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Mucus plugging of the respiratory tract occurs in airway diseases, including asthma, chronic obstructive pulmonary disease, and cystic fibrosis. It can cause blockage of the airways, leading to breathlessness and lung failure. Here, we used a ventilatory setup to demonstrate the effect of BromAc® in dissolving mucus plugs in a novel ex vivo ovine obstructive lung model. Mucus simulant was filled into the trachea of freshly slaughtered ovine lungs and ventilated via an endotracheal tube (ETT) using Continuous Mandatory Ventilation. Predetermined single or repeated doses of Bromelain, Acetylcysteine (Ac), BromAc®, and saline control were administered via an Aerogen® vibrating nebulizer and ventilated for 30 or 60 min. Ventilatory recording of resistance, compliance, and tidal volume was conducted, and rheology pre- and post-treatment were measured. A significant decline in airway resistance (p < 0.0001) compared to the saline control was observed when treated with Bromelain, Ac, and BromAc®, with the latter showing a stronger mucolytic effect than single agents. The decline in resistance was also effective in shorter time points (p < 0.05) at lower doses of the drugs. Changes in compliance, peak pressure, and tidal volume were not observed after administration of the drugs. Rheology measurements revealed that BromAc®TM significantly reduced the viscosity of the mucin at the end of 30 min and 60 min time points (p < 0.001) compared to the saline control. BromAc® showed complete dissolution of the respiratory mucus simulant and improved ventilatory airflow parameters in the ex vivo ovine model.

Background and objectives: Pre-eclampsia (PE) is a pregnancy-specific condition characterized by significant health risks for pregnant women worldwide due to its status as a multi-organ disorder. High blood pressure (hypertension) with or without proteinuria is usually considered an initial clinical sign of PE. The pathogenesis of pre-eclampsia is highly complex and likely involves multiple factors, including poorly developed uterine spiral arterioles, immunological issues, placental ischemia or infarction, and genetic abnormalities. Inflammatory cytokine production, regulated by cytokine gene polymorphisms, is one of the factors likely contributing to the development of PE. The present study aimed to assess IL-6, IL-1β, and Apo B-100 gene polymorphism and to evaluate the association of these polymorphisms with PE. Materials and Methods: This cross-sectional observational study involved 99 participants aged 16 to 45 years from Bahawal Victoria Hospital Bahawalpur, Punjab, Pakistan. The participants were divided into three groups: Group 1 (PE with severe hypertension), Group 2 (PE with hypertension), and Group 3 (control), each comprising 33 individuals. Maternal blood samples were collected, DNA was extracted, and molecular genetic analysis of the IL-6, IL-1β, and Apo B-100 genes was performed using the PCR-RFLP method. Allelic frequencies were compared, and statistical analysis was conducted using SPSS 25, applying the Hardy–Weinberg equation and chi-square test to evaluate the results. Results: There are differences in the distribution of allelic frequencies for IL-6 -174G/C (CC, GC, GG), IL-1β-511C/T (CC, CT, TT), and Apo B-100 2488 C/T (CC, CT, TT) between pre-eclamptic patients and the control group. The analysis using the Hardy–Weinberg equilibrium and chi-square test showed an association between the IL-6-174 G/C polymorphism and the severity of pre-eclampsia. Conclusions: The polymorphisms of the IL-6, IL-1β, and Apo B-100 genes revealed different alleles. The IL-6 gene alone was found to be in disequilibrium according to the Hardy–Weinberg equation, indicating a potential link to the severity of pre-eclampsia in the population studied.

Dengue fever, caused by the dengue virus (DENV), poses a significant global health threat, with a dramatic increase in cases driven by climate change, urbanization, and mosquito resistance. In Pakistan, a country with a population of 240 million, the world’s fifth largest, dengue has emerged as an escalating public health crisis, with seasonal outbreaks severely straining the healthcare system. Despite decades of vector control efforts, there has not been much success, necessitating the introduction of dengue vaccination to boost population immunity. Recent advancements in vaccine development demonstrate promising efficacy and safety profiles, even in dengue-naive individuals. Implementing a dengue vaccination program in Pakistan could significantly reduce the disease burden, lower healthcare costs, and prevent future outbreaks. Integrating vaccination with existing public health initiatives can achieve high coverage and improve overall public health outcomes.

Background: Apolipoprotein E (APOE) gene polymorphism has been implicated in the pathogenesis of various metabolic disorders, including type 2 diabetes mellitus (T2DM). Type 2 diabetes mellitus (T2DM) is a major public health concern worldwide, including in Pakistan. Cardiovascular problems linked with T2DM have a significant impact on individuals and society. The goal of this study is to investigate the relationship between Apolipoprotein E (ApoE) genotypes, dyslipidemia, and cardiovascular complications such as ischemic heart disease (IHD) and stroke. Methods: This study was carried out on 260 subjects divided into controls and diabetics. The diabetics were further divided into four subgroups such as D1: diabetics without cardiovascular issues, D2: diabetics with heart disease, D3: diabetics with stroke, and D4: diabetics with both heart disease and stroke. Anthropometric parameters (age, BMI) and risk factors (smoking, diabetes duration, hypertension) were assessed in all groups. Serum levels of TC, TG, LDL, HDL, VLDL, creatinine, BSF, and HbA1c were also measured. Apolipoprotein E gene polymorphism was determined using PCR-RFLP. Results: Hypertension, BMI, and dyslipidemia are defined as elevated levels of total cholesterol, triglycerides, LDL, and VLDL, and decreased levels of HDL. Uncontrolled hyperglycemia (elevated fasting blood sugar and glycated hemoglobin) in T2DM was linked to vascular complications such as IHD and stroke. Hypertension was prevalent in 79.3% of the population. Stage 2 hypertension was more prevalent in all age groups. It was also noted that common genotypes in the Pakistani population are 3/3, 4/4, 2/3, and 3/4. The frequency of genotypes 3/4 and 2/3 is highest in diabetics with stroke. Genotype 3/3 is present frequently in diabetics with IHD/stroke and patients with both these complications. However, genotype 4/4 is most frequently found in diabetics with IHD. Conclusions: It is concluded that BMI, hypertension, hyperglycemia, atherosclerosis, and dyslipidemia are linked with cardiovascular complications of type 2 diabetes. Apolipoprotein E gene polymorphism is associated with cardiovascular disease in patients with diabetes by affecting the lipid profile.

Respiratory diseases such as cystic fibrosis, COPD, and COVID-19 are difficult to treat owing to viscous secretions in the airways that evade mucocilliary clearance. Earlier studies have shown success with BromAc as a mucolytic agent. Hence, we tested the formulation on two gelatinous airway representative sputa models, to determine whether similar efficacy exist. Sputum lodged in an endotracheal tube was treated to aerosol N-acetylcysteine, bromelain, or their combination (BromAc). After measuring the particle size of aerosolized BromAc, the apparent viscosity was measured using a capillary tube method, whilst the sputum flow was assessed using a 0.5 mL pipette. Further, the concentration of the agents in the sputa after treatment were quantified using chromogenic assays. The interaction index of the different formulations was also determined. Results indicated that the mean particle size of BromAc was suitable for aerosol delivery. Bromelain and N-acetylcysteine affected both the viscosities and pipette flow in the two sputa models. BromAc showed a greater rheological effect on both the sputa models compared to individual agents. Further, a correlation was found between the rheological effects and the concentration of agents in the sputa. The combination index using viscosity measurements showed synergy only with 250 µg/mL bromelain + 20 mg/mL NAC whilst flow speed showed synergy for both combinations of bromelain (125 and 250 µg/mL) with 20 mg/mL NAC. Hence, this study indicates that BromAc may be used as a successful mucolytic for clearing airway congestion caused by thick mucinous immobile secretions.

