Atatürk University

Abiotic stresses, including drought, salinity, heat, and nutrient imbalances, severely constrain cereal crop productivity and pose a growing threat to global food security under climate change. While traditional breeding has contributed to crop improvement, its limited speed and resolution necessitate the integration of advanced biotechnological approaches. Recent developments in genome editing, particularly CRISPR/Cas systems, alongside marker-assisted selection, genomic selection, and multi-omics technologies, have enabled precise manipulation of stress-responsive genes and accelerated trait discovery in major cereals such as wheat, rice, and maize. This review synthesises current advances in the physiological, molecular, and genomic mechanisms underlying abiotic stress tolerance, with a particular emphasis on integrative frameworks that combine genomics, phenomics, and computational approaches. Importantly, emerging constraints associated with genome editing, including off-target effects, delivery challenges, and mosaicism, highlight the need for complementary strategies. The integration of pangenomics, high-throughput phenotyping, artificial intelligence-assisted selection, and speed breeding represents a transformative shift toward systems-level crop improvement. Overall, this review proposes that future progress in developing climate-resilient cereals will depend not on individual technologies alone, but on their coordinated deployment within holistic, data-driven breeding pipelines capable of addressing complex and dynamic stress environments.

BackgroundAccurate modelling of airflow and aerosol/particle dynamics within the human respiratory system is essential for improving inhalation-based drug delivery strategies and for evaluating the health risks associated with hazardous particulates. Owing to the complex geometry of the human airways, inter-individual anatomical variations, and variable breathing patterns, this process constitutes a highly complex multiphase flow problem. To address the constraints associated with in vivo and in vitro techniques, in silico approaches based on computational fluid dynamics (CFD) have been extensively utilized to examine respiratory airflow and aerosol dynamics at microscopic scales.ObjectivesThe aim of this study is to review recent CFD-based approaches for modeling airflow and aerosol behavior in the human respiratory system, summarize key modeling strategies and influential parameters, and identify future research directions.ResultsRecent studies indicate a transition of respiratory tract models toward more physiologically realistic and whole-lung representations. These studies demonstrate that coupling CFD with particle models enables reliable prediction of aerosol transport and deposition by accounting for the effects of geometric variations, breathing conditions, turbulence characteristics, and particle physical and chemical properties.ConclusionCFD-based modeling, particularly when integrated with particle dynamics, provides a powerful and reliable framework for investigating airflow and aerosol behavior in the human respiratory system. Continued advancements toward realistic whole-lung models and improved representation of physiological and particle-related parameters are expected to further enhance predictive accuracy and support both clinical and environmental health applications.

Early and accurate diagnosis of major production diseases in cattle, many of which may be subclinical, such as mastitis, ketosis, and bovine respiratory disease (BRD), is essential for herd efficiency, animal welfare, and long-term sustainability. Conventional diagnostic approaches based on clinical observation and microbiological assays often lack sensitivity for early-stage detection and show limited predictive value in multifactorial conditions, thereby limiting early risk stratification and timely intervention. Biomarkers derived from accessible matrices such as blood and milk provide valuable insight into inflammatory, metabolic, and reproductive disturbances, especially at subclinical stages. However, their clinical implementation remains limited by pre-analytical and analytical variability (e.g., sample collection, storage, and processing), the absence of standardized thresholds, and the lack of reliable cow-specific decision cut-offs. The integration of multi-omics data, including genomic, transcriptomic, proteomic, and metabolomic layers, with artificial intelligence (AI) enables high-dimensional data integration, automated classification, and predictive modeling in cattle health. While AI-driven approaches show promise in supporting biomarker network interpretation, their translation from predictive modeling frameworks to clinically validated diagnostic systems remains limited. This review critically synthesizes current evidence on the utility and limitations of biomarkers and AI-assisted diagnostics in cattle health management, while identifying key methodological constraints and outlining practical pathways toward standardized and interpretable AI-driven systems.

In this study, the potential protective effects of syringic acid (SA) on gastric tissue were investigated in an indomethacin (INDO)-induced gastric ulcer model. A total of 84 male Sprague–Dawley rats were randomly divided into seven groups. In the in vivo experiments, rats were administered SA at doses of 5, 50, and 100 mg/kg and omeprazole (OMP) at a dose of 5 mg/kg intragastrically (i.g.) for 14 days, and indomethacin (100 mg/kg, i.g.) was administered on the final day. Following INDO administration, the rats were sacrificed under anesthesia, and gastric tissues were carefully excised for further analyses. The collected gastric tissues were subjected to biochemical, histopathological, and immunofluorescence analyses. In addition, in silico analyses were performed to support the INDO-induced gastric ulcer model. Using the licensed Schrödinger Maestro 2025/1 software, the binding properties of INDO to the COX-1 receptor were evaluated through molecular docking, MM-GBSA, and pharmacophore matching analyses. INDO administration was associated with oxidative stress, inflammation, apoptosis, and histopathological damage in gastric tissue. SA treatment appeared to alleviate INDO-induced gastric injury through its antioxidant, anti-inflammatory, and anti-apoptotic properties. SA treatment ameliorated histopathological alterations in ulcerated areas, particularly at doses of 50 and 100 mg/kg, whereas the 5 mg/kg dose did not show a significant protective effect. In addition, in silico analyses suggested that INDO may contribute to ulcer formation by inhibiting COX-1, thereby reducing prostaglandin production in the gastric mucosa. Overall, the findings of this study suggest that SA may reduce oxidative stress, suppress inflammatory responses, and inhibit apoptosis, thereby contributing to the protection of gastric tissue against INDO-induced injury. These results indicate that SA may have therapeutic potential for the prevention of NSAID-induced gastric injury; however, further experimental and clinical studies are needed to confirm these effects and clarify the underlying mechanisms.

The sustainability of aquaculture is increasingly challenged by reliance on fish oil as the primary dietary lipid, raising concerns over cost, supply stability, and pressure on marine ecosystems. Plant-based oils offer promising alternatives, yet their effects on fish growth, lipid metabolism, and physiological health remain to be fully elucidated. Argan (Argania spinosa L.) oil, rich in unsaturated fatty acids and bioactive antioxidant compounds, represents a novel feed ingredient whose potential in aquaculture nutrition has not yet been explored. This study assessed the replacement of dietary fish oil with argan oil at inclusion levels of 0 (control; AO0), 33 (AO33), 67 (AO67), and 100