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In the modern era, artificial intelligence (AI) has been applied as one of the transformative factors for scientific research in many fields that could provide new solutions to extremely complicated and complex physical models. In this paper, a multi-layer neural network combined with Bayesian regularization procedure (MNNs-BRP) is utilized to evaluate the model MHD radiative heat in non-uniform heating Eyring Powell fluid (EPF-MHD-RHS). The mixed convection parameter, Prandtl number, and heat emission or immersion parameter are studied in relation to momentum and heat transfer. To facilitate analysis, governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) only with the aid of similarity transformations. From there, a dataset is created and later trained, tested, and validated by the MNNs-BRP model for efficient estimating of fluid models. The MNNs-BRP model is robust and demonstrates high accuracy, which compares well with the benchmark solutions. Performances of which are confirmed by metrics like error histograms, mean squared-error (MSE) check-ups, and regression analysis with MSEs all over the interval [10−09 – 10−04] to ensure a sustained model. The obtained outcomes indicate that the model built and implemented utilizing a neural network offers precise performance ranging from 3.25E-13 to 5.41E-13 with a mean error of around 3.25E-13, 1.56E-11, 5.41E-13, 2.97E-12, 1.03E-11, 2.05E-12, 1.49E-12, and 5.01E-12, across eight different circumstances, suggesting improved capability and reliability of the developed predictive model. After careful review and analysis, we have found that the temperature of the fluid decreases with the increase of Prandl number, while an inverse result is noticed with heat emission. However, the velocity of the fluid have an increasing trend with increasing values of mixed convection parameter, and heat generation or absorption parameter. In contrast the velocity profile have a decreasing trend with the increasing values of magnetic field parameter and stratification parameter. This work on integrating AI into this classical fluid dynamics problem is an entirely new paradigm that involves a smart combination of computational strategies with advanced physical modeling techniques. Our investigation is not just raising the bar for predicting complex fluid dynamics but also showing how AI can truly transform the entire research domain of fluid mechanics and related scientific disciplines. All the numerical and graphical illustrations attained by employing the AI-based techniques authenticates the solution methodology for the evaluation of fluid dynamics problems.

Thermal management is very important in engineering applications to improve the systems’ performance and to reduce the environmental impact. This research works to establish the convective heat transfer coefficient (CHTC) of new improved ternary hybrid functionalized nanofluids with CuO, Fe2O3, and SiO2 nanoparticles in polymeric fluid. The investigations are aimed at the use of the state-of-the-art AI techniques for predicting and simulating the heat transfer processes in radiated channels as well as incorporating the effects of viscous dissipation and radiation. A new computational process tools up Python, Mathematica, and MATLAB to solve the transformed system of PDEs a LMNNA. These results support the qualitative understanding regarding flow rate dependency on R but dependency of flow rate on γ. Likewise, temperature profiles increase with increase in Eckert number (Ec) and Prandtl ratio (Pr) but decreases as radiating parameter (Rd) increases. The use of AI in creating the simulations is more accurate for prediction than traditional numerical methods with an improved MSE of up to 10−14 through the Python model. With focus on technological advancements in the field of thermal heat, these studies show great promise of THF in enhancing rate of heat transfer-issues which complete several energy storage systems, cooling techniques in aeronautics as well as electric vehicle operational convenience via thermal layout.A synergetic composition of three distinct nanomaterial oxides of Copper, Iron and Silicon in engine oil, contributes unique thermophysical character in thermal management. Advance computational technique with combination of AI with Python, Mathematica and Matlab (AIPMM) employing Levenberg Marquardt Neural Network Algorithm (LMNNA), is used for solving a transformed system of ODEs, which was obtained from the system of PDEs of present model. Dataset generated from Python and Mathematica is filtered and embedded into LMNNA for evaluation and comparison of results.Temperature and flow rate profile are analyzed against variations in sundry characteristics. The profile of flow rate shows it increases with fluidity parameter R and decreases with increasing deviation parameter γ. Temperature outline shows it enhances with Eckert Ec and Prandtl Pr ratio but decreases with increase in radiating parameter Rd.

