Cities Of Iran Research Articles

Association between TMPRSS2 rs2070788 polymorphism and COVID-19 severity: a case-control study in multiple cities of Iran.

Host genetic variations have been identified as potential influencers of COVID-19 infection. This study aimed to examine the association between transmembrane serine protease type 2 (TMPRSS2) rs2070788 single nucleotide polymorphism (SNP) and the prognosis of COVID-19 in Iranian populations. This case-control study was performed on 756 COVID-19 patients and 59 healthy individuals across Iran. Clinical data, blood samples, and the presence of the TMPRSS2 rs2070788: G>A SNP were determined using T-ARMS-PCR. Additionally, serum levels of tumor necrosis factor α (TNF-α), C-reactive protein (CRP), interleukin-6 (IL-6), and IL-1β were evaluated in the collected blood samples. No significant association was found between the genotypes and allele frequencies of TMPRSS2 rs2070788 SNP and susceptibility to or mortality from COVID-19 infection. However, we observed a substantial increase in IL-6 and CRP levels associated with the severity of COVID-19, while no such trend was observed for IL-1β and TNF-α. This study showed a considerable rise in TNF-α and IL-1β serum levels exclusively in COVID-19 patients with TT rs2070788 TMPRSS2 SNP genotype compared to healthy controls. In this study conducted across multiple cities in Iran, no significant association was found between the TMPRSS2 rs2070788 SNP genotypes and COVID-19 severity or mortality.

Preferences of Iranian smokers regarding smart smoking cessation technologies: a parallel convergent mixed methods study

BackgroundConsidering the values and preferences of individuals who attempt to quit smoking is a crucial step in the development of smoking cessation technologies. This study aimed to explore preferences regarding smart smoking cessation technologies.MethodsThis parallel convergent mixed-methods study was conducted in two phases: quantitative and qualitative. In the quantitative phase, a cross-sectional study was conducted with 360 participants selected through stratified random sampling from technology-based smoking cessation clinics in Tabriz, Tehran, and Karaj cities in Iran. Data on demographic characteristics and preferences for smart smoking cessation technologies were collected using questionnaires and analyzed using descriptive statistics. In the qualitative phase, 25 users of these technologies were selected through purposeful and snowball sampling. The data were gathered through in-depth semistructured interviews and analyzed using qualitative content analysis with a conventional approach. Quantitative and qualitative data were integrated using the merging strategy and convergence model.ResultsThe quantitative phase results indicated that the highest preference was related to wearing and using a smartwatch for smoking cessation and using mobile apps. In the qualitative phase, 17 subcategories were extracted and classified into 8 main categories: high effectiveness, better management of the smoking cessation process, personalized technology, safe and uncomplicated technologies, attractiveness and innovative design, scientific basis, mobile applications, and smart monitoring devices.ConclusionBy combining and integrating quantitative and qualitative results, it can be concluded that users are more interested in wearable technologies and interactive mobile applications. The findings of this study can assist smoking cessation technology developers in designing and improving their tools based on user needs and preferences to enhance their effectiveness and acceptability.

Sleep quality and associated factors among people who inject drugs in Iran: a nationwide survey using respondent-driven sampling

BackgroundPoor sleep quality is a significant issue among people who inject drugs (PWID). This study aimed to evaluate sleep quality and associated factors among PWID in Iran.MethodsUsing respondent-driven sampling, 2,652 PWID (2,563 male) were recruited in 11 major cities in Iran between 2019 and 2020. The Pittsburgh Sleep Quality Index was utilized to measure sleep quality, and logistic regression was used to assess associations in RDSAnalyst, a software designed for respondent-driven sampling.ResultsThe overall prevalence of poor sleep quality was 68.4% (68.3% among males and 70.2% among females). Married PWID had higher odds of poor sleep quality (Adjusted Odds Ratio (AOR): 1.41; 95% CI: 1.05, 1.91). Lack of access to sufficient food in the past 12 months was also associated with poor sleep quality (AOR: 1.73; 95% CI: 1.17, 2.57 for sometimes having no access, and AOR: 2.95; 95% CI: 1.93, 4.52 for always having no access compared to always having access). Additionally, good self-rated health was significantly associated with lower odds of poor sleep quality (AOR: 0.19; 95% CI: 0.11, 0.31).ConclusionPoor sleep quality is prevalent among PWID in Iran. It is recommended to mitigate the adverse effects of this issue and enhance the overall quality of life for PWID. Supportive interventions aimed at preventing and treating poor sleep quality, as well as improving overall health outcomes, are essential.

