Investigating the effect of curing temperature on the corrosion resistance of epoxy-based composite coatings for aluminium alloy 7075 in artificial seawater.

Araldite LY5052 epoxy resin and Aradur HY5052 hardener were used in a ratio of 100 : 38 to produce composite coatings containing 0.05 proportion of functionalized SiO2. Coating samples were cured at curing temperatures of 60, 80, 100, 120, and 140 °C. The results of Fourier Transform Infrared Spectroscopy (FTIR) verified that silica particles were successfully functionalized with methyltrimethoxysilane (MTMS)/3-aminopropyl-triethoxysilane (APTES). The epoxide and Si-O bond peaks in the EHS100 coating were present due to the effective incorporation of functionalized silica (FSiO2) particles in the polymeric matrix (epoxy resin). The surface morphology of the bare aluminium alloy AA7075 and EHS100 coating was investigated by Field Emission Scanning Electron Microscopy (FE-SEM). Additionally, corrosion analysis was conducted at room temperature using an electrolytic solution of artificial seawater, prepared according to ASTM standard D1141-98. Charge transfer resistance (Rct) was shown to increase by 86.43, 92.15, 94.76, 90.65, and 83.96% for EHS60, EHS80, EHS100, EHS120, and EHS140 in comparison to bare AA7075 substrate using electrochemical impedance spectroscopy (EIS) examination. Furthermore, potentiodynamic polarization (PDP) measurements were carried out to determine the corrosion rates, which demonstrated a drop of 55.98, 98.96, 99.37, 98.33, and 50.39% for EHS60, EHS80, EHS100, EHS120, and EHS140, as compared to the bare AA7075 sample. The highest charge transfer resistance (29.77 kΩ) and lowest corrosion rate (0.00078 mm per year) were recorded for EHS100, which reveals that the EHS100 coating has the best anti-corrosion performance and provides the maximum corrosion protection for the aluminium alloy AA7075 substrate.

Lopinavir-Loaded Self-Nanoemulsifying Drug Delivery System for Enhanced Solubility: Development, Characterisation and Caco-2 Cell Uptake.

BACKGROUND: The antiretroviral protease inhibitor drug, lopinavir (LPV), is used to treat HIV-1 infection. LPV is known to have limited oral bioavailability, which may be attributed to its poor aqueous solubility, low efficacy and high first-pass metabolism. Self-nanoemulsifying drug delivery systems (SNEDDS) for LPV have been developed and optimised to counter the current issues. METHODS: The titration method was used to prepare LPV-loaded SNEDDS (LPV-SNEDDS). Six different pseudo-ternary phase diagrams were constructed to identify the nanoemulsifying region. The developed formulations were chosen in terms of globule size < 100 nm, dispersity ≤ 0.5, dispersibility (Grade A) and% transmittance > 85. Heating-cooling cycle, freeze-thaw cycle, and centrifugation studies were performed to confirm the stability of the developed SNEDDS. RESULTS: The final LPV-SNEDDS (L-14) droplet size was 58.18 ± 0.62 nm, with polydispersity index, zeta potential, and entrapment efficiency (EE%) values of 0.326 ± 0.005, -22.08 ± 1.2 mV, and 98.93 ± 1.18%, respectively. According to high-resolution transmission electron microscopy (HRTEM) analysis, the droplets in the optimised formulation were < 60 nm in size. The selected SNEDDS released nearly 99% of the LPV within 30 min, which was significantly (p < 0.05) higher than the LPV-suspension in methylcellulose (0.5% w/v). It indicates the potential use of SNEDDS to enhance the solubility of LPV, which eventually could help improve the oral bioavailability of LPV. The Caco-2 cellular uptake study showed a significantly (p < 0.05) higher LPV uptake from the SNEEDS (LPV-SNEDDS-L-14) than the free LPV (LPV-suspension). CONCLUSION: The LPV-SNEDDS could be a potential carrier for LPV oral delivery.

Optimization of a Cefuroxime Axetil-Loaded Liquid Self-Nanoemulsifying Drug Delivery System: Enhanced Solubility, Dissolution and Caco-2 Cell Uptake.

