Eman A. Al-Shahari’s research while affiliated with King Khalid University and other places

Cultivation of horticultural crops in riverbank soils which are enriched by deposition of nutrient-rich sediments is one of the most common practices in arid and semi-arid regions around the globe. However, contamination of river streams by urban, industrial, and agricultural runoffs resulted in the accumulation of excessive levels of certain nutrients and heavy metals in such crops. Keeping in view, the present study was conducted to understand the impact of Yamuna (non-contaminated) and Solani (urban sewage contaminated) Rivers streams on riverbank soils and planted Armenian cucumber [Cucumis melo var. flexuosus (L.) Naudin.] in a two-year (2022 and 2023) study. The results showed that C. melo cultivated in Solani River water irrigated soils had significantly (p < 0.05) higher growth, yield, and biochemical constitutes as compared to Yamuna River water irrigated soils. The bioaccumulation factor (BAF) studies showed that concentrations of eight heavy metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) were higher in C. melo grown in Solani River soils. However, the levels of heavy metals were found below the safe limits as indicated by dietary intake of metal (DIM), health risk index (HRI < 1), and target hazard quotient (THQ < 1) indices. Overall, this study evidenced that river water impacted by urban sewage could result in elevated levels of heavy metals in river water, soil, and cultivated food crops, thus, signifying their regular biomonitoring in order to mitigate any associated health risk.

Dengue virus infection, caused by a single positive-stranded RNA virus from the Flaviviridae family, represents a significant public health challenge in tropical and subtropical regions. This virus has four serotypes (DENV-1, 2, 3, and 4), primarily transmitted by Aedes mosquitoes. Despite extensive research, effective antiviral treatments and vaccines remain elusive due to the viral diversity and the complex mechanisms such as antibody-dependent enhancement (ADE). In the current study, NS1-positive serum samples from dengue cases in Pakistan (2023–2024), were analyzed to determine the predominant serotype and characterize the envelope (E) gene for further exploration of antiviral targets. Out of 100 samples, 63 (63%) tested positive for DENV-2, indicating its predominance during this period, while two samples showed mixed infections with DENV-2 and DENV-3. The envelope gene was successfully amplified using nested PCR, validated through gel electrophoresis and sanger sequencing. Phylogenetic analysis revealed high similarity of the DENV-2 isolates to strains from China and India. Computational modeling of the envelope protein structure identified potential antiviral binding sites and further molecular docking studies suggested that specific antiviral compounds like Arbidol and Quercetin can inhibit early steps in viral infection. Additionally, BepiPred-3.0 predicted several B-cell epitopes, which could be useful for vaccine development. These findings enhance our understanding of dengue epidemiology in Pakistan and contribute to the development of targeted antiviral therapies, potentially informing future vaccination strategies and outbreak management.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R² of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R² = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R² = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R² values with respect to Chl a (R² = 0.80) for mandarin fruits while the GI had the greatest R² with Chl a (R² = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R² of 0.993 with an RMSE of 0.149 for the training set, and an R² of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R² of 0.905 and an RMSE of 0.077 for the training dataset, and an R² of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

The cultivation of ornamental horticultural crops under salinity stress has been a challenge for growers all over the world. In this study, an attempt was made for pot cultivation of Marigold (Tagetes erecta L. var. Pusa Basanti Gainda) in salt-stressed (SS) soil (150 mM) with the combined use of mushroom compost leachate (CL) and foliar application of titanium dioxide nanoparticles (TiO2-NPs). For this purpose, a total of six pot treatments, i.e., borewell water (BW; control), T1 (BW with SS), T2 (BW with SS and TiO2-NPs), T3 (CL supplemented), T4 (CL with SS), and T5 (CL with SS and TiO2-NPs) were conducted in triplicate. The results of this study showed that CL supplementation significantly (p < 0.05) improved the physicochemical i.e., pH (14.5%), electrical conductivity (32.9%), total nitrogen (27.4%), total phosphorus (247.6%)), and nutrient (organic matter: 119.6%) profiles of soil which later helped in higher growth (30–35%) and yield (5.4–40.7%) of T. erecta. In CL-based treatments, the biochemical constituents were significantly (p < 0.05) higher than those in BW-irrigated ones. Also, the levels of selected stress defense enzymes were significantly increased under SS treatment but reduced under TiO2-NP application. Overall, it was observed that the combined application of CL and TiO2-NPs (T5 treatment) was the most helpful treatment for enhanced germination, growth, yield, biochemical parameters, and better plant enzymatic activities to cope with saline stress. This study provides a mechanistic understanding of T. erecta plants under saline stress which is crucial for the development of targeted interventions aimed at improving plant tolerance to saline conditions.

