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Background: While home is frequently expressed as the favorite place of death (PoD) among terminally ill cancer patients, various factors affect the fulfillment of this wish. The determinants of the PoD of cancer patients in countries without healthcare system-integrated palliative and supportive care have not been studied before. This study aimed at identifying the predictors of the PoD of patients who suffer from advanced cancer by developing a reliable predictive model among who received home-based palliative care in Iran as a representative of the countries with isolated provision of palliative care services. Methods: In a cross-sectional study, electronic records of 4083 advanced cancer patients enrolled in the Iranian Cancer Control Center (MACSA) palliative homecare program, who died between February 2018 and February 2020 were retrieved. Multivariable binary logistic regression analysis as well as subgroup analyses (location, sex, marital status, and tumor topography) was performed to identify the predictors of PoD. Results: Of the 2398 cases included (mean age (SD) = 64.17 (14.45) year, 1269 (%52.9) male), 1216 (50.7%) patients died at home. Older age, presence and intensity of medical homecare in the last two weeks and registration in the Tehran site of the program were associated with dying at home (P < 0.05). Gynecological or hematological cancers, presence and intensity of the calls received from the remote palliative care unit in the last two weeks were predictors of death at the hospital (p < 0.05). The model was internally and externally validated (AUC = 0.723 (95% CI = 0.702–0.745; P < 0.001) and AUC = 0.697 (95% CI = 0.631–0.763; P < 0.001) respectively). Conclusion: Our model highlights the demographic, illness-related and environmental determinants of the PoD in communities with patchy provision of palliative care. It also urges policymakers and service providers to identify and take the local determinant of the place of death into account to match the goals of palliative and supportive services with the patient preferences. [ABSTRACT FROM AUTHOR]

Background: The Lipid Accumulation Product (LAP) is a measure that indicates excessive fat accumulation in the body. LAP has been the focus of research in epidemiological studies aimed at forecasting chronic and metabolic diseases. This study aimed to evaluate the association between LAP and type 2 diabetes mellitus (T2DM) among adults in western Iran. Methods: The study involved 9,065 adults who participated in the initial phase of the Ravansar non-communicable diseases study (RaNCD) cohort. To investigate the association between LAP and T2DM, multiple logistic regressions were employed. Additionally, the receiver operating characteristic (ROC) curve was used to evaluate LAP's predictive ability concerning T2DM. Results: The participants had an average age of 47.24 ± 8.27 years, comprising 49.30% men and 50.70% women. The mean LAP was 53.10 ± 36.60 for the healthy group and 75.51 ± 51.34 for the diabetic group (P < 0.001). The multiple regression analysis revealed that the odds of T2DM in the second quartile of LAP were 1.69 (95% CI: 1.25, 2.29) times greater than in the first quartile. Furthermore, the odds in the third and fourth quartiles were 2.67 (95% CI: 2.01, 3.55) and 3.73 (95% CI: 2.83, 4.92) times higher, respectively. The ROC analysis for predicting T2DM showed that the LAP index had an area under the curve (AUC) of 0.66 (95% CI: 0.64, 0.68). Conclusion: A strong association was identified between elevated LAP levels and T2DM in the adult population of western Iran. LAP is recommended as a potential tool for screening diabetes susceptibility. [ABSTRACT FROM AUTHOR]

The probabilistic prediction of monthly precipitation events, specifically more‐than‐normal (MN), normal (N), and less‐than‐normal (LN) events, holds significant importance in the field of water resource management. In this paper, we employ the Bayesian Joint Probability (BJP) modeling approach to generate monthly probabilistic forecasts for LN, N, and MN events in Northwest Iran. To model the joint distribution of observed precipitation and model precipitation, we propose the utilization of a log‐sinh transformed multivariate normal distribution. The prior distribution for the parameters of this multivariate normal distribution is established as the normal‐inverse‐Wishart, which is known to be the conjugate of the multivariate normal distribution. Bayesian inference of model parameters and associated uncertainties is conducted using STAN Markov Chain Monte Carlo (MCMC) sampling. The methodology is applied to the outputs of four North American Multi‐Model Ensemble (NMME) models, namely CanCM4i, CCSM4, NEMO, and NASA, spanning the period from 1982 to 2017 for a 4‐month forecast horizon. The accuracy of predictions is assessed through various statistical measures and graphical methods using a 12‐fold cross‐validation strategy. The results demonstrate that (a) BJP probabilistic predictions of precipitation consistently outperform raw predictions across all models, months, lead times. (b) The BJP‐NASA model exhibits superior discrimination and reliability compared to other models. (c) May and November show the least amount of discrimination. This observation may be attributed to the fact that these months serve as transitional periods in Iran. (d) Fall forecasts are the most reliable, while summer forecasts are the least reliable. Key Points: Bayesian joint probability (BJP) probabilistic predictions of precipitation have more Ranked Probability Skill Score (RPSS) than raw predictions for all models, months, and lead times, and generally outperform the raw predictionsThe BJP‐NASA models have better discrimination and higher reliability compared to othersSpring and fall have lower discrimination compared to summer and winter. On the other hand, summer is less reliable, and the fall is the most [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0–30 and 30–60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority. [ABSTRACT FROM AUTHOR]

Objective: During the past few decades, many scoring systems have been developed to evaluate the severity of injury and predict the outcome in trauma patients. This study aimed to assess the capacity of three common trauma scoring systems: injury severity score (ISS), Glasgow coma scale (GCS), and revised trauma score (RTS) in predicting in-hospital mortality and ICU admission in patients with traumatic injury. Methods: This is a multicenter study of the hospital-based national trauma registry of Iran (NTRI), an ongoing registry-based trauma database. This study included trauma cases from 12 major trauma centers throughout the country admitted between July 2016 and November 2023. The inclusion criteria were all patients admitted to the emergency department due to trauma, hospitalized for at least 24 hours, deceased within the first 24 hours of admission, and patients transferred from the intensive care unit (ICU)s of other hospitals. Results: A total of 50,458 traumatic patients, with 38,740 (76.9%) being male, were included in this study. After adjustment for confounders, head, face, and neck injuries were associated with the highest odds of death (OR: 7.51, P-value<0.001), whereas abdominal injuries were associated with the highest odds of ICU admission (OR: 4.58, P-value<0.001). Each Unit increase in RTS score was accompanied by a 61% decrease in odds of death (OR: 0.39, P-value<0.001). The area under the ROC curve for predicting in-hospital mortality was 0.81 (0.79 to 0.82) in ISS, 0.78 (0.77 to 0.80) in GCS, and 0.75 (0.73 to 0.76) in RTS. There was a significant difference between RTS and GCS, aswell as RTS and ISS for in-hospital mortality prediction (P-values< 0.001). The area under the ROC curve for the prediction of ICU admission was 0.75 (0.74 to 0.75) in ISS, 0.63 (0.62 to 0.63) in GCS, and 0.62 (0.61 to 0.63) in RTS. There was a statistically significant difference between ISS and GCS, as well as ISS and RTS, for ICU admission prediction (P-value<0.001). Conclusion: ISS is the best predictor of in-hospital mortality and ICU admission, compared to GCS and RTS. [ABSTRACT FROM AUTHOR]

