Background: Bowel cancer is the most prevalent digestive system cancer and is the 4th largest cause of cancer-related death worldwide. In Iraq, colon and rectal cancer (CRC) is the 6th most common malignancy in males and the 5th in females. This cancer is sluggish in growth, which gives a window of opportunity to screen for both precursor lesions and early cancer. The Cluster of Differentiation 47 (CD47) protein is a type of transmembrane glycoproteins found on nearly all human cells, including non-hematopoietic and hematopoietic cells. CD47 promotes CRC growth by triggering angiogenesis and apoptosis of tumor cell. Objectives: To evaluate the immunohistochemical expression of (CD47) in various colorectal samples from Iraqi patients

Nanocrystalline micro-mesoporous ZSM/MCM-41 composite was synthesized using alkaline treatment method and two step of crystallization in poly tetraflouroethylene (PTFE) lined autoclave. The synthesized zeolites was characterized by X-Ray diffraction (XRD), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), Atomic force microscopy (AFM), Fourier transport infrared (FTIR), and N₂ adsorption-desorption (BET). It was approved that the best results for alkaline leaching can be got with 1.5M NaOH solution. High surface (BET) area of 630 m²/g with pore volume of 0.55 cm³/g has been got. AFM reports showed a nano-level size for average particle size of 50nm.

The current study included a detail morphological study of all parts of the species Ipomoea carnea Jacq. (Convolvulaceae) cultivated in different gardens, the roots, stems, leaves, flowers and fruit were studied in detail, also the pollen grains were studied and there are photographs for all the parts were putted.

Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.

Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft

In this study, three strengthening techniques, near-surface mounted NSM-CRFP, NSM-CFRP with externally bonding EB-CFRP, and hybrid CFRP with circularization were studied to increase the seismic performance of existing RC slender columns under lateral loads. Experimentally, 1:3 scale RC models were studied and subjected to both lateral static load and seismic excitation. In the dynamic test, a model was subjected to El Centro 1940 NS earthquake excitation by using a shaking table. According to the test results, the strengthening techniques showed a significant increase in load carrying capacity, of about 86.6%, and 46.6%, for circularization and NSM-CFRP respectively, of the reference unstrengthened columns. On the other hand, column