Modeling of solvent flow effects in enzyme catalysis under physiological conditions

A stochastic model for the dynamics of enzymatic catalysis in explicit, effective solvents under physiological conditions is presented. Analytically-computed first passage time densities of a diffusing particle in a spherical shell with absorbing boundaries are combined with densities obtained from explicit simulation to obtain the overall probability density for the total reaction cycle time of the enzymatic system. The method is used to investigate the catalytic transfer of a phosphoryl group in a phosphoglycerate kinase-ADP-bis phosphoglycerate system, one of the steps of glycolysis. The direct simulation of the enzyme-substrate binding and reaction is carried out using an elastic network model for the protein, and the solvent motions are described by multiparticle collision dynamics, which incorporates hydrodynamic flow effects. Systems where solvent-enzyme coupling occurs through explicit intermolecular interactions, as well as systems where this coupling is taken into account by including the protein and substrate in the multiparticle collision step, are investigated and compared with simulations where hydrodynamic coupling is absent. It is demonstrated that the flow of solvent particles around the enzyme facilitates the large-scale hinge motion of the enzyme with bound substrates, and has a significant impact on the shape of the probability densities and average time scales of substrate binding for substrates near the enzyme, the closure of the enzyme after binding, and the overall time of completion of the cycle.Comment: 15 pages in double column forma

Organotypic Cerebellar Cultures: Apoptotic Challenges and Detection

Organotypic cultures of neuronal tissue were first introduced by Hogue in 1947 1,2 and have constituted a major breakthrough in the field of neuroscience. Since then, the technique was developed further and currently there are many different ways to prepare organotypic cultures. The method presented here was adapted from the one described by Stoppini et al. for the preparation of the slices and from Gogolla et al. for the staining procedure 3,4

Endovascular Treatment of Acute Ischemic Stroke With the Penumbra System in Routine Practice: COMPLETE Registry Results

Background and Purpose: The purpose of the COMPLETE (International Acute Ischemic Stroke Registry With the Penumbra System Aspiration Including the 3D Revascularization Device) registry was to evaluate the generalizability of the safety and efficacy of the Penumbra System (Penumbra, Inc, Alameda) in a real-world setting. Methods: COMPLETE was a global, prospective, postmarket, multicenter registry. Patients with large vessel occlusion–acute ischemic stroke who underwent mechanical thrombectomy using the Penumbra System with or without the 3D Revascularization Device as frontline approach were enrolled at 42 centers (29 United States, 13 Europe) from July 2018 to October 2019. Primary efficacy end points were successful postprocedure angiographic revascularization (modified Thrombolysis in Cerebral Infarction ≥2b) and 90-day functional outcome (modified Rankin Scale score 0–2). The primary safety end point was 90-day all-cause mortality. An imaging core lab determined modified Thrombolysis in Cerebral Infarction scores, Alberta Stroke Program Early CT Scores, clot location, and occurrence of intracranial hemorrhage at 24 hours. Independent medical reviewers adjudicated safety end points. Results: Six hundred fifty patients were enrolled (median age 70 years, 54.0% female, 49.2% given intravenous recombinant tissue plasminogen activator before thrombectomy). Rate of modified Thrombolysis in Cerebral Infarction 2b to 3 postprocedure was 87.8% (95% CI, 85.3%–90.4%). First pass and postprocedure rates of modified Thrombolysis in Cerebral Infarction 2c to 3 were 41.5% and 66.2%, respectively. At 90 days, 55.8% (95% CI, 51.9%–59.7%) had modified Rankin Scale score 0 to 2, and all-cause mortality was 15.5% (95% CI, 12.8%–18.3%). Conclusions: Using Penumbra System for frontline mechanical thrombectomy treatment of patients with large vessel occlusion–acute ischemic stroke in a real-world setting was associated with angiographic, clinical, and safety outcomes that were comparable to prior randomized clinical trials with stringent site and operator selection criteria. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT0346456

An allometric scaling relationship in the brain of preterm infants

Allometry has been used to demonstrate a power–law scaling relationship in the brain of premature born infants. Forty-nine preterm infants underwent neonatal MRI scans and neurodevelopmental testing at age 2. Measures of cortical surface area and total cerebral volume demonstrated a power–law scaling relationship (α = 1.27). No associations were identified between these measures and investigated clinical variables. Term equivalent cortical surface area and total cerebral volume measures and scaling exponents were not related to outcome. These findings confirm a previously reported allometric scaling relationship in the preterm brain, and suggest that scaling is not a sensitive indicator of aberrant cortical maturation

Prognostic utility of magnetic resonance imaging in neonatal hypoxic-ischemic encephalopathy: substudy of a randomized trial

Objective: To investigate the effects of hypothermia treatment on magnetic resonance imaging (MRI) patterns of brain injury in newborns with hypoxic-ischemic encephalopathy compared with normothermia, including the prognostic utility of MRI for death and/or disability at a postnatal age of 2 years

Phosphite deoxygenation of steroidal[3,4-c] and [16,17-c] furazan N-oxides

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Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Trial of SAGE-217 in Patients with Major Depressive Disorder

BACKGROUND: Altered neurotransmission of gamma-aminobutyric acid (GABA) has been implicated in the pathogenesis of depression. Whether SAGE-217, an oral, positive allosteric modulator of GABA type A receptors, is effective and safe for the treatment of major depressive disorder is unknown. METHODS: In this double-blind, phase 2 trial, we enrolled patients with major depression and randomly assigned them in a 1:1 ratio to receive 30 mg of SAGE-217 or placebo once daily. The primary end point was the change from baseline to day 15 in the score on the 17-item Hamilton Depression Rating Scale (HAM-D; scores range from 0 to 52, with higher scores indicating more severe depression). Secondary efficacy end points, which were assessed on days 2 through 8 and on days 15, 21, 28, 35, and 42, included changes from baseline in scores on additional depression and anxiety scales, a reduction from baseline of more than 50% in the HAM-D score, a HAM-D score of 7 or lower, and a Clinical Global Impression of Improvement score of 1 (very much improved) or 2 (much improved) (on a scale of 1 to 7, with a score of 7 indicating that symptoms are very much worse). RESULTS: A total of 89 patients underwent randomization: 45 patients were assigned to the SAGE-217 group, and 44 to the placebo group. The mean baseline HAM-D score was 25.2 in the SAGE-217 group and 25.7 in the placebo group. The least-squares mean (+/-SE) change in the HAM-D score from baseline to day 15 was -17.4+/-1.3 points in the SAGE-217 group and -10.3+/-1.3 points in the placebo group (least-squares mean difference in change, -7.0 points; 95% confidence interval, -10.2 to -3.9; P \u3c 0.001). The differences in secondary end points were generally in the same direction as those of the primary end point. There were no serious adverse events. The most common adverse events in the SAGE-217 group were headache, dizziness, nausea, and somnolence. CONCLUSIONS: Administration of SAGE-217 daily for 14 days resulted in a reduction in depressive symptoms at day 15. Adverse events were more common in the SAGE-217 group than in the placebo group. Further trials are needed to determine the durability and safety of SAGE-217 in major depressive disorder and to compare SAGE-217 with available treatments. (Funded by Sage Therapeutics; ClinicalTrials.gov number, NCT03000530.)

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors