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Cancer is a leading cause of death worldwide. Cervical cancer caused by human papillomavirus (HPV) is a major health problem in women. DNA vaccines are a perfect approach to immunization, but their potency in clinical trials has been insufficient for generating effective immunity, which may be related to the degradation of the DNA via nucleases, poor delivery to antigen-presenting cells (APCs), and insufficient uptake of DNA plasmids by cells upon injection. Archaeosome is a nano-delivery systems based on liposomes with their immunological role have been developed for gene delivery. In this study, human papillomavirus type 16 genes, containing truncated L1, E6, and E7, were simultaneously used in combination therapy with archaeosome and assessed in vivo . Findings supported that archaeosomes promotes immune responses to DNA vaccines and a long-term CTL response was generated with a low antigen dose. Combination therapy with archaeosome/L1/E6/E7 vaccines exhibited a strong cytolytic activity against tumor cells and induced prophylactic and therapeutic effect against the development of tumor in the animal model.

Iranian Barbel taxonomy and evolutionary history (Cyprinidae: Barbinae and Torinae) remain contentious due to overlapping morphological traits and limited molecular data. This study applies an integrative taxonomic framework to elucidate species boundaries, phylogenetic relationships, and the environmental drivers of diversification within Barbels lineages across Iran. We analyzed mitochondrial DNA (Cytb and COI genes), seven meristic morphological characters, and five spatial environmental predictors from specimens collected across localities representing major Iranian basins. Phylogenetic reconstructions using Maximum Likelihood and Bayesian Inference revealed three main monophyletic groups: (1) Arabibarbus, Mesopotamichthys, and Carasobarbus (Torinae); (2) Luciobarbus; and (3) Barbus sensu stricto (Barbinae). Principal Component and Canonical Variate Analyses of meristic data corroborated molecular findings, supporting the delineation of this taxa. Ecological Niche Evolution analysis indicated several species occupy similar climatic niches, suggesting parallel evolutionary responses to environmental pressures. Divergence time estimates and lineage-through-time analyses linked major cladogenic events to regional orogeny and Quaternary climatic fluctuations. Species delimitation analyses suggested potential synonymy among specific taxa (e.g., L. capito with L. conocephalus; L. esosinus with L. xanthopetrus), highlighting the need for taxonomic revision. Our integrative approach demonstrates that geological history and climatic factors have shaped the diversity and distribution of Barbels in Iran. These findings provide a robust framework for future taxonomic, conservation, and biogeographic studies of Iranian freshwater fishes.

The present study develops a novel form of optimization framework to assess environmental flow in the reservoirs in which upstream and downstream river ecosystems and the lake ecosystem are assessed simultaneously. Physical habitat simulation was applied to compute ecological suitability in the river ecosystems for the fish habitats. Moreover, an adaptive neuro fuzzy inference system was utilized to simulate normalized population of the fish in the reservoir ecosystem. The ecological models were used in the structure of the reservoir operation optimization. Different measurement indices were used in the system performance measurement including reliability index, vulnerability index and root means square error. Three evolutionary algorithms were applied to optimize release including particle swarm optimization, biogeography-based optimization, and differential evolution algorithm. Based on the results in the case study, particle swarm optimization was the best algorithm to optimize ecological flow of the reservoir. The average physical habitat loss at upstream rivers as well as downstream river is less than 20%, which implies the proposed method is robust in terms of ecological flow assessment in rivers. Furthermore, normalized population in the lake is more than 50% that demonstrates the capability of the proposed method to balance ecological needs in the river and lake ecosystems.

The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was established that is able to convert spectral images to TDS distribution. Moreover, a simulation model was developed to generate a TDS distribution map for unseen scenarios for which no spectral images are available. Outputs of the monitoring model were applied as the observations for training the simulation model. The Nash–Sutcliffe model efficiency coefficient (NSE) was utilized in the system performance measurement of the models. Based on the results in the case study, the monitoring model was sufficiently robust to convert the operational land imager spectral bands of Landsat 8 to the TDS distribution map. The NSE was more than 0.6 for the monitoring model, which confirms the predictive skills of the model. Furthermore, the simulation model was highly reliable in generating the TDS distribution map of the lakes. Three tests were carried out to demonstrate the reliability of the model. When comparing the results of the monitoring model and simulation model, an NSE of more than 0.6 was found for all the tests. It is recommendable to apply the proposed method instead of conventional hydrodynamic models that might be highly time consuming for simulating water quality parameters distribution in lakes. Low computational complexity is the main advantage of the proposed method.

SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has undergone various genetic alterations due to evolutionary pressures exerted by host cells, including intracellular antiviral mechanisms such as targeting by human microRNAs (miRNAs). This study investigates the impact of miRNAs hsa-miR-3132 and hsa-miR-4650 on the viral genome. Sequence alignment revealed conserved mutations in the binding sites of these miRNAs in adapted strains compared to the original Wuhan-Hu-1 strain, leading to their deletion. Despite modest expression of these miRNAs in SARS-CoV-2 target tissues, their efficacy against mutant strains is reduced due to the loss of binding sites. Structural analysis indicates that the mutant genome is more stable than the Wuhan-Hu-1 genome. Luciferase and virus titration assays demonstrate that hsa-miR-3132 and hsa-miR-4650 effectively target the Nsp3 gene in the Wuhan-Hu-1 strain but not in mutant strains lacking their binding sites. These findings suggest that the observed mutations help the virus evade selective pressure from human miRNAs, contributing to its adaptation

The South Caspian Basin of Iran (SCBI), a vital ecosystem for unique and valuable fish species, is under severe threats due to anthropogenic activities that are rapidly deteriorating its fish biodiversity. The initial step to effectively combat or mitigate threats to biodiversity is to precisely identify these threats. While such threats are often categorized qualitatively, there is a lack of a comparative quantitative assessment of their severity. This means that although we may have a general understanding of the threats, we do not have a clear picture of how serious they are relative to one another. This study aimed to quantify and prioritize these threats using a modified quantitative “SWOT” (Strengths, Weaknesses, Opportunities, Threats) analysis. Twenty multidisciplinary experts identified and evaluated 26 threats, and we used multivariate cluster analysis to categorize them as “High”, “Medium”, and “Low” based on their quantitative contributions to overall threat. Invasive non-native species and global warming emerged as the most significant threats, followed by resource exploitation, habitat destruction, and pollution. We then used this information to develop a “Situation Model” and “Results Chains” to guide responses to the threats. According to the Situation Model, these threats are interconnected, driven by factors such as population growth, unsustainable resource use, and climate change. To address these challenges, we propose the Results Chains, including two strategies focused on scientific research, land-use planning, public awareness, and community engagement. Prioritizing these actions is crucial for conserving the Caspian Sea’s unique fish fauna and ensuring the region’s ecological and economic sustainability.

Freshwater ecosystems are increasingly imperiled by the dual pressures of biological invasions and climate change, necessitating robust predictive frameworks for effective management. This study integrates advanced ensemble machine learning (EML) within a species distribution modeling (SDM) framework to assess the current and future invasion risk of Carassius species ( C. auratus , C. gibelio , and C. langsdorfii ) across Iranian inland waters. A comprehensive dataset of 486 occurrence records was analyzed alongside eight rigorously selected environmental predictors encompassing climatic, topographical, hydrological, and anthropogenic variables. The BIOMOD2 R package facilitated the construction of an EML-based SDM, leveraging six algorithms weighted by AUC to maximize predictive accuracy. Model performance, evaluated via AUC and true skill statistic (TSS), demonstrated high discriminatory power. Projections under two CMIP6 climate scenarios (SSP 126 and SSP 585) reveal significant potential for range expansion and spatial redistribution of Carassius species, particularly under high-emission trajectories, highlighting increased invasion risks in ecologically sensitive basins. Variable importance analysis underscored the primacy of temperature, precipitation, terrain ruggedness, and human footprint in shaping invasion potential. Additionally, using kernel density estimation (KDE) analysis, the Caspian basin emerged as a critical invasion region for Carassius populations. These findings underscore the urgent need for targeted monitoring and management strategies and demonstrate the utility of EML-SDMs in anticipating biological invasions under global change. The integrative approach presented here provides a scalable framework for proactive biodiversity conservation and policy development in freshwater systems facing multifaceted anthropogenic threats and provides a replicable framework for forecasting biological invasions in other vulnerable freshwater systems.