Background & Objective: Anemia, thrombocytosis, and leukocytosis, are commonly found in a variety of malignancies. The prognostic role of preoperative leukocytosis, anemia, and thrombocytosis on stages and grades of endometrial cancer (EC) in women undergoing surgery was the subject of this study. Materials & Methods: In this cross-sectional study, 105 women suffering from EC at Babol Ayatollah Rouhani Hospital from 2011 to 2018 were considered. Anemia, thrombocytosis, and leukocytosis were defined as prognostic factors. Then, the patients were placed at stages I, II, III, and IV, and ECs were graded as well, moderately and poorly differentiated or undifferentiated. SPSS 22 software, Chi-square, analysis of variance (ANOVA), and receiver operating characteristic (ROC) were used for statistical analysis (p < 0.05). Results: Higher rates of leukocytosis (54.3%), anemia (47.4%), and thrombocytosis (66.7%) were detected in tumor grades III, II, and III, respectively. A higher rate of leucocytosis (51.4%), anemia (60.5%), and thrombocytosis (51.9%) were reported in tumor stages III, I, and III, respectively. The relationship between tumor grade and tumor stage with leucocytosis and thrombocytosis was statistically significant (p <0.001). The white blood cell (WBC) sensitivity (70%) and specificity (70%), Hb sensitivity (37%) and specificity (39%), and platelets sensitivity (73%) and specificity (74%) were determined. There was a significant relationship between levels below the 3-variable curve and tumor stage with WBC (p <0.001) and platelet (p <0.001). Conclusion: Preoperative leukocytosis and thrombocytosis are related to the severity, stages, and grades of EC. Both of them have a prognostic role in women suffering from EC. [ABSTRACT FROM AUTHOR]

Background & Objective: We aimed to compare the correlation between nucleated red blood cell (NRBC) and creatine kinase (CK) levels with hypoxic-ischemic encephalopathy (HIE) in asphyxiated newborns. Materials & Methods: This case-control study included 30 asphyxiated (case) and 30 healthy neonates (control) born at Al-Zahra Hospital in Rasht, Iran. We collected the cord blood samples for CK level and NRBC at birth. The clinical stage of Sarnat indicated the severity of HIE. Data were analyzed in the two groups using IBM SPSS Statistics for Windows, version 22. Results: The case group had a higher NRBC count (P=0.001). The CK level was significantly different between the two groups and was higher in the case group than in the control group (P= 0.002). Results demonstrated a significant association between NRBC count and the occurrence of HIE in neonates with asphyxia (P = 0.021). Besides, a positive correlation was found between HIE and CK levels in the case group (r = 0.7, P=0.001). Conclusion: According to our results, NRBC count and umbilical cord CK level measurement are valuable predictors of asphyxia and HIE in neonates. In addition, measuring these parameters may help clinicians for faster diagnosis and better management. [ABSTRACT FROM AUTHOR]

According to various human needs, such as mineral supply, transportation, and underground storage, tunnel excavation has become commonplace today. However, one of the undesirable phenomena encountered during tunnel excavating, particularly when employing drilling and blasting techniques, is the overbreak phenomenon (OBP). This phenomenon not only compromises the safety of the work environment but also leads to increased operating costs. Therefore, accurately predicting the occurrence of OBP in tunnel excavation is of utmost importance. Due to the numerous uncertainties and complexities associated with rock formations, traditional methods have limited accuracy in predicting OBP. Consequently, this research used the rock engineering system (RES) method to comprehensively understand the physical and mechanical characteristics of the rock mass at any given point. By employing the RES method, accurate modeling of the OBP can be achieved in a time- and cost-efficient manner without requiring extensive and complicated coding. To construct the model using the RES method, 217 data points from three case studies were utilized: the Tarzeh underground coal mine in Iran, the Azad tunnel on the Tehran-North road in Alborz, Iran, and an Indian mine. The parameters considered in this study that affect the OBP (%) included perimeter hole powder factor (PPF, kg/m3), spacing-to-burden ratio of contour holes (S/B, kg), tunnel section area (A, m2), specific drilling (SD, m/m3), and rock mass rating (RMR, %). To compare and evaluate the RES method, various regression methods were also employed. Statistical criteria revealed that the RES method (RMSE = 0.0046, MSE = 0.068, R2 = 0.945) demonstrated high accuracy compared to other regression methods. Consequently, it can be concluded that the RES is a highly valuable and essential tool for rock engineers. [ABSTRACT FROM AUTHOR]

Background: Drug addiction significantly impacts employee performance, leading to increased absenteeism, reduced productivity, and higher rates of occupational accidents. Therefore, identifying predictive factors for addiction potential is crucial in preventing drug dependence. Objectives: This study aimed to develop a group membership model using discriminant analysis to predict employees' potential for addiction based on key psychological variables. Methods: A correlational study was conducted with a statistical population consisting of all employees working in public departments in Bojnord City, Iran, in 2021 (N = 2,837). A random sample of 303 employees was selected to complete the Iranian Form of the Addiction Potential Scale, the Big Five Inventory, the Cognitive Emotional Regulation Questionnaire, and the Generalized Self-Efficacy Scale. Data analysis was performed using the discriminant analysis method in SPSS software version 23. Results: The findings revealed that employees' addiction potential can be predicted using personality and cognitive variables. The discriminant analysis equation effectively distinguishes employees with high and low addiction potential based on psychological variables, including extroversion, neuroticism, self-efficacy, and cognitive emotional regulation. Conclusions: The study concluded that self-efficacy, cognitive emotional regulation, neuroticism, and extroversion are significant predictors of addiction potential. It is recommended to incorporate these variables into training courses and personnel selection processes for government jobs. [ABSTRACT FROM AUTHOR]

In this study, the effect of median guardrails in no-passing zone sections of a two-lane two-way highway was investigated. In this regard, the Firuzkuh-Qaemshahr highway in Mazandaran province of Iran which has 5.5 km median guardrails was selected. All types of crashes studied in two periods, before-and after-the implementation of median guardrails, were considered, and using crash predictive models for the two-way two-lane highway, the effect of implementation was explored. To evaluate the economic effects of this treatment, various crash cost factors were converted to monetary equivalents to compare the cost of crashes before-and after-applying the treatment. This study revealed that using median guardrails caused the monthly average of fatal and injury crashes to decrease by 84% and 29%, respectively. Moreover, the proportion of fatal and injury crashes to total crashes had a 55% drop, indicating that the severity of crashes was reduced, leading to fewer deaths or injuries. Also, this treatment had an impressive reduction in the average monthly cost of crashes, so that the largest reduction among all cost factors belonged to the cost of fatalities in traffic crashes with an 85% reduction, followed by the cost of lost time due to the traffic jams caused by fatal crashes with an 84% decrease. Furthermore, the economic analysis of the implementation of median guardrails revealed that this treatment reduced the average monthly traffic crash cost by 65%. Therefore, the median guardrails in no-passing zone sections of the two-way two-lane Firuzkuh-Qaemshahr highway had a remarkable economic benefit. [ABSTRACT FROM AUTHOR]

Introduction: Cardiovascular disease (CVD) is one of the most important health problems and the leading cause of mortality worldwide. This study aimed to estimate the risk of CVD using the World Health Organization/International Society of Hypertension (WHO/ISH) risk prediction charts. Methods: The demographic characteristics of all participants of this study aged 40-70 years who did not have a prior coronary event were collected. The 10-year CVD risk was estimated using the laboratory version of the WHO/ISH risk score charts. The risk scores for 11678 participants of the Azar cohort population were calculated. Participants were classified as low risk, moderate risk, or high risk. Results: According to the WHO/ISH charts, only 0.1 % of the population was classified as high-risk (> 40%), and 96.8% had a 10-year CVD risk of < 10%. Also, participants with overweight (P = 0.002), obesity, and abdominal obesity had higher CVD risk(P < 0.001). Conclusion: There was a low burden of 10-year CVD risk among the Azar cohort population without prior coronary events. It appears the percentage of people in the high-risk group is underestimated in the WHO/ISH risk prediction charts, leading to delays in receiving appropriate management in the population concerned. Therefore, using other charts alongside the WHO/ISH risk prediction charts is advisable. [ABSTRACT FROM AUTHOR]

