Identify structures underlying out-of-equilibrium reaction networks with random graph analysis.

Network measures have proven very successful in identifying structural patterns in complex systems (, a living cell, a neural network, the Internet). How such measures can be applied to understand the rational and experimental design of chemical reaction networks (CRNs) is unknown. Here, we develop a procedure to model CRNs as a mathematical graph on which network measures and a random graph analysis can be applied.

A novel surgical technique of use of the Goligher self-retaining retractor for microvascular anastomosis in reconstruction after minimally invasive mastectomy.

In recent years, minimally invasive breast surgery (MIBS) has revolutionized breast cancer treatment, allowing for preservation of aesthetic outcomes while ensuring oncological safety. However, this has created a new challenge in maintaining optimal visualization and dexterity during microvascular anastomosis which is critical for successful autologous tissue reconstruction. Traditional retractors often limit maneuverability, potentially impacting the outcomes of anastomotic procedures.

Machine Learning Models For Preventative Mobile Health Asthma Control.

IntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.

Correction to "Optochemical Control of mTOR Signaling and mTOR-Dependent Autophagy".

[This corrects the article DOI: 10.1021/acsptsci.1c00230.

Metabolomic in severe traumatic brain injury: exploring primary, secondary injuries, diagnosis, and severity.

Background: Traumatic brain injury (TBI) is a major public health concern worldwide, contributing to high rates of injury-related death and disability. Severe traumatic brain injury (sTBI), although it accounts for only 10% of all TBI cases, results in a mortality rate of 30-40% and a significant burden of disability in those that survive. This study explored the potential of metabolomics in the diagnosis of sTBI and explored the potential of metabolomics to examine probable primary and secondary brain injury in sTBI.