Despite the recent advances in food preservation techniques and food safety, significant disease outbreaks linked to foodborne pathogens such as bacteria, fungi, and viruses still occur worldwide indicating that these pathogens still constitute significant risks to public health. Although extensive reviews of methods for foodborne pathogens detection exist, most are skewed towards bacteria despite the increasing relevance of other pathogens such as viruses. Therefore, this review of foodborne pathogen detection methods is holistic, focusing on pathogenic bacteria, fungi, and viruses. This review has shown that culture-based methods allied with new approaches are beneficial for the detection of foodborne pathogens. The current application of immunoassay methods, especially for bacterial and fungal toxins detection in foods, are reviewed. The use and benefits of nucleic acid-based PCR methods and next-generation sequencing-based methods for bacterial, fungal, and viral pathogens’ detection and their toxins in foods are also reviewed. This review has, therefore, shown that different modern methods exist for the detection of current and emerging foodborne bacterial, fungal, and viral pathogens. It provides further evidence that the full utilization of these tools can lead to early detection and control of foodborne diseases, enhancing public health and reducing the frequency of disease outbreaks.

Carnosic acid (CA) is a natural phenolic compound with several biomedical actions. This work was performed to study the use of CA-loaded polymeric nanoparticles to improve the antitumor activity of breast cancer cells (MCF-7) and colon cancer cells (Caco-2). CA was encapsulated in bovine serum albumin (BSA), chitosan (CH), and cellulose (CL) nanoparticles. The CA-loaded BSA nanoparticles (CA-BSA-NPs) revealed the most promising formula as it showed good loading capacity and the best release rate profile as the drug reached 80% after 10 h. The physicochemical characterization of the CA-BSA-NPs and empty carrier (BSA-NPs) was performed by the particle size distribution analysis, transmission electron microscopy (TEM), and zeta potential. The antitumor activity of the CA-BSA-NPs was evaluated by measuring cell viability, apoptosis rate, and gene expression of GCLC, COX-2, and BCL-2 in MCF-7 and Caco-2. The cytotoxicity assay (MTT) showed elevated antitumor activity of CA-BSA-NPs against MCF-7 and Caco-2 compared to free CA and BSA-NPs. Moreover, apoptosis test data showed an arrest of the Caco-2 cells at G2/M (10.84%) and the MCF-7 cells at G2/M (4.73%) in the CA-BSA-NPs treatment. RT-PCR-based gene expression analysis showed an upregulation of the GCLC gene and downregulation of the BCL-2 and COX-2 genes in cells treated with CA-BSA-NPs compared to untreated cells. In conclusion, CA-BSA-NPs has been introduced as a promising formula for treating breast and colorectal cancer.

Insect pests and crop diseases are considered the major problems for agricultural production, due to the severity and extent of their occurrence causing significant crop losses. To increase agricultural production, it is significant to protect the crop from harmful pests which is possible via soft computing techniques. The soft computing techniques are based on traditional machine and deep learning-based approaches. However, in the traditional methods, the selection of manual feature extraction mechanisms is ineffective, inefficient, and time-consuming, while deep learning techniques are computationally expensive and require a large amount of training data. In this paper, we propose an efficient pest detection method that accurately localized the pests and classify them according to their desired class label. In the proposed work, we modify the YOLOv5s model in several ways such as extending the cross stage partial network (CSP) module, improving the select kernel (SK) in the attention module, and modifying the multiscale feature extraction mechanism, which plays a significant role in the detection and classification of small and large sizes of pest in an image. To validate the model performance, we develop a medium-scale pest detection dataset that includes the five most harmful pests for agriculture products that are ants, grasshopper, palm weevils, shield bugs, and wasps. To check the model’s effectiveness, we compare the results of the proposed model with several variations of the YOLOv5 model, where the proposed model achieved the best results in the experiments. Thus, the proposed model has the potential to be applied in real-world applications and further motivate research on pest detection to increase agriculture production.

Nano-drug delivery is a promising tactic to enhance the activity and minimize the cytotoxicity of antimicrobial drugs. In the current study, chitosan nanoparticles (CSNPs) were used as a carrier for the delivery of gentamicin sulfate (GM) and ascorbic acid (AA). The particles were synthesized by ionotropic gelation method and characterized by FT-IR, Zeta potential, and transmission electron microscope imaging. The obtained particles were evaluated for their in vitro antimicrobial activity and cytotoxicity. The prepared particles (GM–AA–CSNPs) under the optimal condition of 4:1:1 of chitosan to drug ratio showed encapsulation efficiency and loading capacities of 89% and 22%, respectively. Regarding biological activities, GM–AA–CSNPs showed a lower minimum inhibitory concentration (MIC) than free gentamicin sulfate and GMCSNPs mixture without presenting cytotoxicity against normal cells (HSF). Moreover, the GM–AA–CSNPs did not exhibit hemolytic activity. These results highlight that the GM–AA–CSNPs are confirmed as a hopeful formula for future investigations on the development of antimicrobial preparations.

Solenostemma argel is a desert medicinal plant indigenous to African countries. This research aims to study the pharmacological properties of Solenostemma argel plant. Aerial parts (leaves and flowers) of Solenostemma argel (Delile) Hayane were tested for antibacterial activity, antioxidant activity, anticancer, and anti-inflammatory activity. Phenolic and flavonoid contents of the plant were characterized. There was an increase in the antioxidant activity of Solenostemma argel extract from 12.16% to 94.37% by increasing concentration from10 µg/mL to 1280 µg/mL. The most sensitive organism was S. epidermidis with chloroform extract. The MTT assay revealed that methanolic extracts of Solenostemma argel showed potent cytotoxic effects on the A549, Caco-2, and MDAMB-231 cell lines, respectively. The anti-inflammatory activity increased by increasing the concentration of methanolic extract of Solenostemma argel, using indomethacin as a standard. Gallic acid was the most abundant phenolic acid, followed by synergic acid and p-coumaric acid, respectively. Catechin, quercetin, luteolin, kaempferol and rutin flavonoids were also found in the methanolic extract. GC-mass analysis showed that aerial parts of Solenostemma argel were rich in 2-(5-methyl-5 vinyl tetrahydro-2-furanyl)-2-propanol (11.63%), hexanoic acid methyl ester (10.93%), 3-dioxolane,4-methyl-2-pentadecyl (9.69%), phenol, 2-(1,1-dimethylethyl) (8.50%). It can be concluded that Solenostemma argel methanolic extract contain natural bioactive constituents with potential medicinal importance such as antioxidants, antimicrobial, anti-inflammatory, and anticancer activities.