Mitigating development barriers and addressing disparities in border cities of Iran: a comprehensive analysis of border provinces and influential factors

Mitigating development barriers and addressing disparities in border cities of Iran: a comprehensive analysis of border provinces and influential factors

Determination of the kidney stone composition using infrared spectroscopy in Iran at a national referral center during 2019–2023

ObjectiveThe national lifetime prevalence of urolithiasis is estimated at 6.6% in Iran. However, reports on the composition of kidney stones have been based on imprecise methods like chemical analysis. No prior large-scale study has reported the composition of kidney stones based on the gold-standard methods (X-ray diffraction or infrared spectroscopy) in Iran. This study aimed to provide the composition of kidney stones based on Fourier transform infrared. MethodsThis is a cross-sectional study assessing urinary stone composition from various cities in Iran at a referral center using infrared spectroscopy from February 2019 to March 2023. ResultsThis study determined the stone composition of 1092 samples from 10 cities or provinces in Iran. Overall, the majority of stones were composed of calcium (n=522; 47.8%), uric acid (UA, n=488; 44.7%) followed by cystine (n=49; 4.5%), and struvite (n=28; 2.6%). Stone composition in Shiraz and Isfahan was roughly similar with a higher percentage of UA stones (53.4% and 53.6%, respectively) while the capital city of Iran (Tehran) had less frequent UA stones (39.9%) with a higher percentage of calcium oxalate stones. The percentage of UA stones increased with age as it was 11.1% in children, 42.7% in adults, and 83.3% in geriatric patients (p<0.001). About 29.6% of cystine stones were observed in children. ConclusionThe most frequent stone composition among kidney stones in Iran were calcium oxalate and UA stones. This relative frequency of UA stones is considerably higher than many international reports from neighboring as well as distant countries. More cystine stones were observed in children and women. Geriatric patients’ stones were mostly composed of UA.

An Update on Eradication of Helicobacter Pylori in Iran: A Review.

Helicobacter pylori, the most prevalent infection in the world, has great importance due to being related to peptic ulcer disease, gastric metaplasia, dysplasia, and even gastric adenocarcinoma or mucosa-associated lymphoid tissue (MALT) lymphoma. The standard H. pylori eradication regimen is based on antibiotic susceptibility testing. If susceptibility testing is not available, a standard treatment regimen will be recommended based on records of H. pylori resistance rates to antibiotics in a region or locally proven highly effective regimens (equal to or higher than 90% eradication rate). The aim of this review was to define suitable recommendations for local treatment in different cities of Iran. This review article consists of randomized controlled trials related to H. pylori eradication in Iran. Data including the kind of therapy, number of patients and per-protocol H. pylori eradication rates were recorded in data gathering forms. Data search was conducted in PubMed and Google Scholar databases from 2018 to December 2023. According to our review of Iranian articles regarding first-line H. pylori eradication regimens, these treatment protocols could be recommended: Bismuth-clarithromycin quadruple therapy in Ardabil, bismuth-clarithromycin quadruple therapy with probiotics in Birjand, standard triple therapy in Ilam, bismuth quadruple therapy or bismuth triple therapy or concomitant regimen in Sari, sequential therapy in Tehran and bismuth quadruple therapy in Yazd. These regimes can be extended to other regions that have a similar situation. According to the reports of Iranian researchers, a quinolone-containing regimen (levofloxacin preferred) is recommended for second-line eradication therapy. Various H. pylori eradication regimens can be used as first-line therapy; however, choices for second-line therapy are limited. We recommend the quinolone-containing regimens as the preferred second-line therapy.

