96 research outputs found

    Hot melt extrusion processing parameters optimization

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    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. The aim of this study was to demonstrate the impact of processing parameters of the hot-melt extrusion (HME) on the pharmaceutical formulation properties. Carbamazepine (CBZ) was selected as a model water-insoluble drug. It was incorporated into Soluplus®, which was used as the polymeric carrier, to produce a solid dispersion model system. The following HME-independent parameters were investigated at different levels: extrusion temperature, screw speed and screw configuration. Design of experiment (DOE) concept was applied to find the most significant factor with minimum numbers of experimental runs. A full two-level factorial design was applied to assess the main effects, parameter interactions and total error. The extrudates’ CBZ content and the in vitro dissolution rate were selected as response variables. Material properties, including melting point, glass transition, and thermal stability, and polymorphs changes were used to set the processing range. In addition, the extruder torque and pressure were used to find the simplest DOE model. Each change of the parameter showed a unique pattern of dissolution profile, indicating that processing parameters have an influence on formulation properties. A simple, novel and two-level factorial design was able to evaluate each parameter effect and find the optimized formulation. Screw configuration and extrusion temperature were the most affecting parameters in this study

    Modeling and validation of drug release kinetics using hybrid method for prediction of drug efficiency and novel formulations

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    This paper presents a thorough examination for drug release from a polymeric matrix to improve understanding of drug release behavior for tissue regeneration. A comprehensive model was developed utilizing mass transfer and machine learning (ML). In the machine learning section, three distinct regression models, namely, Decision Tree Regression (DTR), Passive Aggressive Regression (PAR), and Quadratic Polynomial Regression (QPR) applied to a comprehensive dataset of drug release. The dataset includes r(m) and z(m) inputs, with corresponding concentration of solute in the matrix (C) as response. The primary objective is to assess and compare the predictive performance of these models in finding the correlation between input parameters and chemical concentrations. The hyper-parameter optimization process is executed using Sequential Model-Based Optimization (SMBO), ensuring the robustness of the models in handling the complexity of the controlled drug release. The Decision Tree Regression model exhibits outstanding predictive accuracy, with an R2 score of 0.99887, RMSE of 9.0092E-06, MAE of 3.51486E-06, and a Max Error of 6.87000E-05. This exceptional performance underscores the model’s capability to discern intricate patterns within the drug release dataset. The Passive Aggressive Regression model, while displaying a slightly lower R2 score of 0.94652, demonstrates commendable predictive capabilities with an RMSE of 6.0438E-05, MAE of 4.82782E-05, and a Max Error of 2.36600E-04. The model’s effectiveness in capturing non-linear relationships within the dataset is evident. The Quadratic Polynomial Regression model, designed to accommodate quadratic relationships, yields a noteworthy R2 score of 0.95382, along with an RMSE of 5.6655E-05, MAE of 4.49198E-05, and a Max Error of 1.86375E-04. These results affirm the model’s proficiency in capturing the inherent complexities of the drug release system

    Characteristics and anticancer properties of Sunitinib malate-loaded poly-lactic-co-glycolic acid nanoparticles against human colon cancer HT-29 cells lines

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    Purpose: To develop poly-lactic-co-glycolic acid (PLGA) -based nanoparticles (NPs) for the delivery of sunitinib malate (STM) to colon cancer cells.Methods: Three different formulations (F1 – F3) were developed by nano-precipitation technique using various concentrations of PLGA. The NPs were evaluated for particle size, polydispersity index, zeta potential, drug entrapment, and drug loading, using differential scanning calorimetry (DSC), Fouriertransform infrared spectroscopy (FTIR), x-ray diffraction (XRD), and scanning electron microscopy (SEM). Furthermore, in vitro drug release and anticancer studies were carried out on the formulations.Results: Among the three NPs, optimized NP (F3) of STM was chosen for in vitro anti-cancer study against H-29 human colon cancer cells lines based on its particle size (132.9 nm), PDI (0.115), zeta potential (-38.12 mV), entrapment efficiency (52.42 %), drug loading (5.24 %), and drug release (91.26 % in 48 h). A significant anti-cancer activity of the optimized NPs was observed, relative to free STM.Conclusion: These findings suggest that STM-loaded NPs possess significant anti-cancer activity against human colon cancer HT-29 cells lines.Keywords: Sunitinib malate, Poly-lactic-co-glycolic acid, Nanoparticles, Colon cance

    Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

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    The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates

    Impact of Baseline Characteristics on Stroke Outcomes in Pakistan: A Longitudinal Study Using the Modified Rankin Scale

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    Introduction. Stroke is a leading cause of disability and mortality globally, with a significant impact on healthcare systems. Various factors, including age, gender, comorbidities, and the type of stroke, influence the burden of stroke and its outcomes. The study was conducted with an objective to determine the impact of baseline characteristics on the long-term functional outcome of stroke patients. Methods. This prospective observational study was conducted between April 6, 2022 - December 31, 2023, at a tertiary hospital. The study included patients with radiologically confirmed stroke, selected through convenience sampling. Stroke patients of any gender and all age groups, with any comorbidity, were included. The Modified Rankin Scale (mRS) assessed disability on admission and three months post-stroke. Results. Of the 213 patients, 122 (57.3%) were males and the majority, 199 (93.4%) individuals, had acute ischemic stroke. The median age of the participants was 60 years (range: 13-97 years; IQR=18 years). The mRS score on admission was poor (5.0; IQR=1.0) for patients ≥ 60 years. In 74 (34.74%) participants, the left middle cerebral artery was a frequently involved site. Age of ≥ 60 years (mRS=4.0; IQR=4.0; p=0.001) and the presence of ≥ 3 comorbidities (mRS=5.0; IQR=1.0; p=0.001) were significantly associated with poor outcomes three months post-stroke. Ordinal logistic regression revealed that a mRS score of 4 (OR=14.20; 95% CI=1.70-145.25; p=0.02) and a mRS score of 5 (OR=78.84; 95% CI=9.35-820.25; p < 0.001) on admission were associated with poor outcomes. In addition, the presence of ≥ 3 comorbidities (OR=4.59; 95% CI=14.65; p < 0.01) and increasing age (OR=1.04; 95% CI=1.01-1.07; p=0.02) were predictors of poor outcomes three months post-stroke. Conclusions. The study underscores the importance of early intervention and effective management of comorbidities to improve functional outcomes in stroke patients. It highlights the need for targeted stroke care and rehabilitation strategies

