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Inorganic carbon is the largest source of carbon in terrestrial surface, particularly in arid and semiarid regions, including the Chahardowli Plain in western Iran. Inorganic carbon plays an equal or greater role than organic soil carbon in these areas, although less attention has been paid in quantifying their variability. The objective of this study was to model and map calcium carbonate equivalent (CCE) presenting inorganic carbon in soil using machine learning and digital soil mapping techniques. Chahardowli Plain in foothills of the Zagros Mountains in the southeast of Kurdistan Province in Iran was taken as a case study area. CCE was measured at 0–5, 5–15, 15–30, 30–60, and 60–100 cm depths following GloalSoilMap.net project specifications. A total of 145 samples were collected from 30 soil profiles using the conditional Latin hypercube (cLHS) method of sampling. Relationships between CCE and environmental predictors were modeled using random forest (RF) and decision tree (DT) models. In general, the RF model performed slightly superior than the DT model. The mean value of CCE increased with soil depth, from 3.5% (0–5 cm) to 63.8% (30–60 cm). Remote sensing (RS) variables and terrestrial variables were equally important. The importance of RS variables was higher at the surface than terrestrial variables, and vice versa. The most significant variables were Channel Network Base Level (CNBL) variable and Difference Vegetation Index (DVI) with the same variable importance value (21.1%). In areas affected by river activities, the use of the CNBL and vertical distance to channel networks (VDCN) as variables in digital soil mapping (DSM) could increase the accuracy of soil property prediction maps. The VDCN played a principal role in soil distribution in the study area by affecting the rate of discharge and, thus, erosion and sedimentation. A high percentage of carbonate in parts of the region could exacerbate nutrient deficiencies for most crops and provide information for sustainably managing agricultural activity. [ABSTRACT FROM AUTHOR]

The present study was conducted to compare generalized linear model (GLM), random forest (RF), and Cubist to produce available phosphorus (AP) and potassium (AK) maps and to identify the covariates that control mineral distribution in Lorestan Province, Iran. To this end, the locations for collecting 173 soil samples were determined through the conditioned Latin hypercube sampling (cLHS) method, at four different land-uses (orchards, paddy fields, agricultural, and abandoned fields). The performance of the models was assessed by coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) indices. The results showed that the RF model fitted better than GLM and Cubist models and could explain 40 and 57% of AP and AK distribution, respectively. The R2, RMSE, and MAE of the RF model were 0.4, 2.81, and 2.43 for predicting AP and equal to 0.57, 143.77, and 116.61 for predicting AK, respectively. The most important predictors selected by the RF model were valley depth and soil-adjusted vegetation index (SAVI) for AP and AK, respectively. The maps showed higher AP and AK content in apricot orchards compared to other land-uses. No difference was observed between AP and AK content on paddy fields, agricultural, and abandoned areas. The higher AP and AK contents were related to orchard management practices, such as failure to dispose of plant residuals and fertilizer consumption. It can be concluded that the orchards (by increasing soil quality) was the best land-use in line with sustainable management for the study area. However, generalizing the results needs more detailed research. [ABSTRACT FROM AUTHOR]

Background & Objective: Acute myocardial infarction (MI) is caused due to coronary artery occlusion and divided into two forms of ST-elevation (STEMI) and non-ST-elevation (NSTEMI) myocardial infarction. This study aimed to determine the success rate of treatment with primary PCI (percutaneous coronary intervention) versus thrombolysis in the establishment of perfusion and to evaluate the short-term complications caused by each method. Materials & Methods: In this prospective cohort study, 90 patients were selected for primary PCI treatment and 90 patients for thrombolytic treatment. Clinical data and basic demographic characteristics of the patients and cardiovascular risk factors were recorded. The success rate of coronary reperfusion by primary PCI and thrombolytic therapy was then determined by angiography and 90-minute ECG with resolution or improvement of chest pain. All patients were examined for any serious complications such as cerebrovascular accidents, shock, and heart failure for 30 days after treatment. Data were analyzed using SPSS ver. 20. Results: In the primary PCI group, 75.6% of the patients were males, and 24.4% were females, but in the thrombolytic group, 76.6% and 23.3% of the cases were males and females, respectively. The mean door-to-balloon time for PCI was 63.60 ± 23.92 and was 53.70 ±21.52 min in the thrombolytic group. Thrombolysis in myocardial infarction (TIMI) flow grade III and TIMI-II were the most frequent in primary PCI and thrombolytic groups, respectively. The odds ratio (OD) of mechanical MI and major arrhythmia in patients received the primary PCI compared to those received thrombolytic therapy was 0.24 and 0.66, respectively. Also, the OD of mortality in the primary PCI group was 2.12. Conclusion: Our findings suggest that in STEMI patients, the chances of short term complications such as post-MI mechanical complications, major arrhythmia, and bleeding requiring blood transfusion were lower in patients who were treated with primary PCI than those who received thrombolytic treatment. Also, the average hospitalization in CCU and hospital in the primary PCI group was lower than the thrombolytic group. [ABSTRACT FROM AUTHOR]