Drinking water defluoridation has attracted significant attention in the scientific community, from which membrane technology, by exploring thin film nanocomposite (TFN) membranes, has demonstrated a great potential for treating fluoride-contaminated water. This study investigates the development of a TFN membrane by integrating titanium oxide nanosheets (TiO2 NSs) into the polyamide (PA) layer using interfacial polymerization. The characterization results suggest that successfully incorporating TiO2 NSs into the PA layer of the TFN membrane led to a surface with a high negative charge, hydrophilic properties, and a smooth surface at the nanoscale. The TFN membrane, containing 80 ppm of TiO2 NSs, demonstrated a notably high fluoride rejection rate of 98%. The Donnan-steric-pore-model-dielectric-exclusion model was employed to analyze the effect of embedding TiO2 NSs into the PA layer of TFN on membrane properties, including charge density (Xd), the pore radius (rp), and pore dielectric constant (εp). The results indicated that embedding TiO2 NSs increased Xd and decreased the εp by less than the TFC membrane without significantly affecting the rp. The resulting TFN membrane demonstrates promising potential for application in water treatment systems, providing an effective and sustainable solution for fluoride remediation in drinking water.

The proteolytic activity of enzymes may be evaluated by a colorimetric method with azocasein. Hence, we developed a micro-assay to quantify bromelain using azocasein. A total of 250 µL of 1.0% azocasein in dH2O was added to 250 µL of test solution, vortexed and incubated at ambient room temperature/30 min. The reaction was terminated with 1500 µL of 5% trichloroacetic acid, vortexed and centrifuged. A total of 150 µL of 0.5M NaOH was added to 150 µL of supernatant in triplicates, and absorbance was recorded at 410 nm. The linearity of the calibration curve was tested with 200–800 µg/mL serial dilutions. The detection limit, precision, accuracy, and robustness were tested along with the substrate enzyme reaction time and solvent matrix effect. Good linearity was seen with serially diluted 200 µg/mL bromelain. The limit of quantification and limit of detection were 5.412 and 16.4 µg/mL, respectively. Intra-day and inter-day analyses showed a relative standard deviation below 2.0%. The assay was robust when tested over 400–450 nm wavelengths. The assays performed using dH2O or PBS diluents indicated a higher sensitivity in dH2O. The proteolytic activity of bromelain was enhanced with L-cysteine or N-acetylcysteine. Hence, this micro-azocasein assay is reliable for quantifying bromelain.

Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.

The genus Vigna is pan-tropical, having more than 200 species with many desirable economically important traits. This study aimed to validate the in silico polymorphism of whole-genome-sequence-developed mungbean-specific SSR markers and their transferability among different Vigna species. The present study utilized a set of 200 SSR markers developed from the whole-genome sequence of mungbean and validated them using a diversity panel of 25 accessions that belong to 13 Vigna species. Out of 200 SSR markers, 130 markers (65%) were polymorphic across the various Vigna species, and the number of alleles amplified varied from 7 to 24. The SSR markers showed more than 90 percent transferability across the different Vigna species accessions. Utilizing allelic data, the 25 Vigna accessions were clustered into three groups using the unweighted pair group method with arithmetic mean (UPGMA). The two integral coalitions explained 50.79 and 15.42% of the total variance. The principal coordinate analysis (PCA) biplot graph and UPGMA-based neighbor-joining clustering diagram showed a similar pattern of Vigna accession distribution. A population structure assessment grouped the cultivated and wild species accessions into two sub-populations based upon a maximum delta K value of 144.79, which drew a sharp peak at K = 2. The estimated marker parameters, such as the polymorphic information content (0.09–0.84), marker index (0.091–3.342), and effective multiplex ratio (1.0–4.0), suggested their adequacy for several genetic studies, such as parental selection, hybrid testing, genetic mapping, and marker-aided breeding programs, for the genetic enhancement of species belonging to the Vigna genus.

This study introduces a promising and practical method for the removal of paracetamol from aqueous environments, employing graphene oxide–polymer nanocomposite beads. The approach involves the utilization of a straightforward and facile phase inversion method, offering a convenient and efficient one-step process for the creation of adsorbent beads by integrating polymers and graphene oxide (GO). The synthesized nanocomposite beads are tailored for the removal of paracetamol from simulated wastewater in batch systems. Extensive characterization techniques including XPS, FTIR, SEM, TGA, and zeta potential analysis are employed to scrutinize the chemical properties and structural attributes of the prepared beads. The investigation explores the impact of critical parameters such as adsorbent dosage, adsorption duration, initial paracetamol concentration, and solution pH on the adsorption process. These nanocomposite beads exhibit an exceptional paracetamol removal efficiency, achieving up to 99% removal. This research not only contributes to the advancement of efficient and sustainable adsorbent materials for pollutant removal but also underscores their potential for environmentally friendly and cost-effective solutions in the domain of wastewater treatment.

The biofloc technology system (BFT) is considered to be one of the sustainable aquaculture systems, which is based on the principle of nutrient recycling with the addition of a carbon source to give dominance to heterotrophic microorganisms. The objective of this study was to evaluate the effect of sugar cane molasses and tapioca flour as carbon sources on the water quality, growth, hematology, immune status, and non-specific antioxidant status of Oreochromis juveniles. Methodologically, the experiment was carried out for 10 weeks on 225 juvenile Nile tilapia with initial body weights of 47.0 ± 1.3 g that were randomly distributed in 09 tanks (1000 L) with a stocking density of 25 tilapias per tank; the treatments were: BFT + SM (S molasses), BFT + TF tapioca flour (TF), and a control with no carbon source added. The control group was fed 100% feed, while the BFT experimental groups were fed microbial flocs along with 75% feed. The results revealed that the water quality parameters were affected by the carbon sources, but were adequate for normal fish welfare, and the biofloc volume was higher (28.94) with the TF carbon source. The growth performance, such as weight gain (98.61), survival (99.01), and improved feed conversion ratio (FCR) (1.69), was recorded in BFT + TF. Significant improvements in WBCs, HCT, HB, lymphocytes, plasma proteins, albumin, and non-specific immune factors (lysozyme activity, immunoglobulins levels, and ACH50) were observed in biofloc-reared fish with tapioca flour as the carbon source compared to the control and sugarcane molasses groups. Moreover, significant increases in catalase (CAT) and superoxide dismutase (SOD) were found in the biofloc-reared fish with different carbon sources. In conclusion, the use of BFT + TF was found to affect improving the water quality, growth, hematology, immunity, and antioxidant status of juvenile Tilapia.

We investigated the anticancer mechanism of a chloroform extract of marine sponge (Haliclona fascigera) (sample C) in human breast adenocarcinoma (MCF-7) cells. Viability analysis using MTT and neutral red uptake (NRU) assays showed that sample C exposure decreased the proliferation of cells. Flow cytometric data exhibited reactive oxygen species (ROS), nitric oxide (NO), dysfunction of mitochondrial potential, and apoptosis in sample C-treated MCF-7 cells. A qPCR array of sample C-treated MCF-7 cells showed crosstalk between different pathways of apoptosis, especially BIRC5, BCL2L2, and TNFRSF1A genes. Immunofluorescence analysis affirmed the localization of p53, bax, bcl2, MAPKPK2, PARP-1, and caspase-3 proteins in exposed cells. Bioassay-guided fractionation of sample C revealed Neviotin A as the most active compound triggering maximum cell death in MCF-7, indicating its pharmacological potency for the development of a drug for the treatment of human breast cancer.

