The prevalence of Burnout Syndrome was 73.6%. The occurrence of adverse events was minimal. Discussion: The transformation of an anesthetic-surgical unit into a COVID critical patient one, demands a complex net of coordinated strategies to allow facing the attention demand with positive clinical results, at the expense of the health care team mental health. © 2021 Sociedad de Anestesiologia de Chile. All rights reserved.

Valuable Projects: Political Emotions in the Pandemic

Political emotions can have a considerable effect on people's social behavior, so states should seek to promote those that are beneficial to collective enterprises. Following Martha Nussbaum's approach, emotions such as love, compassion, grief and empathy contribute to the achievement of these common goals, while fear, envy, disgust and shame can become obstacles. This article analyzes the coverage of the COVID-19 pandemic by three of the most read digital media in Colombia, in order to establish what type of emotions they recreated in their news, since they play a relevant role in the reproduction of principles and values in society as a source of public information.

A Hawkes process model for the propagation of COVID-19: Simple analytical results

We present a model for the COVID-19 epidemic that offers analytical expressions for the newly registered and latent cases. This model is based on an epidemic branching process with latency that is greatly simplified when the bare memory kernel is given by an exponential function as observed in this pandemic. We expose the futility of the concept of "bending the curve" of the epidemic as long as the number of latent cases is not depleted. Our model offers the possibility of laying out different scenarios for the evolution of the epidemic in different countries based on the most recent observations and in terms of only two constants obtained from clinical trials.