    ANN and CFD driven research on main performance characteristics of solar chimney power plants: Impact of chimney and collector angle

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    Solar energy systems operate directly connected to the sun. Solar chimney power plants are privileged systems that can provide power output even in cloudy weather and during hours when there is no sun. The design and sizing of this system, which researchers focused on after its first application in the 1980s, is very effective on its performance. In this study, the collector slope and chimney slope that give maximum power output for the Manzanares pilot plant are investigated with a 3D CFD model. Simulations made using the RNG k-e turbulence model and the DO (discrete ordinates) solar ray tracing algorithm provide results that are in high compatibility with experimental data and literature. It is understood that the system provides maximum power at 0.6° collector slope and 1.5° chimney divergence angle. It is seen that the system, which gives a power output of approximately 46 kW in the reference case, exceeds the power output by 4.5 times and reaches 216.853 kW in the design that includes the collector and chimney slope. The effects of the main elements of the system on the performance are also included by changing the collector radius and chimney height while preserving these inclination angles. More than the power output in the reference case, 49.233 kW, can be achieved with the inclined design, with a collector radius of 73.2 m and a chimney height of 155.68 m. Although the effect of increasing the chimney height on power output continues after 1.2 floors, its effect decreases. In the study, it is seen that increasing the chimney height and changing the collector radius provide a greater increase in power output. Furthermore, the scope extends to the incorporation of an Artificial Neural Network (ANN) model, presenting a novel approach to predicting SCPP system performance. The findings ascertain the utilisation of 9 neurons in the hidden layer of the ANN, demonstrating a precise alignment with the study data

    Waste Animal Bones as Catalysts for Biodiesel Production; A Mini Review

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    Slaughterhouse waste is considered to be an emerging issue because of its disposal cost. As an alternative, it would be a great prospect for the bioeconomy society to explore new usages of these leftover materials. As per food safety rules mentioned by EU legislation, all bone waste generated by slaughterhouses ought to be disposed of by rendering. The huge quantity of worldwide bone waste generation (130 billion kilograms per annum) is an environmental burden if not properly managed. The waste animal bones can be efficiently employed as a heterogeneous catalyst to produce biodiesel. This mini review summarized the recent literature reported for biodiesel generation using waste animal bones derived heterogeneous catalyst. It discusses the sources of bone waste, catalyst preparation methods, particularly calcination and its effects, and important characteristics of bones derived catalyst. It suggests that catalysts extracted from waste animal bones have suitable catalytic activity in transesterification of different oil sources to generate a good quality biodiesel

    A robust computational investigation on C₆₀ fullerene nanostructure as a novel sensor to detect SCNˉ

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    This study explored on the adsorption properties and electronic structure of SCNˉ via density functional theory analysis on the exterior surfaces of C₆₀ and CNTs using B3LYP functional and 6-31G** standard basis set. Then adsorption of SCNˉ through nitrogen atom on the C60 fullerene is electrostatic (₋48.02 kJ molˉ1) in comparison with the C₅₉Al fullerene that shows covalently attached to fullerene surface (₋389.10 kJ mol̄ˉ1). Our calculations demonstrate that the SCNˉ adsorption on the pristine and Al-doped single-walled CNTs are ₋173.13 and ₋334.43 kJ molˉ1, indicating that the SCNˉ can be chemically bonded on the surface of Al-doped CNTs. Moreover, the adsorption of SCNˉ on the C₆₀ surface is weaker in comparison with C₅₉B, C₅₉Al, and C₅₉Ga systems but its electronic sensitivity improved in comparison with those of C₅₉B, C₅₉Al, and C₅₉Ga fullerenes. The evaluation of adsorption energy, energy gap, and dipole moment demonstrates that the pure fullerene can be exploited in the design practice as an SCNˉ sensor and C₅₉Al can be used for SCNˉ removal application

    Assessment and Management of Atopic Dermatitis in Primary Care Settings

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    An increasingly common chronic inflammatory skin condition is atopic dermatitis (AD). It exhibits severe itching as well as recurring eczematous lesions. New difficulties for treatment selection and approach occur with the expansion of available therapy alternatives for healthcare professionals and patients.  The article highlights recent developments in scientific research on atopic dermatitis diagnosis and assessment that have led to the identification of novel therapeutic targets and the development of targeted therapies, both of which have the potential to completely change the way AD is treated, particularly in a primary care setting

    Elective Cancer Surgery in COVID-19-Free Surgical Pathways During the SARS-CoV-2 Pandemic: An International, Multicenter, Comparative Cohort Study.

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    PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks
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