Applying protection motivation theory to examine tourist’s pro-environmental behaviour: case study of Lahijan, Iran

Purpose This study aims to examine the pro-environmental behavior of tourists in one of the northern cities of Iran, Lahijan, by applying the Protection Motivation Theory (PMT). Design/methodology/approach The sample consists of a total of 180 tourists who had traveled to Lahijan city in Guilan province. Data is analyzed using descriptive analysis and structural equation modeling (SEM) technique using Smart PLS 3.0. Findings The results confirm the importance of PMT in explaining the pro-environmental behavior of tourists. The findings of this study show the significant relationship between vulnerability, severity, self-efficacy, response efficacy, response cost and maladaptive perception with tourist’s pro-environmental behavior. Moreover, rewards are not related to pro-environmental behavior. Practical implications This study provides a better understanding of how to motivate tourists toward pro-environmental behavior. It also contributes to reinforcing PMT by adding evidence from tourism research and delivers profiles for tourism providers. Originality/value The study contributes to the understanding of the predictors of pro-environmental behavior of tourists in Iran using a full PMT which encompasses three concepts of threats appraisal, coping appraisal and maladaptive perception.

Psychometric Evaluation of Quality of Life Questionnaire in Breast Cancer Women in Iran

Background: Given the importance of quality of life, especially in women with breast cancer, it is essential to have a valid scale to measure their quality of life. Objectives: Therefore, we conducted a study to examine the psychometric properties of the Persian version of the quality of life questionnaire for women with breast cancer within the cultural context of Iranian society. Methods: This methodological study was conducted on 404 women with breast cancer, aged 20 to 60 years, from September 2021 to April 2022 in three cities in Iran. After translating the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (version 3) into Farsi and obtaining experts' opinions, the content, construct, convergent, and discriminant validity, as well as the reliability of the scale, were assessed using exploratory and confirmatory factor analysis. The construct validity of the scale was investigated through exploratory factor analysis. Results: Through exploratory factor analysis of this scale, a version with 24 questions was confirmed, identifying five dimensions: Psychological symptoms, daily activities, physical problems, physical activity, and social support. Four items were removed due to factor loadings less than 0.3 and the presence of commonalities. In this study, Cronbach's alpha, McDonald's omega, CR, MaxR, and convergent validity coefficients for each factor were greater than 0.7. Conclusions: This study demonstrated that the Persian version of the quality of life questionnaire is a valid and reliable scale within the cultural context of Iranian society, with potential applications in clinical environments.

Enhancing spatial modeling and risk mapping of six air pollutants using synthetic data integration with convolutional neural networks

Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model’s impressive precision when generating the pollution risk map.

Wind energy integrated green hydrogen system as sustainable solution to decarbonize Iranian Industrial Cities

Industrial cities are metropolitans where economies are based on industrial development. However, in fossil rich countries, environmental challenges are significant. Green hydrogen, as a solution, can be used in energy-intensive processes and chemical feedstocks, contributing to a more sustainable and low-carbon industrial sector. In this regard, four industrial cities in Iran with different climatic conditions were selected, and a novel grid connected wind-electrolyzer-reformer system was investigated to meet the electricity/hydrogen demand. Initially, the windfarm layout, including 20 wind turbines with a capacity of 850 kW, was optimized using genetic algorithm to minimize the wake impacts. The Jensen model was employed in this assessment, and calculations were performed using the Python codes Foxe and Pyomo. Subsequently, a two-objective genetic algorithm (minimizing costs and natural gas consumption) was utilized to optimize the size of the electrolyzer, storage tank, and reformer. The results showed the wind farm generate electricity about 17.8–36.4 GWh/yr. The natural gas consumption varied 189,766–253,568 m3/yr, with the highest(lowest) consumption in Isfahan(Tabriz), accounting for about one-fourth of the natural gas consumed in the reforming process. The served hydrogen cost (mixed of grey and green hydrogen) in Tabriz was lower than grey hydrogen (1.74$/kg) at approximately 0.9 $/kg and is cost-effectiveness.

Source Identification of Urban dust Heavy Metals Using in Situ and Satellite Data in Three Mega Cities of Iran.

The aim of this study was an integrative assessment of heavy metals associated with urban dust data in Iran (Ahvaz, Isfahan, and Shiraz). Samples of urban dust from four sites (traffic, industrial, residential, and Greenland) were collected, and ten heavy metal concentrations were determined using ICP_MS in each sample. The highest average concentrations of metals were at the traffic site for the Mn, Zn, and Cr metals. The PMF model indicates a higher percentage of Pb participation, which shows the importance of traffic resources. The highest non-carcinogenic risk (HI) was for the Cr and the carcinogenic risk was tolerable. To evaluate aerosol and its effects on urban dust, Aerosol Optical Depth (AOD) data were used during 2003-2023. According to the Mankendall test, the trend of AOD has been increasing in Esfahan (p_value < 0.05) and Shiraz. Although Ahvaz's AOD is about two times greater than other cities, the aerosol trend in Ahvaz is decreasing.

