Islamia University of Bahawalpur

Chromium (Cr) toxicity adversely affects crop productivity and poses significantly health risks. However, silicon (Si), an inorganic amendment, has potential to mitigate these effects in wheat crop. A pot study was conducted to examine the ameliorative role of Si in enhancing grain yield, reducing metal accumulation and improving overall crop performance by influencing morphological, physiological and biochemical parameters under Cr stress. The experiment followed a completely randomized design with four replications. The pots were irrigated weekly with Cr contaminated water after germination, and two levels of silicon (Si at 50 mg kg⁻¹ and Si at 100 mg kg⁻¹) were applied. Chromium stress reduced wheat growth; however, the application of both levels of silica to Cr stressed plants improved crop growth by reducing the Cr toxicity and enhancing physiological and biochemical attributes. The addition of silicon under Cr stress led to a significant increase in plant height (up to 11%), and improved root and shoot biomass, with fresh biomass increased by 60 and 70%, and dry biomass by 47 and 162%, respectively. Root morphological parameters improved by up to 100%, and membrane stability increased by 67%. Additionally, Si modulated antioxidant enzymatic activity, which provided protection against Cr stress. Silicon application also reduced Cr concentration in plant tissues by up to 35%. Daily intake of Cr and the Health Risk Index were lowered with Si application. Overall, the exogenous application of Si reduced Cr phytoaccumulation, thereby lowering health risks and promoting wheat crop growth and development.

Network security is crucial in today’s digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.Intrusion detection systems (IDSs) are one of the significant aspect of cybersecurity that involve the monitoring and analysis, with the intention of identifying and reporting of dangerous activities that would help to prevent the attack.Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN) are the vector figures incorporated into the study through the results. These models are subjected to various test to established the best results on the identification and prevention of network violation. Based on the obtained results, it can be stated that all the tested models are capable of organizing data originating from network traffic. thus, recognizing the difference between normal and intrusive behaviors, models such as SVM, KNN, RF, and DT showed effective results. Deep learning models LSTM and ANN rapidly find long-term and complex pattern in network data. It is extremely effective when dealing with complex intrusions since it is characterised by high precision, accuracy and recall.Based on our study, SVM and Random Forest are considered promising solutions for real-world IDS applications because of their versatility and explainability. For the companies seeking IDS solutions which are reliable and at the same time more interpretable, these models can be promising. Additionally, LSTM and ANN, with their ability to catch successive conditions, are suitable for situations involving nuanced, advancing dangers.

Drought is the primary and most pervasive environmental stress that plants face, and it has the potential to cause significant harm to crops. Selenium (Se) possesses various advantageous characteristics that comprise promoting plant growth, augmenting crop resistance to oxidative stress, and retarding senescence. Here, we investigated the combined effect of drought stress and Se on the growth, physiology, yield and quality of quinoa. Se was applied as a foliar application (Se0 = 0 mg L− 1 Se, Se1 = 10 mg L− 1 Se, Se2 = 15 mg L− 1 Se, Se3 = 20 mg L− 1 Se) to quinoa plants grown under drought conditions (control, drought at multiple leaf stage, drought at flowering stage, and drought at grain-filling stage). Drought stress significantly reduced the growth and yield-contributing parameters, antioxidant enzyme activity, physiological attributes and quality parameters. However, Se foliar application mitigated the adverse effects of drought by improving leaf area index (20.53%), crop growth rate (15.55%), leaf chlorophyll contents (26.47%), water use efficiency (16.28%), phenolics (11.25%), peroxidase (27.30%), superoxide dismutase (30.72%), glutathione peroxidase (12.46%), catalase (29.68%) activities, economic yield (16.51%), and grain protein contents (4.23%). The best-suited concentration to improve the growth, yield and nutritional quality of quinoa under drought conditions was found to be 15 mg L− 1. This study highlights the potential of Se foliar application in enhancing the resilience of quinoa to drought stress.

