The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when diagnosing a tissue sample. Small, unnoticeable changes in pixel density may indicate the beginning of cancer or tear tissue in the early stages. These details even expert pathologists might miss. Artificial intelligence (A.I.) and D.L. revolutionized radiology by enhancing efficiency and accuracy of both interpretative and non-interpretive jobs. When you look at AI applications, you should think about how they might work. Convolutional Neural Network (C.N.N.) is a part of D.L. that can be used to diagnose knee problems. There are existing algorithms that can detect and categorize cartilage lesions, meniscus tears on M.R.I., offer an automated quantitative evaluation of healing, and forecast who is most likely to have recurring meniscus tears based on radiographs.
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In this paper, we investigate the connection between the hierarchical models and the power prior distribution in quantile regression (QReg). Under specific quantile, we develop an expression for the power parameter ( ) to calibrate the power prior distribution for quantile regression to a corresponding hierarchical model. In addition, we estimate the relation between the and the quantile level via hierarchical model. Our proposed methodology is illustrated with real data example.
The study included 200 samples were collected from children under two years included (50 samples from each of Cerebrospinal fluid, Blood, Stool and Urine) from, (Central Children Hospital and Children's Protections Educational Hospital) The Iraqi Ministry of Health, the Department of Health Baghdad .the period from the first of 2015 September to the first of December 2015, Were obtained isolates bacterial subjected to the cultural, microscopic and biochemical examination and diagnosed to the species by using vitek2 system .The results showed there were contamination in 6.5% of clinical samples. The diagnosed colonies which gave pink color on the MacConkey agar, golden yellow color on the Trypton Soy agar and green color on t
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Background: Molars and premolars are considered as the most vulnerable teeth of caries attack, which is related to the morphology of their occlusal surfaces along with the difficulty of plaque removal. different methods were used for early caries detection that provide sensitive, accurate preoperative diagnosis of caries depths to establish adequate preventive measures and avoid premature tooth treatment by restoration. The aim of the present study was to evaluate the clinical sensitivity and specificity rates of DIAGNOdent and visual inspection as opposed to the ICDAS for the detection of initial occlusal caries in noncavitated first permanent molars. Materials and Methods: This study examined 139 occlusal surface of the first permanent
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In this work Different weight of pure Zinc powder suspended particles in 4ml base engine Oil were used.
Intensity of Kα Line was measured for the suspended particles ,also for mixture which consist from Zinc particle blended with Engine base Oil. Calibration Curve was drawn between Ikα line Intensity and Zinc concentration at different operation condition. The Lower Limit detection (LLD) and Sensitivity (m) of Spectrometer were determined for different Zinc Concentration (Wt%). The results of LLD and m for Samples were analyzed at Operation Condition of 30KV,17mA is best from Samples were analyzed at Operation Condition of 25KV,15mA
Salmonella infections are a major cause of foodborne infection in both humans and animals. Some serotypes are capable of inducing severe clinical manifestations, including gastroenteritis, septicemia, and abortion. The present study aimed to identify, isolate, and detect Salmonella Agona in cats with diarrhea in Al-Anbar Governorate, Iraq, and to evaluate the antimicrobial resistance profiles of the recovered isolates. Between November 2024 and May 2025, 100 fecal samples were collected from cats of different breeds presenting with diarrhea at veterinary clinics in Iraq. These samples were processed using established culture methods, including pre-enrichment in buffered peptone water followed by selective enrichment in tetrathionate
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Dust storms are typical in arid and semi-arid regions such as the Middle East; the frequency and severity of dust storms have grown dramatically in Iraq in recent years. This paper identifies the dust storm sources in Iraq using remotely sensed data from Meteosat-spinning enhanced visible and infrared imager (SEVIRI) bands. Extracted combined satellite images and simulated frontal dust storm trajectories, using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, are used to identify the most influential sources in the Middle East and Iraq. Out of 132 dust storms in Iraq during 2020–2023, the most frequent occurred in the spring and summer. A dust source frequency percentage map (DSFPM) is generated using ArcGIS so
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CD-nanosponges were prepared by crosslinking B-CD with diphenylcarbonate (DPC) using ultrasound assisted technique. 5-FU was incorporated with NS by freeze drying, and the phase solubility study, complexation efficiency (CE) entrapment efficiency were performed. Also, the particle morphology was studied using SEM and AFM. The in-vitro release of 5-FU from the prepared nanosponges was carried out in 0.1N HCl.
5-FU nanosponges particle size was in the nano size. The optimum formula showed a particle size of (405.46±30) nm, with a polydispersity index (PDI) (0.328±0.002) and a negative zeta potential (-18.75±1.8). Also the drug entrapment efficiency varied with the CD: DPC molar ratio from 15.6 % to 30%. The SEM an
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