An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology.

Background: The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, time-consuming nature, and lack of sensitivity, thus paving the way for automated analysis approaches.

Methods: Hereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images.

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water.

Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP.

Prediction of the synergistic effect of antimicrobial peptides and antimicrobial agents via supervised machine learning.

Background: Infectious diseases not only cause severe health problems but also burden the healthcare system. Therefore, the effective treatment of those diseases is crucial. Both conventional approaches, such as antimicrobial agents, and novel approaches, like antimicrobial peptides (AMPs), are used to treat infections.

Mesoporous silica nanoparticles accommodating electrospun nanofibers as implantable local drug delivery system processed by cold atmospheric plasma and spin coating approaches.

Nanofibers (NF) and nanoparticles are attractive for drug delivery to improve the drug bioavailability and administration. Easy manipulation of NF as macroscopic bulk material give rise to potential usages as implantable local drug delivery systems (LLDS) to overcome the failures of systemic drug delivery systems such as unmet personalized needs, side effects, suboptimal dosage. In this study, poly(ethylene glycol) polyethyleneimine (mPEG:PEI) copolymer blended poly-caprolactone NFs, NFaccommodating mesoporous silica nanoparticles (MSN) as the implantable LLDS was achieved by employing spin coating and cold atmospheric plasma (CAP) as the post-process for accommodation on NF.

Irrigation of peritoneal cavity with cold atmospheric plasma treated solution effectively reduces microbial load in rat acute peritonitis model.

Accurate and timely diagnosis of appendicitis in children can be challenging, which leads to delayed admittance or misdiagnosis that may cause perforation. Surgical management involves the elimination of the focus (appendectomy) and the reduction of the contamination with peritoneal irrigation to prevent sepsis. However, the validity of conventional irrigation methods is being debated, and novel methods are needed.

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning.

Background: Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.