The prediction of drought is critically important for early warning and mitigation of its impacts. Selecting the most appropriate prediction model provides opportunities for reducing the drought's adverse effects on different sectors. So, in this study, the capability of several prediction models to predict drought (based on the Reconnaissance Drought Index (RDI) in 1 and 3-month time scales) was compared. The models included stationary time series models (ST), cyclostationary time series (CST) models, autoregressive fractionally integrated moving average (ARFIMA), periodic autoregressive fractionally integrated moving average (PARFIMA), first-order Markov chain (FOMC), and second-order Markov chain (SOMC). For choosing the best-fitted model, the correlation coefficient (R2) and absolute values of T-Statistics (AVTS) between predicted (using each model) and observed RDI in 1 and 3-month time scales at 15 stations during the period 2017–2021 in Iran were used. For this purpose, the 1967 to 2016 data series was used. Then the best prediction model (with the highest performance level) was used to predict 1 and 3-month RDI in the investigated stations from 2022 to 2031. For this purpose, 1967 to 2021 data series was used. The results showed that CST models with the highest R2 values (significantly at the 5% level in both time scales in all stations) and the lowest AVTS values (significantly at the 1% level in both time scales in all stations) for the best-fitted models in 1 and 3-month time scales had the best performance in predicting monthly and seasonal RDI. To predict monthly and seasonal RDI in all stations, the PARIMA (24, 0, 0), 12 and PARIMA (20, 0, 0), 4 models were used as the best models, respectively. The predictions indicated that normal (No) and moderately (Mod) dry classes would be more frequent in both time scales. This study demonstrates that CST models can be useful tools for drought prediction and management. [ABSTRACT FROM AUTHOR]

Iran is a developing country with low levels of economic development and globalization and is ruled by a theocratic government. To address the lack of national research on well-being in Iran, this retrospective observational study aims to examine life satisfaction and its main determinants among Iranian adults. Using World Gallup Poll data collected between 2006 and 2017, we examined life satisfaction as a cognitive aspect of subjective well-being in relation to various factors. Our results show that income is the strongest predictor of life satisfaction, followed by standard of living, gender, social support, age, negative affect, and education. In developing countries such as Iran, which face significant economic, political, and social challenges, individuals prioritize the satisfaction of basic needs by emphasizing factors such as the socioeconomic status. In contrast, developed countries with established welfare systems may emphasize other values such as social connections and healthy lifestyle behaviors as key factors in life satisfaction. This study contributes to a deeper understanding of the determinants of life satisfaction in Iran and provides insights for future research and policy making. [ABSTRACT FROM AUTHOR]

Objectives: Various risk assessment tools have been developed to evaluate the risk of hereditary breast/ovarian cancer. This study aims to estimate the risk of breast/ovarian cancer in a sample of the general population in Iran using the IBIS software. Methods: A total of 2020 women aged 18-79 years were consecutively selected from the gynecology clinic of Imam Hossein Medical Center between April 2019 and 2021. The Tyrer-Cusick (IBIS) questionnaire was used to collect data on variables such as age, BMI, age at menarche, age at first pregnancy, menopausal status, history of hormone replacement therapy (HRT), and family history of breast/ovarian cancer. The risks of developing breast/ovarian cancer were calculated, with patients having a calculated risk of more than 20% considered high-risk and those below 20% considered low-risk. Results: Out of the 2020 cases studied, 69 (3.4%) were classified as high-risk for developing breast/ovarian cancer. The moderate risk of breast/ovarian cancer in patients with a positive family history was 18.47%, compared to 10.15% in those without a family history. All high-risk individuals had a positive family history, while only 9.4% of the low-risk population had a family history; this difference was statistically significant (p<0.001). Conclusion: Routine assessment of family history of cancer in all patients is recommended, with positive cases being further evaluated using a cancer risk assessment tool and referred for genetic counseling as appropriate. [ABSTRACT FROM AUTHOR]

Background: While there has been extensive research on colorectal cancer (CRC) incidence and its associated factors in Iran, a significant gap exists in studies predicting its future trends. Our study aimed to thoroughly report CRC incidence across Iran from 2014 to 2017, by sex, age, and geographical regions, and provide a projection for 2025. Methods: This retrospective study utilized data from the Iranian National Population-based Cancer Registry (INPCR). Patients with the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) codes C18 to C21 were included. The age-standardized incidence rate (ASR), was calculated per 100 000 individuals annually, and crude incidence rates were retrieved for various demographic groups and years. Results: Between 2014 and 2017, a total of 43 580 new CRC cases (55.96% males) were registered. Men exhibited an ASR of 134.45, while women's ASR was 94.85. The highest ASRs were observed in Tehran, Qom, and Ilam (18.99, 18.26, and 18.06, respectively). Incidence rates surpassed 20 after age 50 for both genders, reaching their peak within the 80-84 age group. Adenocarcinoma was the most frequent histological type of CRC in nearly all provinces. Case numbers and ASRs are projected to continuously rise until 2025, with a predominance of male cases. Conclusion: The anticipated increase in CRC incidence in Iran emphasizes the need for additional studies to better identify risk factors. Furthermore, implementing screening programs is recommended for individuals at a higher risk of CRC, including men, the elderly population, and those residing in regions with a notable prevalence of CRC. [ABSTRACT FROM AUTHOR]

The field of aftershock prediction has evolved over the years, transitioning from traditional statistical models to more advanced machine learning and deep learning methodologies. The USGS relies on the Reasenberg and Jones (1989, 1994) statistical model which subsequent researchers have refined to adapt to various tectonic regimes globally. Meanwhile, DeVries et al. (2018) explored the potential of Deep Learning in predicting aftershock locations, albeit facing critique for potentially over-complicating the issue. Other researchers have applied machine learning algorithms, such as in predicting aftershock patterns following the Kermanshah Earthquake in Iran, demonstrating the capability of machine learning to outperform traditional Coulomb maps. The dynamic discourse among these varying methodologies highlights the ongoing efforts to improve aftershock prediction, aiming at better preparedness and response strategies in seismic-prone regions. [ABSTRACT FROM AUTHOR]

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios. [ABSTRACT FROM AUTHOR]

Purpose: A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran. Design/methodology/approach: In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted. Findings: The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed. Originality/value: The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient's condition becomes more dangerous. [ABSTRACT FROM AUTHOR]

