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Abstract This study employed physiologically‐based pharmacokinetic–pharmacodynamics (PBPK/PD) modeling to predict the effect of obesity and gastric bypass surgery on the pharmacokinetics and intragastric pH following omeprazole treatment. The simulated plasma concentrations closely matched the observed data from non‐obese, morbidly obese, and post‐gastric bypass populations. Obesity significantly reduces CYP3A4 and CYP2C19 activities, as reflected by the metabolic ratio [omeprazole sulphone]/[omeprazole] and [5‐hydroxy‐omeprazole]/[omeprazole]. The morbidly obese model accounted for the down‐regulation of CYP2C19 and CYP3A4 to recapitulate the observed data. Sensitivity analysis showed that intestinal CYP3A4, gastric pH, small intestine bypass, and the delay in bile release do not have a major influence on omeprazole exposure. Hepatic CYP3A4 had a significant impact on the AUC of (S)‐omeprazole, while hepatic CYP2C19 affected both (R)‐ and (S)‐omeprazole AUC. After gastric bypass surgery, the activity of CYP3A4 and CYP2C19 is restored. The PBPK model was linked to a mechanism‐based PD model to assess the effect of omeprazole on intragastric pH. Following 40 mg omeprazole, the mean intragastric pH was 4.3, 4.6, and 6.6 in non‐obese, obese, and post‐gastric bypass populations, and the daily time with pH >4 was 14.7, 16.4, and 24 h. Our PBPK/PD approach provides a comprehensive understating of the impact of obesity and weight loss on CYP3A4 and CYP2C19 activity and omeprazole pharmacokinetics. Given that simulated intragastric pH is relatively high in post‐RYGB patients, irrespective of the dose of omeprazole, additional clinical outcomes are imperative to assess the effect of proton pump inhibitor in preventing marginal ulcers in this population.

BackgroundCare for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML). ObjectiveThis study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system’s outputs to analyze usability aspects and obtain insights related to future implementation. MethodsA quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients’ scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients’ data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems. ResultsThe project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024. ConclusionsThis study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. International Registered Report Identifier (IRRID)PRR1-10.2196/52744

Abstract Background In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

BackgroundSensitive and interpretable machine learning (ML) models can provide valuable assistance to clinicians in managing patients with heart failure (HF) at discharge by identifying individual factors associated with a high risk of readmission. In this cohort study, we delve into the factors driving the potential utility of classification models as decision support tools for predicting readmissions in patients with HF. ObjectiveThe primary objective of this study is to assess the trade-off between using deep learning (DL) and traditional ML models to identify the risk of 100-day readmissions in patients with HF. Additionally, the study aims to provide explanations for the model predictions by highlighting important features both on a global scale across the patient cohort and on a local level for individual patients. MethodsThe retrospective data for this study were obtained from the Regional Health Care Information Platform in Region Halland, Sweden. The study cohort consisted of patients diagnosed with HF who were over 40 years old and had been hospitalized at least once between 2017 and 2019. Data analysis encompassed the period from January 1, 2017, to December 31, 2019. Two ML models were developed and validated to predict 100-day readmissions, with a focus on the explainability of the model’s decisions. These models were built based on decision trees and recurrent neural architecture. Model explainability was obtained using an ML explainer. The predictive performance of these models was compared against 2 risk assessment tools using multiple performance metrics. ResultsThe retrospective data set included a total of 15,612 admissions, and within these admissions, readmission occurred in 5597 cases, representing a readmission rate of 35.85%. It is noteworthy that a traditional and explainable model, informed by clinical knowledge, exhibited performance comparable to the DL model and surpassed conventional scoring methods in predicting readmission among patients with HF. The evaluation of predictive model performance was based on commonly used metrics, with an area under the precision-recall curve of 66% for the deep model and 68% for the traditional model on the holdout data set. Importantly, the explanations provided by the traditional model offer actionable insights that have the potential to enhance care planning. ConclusionsThis study found that a widely used deep prediction model did not outperform an explainable ML model when predicting readmissions among patients with HF. The results suggest that model transparency does not necessarily compromise performance, which could facilitate the clinical adoption of such models.