Cefuroxime axetil (CA) is an oral cephalosporin which hydrolyzes rapidly to the active parent compound cefuroxime. CA is known to have incomplete oral bioavailability (30−50%) due to its poor solubility and enzymatic conversion to cefuroxime in the gut lumen. In order to overcome these drawbacks, a lipid-based self-nanoemulsifying drug delivery system (SNEDDS) has been developed and optimized. The SNEDDS formulations were prepared using the aqueous phase titration method. The greatest self-emulsifying area was found in the 2:1 Smix ratio. As a result, different SNEDDS formulations were carefully selected from this phase diagram based on their smaller droplet size < 100 nm, polydispersity index ≤ 0.5, dispersibility (Grade A), and transmittance (%) > 85%. Thermodynamic stability tests were carried out in order to rule out any metastable/unstable SNEDDS formulations. The droplet size, polydispersity index, zeta potential, and entrapment efficiency (% EE) of optimized CA-loaded SNEDDS (C-3) were 18.50 ± 1.83 nm, 0.064 ± 0.008, −22.12 ± 1.20 mV, and 97.62 ± 1.06%, respectively. In vitro release studies revealed that the SNEDDS formulation had increased CA solubility. CA-SNEDDS-C3 increased CA cellular uptake, possibly due to increased CA solubility and the inhibition of enzymatic conversion to cefuroxime. Finally, in terms of the improvement of oral bioavailability, CA-loaded-SNEDDS could be a viable alternative to commercially available CA formulations.

Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.

Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.

Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches.

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low-middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case-control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities.

Self-assembled insulin and nanogels polyelectrolyte complex (Ins/NGs-PEC) for oral insulin delivery: characterization, lyophilization and in-vivo evaluation.

Introduction: Insulin is given by injection, because when administered orally, it would be destroyed by enzymes in the digestive system, hence only about 0.1% reaches blood circulation. The purpose of the present study was to use pH sensitive polyelectrolyte methyl methacrylate (MMA)/itaconic acid (IA) nanogels as carriers in an attempt to improve absorption of insulin administered orally. Methods: Insulin (Ins) was incorporated into the MMA/IA nanogels (NGs) using the polyelectrolyte complexation (PEC) method to form Ins/NGs-PEC. Several parameters, including Ins:NGs ratio, pH, incubation time and stirring rate were optimized during preparation of InsNGs-PEC. The prepared formulations were characterized in terms of particle size (PS), polydispersity index (PdI), zeta potential (ZP) and percent entrapment efficiency (% EE). Results: The optimized InF12 nanogels had a PS, PdI, ZP and %EE of 190.43 nm, 0.186, -16.70 mV and 85.20%, respectively. The InF12 nanogels were lyophilized in the presence of different concentrations of trehalose as cryoprotectant. The lyophilized InF12 containing 2%w/v trahalose (InF12-Tre2 nanogels) was chosen as final formulation which had a PS, PdI, ZP and %EE of 430.50 nm, 0.588, -16.50 mv and 82.10, respectively. The in vitro release of insulin from InF12-Tre2 nanogels in the SGF and SIF were 28.71% and 96.53%, respectively. The stability study conducted at 5±3°C for 3 months showed that lnF12-Tre2 nanogels were stable. The SDS-PAGE assay indicated that the primary structure of insulin in the lnF12-Tre2 nanogels was intact. The in-vivo study in the diabetic rats following oral administration of InF12-Tre2 nanogels at a dose of 100 IU/kg body weight reduced blood glucose level significantly to 51.10% after 6 hours compared to the control groups. Conclusions: The pH sensitive MMA/IA nanogels are potential carriers for oral delivery of insulin as they enhanced the absorption of the drug.

Freeze-Dried Lopinavir-Loaded Nanostructured Lipid Carriers for Enhanced Cellular Uptake and Bioavailability: Statistical Optimization, in Vitro and in Vivo Evaluations.

