Zabol University

The clinical utility of doxorubicin (DOX) has been widely hampered by a dose-dependent systemic toxicity, in particular cardiotoxicity. While nanocarrier systems represent encouraging solutions, their optimization is not an easy task due to complex, nonlinear relationships between physicochemical properties and biological outcomes. This study presents a hybrid computational framework that incorporates both classical machine learning and physics-informed machine learning to predict and optimize DOX-loaded nanocarrier cytotoxicity toward normal cells. For this purpose, we compiled an extensive dataset of 77 unique nanocomposite systems with their detailed physicochemical characterizations and biological evaluations. Several ML models were trained and compared, whereas a Physics-Informed Neural Network implemented domain knowledge such as drug release kinetics, colloidal stability constraints, and diffusion limitations. The proposed PINN model showed better predictive capability (R2 = 0.89, RMSE = 0.14) compared to conventional ML methods. SHAP analysis revealed that zeta potential and size are the most governing features on cytotoxicity. Bayesian optimization revealed an optimal design space: sizes of 120-150nm, zeta potentials between - 25 and - 35 mV, loading efficiency of 5-10%, and encapsulation efficiency > 85%. Experimental validation on independent studies confirmed the model's accuracy with prediction errors < 3%. The proposed PIML framework offers a robust yet interpretable method for rational nanocarrier design that significantly improves the development of safer chemotherapeutic delivery systems by reducing the reliance on empirical optimizations.

Living Labs in agricultural systems serve as real-world platforms where farmers, researchers, policymakers, and communities co-create solutions to address complex sustainability and biodiversity challenges. Bridging the gap between scientific knowledge and local practice remains a key challenge for advancing sustainable agroecosystems. Recent studies highlight the potential of participatory modelling, such as agent-based models, to support collaborative decision-making, with broader implications for environmental management and governance. Participatory modelling facilitates structured interactions between scientific teams and stakeholders to address trade-offs, synergies, hidden causalities, and systemic solutions.Despite the growing application of living lab approaches, a systematic understanding of how participatory modelling can support stakeholder engagement and decision-making within these processes remains underexplored.This study reviews participatory modelling approaches in the context of agricultural Living Labs, highlighting their potential, challenges, and opportunities for supporting collaborative decision-making. Agent-based models are discussed as a prominent example of such approaches, illustrating how computational simulations can capture stakeholder interactions, emergent patterns, feedback, and system-level outcomes. For this purpose, peer-reviewed studies from major databases, including Web of Science and Scopus, were screened to identify key participatory modelling approaches, stakeholder engagement strategies, and reported outcomes.Findings indicate that participatory modelling and agent-based models contribute to living lab processes by exploring complex system interactions and trade-offs; supporting biodiversity-centric scenarios analysis for sustainable agricultural management; fostering co-learning and shared understanding among stakeholders; and enabling biodiversity-oriented decision-making at multiple spatial scales. Despite challenges such as data availability, stakeholder capacity, and model complexity, participatory modelling shows substantial potential as a facilitation and transformation tool in living labs. Furthermore, integrating agent-based model outputs with artificial intelligence–driven analyses enables multi-scale assessment, identification of biodiversity hotspots, and optimization of management strategies.This overview lays the foundation for future empirical studies and demonstrates how participatory agent-based models, complemented by AI, can bridge science, practice, and policy toward more sustainable and resilient agricultural systems.

Seed priming is a widely used pre-germination technique that enhances sprouting efficiency, synchrony, and the initial development of plantlets, particularly under suboptimal environmental conditions. This study aimed to evaluate the impact of different seed priming treatments on the germination, growth, and yield performance of quinoa (Chenopodium quinoa Willd.) through a series of laboratory, greenhouse, and field experiments conducted during the 2024 cropping season. The laboratory experiment was arranged in a completely randomized design (CRD) with four replications. Treatments included potassium nitrate (5% w/w), potassium chloride (1% w/w), zinc sulfate (6% w/w), gibberellic acid (30mg.kg⁻¹), polyethylene glycol 6000 (10% v/v), salicylic acid (200mg. L⁻¹), humic acid (54mg. L⁻¹), hydropriming (distilled water), and a non-primed control. The same treatments were evaluated in a greenhouse trial (CRD with four replications) and a field trial (randomized complete block design with three replications). The overall results of the first experiment showed that treatments of potassium chloride, hydropriming, potassium nitrate, and gibberellic acid had positive effects on the germination characteristics of quinoa seeds. The greenhouse experiment also clearly demonstrated the superiority of potassium chloride and gibberellic acid treatments. In the field experiment, salicylic acid and potassium chloride treatments led to the highest economic yield. Based on the combined results of the experiments in this study, priming quinoa seeds with potassium chloride and salicylic acid is recommended.