The death rate due to liver cancer approaches 2 million annually, the majority is attributed to fibrosis. Currently, there is no efficient, safe, non-toxic, and anti-fibrotic drug available, suggesting room for better drug discovery. The current study aims to evaluate the anti-fibrotic role of reserpine, an alkaloid plant compound against CCl4-induced liver fibrosis. In-silico docking analysis showed the interaction of reserpine with keap1 protein with the binding energy -9.0 kcal/mol. In-vitro, biochemical analysis, anti-oxidative indexes, and inflammatory cytokines analysis were performed in HepG2 cells. The non-toxic nature of the compound (<100μg/ml) was evaluated through MTT assay in HepG2 and Vero cell lines. The antifibrotic potential of the reserpine compound (dose of 0.5mg/kg) was assessed in CCl4-administered C57BL/6J mice models. Hematoxylin & Eosin and Masson staining were performed to study the morphological changes of liver tissues. Immune histochemistry (IHC) analysis was performed to evaluate the effect of reserpine on the liver fibrosis marker. The biochemical assay indicated a significant decrease in ALT, AST, and MDA levels and increased catalase enzyme post-6-week reserpine treatment in mice models. Gene expression analysis revealed that the reserpine targets oxidative stress Keap1/Nrf2 pathway and down-regulated Keap1 expression by 5-fold and up-regulated Nrf2 and Nqo1 expression by 6 and 4.5-fold respectively showing its antioxidant response. It suppressed the expression of Cyp2e1 by 2.2-fold, illustrating the compound's ability to block lipid peroxidation. Histological and immunostaining exhibited improved hepatocyte morphology and reduced collagen deposition in liver tissues due to reserpine. Reserpine treatment lowered the fibrotic markers α-SMA and Col-1 by 1.3 and 1.5 folds respectively as compared to the control group and increased the expression of miR-200a and miR-29b by 15.5 and 8.2 folds (p<0.05) while decreased miR-128-1-5p expression by 5-fold. A comprehensive In-silico, In-vitro, and In-vivo analysis revealed that reserpine has a strong anti-fibrotic effect against the CCl4-induced liver fibrosis in C57BL/6J mice model by targeting the Keap1/Nrf2 pathway.

This study explored the role of technology systems in influencing nurses’ intentions to adopt medical applications that enhance their performance and how technology contributes to improvements in hospital systems. The study examines the intention to use technology through the mediating effects of perceived usefulness and perceived ease of use, with technology sophistication. A random sampling method was employed to gather 687 responses from nurses. The statistical analysis was conducted using AMOS version 25.0 and SPSS. The findings indicate a significant association between technology sophistication (TS), perceived usefulness (PU), perceived ease of use (PEU), and intention to use (IU). Additionally, PU and PEU positively mediate the relationship between TS and IU. This research will benefit policymakers aiming to enhance nurses’ performance by adopting modern technology. Authorities should consider introducing advanced technology systems to meet the goals of hospital administration and support nurses effectively.

Aquaculture is an interdisciplinary approach that is based on water-food-energy nexus and involves circular bio-based economy concept. This approach has shown immense potential for reduced resource consumption, anthropogenic discharge mitigation, and recycling of nutrients, energy, and agricultural wastes in meeting the global food demands of ever-increasing population. Thus, in this study, we have analyzed the integration of two farming systems, i.e., lettuce (Lactuca sativa) and gulfam fish (Cyprinus carpio), into an agri-aquaculture and compared them with corresponding non-integrated systems or partially integrated systems. The results showed that both lettuce and gulfam fish supported each other as lettuce provided shade and attracted insects for fish feed and fish nitrogenous wastes were utilized by the lettuce for production of green biomass. Consequently, the fully integrated system showed better biomass production with sustainable resource consumption. The gross revenue, cost variable, and net returns of net cost and benefit flow of lettuce and gulfam fish were found considerably high in fully integrated system compared to partially and non-integrated systems. Overall, the net economic return in fully integrated agri-aquaculture systems (PKR 746.57 ± 61.77) was significantly higher than non-integrated (PKR 4181 ± 4.00) and partially integrated system (PKR 326.66 ± 34.26). In brief, the lettuce-gulfam fish agri-aquaculture system could be adopted as a profitable farming system, especially for resource-constrained small-scale farmers as it requires less expense.