This study focuses on the synthesis and characterization of dual-layer sulfonated polyphenylenesulfone (SPPSu) nanocomposite hollow fiber nanofiltration membranes incorporating titanium dioxide (TiO2) nanoparticles through the phase inversion technique. Advanced tools and methods were employed to systematically evaluate the properties and performance of the newly developed membranes. The investigation primarily centered on the impact of TiO2 addition in the SPPSu inner layer on pure water permeability and salt rejection. The nanocomposite membranes exhibited a remarkable three-fold increase in pure water permeability, achieving a flux of 5.4 L/m2h.bar compared to pristine membranes. The addition of TiO2 also enhanced the mechanical properties, with an expected tensile strength increase from 2.4 to 3.9 MPa. An evaluation of salt rejection performance using a laboratory-scale filtration setup revealed a maximal rejection of 95% for Mg2SO4, indicating the effective separation capabilities of the modified dual-layer hollow fiber nanocomposite membranes for divalent ions. The successful synthesis and characterization of these membranes highlight their potential for nanofiltration processes, specifically in selectively separating divalent ions from aqueous solutions, owing to their improved pure water flux, mechanical strength, and salt rejection performance.

This paper presents a novel topology of a modified isolated single-ended-primary-inductance converter (SEPIC) with a model predictive control (MPC) approach applied to direct current (DC) electronic loads. The proposed converter uses an actual transformer rather than a coupled inductor for isolation between the source and the load. The transformer allows the proposed converter to operate at a higher switching frequency, ultimately reducing the passive components’ size. A low-power hardware prototype is developed and tested with a model predictive control algorithm under variable input voltages and load conditions. The performance of the proposed converter is demonstrated to be satisfactory under steady state, as well as sudden input voltage transients. The proposed converter utilizes a switched capacitor technique to generate alternating current in both windings of the transformer. As the coupled inductor is eliminated from the circuit, the problem of high voltage spikes occurring due to leakage inductances is also eliminated for the proposed converter. Therefore, the proposed converter can be used for isolated medium power applications. The experimental results show that the efficiency of the proposed converter reached 96%. The MPC allows this converter’s DC voltage level to remain stable even as the input voltage and output terminal load change. Lastly, this converter with an MPC approach can be applied to different DC electronic loads, improving DC power quality and DC electronic load life.

Horizontal-axis wind turbines are the most popular wind machines in operation today. These turbines employ aerodynamic blades that may be oriented either upward or downward. HAWTs are the most common non-conventional source of energy generation. These turbine blades fail mostly due to fatigue, as a large centrifugal force acts on them at high rotational speeds. This study aims to increase a turbine’s service life by improving the turbine blades’ fatigue life. Predicting the fatigue life and the design of the turbine blade considers the maximum wind speed range. SolidWorks, a CAD program, is used to create a wind turbine blade utilizing NACA profile S814. The wind turbine blade’s fatigue life is calculated using Morrow’s equation. A turbine blade will eventually wear out due to several forces operating on it. Ansys software is used to analyze these stresses using the finite element method. The fatigue study of wind turbine blades is described in this research paper. To increase a turbine blade’s fatigue life, this research study focuses on design optimization. Based on the foregoing characteristics, an improved turbine blade design with a longer fatigue life than the original one is intended in this study. The primary fatigue parameters are the length of a chord twist angle and blade length. The experimental data computed with the aid of a fatigue testing machine are also used to validate the numerical results, and it is found that they are very similar to one another. By creating the most effective turbine blades with the longest fatigue life, this research study can be developed further. The most effective turbine blades with the longest fatigue life can be designed to further this research investigation.

3D printing technology in medicine is gaining great attention from researchers since the FDA approved the first 3D-printed tablet (Spritam®) on the market. This technique permits the fabrication of various types of dosage forms with different geometries and designs. Its feasibility in the design of different types of pharmaceutical dosage forms is very promising for making quick prototypes because it is flexible and does not require expensive equipment or molds. However, the development of multi-functional drug delivery systems, specifically as solid dosage forms loaded with nanopharmaceuticals, has received attention in recent years, although it is challenging for formulators to convert them into a successful solid dosage form. The combination of nanotechnology with the 3D printing technique in the field of medicine has provided a platform to overcome the challenges associated with the fabrication of nanomedicine-based solid dosage forms. Therefore, the major focus of the present manuscript is to review the recent research developments that involved the formulation design of nanomedicine-based solid dosage forms utilizing 3D printing technology. Utilization of 3D printing techniques in the field of nanopharmaceuticals achieved the successful transformation of liquid polymeric nanocapsules and liquid self-nanoemulsifying drug delivery systems (SNEDDS) to solid dosage forms such as tablets and suppositories easily with customized doses as per the needs of the individual patient (personalized medicine). Furthermore, the present review also highlights the utility of extrusion-based 3D printing techniques (Pressure-Assisted Microsyringe—PAM; Fused Deposition Modeling—FDM) to produce tablets and suppositories containing polymeric nanocapsule systems and SNEDDS for oral and rectal administration. The manuscript critically analyzes contemporary research related to the impact of various process parameters on the performance of 3D-printed solid dosage forms.

Load balancing is a very important concept in cloud computing. In this work, studies are conducted on workload traces at Los Alamos National Lab (LANL). The jobs in this trace are asymmetric in nature as most of them have small time limit. Two hybrid algorithms, a Genetic Algorithm combined with First Come First Serve (GA_FCFS) and Genetic Algorithm combined with Round Robin (GA_RR), are proposed here. The results obtained are compared with the existing First Come First Serve (FCFS), Round Robin (RR) and Genetic Algorithm (GA). Makespan and Resource Utilization are used for the comparison of results. In terms of Makespan, it is observed that GA_RR outperforms the other methods for all the batch sizes. Although the performance of GA_FCFS is much better than that of the other three well-established algorithms FCFS, RR and GA, it is still worse than that of the GA_RR algorithm for all the cases. GA_RR performs best in terms of Resource Utilization also and GA_FCFS is a close competitor. Overall, GA_RR outperforms all the other algorithms.

The current healthcare system is widely based on the concept of “one size fit for all”, which emphasizes treating a disease by prescribing the same drug to all patients with equivalent doses and dosing frequency. This medical treatment scenario has shown varied responses with either no or weak pharmacological effects and exaggerated adverse reactions preceded by more patient complications. The hitches to the concept of “one size fits all” have devoted the attention of many researchers to unlocking the concept of personalized medicine (PM). PM delivers customized therapy with the highest safety margin for an individual patient’s needs. PM has the potential to revolutionize the current healthcare system and pave the way to alter drug choices and doses according to a patient’s clinical responses, providing physicians with the best treatment outcomes. The 3D printing techniques is a solid-form fabrication method whereby successive layers of materials based on computer-aided designs were deposited to form 3D structures. The 3D printed formulation achieves PM goals by delivering the desired dose according to patient needs and drug release profile to achieve a patient’s personal therapeutic and nutritional needs. This pre-designed drug release profile attains optimum absorption and distribution, exhibiting maximum efficacy and safety profiles. This review aims to focus on the role of the 3D printing technique as a promising tool to design PM in metabolic syndrome (MS).