Accurate prediction of the spatial distribution of potentially toxic elements (PTEs) and identification of the most important environmental drivers are essential for reducing their adverse effects on human health and the environment. In this regard, the present study was conducted to predict the spatial distribution of arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), and lead (Pb) in a dust-prone area of central Iran using the RF model under 11 scenarios constructed based on human activity-based factors (HAF), land-based factors (LSF), physicochemical soil properties) PSP), meteorological factors (MF), and remote sensing auxiliary data (RSAD). The overall contribution of the influencing factors in predicting soil PTEs was determined using the SHapley additive exPlanations (SHAP) analysis. Soil PTEs and some other properties of 107 surface soil samples were measured in the laboratory. The best performance of RF in predicting As, Co, and Cr was observed under scenario VI (HAF + PSP) with the R2 value of 0.59, 0.60, and 0.58, respectively. The RF under Scenario X (PSP + LSF+HAF+RSAD) showed the best performance in predicting Cd (R2 = 0.67). The performance of RF for predicting Pb was weak in all scenarios (R2 < 0.38). The contributions of HAF, LSF, PSP, and RSAD in predicting Cd were 54.9, 21.5, 18.3, and 5.2%, respectively. On average, the contributions of HAF and PSP to the prediction of the other three PTEs were 55.6 and 44.4%, respectively. Among these categories, distance to industries, calcium, magnesium, magnetic susceptibility, terrain ruggedness index, and distance to rivers were identified as the most important predictors. Our findings are useful for improving soil management to reduce the adverse effects of PTEs in arid environments.

Urban thermal comfort analysis in hot-arid climates requires robust methodological frameworks to address intensifying heat stress. This study introduces a reliability-based thermal comfort framework using Monte Carlo simulation to evaluate six urban design interventions in Zahedan, Iran. Field measurements encompassed reference conditions, concrete gazebos with varying vegetation covers, and tree shade configurations. Monte Carlo simulation (n = 10,000) computed prediction uncertainties and probabilistic thermal comfort evaluations for Physiological Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI). A novel Thermal Comfort Reliability Index (TCRI) was developed integrating comfort probability and system reliability. The approach demonstrated excellent convergence (CV < 1%) and predictive capability (R2 > 0.999, RMSE < 1.0 °C). Results revealed critical deficiencies across all interventions. The best-performing configuration (concrete gazebo with full tree shade) achieved only 20.56% comfortable conditions. Hot failure probabilities ranged from 29.85% to 65.25%. UTCI demonstrated superior reliability versus PET, with 3.14 × higher performance and improvement factors of 1.7 × to 58.2 × . Sensitivity analysis identified mean radiant temperature (r = 0.942) and air temperature (r = 0.896) as dominant variables. Extremely low TCRI values (< 5%) indicate systematic failures requiring radical climate adaptation measures in hot-arid urban areas.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-33440-6.

This study evaluated the effects of graded dietary tryptophan (Trp) supplementation on growth performance, carcass traits, hepatic enzyme activities, and immune response in growing Japanese quails. A total of 525 seven-day-old quail chicks were distributed in a completely randomized design with seven dietary Trp levels (0.21 %, 0.22 %, 0.23 %, 0.24 %, 0.25 %, 0.26 %, and 0.27 %) and five replicates of 15 birds each. Growth performance traits, including weight gain (G) and feed conversion ratio (FCR), responded significantly (P < 0.05) to increasing dietary Trp. The rational regression model estimated optimal Trp levels of 0.237 % for G and 0.238 % for FCR, corresponding to 7.9 % and 8.3 % higher requirements than NRC (1994) recommendations, respectively. Hepatic enzyme activities exhibited pronounced quadratic responses, with minimum alkaline phosphatase (ALP) and aspartate aminotransferase (AST) activities estimated at 0.242 % and 0.232 % Trp, representing 10.2 % and 5.6 % higher requirements than NRC (1994), respectively. The antibody titer against sheep red blood cells peaked at 0.247 % Trp (12.2 % higher than NRC, 1994), indicating enhanced immune responsiveness. Carcass traits, including thigh and breast meat yields, were optimized at 0.249 % and 0.251 % Trp, exceeding NRC recommendations by 13.0 % and 14.1 %, respectively. Overall, the estimated optimal Trp requirements for maximizing growth, immunity, and carcass yield ranged from 5.6 % to 14.1 % above NRC (1994) values, underscoring the elevated amino acid demands of modern quail genotypes. These findings highlight the need to update current nutritional guidelines to accommodate contemporary genetic potential, improved metabolic efficiency, and functional amino acid utilization, thereby supporting optimal growth performance, liver health, and immune competence.