Influence of tetrahydrobiopterin supplementation on rate pressure product

Augmentation of tetrahydrobiopterin (BH4) could potentially improve eNOS uncoupling by increasing Nitric oxide (NO) bioavailability to improve endothelial health in cardiovascular diseases. With age, the bioactivity of eNOS decreases resulting in a decrease in concentration and bioavailability of NO. Elevated levels of eNOS cofactor BH4 helps in synthesizing NO, whereas limited concentrations of BH4 production potentiallyleads to uncoupling of eNOS and the production of superoxides. A study conducted by Pierce et al., 2012 on young and old men showed that limited BH4 bioavailability contributed to impaired arterial compliance, elasticity and other hemodynamics of vascular tissue. Experiments on sedentary and aerobically trained men by Eskurza et al., (2005) indicated that flow mediated dilation (FMD) increased by approximately 45% in old sedentary men but did not affect FMD in young sedentary or old aerobically trained individuals. Thus BH4 supplementation is a potential therapeutic target in regulation of eNOS and NO generation in vascular diseases. We hypothesize that acute oral tetrahydrobiopterin (BH4) supplementation can influence the heart work through rate pressure product that would negatively affect with age among male and female participants. Methods: A double-blinded study conducted on young men and women (21-45yrs), old men and women (60-75yrs), who were asked to consume an acute dose of BH4 supplements (10mg/kg) or equal dose of placebo supplementation (cellulose) on two separate visits. Single leg knee kick exercise with increase in resistance (0watt, 7watt, 15watt and 20 watt) was performed and measurements of heart rate (ECG) , beat-to-beat blood pressure (CNAP finger plethysmography), leg blood flow (Doppler ultrasound) were recorded. Work of heart was calculated as the Rate Pressure Product (RPP), which is a product of Heart rate (HR) and Systolic blood pressure (SBP). Results: RPP is generally higher in sedentary old men and women, with treatment RPP decreased in older participants (P\u3c 0.05) when compared to young men and women. Conclusion: Low RPP may be due to an increase in compliance of arteries and a decrease in the vascular tone of the resistance vessels and workload conducted by heart. Thus lowering heart rate and systolic blood pressure with BH4 therapy would be beneficial to patients with systemic hypertension and cardiovascular disease

The Effects of Metabolic Syndrome on the Increased Prevalence of Cognitive Decline in Minority Groups.

Alzheimer’s disease is one of the leading causes of dementia, affecting over five million people in the United States. It is clinically diagnosed by the presence of amyloid plaques and hyperphosphoryated tau. Alzheimer’s disease patients present with memory and cognitive decline. The cumulative effects of an increasing elderly population and the elevation in the number of persons with diseases such as hypertension, diabetes and obesity, which are risk factors for Alzheimer’s disease elevated the interest in understanding the interrelatedness between factors of metabolic syndrome and Alzheimer’s disease. The disparity between the incidences of Alzheimer’s disease among racial groups in the United States also correlates with the differences in the incidence of metabolic syndrome (MetSyn) among these groups. We hypothesized that persons who are classified as MetSyn will also show deficits in cognition, carotid blood flow and above normal levels of IL-6 and C-reactive protein. We believe that the amalgamation of risk factors associated with MetSyn might offer an explanation for the differential occurrence of Alzheimer’s disease in ethnic minority populations in the United States. The study has enrolled 15 participants from the community. Of the 15 participants there are 5 males and 10 females between the ages of 19 and 62, 5 of the participants have two or three risk factors for MetSyn and 7 are members of a minority population. The study is divided into 2 visits; during the first visit, anthropometric measurements and a blood draw for the plasma analysis of interleukin-6 and C-reactive protein are taken. The second visit consisted of the imaging of the carotid artery and the administration of the Penn CNP neurocognitive battery. The battery included measures of working memory, attention, executive function and verbal learning and memory. The data did not show any significant difference between persons that are metabolically compromised and normal controls in the areas of cognitive ability and inflammatory marker concentration. There were gender and racial difference in response times in the cognitive area of working memory with males having lower response times than females and Caucasian having lower response times than minorities, however the differences are not currently significant. The study continues to enroll participants, we believe that with a greater sample size the trends seen in gender and racial population differences will become significant; particularly if we are able to increase the number of persons with metabolic syndrome

Development of a physiologically based pharmacokinetic model of actinomycin D in children with cancer