The yield and nutritional profile of grass and legume species in Kashmir Valley’s rangelands are scantly reported. The study area in this paper included three types of sites (grazed, protected, and seed-sown) divided into three circles: northern, central, and southern Kashmir. From each circle, three districts and three villages per district were selected. Most sites showed higher aboveground biomass (AGB) compared to belowground biomass (BGB), which showed low to moderate effects on biomass. The comparison between northern, central, and southern Kashmir regions revealed that AGB (86.74, 78.62, and 75.22 t. ha−1), BGB (52.04, 51.16, and 50.99 t. ha−1), and total biomass yield (138.78, 129.78, and 126.21 t. ha−1) were the highest in central Kashmir region, followed by southern and northern Kashmir regions, respectively. More precisely, AGB and total biomass yield recorded the highest values in the protected sites of the central Kashmir region, whereas BGB scored the highest value in the protected sites of southern Kashmir region. The maximum yield (12.5 t. ha−1) recorded among prominent grasses was attributed to orchard grass, while the highest crude fiber and crude protein contents (34.2% and 10.4%, respectively), were observed for Agrostis grass. The maximum yield and crude fiber content (25.4 t. ha−1 and 22.7%, respectively), among prominent legumes were recorded for red clover. The highest crude protein content (33.2%) was attributed to white clover. Those findings concluded the successful management of Kashmir rangelands in protected sites, resulting in high biomass yields along with the considerable nutritional value of grasses and legumes.

With the application of chemical enhanced oil recovery methods, water separation is a major issue in a production facility. Oil/water separation is suppressed with a stable emulsion. The present study evaluated the impact of different emulsifiers in enhanced oil recovery. The effectiveness of each additive such as an alkali, surfactant, or polymer on the stability of the emulsion was anticipated using laser scattering to measure the emulsion’s stability. An artificial neural network was applied to predict the effectiveness of the additives on stabilization/destabilization and to assess how alkali/surfactants, surfactant/polymers, and polymer/surfactants affect the separation profiles. Measurements of the viscosity and zeta potential of residual emulsion clarify that the increase in surfactant makes the emulsion stable and became unstable with the increase in the alkali and polymer. The droplet zeta potential was within −i9~−i5 mV. The absolute value of the zeta potential decreased at a high polymer concentration with a low surfactant concentration resulting in fast flocculence phenomena. With an increase in the surfactant concentration and the presence of high alkali, the droplet’s absolute zeta potential demonstrated an increase in the repulsion force in the emulsions. The study also focuses on the carbon number distribution, wax appearance temperature (WAT) and wax content of four crude oil samples from different field locations (Miri, Angsi, Penara and Dulang). Findings show that crude oil samples with higher mol percent of carbon distribution from C20 to C40 (paraffinic composition) contains higher wax content, wt% and subsequently results in higher wax appearance temperature (WAT). This is obviously shown by the crude oil sample from Penara field. Further similar investigation on other field locations will assist in characterizing the paraffinic composition in Malaysian oil basins.

Nanofluids and nanotechnology are very important in enhancing heat transfer due to the thermal conductivity of their nanoparticles, which play a vital role in heat transfer applications. Researchers have used cavities filled with nanofluids for two decades to increase the heat-transfer rate. This review also highlights a variety of theoretical and experimentally measured cavities by exploring the following parameters: the significance of cavities in nanofluids, the effects of nanoparticle concentration and nanoparticle material, the influence of the inclination angle of cavities, heater and cooler effects, and magnetic field effects in cavities. The different shapes of the cavities have several advantages in multiple applications, e.g., L-shaped cavities used in the cooling systems of nuclear and chemical reactors and electronic components. Open cavities such as ellipsoidal, triangular, trapezoidal, and hexagonal are applied in electronic equipment cooling, building heating and cooling, and automotive applications. Appropriate cavity design conserves energy and produces attractive heat-transfer rates. Circular microchannel heat exchangers perform best. Despite the high performance of circular cavities in micro heat exchangers, square cavities have more applications. The use of nanofluids has been found to improve thermal performance in all the cavities studied. According to the experimental data, nanofluid use has been proven to be a dependable solution for enhancing thermal efficiency. To improve performance, it is suggested that research focus on different shapes of nanoparticles less than 10 nm with the same design of the cavities in microchannel heat exchangers and solar collectors.

Osteoporosis, a chronic bone disorder, is one of the leading causes of fracture and morbidity risk. Numerous medicinally important herbs have been evaluated for their efficacy in improving bone mass density in exhaustive preclinical and limited clinical studies. Nigella sativa L. has been used as local folk medicine, and traditional healers have used it to manage various ailments. Its reported beneficial effects include controlling bone and joint diseases. The present manuscript aimed to provide a sound discussion on the pharmacological evidence of N. sativa and its active constituent, thymoquinone, for its utility in the effective management of osteoporosis. N. sativa is reported to possess anti-IL-1 and anti-TNF-α-mediated anti-inflammatory effects, leading to positive effects on bone turnover markers, such as alkaline phosphatase and tartrate-resistant acid phosphatase. It is reported to stimulate bone regeneration by prompting osteoblast proliferation, ossification, and decreasing osteoclast cells. Thymoquinone from N. sativa has exhibited an antioxidant effect on bone tissue by reducing the FeNTA-induced oxidative stress. The present manuscript highlights phytochemistry, pharmacological effect, and the important mechanistic perspective of N. sativa and its active constituents for the management of osteoporosis. Further, it also provides sound discussion on the utilization of a nanotechnology-mediated drug delivery approach as a promising strategy to improve the therapeutic performance of N. sativa and its active constituent, thymoquinone, in the effective management of osteoporosis.

Glaucoma is a degenerative, chronic ocular disease that causes irreversible vision loss. The major symptom of glaucoma is high intraocular pressure, which happens when the flow of aqueous humor between the front and back of the eye is blocked. Glaucoma therapy is challenging because of the low bioavailability of drugs from conventional ocular drug delivery systems such as eye drops, ointments, and gels. The low bioavailability of antiglaucoma agents could be due to the precorneal and corneal barriers as well as the low biopharmaceutical attributes of the drugs. These limitations can be overcome by employing nanoparticulate drug delivery systems. Over the last decade, there has been a lot of interest in chitosan-based nanoparticulate systems to overcome the limitations (such as poor residence time, low corneal permeability, etc.) associated with conventional ocular pharmaceutical products. Therefore, the main aim of the present manuscript is to review the recent research work involving the chitosan-based nanoparticulate system to treat glaucoma. It discusses the significance of the chitosan-based nanoparticulate system, which provides mucoadhesion to improve the residence time of drugs and their ocular bioavailability. Furthermore, different types of chitosan-based nanoparticulate systems are also discussed, namely nanoparticles of chitosan core only, nanoparticles coated with chitosan, and hybrid nanoparticles of chitosan. The manuscript also provides a critical analysis of contemporary research related to the impact of this chitosan-based nanomedicine on the corneal permeability, ocular bioavailability, and therapeutic performance of loaded antiglaucoma agents.

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%.

The current study was designed to synthesize, characterize, and screen the molecular and biological activities of different metformin derivatives that possess potent antidiabetic potential with minimal side-effects. Metformin-based derivatives containing the metal complexes Cu II (MCu1–MCu9) and Zn II (MZn1–MZn9) were generated using aromatic aldehydes and ketones in a template process. The novel metal complexes were characterized through elemental analysis, physical state, melting point, physical appearance, Fourier-transform infrared (FTIR) spectroscopy, UV/visible (UV/Vis) spectroscopy, 1H nuclear magnetic resonance (NMR) spectroscopy, and 13C-NMR spectroscopy. Screening for inhibitory activity against the enzymes α-amylase and α-glucosidase, and molecular simulations performed in Schrödinger were used to assess the synthesized derivatives’ biological potential. Met1, Met2, Met3, and Met8 all displayed activities that were on par with the reference in an enzymatic inhibition assay (amylase and glucosidase). The enzyme inhibition assay was corroborated by molecular simulation studies, which also revealed a competitive docking score compared to the gold standard. The Swiss ADME online web server was utilized to compute ADME properties of metformin analogues. Lipinski’s rule of five held true across all derivatives, making it possible to determine the percentage of absorption. Metformin derivatives showed significant antidiabetic activities against both targeted enzymes, and the results of this work suggest that these compounds could serve as lead molecules for future study and development.