Background: The ability to predict the survival of patients with non-small cell lung cancer (NSCLC) can provide a basis for individualized treatment and follow-up. Determination of positron emission tomography/computed tomography (PET/CT) parameters, i.e., maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), may improve predictions of survival for NSCLC patients. Objectives: To determine the relationship between the PET/CT-derived parameters SUVmax, SUVmean, MTV, and TLG and survival in NSCLC patients. Patients and Methods: Patients with NSCLC diagnosed at our clinic between January 2019 and October 2020 were evaluated retrospectively using data obtained from the electronic database. The study population consisted of 132 patients over 18 years of age who had a PET/CT scan before receiving any treatment for NSCLC. During their initial PET/CT evaluation, SUVmax, SUVmean, MTV, and TLG were calculated. Correlations between the variables were analyzed using Spearman's correlation test, and the associations of the variables with patient survival were determined using the Cox proportional hazards regression model. Results: The overall 2-year survival rate of the patients was 36.6%. In the univariate analysis, MTV and TLG, but not SUVmax or SUVmean, were significantly associated with survival (P = 0.8, P = 0.003, and P = 0.045, respectively). In the multivariate analysis, MTV was related to a higher risk of death in patients with adenocarcinoma, lymph node involvement, or distant metastasis. By contrast, TLG was associated with a lower risk of death in patients with adenocarcinoma (ACA) or distant metastasis. Conclusion: Among the parameters obtained in PET/CT studies, SUVmax and SUVmean were not related to the survival of patients with NSCLC. However, MTV was associated with a higher risk of death, while TLG was associated with a lower risk of death in patients with adenocarcinoma or distant metastasis. Further studies with larger samples are needed to validate these results. [ABSTRACT FROM AUTHOR]

This study examined the efficiency of the health belief model in understanding preventive behaviors of pregnant women in Iran. A cross-sectional descriptive-analytical methodology study was conducted of pregnant women who were referred to a healthcare center in 2021. The data were the responses to a questionnaire designed for this study. The data were analyzed with SPSS 24 software, Amos 22 software, correlation tests, regression analysis, and independent t tests. In terms of the health belief model, pregnant women with high perceived susceptibility showed the highest vaccination rate, while those with high perceived barriers had the lowest. The model predicted 20% variance in the preventive behavior from COVID-19, with perceived susceptibility and cues to action being the strongest and weakest predictors of behavior, respectively. The conclusion of the study was that the health belief model was an appropriate model to guide the care of pregnant women. [ABSTRACT FROM AUTHOR]

In the present study, four global climate models MRI-ESM2-0, IPSL-CM6ALR, CanESM5, and GFDL-ESM4 from the set of CMIP6 models are assessed to select the best model and determine the effects of climate change on temperature and precipitation parameters under three shared socioeconomic pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the base period (1988–2017) and a future period (2020–2049) in the Samalghan basin. Statistical measures such as mean absolute error, root mean square error, mean bias error are applied to test the models, and the correlation coefficient is used to compare the results of the historical period of the models with the observational data of the selected stations. Taking the obtained results into account, the global climatic model IPSLCM6A-LR is chosen to study the trend of temperature and precipitation changes in the future period under scenarios. The results of this study indicate an increasing trend of the average annual precipitation in the desied period compared to the base period for the SSP1- 2.6 and SSP2-4.5 scenarios at all stations. Also, it increases in the SSP5-8.5 scenario for all stations except Besh Ghardash, Hesegah and Darkesh stations. The predictions of temperature show an increase in the minimum and maximum temperature values under all scenarios compared to the base period. [ABSTRACT FROM AUTHOR]

Aim: The present study aimed to evaluate the predictive role of the Health Belief Model (HBM) constructs and health literacy (HL) in shaping the coronavirus disease 2019 (COVID-19) preventive health behaviors (PHBs) among adolescents. Methods: This cross-sectional study was conducted with 503 adolescent girls and boys, randomly selected via cluster sampling. For this purpose, the data were collected online through four research tools, including the demographic-clinical characteristics information questionnaire, the COVID-19 PHB Scale based on HBM, and the Health Literacy Scale for COVID-19. The data analysis was then performed by regression analysis along with the structural equation modeling (SEM), considering the significance level of 0.05. Results: The regression analysis results demonstrated that following the one-unit increase in the values of self-efficacy and cues to action, the COVID-19 PHBs elevated by 0.063 and 0.078 units, respectively. In addition, the COVID-19 PHBs subsided by 0.018 with the rise in the value of perceived barriers (P < 0.001). According to the path analysis, the direct path from the COVID-19-related HL to the COVID-19 PHBs (B = 0.097, β = 0.087, 95% confidence interval [CI] = 0.005 to 0.189) was significant. Furthermore, the indirect path from the COVID-19-related HL to the COVID-19 PHBs through perceived susceptibility (B = 0.017, β = 0.015, 95% CI = 0.001 to 0.032), perceived barriers (B = 0.029, β = 0.026, 95% CI = 0.004 to 0.055), self-efficacy (B = 0.094, β = 0.084, 95% CI = 0.031 to 0.156), and cues to action (B = 0.153, β = 0.137, 95% CI = 0.092 to 0.215) was significant. Conclusion: In keeping with the study results, it is essential to take some effective measures to boost the HBM constructs and HL among adolescents to improve their PHBs during pandemics such as COVID-19. [ABSTRACT FROM AUTHOR]

Purpose: A variety of prediction equations have been able to estimate 24-h urinary sodium excretion from spot urine samples; however, Iranians over the age of 50 have not been compared and verified. Using spot urine samples as a substitute for 24-h urine samples to estimate 24-h urine sodium excretion among the population age 50 and older are the purpose of this study. Methods: A 24-h urinary sodium excretion was studied by well-known Kawasaki, INTERSALT, Tanaka, and World Health Organization/Pan American Health Organization (WHO/PAHO) formulas. On 360 individuals, the mean bias, agreements between estimated and measured values, correlation, absolute and relative differences, and misclassification rates were evaluated for four equations. Results: As a result, the mean urinary sodium excretion for a 24-h period was 136.3 ± 52.21 mmol/24-h, which corresponds to a calculated intake of 9.1 ± 3.8 g of salt per day. According to the WHO/PAHO formula, the mean bias between measured values and estimated 24-h urinary sodium excretion is − 21.6 mg/day (95% confidence interval (CI) − 144.8, 101.6 mg/day), which is the smallest difference compared with the other three formulas. The lowest rate of individual misclassification of salt intake was 40% for WHO/PAHO, especially for those who consumed less than 9 g/day, while Kawasaki had the lowest misclassification rate at higher levels of salt intake. Conclusion: As a result of our research, the WHO/PAHO equations accurately predict 24-h urinary sodium excretion among Iranians aged ≥ 50 more than other equations, both at the population level and at the individual level. However, further study is needed in regard to different ages in Iran. [ABSTRACT FROM AUTHOR]

We aimed to assess the effects of pretreatment neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios on the response to neoadjuvant chemotherapy and survival rates in patients with extremity osteosarcoma. Patients with high-grade osteosarcoma admitted to oncologic centers affiliated with Iran University of Medical Sciences, Tehran, Iran from 2015 to 2021 were evaluated retrospectively to assess the impact of complete blood count-related parameters on the pathologic response after neoadjuvant chemotherapy. Then, patients were followed up prospectively to evaluate the survival rates. All patients received at least three cycles of cisplatin/doxorubicin regimen, preoperatively. In this study, the cut-off values for high neutrophil-to-lymphocyte and high platelet-to-lymphocyte ratio were considered 3.28 and 128, respectively. One hundred eighty-six patients were enrolled. Patients with high neutrophil-to-lymphocyte ratio and high platelet-to-lymphocyte ratio had a significantly lower overall survival rates (20.7 [95% CI 18–23.5] month vs. 34.6 [95% CI 33.2–36], p = 0.003 and 21.9 [95% CI 20.2–23.6] month versus 35.3 [95% CI 33.9–36.7], p = 0.002; respectively). Moreover, disease-free survival of patients with high platelet-to-lymphocyte ratio was worse than patients with low platelet-to-lymphocyte ratio (20.4 [95% CI 18.4–22.4] month vs. 32.7 [95% CI 30.8–34.7], p = 0.02). Our study showed that neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios at the baseline can predict the survival of patients with high-grade osteosarcoma. [ABSTRACT FROM AUTHOR]