More has yet to be indicated on the adsorption and degradation processes, determining herbicides recycling in the environment. The sorption and degradation of 2, 4-D, affected by organic carbon (1.92–2.81%), soil clay (20–30%) and pH of the citrus orchards of Mazandaran province, Iran was investigated using HPLC equipped with UV detector for the identification and quantification of soil 2, 4-D. The adsorption (kd) and degradation (Kdeg) coefficients were determined using Freundlich and the first-degree kinetic equations. Gardens C (2.45 mL g−1), and B (0.3 mL g−1), with the highest (8.2 g day−1) and least (2.7 g day−1) degradation coefficients, had the highest and lowest Kd values. Kd variations with pH indicated higher adsorption of 2, 4-D in acidic pH. Due to the high presence of functional groups and soil biological activities, organic carbon affected the adsorption and degradation rates more effectively, which is of economic and environmental significance. [ABSTRACT FROM AUTHOR]

The definition of an exclusive panel of genetic markers is of high importance to initially detect among this review population. Therefore, we gave a summary of each main genetic marker among Iranian patients with thyroid cancer for the first time which were classified based on their cellular function. Due to the results, a significant relationship was found between SNP in codons 194, 280, and 399 (XRCC1), Allele 3434Thr (XRCC7), GC or CC genotype 31, G/C (Survivin), 399G>A (XRCC1), Tru9I (vitamin D receptor), G‐D haplotype (MDM2), TT genotype, −656 G/T (IL-18), TAGTT haplotype (IL-18), G allele in +49 A>G (CTLA-4), +7146 G/A (PD-1.3), +7785 C/T (PD-1.5), rs1143770 (let7a‐2), rs4938723 (pri‐mir‐34b/c) genes, and thyroid cancers. Moreover, SNP in 677C-->T (MTHFR), GG genotype Asp1312Gly (thyroglobulin), 2259C>T (Rad52), R188H, (XRCC2), T241M (XRCC3) had higher risks of thyroid cancer and lower risks were observed in −16 Ins-Pro (p53), rs3742330 (DICER1). At last, the protective effects were explored in 127 CC genotype (IL-18), rs6877842 (DROSHA). Conduct further studies on the types of DNA repair gene polymorphisms with a larger number in the thyroid cancer using modern methods such as SNP array so that these genes could be used as a biomarker in prediction, diagnosis, and treatment of thyroid cancer. This review presents for the first time a summary of important genetic markers in Iranian patients with thyroid cancer. [ABSTRACT FROM AUTHOR]

Background and Objectives: The decreased level of self-esteem and life satisfaction is prevalent among patients with coronary artery disease (CAD). The present study aimed to investigate the effectiveness of an Islamic approach to hope therapy on the promotion of self-esteem and life satisfaction in comparison with its conventional approach in patients with CAD. Methods: The study was conducted in a quasi-experimental control group pretest-posttest design. The participants were 60 patients with CAD selected by convenience sampling method from one of Qom hospitals. Data collection tools included the Self-Esteem Scale, Satisfaction with Life Scale, and Hope State Scale. After collecting pre-test data, participants were divided into three peer groups, and the intervention was performed in eight sessions each lasting for 90-minute. An experimental group was exposed to Islamic hope therapy and the other received conventional hope therapy, while the control group received a stress management package. Finally, all the groups undertook the same posttests and the data obtained were analyzed in SPSS22 software using the Covariance test. Results: Both Islamic and conventional hope therapy increased hope (7, 10.5), life satisfaction)9, 12.8), and self-esteem (7.5, 13.9) of the patients with CAD significantly. Moreover, it was found that Islamic hope therapy was more effective in the enhancement of self-esteem and life satisfaction, compared to conventional hope therapy. Conclusion: Islamic hope therapy outperformed the conventional hope therapy in terms of increasing the self-esteem and life satisfaction of patients with CAD. Therefore, it is recommended to use Islamic hope therapy for patients with CAD and other similar chronic diseases. [ABSTRACT FROM AUTHOR]

Colorectal cancer has been considered as one of the complicated multi-stage processes after adenoma-carcinoma sequence. Therefore, studies of the molecular dysregulation basis could present information on the recognition of the potent biomarkers and treatment targets for this disease. Even though outcomes of the patients with colorectal cancer have been improved largely with current annual screening plans, it is necessary to have reliable prognostic biomarkers because of the disease heterogeneity. There is a significant relationship between SNP in IL1RN* 2 (IL1ra), − 509 C/T (TGFB1), rs11556218 T>G and rs4778889 T/C (IL16), miRNA-binding site polymorphisms in IL16, rs4464148 (SMAD7), rs6983267 (EGF), GSTT1, TACG haplotype (CTLA4), 1793G> A (MTHFR), Leu/Leu genotype of (EXO1), − 137 G/C (IL18), C/T genotype (XRCC3), I3434T (XRCC7), MGMT, C3435T (MDR1), ff genotype of FokI, 677CT+TT (MTHFR), G2677T/A (MDR1) and CRC. Increased risk has been observed in VDR ApaI genotype "aa". Finally, the protective effect has been explored in the TACA haplotype (CTLA4). According to the findings, the genetic polymorphisms in the immunity-associated genes are related to the CRC amongst the Iranian patients. Therefore, more large-scale functional investigations are necessary for confirming the results. [ABSTRACT FROM AUTHOR]

