•Adomian decomposition is used to study a moving fin.•Effects of different parameters on the temperature and efficiency are studied.•Binary-coded GA is used to solve an inverse problem.•Sensitivity ...analyses of important parameters are carried out.•Measurement error up to 8% is found to be tolerable.
The application of the Adomian decomposition method (ADM) is extended to study a conductive–convective and radiating moving fin having variable thermal conductivity. Next, through an inverse approach, ADM in conjunction with a binary-coded genetic algorithm (GA) is also applied for estimation of unknown properties in order to satisfy a given temperature distribution. ADM being one of the widely-used numerical methods for solving non-linear equations, the required temperature field has been obtained using a forward method involving ADM. In the forward problem, the temperature field and efficiency are investigated for various parameters such as convection–conduction parameter, radiation–conduction parameter, Peclet number, convection sink temperature, radiation sink temperature, and dimensionless thermal conductivity. Additionally, in the inverse problem, the effect of random measurement errors, iterative variation of parameters, sensitivity coefficients of unknown parameters are investigated. The performance of GA is compared with few other optimization methods as well as with different temperature measurement points. It is found from the present study that the results obtained from ADM are in good agreement with the results of the differential transformation method available in the literature. It is also observed that for satisfactory reconstruction of the temperature field, the measurement error should be within 8% and the temperature field is strongly dependent on the speed than thermal parameters of the moving fin.
In vitro release studies in USP II apparatus indicate significant improvement (
p
<
0.001) in dissolution of lercanidipine from SNEDDS (F4) over marketed tablet “Zanidip” (10
mg) and plain ...lercanidipine in different dissolution media.
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► In phase diagram, effect of cosurfactants on the nanoemulsifying area was proved. ► Length of hydrophobic alkyl chains in cosurfactants defines emulsification capacity. ► Careful selection of cosurfactant is required for effective nanocarrier based system. ► Smix ratio of 3:1 gave best self-nanoemulsifying lercanidipine loaded formulation. ► Results of 3 months stability studies indicated no significant change in parameters. ► SNEDDS increased
in vitro release of drug than marketed tablet and plain drug.
The present study deals with the development and characterization of self-nanoemulsifying drug delivery system (SNEDDS) to improve the oral bioavailability of poorly soluble third generation calcium channel blocker lercanidipine (LER). Solubility of the LER was estimated in various oils, cosurfactants and surfactants which were grouped into two different combinations to construct pseudoternary phase diagrams. Various thermodynamic stability and dispersibility tests were performed on the formulations from phase diagram. After constructing phase diagram of two different combinations NL-I and NL-II, the effect of cosurfactants on the nanoemulsifying area was studied and the effect of number and length of hydrophobic alkyl chains of cosurfactant in its emulsification capacity was proved. Percentage transmittance, emulsification time, viscosity and droplet size analysis were used to characterize optimized formulations. The optimized formulation composed of Cremophor EL (45% wt/wt), (13.5% wt/wt) Caproyl 90 with (1.5% wt/wt) Transcutol® HP as per limits of inactive ingredients guidelines of FDA and Maisine oil (10% wt/wt). The mean droplet size in selected nanocarrier system was 20.01
nm. The
in vitro dissolution profile of LER SNEDDS was found significant in comparison to the marketed LER (Zanidip) tablet and pure drug in pH 1.2, 4.5 and 6.8 buffers. Empty hard gelatin capsule shells were filled using Pfizer's Licap technology and charged on stability conditions of 30
°C/65% RH, 40
°C/65%RH and 50
°C/75% in glass bottles where no significant degradation (
p
>
0.05) was observed in 3 months. The results indicate that SNEDDS of LER, owing to nanosized, has potential to enhance the absorption of drug.
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•Pluronic P84 was found to be better solublizer than pluronic F127 and F108.•The solublization capacity was found to be dependent upon the length of corona.•Invitro drug release time ...depend upon the locus of solublization of OXC drug.
The solubilization of hydrophobic drug oxcarbazepine (OXC) in different pluronics viz. F108, F127 and P84 have been studied employing state of art techniques. The results from UV–vis spectroscopy reveals that the solublization capacity of pluronic P84 is higher as compared to other two pluronics F108 and F127 from where we speculated that larger corona region of pluronic micelles acted as barrier for the movement of drug into core. The locus of OXC in different pluronic micelles has been adjudged by 1H NMR and isothermal titration calorimetry (ITC) measurements. In case of P84, an upfield chemical shifts in PPO (poly-propylene oxide) protons and a net endothermic process in the presence of OXC confirms that more amount of OXC is solubilized in core region whereas opposite results have been observed in case of pluronic F108 and F127. Dynamic light scattering (DLS) has been employed to determine the hydrodynamic diameter (Dh) of loaded and unloaded micelles. A significant difference between Dh of loaded and unloaded micelles assure that OXC was solubilized in pluronic micelles. In vitro drug release study of three different pluronic formulations show sustained release behaviour according to their locus of solubilization. The present results demonstrate that via drug-pluronics interaction and drug location study, we can tune the drug release behaviour from the pluronic micelles.