AIMS: Use of the anti‐tumour antibiotic actinomycin D is associated with development of hepatotoxicity, particularly in young children. A paucity of actinomycin D pharmacokinetic data make it challenging to develop a sound rationale for defining dosing regimens in younger patients. The study aim was to develop a physiologically based pharmacokinetic (PBPK) model using a combination of data from the literature and generated from experimental analyses. METHODS: Assays to determine actinomycin D Log P, blood:plasma partition ratio and ABCB1 kinetics were conducted. These data were combined with physiochemical properties sourced from the literature to generate a compound file for use within the modelling‐simulation software Simcyp (version 14 release 1). For simulation, information was taken from two datasets, one from 117 patients under the age of 21 and one from 20 patients aged 16–48. RESULTS: The final model incorporated clinical renal and biliary clearance data and an additional systemic clearance value. The mean AUC(0‐26h) of simulated subjects was within 1.25‐fold of the observed AUC(0‐26h) (84 ng h ml(−1) simulated vs. 93 ng h ml(−1) observed). For the younger age ranges, AUC predictions were within two‐fold of observed values, with simulated data from six of the eight age/dose ranges falling within 15% of observed data. Simulated values for actinomycin D AUC(0‐26h) and clearance in infants aged 0–12 months ranged from 104 to 115 ng h ml(−1) and 3.5–3.8 l h(−1), respectively. CONCLUSIONS: The model has potential utility for prediction of actinomycin D exposure in younger patients and may help guide future dosing. However, additional independent data from neonates and infants is needed for further validation. Physiological differences between paediatric cancer patients and healthy children also need to be further characterized and incorporated into PBPK models

Twitter Based Sentiment Analysis of Impact of COVID-19 on Education Globally

Education system has been gravely affected due to widespread of Covid-19 across the globe. In this paper we present a thorough sentiment analysis of tweets related to education available on twitter platform and deduce conclusions about its impact on people’s emotions as the pandemic advanced over the months. Through twitter over ninety thousand tweets have been gathered related to the circumstances involving the change in education system over the world. Using Natural language tool kit (NLTK) functionalities and Naive Bayes Classifier a sentiment analysis has been performed on the gathered dataset. Based on the results of this analysis we infer to exhibit the impact of covid-19 on education and how people’s sentiment altered due to the changes with regard to the education system. Thus, we would like to present a better understanding of people’s sentiment on education while trying to cope with the pandemic in such unprecedented times

Cross-domain sentiment classification using grams derived from syntax trees and an adapted naive Bayes approach

Master of ScienceDepartment of Computing and Information SciencesDoina CarageaThere is an increasing amount of user-generated information in online documents, includ- ing user opinions on various topics and products such as movies, DVDs, kitchen appliances, etc. To make use of such opinions, it is useful to identify the polarity of the opinion, in other words, to perform sentiment classification. The goal of sentiment classification is to classify a given text/document as either positive, negative or neutral based on the words present in the document. Supervised learning approaches have been successfully used for sentiment classification in domains that are rich in labeled data. Some of these approaches make use of features such as unigrams, bigrams, sentiment words, adjective words, syntax trees (or variations of trees obtained using pruning strategies), etc. However, for some domains the amount of labeled data can be relatively small and we cannot train an accurate classifier using the supervised learning approach. Therefore, it is useful to study domain adaptation techniques that can transfer knowledge from a source domain that has labeled data to a target domain that has little or no labeled data, but a large amount of unlabeled data. We address this problem in the context of product reviews, specifically reviews of movies, DVDs and kitchen appliances. Our approach uses an Adapted Naive Bayes classifier (ANB) on top of the Expectation Maximization (EM) algorithm to predict the sentiment of a sentence. We use grams derived from complete syntax trees or from syntax subtrees as features, when training the ANB classifier. More precisely, we extract grams from syntax trees correspond- ing to sentences in either the source or target domains. To be able to transfer knowledge from source to target, we identify generalized features (grams) using the frequently co-occurring entropy (FCE) method, and represent the source instances using these generalized features. The target instances are represented with all grams occurring in the target, or with a reduced grams set obtained by removing infrequent grams. We experiment with different types of grams in a supervised framework in order to identify the most predictive types of gram, and further use those grams in the domain adaptation framework. Experimental results on several cross-domains task show that domain adaptation approaches that combine source and target data (small amount of labeled and some unlabeled data) can help learn classifiers for the target that are better than those learned from the labeled target data alone

Implementation of Heavy Fleet Routes and Facilities Location Optimization

This study\u27s main objective was to enhance the efficiency of winter salting operations for the Indiana Department of Transportation (INDOT) through the following objectives. First, we aimed to optimize the routing of salting vehicles from current facility locations to assigned road segments. The second and more pivotal objective was to identify the locations for future facilities based on “what-if” scenarios, such as the number of facilities and the composition of trucks at said facilities

Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with RET-altered solid tumors