The current work attempted to examine the histological and immunohistochemical features of the pancreas in the southern white-breasted hedgehog (Erinaceus concolor). Five adult male individuals were used in this study. Utilizing hematoxylin and eosin as well as Masson's trichrome, staining was performed on paraffin sections of the pancreas. Immunofluorescent staining investigated the expression pattern of glucagon, insulin, somatostatin, neuropeptide Y (NY), and pancreatic polypeptide (PP). The exocrine pancreas comprised pyramidal and oval-shaped acini, exhibiting the clustering of zymogen droplets in the apical cytoplasm of acinar cells. The endocrine pancreas was distinguished as a large α islet of Langerhans and small β islets. Alpha cells were localized on the periphery of islets with an intensity of 20.6% ± 12.56. In comparison, Beta cells with 25.14% ± 5.89 intensity were gathered in the middle of the pancreatic islets. Delta cells exhibited 25.69% ± 5.13 intensity and were distributed through the islets. NY and PP cells were detected with 22.74% ± 3.25 and 16.80% ± 4.77 intensity, respectively. These cells showed prevalent distribution in the islets. In conclusion, it appears that although the pancreas of the southern white-breasted hedgehog is generally like other mammalian species, there are some species-specific features in density and dispersal of endocrine cells which can be attributed to the physiological purpose of the pancreas in this species. Future histophysiological investigations are still needed to explore the exact relationship between the histological organization of the pancreas and its function in that animal.

Finding sustainable methods for utilizing biosolids, also known as municipal sewage sludge (SS), presents a pressing challenge in modern waste management practices. Therefore, this study aimed to evaluate the impact of SS amendment on growth, biochemical, proximate, and heavy metal bioaccumulation parameters of berseem clover (Trifolium alexandrinum L.) fodder crop under field conditions. Trifolium alexandrinum was cultivated using different rates of SS mixing (i.e., T0: 0% as control with no SS application, T1: 5%, and T2: 10%). The results obtained showed a significant (p < 0.05) increment in growth, biochemical, and proximate parameters of T. alexandrinum with an increasing SS mixing rate. The highest productivity of T. alexandrinum fodder (1.92 kg m-2 fw) was observed in the T2 treatment as compared to the control treatment. The heavy metal analysis of shoot and root parts of T. alexandrinum showed that the contents (mg kg-1) of eight elements (Cd 0.02-0.13, Co 0.04-0.08, Cu 5.94-0.05, Cr 0.43-1.68, Fe 7.08-15.93, Ni 0.89-2.90, Mn 1.62-5.38, and Zn 3.30-7.04) increased significantly (p < 0.05) with SS mixing rate. The bioaccumulation factor (BAF) was below 1 except for Cu and Zn exhibiting their rapid uptake by plants from SS-treated soils. However, dietary intake modeling (DIM < 1) and health risk index (HRI < 1) studies showed that the levels of heavy metals did not exceed the permissible limits in any SS treatment. Overall, SS amendment has a positive impact on the growth, biochemical, proximate, and heavy metal characteristics of T. alexandrinum. Therefore, this study suggested a strategy for low-cost soil fertilization and fodder crop production which could sustainably benefit waste recycling.

