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Nano-magnesium oxide (MgO) was prepared by wet chemical method using magnesium chloride and sodium hydroxide as precursors and soluble gelatin as stabilizing agent in this paper. The synthesized nano MgO was characterized by XRD, SEM, and FTIR. The results showed that the size of nano-MgO was about 20.62 nm. Polyvinyl alcohol (PVA) polymer based nanocomposites, with different concentrations of MgO (1, 2, 3, 4 wt%), have been prepared using solvent casting technique. The results of SEM revealed that the MgO nanoparticles are uniformly distributed in PVA polymer matrix. FTIR analysis evidently saw the interaction between MgO with hydroxyl group of PVA through hydrogen bonding. The influences of MgO nanoparticle on the optical characterisation of PVA have been considered using UV–Vis–NIR spectroscopy. Energy band gap and tail of localized state of PVA/MgO nanocomposites have been calculated by using Tauc and Urbach relations, respectively. The band gap of the nanocomposites samples decreases as MgO wt% increases. Wemple-DiDomenico single-oscillator model has been applied to analyze the dispersion of the refractive index of the films, and the dispersion parameters are calculated to obtain the information about disorder degree.

Lung cancer is the most common malignancy worldwide, with approximately 1.8 million new cases yearly. Cytotoxic drugs are frequently used in cancer treatment. Even though the medicine enhances patients' quality of life, several drawbacks diminish its efficacy. Drug resistance and many disadvantages associated with chemotherapeutic drug side effects continue to be significant factors limiting the efficiency of cancer treatment. This necessitates developing new effective strategies that target tumors with minimal adverse effects. This research aims to overcome these issues by synthesizing a new series of compounds with an isoxazole ring attached by Schiff bases and azo bonds based on molecular docking with the (Genetic Optimization of Ligand Docking) program and estimating the pharmacokinetic properties with the Swiss ADME. The greatest-fitting compounds were then manufactured and verified by spectral analysis (FT-IR, 1H NMR, and 13C NMR), in vitro MTT assay for assessment of antiproliferative activities against A549 lung cancer cell lines showed that compounds 5a and 5b had an inhibitory concentration half-maximal inhibitory concentration (IC50) (17.34 and 18.32 μM), respectively, which was significantly lower than the inhibitory concentration of erlotinib (IC50 = 25.06 μM).

Background There is no doubt that vaccination is crucial for preventing the spread of diseases; however, not every vaccine is perfect or will work for everyone. The main objective of this work is to predict which vaccine will be most effective for a candidate without causing severe adverse reactions and to categorize a patient as potentially at high risk of death from the COVID-19 vaccine. Methods A comprehensive analysis was conducted using a dataset on COVID-19 vaccine adverse reactions, exploring binary and multiclass classification scenarios. Ensemble models, including Random Forest, Decision Tree, Light Gradient Boosting, and extreme gradient boosting algorithm, were utilized to achieve accurate predictions. Class balancing techniques like SMOTE, TOMEK_LINK, and SMOTETOMEK were incorporated to enhance model performance. Results The study revealed that pre-existing conditions such as diabetes, hypertension, heart disease, history of allergies, prior vaccinations, other medications, age, and gender were crucial factors associated with poor outcomes. Moreover, using medical history, the ensemble learning classifiers achieved accuracy scores ranging from 75% to 87% in predicting the vaccine type and mortality possibility. The Random Forest model emerged as the best prediction model, while the implementation of the SMOTE and SMOTETOMEK methods generally improved model performance. Conclusion The random forest model emerges as the top recommendation for machine learning tasks that require high accuracy and resilience. Moreover, the findings highlight the critical role of medical history in optimizing vaccine outcomes and minimizing adverse reactions.

The material characteristics of foreign Object Debris (FOD) are the essential criteria in determining the extent of an aircraft’s damage. Foreign object debris (FOD) can cause significant accidents and financial losses on airport runways. A new FOD material recognition strategy is proposed in this paper using an ensemble learning algorithm, namely KNN, Adaboost, and Random Forest Tree, to classify FOD images. In addition, this study uses different feature extraction methods like Linear Discriminant Analysis (LDA) and Gray-level co-occurrence matrix(GLCM) to extract FOD features. The KNN, Adaboost, and Random Forest Tree precision are 94.20%, 98.9%, and 99.7%, respectively. The dataset that was used has been collected by researchers from several datasets. As a result, the experiment results reveal that the proposed framework is effective and accurate. The results showed that the best classification machine algorithm is Random Forest Tree.