Most common machine learning (ML) algorithms usually work well on balanced training sets, that is, datasets in which all classes are approximately represented equally. Otherwise, the accuracy estimates may be unreliable and classes with only a few values are often misclassified or neglected. This is known as a class imbalance problem in machine learning and datasets that do not meet this criterion are referred to as imbalanced data. Most datasets of soil classes are, therefore, imbalanced data. One of our main objectives is to compare eight resampling strategies that have been developed to counteract the imbalanced data problem. We compared the performance of five of the most common ML algorithms with the resampling approaches. The highest increase in prediction accuracy was achieved with SMOTE (the synthetic minority oversampling technique). In comparison to the baseline prediction on the original dataset, we achieved an increase of about 10, 20 and 10% in the overall accuracy, kappa index and F‐score, respectively. Regarding the ML approaches, random forest (RF) showed the best performance with an overall accuracy, kappa index and F‐score of 66, 60 and 57%, respectively. Moreover, the combination of RF and SMOTE improved the accuracy of the individual soil classes, compared to RF trained on the original dataset and allowed better prediction of soil classes with a low number of samples in the corresponding soil profile database, in our case for Chernozems. Our results show that balancing existing soil legacy data using synthetic sampling strategies can significantly improve the prediction accuracy in digital soil mapping (DSM). Highlights: Spatial distribution of soil classes in Iran can be predicted using machine learning (ML) algorithms.The synthetic minority oversampling technique overcomes the drawback of imbalanced and highly biased soil legacy data.When combining a random forest model with synthetic sampling strategies the prediction accuracy of the soil model improves significantly.The resulting new soil map of Iran has a much higher spatial resolution compared to existing maps and displays new soil classes that have not yet been mapped in Iran. [ABSTRACT FROM AUTHOR]

Objective(s): Plasmid-mediated quinolone resistance (PMQR) determinants and integrons have a considerable contribution to bacterial drug resistance in gram-negative pathogens. We studied the prevalence of PMQR genes and integron carriage in multidrug-resistant community isolates of Klebsiella spp. Materials and Methods: Two hundred and fifty Klebsiella spp. isolates were collected from outpatient specimens between August 2015 and October 2017 in Yazd central Laboratory, Iran. Antibiotic susceptibility was determined against 17 antibiotics and minimum inhibitory concentration (MIC) of ciprofloxacin was measured by E-test. Polymerase chain reaction (PCR) was employed for detection of qnrA, qnrB, qnrS, aac(6’)-Ib-cr, oqxAB and qepA genes. Results: Disc diffusion results showed that 17 isolates (6.8%) were multidrug resistant (MDR), two of which were Klebsiella oxytoca and 15 were Klebsiella pneumoniae. MIC measurements revealed 11 ciprofloxacin-resistant isolates (including the two K. oxytoca), three intermediately-resistant and three ciprofloxacin-susceptible isolates. All ciprofloxacin-resistant and intermediately-resistant isolates carried at least one and up to four PMQR genes. The most prevalent PMQR gene was oqxAB (93.75%) followed by aac(6’)-ib-cr (50.0%), qnrB (25.0%) and qnrS (18.75%) but qnrA and qepA were not detected. Class 1 integron was observed in 14 (82.3%) isolates including nine ciprofloxacin-resistant, two intermediately-resistant, and three susceptible isolates. Class 2 and 3 integrons were not observed. Conclusion: Presence of MDR, multiple PMQR determinants as well as class 1 integron in community isolates of Klebsiella spp. can be an important source of transmission of these opportunistic pathogens. [ABSTRACT FROM AUTHOR]

Many regions of Iran lack digital map of soil properties. The Chahardowli plain in western Iran is one of these areas. Due to the importance of sand, silt, and clay components, having quantitative and continuous data on abrupt changes in these two properties in this area is very critical. Therefore, to study sand, silt, and clay, samples were taken at depths of 0-5, 5-15, 15-30, 30-60, and 60-100 cm, according to GlobalSoilMap. Finally, 145 samples were collected from 30 soil profiles. The significant covariates were selected by Random Forest Recursive Feature Elimination (RF-RFE). Relationships between these characteristics and environmental predictors were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. The accuracy and precision of the models used for all three particles showed that the RF model had the most accurate prediction with R² and RMSE of 0.82 and 2.34 for clay, 0.80 and 3.87 for sand, and 0.85 and 2.89 for silt, respectively. In this study, terrain-based variables had a greater impact on improving accuracy than remote-sensing variables. The current study showed that even with limited information, digital mapping of sand, silt, and clay particles under GlobalSoilMap and the use of environmental factors can provide acceptable results. [ABSTRACT FROM AUTHOR]