The building sector is targeting net-zero emissions through the integration of renewable energy technologies, especially for space cooling and heating applications. In this regard, the use of solar thermal concentrating collectors is of vital importance. The performance of these collectors increases by using an efficient fluid such as a nanofluid due to their high thermal conductivity. This research addresses the preparation, stability analysis, and characterisation of metallic and non-metallic oxide nanofluids and their experimental analysis in a compound parabolic collector (CPC) system. Five different combinations of nanofluids are used with different volumetric concentrations (0.025%, 0.05%, and 0.075%) including multi-wall carbon nanotube with water (MWCNT–H2O), multi-wall carbon nanotube with ethylene glycol (MWCNT–EG), aluminium oxide with water (Al2O3–H2O), aluminium oxide with ethylene glycol (Al2O3–EG), and magnesium oxide with ethylene glycol (MgO–EG). The prepared nanofluids are characterised in terms of thermal conductivity and viscosity. Detailed experimentation is performed to investigate the CPC system integrated with the nanofluids. The results obtained from the detailed characterisation of the MWCNT–H2O nanofluid showed that the nanofluids have a 37.17% better thermal conductivity than distilled water as a primary fluid, and the MWCNT–EG nanofluid has demonstrated an increase in viscosity by 8.5% compared to ethylene glycol (EG). The experimental analysis revealed that the thermal efficiency of the collector integrated with the MWCNT–H2O nanofluid is increased by 33% compared to water. Meanwhile, the thermal efficiency of the collector with MWCNT–EG was increased by 24.9% compared to EG. Moreover, a comparative analysis among metallic nanofluids was also performed, i.e., Al2O3–H2O, Al2O3–EG, and MgO–EG. In each case, the thermal efficiency of the collector was recorded, which was greater than the base fluid by percentages of 29.4%, 22.29%, and 23.1%, respectively. The efficiency of non-metallic nanofluids is better than metallic nanofluids by 7.7%. From the obtained results, it can be concluded that the CPC system performed best with MWCNT–H2O compared to any other combination of nanofluids.

These days an extensive amount of the attention of researchers is focused towards exploring bioactive compounds of natural or herbal origin for therapeutic intervention in different ailments of significant importance. One such novel bioactive compound that has a variety of biological properties, including anti-inflammatory and antioxidant activities, is piperine. However, until today, piperine has not been explored for its potential to improve inflammation and enhance healing in acute and chronic wounds. Therefore, the present study aimed to investigate the wound healing potential of piperine hydrogel formulation after topical application. Hydrogels fit the need for a depot system at the wound bed, where they ensure a consistent supply of therapeutic agents enclosed in their cross-linked network matrices. In the present study, piperine-containing carbopol 934 hydrogels mixed with Aloe vera gels of different gel strengths were prepared and characterized for rheological behavior, spreadability, extrudability, and percent (%) content uniformity. Furthermore, the wound healing potential of the developed formulation system was explored utilizing the excision wound healing model. The results of an in vivo study and histopathological examination revealed early and intrinsic healing of wounds with the piperine-containing bioactive hydrogel system compared to the bioactive hydrogel system without piperine. Therefore, the study’s findings establish that the piperine-containing bioactive hydrogel system is a promising therapeutic approach for wound healing application that should be diligently considered for clinical transferability.

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

Psoriasis is a typical dermal condition that has been anticipated since prehistoric times when it was mistakenly implicit in being a variant of leprosy. It is an atypical organ-specific autoimmune disorder, which is triggered by the activation of T-cells and/or B-cells. Until now, the pathophysiology of this disease is not completely explicated and still, many research investigations are ongoing. Different approaches have been investigated to treat this dreadful skin disease using various anti-psoriatic drugs of different modes of action through smart drug-delivery systems. Nevertheless, there is no ideal therapy for a complete cure of psoriasis owing to the dearth of an ideal drug-delivery system for anti-psoriatic drugs. The conventional pharmacotherapy approaches for the treatment of psoriasis demand various classes of anti-psoriatic drugs with optimum benefit/risk ratio and insignificant untoward effects. The advancement in nanoscale drug delivery had a great impact on the establishment of a nanomedicine-based therapy for better management of psoriasis in recent times. Nanodrug carriers are exploited to design and develop nanomedicine-based therapy for psoriasis. It has a promising future in the improvement of the therapeutic efficacy of conventional anti-psoriatic drugs. The present manuscript aims to discuss the pathophysiology, conventional pharmacotherapy, and contemporary research in the area of nanoscale topical drug delivery systems for better management of psoriasis including the significance of targeted pharmacotherapy in psoriasis.

The Sultanate of Oman is an arid country in the Arabian Peninsula suffering from insufficient freshwater supplies and extremely hot weather conditions. The only source of recharge is rainfall, which is scarce and varies with space and time, for the aquifers being overexploited for the last few decades. This has led to depleting groundwater levels and seawater intrusion into coastal aquifers. In the present study, Ground Modeling System (GMS) was employed in Wadi Al-Jizi, which is one of the important aquifers in the Al Batinah coastal plain that caters to the needs of the country’s 70% agriculture. MODFLOW and MT3DMS were used to simulate the groundwater levels and solute transport, respectively. These models were calibrated under steady and transient conditions using observed data from twenty monitoring wells for a period of seventeen years (year 2000–2016). After validation, the model was utilized to predict the salinity intrusion due to changes in groundwater abstraction rates and sea level rise owing to climatic change. These predictions show that, by the year 2040, salinity intrusion (TDS > 12,800 mg/L) will transgress by 0.80 km inland if the current abstraction rates are allowed to be maintained. Further deterioration of groundwater quality is anticipated in the following years due to the increased pumping rates. The models and the results from the present study may be utilized for the effective management of groundwater resources in the Wadi Al-Jizi aquifer.

Diabetes mellitus is one of the most prevalent metabolic disorders characterized by hyperglycemia due to impaired glucose metabolism. Overproduction of free radicals due to chronic hyperglycemia may cause oxidative stress, which delays wound healing in diabetic conditions. For people with diabetes, this impeded wound healing is one of the predominant reasons for mortality and morbidity. The study aimed to develop an Ocimum sanctum leaf extract-mediated green synthesis of titanium dioxide (TiO2) nanoparticles (NPs) and further incorporate them into 2% chitosan (CS) gel for diabetic wound healing. UV-visible spectrum analysis recorded the sharp peak at 235 and 320 nm, and this was the preliminary sign for the biosynthesis of TiO2 NPs. The FTIR analysis was used to perform a qualitative validation of the biosynthesized TiO2 nanoparticles. XRD analysis indicated the crystallinity of TiO2 NPs in anatase form. Microscopic investigation revealed that TiO2 NPs were spherical and polygonal in shape, with sizes ranging from 75 to 123 nm. The EDX analysis of green synthesized NPs showed the presence of TiO2 NPs, demonstrating the peak of titanium ion and oxygen. The hydrodynamic diameter and polydispersity index (PDI) of the TiO2 NPs were found to be 130.3 nm and 0.237, respectively. The developed TiO2 NPs containing CS gel exhibited the desired thixotropic properties with pseudoplastic behavior. In vivo wound healing studies and histopathological investigations of healed wounds demonstrated the excellent wound-healing efficacy of TiO2 NPs containing CS gel in diabetic rats.