Background There is limited evidence regarding the safety and efficacy of tenecteplase 0.25 mg/kg in patients with acute ischemic stroke in the extended time window, either with large vessel occlusion or not. Therefore, we aim to assess its safety and efficacy for these patients. Methodology We searched PubMed, Scopus, and Web of Science for randomized controlled trials that compared tenecteplase 0.25 mg/kg with the best medical management. Our primary efficacy outcomes included favorable and excellent functional outcomes, measured via the modified Rankin score (mRS), and early neurological improvements. Primary safety outcomes were mortality and symptomatic intracranial hemorrhage rates. Subgroup analyses were performed based on mRs, and the inclusion of patients eligible for mechanical thrombectomy in addition to TNK. Results Our search found nine randomized controlled trials, of which 8 were included in the meta-analysis. A total of 3068 patients were included. Tenecteplase 0.25 mg/kg did not differ from the control group in achieving excellent functional outcomes and favorable functional outcomes ( P values: 0.15 and 0.69), while there was significant improvement in early neurological improvement ( P value: 0.02). Symptomatic intracranial hemorrhage was statistically higher in the tenecteplase 0.25 mg/kg group ( P value 0.008). Subgroup analyses indicated that patients with mRS 0–1 who receive TNK 0.25 mg/kg do better than those with mRS 0–2. Better results for TNK 0.25 mg/kg were observed in studies that excluded patients eligible for mechanical thrombectomy in addition to TNK. Conclusions TNK 0.25 mg/kg appears to significantly achieve early neurological improvement with no effect in achieving excellent or favorable functional outcomes; however, it showed a significant increase in symptomatic intracranial hemorrhage. Patients who performed endovascular thrombectomy after administering TNK 0.25 mg/kg had statistically significantly higher rates of symptomatic intracranial hemorrhage; on the other hand, current evidence endorses the use of tenecteplase 0.25 mg/kg in patients with ischemic strokes whose baseline mRs ranges from 0 to 1. Further RCTs are necessary to validate these findings. Prospero registration number : CRD42025636473.

In this paper, we successfully solve some linear [DELTA]-fractional dynamic equations ([DELTA]-FDE) with Caputo [DELTA]-derivative analytically by solving an auxiliary linear [DELTA]-differential equation ([DELTA]-DE) with an integer order. The idea of the proposed method is based on transforming the given [DELTA]-FDE into a linear [DELTA]-DE with an integer order. This transformation removes certain terms of the solution of the considered [DELTA]-FDE, resulting in the remaining terms being a solution to the auxiliary equation. To demonstrate the ability and efficacy of this idea, several examples have been provided. Keywords: Time Scale Calculus, Fractional time scale calculus, Caputo fractional [DELTA]-derivatives. AMS Subject Classification: 34-XX and MSC 35R07 and MSC 34A08.

Background: There is no doubt that vaccination is crucial for preventing the spread of diseases; however, not every vaccine is perfect or will work for everyone. The main objective of this work is to predict which vaccine will be most effective for a candidate without causing severe adverse reactions and to categorize a patient as potentially at high risk of death from the COVID-19 vaccine. Methods: A comprehensive analysis was conducted using a dataset on COVID-19 vaccine adverse reactions, exploring binary and multiclass classification scenarios. Ensemble models, including Random Forest, Decision Tree, Light Gradient Boosting, and extreme gradient boosting algorithm, were utilized to achieve accurate predictions. Class balancing techniques like SMOTE, TOMEK_LINK, and SMOTETOMEK were incorporated to enhance model performance. Results: The study revealed that pre-existing conditions such as diabetes, hypertension, heart disease, history of allergies, prior vaccinations, other medications, age, and gender were crucial factors associated with poor outcomes. Moreover, using medical history, the ensemble learning classifiers achieved accuracy scores ranging from 75% to 87% in predicting the vaccine type and mortality possibility. The Random Forest model emerged as the best prediction model, while the implementation of the SMOTE and SMOTETOMEK methods generally improved model performance. Conclusion: The random forest model emerges as the top recommendation for machine learning tasks that require high accuracy and resilience. Moreover, the findings highlight the critical role of medical history in optimizing vaccine outcomes and minimizing adverse reactions.

