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Schizophrenia is a debilitating condition necessitating more efficacious therapies. Previous studies suggested that schizophrenia development is associated with aberrant synaptic pruning by glial cells. We pursued an interdisciplinary approach to understand whether therapeutic reduction in glial cell-specifically astrocytic-phagocytosis might benefit neuropsychiatric patients. We discovered that beta-2 adrenergic receptor (ADRB2) agonists reduced phagocytosis using a high-throughput, phenotypic screen of over 3200 compounds in primary human fetal astrocytes. We used protein interaction pathways analysis to associate ADRB2, to schizophrenia and endocytosis. We demonstrated that patients with a pediatric exposure to salmeterol, an ADRB2 agonist, had reduced in-patient psychiatry visits using a novel observational study in the electronic health record. We used a mouse model of inflammatory neurodegenerative disease and measured changes in proteins associated with endocytosis and vesicle-mediated transport after ADRB2 agonism. These results provide substantial rationale for clinical consideration of ADRB2 agonists as possible therapies for patients with schizophrenia.

Abstract This study aimed to develop a highly efficient nanocomposite composed of magnetic chitosan/molybdenum disulfide (CS/MoS2/Fe3O4) for the removal of three polycyclic aromatic hydrocarbons (PAHs)—pyrene, anthracene, and phenanthrene. Novelty was introduced through the innovative synthesis procedure and the utilization of magnetic properties for enhanced adsorption capabilities. Additionally, the greenness of chitosan as a sorbent component was emphasized, highlighting its biodegradability and low environmental impact compared to traditional sorbents. Factors influencing PAH adsorption, such as nanocomposite dosage, initial PAH concentration, pH, and contact time, were systematically investigated and optimized. The results revealed that optimal removal efficiencies were attained at an initial PAH concentration of 150 mg/L, a sorbent dose of 0.045 g, pH 6.0, and a contact time of 150 min. The pseudo-second-order kinetic model exhibited superior fitting to the experimental data, indicating an equilibrium time of approximately 150 min. Moreover, the equilibrium adsorption process followed the Freundlich isotherm model, with kf and n values exceeding 7.91 mg/g and 1.20, respectively. Remarkably, the maximum absorption capacities for phenanthrene, anthracene, and pyrene on the sorbent were determined as 217 mg/g, 204 mg/g, and 222 mg/g, respectively. These findings underscore the significant potential of the CS/MoS2/Fe3O4 nanocomposite for efficiently removing PAHs from milk and other dairy products, thereby contributing to improved food safety and public health.

Microfluidic devices have gained popularity in recent years due to their low cost and simplicity. Cell focusing linked with cell counting and cell separation used in clinical tests and point-of-care devices stands towards the top of every list of microfluidic applications. To focus and manipulate particles in a Newtonian medium, inertial microfluidics has been widely used. There is a requirement to replace a significantly more efficient approach given various particle-focusing positions near the channel walls recorded by inertial microfluidics. Here, the synergetic effect of elastic and inertial forces in relatively low-concentration viscoelastic fluids was investigated numerically. Utilizing particle-focusing behavior in straight and serpentine channels with different corner angles, by applying the numerical results obtained using the developed direct numerical simulation method (DNS) for elastic and inertial forces, we created a microchannel with a remarkable performance. As the flow regime transitions to elasto-inertial, where inertial forces are comparable to elastic forces in a straight microchannel, particles tend to concentrate at the channel centerline. As the flow rate increases, however, particle defocusing develops, and shear gradient lift forces ultimately overcome elastic forces in the channel's central region. Given that fluid flows at a higher velocity in the serpentine channel with a corner angle of 75° than in other channels, the Dean drag force induced by secondary flow serves to reduce particle defocusing rate at a given flow rate, making this channel a desirable platform for Elasto-inertial microfluidics. Particle focusing in a vertical direction was noticed as a benefit of elasto-inertial microfluidics, regardless of the geometry or flow rate of the particles passing through the channel. Finally, the impact of particle diameter on focusing behavior was studied. As particle diameter decreases, focusing width decreases and the focusing band shifts to the left side of the cross-section. We expect that the numerical evaluation of using viscoelastic medium in complex geometries can contribute to accelerate the three dimensional particle-focusing mechanism and enhance the efficiency of microdevices for cell and particle manipulation.