Control of the cattle tick Rhipicephalus (R.) microplus mainly relies on chemical acaricides and cypermethrin is the most widely used acaricide in Pakistan. Farmers frequently complain about its low efficacy, thus, the present study was designed to quantify the frequency of cypermethrin resistance in cattle ticks. Engorged female R. microplus were collected and tested for the efficacy of cypermethrin using the FAO-recommended larval packet test. Resistance factors (RF) were estimated at both the lethal concentration for 50% (LC50) and 99% (LC99) of ticks. Thirty-three samples were tested, of which 8/33 (24.24%) were classified as resistant based on the RF50, and all 33 were classified as resistant based on the RF99. In District Sargodha, when only the RF50 was considered, 45.5% of samples were classified as resistant, but at RF99, all tested samples were identified as resistant. In District Okara, the variation in RF50 estimates was 2.2–8.3 and variation in RF99 estimates was 10.6–1139.8. Similar results were found in District Attock, where variations in RF50 were 0.8–8.5 and RF99 ranged from 9–237.3. The study showed that cypermethrin resistance is prevalent in these three districts of Pakistan and is likely to be overestimated by classification based on the RF99.

Several technical factors contribute to the flow of cuttings from the wellbore to the surface of the well, some of which are fundamentally due to the speed and inclination of the drill pipe at different positions (concentric and eccentric), the efficacy of the drilling mud considers plastic viscosity (PV) and yield point (YP), the weight of the cuttings, and the deviation of the well. Moreover, these overlaying cutting beds breed destruction in the drilling operation, some of which cause stuck pipes, reducing the rate of rotation and penetration. This current study, while it addresses the apropos of artificial intelligence (AI) with symmetry, employs a three-dimensional computational fluid dynamic (CFD) simulation model to validate an effective synthetic-based mud-drilling and to investigate the potency of the muds’ flow behaviours for transporting cuttings. Furthermore, the study examines the ratio effects of YP/PV to attain the safe transport of cuttings based on the turbulence of solid-particle suspension from the drilling fluid and the cuttings, and its velocity–pressure influence in a vertical well under a concentric and eccentric position of the drilling pipe. The resulting CFD analysis explains that the YP/PV of SBM and OBM, which generated the required capacity to suspend the cuttings to the surface, are symmetric to the experimental results and hence, the position of the drill pipe at the concentric position in vertical wells required a lower rotational speed. A computational study of the synthetic-based mud and its potency of not damaging the wellbore under an eccentric drill pipe position can be further examined.

A meta-QTL analysis was conducted in Indian mustard to identify robust and stable meta-QTLs (MQTLs) by utilizing 1504 available QTLs, which included 891 QTLs for yield-related traits and 613 QTLs for quality traits. For yield-related traits, a total of 57 MQTLs (YRTs_MQTLs) were uncovered from the clustering of 560 projected QTLs, which had a 4.18-fold smaller confidence interval (CI) than that of the initial QTLs, whereas, for quality traits, as many as 51 MQTLs (Quality_MQTLs) were derived from 324 projected QTLs, which had a 2.65-fold smaller CI than that of the initial QTLs. Sixteen YRTs_MQTLs were observed to share chromosomal positions with 16 Quality_MQTLs. Moreover, four most promising YRTs_MQTLs and eight Quality-MQTLs were also selected and recommended for use in breeding programs. Four of these selected MQTLs were also validated with significant SNPs that were identified in previously published genome-wide association studies. Further, in silico functional analysis of some promising MQTLs allowed the detection of as many as 1435 genes, which also involved 15 high-confidence candidate genes (CGs) for yield-related traits and 46 high-confidence CGs for quality traits. After validation, the identified CGs can also be exploited to model the plant architecture and to improve quality traits through marker-assisted breeding, genetic engineering, and genome editing approaches.

Nanogel is a promising drug delivery approach to improve the pharmacokinetics and pharmacodynamic prospect of phytopharmaceuticals. In the present review, phytopharmaceuticals with astonishing therapeutic utilities are being explored. However, their in vivo delivery is challenging, owing to poor biopharmaceutical attributes that impact their drug release profile, skin penetration, and the reach of optimal therapeutic concentrations to the target site. Nanogel and its advanced version in the form of nanoemulgel (oil-in-water nanoemulsion integrated gel matrix) offer better therapeutic prospects than other conventional counterparts for improving the biopharmaceutical attributes and thus therapeutic efficacy of phytopharmaceuticals. Nanoemulgel-loaded phytopharmaceuticals could substantially improve permeation behavior across skin barriers, subsequently enhancing the delivery and therapeutic effectiveness of the bioactive compound. Furthermore, the thixotropic characteristics of polymeric hydrogel utilized in the fabrication of nanogel/nanoemulgel-based drug delivery systems have also imparted improvements in the biopharmaceutical attributes of loaded phytopharmaceuticals. This formulation approach is about to be rife in the coming decades. Thus, the current review throws light on the recent studies demonstrating the role of nanogels in enhancing the delivery of bioactive compounds for treating various disease conditions and the challenges faced in their clinical translation.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Curcumin (CUR) has impressive pharmacologic properties, including cardioprotective, neuroprotective, antimicrobial, and anticancer activity. However, the pharmaceutical application of CUR is limited due to its poor aqueous solubility and low bioavailability. The development of novel formulations has attracted considerable attention to the idea of applying nanobiotechnology to improve the therapeutic efficacy of these challenging compounds. In this study, CUR-loaded lecithin–chitosan nanoparticles (CUR/LCSNPs) were developed and optimized by the concentration of chitosan, lecithin, and stirring speed by a 3-factorial Box-Behnken statistical design, resulting in an optimal concentration of chitosan (A) and lecithin (B) with a 1200 rpm stirring speed (C), with applied constraints of minimal average particle size (Y1), optimal zeta potential (Y2), and maximum entrapment efficiency (%EE) (Y3). The mean particle size of the checkpoint formulation ranged from 136.44 ± 1.74 nm to 267.94 ± 3.72, with a zeta potential of 18.5 ± 1.39 mV to 36.8 ± 3.24 mV and %EE of 69.84 ± 1.51% to 78.50 ± 2.11%. The mean particle size, zeta potential, %EE, and % cumulative drug release from the optimized formulation were 138.43 ± 2.09 nm, +18.98 ± 0.72 mV, 77.39 ± 1.70%, and 86.18 ± 1.5%, respectively. In vitro drug release followed the Korsmeyer–Peppas model with Fickian diffusion (n < 0.45). The optimized technique has proven successful, resulting in a nanoformulation that can be used for the high loading and controlled release of lipophilic drugs.

Vaccine hesitancy among displaced populations is associated with inequitable access to services and mistrust of authorities, among other factors. This study evaluated variations in attitudes toward COVID-19 vaccines and factors associated with vaccine acceptance among refugees and Lebanese nationals accessing 60 International Medical Corps-supported health facilities through two cross-sectional surveys pre- (n = 3927; Survey 1) and post- (n = 4174; Survey 2) vaccine rollout. Logistic regression was used to assess predictors of vaccine acceptance using the health beliefs model. Refugees comprised 52.9% (Survey 1) and 54.2% (Survey 2) of respondents. Vaccine acceptance was low among both groups in Survey 1 (25.9% refugees vs. 23.1% Lebanese nationals), but higher in Survey 2 in Lebanese (57.6%) versus refugees (32.9%). Participants reported greater perceived benefits of vaccination, higher perceived COVID-19 susceptibility, and lower perceived vaccination barriers in Survey 2 versus Survey 1. Post-vaccine rollout, refugees had lower odds of vaccine acceptance compared to Lebanese (OR 0.50, 95%CI 0.41–0.60), while older age (OR 1.37, 95%CI 1.06–1.78, ≥51 years vs. 18–30 years) was associated with greater vaccine acceptance. Health beliefs model variables were associated with vaccine acceptance in both surveys. Tailored strategies to respond dynamically to changes in vaccine attitudes among vulnerable groups in Lebanon are essential for equitable vaccine uptake.