We study dynamic systems on time scales that are generalizations of classical differential or difference equations. In this paper, we present the asymptotic stability of linear fractional time-invariant systems with the Caputo [DELTA]--derivative on time scale. To ensure the asymptotic stability of this kind of system, some results about necessary and sufficient conditions are investigated, resulting in a region of asymptotic stability. Furthermore, we obtain the results of the asymptotic stability by transforming the stability region of the continuous-time case through suitable Mobious transformations. Keywords: Time scale calculus; Linear dynamical systems; Fractional calculus; Stability. AMS Subject Classification: 34-XX and MSC 35R07 and MSC 34A08.

Background: Vemurafenib (VEM) was a licensed drug for the treatment of skin melanoma and is available only in the market as oral tablets prescribed in huge doses (1920 mg/day). One reason for the high dose is vemurafenib's low oral bioavailability. Methods: VEM-lipid complex (DLC) was predicted based on Conquest and Mercury programs and prepared using the solvent evaporation method using the lipid (phosphatidylethanolamine). DLC was subjected to characterization (FT-IR, Raman spectroscopy, DSC, TGA, P-XRD, and FESEM) to confirm complexation.  DLC was used to prepare solid in oil nanodispersion (DLC-SON) and subjected to in vitro, ex vivo, and in vivo evaluation in comparison to our recently prepared conventional SON (VEM-SON) and DLC-control. Results: Conquest and Mercury predict the availability of intermolecular hydrogen bonding between VEM and phosphatidylethanolamine (PE). All characterization tests of DLC ensure the complexation of the drug with PE. Ex vivo studies showed that the drug in DLC-SON has significantly (P<0.05) higher skin permeation than DLC-control but lower drug permeation than conventional SON but it has a higher % skin deposition (P<0.05) than others. The half-maximal inhibitory concentration (IC50) of the prepared DLC-SON is significantly high (P<0.05) in comparison to the conventional SON and pure VEM. In vivo permeation using confocal laser scanning microscopy (on the rat) results indicated that both conventional SON and DLC-SON can cross the SC and infiltrate the dermis and epidermis but DLC-SON has a higher luminance/gray value after 24 h in the dermis in comparison to the conventional SON. Conclusion: The novel lipid complex for VEM prepared using PE as a lipid and enclosed in SON showed higher anticancer activity and topical permeation as well as sustained delivery and good retention time in the dermis that localize the drug in a sufficient concentration to eliminate early diagnosed skin melanoma.

Mapping the soil moisture is a key activity in water management and sustainable agriculture, especially in regions characterised by fragile agri-food systems and water scarcity. Cosmic Ray Neutron Sensors (CRNS) is a contactless nuclear technology used for estimating soil moisture (SM) content on a 20–30 m scale at the landscape level. Very interestingly, this corresponds to the so-called intermediate scale gap between the local probes, operating on the meter scale, and the satellite-based technologies, working on the kilometre scale and above. In state-of-art CRNS, the cosmic neutrons degraded by the soil are simply counted by a slightly moderated thermal neutron counter. After a calibration procedure, the SM is inferred by combining this count rate with environmental parameters: the atmospheric pressure, temperature and the air humidity. As the SM affects not only the environmental neutron fluence rate but also its energy distribution, this study was organised in such a way to understand if a CRNS with spectrometric capabilities could provide improved information on the SM distribution. To this aim, an environmental neutron spectrometer was designed by extending the Bonner Spheres to a more sensitive system made of moderating cylinders embedding long BF 3 proportional counters, the Moderating Cylinders Spectrometer (MCS). Relying on literature environmental neutron spectra, corresponding to different SM values in a standardised soil, the count rates in the MCS were calculated for different values of SM. To simulate various counting scenarios, these count rates were associated to different levels of “realistic” uncertainties and unfolded by means of the FRUIT code. The resulting neutron spectra are compared to the literature ones, allowing at estimating the resolving power of the spectrometer in terms of SM.