This article examines the connection between religious and musical experiences and the shared elements that have contributed to their potent emotional impact. Both music and religion have improved the lives of various cultures around the world for decades because of their capacity to foster emotions of joy, clarity and escape from daily life. Several viewpoints are discussed in relation to this link, through the topic of music composed for religious ceremonies and prayers. These pieces, which are intended to bring listeners closer to their higher selves, may be beneficial for both individuals and groups in a range of favourable and unfavourable circumstances and serve as a suitable counterbalance to the solace that comes from religion. This study aims to shed more light on the similarities between music and religion and what it is about them that appeals to people.

Maize (Zea mays L.) faces significant challenges to its growth and productivity from heavy metal stress, particularly Chromium (Cr) stress, which induces reactive oxygen species (ROS) generation and damages photosynthetic tissues. This study aimed to investigate the effects of fulvic acid (FA) application, via foliar spray or root irrigation, on mitigating chromium stress in maize by evaluating its impact on antioxidant activity and growth parameters. Two maize varieties, P3939 and 30Y87, were subjected to chromium stress (CrCl3·6H2O) at concentrations of 300 µM and 100 µM for a duration of 5 weeks. The experiment was conducted in a wire house under natural environmental conditions at the Seed Centre, Institute of Botany, University of the Punjab, Lahore, Pakistan. Physiological assessments included electrolyte leakage, chlorophyll pigment content, malondialdehyde (MDA) levels, and activities of antioxidant enzymes such as catalase (CAT), ascorbate peroxidase (APX), and guaiacol peroxidase (GPX) in maize leaves. Growth parameters were also monitored. The results revealed that chromium stress significantly reduced chlorophyll content and increased oxidative stress, as evidenced by elevated MDA levels and electrolyte leakage. However, FA application notably mitigated these effects: chlorophyll content improved by 15%, and MDA levels decreased significantly. Irrigation with FA was particularly effective, reducing MDA levels by 40% compared to the 300 µM chromium treatment. Furthermore, while chromium stress enhanced antioxidant enzyme activities, FA application further boosted total soluble protein levels and antioxidant enzyme activities under stress conditions. In conclusion, FA application demonstrates potential in improving maize tolerance to heavy metal stress by enhancing the antioxidant defense system and preserving photosynthetic pigments. These findings highlight FA’s promise as a practical strategy for mitigating the negative impacts of chromium stress on maize, promoting sustainable agricultural practices in contaminated environments.

Foxtail millet (Setaria italica L.) is nutritionally superior to other cereals of the family Poaceae, with the potential to perform better in marginal environments. In the present context of climate change, ecologically sound and low-input foxtail millet varieties can be chosen for agricultural sustainability. The planned research was carried out at the green house of the Department of Agronomy, University of Agriculture, Faisalabad, Pakistan, to investigate the impact of various levels of NPK fertilizer on the growth, development, and yield of foxtail millet lines from USDA germplasm. Eight lines of foxtail millet; U2, V19, V73, V93, V101, V106, V107, and V111, were under study along with NPK fertilizers’ treatments; T1 = 000 NPK as a control, T2 = 20:15:15 NPK, T3 = 30:20:20 NPK, T4 = 40:25:25 NPK, and T5 = 50:30:30 NPK (kg ha− 1). NPK treatments were applied twice during the study periods: first dose was applied after one week of the emergence of seedlings and the second dose was applied at the age of four weeks of seedlings. The time to 50% emergence ranged from 4.33 (V111) to 5.92 (U2) days, and the emergence was highest in V111 (10.02), and V19 had the lowest emergence index of 4.95. Furthermore, all genotypes achieved a complete final emergence percentage of 100, except U2 (92.89%) and V19 (89.33%). The highest growth rate and assimilation rate were observed in V111 and V107 under the impact of treatment 5. Among the different treatments, T3 resulted in the maximum plant height, panicle length, and grain yield per panicle. The highest panicle weight and grain yield per panicle were observed in line V106. Line V107 synthesized the highest chlorophyll a while V93 produced highest chlorophyll b contents which is statistically similar toV19. Line V19 had the highest total chlorophyll and V93 produced the highest carotenoid contents. Application of NPK at the rate of 50:30:30 kg ha− 1 produced maximum chlorophyll a (23%), b (15.8%), total chlorophyll contents (14.2%), plant fresh biomass (2.06%), and grain yield (23.6%) as compared to control treatment. Overall, T3 (30:20:20) and T5 (50:30:30) were observed to be better as compared to other treatments. With respect to growth, yield, and chlorophyll contents, lines U2, V19, V93, V106, V107, and V111 were observed to be potentially superior.