Background and Objectives: Candida parapsilosis is the second most common species causing infectious diseases and can lead to biofilm resistance. This study aims to adjust and synthesize a liposomal compound of Nigella sativa and evaluate its antifungal properties against C. parapsilosis isolates. Materials and Methods: The liposomal formulation of N. sativa was optimized through the utilization of transmission electron microscopy (TEM), particle size analysis, zeta potential measurement, and UV-visible spectrophotometry. Furthermore, an MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay was conducted on peripheral blood mononuclear cells (PBMCs). The antifungal efficacy was evaluated in accordance with the M27-A3 guideline. Results: The minimum inhibitory concentrations (MICs) of N. sativa oil and the liposomal formulation on C. parapsilosis isolates ranged from 128 to 8 µg/mL and from 250 to 31.25 µg/mL, respectively. The MIC50 and MIC90 values of N. sativa oil and the liposomal formulation were 125, 187, and 32, 96 µg/mL, respectively. The viability percentage of cells treated with the liposomal formulation and free N. sativa oil was 91% and 85%, respectively. Conclusion: The cytotoxicity of free N. sativa was significantly reduced when using nanoliposomes. The liposomal form of N. sativa showed greater antifungal properties compared to the free N. sativa extract against C. parapsilosis isolates.

There is a broad consensus that magma eruptibility—the ability of magma stored in the subsurface to erupt onto Earth's surface—is strongly controlled by viscosity. Related to this, a critical parameter that controls viscosity is crystallinity. However, there is uncertainty in the crystallinities that distinguish eruptible from non-eruptible magmas, and whether highly crystalline magmas (>60 vol.%) could be erupted in some conditions. An underutilized but important source of information for understanding this relationship is the observed crystallinities in erupted volcanic materials, which by definition represent a set of eruptible magmas. Here we present a compilation of reported crystallinities for nearly 1000 volcanic samples of differing composition, tectonic setting, and eruption style, which provides valuable insight into the fundamental mechanisms which drive eruptions. Overall, the 95th percentile crystallinity value of our full dataset is 57 vol.%, and \>99 % of all non-dome samples have crystallinity ≤53 vol.%. This suggests that 50–60 vol.% crystallinity represents a fundamental limit for eruptibility for most volcanic rocks. Some dome samples are clear exceptions to this and are erupted with considerably higher crystallinities. There is also a significant correlation between crystallinity and whole rock SiO2 content as observed previously, but a shallow slope suggests whole rock and melt silica content have less impact on critical crystallinity for erupted magma than previously thought. Melt viscosity (as a function of SiO2, temperature, and H2O content) and crystallinity both play important roles on increasing effective viscosity, where melt viscosity plays a more important role at low crystal fractions, and crystallinity plays a more important role at crystallinities greater than ~40 vol.%.

The extraction of DNA from isolated cells is crucial for diagnosing and treating various diseases, including cancer, which has become a leading cause of death in recent years. Circulating tumor cells (CTCs) in the blood can be separated from other blood cells, allowing for DNA extraction and analysis for further research. Microfluidic methods are more cost-effective than conventional DNA extraction techniques and have significant potential for high-throughput mixing in lab-on-a-chip (LOC) and lab-on-a-disk (LOD) devices without damaging DNA. Chemical methods for cell lysis, where the mixing of reagents is essential, are particularly efficient on these platforms due to the generation of secondary flows. This is especially true in microchannels with a serpentine design, which enhances mixing quality—a critical parameter for cell lysis efficiency. This study presents and compares the mixing capabilities of the two microchannel platforms (LOC and LOD). Additionally, we introduce a new Omega-shaped serpentine microchannel that combines features of both straight and curved serpentine channels. The mixing quality across all geometries and both platforms has been investigated. Our findings indicate that the centrifugal platform is the most effective for mixing at low inlet velocities. Furthermore, we examine the number of stages in the Omega channel and conclude that 7 Ω stages are sufficient for achieving high mixing quality (∼100 %).