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types

Diagnostic implications of neuroimaging in epilepsy and other seizure disorders

La epilepsia es el trastorno neurológico más común que afecta a aproximadamente el 1-2% de la población mundial y que conduce a la presentación en la sala de emergencias. Las modalidades de neuroimagen tienen una aplicación importante en el diagnóstico de nuevas convulsiones no provocadas y epilepsia. Este artículo analiza las diversas modalidades de neuroimagen para diagnosticar convulsiones y epilepsia y aborda que la resonancia magnética es la investigación de elección, y la obtención de imágenes urgentes se realiza más comúnmente mediante tomografía computarizada en pacientes con convulsiones de nueva aparición. El objetivo del artículo fue diagnosticar convulsiones y epilepsia para una intervención temprana para prevenir complicaciones o daños al cerebro. La resonancia magnética detecta incluso pequeñas lesiones epileptogénicas corticales, mientras que la tomografía computarizada se utiliza en la detección, el diagnóstico, la evaluación y el seguimiento del pronóstico de las convulsiones en niños. La espectroscopia de resonancia magnética proporciona mediciones bioquímicas de la reducción de N-acetil aspartato y el aumento de creatinina y colina en zonas epilépticas disfuncionales. La resonancia magnética volumétrica es muy sensible y específica para determinar las convulsiones que se originan en sitios extratemporales y extrahipocampales. Aunque la resonancia magnética con tensor de difusión tiene un papel limitado, se utiliza en grupos específicos de pacientes pediátricos con epilepsia del lóbulo temporal. Las modalidades de imágenes funcionales con radionúclidos (tomografía por emisión de positrones y tomografía computarizada por emisión monofotónica) son cada vez más importantes para la identificación de la región epiléptica. Además, los autores recomiendan el uso de inteligencia artificial y más investigación sobre las modalidades de imágenes para el diagnóstico temprano de las convulsiones y la epilepsiaEpilepsy is the most common neurological disorder that affects ~1–2% of the global population, leading to presentation in the emergency room. The neuroimaging modalities have an important application in diagnosing new onset unprovoked seizures and epilepsy. This article discusses the various neuroimaging modalities for diagnosing seizures and epilepsy and addresses that the MRI is the investigation of choice, and urgent imaging is more commonly done by computed tomography in patients with new-onset seizures. The goal of the article was to diagnose seizures and epilepsy for early intervention to prevent complications or damage to the brain. MRI detects even small cortical epileptogenic lesions, whereas computed tomography is used in screening, diagnosis, evaluation, and monitoring of the prognosis of seizures in children. Magnetic resonance spectroscopy provides biochemical measurements of reduced N-acetyl aspartate and increased creatinine and choline in dysfunctioning epileptic zones. Volumetric MRI is very sensitive and specific in determining seizures originating in extratemporal and extrahippocampal sites. Even though diffusion tensor magnetic resonance imaging has a limited role, it is used in specific pediatric patient groups with temporal lobe epilepsy. Functional radionuclide imaging modalities (positron emission tomography and single-photon emission computerized tomography) are increasingly significant for the identification of the epileptic region. Furthermore, the authors recommend the use of artificial intelligence and further research on imaging modalities for early diagnosis of seizures and epilepsyPor pare

Physiologically Based Pharmacokinetic Modelling of Cytochrome P450 2C9-Related Tolbutamide Drug Interactions with Sulfaphenazole and Tasisulam

Background and Objectives: Cytochrome P450 2C9 (CYP2C9) is involved in the biotransformation of many commonly used drugs, and significant drug interactions have been reported for CYP2C9 substrates. Previously published physiologically based pharmacokinetic (PBPK) models of tolbutamide are based on an assumption that its metabolic clearance is exclusively through CYP2C9; however, many studies indicate that CYP2C9 metabolism is only responsible for 80–90% of the total clearance. Therefore, these models are not useful for predicting the magnitude of CYP2C9 drug–drug interactions (DDIs). This paper describes the development and verification of SimCYP-based PBPK models that accurately describe the human pharmacokinetics of tolbutamide when dosed alone or in combination with the CYP2C9 inhibitors sulfaphenazole and tasisulam. Methods: A PBPK model was optimized in SimCYP for tolbutamide as a CYP2C9 substrate, based on published in vitro and clinical data. This model was verified to replicate the magnitude of DDI reported with sulfaphenazole and was further applied to simulate the DDI with tasisulam, a small molecule investigated for the treatment of cancer. A clinical study (CT registration # NCT01185548) was conducted in patients with cancer to assess the pharmacokinetic interaction of tasisulum with tolbutamide. A PBPK model was built for tasisulam, and the clinical study design was replicated using the optimized tolbutamide model. Results: The optimized tolbutamide model accurately predicted the magnitude of tolbutamide AUC increase (5.3–6.2-fold) reported for sulfaphenazole. Furthermore, the PBPK simulations in a healthy volunteer population adequately predicted the increase in plasma exposure of tolbutamide in patients with cancer (predicted AUC ratio = 4.7–5.4; measured mean AUC ratio = 5.7). Conclusions: This optimized tolbutamide PBPK model was verified with two strong CYP2C9 inhibitors and can be applied to the prediction of CYP2C9 interactions for novel inhibitors. Furthermore, this work highlights the utility of mechanistic models in navigating the challenges in conducting clinical pharmacology studies in cancer patients