The glass manufacturing industry produces hazardous effluent that is difficult to manage and causes numerous environmental problems when disposed of in the open. In this study, an attempt was made to study the phytoremediation feasibility of water lettuce (Pistia stratiotes L.), a free-floating aquatic macrophyte, for the removal of six heavy metals from glass industry effluent (GIE) at varying concentrations (0, 25, 50, 75, and 100%). After a 40-day experiment, the results showed that 25% GIE dilution showed maximum removal of heavy metals i.e., Cu (91.74%), Cr (95.29%), Fe (86.47%), Mn (92.95%), Pb (87.10%), and Zn (91.34%), respectively. The bioaccumulation, translocation, and Pearson correlation studies showed that the amount of heavy metals absorbed by vegetative parts of P. stratiotes was significantly correlated with concentrations. The highest biomass production, chlorophyll content, relative growth rate, and biomass productivity were also noted in the 25% GIE treatment. Moreover, the multiple linear regression models developed for the prediction of heavy metal uptake by P. stratiotes also showed good performance in determining the impact of GIE properties. The models showed a high coefficient of determination (R² > 0.99), low mean average normalizing error (MANE = 0.01), and high model efficiency (ME > 0.99) supporting the robustness of the developed equations. This study outlined an efficient method for the biological treatment of GIE using P. stratiotes to reduce risks associated with its unsafe disposal.

This study aimed to assess the mushroom farm wastewater (MFW) treatment potential using novel isolated microalga Chlamydomonas asymmetrica strain SAG70.72. Microalga was isolated from the Ganga River located in Haridwar, India, and identified using an 18S rRNA-based sequencing technique. For phycoremediation experiments , different concentrations of MFW (i.e., 0 % as control, 50, and 100 %) were used to cultivate isolated C. asymmetrica under laboratory conditions. The results showed that the maximum significant (p < 0.05) reduction in total dissolved solids (TDS: 87 %), biochemical oxygen demand (BOD: 93 %), chemical oxygen demand (COD: 92 %), total Kjeldahl's nitrogen (TKN: 87 %), and total phosphorus (TP: 97 %) were observed in 50 % MFW treatment. Moreover, proximate, biochemical, and fatty acids methyl esters (FAMEs) composition of C. asymmetrica were also found highest in 50 % MFW treatment. The first-order reaction-based kinetic model also depicted the highest value of rate constant (k) in the same treatment. Moreover, the growth of C. asymmetrica was best simulated using the logistic model as compared to the modified Gompertz models. The findings of this study suggested that the implementation of isolated C. asymmetrica in MFW treatment systems could contribute to the mitigation of environmental impacts associated with mushroom cultivation.

The integration of Internet of Things (IoT) technology with deep learning (DL) algorithms has revolutionized plant disease detection and crop management and paved the way for sustainable agricultural practices. Real-time information on soil moisture, plant health, and environmental conditions can be collected by deploying a network of connected devices and sensors in agricultural fields. DL algorithms, specifically convolutional neural networks (CNN), analyze this massive dataset, facilitating timely and accurate recognition of plant diseases. This early detection allows farmers to implement targeted interventions, like adjustment to irrigation or precision application of pesticides, maximizing crop yield, and minimizing resource wastage. Therefore, this article develops an automated Plant Disease Detection and Crop Management using a spotted hyena optimizer with deep learning (APDDCM-SHODL) technique for Sustainable Agriculture. The APDDCM-SHODL approach aims to detect the existence of plant diseases and improve crop productivity in the IoT infrastructure. To achieve this, the APDDCM-SHODL method primarily employs the Vector Median Filter (VMF) technique. In addition, the Densely Connected Networks (DenseNet201) model is deployed for feature extraction. In addition, the SHO technique is exploited for optimum hyperparameter tuning of the DenseNet201 model. Furthermore, the classification algorithm is implemented by using the recurrent spiking neural network (RSNN) model. A brief set of experiments has been made to determine the experimental validation of the APDDCM-SHODL model. The comprehensive results inferred that the APDDCM-SHODL method reaches remarkable performance over other existing methods with the highest accuracy of 98.60%.