Exposures to hazardous chemicals including formaldehyde are harmful to human health. In this study, the authors investigate the protective effects of pumpkin seed oil (PSO) extract against formaldehyde-induced major organ damages in mice. Administration of formaldehyde (FA) caused significant elevation of serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvic transaminase (SGPT), serum creatinine, etc. Histopathological examinations of liver, kidney, and brain tissues showed the degenerations of those organs. Mice pretreated with PSO extract significantly attenuated the FA-induced elevation of SGOT (39.0 ± 1.30 vs 20.5 ± 0.65 IU/L; FA-group vs PSO treatment group), SGPT (91.8 ± 1.65 vs 51.0 ± 1.29 IU/L), serum creatinine (1.05 ± 0.07 vs 0.65 ± 0.07 IU/L), and preserved the normal histology of organ tissues. The FA-induced elevation of malondialdehyde (MDA) in the brain, liver, and kidneys was suppressed by pretreatment with PSO extract. The extract also attenuated the FA-induced reduction of endogenous antioxidant pools. In vitro phytochemical analyses showed that PSO extract possesses free radical scavenging and total antioxidant activities due to the presence of phenolic and flavonoid compounds. Thus, PSO extract has significant protective effects against FA-induced organ toxicities by scavenging oxidative stress and inhibiting lipid peroxidation.
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http://dx.doi.org/10.1016/j.heliyon.2020.e04587 | DOI Listing |
Dent Traumatol
January 2025
Department of Endodontology, Maurice and Gabriela Goldschleger School of Dental Medicine, Tel Aviv University, Tel Aviv, Israel.
Background/aim: To explore transfer learning (TL) techniques for enhancing vertical root fracture (VRF) diagnosis accuracy and to assess the impact of artificial intelligence (AI) on image enhancement for VRF detection on both extracted teeth images and intraoral images taken from patients.
Materials And Methods: A dataset of 378 intraoral periapical radiographs comprising 195 teeth with fractures and 183 teeth without fractures serving as controls was included. DenseNet, ConvNext, Inception121, and MobileNetV2 were employed with model fusion.
BMC Cardiovasc Disord
January 2025
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India.
Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. Diabetic Retinopathy (DR) is a kind of eye disease was associated with increasing risk factors for all-causes of CVD events.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamilnadu, India, 641010.
The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals.
View Article and Find Full Text PDFPLoS One
January 2025
Clinic for Orthopaedics, Heidelberg University Hospital, Heidelberg, Germany.
Duchenne gait, characterized by an ipsilateral trunk lean towards the affected stance limb, compensates for weak hip abductor muscles, notably the gluteus medius (GM). This study aims to investigate how electromyographic (EMG) cluster analysis of GM contributes to a better understanding of Duchenne gait in patients with cerebral palsy (CP). We analyzed retrospective gait data from 845 patients with CP and 65 typically developed individuals.
View Article and Find Full Text PDFBiomed Tech (Berl)
January 2025
Department of Computer Science, 72937 Centre for Machine Learning and Intelligence (CMLI), Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.
Objectives: Diabetic retinopathy (DR) is associated with long-term diabetes and is a leading cause of blindness if it is not diagnosed early. The rapid growth of deep learning eases the clinicians' DR diagnosing procedure. It automatically extracts the features and performs the grading.
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