Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid disease predictions. A systematic literature review (SLR) strategy is used in this study to give a comprehensive overview of the existing literature on forecasting data on thyroid disease diagnosed using ML. This study includes 168 articles published between 2013 and 2022, gathered from high-quality journals and applied meta-analysis. The thyroid disease diagnoses (TDD) category, techniques, applications, and solutions were among the many elements considered and researched when reviewing the 41 articles of cited literature used in this research. According to our SLR, the current technique's actual application and efficacy are constrained by several outstanding issues associated with imbalance. In TDD, the technique of ML increases data-driven decision-making. In the Meta-analysis, 168 documents have been processed, and 41 documents on TDD are included for observation analysis. The limits of ML that are discussed in the discussion sections may guide the direction of future research. Regardless, this study predicts that ML-based thyroid disease detection with imbalanced data and other novel approaches may reveal numerous unrealised possibilities in the future
The aim of this study was to increasing natural carotenoides production by a locally isolate Rodotorula mucilagenosa M. by determination of the optimal conditions for growth and production of this agents, for encouragest to use it in food application permute artificial pigments which harmfull for consumer health and envieronmental. The optimal condition of carotenoides production from Rhodotorula mucilaginosa M were studied. The results shows the best carbon and nitrogen source were glucose and yeast extract. The carotenoids a mount production was 47430 microgram ̸ litter and 47460 microgram ̸ litter, respectively, and the optimum temperature was 30°C, PH 6, that the carotenoides a mount was 47470 microgram ̸ litter and 47670 microgr
... Show MoreImpact strength of self-compacted concrete is a field of interest, mostly when the concrete is produced from sustainable materials. This research's main objective is to clarify the ability to use two types of Portland limestone cement (Karasta and Tasluja) in self compacted concrete under impact loading, further to the economic and environmental benefits of the limestone cement. The impact loading was applied by a low-speed test, using the drop ball on concrete. Moreover, the study reveals the resistance of the grids reinforced concrete to impact loading by using polymer grid, and steel grid reinforced concrete slabs. Mixes reinforced by steel mesh had the highest results, indicating that the steel mesh was more robust because it had
... Show MoreThe extraction of Eucalyptus oil from Iraqi Eucalyptus Camadulensis leaves was studded using water distillation methods. The amount of Eucalyptus oil has been determined in a variety of extraction temperature and agitation speed. The effect of water to Eucalyptus leaves (solvent to solid) ratio and particle size of Eucalyptus leaves has been studied in order to evaluate the amount of Eucalyptus oil. The optimum experimental condition for the Eucalyptus oil extraction was established as follows: 100 C extraction temperature, 200 rpm agitation speed; 0.5 cm leave particle size and 6: 1 ml: g amount of water to eucalyptus leaves Ratio.
Specialized Escherichia coli (E. coli) isolates, called uropathogenic E. coli (UPEC), cause most of urinary tract infections (UITs). Once bacteria reached the urinary tract of the host, they have to adhere to the host cell for the colonization. For this purpose, bacteria have different structures including fimbrial adhesins. Most of the UPECs contain type 1 fimbriae encoded by fim operon (fimB, E, A, I, C, D, F, G, H) which is responsible for the adhesive ability in these isolates. Ninety-four isolates of UPEC were obtained from UTI patients in Baghdad hospitals and their diagnosis were confirmed by the PCR method using 16srDNA as a housekeeping gene. The UPEC isolates were tested for their ability of adherence to the urothelial cells obtai
... Show MoreObjective Using two complementary techniques of virus detection human papillomavirus (HPV)[capture of hybrids (CH) and polymerase chain reaction (PCR)], relate the cytological study and/or cervical biopsy with high-risk HPV (HPV-HR) genotypes presence, as well as relating their viral load (VL). Methods About 272 women, who presented most cell alterations compatible with lesions cervical HPV, which has been detected in all high risk by the CH method and HPV genotype detection by PCR. Results In 22% of the patients it was not detected HPV DNA. Genotype 16 and/or 18 was prevalent and was found in 33% of the 212 women studied, meanwhile, mixed infections were found by several genotypes in 25%. In as for the histological lesions found, in 61 pat
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