Flurbiprofen (FBP) is poorly water-soluble BCS class II drug with anti-inflammatory and analgesic effects, used to treat arthritis and degenerative joint diseases. This study was aimed to develop SNEDDS loaded with FBP. Six SNEDDS using two oils olive oil (F1OLV, F2OLV, F3OLV) and castor oil (F4CAS, F5CAS, F6CAS) with three different Smix ratios consisting of Tween 20 and PEG 400 (1:1, 1:2, 2:1) were prepared and characterized. Compatibility between FBP and polymers was investigated using FTIR. SNEDDS were characterized for physicochemical attributes. Two optimized formulations were investigated at 10 mg/kg dose given orally in Wistar rats for analgesic activity by hot plate and tail flick methods, and anti-inflammatory activity by carrageenan induced paw edema method. Anti-inflammatory activity was further explored by motor coordination and motility by Rota rod and cage activity tests. Following anesthesia blood samples were collected before dissection to measure inflammatory mediators and oxidative stress markers. Sciatica nerves and hind paws of rats were also removed for histopathological evaluation. FTIR studies revealed compatibility of FBP with other components. Droplet size of F1OLV, F2OLV, F3OLV was 128.5 ± 0.7 nm, 202.5 ± 1.3 nm, and 541.5 ± 1.7 nm, whereas it was 142.5 ± 1.1 nm, 215.4 ± 1.2 nm and 349.9 ± 1.8 nm for F4CAS, F5CAS, F6CAS. %EE of F1OLV, F2OLV, F3OLV was found 85 ± 4.89%–91 ± 4.67%, whereas the %EE F4CAS, F5CAS, F6CAS was 84 ± 4.15%–90 ± 4.21%. DSC curves of F1OLV and F4CAS revealed amorphous nature of the FBP. SEM showed spherical shape of globules. % of drug released in the pH medium 1.2 for plain FBP, F1OLV and F4CAS was 25%, 59% and 57%. % drug released in the pH 6.8 for plain FBP, F1OLV and F4CAS was 59%, 85% and 83%. Oral administration of FBP-loaded SNEDDS (F1OLV and F4CAS) significantly decreased paw diameter and enhanced motor coordination in rats when compared to the disease control group. This was linked to the ability of FBP to reduce inflammation and oxidative stress, with histological studies indicating decreased tissue damage in SNEDDS treated groups, implying the possibility of tissue recovery. Administration of both formulations started to demonstrate analgesic and anti-inflammatory effects after one hour of administration. In addition to anti-inflammatory effect, both formulations improved motor coordination, motility, and reduced infiltration of inflammatory cells in the inflamed paws. The anti-inflammatory and analgesic activities were attributed to decreased serum levels of IL-6 and TNF-α, increased activity of SOD and reduced nitrite content in sciatic nerves. Histopathological evaluation revealed reduced vascularity, inflammation and synovial hyperplasia. The overall findings suggest that the FBP loaded SNEDDS can be used as carriers for improved delivery of FBP which can effectively be used to cure pain and inflammation.