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time–frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.

Interest in nanoemulsion technology has increased steadily in recent years for its widespread applications in the delivery of pharmaceuticals, nutraceuticals, and cosmeceuticals. Rational selection of the composition and the preparation method is crucial for developing a stable nanoemulsion system with desired physicochemical characteristics. In the present study, we investigate the influence of intricate factors including composition and preparation conditions that affect characteristic parameters and the stability of the nanoemulsion formation prepared by the spontaneous emulsification method. Octanoic acid, capryol 90, and ethyl oleate were selected to represent oil phases of different carbon–chain lengths. We explored the impact of the addition mode of the oil–Smix phase and aqueous phase, vortexing time, Km (surfactant/cosurfactant) ratio, and the replacement of water by buffers of different pH as an aqueous system. The phase behavior study showed that the Smix phase had a significant impact on the nanoemulsifying ability of the nanoemulsions composed of oil phases of varying carbon-chain lengths. The mode of mixing of the oil–Smix phase to the aqueous phase markedly influenced the mean droplet size and size distribution of the nanoemulsions composed of oil phases as capryol 90. Vortexing time also impacted the mean droplet size and the stability of the generated nanoemulsion system depending on the varying carbon-chain length of the oil phase. The replacement of the water phase by aqueous buffers of pH 1.2, 5.5, 6.8, and 7.4 has altered the mean droplet size and size distribution of the nanoemulsion system. Further, the Km ratio also had a significant influence on the formation of the nanoemulsion system. The findings of this investigation are useful in understanding how the formulation composition and process parameters of the spontaneous emulsification technique are responsible for affecting the physicochemical characteristics and stability of the nanoemulsion system composed of oil of varying carbon-chain (C8-C18) length.

Vitamin D deficiency is highly prevalent in India and worldwide. Mushrooms are important nutritional foods, and in this context shiitake (Lentinula edodes), button (Agaricus bisporus) and oyster (Pleurotus ostreatus) mushrooms are known for their bioactive properties. The application of ultraviolet (UV) irradiation for the production of substantial amounts of vitamin D2 is well established. Levels of serum 25-hydroxy vitamin D (25-OHD), parathyroid hormone (PTH), calcium, phosphorus and alkaline phosphatase (ALP) were significantly (p < 0.05) improved in vitamin-D-deficient rats after feeding with UVB irradiated mushrooms for 4 weeks. Further, microscopic observations indicate an improvement in the osteoid area and the reduction in trabecular separation of the femur bone. In addition, the level of expression of the vitamin D receptor (VDR) gene and genes metabolizing vitamin D were explored. It was observed that in mushroom-fed and vitamin-D-supplemented groups, there was upregulation of CYP2R1 and VDR, while there was downregulation of CYP27B1 in the liver. Further, CYP2R1 was downregulated, while CYP27B1 and VDR were upregulated in kidney tissue.

The solid self-nanoemulsifying drug delivery system (s-SNEDDS) is a growing platform for the delivery of drugs via oral route. In the present work, tamoxifen (TAM) was loaded in SNEDDS with resveratrol (RES), which is a potent chemotherapeutic, antioxidant, anti-inflammatory and P-gp inhibitor for enhancing bioavailability and to obtain synergistic anti-cancer effect against breast cancer. SNEDDS were developed using capmul MCM as oil, Tween 80 as surfactant and transcutol-HP as co-surfactant and optimized by central composite rotatable design. Neusilin US2 concentration was optimized for adsorption of liquid SNEDDS to prepare s-SNEDDS. The developed formulation was characterized and investigated for various in vitro and cell line comparative studies. Optimized TAM-RES-s-SNEDDS showed spherical droplets of a size less than 200 nm. In all in vitro studies, TAM-RES-s-SNEDDS showed significantly improved (p ˂ 0.05) release and permeation across the dialysis membrane and intestinal lumen. Moreover, TAM-RES-s-SNEDDS possessed significantly greater therapeutic efficacy (p < 0.05) and better internalization on the MCF-7 cell line as compared to the conventional formulation. Additionally, oral bioavailability of TAM from SNEDDS was 1.63 folds significantly higher (p < 0.05) than that of combination suspension and 4.16 folds significantly higher (p < 0.05) than TAM suspension. Thus, findings suggest that TAM- RES-s-SNEDDS can be the future delivery system that potentially delivers both drugs to cancer cells for better treatment.

Zoonotic coronaviruses (CoV) have emerged twice and have caused severe respiratory diseases in humans. Due to the frequent outbreaks of different human coronaviruses (HCoVs), the development of a pan-HCoV vaccine is of great importance. Various conserved epitopes shared by HCoVs are reported to induce cross-reactive T-cell responses. Therefore, this study aimed to design a multi-epitope vaccine, targeting the HCoV spike protein. Genetic analysis revealed that the spike region is highly conserved among SARS-CoV-2, bat SL-CoV, and SARS-CoV. By employing the immunoinformatic approach, we prioritized 20 MHC I and 10 MHCII conserved epitopes to design a multi-epitope vaccine. This vaccine candidate is anticipated to strongly elicit both humoral and cell-mediated immune responses. These results warrant further development of this vaccine into real-world application.

The suspected cases of COVID-19 must be detected quickly and accurately to avoid the transmission of COVID-19 on a large scale. Existing COVID-19 diagnostic tests are slow and take several hours to generate the required results. However, on the other hand, most X-rays or chest radiographs only take less than 15 min to complete. Therefore, we can utilize chest radiographs to create a solution for early and accurate COVID-19 detection and diagnosis to reduce COVID-19 patient treatment problems and save time. For this purpose, CovidDetNet is proposed, which comprises ten learnable layers that are nine convolutional layers and one fully-connected layer. The architecture uses two activation functions: the ReLu activation function and the Leaky Relu activation function and two normalization operations that are batch normalization and cross channel normalization, making it a novel COVID-19 detection model. It is a novel deep learning-based approach that automatically and reliably detects COVID-19 using chest radiograph images. Towards this, a fine-grained COVID-19 classification experiment is conducted to identify and classify chest radiograph images into normal, COVID-19 positive, and pneumonia. In addition, the performance of the proposed novel CovidDetNet deep learning model is evaluated on a standard COVID-19 Radiography Database. Moreover, we compared the performance of our approach with hybrid approaches in which we used deep learning models as feature extractors and support vector machines (SVM) as a classifier. Experimental results on the dataset showed the superiority of the proposed CovidDetNet model over the existing methods. The proposed CovidDetNet outperformed the baseline hybrid deep learning-based models by achieving a high accuracy of 98.40%.

Forty-day-old Brassica juncea (var. Pusa Jai Kisan) plants were exposed to arsenic (As, 250 µM Na2HAsO4·7H2O) stress. The ameliorative role of ferrous sulfate (2 mM, FeSO4·7H2O, herein FeSO4) was evaluated at 7 days after treatment (7 DAT) and 14 DAT. Whereas, As induced high magnitude oxidative stress, FeSO4 limited it. In general, As decreased the growth and photosynthetic parameters less when in the presence of FeSO4. Furthermore, components of the antioxidant system operated in better coordination with FeSO4. Contents of non-protein thiols and phytochelatins were higher with the supply of FeSO4. Blue-Native polyacrylamide gel electrophoresis revealed an As-induced decrease in almost every multi-protein-pigment complex (MPC), and an increase in PSII subcomplex, LHCII monomers and free proteins. FeSO4 supplication helped in the retention of a better stoichiometry of light-harvesting complexes and stabilized every MPC, including supra-molecular complexes, PSI/PSII core dimer/ATP Synthase, Cytochrome b6/f dimer and LHCII dimer. FeSO4 strengthened the plant defence, perhaps by channelizing iron (Fe) and sulfur (S) to biosynthetic and anabolic pathways. Such metabolism could improve levels of antioxidant enzymes, and the contents of glutathione, and phytochelatins. Important key support might be extended to the chloroplast through better supply of Fe-S clusters. Therefore, our results suggest the importance of both iron and sulfur to combat As-induced stress in the Indian mustard plant at biochemical and molecular levels through enhanced antioxidant potential and proteomic adjustments in the photosynthetic apparatus.

Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on Kaggle. Additionally, all deep learning (DL) models are fine-tuned with their default values. The fine-grained classification experiment demonstrates that the inceptionresnetv2 TL algorithm performs better and achieves the highest accuracy in detecting and classifying glioma, meningioma and pituitary brain tumors, and hence it can be classified as the best classification algorithm. We achieve 98.91% accuracy, 98.28% precision, 99.75% recall and 99% F-measure values with the inceptionresnetv2 TL algorithm, which out-performs the other DL algorithms. Additionally, to ensure and validate the performance of TL classifiers, we compare the efficacy of the inceptionresnetv2 TL algorithm with hybrid approaches, in which we use convolutional neural networks (CNN) for deep feature extraction and a Support Vector Machine (SVM) for classification. Similarly, the experiment’s results show that TL algorithms, and inceptionresnetv2 in particular, out-perform the state-of-the-art DL algorithms in classifying brain MRI images into glioma, meningioma, and pituitary. The hybrid DL approaches used in the experiments are Mobilnetv2, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet.

Natural nanofibers are widely used in the field of medicine, but the low strength of these nanofibers is one of the major concerns. A number of factors, importantly the composition, affect the strength of natural nanofibers. The purpose of the current study is to ascertain the effect of the composition of natural nanofibers on the strength of hybrid composites formed using these nanofibers. Hybrid composites formed using 32% volume glass fibre with optimized volume fraction of 0.5% of pure Cellulose Acetate (CA), and 0.5% CA + Hemp Seed (HS) for this study to carry out the analysis. Hybrid composites were produced with vacuum-assisted resin transfer molding (VARTM) by collecting natural nanofibers, produced using the electrospinning process, over glass fiber mats. The electrospinning process was carried out with 12 kV, 10 cm tip to the collector gap, and 12% concentration of the solution. The tensile strength of the hybrid composites was measured using the universal testing machine (UTM). The results showed that the diameter of the electrospun nanofiber varied between 50 and 1400 nm and was affected by solution concentration, voltage, tip-to-collector distance, flow rate, and inclusion of HS in CA. The inclusion of HS in CA, for all compositions, decreased the fiber diameter and caused the formation of beads prominently at higher concentrations. Hybrid composites formed from nanofibers produced using CA and HS showed higher elastic modulus (232 MPa) and tensile strength (20.4 GPa) as compared with nanofibers produced using CA only (elastic modulus = 110 MPa and 13.7 GPa).

This study aimed to develop and evaluate sustained-release (SR) long-acting oral nanocomposites in-situ gelling films of resveratrol (Rv) to treat colorectal cancer. In these formulations, Rv-Soy protein (Rv-Sp) wet granules were prepared by the kneading method and then encapsulated in the sodium alginate (NA) dry films. The prepared nanocomposite in-situ gels films were characterized using dynamic light scattering, Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. The optimized formulations were further evaluated based on drug encapsulation efficiency, pH-drug release profile, swelling study, and storage time effects. The optimized formulation was tested for its anticancer activity against colorectal cancer cells using the cytotoxicity assessment, apoptosis testing, cell cycle analysis, gene expression analysis, and protein estimation by the reverse-transcriptase polymerase chain reaction and enzyme-linked immunosorbent assay methods, respectively. The optimum film showed encapsulation efficiency of 97.87% ± 0.51 and drug release of 14.45% ± 0.043 after 8 h. All physiochemical characterizations confirmed, reasoned, and supported the drug release experiment’s findings and the encapsulation assay. The Rv nanocomposite formulation showed concentration-dependent cytotoxicity enhanced apoptotic activity as compared to free Rv (p < 0.05). In addition, Rv nanocomposite formulation caused a significant increase in Bcl-2-associated protein X (Bax) and a decrease in expression of B-cell lymphoma 2, interleukin 1 beta, IL-6, and tumor necrosis factor-alpha (Bcl2, IL-1β, IL-6, and TNF-α respectively) compared to that of free Rv in HCT-116 cells. These results suggest that long-acting Rv nanocomposite gels could be a promising agent for colorectal cancer treatment.

The current study focuses on the natural-convection flow of nanofluids with boundary layer over a circular cylinder of uniform thermal wall with varying magnetic force from 0 to 1.5, radiative effects from 0 to 1, heat generation effects from 0 to 1, and Joule heating effects from 0 to 1. The problem is represented in the form of partial differential equations. The dimensional form of the equations is converted into a dimensionless form with the help of suitable stream functions. Then, the resultant equations are further reduced into the system of first-ordered differential equations, and the Keller box scheme is applied to obtain a solution numerically with the help of MATLAB code. The numerical solutions for Nusselt number, skin friction coefficient, Sherwood number, velocity profile, temperature profile, and concentration profile are represented with the help of graphs. The most interesting fact of the analysis is the flow of the fluid; the heat-mass and energy transfer rates could be managed in a controlled way through slight variations in the Brownian motion parameter from 0.1 to 0.7, in the Lewis number from 1 to 40, in the Eckert number from 0.1 to 0.4, in the thermophoresis parameter from 0.1 to 0.7, in the Prandtl number from 0.1 to 0.7, and in the buoyancy ratio from 0.1 to 0.7, as it is here analyzed and discussed.

The rotating biological contactor (RBC) is resistant to toxic chemical and shock loadings, and this results in significant organic and nutrient removal efficiencies. The RBC system offers a low-energy footprint and saves up to 90% in energy costs. Due to the system’s low-energy demand, it is easily operable with renewable energy sources, either solar or wind power. An RBC was employed to degrade pollutants in domestic wastewater through biodegradation mechanisms in this study. The high microbial population in the RBC bioreactor produced excellent biological treatment capacity and higher effluent quality. The results showed that the RBC bioreactor achieved an average removal efficiency of 73.9% of chemical oxygen demand (COD), 38.3% of total nitrogen (TN), 95.6% of ammonium, and 78.9% of turbidity. Investigation of operational parameters, disk rotational speed, HRT, and SRT, showed the biological performance impact. Disk rotational speed showed uniform effluent quality at 30–40 rpm, while higher values of disk rotational speed (>40 rpm) resulted in lower effluent quality in COD, TN, and turbidity. The longer hydraulic retention time and sludge retention time (SRT) facilitated higher biological performance efficiency. The longer SRTs enabled the higher TN removal efficiency because of the higher quantity of microbial biomass retention. The longer SRT also resulted in efficient sludge-settling properties and reduced volume of sludge production. The energy evaluation of the RBC bioreactor showed that it consumed only 0.14 kWh/m3, which is significantly lower than the conventional treatment methods; therefore, it is easily operable with renewable energy sources. The RBC is promising substitute for traditional suspended growth processes as higher microbial activity, lower operational and maintenance costs, and lower carbon foot print enhanced the biological performance, which aligns with the stipulations of ecological evolution and environment-friendly treatment.