BACKGROUND: Assessment of maximal oxygen consumption is important in both general community and occupational settings. Validity and reliability tests are needed to indicate the functionality of the cardiopulmonary system. OBJECTIVE: This study aimed to establish a maximal oxygen consumption (VO2max) prediction model using anthropometric and demographic variables for young adults in Iran. METHODS: This cross-sectional study was conducted on 64 healthy young men aged 19–29 years. Oxygen consumption was measured directly and the prediction models to estimate VO2max were determined by multiple linear regression. The accuracy of the prediction models was considered using regression coefficient (R), coefficient of determination (R2), and standard error of estimate (SEE). RESULTS: The average VO2max was 44.02±4.31 ml/kg/min. Significant correlations were found between the measured VO2max and the anthropometric and demographic variables (r = 0.16–0.86, P < 0.05). Three significant regression models with acceptable accuracy were developed (R2 = 0.67–0.71, SEE = 3.19–3.21). CONCLUSION: The predictive models consisted of 3–5 variables as significant predictors of VO2max and had acceptable accuracy for Iranian young adults. The proposed models are a simple and valid tool that can be used to estimate the VO2max in the field and in laboratory settings. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is a leading cause of cancer-related deaths, emphasizing the importance of timely diagnosis for effective treatment. Machine learning models have shown promise in assisting with GC diagnosis. Objectives: This study aimed at comparing the performance of various feature selection methods in identifying influential factors related to GC based on lifestyle using machine learning models. The ultimate goal was to enhance early detection and treatment of the disease. Methods: The data of patients from Shahid Ayatollah Modarres Hospital and Shohadaye Tajrish Hospital between 2013 and 2021 were utilized. Three feature selection methods (filter, wrapper, and filter-wrapper) were employed. The k-fold method validated each model. Four classifiers k Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT) compared their outputs based on feature selection methods. Results: The filter-wrapper method outperformed others, achieving an area under the ROC curve and Fl score of 95.8% and 94.7%, respectively. GBDT also performed well. The wrapper and RF classifiers achieved an area under the ROC curve and Fl scores of 95.7% and 93.6%, respectively, after the filter-wrapper method. Without feature selection methods, the RF classifier had an area under the ROC curve and F1 scores of 95.6% and 91.7%, respectively, surpassing other classifiers. Conclusions: This study suggests that appropriate feature selection methods for identifying influential factors related to GC based on lifestyle can facilitate early diagnosis and treatment. The filter-wrapper method demonstrated the best performance in this regard. [ABSTRACT FROM AUTHOR]

Background. It is well known that laboratory markers could help in identifying risk factors of severe illness and predicting outcomes of diseases. Here, we performed a retrospective modeling study of severity and mortality predictors of hematological and biochemical laboratory parameters in Iranian COVID-19 patients. Methods. Data were obtained retrospectively from medical records of 564 confirmed Iranian COVID-19 cases. According to the disease severity, the patients were categorized into two groups (severe or nonsevere), and based on the outcome of the disease, patients were divided into two groups (recovered or deceased). Demographic and laboratory data were compared between groups, and statistical analyses were performed to define predictors of disease severity and mortality in the patients. Results. The study identified a panel of hematological and biochemical markers associated with the severe outcome of COVID-19 and constructed different predictive models for severity and mortality. The disease severity and mortality rate were significantly higher in elderly inpatients, whereas gender was not a determining factor of the clinical outcome. Age-adjusted white blood cells (WBC), platelet cells (PLT), neutrophil-to-lymphocyte ratio (NLR), red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), erythrocyte sedimentation rate (ESR), mean corpuscular hemoglobin (MCHC), blood urea nitrogen (BUN), and creatinine (Cr) also showed high accuracy in predicting severe cases at the time of hospitalization, and logistic regression analysis suggested grouped hematological parameters (age, WBC, NLR, PLT, HGB, and international normalized ratio (INR)) and biochemical markers (age, BUN, and lactate dehydrogenase (LDH)) as the best models of combined laboratory predictors for severity and mortality. Conclusion. The findings suggest that a panel of several routine laboratory parameters recorded on admission could be helpful for clinicians to predict and evaluate the risk of disease severity and mortality in COVID-19 patients. [ABSTRACT FROM AUTHOR]

Purpose. ITP is the most prevalent autoimmune blood disorder. The lack of predictive biomarkers for therapeutic response is a major challenge for physicians caring of chronic ITP patients. This study is aimed at identifying predictive biomarkers for drug therapy responses. Methods. 2D gel electrophoresis (2-DE) was performed to find differentially expressed proteins. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS) analysis was performed to identify protein spots. The Cytoscape software was employed to visualize and analyze the protein-protein interaction (PPI) network. Then, enzyme-linked immunosorbent assays (ELISA) were used to confirm the results of the proteins detected in the blood. The DAVID online software was used to explore the Gene Ontology and pathways involved in the disease. Results. Three proteins, including APOA1, GC, and TF, were identified as hub-bottlenecks and confirmed by ELISA. Enrichment analysis results showed the importance of several biological processes and pathway, such as the PPAR signaling pathway, complement and coagulation cascades, platelet activation, vitamin digestion and absorption, fat digestion and absorption, cell adhesion molecule binding, and receptor binding. Conclusion and Clinical Relevance. Our results indicate that plasma proteins (APOA1, GC, and TF) can be suitable biomarkers for the prognosis of the response to drug therapy in ITP patients. [ABSTRACT FROM AUTHOR]

Background. Polypharmacy can be an area of concern for the older population. A set of internal and external factors determine the degree of adherence to treatment in older adults. Objectives. We aimed to investigate whether medication belief and social support, taking into account the role of socio-demographic and clinical factors, are predictors of medication adherence among illiterate, low-literate community-dwelling older adults with polypharmacy. Material and methods. A cross-sectional study was conducted in the health centres of Tabriz-Iran in 2022. The data was collected using the socio-demographic and clinical questionnaires, Morisky, Green and Levine’s adherence scale, Belief about Medicines Questionnaire (BMQ) and the Multidimensional Scale of Perceived Social Support (MSPSS). Hierarchical Multiple Linear Regression analysis was used to identify medication adherence predictors based on a conceptual framework. Results. The final sample size was 318 people. The results showed that age, education years, medication satisfaction, side effects of medications, doctor checkups, medication belief and social support were significant predictors of medication adherence. The necessity part of medication belief had a negative significant relationship, and the concern part had a positive significant relationship with medication adherence. Conclusions. A strong belief along with sufficient social support could be a good predictor of medication adherence. The results showed that elementary education has a positive relationship with people’s medication adherence, even among low-literacy populations. The development of literacy movement programmes within communities to promote primary education among illiterate older adults is recommended. Our findings also highlight the importance of improving patient-physician communication skills and clear communication in the formation of patients’ behaviour. [ABSTRACT FROM AUTHOR]