Evaluation of the cooling, heating and water demands for tomato production in a solar greenhouse by integrating the solar energy, evapotranspiration and TOMGRO models

The design, operation and maintenance of solar greenhouses in optimum condition can improve the water and energy consumption and agricultural production. This study has been integrated the energy, humidity and growth models to determine the cooling, heating and water requirements for producing tomato in the even span, uneven span and arc roof type greenhouse structures with north–south and east–west orientations. In the proposed integrated framework, firstly, the incident and transmitted diffuse, direct and ground solar radiations for any side of solar greenhouse, along with different evapotranspiration models and TOMGRO model for tomato growth and production have been assessed comprehensively. Then, by using the set point day and night temperatures and relative humidity the phenomena of the energy and water transfer such as convection, longwave radiation, infiltration, and cover condensation as well as TOMGRO state variables have been calculated. By considering the temperature, humidity, structure and orientation as decision variables, the east–west uneven span structure with day/night temperatures of 20/16 °C and relative humidity of 80 % is recommended as the best case in Pakdasht city of Iran. This solar greenhouse requires 0.92 MW/m2 and 3.83 MW/m2 heating and cooling energy, respectively, to produce 46.68 kg/m2 mature tomatoes during the whole cultivation period. This research has provided a tool to select the most appropriate structure and orientation of a solar greenhouse in any location based on the trade-off between energy and water consumption and tomato production.

Geometrical optimization of solar venetian blinds in residential buildings to improve the economic costs of the building and the visual comfort of the residents using the NSGA-II algorithm

The entering sunlight from the building's windows mainly affects the heating and visual comfort of the occupants. The applications of Venetian blinds are a solution to improve the heating and visual comfort of the occupants. However, reducing the sunlight that enters the space can result in a rise in the building's electricity consumption. While most studies focus on the electricity production of solar panels, present study aims to examine the effect of solar venetian blinds on the indoor visual and thermal comfort of the occupants and optimize their geometry considering different geographical specifications. In the present paper, efforts are made to numerically install solar panels on Venetian blinds and analyze the effect of changing the geometrical parameters of solar Venetian blinds and the building's window dimensions on visual comfort and net electricity. Therefore, the target functions in the present paper are an improvement percentage in the daylight glare index and an improvement percentage in the net electricity costs for the analyzed building. As a result, five cities in Iran that have different climatic conditions are targeted to model the building. EnergyPlus software is employed to conduct the energy-based calculations, and the design variables and target functions are defined using JEPLUS software. The outputs are next inserted in JEPLUS+EA software to process a multi-objective optimization using the NSGA-II algorithm. The results demonstrate that the visual comfort and net electricity can be optimized by ranges of 10–100% and 1.5–10%, respectively. Furthermore, Venetian blinds are proven to have higher reception of sun radiations and better efficiency in southern cities and they can have a more proper performance while being installed for windows of southern building wall.

Multi-objective optimization of energy and thermal comfort using insulation and phase change materials in residential buildings

Recently, due to the increasing demand for energy and the identification of fossil fuels and their pollution, the focus on renewable energy is increasing day by day, so the use of renewable energy and energy-saving techniques is very important. PCMs are one of the most effective means of thermal energy storage, whose task is to change phase with increasing temperature, store thermal energy, and release this energy with decreasing temperature. This is one of the applications of these materials in building walls. In this research, the aim is to analyze and investigate the use of phase change materials in addition to thermal insulation. For this purpose, optimization in terms of energy and thermal comfort of residents has been done by the response surface method. Four Iranian cities with different climates—Tehran, Bandar Abbas, Tabriz, and Rasht—have undergone this optimization. According to the results, it can be concluded that the best temperature for the heating thermostat is approximately 20 °C and for the cooling thermostat, approximately 28 °C and 25 °C. Also, the best thermal insulation and PCMs are polyurethane and BioPCMDSCM27Q21. The thickness of the thermal insulation in the range between 6.9 cm and 9.8 cm is chosen as optimal, and the thickness of the phase change material is approximately 5 cm optimal for all cities (except Tehran). Due to the optimization of thermal comfort between 25 and 60 %, heating electricity between 43 and 99 % and cooling electricity between 38 and 52 % have been improved.