The aim of this research work is to estimate and analyze the solution of rheological chemical reactive Carreau nanofluid (CNRFM) induced by exponentially extended surface (EES) subject to variable physical attributes by using stochastic analysis on Levenberg Marquardt backpropagation neural networks (SALMBNNs). The non-linear Partial Differential Equations (PDEs) are transformed by using the similarity transformation variables into their corresponding ODEs. The reference values are created with ARK (adaptive Runge-Kutta) scheme. The ensuing results are explained for the variable viscosity, Weissenberg number (material number), Brownian movement factor, LRF (local rotation factor), LN (Lewis number) and activation energy with chemical reaction in addition. Numerical calculations of different physical quantities are approximated with artificial intelligence based SALMBNNs from dataset created with ARK method. The convergence, accuracy, and efficiency of the proposed stochastic analysis on Levenberg Marquardt backpropagation neural network (SALMBNNs) are established and endorsed through iterative learning curves at each incremental step in epoch, statistical instance distribution studies of error-histograms, analysis of adaptive controlling parameters of SALMBNNs, and evaluation of regression metric.

The objective of this article is to investigate the mechanism of unsteady heat and mass transfer (HMT) in a magnetohydrodynamics Carreau nanofluid flow (MHD-CNFF). To accomplish this, we utilized a novel artificial intelligence-based (AI) method, deep learning predictive technique combined with Levenberg-Marquardt scheme (DLPT-LMS). The similarity transformation is applied to convert the partial differential equations (PDEs) into a set of non-linear ordinary differential equations (ODEs). We numerically solved these resulting ODEs using the Adam numerical approach in sophisticated “Mathematica” software. The influence of Brownian motion parameter, thermophoresis parameters, Prandtl number, Lewis number, and mass transfer parameters on the temperature, velocity, and concentration profile is analyzed using a synthetic dataset. This evaluation is conducted across different variation of MHD-CNFF by leveraging DLPT-LMS. The absolute error (AE) across various iteration of MHD-CNFF demonstrated a progressive drop, indicating the accuracy of DLPT-LMS. The outcomes of this investigation revealed that the AE ranged from 10−3 to 10−8 which shows a minimum error between actual and predicted value. Additionally, the MSE confirmed the accuracy of proposed DLPT-LMS in predicting the behavior of velocity, temperature, and concentration profiles, with lower values showing better predictions. The model accuracy was reinforced by performance results, including regression analysis and error histograms. Error values gradually decreased during training, validation, and testing phases, exhibiting a robust convergence and indicating the highly reliability of AI-based algorithm for investigating intricate fluid dynamics in MHD-CNFF systems.

Our study delves into the dynamics of a convective Magneto-Hydrodynamic Bioconvective Nanofluid model (MHD-BCNFM) flowing over a convectively heated stretched sheet. To accomplish this, we utilize the distinctive capabilities of the Supervised Stochastic Approach for Computational Analysis (SSACA). By integrating similarity transformations, we convert the partial differential equations (PDEs) governing the system into coupled ordinary differential equations (ODEs). We generate the dataset for our approach using the Adam numerical technique specifically tailored for the (MHD-BCNFM). Achieving this involves systematic modulation of parameters such as λ, bioconvection Péclet number Pe, Bioconvection constant σ, Brownian motion parameter Nb, and thermophoresis parameter Nt. ''Moreover, we utilize a reference dataset to compute numerical values of various physical quantities in the (MHD-BCNFM) employing SSACA-based Artificial Intelligence methods. The effectiveness of our devised SSACA approach is demonstrated by a negligible mean squared error, ranging from approximately 10−8 to 10−10. Histograms exhibit a maximum error range of 10−5, closely aligning with optimal correlation/regression measures. Outstanding performance metrics in terms of Mean Squared Error (MSE) are attained at levels such as l 9.93E−12, 1.07E−11, 6.28E−10, 1.43E−11, 2.09E−09, 3.65E−11, 2.97E−11, and 2.80E−13 against 320, 430, 209, 85, 356, 295, 66, and 136 epochs. A comparative study between the proposed and reference datasets underscores the authenticity and precision of SSACA, supported by error analyses ranging from E-05 to E-09 across all scenarios.