Background: Maternal health literacy (MHL) and health information-seeking behavior (HISB) could impact maternal health and pregnancy outcomes. Purpose and Research Design: This cross-sectional study investigated MHL, HISBs, and pregnancy outcomes in Iranian pregnant women. This study was also aimed to determine the predictors of MHL and pregnancy outcomes. Study smple and Data Collection : In this study, 337 randomly selected postpartum women completed three questionnaires, including a demographics form, an obstetrics history checklist, and the Maternal Health Literacy and Pregnancy Outcome Questionnaire (MHLAPQ). Results: The mean MHL and pregnancy outcome scores were 60.80 ± 9.91 and 49.88 ± 5.69, respectively. Of the participants, 56% adopted both formal and informal HISB. Education and internet HISB was associated with the odds of being in the group with higher MHL. Participants with high MHL had more than twice higher odds for better pregnancy outcomes (OR = 2.25, p < 0.004). Conclusions: Continued efforts are required to make health information sources more accessible to pregnant women to improve their health literacy and pregnancy outcomes. [ABSTRACT FROM AUTHOR]

Background: Epithelial ovarian cancer is one of the leading causes of death from gynecological cancers in the Western world. One of the important objectives of medical research is to determine predictors of an event. Regarding the interaction of risk factors, regression methods are unsuitable when the number of factors is high. Objectives: Regarding frequency predictors of recurrence-free survival in epithelial ovarian cancer, our aim in this article is to determine predictors and time to first recurrence using a classification and regression tree model. Methods: This retrospective analysis used medical and chemotherapy records of 141 patients with epithelial ovarian cancer between 2007 and 2018. They were referred to Imam Hossein Hospital in Tehran. Data were analyzed using classification and regression trees in Rver3.4.3. Results: The regression tree results showed that the worst recurrence-free survival in metastatic patients was in grade II patients (15.03 ± 11 months), but in patients without metastases were in patients with CA125 tumor marker above 207 that used 3-week chemotherapy courses (14.53 ± 6.4 months). The classification tree also showed that the most probability of the first recurrence in metastatic patients was in patients with adjuvant chemotherapy (0.81), and patients without metastases were among those with stages 2, 3, and 4 with the maximum platelet count above 305,000 and less than 35 years old (0.75). Conclusions: The classification and regression tree models, without any assumptions, can estimate the probability of recurrence in different subgroups. These models can be used in deciding due to the ease of interpretation by physicians and paramedic [ABSTRACT FROM AUTHOR]

Illegal parking during peak hours and on heavily traveled streets can significantly impact the severity of traffic congestion and related problems. A double parking violation is among the most significant instances of illegal parking. Double parking violations reduce road capacity and increase traffic congestion. Intensified traffic congestion also increases travel time and reduces road user satisfaction. Thus, the management of double parking violations is critical to managing traffic congestion in urban streets. In this study, the factors influencing the drivers' tendency to perpetrate a double parking violation were identified using the ordinal logistic regression model in the central area of Mashhad. Then, by redesigning the ordinal logistic regression model and utilizing the identified effective factors, a model was developed to predict the level of drivers' propensity to perpetrate a double parking violation during urban trips. Mashhad is one of the largest cities in Iran, and its central district experiences a high volume of daily traffic. The results indicated that parking duration is the main factor affecting drivers' propensity to perpetrate a double parking violation. Driver education level and driver presence in the vehicle ranked second and third, respectively. The P-value in the goodness-of-fit test was obtained at less than 0.05, indicating the designed model's acceptable accuracy. • The greater the necessity of parking, the greater the likelihood that the driver will commit a double parking violation. • Reducing the educational level of drivers to B.Sc. or less increase the likelihood that they will perpetrate double parking. • If the parking duration is less than 25 min, the driver's visual search distance for parking will be significantly reduced. [ABSTRACT FROM AUTHOR]

Land use / land cover (LULC) has changed dramatically in recent years, especially in areas that have experienced severe climate change and population growth. Evaluation of LULC changes is the most accurate method to understand past land uses and predict their future changes. Analysis of multi-temporal optic and radar remotely sensed images is an effective tool for monitoring LULC changes, and fusion of these datasets has provided significant developments in the detection and modeling of land surface changes due to their unique features individually. Time-series image (TSI) analysis provides a history of changes in an area and allows for predicting future changes. This study aims to develop a method for modeling LULC changes and predicting future trends. To this end, a spatiotemporal regression model is proposed based on TSI. In this method, land surface changes are first modeled by considering the spatiotemporal behavioral patterns of cover changes in a given period. Then, future land covers are predicted by a spatiotemporal regression. To evaluate the proposed method, LULC changes of the Maharloo Lake (Fars province, Iran) were modeled using fused optical and radar TSI (12 consecutive image sets) and then predicted for time t = 13. Compared to ground truth data, the different land covers including; water bodies, vegetation cover, bare land, and saline area were predicted by accuracies of 84.9, 98.4, 90.7, and 97.3%, respectively, indicating the remarkable efficiency of the model proposed for predicting LULC. [ABSTRACT FROM AUTHOR]

Background & Objective: Psoriasis is one of the most common skin disorders in humans and is believed to have genetic foundations. The aim of this study is to identify potential genetic biomarkers for psoriasis using penalized methods. Materials & Methods: The gene chip GSE55201, which included 74 individuals (34 patients with psoriasis and 30 healthy individuals), was obtained from GEO. Three penalized approaches were used in logistic regression, including Least Absolute Shrinkage Selection Operator, Minimax Concave Penalty, and Smoothing Clipped Absolute Deviation, to identify the most important genes associated with psoriasis. To validate the results, Random Forest was used to assess the predictive power of the selected genes in a validation dataset. Results: The analysis identified ADORA3 and C16orf72 as two genes that were commonly associated with psoriasis. The independent samples t-test revealed significantly higher expression of ADORA3 and C16orf72 among psoriasis cases (p<0.001). The area under the ROC curve for predicting psoriasis was 0.88 (95% CI: 0.80-0.96) for ADORA3 and 0.75 (95% CI: 0.75-0.94) for C16orf72. The Random Forest analysis showed that the model using these genes had a prediction probability of 0.68 (95% CI: 0.53-0.83). Conclusion: Among all the methods used, MCP outperformed other penalties, selecting a smaller subset with compatible performance. Two key genes, ADORA3 and C16orf72, were found to be associated with psoriasis and were identified for further study. These genes may serve as genetic biomarkers for predicting psoriasis. [ABSTRACT FROM AUTHOR]

Background: Diagnosing patient deterioration and preventing unexpected deaths in the emergency department is a complex task that relies on the expertise and comprehensive understanding of emergency physicians concerning extensive clinical data. Objectives: Our study aimed to predict emergency department mortality and compare different models. Methods: During a one-month period, demographic information and records were collected from 1,000 patients admitted to the emergency department of a selected hospital in Tehran. We rigorously followed The Cross Industry Standard Process for data mining and methodically progressed through its sequential steps. We employed Cat Boost and Random Forest models for prediction purposes. To prevent overfitting, Random Forest feature selection was employed. Expert judgment was utilized to eliminate features with an importance score below 0.0095. To achieve a more thorough and dependable assessment, weimplemented a K-fold cross-validation method with a value of 5. Results: The Cat Boost model outperformed Random Forest significantly, showcasing an impressive mean accuracy of 0.94 (standard deviation: 0.03). Ejection fraction, urea (body waste materials), and diabetes had the greatest impact on prediction. Conclusions: This study sheds light on the exceptional accuracy and efficiency of machine learning in predicting emergency department mortality, surpassing the performance of traditional models. Implementing such models can result in significant improvements in early diagnosis and intervention. This, in turn, allows for optimal resource allocation in the emergency department, preventing the excessive consumption of resources and ultimately saving lives while enhancing patient outcomes. [ABSTRACT FROM AUTHOR]