The present study proposes an integrated simulation–optimization framework to assess environmental flow by mitigating environmental impacts on the surface and ground water resources. The model satisfies water demand using surface water resources (rivers) and ground water resources (wells). The outputs of the ecological simulation blocks of river ecosystem and the ground water level simulation were utilized in a multiobjective optimization model in which six objectives were considered in the optimization model including (1) minimizing losses of water supply (2) minimizing physical fish habitat losses simulated by fuzzy approach (3) minimizing spawning habitat losses (4) minimizing ground water level deterioration simulated by adaptive neuro fuzzy inference system(ANFIS) (5) maximizing macroinvertebrates population simulated by ANFIS (6) minimizing physical macrophytes habitat losses. Based on the results in the case study, ANFIS-based model is robust for simulating key factors such as water quality and macroinvertebrate’s population. The results demonstrate the reliability and robustness of the proposed method to balance environmental requirements and water supply. The optimization model increased the percentage of environmental flow in the drought years considerably. It supplies 69% of water demand in normal years, while the environmental impacts on the river ecosystem are minimized. The proposed model balances the portion of using surface water and ground water in water supply considering environmental impacts on both sources. Using the proposed method is recommendable for optimal environmental management of surface water and ground water in river basin scale.

Co-occurring biodiversity and global heating crises are systemic threats to life on Earth as we know it, especially in relatively rare freshwater ecosystems, such as in Iran. Future changes in the spatial distribution and richness of 131 riverine fish species were investigated at 1481 sites in Iran under optimistic and pessimistic climate heating scenarios for the 2050s and 2080s. We used maximum entropy modeling to predict species’ potential distributions by hydrologic unit (HU) occupancy under current and future climate conditions through the use of nine environmental predictor variables. The most important variable determining fish occupancy was HU location, followed by elevation, climate variables, and slope. Thirty-seven species were predicted to decrease their potential habitat occupancy in all future scenarios. The southern Caspian HU faces the highest future species reductions followed by the western Zagros and northwestern Iran. These results can be used by managers to plan conservational strategies to ease the dispersal of species, especially those that are at the greatest risk of extinction or invasion and that are in rivers fragmented by dams.

Background Leuciscinae is a subfamily belonging to the Cyprinidae fish family that is widely distributed in Circum-Mediterranean region. Many efforts have been carried out to deciphering the evolutionary history of this group. Thus, different biogeographical scenarios have tried to explain the colonization of Europe and Mediterranean area by cyprinids, such as the \"north dispersal\" or the \"Lago Mare dispersal\" models. Most recently, Pleistocene glaciations influenced the distribution of leuciscins, especially in North and Central Europe. Weighing up these biogeographical scenarios, this paper constitutes not only the first attempt at deciphering the mitochondrial and nuclear relationships of Mediterranean leuciscins but also a test of biogeographical hypotheses that could have determined the current distribution of Circum-Mediterranean leuciscins. Results A total of 4439 characters (mitochondrial + nuclear) from 321 individuals of 176 leuciscine species rendered a well-supported phylogeny, showing fourteen main lineages. Analyses of independent mitochondrial and nuclear markers supported the same main lineages, but basal relationships were not concordant. Moreover, some incongruence was found among independent mitochondrial and nuclear phylogenies. The monophyly of some poorly known genera such as Pseudophoxinus and Petroleuciscus was rejected. Representatives of both genera belong to different evolutionary lineages. Timing of cladogenetic events among the main leuciscine lineages was gained using mitochondrial and all genes data set. Conclusions Adaptations to a predatory lifestyle or miniaturization have superimposed the morphology of some species. These species have been separated into different genera, which are not supported by a phylogenetic framework. Such is the case of the genera Pseudophoxinus and Petroleuciscus , which real taxonomy is not well known. The diversification of leuciscine lineages has been determined by intense vicariant events following the paleoclimatological and hydrogeological history of Mediterranean region. We propose different colonization models of Mediterranean region during the early Oligocene. Later vicariance events promoted Leuciscinae diversification during Oligocene and Miocene periods. Our data corroborate the presence of leuciscins in North Africa before the Messinian salinity crisis. Indeed, Messinian period appears as a stage of gradually Leuciscinae diversification. The rise of humidity at the beginning of the Pliocene promoted the colonization and posterior isolation of newly established freshwater populations. Finally, Pleistocene glaciations determined the current European distribution of some leuciscine species.