Waterlogging effects on some antioxidant enzymes activities and yield of three wheat promising lines

Waterlogging is one of the most important environmental stresses that have negative effects on wheat growth and yield. The purpose of this study was to investigate the effect of waterlogging (0, 7, 14 and 21 d) at tillering (ZG21) and stem elongation (ZG31) stages on the content of photosynthetic pigments, proline, malondialdehyde (MDA), antioxidant enzymes, grain yield and yield components of three wheat promising lines (N-93-19, N-93-9 and N-92-9). Increasing waterlogging stress reduce the photosynthetic pigments contents and the activity of catalase enzyme while increase the proline content, MDA, superoxide dismutase and peroxidase enzymes in three wheat genotypes in both tillering and stem elongation stages. Waterlogging also reduced yield and yield components in three wheat genotypes. The results showed that N-92-9 genotype had better response than other two genotypes in all studied traits under waterlogging conditions.</p

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning

Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient’s background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the vari- ables related to the patient’s background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management. Keywords COVID-19 · Opioid addiction · Oxygen treatment · Prediction · Machine learning · XGBoos

Comparison of lead content absorption in different parts of Eldar pine (Pinus eldarica Medw.) in Tehran city

Evaluation of toxic metal concentration in soil and plants are the most important subject according to the health of ecosystem. This study was carried out to investigate on the lead content absorption in different parts (leaf, root and branch) of Eldar pine (Pinus eldarica Medw.) trees in Tehran city. For this aim in polluted sites (Azadi, Bahman and Bazar) and controlled site (Aghdasiyeh), in defferent seasons (January, March, July and September) and in different distance of air pollution measurment station (0, 500m and 1000m), 432 samples from leaves, branches and top root were collected and lead content density in each samples determined by atomic absorption instrument model Varian 220. Result indicated that lead content absorption in root of pine was higher than aerial parts (leaf and branch). Lead absorption in parts of tree in Azadi site was higher than other sites and the lowest content of lead was measured in Aghdasiyeh site. However, the highest lead content in parts of trees was observed in September and the lowest in March. The results also showed that by increasing of distance from air pollution measurment station, lead content absorption in parts of trees decreased

Human Olfactory Mucosa Stem Cells Delivery Using a Collagen Hydrogel: As a Potential Candidate for Bone Tissue Engineering

For bone tissue engineering, stem cell-based therapy has become a promising option. Recently, cell transplantation supported by polymeric carriers has been increasingly evaluated. Herein, we encapsulated human olfactory ectomesenchymal stem cells (OE-MSC) in the collagen hydrogel system, and their osteogenic potential was assessed in vitro and in vivo conditions. Collagen type I was composed of four different concentrations of (4 mg/mL, 5 mg/mL, 6 mg/mL, 7 mg/mL). SDS-Page, FTIR, rheologic test, resazurin assay, live/dead assay, and SEM were used to characterize collagen hydrogels. OE-MSCs encapsulated in the optimum concentration of collagen hydrogel and transplanted in rat calvarial defects. The tissue samples were harvested after 4- and 8-weeks post-transplantation and assessed by optical imaging, micro CT, and H&amp;E staining methods. The highest porosity and biocompatibility were confirmed in all scaffolds. The collagen hydrogel with 7 mg/mL concentration was presented as optimal mechanical properties close to the naïve bone. Furthermore, the same concentration illustrated high osteogenic differentiation confirmed by real-time PCR and alizarin red S methods. Bone healing has significantly occurred in defects treated with OE-MSCs encapsulated hydrogels in vivo. As a result, OE-MSCs with suitable carriers could be used as an appropriate cell source to address clinical bone complications