It is necessary to select appropriate rainfall series as input to the hydrologic model to access more accurate hydrologic predictions and estimate reliable parameters in the modeling process. For achieving this aim, in the present study, the rainfall multipliers with a combination of Discrete Wavelet Transform (DWT) and principal component analysis (PCA) are applied to select effective rainfall series for modeling. DREAM(ZS) algorithm based on the Markov chain Monte Carlo (MCMC) scheme is used to estimate posterior parameters and investigate prediction uncertainties of a five-parameter hydrologic model, HYMOD. The model's results are then compared to those obtained from the other methods that use only the rainfall multipliers or the raw rainfall data. This study reveals the advantages of using a combined application of DWT and PCA methods to estimate hydrological prediction uncertainty and model parameters accurately. Considering the occasional flash flood incident that occurred in the study region (Tamar basin, which is situated in the Gorganroud river basin, Golestan province, Iran), the results of this research can be useful for forecasting floods accurately and planning for flood control management. [ABSTRACT FROM AUTHOR]

Using visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) spectra can provide a quick, undisturbed, and inexpensive method for predicting deferent soil variables. This study aims to evaluate the ability of hyperspectral information in VIS-NIR-SWIR range to predict heavy metals concentrations including As, Pb, Sb, and Cr in Hormuz Island soils, with a total area of about 42 km2, south of Iran. Consequently, 57 samples were taken from the topsoil. The total As, Pb, Sb, and Cr concentrations were measured by ICP-OES apparatus. Soil sample's spectral reflectance was quantified by an ASD field spectroradiometer. PLSR separately and coupled with Bootstrap aggregation (Bagging-PLSR) methods were employed to predict total concentrations of As, Pb, Sb, and Cr. The findings demonstrated that hyperspectral data in VIS, NIR, and SWIR could reasonably estimate the As, Pb, Sb, and Cr concentrations. The findings revealed that the PLSR method outperformed the bagging-PLSR to predict Pb, Sb, and Cr concentration. Based on the Ratio Performance Deviation (RPD), the prediction models have moderate accuracy (RPD = 1.15–1.79) for predicting heavy metals concentration. Finally, the result of this study revealed the feasibility of hyperspectral data (350–2500 nm) to predict heavy metals concentration on the little island of Hormuz. Nevertheless, it is fundamental to compare these methods with non-linear models for their sensitivity to a limited dataset, and also, for their predictive capability. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the roles of psychological strength and attachment styles to God in predicting Covid-19 anxiety in Iranian nurses.‏ A descriptive cross-sectional study was carried out on 100 nurses in July 2021. The subjects answered the questionnaires of psychological strength, attachment to God, and anxiety of Covid-19. The results showed a negative correlation between psychological strength and Covid-19 anxiety (r = −.894; p <.01) and a negative correlation between secure attachment style and Covid-19 anxiety (r = −.522; p <.01). But the correlation between avoidant attachment styles(r =.387;p <.01) and ambivalent attachment styles(r =.312; p <.01) with Covid-19 anxiety was positive. Also, the coefficient of determination showed that the components of psychological strength and attachment styles to God up to 81% can explain the variance of Covid-19 anxiety.‏ Based on the research results, by investing in attachment styles to God and promoting psychological strength, we can take steps to reduce Covid-19 anxiety in nurses.‏ [ABSTRACT FROM AUTHOR]

Background: Allogenic hematopoietic stem cell transplantation is considered as an effective treatment for patients with acute myeloid leukemia. However, complications of transplantation, like aGVHD, affect the efficiency of allogenic hematopoietic stem cell transplantation. The present study aimed to implement different models of data mining (DM) (single and ensemble) for prediction of allogenic hematopoietic stem cell transplantation in patients with acute myeloid leukemia (transplantation against host disease). Method: We conducted this developmental study on 94 patients with 34 attributes in Taleghani Hospital, Tehran, Iran, during 2009--2017. In this practical study, data were analyzed via decision tree (DT) algorithms, including decision tree, random forest and gradient boosting (ensemble learning), artificial neural network (Single Learning), and support vector machine. Some criteria, like specificity, accuracy, Fmeasure, AUC (area under curve), and sensitivity, were reported in order to evaluate DT algorithms. Results: There were 34 transplantation-related variables; some predictors, such as liver, hemoglobin, and donor blood group, were found to be the most important ones. To predict aGVHD, the two selected algorithms included the most appropriate DM models, artificial neural network and support vector machine classifiers, with ROC of 100. Conclusion: This study indicated that DT algorithms could be successfully used for approving the efficiency of the models predicting allogenic hematopoietic stem cell transplantation. [ABSTRACT FROM AUTHOR]

To control diabetes in a society, risk assessment tools are used to predict disease risk. We aimed to assess the value of different risk factors for diabetes mellitus in a remarkable community in the city of Kerman, one of the vast areas in the southeast of Iran, with the final goal of designing a predictive model for diabetes in this region. This study was a cross-sectional study with the aim of investigating the predictive value of risk factors indicating the presence of diabetes in the population of Kerman City based on the guidelines of the American Diabetes Association (ADA) risk assessment tool. The information of 4000 people participating in the comprehensive screening plan for cardiovascular risk factors in Kerman City was extracted by reviewing the relevant data registry. According to the ADA guideline, 32.5% of participants were at risk for diabetes mellitus. The hazard ratio of diabetes mellitus in the subgroup with the ADA final score =5 as compared to those with a lower final score was 1.9. Advanced age, history of gestational diabetes, family history of diabetes mellitus, history of hypertension, low physical activity, and higher body mass index were the main determinants of diabetes mellitus. According to ADA guidelines and the diabetes mellitus risk assessment tool, 32.5% of the population residents in Kerman City are potentially at risk for diabetes mellitus that can be successfully predicted aide by the ADA risk assessment tool. [ABSTRACT FROM AUTHOR]

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Introduction: This study aimed to predict the risk of mortality among COVID-19 patients in the central region of Iran by employing the Charlson Comorbidity Index (CCI), with adjustments made for age in the predictive model. Material & Methods: In this cross-sectional study, encompassing all probable, suspicious, and confirmed COVID-19 cases from the onset of the pandemic (55307 individuals), 3415 cases resulting in death were designated as the study group, while the survivors constituted the control group. Results: The Charlson Comorbidity Index revealed that over 11 percent of all patients had at least one underlying medical condition. Logistic regression analysis indicated a significantly elevated likelihood of mortality among patients with comorbidities. Specifically, individuals with a CCI score of 6 or higher were more than twice as likely to succumb to the virus compared to those without underlying diseases. Those with a score of 6 or more exhibited the highest odds ratio (OR 2.4; 95% CI 1.3-4.5). Conclusion: The study findings underscore the heightened vulnerability of individuals to COVID-19 mortality, particularly among the elderly with pre-existing health conditions. The coexistence of age and comorbidities substantially increased the risk of death due to COVID-19 in this population. Consequently, targeted interventions and focused care strategies may be crucial for this high-risk demographic in pandemic management efforts. [ABSTRACT FROM AUTHOR]