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood–brain barrier (BBB) permeability evaluation tools are needed to explore drugs’ ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of

Comprehensive urban development and the elimination of slums is the state’s strategy for years, during which it has achieved many achievements through well-thought-out plans carried out by the Slum Development Fund to make Egypt free from unsafe slums, develop unplanned areas, and achieve urban development to stop migration from the countryside to cities and prevent the emergence of slums With the advent of 2030 to achieve social justice and improve the economic and living conditions of citizens.The best way to deal with slums is by addressing the causes of their emergence and trying to prevent their occurrence. After the spread of slums around cities, all the methods used to treat them became useless. All methods used to solve this problem carry negative aspects that affect the sustainability of urban areas. In this research paper, we try to investigate the treatments that achieve sustainable urban development, whether within these slums or in the surrounding urban areas. Reducing the negative aspects associated with traditional treatments for this phenomenon, which some vital areas of the capital suffer from, as a current model (Umm Bayoumi area), which is located in a privileged location as it links the Ring Road with Ahmed Orabi Street and Shubra Street. Therefore, the proposed design contains solutions to the problems that it suffers from Region .

Theatre is one of the most distinguished human arts; due to the concepts it conveys and the recreation it provides. The success of any theatrical play depends on the viewer’s attraction to the play and their interaction with it; which can be accomplished by utilizing an integrated set of different artistic elements. Theatrical costumes and their accessories; including jewelry; are one of main the artistic elements that contribute in the success of theatrical plays, as they play a vital role in identifying its historical era and its different dramatic characters.The circumstances within which theatrical jewelry are used; as fashion accessories; differ from those of jewelry designed for daily life use, thus; this research seeks to identify the basic design requirements for theatrical jewelry in order to efficiently accomplish their desired function.The theoretical aspect of the research discusses theatrical costumes, their development and role in the success of theatrical plays, as well as the significance of theatrical jewelry; as accessories for theatrical costumes; and their role in emphasizing the dramatic effect of these costumes and identifying the characters of the play and its era; the research then reviews the most important design requirements for theatrical jewelry.The practical aspect of the study includes an application of its theoretical study; through a project to design and execute various theatrical jewelries; performed by 3rd year students of the department of metal products & jewelry, Banha University; instructed and supervised by the author; and a demonstration of some examples of these designs.

The present study was carried out under plastic house conditions at the nursery of Zohria garden, Hort. Res. Inst., ARC, Cairo, Egypt throughout the two successive seasons of 2015 and 2016 to reveal the effect of planting dates (winter, spring, summer and autumn), some rooting promoters at different concentrations i.e. IBA, NAA, 2,4-D, catechol, cinnamic acid and tryptophan (alone or in combination) and their interactions on rooting of hard-to-root ornamental tree Phytolacca dioica.Results revealed thatplanting in spring increased rooting percentage (%) and root length (cm) in both seasons to the highest values. Treating the cuttings with IBA at 4000 ppm + NAA at 4000 ppm formulation resulted in the highest rooting percentage (%) in both seasons. Although there was no clear trend that could be observed from the obtained results, planting cuttings in either spring or autumn in addition to treating with IBA at 4000 ppm + NAA at 4000 ppm formulation increased rooting percentage (%) to the highest values.In conclusion, to achieve a successful vegetative propagation and to induce rooting on cuttings it is recommended to plant the cuttings in either spring or autumn in addition to treating with IBA at 4000 ppm + NAA at 4000 ppm formulation.

Background: Alopecia areata (AA) is a common, recurrent, autoimmune hair disorder. It has been found that vitamin D deficiency is associated with many autoimmune diseases. Aims: The current study aimed to estimate serum levels of 25-hydroxy vitamin D in patients with AA. Materials and Methods: This case–control study included 60 patients with AA and 60 age, gender, skin phototype, and body mass index-matched healthy subjects as a control group. Levels of serum 25-hydroxy vitamin D were estimated using ELISA technique. Results: Serum 25-hydroxy vitamin D levels were significantly lower in AA cases when compared with healthy controls (P < 0.001). The least values were significantly associated with alopecia totalis/universalis compared with patchy AA (P< 0.001) and ophiasis (P = 0.04). Severe AA showed significantly the lowest vitamin D levels compared with cases with mild (P = 0.002) and moderate disease (P = 0.03). A significant inverse correlation was found between 25-hydroxy vitamin D levels and age of the patients (r = −0.38; P = 0.03). There was no significant association between serum 25-hydroxy vitamin D levels and gender, disease duration, disease recurrence, nail affection, duration of sun exposure/day, or positive family history of AA. Conclusion: AA patients have lower levels of 25-hydroxy vitamin D than healthy subjects. More studies are required to assess the value of vitamin D supplementation in the treatment of that disease.

