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Background: Currently, the high expression of fetal hemoglobin (HbF) in sickle cell disease is treated by hydroxyurea (HU). However, potential adverse effect regarding the use of HU is a major concern. Therefore, the search for an alternative therapeutic agent is necessary. By using K562 cells as a model, this research aimed to determine the effect of erythropoietin (EPO) and stem cell factor (SCF) combination therapy on the expression of BCL11A, KLF1, and the ERK of mitogen-activated protein kinase (MAPK) pathway. Methods: K562 cells were treated with SCF, EPO, and a combination of SCF and EPO for 24 h. Real-time quantitative polymerase chain reaction was performed to detect γ-globin mRNA expression. Western blotting was performed to determine the expression of BCL11A, KLF1, and ERK of the MAPK pathway. Results: In contrast to HU, the treatment with SCF and EPO, either separately or together, preserved the expression of the γ-globin gene. In addition, SCF and EPO treatment had no effect on the expression of BCL11A or KLF1 in K562 cells. After receiving SCF and EPO treatment, the ERK signaling of the MAPK remained unaltered. Conclusions: We conclude that the expression of the γ-globin gene, BCL11A, KLF1, and ERK of the MAPK pathway is unaffected by the combination of EPO and SCF.

Background: Stage III non-small cell lung cancer (NSCLC) being highly heterogeneous requires multimodal therapeutic strategies for optimal management. We present findings on treatment patterns and their associated survival outcomes in patients with stage III NSCLC from the Egypt subset of the KINDLE global real-world study conducted across countries from Asia, Middle East, Africa, and Latin America. Method: Retrospective data from the Egypt subset (21 centers) of adult patients diagnosed with stage III NSCLC between January 2013 and December 2017 were analyzed. Descriptive and inferential statistics summarized treatment modalities, progression-free survival (PFS), and overall survival (OS). Results: Of 421 patients enrolled (median age: 59.0 years), 77.9% were males, 53.5% had stage IIIA disease, 60.8% had adenocarcinoma, 78.4% had an unresectable disease, and 81.5% had Eastern Cooperative Oncology Group performance status ⩽1. Overall, chemotherapy alone (40.4%) was predominantly used in the initial line, whereas definite radiotherapy was used in only 5.0% of patients. In resectable patients, chemotherapy plus surgery (33.8%), surgery alone (20.6%), or other surgery (20.6%) were the top three modalities used in initial line of treatment. Chemotherapy alone was most preferred (48.8%) in unresectable patients, followed by sequential chemoradiotherapy (CRT) (17.6%) and concurrent CRT (9.3%). The overall median PFS was 10.3 months [95% confidence interval (CI), 9.43–12.02], whereas the median OS was 18.5 months (95% CI, 16.46–21.88). Overall, female gender, adenocarcinoma histology, and radical therapy as surgery or CRT predicted significantly longer OS (all p < 0.05). Conclusion: KINDLE-Egypt cohort revealed wide heterogeneities in the treatment patterns of stage III NSCLC. Although deemed resectable, few patients did not undergo surgery, probably due to high smoking rates leading to poor lung function. Lower survival outcomes than other published real-world studies highlight the need for timely approval and availability of novel targeted and immunotherapies to enhance patient outcomes. Trial registration: NCT03725475 [ABSTRACT FROM AUTHOR]

The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R2) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg−1 for Cr, 20,963.84 ± 4447.83 mg kg−1 for Fe, 1.46 ± 0.42 mg kg−1 for Cd, and 48.71 ± 11.88 mg kg−1 for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination. [ABSTRACT FROM AUTHOR]

The Halayeb area is one of the most important Egyptian places, occupying a portion of the country's southeastern desert zone and serving as a strategic border with Sudan. The major goal of this study is to combine existing data on landforms, soil qualities, and climate data to define agro-ecological zones (AEZ) that are suitable for agricultural development. Thirty-two soil profiles were dug throughout the study region to represent physiographic units, and a digital soil map was generated based on an analysis of the digital elevation model and Landsat 8 satellite data, as well as climatic data from nine sites within the study area. Using the Model Builder in ArcGIS software, AEZ were created based on overlay maps of topography, soil chemical and physical properties, temperature, and precipitation maps. Zone (I) represents highly suitable areas (2.6 percent), zone (II) represents suitable areas (12.4 percent), zone (III) represents moderately suitable areas (26.5 percent), and zone (IV) represents marginally suitable areas (37.3 percent). highly suitable(S1), suitable (S2), and moderately suitable(S3) were the most appropriate classes for all of the crops studied. The findings will aid decision-makers in developing various development plans based on the research area's conditions. The study's methodology and findings can be used to evaluate which land is best suited for agricultural productivity growth. [ABSTRACT FROM AUTHOR]

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique. [ABSTRACT FROM AUTHOR]