Discrimination of bottled mineral water from tap water using a Dip-Type colorimetric paper-based sensor array and chemometrics

Mineral water is a natural water that originated from an underground water table, a well, or a natural spring which is considered microbiologically intact. The revenue from the bottled mineral water industry will be USD 342.40 billion in 2023, and it is expected to grow at a compound annual growth rate (CAGR) of 5.24 %. Consequently, the discrimination of original bottled mineral water from tap water is an important issue that requires designing sensors for simple and portable identification of these two types of water. In this work, we have developed a Dip-Type colorimetric paper-based sensor array with three organic dyes (Bromothymol Blue, Bromophenol Blue, and Methyl Red) followed by chemometrics’ pattern recognition methods (PCA and LDA) for discrimination of original bottled mineral waters from tap waters based on differences in ion variety and ion quantity. Forty brands of mineral water and twenty-six Tap water samples from different regions of Shiraz and other Iranian cities were analyzed by this sensor array. Moreover, these experiments were performed in two consecutive years to check the versatility of the sensor with seasonal changes in waters. This sensor array was able to discriminate these two water types from each other with an accuracy of > 95 % based on the analysis of 85 water samples.

Nurses’ experiences of reporting the medical errors of their colleagues: a qualitative study

BackgroundMedical error is a leading cause of disability and death in healthcare settings and reporting colleagues’ medical errors is one of the main strategies for medical error management and an ethical responsibility of all healthcare providers, including nurses. Most studies into reporting colleagues’ medical errors used quantitative designs while it seems that using qualitative designs can provide better insight in this area.PurposeThis study explored nurses’ experiences of reporting the medical errors of their colleagues.MethodsThis qualitative study was conducted using the conventional content analysis approach. Participants were 22 hospital nurses purposively selected in 2021–2022 from different cities in Iran. Twenty-two in-depth semi-structured interviews were held for data collection. The data were analyzed via Graneheim and Lundman’s conventional content analysis and trustworthiness was maintained using the criteria proposed by Guba and Lincoln.FindingsThe main categories of the study were burnout and intention to leave the profession and growth and development. The two subcategories of the first category were the experience of injury and the experience of violence and the two subcategories of the second category were sense of worthiness and sense of motivation. Moral distress was the most important experience of almost all participants.ConclusionNurses mostly have negative experiences in terms of reporting their colleagues’ medical errors. Negative experiences can act as the barriers to report colleagues’ errors while positive experiences can act as its facilitators. Improvement of the patient safety culture in healthcare settings and interpersonal relationships among healthcare providers can reduce the negative experiences and promote the positive experiences of reporting colleagues’ medical errors.

Spatial distribution and health risk assessment of heavy metals in drinking water in Nikshahr and Iranshahr cities of Iran

ABSTRACT The presence of high concentrations of certain heavy metals (HMs) in drinking water poses a serious threat to the health of consumers. This work was done to investigate the concentration of heavy metals such as Arsenic (As) cadmium (Cd), chromium (Cr), iron (Fe), mercury (Hg), nickel (Ni), lead (Pb) and cobalt (Co) in the water resources of two cities of Iranshahr and Nikshahr in the south of Sistan and Baluchistan province. For this aim, 24 samples of drinking water sources were collected and the concentration of heavy metals was determined by inductively coupled plasma-mass spectrometry (ICP-MS). The results of samples of these networks showed that the average concentration of Pb (11.25 μg/L), Hg (10.33 μg/L), As (10.62 μg/L) and Cd (3.45 μg/L) far exceeds the standard of the World Health Organisation. The average concentration of heavy metals in the drinking water of Iranshahr and Nikshahr is respectively Fe>Pb>As>Hg>Ni>Cr>Co>Cd and Fe>Pb>As> Ni > Cr >Co>Cd> Hg. Potential sources of pollution in this study are attributed to infiltration of industrial effluents and agricultural runoff, which is a significant threat to groundwater quality. The calculated hazard quotients (HQ(values for non-carcinogenic risk show that this value is less than 1 in most metals, but the hazard index)HI) in children is greater than 1, and non-carcinogenic risk is evident. HI was obtained for children 5.4 and adults 2.6. However, the risk of carcinogenics in children and adults is equal to 2.66 E-05 and1.3E–6, respectively, and the increase in the risk of cancer in children is higher due to the consumption of drinking water. Therefore, it is necessary to protect water sources, water treatment and monitor more water distribution networks based on the water safety plan.

