Christophersenwerner0381

COVID-19 is a new human-infecting coronavirus for which the World Health Organization declared a global pandemic. The first Italian cases occurred in February 2020 since then, there has been an exponential increase in new cases, hospitalizations and intensive care assistance demand. This new and sudden scenario led to a forced National Health System reorganization and review of welfare priorities. The aim of this study is to evaluate the effects of this pandemic on ordinary activities in two plastic surgery divisions in Rome, hosted in a COVID-19 and a non-COVID-19 hospital.

The data of this comparative retrospective study was collected between 9 March and 9 April 2019 and the same period of 2020 from two plastic surgery units, one in a COVID-19 hospital and second in a non-COVID-19 hospital in Rome, Italy. The 2019-2020 data of the two hospitals was compared regarding the number of surgeries, post-operative dressings and first consultations performed.

Both units sustained a decrease in workload due to lockdown effects. Statistically significant differences for day surgery procedures (

value = 0.0047) and first consultations (

value < 0.0001) were found between the COVID-19 and non-COVID-19 institutes, with a drastic trend limiting non-urgent access to COVID-19 hospitals.

The long-term effects of healthcare reshuffling in the "COVID-19 era" imply a delay in the diagnosis and treatment of skin cancer and cancellation of many reconstructive procedures. These findings pose a question on the future consequences of a long-term limitation in plastic surgery healthcare.Level of evidence Level III, risk/prognostic study.

The long-term effects of healthcare reshuffling in the "COVID-19 era" imply a delay in the diagnosis and treatment of skin cancer and cancellation of many reconstructive procedures. These findings pose a question on the future consequences of a long-term limitation in plastic surgery healthcare.Level of evidence Level III, risk/prognostic study.In this paper, we describe the new OPTCON3 algorithm, which serves to determine approximately optimal policies for stochastic control problems with a quadratic objective function and nonlinear dynamic models. It includes active learning and the dual effect of optimizing policies, whereby optimal policies are used to learn about the stochastics of the dynamic system in addition to their immediate effect on the performance of the system. The OPTCON3 algorithm approximates the nonlinear model with a time-varying linear model and applies a procedure similar to that of Kendrick to the series of linearized models to calculate approximately optimal policies. The results for two simple economic models serve to test the OPTCON3 algorithm and compare it to previous solutions of the stochastic control problem. Initial evaluations show that the OPTCON3 approach may be promising to enhance our understanding of the adaptive economic policy problem under uncertainty.The concept of similarity is used as the basis for many data exploration and data mining tasks. Nearest neighbor (NN) queries identify the most similar items, or in terms of distance the closest points to a query point. Similarity is traditionally characterized using a distance function between multi-dimensional feature vectors. However, when the data is high-dimensional, traditional distance functions fail to significantly distinguish between the closest and furthest points, as few dissimilar dimensions dominate the distance function. Localized similarity functions, i.e. functions that only consider dimensions close to the query, quantize each dimension independently and only compute similarity for the dimensions where the query and the points fall into the same bin. These quantizations are query-agnostic and there is potential to improve accuracy when a query-dependent quantization is used. In this work we propose a query dependent equi-depth (QED) on-the-fly quantization method to improve high-dimensional similarity searches. The quantization is done for each dimension at query time and localized scores are generated for the closest p fraction of the points while a constant penalty is applied for the rest of the points. QED not only improves the quality of the distancemetric,but also improves query time performance by filtering out non relevant data. check details We propose a distributed indexing and query algorithm to efficiently compute QED. Our experimental results show improvements in classification accuracy as well as query performance up to one order of magnitude faster than Manhattan-based sequential scan NN queries over datasets with hundreds of dimensions. Furthermore, similarity searches with QED show linear or better scalability in relation to the number of dimensions, and the number of compute nodes.This study reveals a new microfluidic biosensor consisting of a multi-constriction microfluidic device with embedded electrodes for measuring the biophysical attributes of single cells. The biosensing platform called the iterative mechano-electrical properties (iMEP) analyzer captures electronic records of biomechanical and bioelectrical properties of cells. The iMEP assay is used in conjunction with standard migration assays, such as chemotaxis-based Boyden chamber and scratch wound healing assays, to evaluate the migratory behavior and biophysical properties of prostate cancer cells. The three cell lines evaluated in the study each represent a stage in the standard progression of prostate cancer, while the fourth cell line serves as a normal/healthy counterpart. Neither the scratch assay nor the chemotaxis assay could fully differentiate the four cell lines. Furthermore, there was not a direct correlation between wound healing rate or the migratory rate with the cells' metastatic potential. However, the iMEP assay, through its multiparametric dataset, could distinguish between all four cell line populations with p-value less then 0.05. Further studies are needed to determine if iMEP signatures can be used for a wider range of human cells to assess the tumorigenicity of a cell population or the metastatic potential of cancer cells.