Nanostructured lipid carriers (NLCs) loaded with lopinavir (LPV) were prepared by the high-shear homogenization method. The LPV-NLCs formulations were freeze-dried using trehalose as a cryoprotectant. In vitro release studies in simulated gastric fluid (pH 1.2) and simulated intestinal fluid (pH 6.8) showed a burst release. The optimized freeze-dried formulation (LPV-NLC-7-Tres) had a particle size (PS), polydispersity index (PdI), zeta potential (ZP) and % entrapment efficiency (%EE) of 286.8 ± 1.3 nm, 0.413 ± 0.017, -48.6 ± 0.89 mV and 88.31 ± 2.04%, respectively. The optimized formulation observed by transmission and scanning electron microscopes showed a spherical shape. Differential scanning calorimetry study revealed the absence of chemical interaction between the drug and lipids. In vitro cellular uptake study using Caco-2 cell line showed a higher LPV uptake from LPV-NLC-7-Tres formulation compared to the free LPV-suspension. The 6-month stability study showed a minimum rise of ~40 nm in PS, while no significant changes in PdI, ZP and drug content of the LPV-NLC-7-Tres formulation stored at 5 °C ± 3 °C. The bioavailability of LPV following oral administration of LPV-NLC-7-Tres in male Wistar rats was found 6.98-fold higher than the LPV-suspension. In conclusion, the nanostructure lipid carriers are potential carriers for improving the oral bioavailability of lopinavir.

Lyophilized Hybrid Nanostructured Lipid Carriers to Enhance the Cellular Uptake of Verapamil: Statistical Optimization and In Vitro Evaluation.

Verapamil is a calcium channel blocker and highly effective in the treatment of hypertension, angina pectoris, and other diseases. However, the drug has a low bioavailability of 20 to 35% due to the first pass effect. The main objective of this study was to develop hybrid verapamil-dextran nanostructured lipid carriers (HVD-NLCs) in an attempt to increase verapamil cellular uptake. The formulations were successfully prepared by a high-shear homogenization method and statistically optimized using 24 full factorial design. The HVD-NLCs formulations were freeze-dried using trehalose as a cryoprotectant. The results showed that the optimized formula (VER-9) possessed a particle size (PS), polydispersity index (PDI), and the percentage of entrapment efficiency (%EE) of 192.29 ± 2.98, 0.553 ± 0.075, and 93.26 ± 2.66%, respectively. The incorporation of dextran sulfate in the formulation had prolonged the release of verapamil (~ 85% in 48 h) in the simulated gastric fluid (pH 1.2) and simulated intestinal fluid (pH 6.8). The differential scanning calorimetry analysis showed no chemical interaction between verapamil and the excipients in the formulation. While wide-angle X-ray scattering studies demonstrated the drug in the amorphous form after the incorporation in the NLCs. The transmission electron microscopy and scanning electron microscopy images revealed that the nanoparticles had spherical shape. The cellular uptake study using Caco-2 cell line showed a higher verapamil uptake from HVD-NLCs as compared to verapamil solution and verapamil-dextran complex. The optimized formulation (VER-9) stored in the refrigerated condition (5 °C ± 3 °C) was stable for 6 months. In conclusion, the HVD-NLCs were potential carriers for verapamil as they significantly enhanced the cellular uptake of the drug.

The prevalence of polypharmacy in elderly attenders to an emergency department - a problem with a need for an effective solution.

We studied the prevalence of polypharmacy in attenders aged 75 years and over to an emergency department (ED) in North London over a period of 1 month. We identified 467 patients in this age group. Analysis of medications being prescribed revealed at least 82 patients on medication with the potential for adverse interaction. There is a need for ED-initiated strategies to identify interactions and for pathways to allow for medication review.

The role of prophylactic anticonvulsants in moderate to severe head injury.

BACKGROUND: Post-traumatic seizures cause secondary brain injury, contributing to morbidity and mortality after traumatic brain injury. Seizure activity may be undetectable if the patient is paralysed and ventilated. AIMS: The effect of prophylactic anticonvulsant therapy on the prevention of seizures after moderate to severe traumatic brain injury was studied. METHODS: A structured systematic literature review was performed. RESULTS: There may be a place for prophylactic anticonvulsants in the prophylaxis of early post-traumatic seizures. CONCLUSION: Further randomised controlled trials are needed to firmly establish the benefits of prophylactic anticonvulsants.

Cerebrospinal shunt malfunction: recognition and emergency management.

Indwelling shunts to divert CSF flow are essential in treating hydrocephalus. There is a high incidence of shunt malfunction, which accounts for the increasing popularity of endoscopic third ventriculostomy. Failure to recognize and act on symptoms and signs of shunt malfunction may lead to loss of life or to permanent neurological dysfunction. This review provides the basis for assessment and management based on updated clinical knowledge.