A facile co-precipitation strategy was employed to fabricate SrFe12O19 (SF1) and Cd/Nd-SrFe12O19 (SF2). The ultrasonication route was followed to prepare CNTs-based composite (SF3). The synthesized catalysts were characterized by various physiochemical techniques. Bandgap energy of pristine and doped catalyst was computed via Tauc plot and it was found out to be 2.14 and 1.86 eV respectively. The charge transfer resistance was calculated via EIS analysis. The charge transfer resistance of the synthesized CNTs based catalyst was less (2.83 ohm) compared to other prepared catalysts. The photocatalytic activity of prepared samples was monitored for the degradation of acetylsalicylic acid and crystal violet. SF3 showed maximum degradation of acetylsalicylic acid (75.72%) and crystal violet (66.36%) compared to its counterparts. SF3 composite showed excellent activity due to more charge transfer and less recombination of electrons and holes. The fabricated composite material can be used as multipurpose catalyst for degrading organic contaminants.

Chickpea (Cicer arietinum L.) productivity is hindered by biotic and abiotic stresses, particularly heavy metal toxicity. The pot experiment was carried out at the botanical garden of The Islamia University of Bahawalpur, Bahawalpur-Pakistan. The experimental treatments comprised of following details: T0 = Control + 0 µM MT, T1 = Control + 15 µM MT, T2= Control + 30 µM MT, T3 = 100 µM Cd + 0 µM MT, T4 = 100 µM Cd + 15 µM MT and T5 = 100 µM Cd + 30 µM MT. A completely randomized design (CRD) with three replicates was used. Cd stress significantly reduced shoot fresh (51.3%) and dry weight (50.4%), total chlorophyll (53.6%), and shoot Ca2+ (56.6%). However, it increased proline (38.3%), total phenolics (74.2%), glycine betaine (46.4%), TSS (67.7%), TSP (50%), SOD (49.5%), POD (107%), and CAT (74.2%). Conversely, 30 µM MT improved shoot fresh (78.5%) and dry weight (76%), total chlorophyll (47%), SOD (26.5%), POD (35.8%), CAT (27.8%), proline (19%), TSS (24.5%), TSP (25.8%), and shoot Ca2+ (56.6%). Results indicated that MT enhanced photosynthetic pigments and antioxidant activities, maintained ion homeostasis, and reduces reactive oxygen species. Desi variety performed better than Kabuli, and 30 µM MT application effectively mitigated Cd toxicity

Pymetrozine (a pyridine azomethine pesticide) is one of the most commonly and frequently used insecticides. Scanty information is available about the deleterious effects of Pymetrozine on fish especially bighead carp. Hence, the current study investigated chronic toxicological effects of pymetrozine in bighead carp. A total of 80 fish were reared and divided into four groups(A-D) each containing 20 fish. Pymetrozine was given to experimental fish of groups B, C, and D mixed in water at doses of 5, 10, and 15 mg/L respectively for 30 days. Group A remained as control group. On days 10, 20, and 30 of the experiment, blood and other visceral tissues were collected for analysis of genotoxic effects, erythrocytic morphological and nuclear changes, antioxidant enzymes, and oxidative stress profile. The results revealed significantly higher values of various nuclear abnormalities (erythrocyte with micronuclei, red blood cells with condensed and lobed nuclei) and morphological changes (pear shaped erythrocyte, spindle shaped erythrocytes and spherocyte) in erythrocytes of bighead carp. The investigations on status of antioxidant enzymes and oxidative stress indicated higher values of oxidative stress biomarkers and lower values of antioxidant enzymes in visceral organs (brain, liver, gills, and kidneys) of treated fish. The findings on genotoxic potential of pymetrozine revealed a considerably increased frequency of DNA damage in isolated cells of multiple tissues (brain, liver, gills, and kidneys) of experimental fish at higher doses. In conclusion, it may be suggested that pymetrozine induces toxic effects via disruption of physiological mechanisms of multiple visceral organs of bighead carp.

In a connected graph space G (a discrete structure/a point space), the navigation of an AI agent (assumed to be a point robot) can be taken into place with the help of fewest landmarks. The sense of distance in G provides a platform to choose such fewest landmarks, which is called a metric basis of G. This concept of graph theory allows an AI agent to locate itself uniquely for navigation. As a metric basis of G is not unique, so it is natural to ask which of the specified metric bases of G is the best landmarks provider by using them for a rapid and distraction-free navigation of an AI agent. Such a metric basis of G is said to be an optimal metric basis. In this paper, we address the aforesaid problem by originating a novel approach of metric differential. We consider all those graph spaces in which landmarks already have been observed by inspecting their metric basis. We use our new approach to explore optimal metric basis out of all the existing metric basis of these graph spaces.

Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model’s ability to learn intricate patterns and features from complex image data. Hyperparameter optimization helps to prevent overfitting by finding the right balance between complexity and generalization of a model. The ensemble genetic algorithm and convolutional neural network (EGACNN) are proposed to enhance image classification by fine-tuning hyperparameters. The convolutional neural network (CNN) model is combined with a genetic algorithm GA) using stacking based on the Modified National Institute of Standards and Technology (MNIST) dataset to enhance efficiency and prediction rate on image classification. The GA optimizes the number of layers, kernel size, learning rates, dropout rates, and batch sizes of the CNN model to improve the accuracy and performance of the model. The objective of this research is to improve the CNN-based image classification system by utilizing the advantages of ensemble learning and GA. The highest accuracy is obtained using the proposed EGACNN model which is 99.91% and the ensemble CNN and spiking neural network (CSNN) model shows an accuracy of 99.68%. The ensemble approaches like EGACNN and CSNN tends to be more effective as compared to CNN, RNN, AlexNet, ResNet, and VGG models. The hyperparameter optimization of deep learning classification models reduces human efforts and produces better prediction results. Performance comparison with existing approaches also shows the superior performance of the proposed model.

Tobacco, being a globally cultivated crop, holds significant social and economic importance. Tobacco plants are susceptible to the adverse effects of heavy metals (HMs), particularly cadmium (Cd), which hinders root development, disrupts water balance, and impedes nutrient absorption. Higher concentrations of HMs, especially Cd, naturally accumulate in tobacco leaves due to complex interactions within the plant–soil continuum. The uptake of Cd by plants from the soil is influenced by several factors, including soil type, pH, irrigation water quality, and the chemical composition of the metal involved. Different techniques, such as bioremediation, phytoremediation, and mycoremediation, have been employed to tackle the issue of HMs. The use of biochar offers a practical solution to mitigate this problem. With its large surface area and porous nature, biochar can effectively alleviate HMs contamination. Under biochar application, metal adsorption primarily occurs through physical adsorption, where metal ions are trapped within the pores of the biochar. Additionally, electrostatic attraction, in which negatively charged biochar surfaces attract positively charged metal ions, is another major mechanism of metal remediation facilitated by biochar. In this review, we documented, compiled, and interpreted novel and recent information on HMs stress on tobacco plants and explored biochar’s role in alleviating HMs toxicity. By providing a comprehensive review of the persistent threat posed by Cd to tobacco crops and exploring biochar’s potential as a remediation measure, this work aims to enhance our understanding of HMs stress in tobacco and contribute to the development of sustainable agricultural practices.

Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the effectiveness of interventions, and predicting future disease trends. This study aims to investigate the modeling and forecasting of CVD mortality, specifically in the Sindh province of Pakistan. The civil hospital in the Nawabshah area of Sindh province, Pakistan, provided the data set used in this study. It is a time series dataset with actual cardiovascular disease (CVD) mortality cases from 1999 to 2021 included. This study analyzes and forecasts the CVD deaths in the Sindh province of Pakistan using classical time series models, including Naïve, Holt-Winters, and Simple Exponential Smoothing (SES), which have been adopted and compared with a machine learning approach called the Artificial Neural Network Auto-Regressive (ANNAR) model. The performance of both the classical time series models and the ANNAR model has been evaluated using key performance indicators such as Root Mean Square Deviation Error, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). After comparing the results, it was found that the ANNAR model outperformed all the selected models, demonstrating its effectiveness in predicting CVD mortality and quantifying future disease burden in the Sindh province of Pakistan. The study concludes that the ANNAR model is the best-selected model among the competing models for predicting CVD mortality in the Sindh province. This model provides valuable insights into the impact of interventions aimed at reducing CVD and can assist in formulating health policies and allocating economic resources. By accurately forecasting CVD mortality, policymakers can make informed decisions to address this public health issue effectively.