The objective of this research is to establish the modelling and evaluation of a differential mathematical system for the radiated Carreau nanofluid model (RCNFM) by exploiting the skills of stochastic computing with Levenberg–Marquardt neural networks (SCLMNNs).The reference dataset is created using the Adams technique in the Mathematica software by variation of various physical quantities. The reference data results are trained using a split of seventy percent for training and thirty percent for validation and testing methods. This approach aims to enhance and compare the estimated outcomes with established solutions. The precision and efficacy of the developed stochastic computing with Levenberg–Marquardt neural networks are illustrated by a comparison of the results obtained from the dataset using Adams technique. This comparison includes variations in values of several influential parameters including Magnetic number, Weissenberg Numbers, Porosity parameter, Brownian movement, Prandtl number, Unsteady parameter, Temperature Difference Parameter, Stretching/shrinking parameter, and Lewis Number. The reference data results are trained by assigning 70% for training, 15 % for validation and 15 % for testing. Fitness curves of mean square error, regression studies, error evaluated with histogram plots, and evaluation on absolute errors all authenticate the reliability and precision of stochastic computing with Levenberg–Marquardt neural networks. Performance metrics in terms of mean square error are excellent at the levels 1.19E−10, 1.92E−10, 9.60E−11, 1.02E−10, 7.09E−11, 2.07E−09, 1.66E−10, 8.34E−11, and 1.17E−13against 117, 194, 144, 117, 237, 260, 96, 128, and 74 epochs. The error analysis of the designed and reference datasets suggests that the stochastic computing with Levenberg–Marquardt neural networks is accurate and reliable, with values ranging from E−08 to E−04 across all scenarios. Radiative transport in three dimensional Carreau nanofluids with activation energy in porous media improves the following domains: biomedical engineering, energy systems, chemical process, environmental engineering, thermal management and material science.

Recent changes in climate have reduced crop productivity throughout much of the world. Drought and heat stress, particularly in arid and semi‐arid regions, have seriously affected barley production.This study explored the separate and interactive effects of silicon (Si) and hydrogen sulphide (H2S) on plant growth and mitigation of the adverse effects of heat stress (DS) and drought stress (HS) in a barley pot experiment.The impacts of simultaneous DS + HS were more severe than individual stresses due to increased ROS production, malondialdehyde (MDA) content and higher electrolyte leakage (EL), thereby leading to reduced water, protein and photosynthetic pigment content. Exogenously applied Si and H2S alleviated the DS‐, HS‐ and DS + HS‐induced effects on barley by reducing ROS production, MDA and EL. A single application of H2S or Si + H2S increased plant biomass under all stress conditions, which can be ascribed to higher Si accumulation in barley shoots. A single application of Si or H2S significantly increased plant biomass. However, Si + H2S was the most effective treatment for metabolite accumulation and elevating activity of antioxidant enzymes to prevent toxicity from oxidative stress. This treatment also modulated osmolyte content, enhanced antioxidant activity and regulated the stress signalling‐related endogenous hormones, abscisic acid (ABA) and indole acetic acid (IAA).Exogenous treatments regulated endogenous H2S and Si and resulted in higher tolerance to individual and combined drought and heat stress in barley. [ABSTRACT FROM AUTHOR]

