Search results for: radiographic abnormalities

Authors: Kuladip Jana, Bhaswati Banerjee, Parimal C. Sen

Abstract:

Benzo(a)pyrene [B(a)P] is an environmental toxicant present mostly in cigarette smoke and car exhaust, is an aryl hydrocarbon receptor (AhR) ligand that exerts its toxic effects on both male and female reproductive systems along with carcinogenesis in skin, prostate, ovary, lung and mammary glands. Our study was focused on elucidating the molecular mechanism of B(a)P induced male reproductive toxicity and its prevention with phytochemical like resveratrol. In this study, the effect of B(a)P at different doses (0.1, 0.25, 0.5, 1 and 5 mg /kg body weight) was studied on male reproductive system of Wistar rat. A significant decrease in cauda epididymal sperm count and motility along with the presence of sperm head abnormalities and altered epididymal and testicular histology were documented following B(a)P treatment. B(a)P treatment resulted apoptotic sperm cells as observed by TUNEL and Annexin V-PI assay with increased Reactive Oxygen Species (ROS), altered sperm mitochondrial membrane potential (ΔΨm) with a simultaneous decrease in the activity of antioxidant enzymes and GSH status. TUNEL positive apoptotic cells also observed in testis as well as isolated germ and Leydig cells following B(a)P exposure. Western Blot analysis revealed the activation of p38 mitogen activated protein kinase (p38MAPK), cytosolic translocation of cytochrome-c, upregulation of Bax and inducible nitric oxide synthase (iNOS) with cleavage of poly ADP ribose polymerase (PARP) and down regulation of BCl2 in testis upon B(a)P treatment. The protein and mRNA levels of testicular key steroidogenesis regulatory proteins like steroidogenic acute regulatory protein (StAR), cytochrome P450 IIA1 (CYPIIA1), 3β hydroxy steroid dehydrogenase (3β HSD), 17β hydroxy steroid dehydrogenase (17β HSD) showed a significant decrease in a dose dependent manner while an increase in the expression of cytochrome P450 1A1 (CYP1A1), Aryl hydrocarbon Receptor (AhR), active caspase- 9 and caspase- 3 following B(a)P exposure. We conclude that exposure of benzo(a)pyrene caused testicular gamatogenic and steroidogenic disorders by induction of oxidative stress, inhibition of StAR and other steroidogenic enzymes along with activation of p38MAPK and initiated caspase-3 mediated germ and Leydig cell apoptosis. Next we investigated the role of resveratrol on B(a)P induced male reproductive toxicity. Our study highlighted that resveratrol co-treatment with B(a)P maintained testicular redox potential, increased serum testosterone level and prevented steroidogenic dysfunction with enhanced expression of major testicular steroidogenic proteins (CYPIIA1, StAR, 3β HSD,17β HSD) relative to treatment with B(a)P only. Resveratrol suppressed B(a)P-induced testicular activation of p38 MAPK, ATF2, iNOS and ROS production; cytosolic translocation of Cytochome c and Caspase 3 activation thereby prevented oxidative stress of testis and inhibited apoptosis. Resveratrol co-treatment also decreased B(a)P-induced AhR protein level, its nuclear translocation and subsequent CYP1A1 promoter activation, thereby decreased protein and mRNA levels of testicular cytochrome P4501A1 (CYP1A1) and prevented BPDE-DNA adduct formation. Our findings cumulatively suggest that resveratrol prevents activation of B(a)P by modulating the transcriptional regulation of CYP1A1 and acting as an antioxidant thus prevents B(a)P-induced oxidative stress and testicular apoptosis.

Keywords: benzo(a)pyrene, resveratrol, testis, apoptosis, cytochrome P450 1A1 (CYP1A1), aryl hydrocarbon receptor (AhR), p38 MAPK/ATF2/iNOS

Procedia PDF Downloads 197

Authors: Pei Yi Lin

Abstract:

Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

Procedia PDF Downloads 37