Introduction: Population dynamics are central to policymaking and future planning, commanding perpetual governmental attention across economic, social, and cultural domains. This study focuses on forecasting fertility indicators in Ilam city, projecting trends in five-year intervals up to 2051. Material & Methods: The "secondary document analysis" method was employed, utilizing data from the country's population censuses (1976-2016), birth events, and population statistics reported by Iran's National Organization for Civil Registration. The urban area of Ilam served as the study's location. A mixed-method approach to population forecasting, involving the use of Spectrum, Mortpak, and Excel, was employed for fertility status prediction and analysis. Results: The research outcomes indicate that under two scenarios with fixed and minimal thresholds (fertility rates of 1.8 and 1.5, respectively), the net reproduction rate (NRR) would fall below one girl. Conclusion: Based on these scenarios, it is evident that girls cannot replace their mothers in bearing children, contributing to the observed decline in fertility and, consequently, the population of Ilam city. [ABSTRACT FROM AUTHOR]

Background: The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA). Materials and methods: A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation. Results: The mean age of the patients was 64 years (IQR:50--76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration. Conclusion: The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors. [ABSTRACT FROM AUTHOR]

Groundwater is one of the major sources of exploitation in arid and semiarid regions. Spatial and temporal quality distribution is an important factor in groundwater management. Thus for protecting groundwater quality, data production on spatial and temporal distribution is essential. The present study has applied multiple linear regression (MLR) techniques to predict the fitness of groundwater quality in Kermanshah province, west of Iran. The parameters examined were Total dissolved solids (TDS), Total hardness (TH), Sodium adsorption ratio (SAR). the quality variables were modelled by MLR. Finally, the performance of the models was assessed using the coefficient of determination (R2). The relationship between parameters by MLR showed that TDS and water quality parameters in semi-deep wells and aquifers had a strong positive correlation (r = 0.94, r = 0.98) and there was a strong positive significant correlation between SAR and water quality parameters in deep wells and aquifers (r = 0.98, r = 0.99). Also, TH and water quality parameters in all water sources had a strong positive correlation (r = 1). The MLR model could serve as an alternative and cost-effective tool for groundwater quality prediction where there is limitation in laboratory facilities, trained expertise or time. Consequently, the usefulness of these linear regression equations in predicting the groundwater quality is an approach, which can be applied in any other locations. [ABSTRACT FROM AUTHOR]

Purpose: Hypercholesterolemia is a major risk factor for a wide range of cardiovascular diseases. Developing countries are more susceptible to hypercholesterolemia and its complications due to the increasing prevalence and the lack of adequate resources for conducting screening and/or prevention programs. Using machine learning techniques to identify factors contributing to hypercholesterolemia and developing predictive models can help early detection of hypercholesterolemia, especially in developing countries. Methods: Data from the nationwide 2016 STEPs study in Iran were used to identify socioeconomic, lifestyle, and metabolic risk factors associated with hypercholesterolemia. Furthermore, the predictive power of the identified risk factors was assessed using five commonly used machine learning algorithms (random forest; gradient boosting; support vector machine; logistic regression; artificial neural network) and 10-fold cross validation in terms of specificity, sensitivity, and the area under the receiver operating characteristic curve. Results: A total of 14,667 individuals were included in this study, of those 12.8% (n = 1879) had (undiagnosed) hypercholesterolemia. Based on multivariate logistic regression analysis the five most important risk factors for hypercholesterolemia were: older age (for the elderly group: OR = 2.243; for the middle-aged group: OR = 1.869), obesity-related factors including high BMI status (morbidly obese: OR = 1.884; obese: OR = 1.499; overweight: OR = 1.426) and AO (OR = 1.339), raised BP (hypertension: OR = 1.729; prehypertension: OR = 1.577), consuming fish once or twice per week (OR = 1.261), and having risky diet (OR = 1.163). Furthermore, all the five hypercholesterolemia prediction models achieved AUC around 0.62, and models based on random forest (AUC = 0.6282; specificity = 65.14%; sensitivity = 60.51%) and gradient boosting (AUC = 0.6263; specificity = 64.11%; sensitivity = 61.15%) had the optimal performance. Conclusion: The study shows that socioeconomic inequalities, unhealthy lifestyle, and metabolic syndrome (including obesity and hypertension) are significant predictors of hypercholesterolemia. Therefore controlling these factors is necessary to reduce the burden of hypercholesterolemia. Furthermore, machine learning algorithms such as random forest and gradient boosting can be employed for hypercholesterolemia screening and its timely diagnosis. Applying deep learning algorithms as well as techniques for handling the class overlap problem seems necessary to improve the performance of the models. [ABSTRACT FROM AUTHOR]

Objective: To explore the modeling of time series of animal bite occurrence in northwest Iran. Methods: In this study, we analyzed surveillance time series data for animal bite cases in the northwest Iran province of Iran from 2011 to 2017. We used decomposition methods to explore seasonality and long-term trends and applied the Autoregressive Integrated Moving Average (ARIMA) model to fit a univariate time series of animal bite incidence. The ARIMA modeling process involved selecting the time series, transforming the series, selecting the appropriate model, estimating parameters, and forecasting. Results: Our results using the Box Jenkins model showed a significant seasonal trend and an overall increase in animal bite incidents during the study period. The best-fitting model for the available data was a seasonal ARIMA model with drift in the form of ARIMA (2,0,0) (1,1,1). This model can be used to forecast the frequency of animal attacks in northwest Iran over the next two years, suggesting that the incidence of animal attacks in the region would continue to increase during this time frame (2018-2019). Conclusion: Our findings suggest that time series analysis is a useful method for investigating animal bite cases and predicting future occurrences. The existence of a seasonal trend in animal bites can also aid in planning healthcare services during different seasons of the year. Therefore, our study highlights the importance of implementing proactive measures to address the growing issue of animal bites in Iran. [ABSTRACT FROM AUTHOR]

Flow prediction is regarded as a major computational process in strategic water resources planning. Prediction's lead time has an inverse relationship with results' accuracy and certainty. This research studies the impact of climate-atmospheric indices on surface runoff predictions with a long lead time. To this end, the correlation of 36 long-distance climate indices with runoff was examined at 10 key nodes of the Great Karun multi-reservoir system in Iran, and indices with higher correlation are divided into 4 different groups. Then, using Artificial Neural Network (ANN) and Ensemble Learning to combine the input variables, flow is predicted in 6-month horizons, and results are compared with observed values. To assess the impact of extending the prediction lead time, results from the proposed model are compared with those of a monthly prediction model. The performed comparison shows that using an ensemble approach improves the final results significantly. Moreover, Tropical Pacific SST EOF, Caribbean SST, and Nino1 + 2 indices are found to be influential parameters to the basin's inflow. It is observed that the performance of the prediction process varies in different hydrological conditions and the best results are obtained for dry seasons. [ABSTRACT FROM AUTHOR]

Road traffic accidents (RTAs) are among the top causes of mortality and disability globally, particularly in developing nations like Iran. In this study, RTAs were analyzed to develop precise predictive models for predicting the frequency of accidents in the Kerman Province (southeastern Iran) using the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modeling methods. The findings demonstrate that including factors regarding humans, vehicles, and elements of nature in the time-series analysis of accident records resulted in the development of a more reliable prediction model than utilizing only aggregated accident count. The understanding of safety on the road is increased by this research, which also offers a method for forecasting that utilizes a variety of parameters relating to people, cars, and the environment. The findings of this research are likely to contribute to lowering the incidence of RTAs in Iran. [ABSTRACT FROM AUTHOR]