Polycyclic aromatic hydrocarbons (PAHs), toxic persistent pollutants, result in adverse impacts to human being health. Among the variety contaminant remediation approaches, nanotechnology was found promising in terms of its efficiency and exceptional size-dependent properties. Nanomaterials also possess high particular surface area, rapid dissolution characteristics, high sorption, magnetic -properties and quantum confinement. Nanoparticles (NPs) have been employed as sorbents in the assessment of PAHs, including carbon NPs, mesoporous silica NPs, metallic species, metal oxides, as well as magnetic and magnetized NPs. Magnetic nanocomposites have demonstrated high efficiency (>99 %) in removing PAHs from food products. Similarly, a magnetic chitosan/molybdenum disulfide nanocomposite exhibited excellent adsorption capacities for PAHs in milk samples. Present research was conducted on multiple academic platforms, including Google Scholar, Science Direct, Elsevier, Springer, Scopus, and PubMed from 2017 to 2024. Various combinations of keywords, such as “PAHs,” “extraction,” “removal,” and “nanomaterials,” were used in the search. The aim of this manuscript is to reviews the application of nanotechnologies for the elimination and extraction of PAHs from contaminated food products. The findings of this study offer novel insights into efficient and cost-saving approach and suggest the potential of NPs as promising agents for preconcentration and remediation of PAHs from variety food samples. Also, the obtained results will pave the way for future explorations that will lead to the achievement of maximum efficiency for the analysis and extraction of materials in more diverse matrices. Therefore, it is suggested to investigate the potential of various nanomaterials regarding various matrices in future.

Tobacco use is a major health concern worldwide, especially in low/middle-income countries. We aimed to assess the prevalence of cigarette smoking, waterpipe, and pipe use in Tehran, Iran. We used data from 8272 participants of the Tehran Cohort Study recruitment phase. Tobacco use was defined as a positive answer to using cigarettes, waterpipes, or pipes. Participants who did not report tobacco use during the interview but had a previous smoking history were categorized as former users. Age- and sex-weighted prevalence rates were calculated based on the national census data, and characteristics of current and former tobacco users were analyzed. Age- and sex-weighted prevalence of current tobacco users, cigarette smokers, waterpipe, and pipe users in Tehran was 19.8%, 14.9%, 6.1%, and 0.5%, respectively. Current tobacco use was higher in younger individuals (35-45 years: 23.4% vs. ≥ 75 years: 10.4%, P  12 years: 19.3% vs. illiterate: 9.7%, P

ABSTRACT Objectives Gingiva is one of the supporting tissues around the teeth that can be affected by various neoplastic or nonneoplastic lesions. Previous studies have examined several types of gingival lesions, but the lack of a standardized classification system has hindered meaningful comparisons. Additionally, many studies focused primarily on reactive lesions. Our study aims to contribute to the understanding of gingival lesions by investigating their prevalence across age groups, genders, sites, and by their clinical presentation. This research could lead to improved diagnostic accuracy and treatment strategies. Materials and Methods This retrospective study explores the prevalence of gingival lesions based on biopsies during a 22‐year span. The patient's demographic details, including age, gender, and lesion's clinical presentation were systematically collected. These lesions were categorized into six groups. Descriptive statistics, χ2 test of independence, and one‐way ANOVA were used for data analysis. Results Among the 7668 biopsied lesions, 684 (8.9%) lesions were located in the gingiva, with a greater occurrence in women (63.5%). Soft tissue tumors represented the most prevalent group in the gingival lesions (72.1%), and peripheral giant cell granuloma (PGCG) was the most frequent lesion (21.2%), followed by, pyogenic granuloma (19.3%), peripheral ossifying fibroma (17.8%) and focal fibrous hyperplasia (7.6%); all of which predominantly affected women, with mean ages falling in the fourth decade of life. Squamous cell carcinoma was recognized as the most common malignancy. Conclusion In this study, PGCG was found to be the most common lesion in the gingiva in Iranian population. Further analysis using a unanimous categorization is required to confirm these results.

