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Lornoxicam-Loaded Cubosomes: - Preparation and In vitro Characterization.
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Cubosomes are nanosized structures self-assembled nanostructured materials used for controlling the release of the entrapped drug molecule. Lornoxicam (LXM) is a potent analgesic nonsteroidal anti-inflammatory (NSAID) drug with a short half-life (3-4) hours. The present study aims to prepare LXM-loaded cubosomes with well-defined morphology, particle size, PDI, high entrapment efficiency, sustained drug release, and high zeta potential value, as a transdermal drug delivery system.

      Twelve formulas of LXM-loaded cubosomal dispersions were prepared by a solvent dilution method using Glyceryl monooleate ( GMO) as polar lipid with different stabilizers as Pluronic® F127 or tween 80 and different types of hydrotrope as ethanol or propylene glycol. These formulas were evaluated for their particle size analysis & PDI, E.E. %, and in-vitro drug release to select a group of the optimum formulas, that further characterized by transmission electron microscopy (TEM) and zeta potential analyzer to select the optimum dispersion. FTIR study was used to investigate the compatibility of the drug with excipients.

The obtained results indicated that F3, composed of GMO, Pluronic® F127, ethanol, drug, and phosphate buffer solution pH 7.4 in the following per cents 7.28%, 1.82%, 8%, 2%, and 80.9% w/w, respectively, prepared in 20min agitation period, as the optimum formula for its high E.E. % (94.30±0.002%), small particle size (16.3±0.19nm), low PDI          (0.06±0.02), and high zeta potential value (-65.9±0.05mV), and well-defined cubic structure. FTIR study indicated no interaction between LXM and other formulas components.

This study's conclusion illustrated that LXM-loaded cubosomal dispersion could be considered a promising nano-carrier for transdermal drug delivery.

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Publication Date
Thu Oct 01 2020
Journal Name
Green Engineering
Sustainability and recovery project management implementation on construction projects in Iraq
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Publication Date
Sun Oct 01 2023
Journal Name
Journal Of Applied Hematology
D-dimer and Ferritin Levels in Prediction of COVID-19 Severity
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Abstract<sec> <title>BACKGROUND:

The most common cause of upper respiratory tract infection is coronavirus, which has a crown appearance due to the existence of spikes on its envelope. D-dimer levels in the plasma have been considered a prognostic factor for COVID-19 patients.

AIM OF THE STUDY:

The aim of the study is to demonstrate the role of COVID-19 on coagulation parameters D-dimer and ferritin with their association with COVID-19 severity and disease progression in a single-center study.

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Publication Date
Thu Sep 01 2016
Journal Name
Current Pharmaceutical Design
Dry Eye Disease: Present Challenges in the Management and Future Trends
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Publication Date
Sat Dec 01 2018
Journal Name
Swarm And Evolutionary Computation
Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
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Publication Date
Wed Oct 01 2025
Journal Name
Results In Engineering
Advances in Piezoelectric Energy Harvesting: Materials, Configurations, and Power Optimisation Strategies
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Publication Date
Mon Jan 01 2024
Journal Name
Explainable Artificial Intelligence In The Digital Sustainability Administration
Artificial Intelligence and Trends Using in Sustainability Audit: A Bibliometric Analysis
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Publication Date
Sun Aug 01 2021
Journal Name
Bulletin Of Electrical Engineering And Informatics
Robust speaker verification by combining MFCC and entrocy in noisy conditions
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Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robu

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Publication Date
Fri Jan 30 2026
Journal Name
Iraqi Journal Of Science
Theoretical Study of Positive and Negative Parity Coulomb Transitions in 40,48Ca
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Hsieh-Wildenthal (HW) and G-matrix PF1, (GXPF1) interactions for 40Ca and 48Ca, respectively. The 40Ca was considered to be consisted of 32S as a core and 1d3/21f7/2 subshells as a model space. The 48Ca was considered to be consisted of 40Ca as a core and 1f7/22p3/21f5/2 2p1/2 subshells as a model space. The obtained one body-density matrix elements (OBDME) for both interactions for the studied Coulomb transitions:  For 40Ca, the three non-normal parity transitions (C3): ,  and  and one C5 transitions,  .  For 48Ca, the two C2 transitions:  and  and one C4 transitions  were used in parallel with the transformed harmonic-oscillator basis to compute charge density, elastic electron scattering differential cross-section and ela

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Publication Date
Sun Jun 05 2016
Journal Name
Baghdad Science Journal
Study of total Immunoglobulin E and Eosinophil count in allergic disease
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The present study aimed to evaluate the levels of total immunoglobulin E and percentage count of eosinophil in some of allergic disease. Blood sample collected from 210 patients (110 female, 100 male) with allergic disease (allergic asthma, allergic rhinitis, and urticaria) their age between 10-70 years and 50 healthy control their age between 23-52 years. A highly significant (P<0.01) increase in the mean serum total IgE in patients with asthma (503.54 ± 63.49 IU/ml), Allergic rhinitis (442.77 ± 95.76 IU/ml) and urticaria (489.53 ± 69.68 IU/ml) as a compared with healthy controls (23.67 ± 5.81 IU/ml).There was a significant difference in percentage count of eosinophil in patients groups allergic asthma 4.37 ± 0.52% ,allergic rhinitis

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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
Concepts of statistical learning and classification in machine learning: An overview
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Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

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