This article explores the enhancement of thermal exchange in a dissipative Triple-nanoparticle (Al2O3+CuO+Cu) hybrid fluid over a stretchable wavy cylindrical surface with slip effect, incorporating Python bvp algorithm with artificial intelligence AI analysis of numerical results. The stochastic AI analysis gives the enhanced and optimized results with predictive modeling, incorporating randomness of influencing parameters and nonlinear turbulent behavior of model. The model has significant importance and application in noise reducing and drag reduction devices or structures. Moreover, the presented geometrical structure is useful in enhancing thermal conduction characteristic. The intricate interplay of constituent nanoparticles and their effect on complex heat transfer in drag optimization devices is the main focus of this study. Mathematical Model of PDEs of this flow problem is converted into system of ODEs by similarity transformations with introducing dimensionless parameters. Numerical solutions of the emerged system are obtained by Python bvp solver algorithm and graphical solutions by Python are presented. To expedite the solution process and enhance the accuracy of prediction, advanced AI algorithm, such as neural network and machine learning technique is adopted. Numerical dataset obtained from Python is embedded for further AI analysis by using Levenberg Marquardt Feed-forward Algorithm (LMFA) with 10 computing neurons and 4 output layers representing results for 4 parametric variations.A rise in the fluid flow speed is observed with higher of value yield stress or Newtonian-behavior i.e. of Casson parameter a1 and stretching parameter λ for the sheet, but shows a decline with enhancing turbulence s2. Temperature profile show a descending behavior with inclination of Eckert ratio Ec, and Prandtl ratio of momentum-thermal diffusivity.

Byline: M. M. Javed, S. Zahoor, H. Sabar, I. U. Haq and M. E. Babar ABSTRACT The importance of thermostable biomolecules in the field of biotechnology has spurred research into [...]

Introduction The purpose of sedation and analgesia is to relieve patient anxiety and discomfort and improve the outcome of the GI endoscopic procedure. The objective is to evaluate and compare the clinical efficacy and safety of Dexmedetomidine + Propofol (DP) and Fentanyl + Propofol (FP) sedation for proclaiming a better drug regime in pediatric lower GI endoscopic procedures. Materials and Methods This prospective, randomized double blind study included hundred pediatric patients, of ASA I-II, aged between 7 to 16 years and were randomly allocated to either Dexmedetomidine and Propofol (DP) or Fentanyl and Propofol (FP) groups. The study group received either Dexmedetomidine 1µg/kg over 10 minutes in DP group or Fentanyl 1µg/kg slow IV bolus in FP group for sedation induction followed by Propofol (50µg/kg/min) for maintainence (± Propofol rescue doses 0.5mg/kg were administered if patients showed discomfort) in both groups. Results The two groups were comparable in terms of age, weight, sex distribution, ASA status, diagnosis, the procedure performed and baseline hemodynamic and respiratory parameters. The mean heart rate, systolic and diastolic arterial pressure during procedure were lower in the DP group as compared to FP group and difference was statistically significant (P < 0.05). Respiratory rate and SpO2 were lower in FP group. Higher Ramsay sedation scores were observed in DP group. The mean recovery time (DP vs FP, 8.7 vs 10.56 mins) and length of stay in recovery (DP vs FP, 12.9 vs 15.14mins) was lesser in DP group and the difference was statistically significant (P < 0.05). The average number of rescue doses of Propofol used during the procedure were significantly less in DP group as compared to the FP group (DP vs FP, 1.84 ± 0.76 vs 3.72 ± 1.16, P Value <0.0001). The adequacy of analgesia in patients of both groups was assessed by Wong Baker Faces Pain Rating Scale and was comparable. A higher percentage of operator satisfaction was observed in patients who underwent colonoscopy in DP group Conclusion Dexmedetomidine-Propofol sedoanalgesia may be considered preferable because it is associated with shorter recovery time, adequate post procedure analgesia, without any significant adverse effects on cardiovascular and respiratory parameters and is a practical alternative in paediatric patients undergoing lower GI endoscopies for monitored anesthesia care. [ABSTRACT FROM AUTHOR]

In the presented study, a novel application of intelligent numerical computing solver based on neural networks backpropagated with the Levenberg-Marquard scheme (NN-BLMS) is presented to interpret the chemical reactions and activation energy in unsteady 3D flow of Eyring-Powell magneto-nanofluidic system for convective heat and mass flux scenarios. The original nonlinear coupled PDEs representing the Eyring-Powell magneto-nanofluidic model (EPMNM) is transformed to an equivalent nonlinear ODEs system by exploiting similarity variables. A dataset for the proposed NN-BLMS is generated for different scenarios of EPMNM by variation of radiation, temperature ratio parameter, heat generation, Brownian motion and thermophoresis parameters by using Adam numerical method. The training, testing, and validation processes of NN-BLMS are performed to determine the approximate solution of EPMNM for different cases and comparison with reference results to verify the correctness of the proposed NN-BLMS. The performance of the proposed NN-BLMS to effectively solve the EPMNM is endorsed through mean squared error, histogram studies and regression analysis. The close matching of the proposed and reference results based on error analysis form level 10−05 to 10-07 validates the correctness of the proposed methodology.