Geospatial analysis for environmental noise mapping: A land use regression approach in a metropolitan city

Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: Lden and Lnight. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of Lden and Lnight during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in Lden and Lnight, respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for Lden and 4.74 dB(A) for Lnight. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both Lden and Lnight, with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that Lden and Lnight levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe.

Cultural landscapes in climate change: A framework for resilience in developing countries

Cultural landscapes, particularly cities with rich historical and cultural heritage, play a crucial role in bolstering the resilience of local communities. The occurrence of climate change-induced phenomena jeopardizes cultural landscapes, resulting in the deterioration of historical structures, natural landscapes, cultural heritage, the economy, and the livelihoods of residents in these areas. Therefore, adopting a resilient approach is essential for the integrated management of cultural landscapes. This study develops a model for enhancing cultural landscape resilience to climate change in Nishapur, a historical and cultural city in Iran. Through desk studies, factors affecting the resilience of cultural landscapes to climate change were extracted in the context of developing countries. Subsequently, a model was developed based on the frequency of the occurrence of dimensions and indicators. Snowball sampling was used to distribute questionnaires to 310 members of the academic and professional communities in the field. Next, confirmatory factor analysis (CFA) was conducted using IBM SEM-AMOS to analyze the data and measure the reliability and validity of the model. The findings indicate that the driving factors of change, such as changes in livelihood and social issues, historical fabric and physical environment, natural hazards, biodiversity patterns, and management patterns, can significantly affect the resilience of cultural landscapes to climate change. The developed model can contribute to policymaking in various fields, including urban design and planning, economics, sociology, and cultural heritage conservation. This can play a vital role in creating cultural landscapes that are resilient to the increasing impacts of climate change.

Coronary artery calcification score as a prognostic indicator for COVID-19 mortality: evidence from a retrospective cohort study in Iran

Background: Coronary artery calcification (CAC) has been established as an independent risk factor for major adverse cardiovascular events. Nevertheless, the effect of CAC on in-hospital mortality and adverse clinical outcomes in patients with COVID-19 has yet to be determined. Objective: To investigate the association between CAC score and in-hospital mortality of COVID-19 patients Method: This retrospective cohort study was conducted across tertiary hospitals of University of Medical Sciences in Babol, a northern city in Iran, and enroled 551 confirmed COVID-19 patients with definitive clinical outcomes of death or discharge between March and October 2021. Demographic and clinical data, along with chest computed tomography (CT) findings and CAC score on admission, were systematically collected. The study utilized logistic regression analysis and Kaplan-Meier plots to explore the association between CAC score and in-hospital death and adverse clinical outcomes. Results: The mean age was 60.05±12.8. A significant difference regarding CAC score, age, history of hypertension, hyperlipidemia, cardiovascular diseases, and respiratory diseases among survivors and non-survivors was observed; however, gender was not found to be different. Furthermore, in multivariate analysis, CAC score greater than or equal to 400 [odds ratio (OR): 4.2, 95% CI: 1.70–10.33, P value: 0.002], hospitalization time (OR: 1.31, 95% CI: 1.13–1.53, P value &lt; 0.001), length of ICU stay (OR: 2.02, 95% CI: 1.47–2.77, P value &lt; 0.001), severe or critical COVID-19 severity in time of admission (95% CI: 1.79–18.29, P value: 0.003), and history of respiratory diseases (95% CI: 2.18–40, P value: 0.003) were found to be associated with higher odds of in-hospital mortality. Log-rank test also revealed a significant difference regarding the time of admission to death between patients with CAC score greater than or equal to 400 and those with CAC score less than 400 (P value &lt; 0.001). Conclusion: Elevated CAC score is a crucial risk factor linked to in-hospital mortality and unfavourable clinical results in confirmed COVID-19 patients. This finding emphasizes the need for careful monitoring of individuals with high CAC scores.