The study investigated the use of Allium Jesdianum plant extract as a natural preservative in sausage dough at varying concentrations. After preparation, chemical and microbial tests were conducted on the samples at zero, 14, 28, and 42 days. The study found no significant changes in pH, moisture, fat, or protein content, but the control samples consistently had the highest total volatile nitrogen (TVN) levels. The peroxide test revealed a significant difference between the control and extract samples. Sensory analysis indicated a significant difference between the control and the 200 and 300 ppm extracts (P

Enterocytozoon bieneusi is one of the most prevalent microsporidia species, responsible for more than 90% of human and animal microsporidiosis. Microsporidia species, particularly E. bieneusi, are frequently reported from waterborne and foodborne outbreaks. Therefore, early detection is crucial in clinics and outbreak investigations. This study aimed to design a loop-mediated isothermal amplification (LAMP) for rapid detection of E. bieneusi. Total DNA was extracted from 30 E. bieneusi –positive samples, which had been confirmed with nested PCR. LAMP primers were designed based on the identical fragment of small subunit ribosomal RNA (SSU rRNA) gene. LAMP reactions were performed at 63 °C for 60 min. The sensitivity and specificity of the assay were analyzed and the results of amplification were compared to real-time PCR. Our results showed that the LAMP assay successfully amplified 25/30 (83.3%) samples. The specificity results indicated no false positive with other microorganisms. Furthermore, the LAMP method exhibited a sensitivity (limit of detection, LoD) as low as 34 ag/μL of total DNA. Compared to the LAMP assay, real-time PCR was able to detect all 30 nested PCR-positive samples. Our findings showed that the LAMP assay was able to detect 83.3% of E. bieneusi-positive samples. Although the current assay was not able to detect all nested PCR-positive samples, the lack of need for specific instruments, rapid processes, and high specificity makes LAMP assay a suitable tool for screening.

Introduction Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting. Materials and Methods In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods. Results and Discussion This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods. Conclusion In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.

Analyzing the relationship between gender and space elucidates the roles and needs of both men and women, particularly in urban spaces. This analysis aids in understanding how the constructive roles of women contribute to the production and reproduction of desirable social relations in urban spaces. The presence of women in public and urban spaces not only reflects but also narrates the desirable relationships within a society. The primary objective of this research is to identify the dimensions and components that influence the formation of desirable urban spaces for women using the meta-analysis method. The searches were conducted manually, focusing on the keywords "urban spaces, women." For sub-keywords, the terms "park, public space, single-gender spaces" replaced the first keyword. The study period spans from 2016 to 2022 in Persian sources and from 2016 to 2022 in English sources. SPSS software was employed to conduct meta-analysis tests, resulting in the production of forest, bubble, heterogeneity, and funnel (diffusion bias) diagrams. VOSviewer software was used to assess scientific databases for content production in this field. The results indicate that the main dimensions of the research encompass physical and functional dimensions, structural and spatial dimensions, individual and personality dimensions, and cultural and social dimensions in the design of urban spaces. The position of research in the field of urban space design for women holds significance in the global research literature.

Abstract This study investigates nanocarriers (NCs) for drug delivery targeting carotid artery atherosclerosis. This targeted drug delivery mechanism is based on ligand–receptor bindings facilitated by coating NCs with P-selectin aptamers, which exhibit high affinities for P-selectin plaque receptors. Recognizing the significant advantages of metal–organic frameworks (MOFs), such as their high drug-loading percentages, we chose them as nanocarriers for this research. Our evaluation considers critical factors: NC surface density (the number of attached nanocarriers per unit of plaque area), toxicity (percentage of NCs missing the target), and efficient drug transfer to plaque tissue. Employing molecular dynamics (MD) for drug loading calculations via van der Waals interactions and computational fluid dynamics (CFD) for toxicity, surface density, and drug transfer assessments, we achieve a comprehensive analysis. A cardiac cycle-based metric guides optimal MOF release conditions, establishing an ideal dosage of 600 NCs per cycle. MOF-801 exhibits outstanding drug delivery performance, particularly in plaque targeting. While a magnetic field enhances NC adhesion, its impact on drug transfer is limited, emphasizing the need for further optimization in magnetic targeting for NC-based therapies. This study provides crucial insights into NC drug delivery performance in carotid artery atherosclerosis, advancing the field of targeted drug delivery for atherosclerosis treatment.