While health effects of conventional tobacco are well defined, data on vaping devices, including one of the most popular e-cigarettes which have high nicotine levels, are less established. Prior acute e-cigarette studies have demonstrated inflammatory and cardiopulmonary physiology changes while chronic studies have demonstrated extra-pulmonary effects, including neurotransmitter alterations in reward pathways. In this study we investigated the impact of inhalation of aerosols produced from pod-based, flavored e-cigarettes (JUUL) aerosols three times daily for 3 months on inflammatory markers in the brain, lung, heart, and colon. JUUL aerosol exposure induced upregulation of cytokine and chemokine gene expression and increased HMGB1 and RAGE in the nucleus accumbens in the central nervous system. Inflammatory gene expression increased in the colon, while gene expression was more broadly altered by e-cigarette aerosol inhalation in the lung. Cardiopulmonary inflammatory responses to acute lung injury with lipopolysaccharide were exacerbated in the heart. Flavor-specific findings were detected across these studies. Our findings suggest that daily e-cigarette use may cause neuroinflammation, which may contribute to behavioral changes and mood disorders. In addition, e-cigarette use may cause gut inflammation, which has been tied to poor systemic health, and cardiac inflammation, which leads to cardiovascular disease.

ABSTRACT: Pigeons are omnipresent birds which belong to the order, Columbiformes and cover a large part of every town and city, worldwide. Among the parasites of pigeons, Capillaria is found to be more prevalent in them after Eimeria. These worms penetrate deeply in the intestinal mucosa leading to haemorrhages and epithelial necrosis thereby presenting clinical disease and death occurs if left untreated. The major purpose of this study was to determine the burden of Capillaria spp. in domestic pigeons(Columba livia domestica) of district Narowal. Total 210 fecal samples from pigeon flocks were processed qualitatively through direct microscopy and floatation method while quantitatively through McMaster technique. The parasitic eggs were diagnosed using standard keys. In this study, 154(73.33%) out of the 210 samples were found infected with Capillaria spp. The quantitative examination presented the lowest eggs per gram(EPG) of 300 in the Narowal tehsil and highest EPG of 800 in Shakargarh tehsil among the study areas. This study will be helpful in raising awareness to pigeon owners to improve pigeons health status through better control of capillariasis. [ABSTRACT FROM AUTHOR]

Chrysoperla carnea (Green lacewing) is voracious predator of insect eggs and varieties of soft-bodied arthropods such as caterpilllars, aphids, jassids and mealy bugs. It is currently mass-reared and used in biological control of insect pests. Experiments were conducted to find out a better and cheaper artificial diet for mass-rearing of adult C. carnea. Three adult diets were tested in the laboratory conditions in comparison with standard diet; that was mixture of yeast extract, casein, honey, sugar and distilled water. Parameters were fecundity, larval period, pupal period and adult longevity. The results revealed that the mixture of egg yolk, milk and honey was better than all other diets.

