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COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

Contamination of farmland soils by trace elements (TEs) has become an international issue concerning food safety and human health risks. In the present research, the concentrations of TEs including cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), zinc (Zn) and iron (Fe) in soils of 16 farmlands were determined in Gonabad, Iran. In addition, the human health risks due to exposure to the TEs from the soils were assessed. Moreover, the soil contamination likelihood was evaluated based on various contamination indices including contamination factor $$(\mathrm{CF}$$ ( CF ), enrichment factor (EF), geo-accumulation index (Igeo), and pollution load index (PLI) calculations. The soil mean concentrations for Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn and Fe ranges as 0.102, 6.968, 22.550, 29.263, 475.281, 34.234, 13.617, 54.482 and 19,683.6 mg/kg in farmland soils. The mean concentrations of the TEs decreased in the order of Fe > Mn > Zn > Ni > Cu > Cr > Pb > Co > As > Cd. Levels of all metals in this study were within the FAO/WHO and Iranian soil standards. The HQ values from investigated elements for adults and children in the studied farms were less than the limit of 1, indicating no health risks for the studied subpopulations. The results of the present research indicated no significant carcinogenic health hazards for both adults and children through ingestion, skin contact and inhalation exposure routes. $$\mathrm{CF}$$ CF values of Ni and Zn in 100% and 6.25% of farmlands were above 1, showing moderate contamination conditions. EF values of metals in farmlands were recorded as “no enrichment”, “minimal enrichment” and “moderate enrichment” classes. Furthermore, it can be concluded that the all farms were uncontaminated except Ni (moderately contaminated) based on Igeo. This is an indication that the selected TEs in the agricultural soils have no appreciable threat to human health.

Loss of pregnancy is a major stressor which can cause mental disorders. In stressful conditions, spiritual intelligence can be used as a coping strategy to manage and cope with the stressor. It may also produce positive outcomes in spontaneous abortion. This study aimed to assess the relationship of spiritual intelligence with depression after spontaneous abortion. This cross-sectional descriptive-correlational study was done in 2015-2016 on 185 women with spontaneous abortion who were hospitalized in the postnatal care ward of Kowsar teaching hospital, Qazvin, Iran. Data collection was performed in two steps. In the first step, a demographic and obstetric characteristics questionnaire and the Spiritual Intelligence Self-Report Inventory were completed by participants during their hospital stay. In the second step, i.e., 7 days after spontaneous abortion, the Edinburgh Postnatal Depression Scale was completed for each participant over the phone. Variables which were found in univariate analysis to have a significant correlation with depression were entered into multiple logistic regression analysis to assess their roles in predicting depression. Most women were depressed (54%). Multiple logistic regression analysis revealed that the significant predictors of depression were women's educational status (OR 0.419; CI 0.18-0.93), gestational age at the time of abortion (OR 1.121; CI 1.02-1.22), and the personal meaning production dimension of spiritual intelligence (OR 0.82; CI 0.73-0.91). Spiritual intelligence is significantly correlated with post-abortion depression. Developing and following strategies for promoting spiritual intelligence can alleviate depression, stress, and anxiety after spontaneous abortion and also help manage complicated pregnancies.

Novel modified activated carbons (ACs) with enhanced adsorptive properties were obtained coating by chitosan (CS), polyethylene glycol (PEG) and blends of the two polymers (0:1, 1:0, 1:1, 1:2 and 2:1 wt/wt) on ACs by an impregnation technique. The adsorption performances of the pristine, acidified and polymer-impregnated ACs were studied using methylene blue as a model adsorbate. The adsorbents were characterized using Fourier transform infrared spectroscopy, scanning electron microscopy and abrasion hardness tests. The average coating thicknesses were between 10 to 23 microns. The pore sizes, pore densities and pore capacities of the activated carbons increased as the wt-% PEG in the coating increased. The highest adsorption capacity (424.7 mg/g) was obtained for the chitosan-coated ACs and this adsorption was well described by the Langmuir isotherm model. The kinetic results were best described by the pseudo-second-order kinetic model. The highest rate constant was obtained with the ACs modified with the CS:PEG (2:1) coating and this result was almost 2.6 times greater than that of the unmodified ACs. The CS/PEG impregnated ACs also displayed superior hardness (~90%), compared to unmodified ACs (~85%). Overall the chitosan had a greater effect on improving adsorption capacity whereas the polyethylene glycol enhanced the adsorption rate.

In this paper, we define the notion of sigma-approximate module amenability of Banach algebras and give some properties about this notion. Also for Banach A -bimodule A , and J , the closed ideal of A generated by elements of form ( α · a ) b - a ( b · α ) , ( a , b ∈ A , α ∈ A ) , we considered some corollaries about σ ^ -approximate amenability of A J as a Banach A -bimodule, where σ ^ : A J → A J by σ ^ ( a + J ) = σ ( a ) + J has a dense range.