PDF Architect 6.1.19.842 Pro OCR (x64) Full With Medicine[Babu Full Version
Reyna Boyenga
The 1,2,3-thiadiazole heterocycle has been explored as a heme ligand and mechanism-based inactivator for the design of cytochrome P450 inhibitors. One 4,5-fused bicyclic and three 4,5-disubstituted monocyclic 1,2,3-thiadiazoles have been examined for their spectral interactions, inhibition, mechanism-based inactivation, and oxidation products by the versatile microsomal P450s 2B4, 2E1, and 1A2. The compounds generally show heteroatom coordination to the heme iron; however, the binding mode is influenced by the architecture of the active site. For example, 4,5-diphenyl-1,2,3-thiadiazole shows type I and type II difference spectra with P450s 2B4 and 2E1, respectively, and no spectral perturbation with P450 1A2. Except for the fused bicyclic compound, the spectral dissociation constants are in the 2-50 microM range. The effectiveness as an inhibitor depends on the substituents at the 4- and 5- positions and on the P450 examined. Inhibition of the P450-catalyzed 1-phenylethanol oxidation to acetophenone by the thiadiazoles does not correlate with either the type of binding spectra or the spectral dissociation constants of the compounds. P450s 2E1 and 2B4 are inactivated by the 4,5-fused bicyclic 1,2,3-thiadiazole in a mechanism-based manner. Inactivation of the P450 correlates with loss in absorbance at 450 nm for the ferrous-CO complex. The monocyclic 1,2,3-thiadiazoles do not inactivate any of the P450s examined. The 1,2,3-thiadiazole ring is oxidized by the P450 system. Oxidation of the monocyclic compounds results in extrusion of the three heteroatoms and formation of the corresponding acetylenes, whereas oxidation of the fused bicyclic compound does not yield an acetylenic product.
Ujjwal Ratan is Principal Machine Learning Specialist Solution Architect in the Global Healthcare and Lifesciences team at Amazon Web Services. He works on the application of machine learning and deep learning to real world industry problems like medical imaging, unstructured clinical text, genomics, precision medicine, clinical trials and quality of care improvement. He has expertise in scaling machine learning/deep learning algorithms on the AWS cloud for accelerated training and inference. In his free time, he enjoys listening to (and playing) music and taking unplanned road trips with his family.
PDF Architect 6.1.19.842 Pro OCR (x64) Full With Medicine[Babu full version
Babu Srinivasan is Senior cloud architect at Deloitte. He works closely with customers in building scalable and resilient cloud-based architectures and accelerate the adoption of AWS cloud to solve business problems. Babu is also an APN (AWS Partner Network) Ambassador, passionate about sharing his AWS technical expertise with the technical community. In his spare time, Babu loves to spend time performing close-up card magic to friends and colleagues, wood turning in his garage woodshop or working on his AWS DeepRacer car.
Congenital heart disease (CHD) is the most common category of birth defect, affecting 1% of the population and requiring cardiovascular surgery in the first months of life in many patients. Due to advances in congenital cardiovascular surgery and patient management, most children with CHD now survive into adulthood. However, residual and postoperative defects are common resulting in abnormal hemodynamics, which may interact further with scar formation related to surgical procedures. Cardiovascular magnetic resonance (CMR) has become an important diagnostic imaging modality in the long-term management of CHD patients. It is the gold standard technique to assess ventricular volumes and systolic function. Besides this, advanced CMR techniques allow the acquisition of more detailed information about myocardial architecture, ventricular mechanics, and fibrosis. The left ventricle (LV) and right ventricle have unique myocardial architecture that underpins their mechanics; however, this becomes disorganized under conditions of volume and pressure overload. CMR diffusion tensor imaging is able to interrogate non-invasively the principal alignments of microstructures in the left ventricular wall. Myocardial tissue tagging (displacement encoding using stimulated echoes) and feature tracking are CMR techniques that can be used to examine the deformation and strain of the myocardium in CHD, whereas 3D feature tracking can assess the twisting motion of the LV chamber. Late gadolinium enhancement imaging and more recently T1 mapping can help in detecting fibrotic myocardial changes and evolve our understanding of the pathophysiology of CHD patients. This review not only gives an overview about available or emerging CMR techniques for assessing myocardial mechanics and fibrosis but it also describes their clinical value and how they can be used to detect abnormalities in myocardial architecture and mechanics in CHD patients.
The packaging should be responsive in that manner such that even if we say we do packs of 30, how possible is it to separate and divide this in the event that you are having a shortage and so that you are not giving one person for one month and the other one is going without, so that you can give for two weeks each and within that time you are able to outsource for medicine and get more. (Hosp3_KII4)
Evelyn Lockhart, MD
Evelyn Lockhart, MD, Professor, is a transfusion medicine physician and clinical pathologist with a specialization in laboratory hemostasis. She has over two decades of experience in medical education, where she has dedicated her time to establishing clear communication with patients and colleagues. She recently completed a Master of Science in Biomedical Communication at the University of Toronto, focusing on visualization design and animation.
Tiffany is a Research Scientist involved in projects ranging from Clinical studies to Regenerative Medicine research at MED Institute. She enjoys studying medicine and learning about innovative therapies to improve the lives of patients. Tiffany graduated with her BS in Biology at the University of Texas at San Antonio and has worked in several Microbiology and Immunology labs.
Abstract:Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.Keywords: artificial intelligence (AI); machine learning; drug discovery; formulation; dosage form testing; pharmacokinetics; pharmacodynamics; PBPK; QSAR
COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-of-the-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.
Chest radiography can potentially be the first-line imaging modality used for patients with suspected COVID-19 [91]. Chest radiography is a fast and relatively inexpensive imaging modality that is available in many resource-constrained healthcare settings. However, one of the biggest bottlenecks faced is the need for expert radiologists to interpret the radiography images, which may not be available in every setting. Research studies have proven that COVID-19 causes abnormalities that are visible in the chest X-rays and CT images, in the form of ground-glass opacities [41, 43]. The existence of X-Ray laboratories across the globe coupled with reliable computation-based methodologies can potentially ease the pressure on the front-line COVID-19 warriors. [3] evaluated the performance of state-of-the-art convolutional neural networks including MobileNet-v2, VGG-19, Inception, Xception and Inception ResNet-v2 for the detection of COVID-19 from chest X-rays. The work by [84] proposes a SqueezeNet-based architecture tuned for the COVID-19 diagnosis with Bayes optimization along with the validation phase. [87] have proposed a deep convolutional neural network design named COVID-Net using a lightweight residual projection-expansion projection-extension design pattern. The work by [63] proposes a patch-based convolutional neural network approach with a relatively small number of trainable parameters along with statistical analysis of the potential imaging biomarkers of the chest X-rays.
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