Background: The ideal treatment strategy for spontaneous coronary artery dissection (SCAD) remains unclear, with patients potentially treated with either conservative medical care or a revascularization approach., Methods: We performed a systematic review and meta-analysis adhering to PRISMA 2020 guidelines. Inclusion criteria involved studies with confirmed SCAD diagnosis, reporting initial management strategies, and original research with ≥ 10 participants. Random-effect models were applied for insignificant heterogeneity with significance at p ≤ 0.05. Sensitivity analysis and funnel plots assessed potential publication bias., Results: Our analysis found no significant differences in major adverse cardiac events (MACE) (OR = 0.61, p = 0.49), unstable angina pectoris (UAP) (OR = 1.04, p = 0.93), non-ST segment elevation myocardial infarction (NSTEMI) (OR = 1.16, p = 0.82), recurrent myocardial infarction (MI) (OR = 0.78, p = 0.56), stroke (OR = 0.35, p = 0.07), heart failure (OR = 0.41, p = 0.24), in-hospital mortality (OR = 0.35, p = 0.09), post-discharge mortality (OR = 1.66, p = 0.27), or ST segment elevation myocardial infarction (STEMI) (OR = 0.45, p = 0.23) between conservative management and revascularization procedures. However, sensitivity analysis reveals significant decreases in odds of inferior wall STEMI (OR = 0.41 [95% CI 0.17-0.97], p = 0.04) and heart failure (OR = 0.18 [95% CI 0.06-0.54], p = 0.002) in conservative treatment compared to revascularization group., Conclusion: Conservative therapy significantly decreased inferior wall STEMI and heart failure as compared to revascularization in SCAD. Although no significant differences in cardiovascular outcomes, sensitivity analysis highlights potential benefits of conservative management., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare that they have no conflict of interest., (© 2024. The Author(s).)

Introduction: Cancer, particularly breast cancer, is a major contributor to mortality and a significant impediment to life expectancy. In 2020, breast cancer accounted for 11.7% of all cancer cases and caused approximately 685,000 deaths worldwide, surpassing lung cancer in prevalence. The study aims to evaluate the impact of an educational intervention on breast cancer awareness among pharmacy students by comparing their understanding before and after the program., Method: A pre-post quasi-experimental study was designed to assess knowledge and awareness of breast cancer, breast self-examination (BSE) practices, and attitudes toward breast cancer among female university students in Karachi, Pakistan. Participants completed a pre-session questionnaire, attended an awareness workshop and video tutorial, and then completed a post-session questionnaire 2 weeks later., Results: Of 1,200 participants, 1,015 of them completed both the pre- and post-intervention questionnaires. Key demographic features included 83.9% of the participants being in the 18-24 age group, 26.8% being married, and only 14.2% having a family history of breast cancer. Before the intervention, 60.7% of the participants were not involved in regular breast self-exams due to a lack of awareness. Post-intervention results showed a significant increase in awareness, with 35.9% rising to 94.9%. The use of screening methods increased from 46.7 to 94.8%. Knowledge of breast cancer improved from 51.2 to 96.7%, and the general perception rose from 48.2 to 93.4%. Attitudes toward self-examination also shifted positively, indicating a significant change in perception., Interpretation and Conclusion: The study concludes the baseline knowledge of breast cancer among female students was inadequate but improved significantly from over 40% to over 90% following the educational intervention. The intervention positively influenced the general perception and attitudes toward breast cancer. These findings highlight the need for regular educational sessions to enhance awareness, improve healthcare outcomes, and reduce mortality rates, particularly in developing countries., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Mansoor, Naveed, Ali, Manzoor, Zahoor and Sheikh.)

Objectives . Current research aims to identify factors that affect the occupational safety climate in university laboratories despite their perception as low-risk areas compared to industrial environments. Methods . A safety climate survey was conducted in science laboratories across various engineering universities in Pakistan. The survey questionnaire was administered to 406 personnel, and a quantitative method for analysis was selected to examine the socio-demographic variables. A 5-point Likert scale (1 =  strongly disagree to 5 =  strongly agree ) was used to perceive responses from participants. Additionally, a scale reliability test was conducted, and multivariate analysis of variance was performed to determine the relationship between selected dependent and independent variables. Results . The study found an overall safety climate score of 3.16 ± 0.55, indicating a moderate to high perception of safety on a scale of 1-5. Parameters such as role in the laboratory, departments/disciplines, accident experience and safety training significantly affected the safety climate score, while gender, age group, duration in university and accident witnessing did not. Conclusion . Upper management involvement, safety communication and direct supervision are crucial for improving the safety climate of university laboratories. The study recommends the consideration of the identified significant safety climate dimensions in laboratory safety policy-making at academic institutes.

