Some Mechanical Constraints to the Biomimicry with Peripheral Nerves.

Novel high technology devices built to restore impaired peripheral nerves should be biomimetic in both their structure and in the biomolecular environment created around regenerating axons. Nevertheless, the structural biomimicry with peripheral nerves should follow some basic constraints due to their complex mechanical behaviour. However, it is not currently clear how these constraints could be defined.

Diagnostic Accuracy of Procalcitonin in Bacterial Infections of the CNS: An Updated Systematic Review, Meta-Analysis, and Meta-Regression.

Objectives: To evaluate diagnostic accuracy of serum and cerebrospinal fluid (CSF) procalcitonin for diagnosing CNS bacterial infections.

Data Sources: We searched MEDLINE, Cochrane Central Register of Controlled Trials, and International Web of Science databases from January 1, 2016, to September 30, 2022.

Study Selection: Randomized controlled trials and observational studies, either prospective or retrospective, focusing on procalcitonin as a biomarker for CNS infections.

A real-time integrated framework to support clinical decision making for covid-19 patients.

Background: The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level.

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death.