Cluster Analysis of Biochemical Markers as Predictor of COVID-19 Severity

Mohammed, Suhayla K.; Taha, Muzahem Mohialdeen; Taha, Ekhlass M.; Mohammad, Massara Najim Abdulla

doi:10.21123/bsj.2022.7454

Cited by 10 publications

(10 citation statements)

References 21 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Psychological stress affects the immune system in many ways. The effect of stress on the immune system varies and can be individual specific 19 . The most frequently observed response is immune suppression, especially when stress is chronic and persistent.…”

Section: The Effect Of Il-6 Levels On Anxiety Model Mice Treatedmentioning

confidence: 99%

Red Ginger's Anti-Anxiety Effect on BALB/c Strain Mice (Mus musculus) Pro-Inflammatory and Anti-Inflammatory Measurements as Anxiety Model

Dania,

Rambe,

Harahap

et al. 2023

Baghdad Sci.J

View full text Add to dashboard Cite

There is a correlation between the occurrence of anxiety and the production of inflammatory mediators, and red ginger rhizome is a well-known herbal product with a high content of phenolic and flavonoid compounds that can be used as anti-inflammatories and antioxidants. The aim of study to evaluate the effect of red ginger as antianxiety in mice (Mus musculus) BALB/c strain by measuring levels of TNF-α, IL-6 and IL-10. Anxiety model mice were carried out by giving treatment with the Forced Swimming Test (FST) for 7 days then assessed by carrying out the Elevated Plus Maze for Mice (EPM) test for one day. After the treatment, the anxiety mice model was made, followed by administration of red ginger ethanol extract therapy for 14 days. The distribution of the experimental animal model groups was divided into control groups (KN, K-, K+) and treatment groups (P, P2, P3).There was a significant difference the decreased of the TNF-α levels at the all of treatment groups with red ginger rhizome extract (P1, P2, P3) compared with the control groups (KN, K-) (p<0.05), the significantly decreased of IL-6 levels in the three doses treatment group (P1, P2, P3) compared to the control group (K-, K+) (p <0.05) and an increase in IL-10 levels in the 50 mg treatment group compared to group K -, statistically not significant (p>0.05).In overall, this study suggests that FST stimulation will create anxiety symptoms and behavior as well as impact cytokine levels, namely elevated levels of TNF-α and IL-6. Giving red ginger ethanol extract has the potential to be researched further for reducing anxiety symptoms because it can block pro-inflammatory cytokines by significantly decreased levels of TNF-α, IL-6, and increased IL-10 cytokines a brief abstract about your paper’s subject of study.

show abstract

Section: The Effect Of Il-6 Levels On Anxiety Model Mice Treatedmentioning

confidence: 99%

Red Ginger's Anti-Anxiety Effect on BALB/c Strain Mice (Mus musculus) Pro-Inflammatory and Anti-Inflammatory Measurements as Anxiety Model

Dania,

Rambe,

Harahap

et al. 2023

Baghdad Sci.J

View full text Add to dashboard Cite

show abstract

“…They used the binary cluster analysis method with the aim of identifying groups of patients with a high risk and low risk of contracting Covid-19 disease, based on a set of vital factors. The study concluded that early detection of the disease can reduce cases of severe infection with the disease 13 . The study aimed to classify the pollution areas in the Orontes River in Syria into low-pollution areas and high-pollution areas through the use of hierarchical cluster analysis.…”

Section: Introductionmentioning

confidence: 97%

Enhancing Fuzzy C-Means Clustering with a Novel Standard Deviation Weighted Distance Measure

Mohammed,

Ashour

2024

Baghdad Sci.J

View full text Add to dashboard Cite

The aim of this paper is to present a new approach to address the Fuzzy C Mean algorithm, which is considered one of the most important and famous algorithms that addressed the phenomenon of uncertainty in forming clusters according to the overlap ratios. One of the most important problems facing this algorithm is its reliance primarily on the Euclidean distance measure, and by nature, the situation is that this measure makes the formed clusters take a spherical shape, which is unable to contain complex or overlapping cases. Therefore, this paper attempts to propose a new measure of distance, where we were able to derive a formula for the variance of the fuzzy cluster to be entered as a weight on the Euclidean Distance (WED) formula. Moreover, the calculation was processed partitions matrix through the use of the K-Means algorithm and creating a hybrid environment between the fuzzy algorithm and the sharp algorithm. To verify what was presented, experimental simulation was used and then applied to reality using environmental data for the physical and chemical examination of water testing stations in Basra Governorate. It was proven through the experimental results that the proposed distance measure Weighted Euclidean distance had the advantage over improving the work of the HFCM algorithm through the criterion (Obj_Fun, Iteration, Min_optimization, good fit clustering and overlap) when (c = 2,3) and according to the simulation results, c = 2 was chosen to form groups for the real data, which contributed to determine the best objective function (23.93, 22.44, 18.83) at degrees of fuzzing (1.2, 2, 2.8), while according to the degree of fuzzing (m = 3.6), the objective function for Euclidean Distance (ED) was the lowest, but the criteria were (Iter. = 2, Min_optimization = 0 and ) which confirms that (WED) is the best.

show abstract

“…Coronavirus disease 2019, also known as COVID-19, is an infectious condition that is brought on by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The first case of COVID-19 was discovered in Wuhan, China in 2019, and since then, it has quickly spread over the world, causing a pandemic that is currently affecting many different nations and areas [2].…”

Section: Introductionmentioning

confidence: 99%

Comparative study of Chest Radiographs and Detection of The Covid 19 Virus Using Machine Learning Algorithm

Sabri,

Arif,

Taqa

et al. 2024

Mesopotamian Journal of Computer Science

View full text Add to dashboard Cite

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that is causing coronavirus disease 2019 is being deemed a pandemic because of its quick spread around the globe. Because chest X-ray pictures have shown to be beneficial in monitoring a variety of lung disorders, they have recently been utilized to monitor COVID-19 disease. It takes time to manually analyze a lot of chest X-ray pictures. Several previous studies have suggested machine-learning (ML)-based techniques for COVID-19 detection from chest X-ray pictures as a solution to this issue. Though little effort has been made to use traditional machine learning (ML) methods, the majority of these investigations use deep learning (DL) based techniques. Conventional ML-based algorithms will be favored for implementation if they can yield identical outcomes as DL-based methods. In this effort, we constructed four classic ML-based models for COVID-19 identification, driven by the need to close the gap in the literature. The accuracy rates for the various classification models were as follows, according to the results: 93.4% for Support Vector Machine (SVM), 93.3% for Random Forest (RF), 90.5% for K-Nearest Neighbors (KNN), and 87.9% for Decision Tree (DT). The results of the study showed that machine learning-based algorithms can produce great results for COVID-19 identification by being refined and improved using several well-known data preparation approaches.

show abstract

Cluster Analysis of Biochemical Markers as Predictor of COVID-19 Severity

Cited by 10 publications

References 21 publications

Red Ginger's Anti-Anxiety Effect on BALB/c Strain Mice (Mus musculus) Pro-Inflammatory and Anti-Inflammatory Measurements as Anxiety Model

Red Ginger's Anti-Anxiety Effect on BALB/c Strain Mice (Mus musculus) Pro-Inflammatory and Anti-Inflammatory Measurements as Anxiety Model

Enhancing Fuzzy C-Means Clustering with a Novel Standard Deviation Weighted Distance Measure

Comparative study of Chest Radiographs and Detection of The Covid 19 Virus Using Machine Learning Algorithm

Contact Info

Product

Resources

About