Abstract Schizophrenia is a debilitating condition necessitating more efficacious therapies. Previous studies suggested that schizophrenia development is associated with aberrant synaptic pruning by glial cells. We pursued an interdisciplinary approach to understand whether therapeutic reduction in glial cell—specifically astrocytic—phagocytosis might benefit neuropsychiatric patients. We discovered that beta-2 adrenergic receptor (ADRB2) agonists reduced phagocytosis using a high-throughput, phenotypic screen of over 3200 compounds in primary human fetal astrocytes. We used protein interaction pathways analysis to associate ADRB2, to schizophrenia and endocytosis. We demonstrated that patients with a pediatric exposure to salmeterol, an ADRB2 agonist, had reduced in-patient psychiatry visits using a novel observational study in the electronic health record. We used a mouse model of inflammatory neurodegenerative disease and measured changes in proteins associated with endocytosis and vesicle-mediated transport after ADRB2 agonism. These results provide substantial rationale for clinical consideration of ADRB2 agonists as possible therapies for patients with schizophrenia.

Abstract Purpose Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This paper introduces a model for detecting cancer patients' opinions about healthcare services in the Persian language, both positive and negative. Method To achieve the objectives of this study, a combination of sentiment analysis (SA) and topic modeling approaches was employed. All pertinent comments made by cancer patients were collected from the patient feedback form of the Tehran University of Medical Science (TUMS) Cancer Institute (CI) in Iran, from March to October 2021. Conventional evaluation metrics such as accuracy, precision, recall, and F-measure were utilized to assess the performance of the proposed model. Result The experimental findings revealed that the proposed SA model achieved accuracies of 89.3%, 92.6%, and 90.8% in detecting patients' sentiments towards general services, healthcare services, and life expectancy, respectively. Based on the topic modeling results, the topic "Metastasis" exhibited lower sentiment scores compared to other topics. Additionally, cancer patients expressed dissatisfaction with the current appointment booking service, while topics such as "Good experience," "Affable staff", and "Chemotherapy" garnered higher sentiment scores. Conclusion The combined use of SA and topic modeling offers valuable insights into healthcare services. Policymakers can utilize the knowledge obtained from these topics and associated sentiments to enhance patient satisfaction with cancer institution services.

Droplet-based microfluidic systems have been employed to manipulate discrete fluid volumes with immiscible phases. Creating the fluid droplets at microscale has led to a paradigm shift in mixing, sorting, encapsulation, sensing, and designing high throughput devices for biomedical applications. Droplet microfluidics has opened many opportunities in microparticle synthesis, molecular detection, diagnostics, drug delivery, and cell biology. In the present review, we first introduce standard methods for droplet generation (i.e. passive and active methods) and discuss the latest examples of emulsification and particle synthesis approaches enabled by microfluidic platforms. Then, the applications of droplet-based microfluidics in different biomedical applications are detailed. Finally, a general overview of the latest trends along with the perspectives and future potentials in the field are provided.

