A Quick Review on Cardiac Image Segmentation

Sharif, Hanan; Rehman, Faisal; Rida, Amina; Usman, Nawaz; Ali, Chaudhry Nouman; Akram, Hira; Zahid, Ammara

doi:10.1109/icit56493.2022.9988971

Cited by 4 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Main components of Styrax benzoin are benzaldehyde, benzoic acid, benzyl benzoate, cinnamic acid and vanillin [16]. Styrax benzoin contains compounds such as benzoic acid, benzaldehyde and benzyl benzoate which are bactericidal, germicidal, antiviral substances and fungicidal [17]. Thus, although the antibacterial property of carbon nanodots are not new, of course the fact that the carbon nanodots in the smoke also possess antibacterial activity is what has been disclosed in this study.…”

Section: Discussionmentioning

confidence: 67%

Extraction of carbon nanodots from benzoin resin soot for multifaceted antibacterial applications

Raja,

Gopal

2024

Preprint

View full text Add to dashboard Cite

Antimicrobial compounds have been gaining paramount importance in recent years since there has been a lot of concern over the increasing threats from microbial infections. Medicinal smoke has always been received with warmth and positivity and being an age-old traditional process, has been practiced down the generations. Nanomaterials have become highly promising when it comes to antimicrobial applications and carbon nanomaterials have also gained preeminence. In the following study, carbon nanodots were extracted and characterized from benzoin resin smoke and tested for its antibacterial, anti oral bacterial and anti-biofilm properties. With rising concerns over chemically synthesized nanomaterials, green extraction of carbon nanodots from benzoin smoke, will come handy for multiple biomedical applications.

show abstract

Section: Discussionmentioning

confidence: 67%

Extraction of carbon nanodots from benzoin resin soot for multifaceted antibacterial applications

Raja,

Gopal

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The amino acid residues involved in the interaction were shown to be SER285, PHE281, VAL113 and SER90. This was followed by the interaction between cannabinoid receptor 2 (CB2) and CBD where the binding affinity of -9.5 kcal/mol.CB1 and CB2 are endocannabinoid cannabinoid receptors or membrane proteins with seven transmembrane helices and belong to the rhodopsin-like Gprotein-coupled receptor superfamily (Sharif et al, 2016). CB1 is predominately found in the central nervous system (CNS) and in addition to its ability to bind to a wide range of cannabinoids, CB1 actively binds and mediates the psychoactive effects of △ 𝟗 -THC (Kendall & Yudowski, 2017).…”

Section: Anti-proliferative Properties Of Amnh and Rdc: In Silico Mol...mentioning

confidence: 99%

Charting the Cannabis plant chemical space with computational metabolomics

Myoli,

Choene,

Kappo

et al. 2024

Preprint

View full text Add to dashboard Cite

IntroductionThe chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant.ObjectivesThus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.MethodsMass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).ResultsThe results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids, polyketides, and phospholipids at varying distributions across the cultivar plant tissues. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally,in silicomolecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.ConclusionThese findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.

show abstract

“…This research concentrated on the case of jobs that span multiple domains yet are performed in a comparable manner (classification). Specifically, an ImageNet-trained CNN was used for another image-related dataset which is a well-known technique in the deep learning literature [24,25,26]. Yolo5 is illustrated in Fig.…”

Section: A Image Data Acquisition and Preprocessingmentioning

confidence: 99%

Classification of Thoracic Abnormalities from Chest X-Ray Images with Deep Learning

Nawaz,

Ashraf,

Iqbal

et al. 2024

IJACSA

View full text Add to dashboard Cite

Most Chest X-Rays (CXRs) are used to spot the existence of chest diseases by radiologists worldwide. Examining multiple X-rays at the busiest medical facility may result in time and financial loss. Furthermore, in the detection of the disease, expert abilities and attention are needed. CXRs are usually used for the detection of heart and lung region anomalies. In this research, multi-level Deep Learning for CXRs ailment detection has been used to identify solutions to these issues. Spotting these anomalies with high precision automatically will significantly improve the processes of realistic diagnosis. However, the absence of efficient, public databases and benchmark analyses makes it hard to match the appropriate diagnosis techniques and define them. The publicly accessible VINBigData datasets have been used to address these difficulties and researched the output of established multi-level Deep Learning architectures on various abnormalities. A high accuracy in CXRs abnormality detection on this dataset has been achieved. The focus of this research is to develop a multi-level Deep Learning approach for Localization and Classification of thoracic abnormalities from chest radiograph. The proposed technique automatically localizes and categorizes fourteen types of thoracic abnormalities from chest radiographs. The used dataset consists of 18,000 scans that have been annotated by experienced radiologists. The YoloV5 model has been trained with fifteen thousand independently labeled images and evaluated on a test set of three thousand images. These annotations were collected via VinBigData's web-based platform, VinLab. Image preprocessing techniques are utilized for noise removal, image sequences normalization, and contrast enhancement. Finally, Deep Ensemble approaches are used for feature extraction and classification of thoracic abnormalities from chest radiograph.

show abstract

A Quick Review on Cardiac Image Segmentation

Cited by 4 publications

References 21 publications

Extraction of carbon nanodots from benzoin resin soot for multifaceted antibacterial applications

Extraction of carbon nanodots from benzoin resin soot for multifaceted antibacterial applications

Charting the Cannabis plant chemical space with computational metabolomics

Classification of Thoracic Abnormalities from Chest X-Ray Images with Deep Learning

Contact Info

Product

Resources

About