A field experiment was consummated at the nursery of Horticulture Research Institute, ARC, Giza, Egypt, throughout two successive seasons (2012/2013 and 2013/2014). It was intended to improve vegetative growth and root parameters of Artocarpus heterophyllus Lam. at the early growth stages by studying the effect of treating the plants with different natural extracts botanicals (Moringa oleifera, Lam., Thymus vulgaris, L. and Majorana hortensis, Moench) at the levels of 0, 5 and 10%, besides the combination of moringa + marjoram, moringa + thyme and marjoram + thyme extracts at 2.5%. All of them were applied as a foliar spray commencing from November 20th in both seasons on transplants of 14-15 cm. height with 4-5 leaves till June 20th of the next year at 15 days interval. The plants were grown in sand + peatmoss mixture (1:1, v/v) under the condition of saran house of 65% shading. The results could be briefed as follows: Most plants which received the highest natural extract level (10%) died after about six times of the beginning of spraying. So, the corresponding data of such treatments were excluded. The prevalence of supplying plants with moringa extract at 5% for improving vegetative growth and root parameters of Artocarpus heterophyllus Lam. plant at the terminate of experiment (July 15th) as it increased leaf area, fresh and dry weights of either leaves or roots/plant. Also, it raised root length, root diameter, number of the secondary roots/ plant, length of the longest secondary root. Meanwhile, using the combination of marjoram + thyme extracts at 2.5% belonged to the first and second degree for improving the above mentioned traits in most cases. In contrast, the least scores were a result of untreated plants (control) and plants which received only thyme extract at 5%. From the aforementioned results, it could be recommended to apply either moringa extract at 5% or the combination of marjoram + thyme extracts at 2.5% on Artocarpus heterophyllus Lam. transplants for improving vegetative growth and root parameters of such economic important plant, besides, getting better plants without being forced to use chemical nutrients or synthetic growth regulators that may pollute the environment with getting a safe clean product.

The experimental trial was consummated under saran house of 65% shading performed at the nursery of Horticulture Research Institute, Giza, Egypt during two successive seasons (2012/2013 and 2013/2014) with the aim of improving plant quality at the early growth stages of Artocarpus heterophyllus Lam. by studying the effect of treating plants with some natural botanical extracts (Moringa oleifera Lam., Thymus vulgaris L. and Majorana hortensis Moench) with different levels (0, 5 and 10%) besides, the combinations of moringa + marjoram, moringa + thyme and marjoram + thyme extracts at 2.5%. All of them were applied as a foliar spray commencing from November 20th in both seasons on transplants of Artocarpus hetrophyllus Lam. 14-15 cm height with 4-5 leaves till June 20th of the following year at 15 days interval. The plants were grown in sand + peatmoss mixture (1:1, v/v). The results emphasized that most plants which received the highest natural extract level (10%) died after about 6 times of spraying. So, the corresponding data of such treatments were excluded. Meanwhile, using either moringa extract at 5% or the combination of marjoram + thyme extracts at 2.5% proved their mastery in improving plant chemical constituents at the terminate of experiment (July,5th) as that treating plants with moringa extract at 5% followed by that of the combination of marjoram + thyme extracts at 2.5% were the best treatments used for improving all the components which were measured in this respect (chlorophyll a, b and carotenoids, total carbohydrates, N, P, K and protein in leaves). From the aforementioned results, it could be recommended to use either moringa extract at 5% or the combination of marjoram + thyme extras at 2.5% for improving chemical constituents of Artocarpus heterophyllus Lam. at the early growth stages of such plants besides, getting better plants without being forced to use chemical nutrients or synthetic growth regulators that may contaminate the environment with getting a safe clean product.

The grid vision, of sharing diverse resources in a flexible, coordinated and secure manner, strongly depends on metadata. Currently, grid metadata is generated and used in an ad-hoc fashion, much of it buried in the grid middleware code libraries and database schemas. This ad-hoc expression and use of metadata causes chronic dependency on human intervention during the operation of grid machinery. Therefore, the Semantic Grid is emerged as an extension of the grid in which rich resource metadata is exposed and handled explicitly, and shared and managed via grid protocols. The layering of an explicit semantic infrastructure over the grid infrastructure potentially leads to increase interoperability and flexibility. In this paper, we present PSG framework architecture that offers semantic-based grid services. PSG architecture allows the explicit use of semantics and defining the associated grid services. PSG architecture is originated from the integration of Peer-to-Peer (P2P) computing with semantics and agents. Ontologies are used in annotating each grid component, developing users/nodes profiles and organizing framework agents. While, P2P is responsible for organizing and coordinating the grid nodes and resources.