Abstract In the present paper, we employ non-equilibrium molecular dynamics approach to simulate impact-induced shock propagation in single-crystal Molybdenum. Shock hugoniot, generated by simulation ...of impact with varied strength shows excellent agreement with experimental data in the strong shock regime. The resulting hugoniot parameters obtained by linear fitting of shock velocity vs. particle velocity data are then used to estimate ambient pressure Grüneisen coefficient. Finally Mie-Grüneisen equation of state (EOS) with hugoniot as reference state, is employed to express pressure-volume-energy relationship for 001 single-crystal Mo. The influence of different analytical forms for volume dependence of Grüneisen parameter on EOS is investigated.
In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar ...forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.
Sepsis, a life-threatening condition resulting from uncontrolled host responses to infection, poses a global health challenge with limited therapeutic options. Due to high heterogeneity, sepsis lacks ...specific therapeutic drugs. Additionally, there remains a significant gap in the clinical management of sepsis regarding personalized and precise medicine.
This review critically examines the scientific landscape surrounding natural products in sepsis and sepsis-mediated inflammation, highlighting their clinical potential.
Following the PRISMA guidelines, we retrieved articles from PubMed to explore potential natural products with therapeutic effects in sepsis-mediated inflammation.
434 relevant in vitro and in vivo studies were identified and screened. Ultimately, 55 studies were obtained as the supporting resources for the present review. We divided the 55 natural products into three categories: those influencing the synthesis of inflammatory factors, those affecting surface receptors and modulatory factors, and those influencing signaling pathways and the inflammatory cascade.
Natural products' potential as game-changers in sepsis-mediated inflammation management lies in their ability to modulate hallmarks in sepsis, including inflammation, immunity, and coagulopathy, which provides new therapeutic avenues that are readily accessible and capable of undergoing rapid clinical validation and deployment, offering a gift from nature to humanity. Innovative techniques like bioinformatics, metabolomics, and systems biology offer promising solutions to overcome these obstacles and facilitate the development of natural product-based therapeutics, holding promise for personalized and precise sepsis management and improving patient outcomes. However, standardization, bioavailability, and safety challenges arise during experimental validation and clinical trials of natural products.
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Artificial intelligence/Machine learning (AI/ML) is transforming all spheres of our life, including the healthcare system. Application of AI/ML has a potential to vastly enhance the reach of diabetes ...care thereby making it more efficient. The huge burden of diabetes cases in India represents a unique set of problems, and provides us with a unique opportunity in terms of potential availability of data. Harnessing this data using electronic medical records, by all physicians, can put India at the forefront of research in this area. Application of AI/ML would provide insights to our problems as well as may help us to devise tailor-made solutions for the same.
•Classification accuracy: The mean classification accuracy of Novel hBCI with distinct colours was 92.30%.•ITR: The average ITR for 10 subjects was highest for SSVEP-P300 hBCI with distinct colours ...(82.38 bits/min.).•FAR: Average FAR was reduced to 2.72 % for the Novel hBCI with distinct colours.•Increased number of target tiers resulted in more targets for the same number of frequency as compared to traditional SSVEP BCI.
Steady-State Visually Evoked Potential (SSVEP) has been one of the most common paradigms for Brain-Computer Interface (BCI) based applications with relatively good accuracy and Information Transfer Rate (ITR). However, limited decision options for a set number of paradigm frequencies, which is the main reason behind low ITR, is a huge hurdle the researchers have been facing in generalising SSVEP based BCI applications.
This paper proposes a novel hybrid Brain-Computer Interface (hBCI) stimuli paradigm to improve ITR by increasing the number of decision options available for SSVEP BCI by introducing P300 as a Time Division Multiplexing (TDM) marker. One of the Hybrid BCI’s used distinct colours along with distinct flickering frequencies for targets, with an aim to improve the performance of the BCI based on the accuracy of classification, the elevation of ITR and reduction of FAR as compared to traditional BCIs.
It was established that the Novel SSVEP-P300 with distinct colours for target frequencies hybrid BCI had average parameters as following: classification accuracy of 92.30 %, ITR of 82.38 bits/min and FAR of 2.72 %.
A comparative study between the two novel paradigms, traditional SSVEP and P300 paradigms in the same environment was conducted. And a statistical inference was established sussing pairedt-tests.
The results of the comparative study were conclusive that the hybrid BCI with distinct colours for each target frequency yielded best results and hence can be considered as a viable paradigm option for the development of an Assistive Device.
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