IntroductionSoil and water salinity has significantly reduced crop yields and threatened human food security worldwide. Understanding the molecular basis of how crop plants receive and respond to salinity and identifying the main components of their stress tolerance are essential for developing tolerant plants through genetic manipulation. Today, functional genomics methods such as transcriptomics, proteomics, and metabolomics have revolutionised the study of plant stress response and facilitated the identification of important pathways and components governing stress tolerance. For example, the production and analysis of EST sequences, RNA microarray, and RNA-sequencing technology (RNA-seq) have made it possible to study and identify gene transcripts in genome-wide stress response networks. Accordingly, this study aimed to analyse the transcript of forage turnip (Brassica rapa L.) under salinity stress, determine the functional orientation of the genome, and identify the important components and genes involved in salinity tolerance.Materials and methodsTwo Expressed Sequenced Tags (ESTs) libraries of forage turnip under non-stress and salinity stress conditions were analysed using bioinformatic and statistical methods. Non-stress and salinity stress libraries had 8408 and 6894 sequences, respectively. After sequences trimming, the genome functional orientation of forage turnip in response to salinity stress was determined based on Fisher's exact test using the DAVID tool. Differentially expressed genes (DEGs) were assigned using the Audio and Claverie statistical test (AC test) implemented in the IDEG6 tool, at a significance level of 5%. Based on Fisher's exact test, a hierarchical gene network among DEGs was predicted. Then, gene network topology analysis was performed using the NetworkAnalyzer plugin in Cytoscape 4.3. One gene was identified as an important gene (Hub gene) based on gene network topology analysis. In a greenhouse experiment using a tolerant genotype and a susceptible genotype of forage turnip, the expression profile of the identified important gene along with some traits related to the plant antioxidant system, including anthocyanin content, antioxidant enzymes activity, and malondialdehyde content, were evaluated in response to different salinity time points. Finally, the relationship among the changes in gene expression, evaluated traits, and salinity tolerance was determined using Correspondence Analysis (CA).Results and discussionPre-processing of 8408 EST sequences of the non-stress library resulted in 8,403 high-quality sequences. Also, out of 6894 EST sequences from the salinity stress library, 6894 high-quality sequences were obtained. BlastX against Arabidopsis proteins showed that 8075 (96.1%) of the non-stress EST sequences had at least one specific hit. 6597 (95.7%) of the EST sequences of the salinity stress library also had at least one specific hit in BlastX. Based on Fisher's exact test, 15 functional groups were significantly (FDR≤0.01) more active in salinity stress conditions than in non-stress conditions. These results clearly showed that under salinity conditions, the genome functional activity of forage turnip was oriented towards response to stresses, especially oxidative stresses, response to internal inductions such as plant hormones, control of metabolic activities, and photosynthetic reactions. Audic and Claverie’s statistical test showed 344 genes were differentially expressed in response to salinity stress; among them, 242 genes were upregulated, and 102 genes were downregulated. The predicted gene network indicated a complex relationship among DEGs, regulatory molecules (especially melatonin and plant hormones), and downstream responsive pathways. Among identified DEGs, the gene encoding transcription factor ZAT6 was assigned as an important gene in the salinity response gene network. The expression profile of the ZAT6 gene, quantity of anthocyanin, the activity of antioxidant enzymes and content of malondialdehyde were significantly different between the two studied genotypes. ZAT6 was significantly more expressed in the stress-tolerant genotype than the stress-susceptible genotype at all time points. In addition, the antioxidant system of the tolerant genotype was more potent than the susceptible genotype. Also, results revealed a significant relationship between the expression profile of ZAT6 and evaluated traits in the context of salinity tolerance. Based on the results, changes in ZAT6 gene expression are directly or indirectly involved in regulating forage turnip plant responses to salinity stress, especially through the control of evaluated traits.ConclusionTranscriptome study clarified some of the molecular bases of the forage turnip response to salinity stress. Accordingly, it seems that the gene encoding the ZAT6 transcription factor plays an important role(s) in the salinity stress tolerance of forage turnip. There was a significant relationship between high expression levels of this gene and enhanced antioxidant activities, which could confirm the hypothesis. However, further studies are needed to assign detailed functions of ZAT6, particularly the association of this gene with the regulatory pathway of melatonin as a major regulatory molecule in plants. This can be an effective starting point for further studies.AcknowledgmentsWe would like to thank the support of the Higher Education Center of Eghlid for this research project.

The COVID-19 pandemic has posed significant challenges to public transportation systems worldwide, resulting in a substantial decrease in demand. In light of this, it is crucial to develop policies that mitigate potential risks and prioritize the health and safety of passengers. While the scientific and collective understanding of the interplay between these two spheres is steadily growing, the absence of a comprehensive review focusing on sound policymaking is deemed significant. This present research aims to quantitatively synthesize and consolidate existing studies in the field to determine the effectiveness of various policies by examining overlapping sources and combining findings. The meta-analysis method was employed to fulfill the research objective, with both Persian and English scientific research articles constituting the statistical population. The search process involved manual selection and utilized relevant keywords such as "public transportation" and "COVID-19." Ultimately, a total of 50 scientific research articles (in Persian and English) were included as the final statistical population. For the execution of the meta-analysis, data were inputted into the Stata software, enabling the creation of a forest diagram (also known as an accumulation diagram). Furthermore, analytical connections between the research articles were established using the VOSviewer‎ software. The findings of the synthesis reveal a profound research gap pertaining to the understanding of resource policies in this domain.