Toxocariasis is an important zoonotic disease caused by Toxocara (T.) canis with considerably higher prevalence in developing countries. The data on its epidemiology, especially in socioeconomically deprived nomadic communities, are scarce in Pakistan. Therefore, this study was conducted to determine the prevalence of anti-T. canis antibodies and its associated risk factors in nomadic communities located in and around Multan, Pakistan. A total of 184 sera samples were collected from nomadic communities by simple random sampling technique. The descriptive epidemiological data of participants were collected on well-designed questionnaires. Prior consent was also obtained from the participants to use the data generated from their samples without showing their identity. All the samples were analysed for the detection of anti-T. canis antibodies using commercially available Enzyme-Linked-Immunosorbent-Assay (ELISA) kits having 91% sensitivity and 96% specificity (Bordier Affinity Products, Switzerland). The overall seroprevalence of toxocariasis among nomadic communities was 27.7% (51/184). Various factors, including age, known disease history, nutritional status, contact with dogs, practice of hand washing after contact with dogs, use of unwashed vegetables, body mass index, and drug abuse, showed significant correlation (p < 0.05) with toxocariasis in nomadic communities. Conversely, other factors, including gender, marital status, educational status, awareness about zoonotic diseases, source of drinking water, occupation, location, hand washing before taking food, exposure to soil, and hygienic eating behaviour, showed non-significant correlation (p > 0.05) with seroprevalence of toxocariasis. Results also showed that >50% of seropositive cases were asymptomatic, whereas cough and abdominal pain were recorded in 19.6% and 11.76% of seropositive cases, respectively. Keeping in view, it is suggested to conduct surveys at mass level to rule out the exact disease status at national level and to include nomadic communities in local, national, and regional disease control programs through provision of better healthcare facilities and awareness about the disease., (© 2023 Wiley-VCH GmbH. Published by John Wiley & Sons Ltd.)

Green nanobiotechnology and beneficial bacterial strains as biofertilizers are crucial in agriculture to achieve food security. Both these strategies have been individually studied in improving plant resistance against phytopathogens along with enhancing plant productivity. Therefore, objective of this study was to explore the eco-friendly and cost-effective approach of utilizing plant growth promoting and disease suppressing bacterial strains and nanoparticles, individually as well as in combination, as bio-stimulants to improve plant growth, antioxidant defense system, nutrition and yield of tomato. A pot experiment was conducted to investigate the zinc sulfide nanoparticles (ZnS NPs) synthesized by using Jacaranda mimosifolia flower extracts (JFE), Acinetobacter pittii and Bacillus velezensis either individually or in combinations to check their potential against Rhizoctonia solani in tomato to suppress root rot infection and improve growth and yield. Among all the combinations the JFE-ZnS NPs + B. velezensis compared to untreated infected plants showed minimum disease incidence and maximum significant protection (66%) against R. solani instigated root rot that was followed by JFE-ZnS NPs + A. pittii and individual application of JFE-ZnS NPs by 58%. The same treatment showed maximum significant increase in plant fresh and dry biomass. B. velezensis significantly increased the photosynthetic pigments when applied individually. However, JFE-ZnS NPs alone and in mixed treatments with B. velezensis efficiently improved total soluble protein, sugar and phenolic contents. The same interactive application of JFE-ZnS NPs + B. velezensis improved the tomato plant nutrition (silicon (Si), magnesium (Mg), calcium (Ca) and potassium (K)) and redox quenching status by improving the activity of antioxidant defense enzymes. Overall, the interactive use of JFE-ZnS NPs with A. pittii and B. velezensis very appropriately prepared the host plant to fight against the negative effects of root rot pathogen in tomato. Advancements in interactively investigating the nanoparticles with beneficial plant growth promoting bacterial strains importantly can contribute in resolving the challenges of food security. According to our information, this is a pioneer report for implying JFE-ZnS NPs in synergism with A. pittii and B. velezensis to hinder the root rot in tomatoes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

