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. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.

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 polyϵ-caprolactone NFs, NFblendaccommodating 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 NFblend. The macroporous morphology, mechanical properties, wettability, andin vitrocytocompatibility of NFblendensured their potential as an implantable LLDS and superior features compared to neat NF. The electron microscopy images affirmed of NFblendrandom fiber (average diameter 832 ± 321 nm) alignments and accessible macropores before and after MSN@Cur accommodation. The blending of polymers improved the elongation of NF and the tensile strength which is attributed as beneficial for implantable LLDS. CAP treatment could significantly improve the wettability of NF observed by the contact angle changes from ∼126° to ∼50° which is critical for the accommodation of curcumin-loaded MSN (MSN@Cur) andin vitrocytocompatibility of NF. The combined CAP and spin coating as the post-processes was employed for accommodating MSN@Cur on NFblendwithout interfering with the electrospinning process. The post-processing aided fine-tuning of curcumin dosing (∼3 µg to ∼15 µg) per dose unit and sustained zero-order drug release profile could be achieved. Introducing of MSN@Cur to cells via LLDS promoted the cell proliferation compared to MSN@Cur suspension treatments and assigned as the elimination of adverse effects by nanocarriers by the dosage form integration. All in all, NFblend-MSN@Cur was shown to have high potential to be employed as an implantable LLDS. To the best of our knowledge, this is the first study in which mPEG:PEI copolymer blend NF are united with CAP and spin coating for accommodating nano-drug carriers, which allows for NF both tissue engineering and drug delivery applications.

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. In the present study, the use of cold plasma treated saline solution as an intraperitoneal irrigation solution for the management of acute peritonitis was investigated. Chemical and in vitro microbiological assessments of the plasma-treated solution were performed to determine the appropriate plasma treatment time to be used in in-vivo experiments. To induce acute peritonitis in rats, the cecal ligation and perforation (CLP) model was used. Sixty rats were divided into six groups, namely, sham operation, plasma irrigation, CLP, dry cleaning after CLP, saline irrigation after CLP, and plasma-treated saline irrigation after CLP group. The total antioxidant and oxidant status, oxidative stress index, microbiological, and pathological evaluations were performed. Findings indicated that plasma-treated saline contains reactive species, and irrigation with plasma-treated saline can effectively inactivate intraperitoneal contamination and prevent sepsis with no short-term local and/or systemic toxicity.

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. METHODS: A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19. RESULTS: Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach. CONCLUSION: Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals. SOURCE CODE: All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classification.