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Dubey VK, Madan S, Rajput SK, Singh AT, Jaggi M, Mittal AK. Single and repeated dose (28 days) intravenous toxicity assessment of bartogenic acid (an active pentacyclic triterpenoid) isolated from Barringtonia racemosa (L.) fruits in mice. Curr Res Toxicol 2021; 3:100057. [PMID: 36504921 PMCID: PMC9731886 DOI: 10.1016/j.crtox.2021.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 10/16/2021] [Accepted: 10/23/2021] [Indexed: 12/15/2022] Open
Abstract
Bartogenic acid (BA), an active pentacyclic triterpenoid, has been reported for anti-diabetic, anti-inflammatory, anti-arthritic, anti-cancer, and anti-tumor activity. However, toxicity profiling of BA has not been reported till date. Hence, this study is designed to evaluate the single dose (12.5, 25, 50 and 100 mg/kg) and repeated dose (1.5, 6, and 24 mg/kg) intravenous toxicity of BA in BALB/c mice. Control group received vehicle. In single dose toxicity study, two mortalities were observed at 100 mg/kg of BA whereas lower doses were well tolerated. In repeated dose toxicity study, no mortality was observed. 1.5 mg/kg of BA was well tolerated in mice of both sexes. At 6 mg/kg of BA, female mice showed significant reduction in the body weight as compared to the control group however no significant change was observed in male mice. 24 mg/kg of BA showed significant reduction in the body weight in mice of both sexes. Further, these mice showed significant change in the relative organ weight. However, no toxicologically relevant changes were observed in hematology, biochemistry, and histopathology. Based on the findings, No-Observed-Adverse-Effect-Level (NOAEL) for BA were found to be<24 mg/kg for male mice and<6 mg/kg for female mice.
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Key Words
- AAALAC, Association For Assessment And Accreditation Of Laboratory Animal Care
- ALP, Alkaline Phosphatase
- ALT, Alanine Aminotransferase
- AST, Aspartate Aminotransferase
- Acute
- BA, Bartogenic Acid
- BUN, Blood Urea Nitrogen
- Barringtonia racemosa
- Bartogenic acid
- FDA, Food And Drug Administration
- GLP, Good Laboratory Practice
- H&E, Hematoxylin–Eosin
- HCT, Hematocrit
- LC/MS, Liquid chromatography–mass spectrometry
- MCH, Mean Corpuscular Hemoglobin
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean Corpuscular Volume
- Mice
- NMR, Nuclear Magnetic Resonance
- NOAEL
- NOAEL, No Observed Adverse Effect Level
- OA, Oleanolic Acid
- OECD, Organization For Economic Co-Operation And Development
- RBC, Red Blood Cells Count
- RDW-CV, Red Cell Distribution Width - Coefficient Of Variation
- SEM, Standard Error Of The Mean
- TLC, Total Leukocyte Count
- Toxicity
- UA, Ursolic Acid
- UHPLC, Ultra High Performance Liquid Chromatography
- VLDL, Very Low Density Lipoprotein
- b.wt., Body Weight
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Affiliation(s)
- Vishal Kumar Dubey
- Dabur Research Foundation, 22, Site IV, Sahibabad, Ghaziabad 201010, Uttar Pradesh, India
- Amity Institute of Pharmacy, Amity University Uttar Pradesh, Sector-125, Noida 201313, Uttar Pradesh, India
- Corresponding author at: Dabur Research Foundation, 22, Site IV, Sahibabad, Ghaziabad 201010, Uttar Pradesh, India
| | - Swati Madan
- Amity Institute of Pharmacy, Amity University Uttar Pradesh, Sector-125, Noida 201313, Uttar Pradesh, India
| | - Satyendra K. Rajput
- Department of Pharmaceutical Sciences, Gurukula Kangri Vishwavidyalaya, Jagjeetpur, Haridwar 249404, Uttarakhand, India
| | - Anu T Singh
- Dabur Research Foundation, 22, Site IV, Sahibabad, Ghaziabad 201010, Uttar Pradesh, India
| | - Manu Jaggi
- Dabur Research Foundation, 22, Site IV, Sahibabad, Ghaziabad 201010, Uttar Pradesh, India
