Adaptive average arterial pressure control by multi-agent on-policy reinforcement learning.

The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages.

Combined effects of α-amylase, xylanase, and cellulase coproduced by Stachybotrys microspora on dough properties and bread quality as a bread improver.

This study aims to explore the feasibility of introducing, during the manufacture of bakery bread, an enzymatic cocktail coproduced by the fungus Stachybotrys microspora: α-amylases, xylanases and cellulases, using wheat bran as a nutrient source. Among the characteristics of the alveograph (dough tenacity "P" and dough extensibility "L"), the addition of a cocktail of enzymes at a concentration of 2 %, to weak wheat flour, has made it possible to significantly reduce its P/L ratio from 2.45 to 1.

A novel PIK3CA hot-spot mutation in breast cancer patients detected by HRM-COLD-PCR analysis.

Background: The PI3K protein is involved in the PI3K/AKT/mTOR pathway. Deregulation of this pathway through PIK3CA mutation is common in various tumors. The aim of this work is to identify hotspot mutation at exons 9 and 20 in Tunisian patients with sporadic or hereditary breast cancer.

National Tunisian Study of Cardiac Implantable Electronic Devices: Design and Protocol for a Nationwide Multicenter Prospective Observational Study.

Background: In Tunisia, the number of cardiac implantable electronic devices (CIEDs) is increasing, owing to the increase in patient life expectancy and expanding indications. Despite their life-saving potential and a significant reduction in population morbidity and mortality, their increased numbers have been associated with the development of multiple early and late complications related to vascular access, pockets, leads, or patient characteristics.

Objective: The study aims to identify the rate, type, and predictors of complications occurring within the first year after CIED implantation.

Epileptic EEG patterns recognition through machine learning techniques and relevant time-frequency features.

Objectives: The present study is designed to explore the process of epileptic patterns' automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via a selection of machine learning (ML) techniques. The primary motivation for conducting such a research lies mainly in the need to investigate the long-term electroencephalography (EEG) recordings' visual examination process, often considered as a time-consuming and potentially error-prone procedure, requiring a great deal of mental focus and highly experimented neurologists. On attempting to resolve such a challenge, a number of state-of-the-art ML algorithms have been evaluated and compare in terms of performance, to pinpoint the most effective algorithm fit for accurately extracting epileptic EEG patterns.