The complications associated with diabetic wounds make their healing process prolonged. Hydrogels could be ideal wound dressings therefore present research was conducted to prepare silk sericin (an adhesive protein polymer) based hydrogels in combination with plant extracts and to evaluate its effectiveness against wound healing process in mice with alloxan induced diabetes. Excision wounds were formed via a biopsy puncture (6 mm). Experimental hydrogels were prepared and applied topically on the diabetic wounds. All the hydrogel treatment groups showed significantly higher (P < 0.001) percent wound contraction from day 3 to day 11 as compared to the negative diabetic control group. The serum level of anti-inflammatory cytokine (Interleukin-10) and tissue inhibitor metalloproteinase (TIMP) was significantly higher (P < 0.001), while the level of pro-inflammatory cytokines (tumor necrosis factor-α, Interleukin-6) and matrix metalloproteinases (MMP-2, MMP-9) was significantly lower (P < 0.001) in hydrogels treatment groups as compared to diabetic control group. Although all the hydrogels showed effective results, however the best results were shown by 4 % sericin+4 % banyan+4 % onion based hydrogel. It can be concluded that Sericin based hydrogel enriched with banyan and onion extracts can be used as an effective remedy for the treatment of diabetic wounds due to their high healing and regenerative properties., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

The current study appraises the variations in the yield and physicochemical and antioxidant attributes among kernel oils from the seven most widely consumed varieties of Pakistani mangoes, namely, Anwar Ratul, Dasehri, Fajri, Laal Badshah, Langra, Safed Chaunsa, and Sindhri. The yield of mango kernel oil (MKO) among the tested varieties of mangoes varied significantly ( p < 0.05), ranging from 6.33% (Sindhri) to 9.88% (Dasehri). Physicochemical properties, including the saponification value, refractive index, iodine no., P.V, % acid value, free fatty acids, and unsaponifiable matter, for MKOs were noted to be 143.00-207.10 mg KOH/g, 1.443-1.457, 28.00-36.00 g/100 g, 5.5-2.0 meq/kg, 1.00-7.7%, 0.5-3.9 mg/g, and 1.2-3.3%, respectively. The fatty acid composition determined by GC-TIC-MS revealed the presence of 15 different fatty acids with variable contributions of saturated (41.92-52.86%) and unsaturated (47.140-58.08%) fatty acids. Among unsaturated fatty acids, values of monounsaturated and polyunsaturated fatty acids ranged from 41.92 to 52.85 and 7.72 to 16.47%, respectively. Oleic acid (25.69-48.57%), stearic acid (24.71-38.53%), linoleic acid (7.72-16.47%), and palmitic acid (10.00-13.26%) were the prominent fatty acids. The total phenolic content (TPC) and DPPH radical scavenging (IC 50 ) capacity of MKOs varied from 7.03 to 11.00 mg GAE/g and 4.33 to 8.32 mg/mL, respectively. The results of most of the tested attributes varied significantly ( p < 0.05) among the varieties selected. It can be concluded from the findings of this research work that MKOs from the tested varieties are potential sources of valuable ingredients for the development of nutrapharmaceuticals due to their potent antioxidant properties and high oleic fatty acid profile., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

The goal of the present work was to invent an apigenin-stacked gastroretentive microsponge to target H. pylori . The quasi-emulsion technique was used to prepare microsponges, which were then tested for various physicochemical properties, in-vivo gastric retention, and in-vitro anti-H. pylori study. The microsponge that demonstrated a comparatively good product yield (76.23 ± 0.84), excellent entrapment efficiency (97.84 ± 0.85), sustained in-vitro gastric retention period, and prolonged drug release were chosen for further investigations. The microsponge's SEM analysis showed that it had a spherical form, porous surface, and interconnected spaces. No drug-polymer interactions were detected in the FTIR investigation. Apigenin was found to be dispersed in the microsponge's polymeric matrix according to DSC & XRD investigations. Moreover, the microsponge in the rat's stomach floated for 4 h, according to the ultrasonography. The antibacterial activity of apigenin against H. pylori was nearly two folds more than the pure apigenin and had a more sustained release in the best microsponge, according to the in vitro MIC data, when compared to pure apigenin. To sum up, the developed gastroretentive microsponge with apigenin offers a viable alternative for the efficient targeting of H. pylori . But more preclinical & clinical studies of our best microsponge would yield considerably more fruitful results., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)