| | - Amit Kumar Mittal
- Amity Institute of Pharmacy, Amity University Noida, Uttar Pradesh, 322230 India
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Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021; 19:2833-2850. [PMID: 34025952 PMCID: PMC8123783 DOI: 10.1016/j.csbj.2021.05.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/01/2021] [Accepted: 05/02/2021] [Indexed: 12/23/2022] Open
Abstract
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
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Key Words
- ABG, Arterial Blood Gas
- ADA, Adenosine Deaminase
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APTT, Activated Partial Thromboplastin Time
- ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer
- AUC, Area Under the Curve
- Acc, Accuracy
- Adaboost, Adaptive Boosting
- Apol AI, Apolipoprotein AI
- Apol B, Apolipoprotein B
- Artificial intelligence
- BNB, Bernoulli Naïve Bayes
- BUN, Blood Urea Nitrogen
- CI, Confidence Interval
- CK-MB, Creatine Kinase isoenzyme
- CNN, Convolutional Neural Networks
- COVID-19
- CPP, COVID-19 Positive Patients
- CRP, C-Reactive Protein
- CRT, Classification and Regression Decision Tree
- CoxPH, Cox Proportional Hazards
- DCNN, Deep Convolutional Neural Networks
- DL, Deep Learning
- DLC, Density Lipoprotein Cholesterol
- DNN, Deep Neural Networks
- DT, Decision Tree
- Diagnosis
- ED, Emergency Department
- ESR, Erythrocyte Sedimentation Rate
- ET, Extra Trees
- FCV, Fold Cross Validation
- FL, Federated Learning
- FiO2, Fraction of Inspiration O2
- GBDT, Gradient Boost Decision Tree
- GBM light, Gradient Boosting Machine light
- GDCNN, Genetic Deep Learning Convolutional Neural Network
- GFR, Glomerular Filtration Rate
- GFS, Gradient boosted feature selection
- GGT, Glutamyl Transpeptidase
- GNB, Gaussian Naïve Bayes
- HDLC, High Density Lipoprotein Cholesterol
- INR, International Normalized Ratio
- Inception Resnet, Inception Residual Neural Network
- L1LR, L1 Regularized Logistic Regression
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LDH, Lactate Dehydrogenase
- LDLC, Low Density Lipoprotein Cholesterol
- LR, Logistic Regression
- LSTM, Long-Short Term Memory
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean corpuscular volume
- ML, Machine Learning
- MLP, MultiLayer Perceptron
- MPV, Mean Platelet Volume
- MRMR, Maximum Relevance Minimum Redundancy
- Multimodal data
- NB, Naïve Bayes
- NLP, Natural Language Processing
- NPV, Negative Predictive Values
- Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer
- OB, Occult Blood test
- PCT, Thrombocytocrit
- PPV, Positive Predictive Values
- PWD, Platelet Distribution Width
- PaO2, Arterial Oxygen Tension
- Paco2, Arterial Carbondioxide Tension
- Prognosis
- RBC, Red Blood Cell
- RBF, Radial Basis Function
- RBP, Retinol Binding Protein
- RDW, Red blood cell Distribution Width
- RF, Random Forest
- RFE, Recursive Feature Elimination
- RSV, Respiratory Syncytial Virus
- SEN, Sensitivity
- SG, Specific Gravity
- SMOTE, Synthetic Minority Oversampling Technique
- SPE, Specificity
- SRLSR, Sparse Rescaled Linear Square Regression
- SVM, Support Vector Machine
- SaO2, Arterial Oxygen saturation
- Screening
- TBA, Total Bile Acid
- TTS, Training Test Split
- WBC, White Blood Cell count
- XGB, eXtreme Gradient Boost
- k-NN, K-Nearest Neighbor
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Affiliation(s)
- Eleni S. Adamidi
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantinos Mitsis
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantina S. Nikita
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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