Background
Comorbidity is a current area of interest in multiple sclerosis (MS) and is essential for multidisciplinary management. Although recent studies suggest that patients with MS have an ...elevated risk of developing inflammatory bowel diseases (IBD), this systematic review and meta-analysis aimed to estimate the overall risk of developing ulcerative colitis (UC), specifically in patients with MS.
Methods
In 2021, a comprehensive literature search was performed on PubMed, Scopus, Embase, and Web of Science to identify studies investigating the association between UC and MS. The selected papers were utilized to estimate the associations, risk ratios (RRs), and a 95% confidence interval (CI).
Results
The analysis revealed a slightly elevated risk of UC incidence in patients with MS compared to controls, but this finding was not statistically significant (RR: 1.27 95% CI: 0.96–1.67). In contrast, the study found that patients with UC have a significantly higher risk of developing MS than controls (RR: 1.66 95% CI: 1.15–2.40).
Conclusion
Our findings highlight that the presence of UC increases the risk of developing MS by more than 50%, whereas the presence of MS does not increase the risk of UC occurrence. These results underscore the importance of considering the potential development of UC in the clinical management and early diagnosis of patients with MS, as it may contribute to better therapeutic outcomes.
Software companies develop different software in parallel, which is a very complex task. Project managers have to manage different software development processes based on different time, cost, and ...number of staff, sequentially. Software time, cost, and number of staff estimates are the critical tasks for project managers in software companies. Estimation of these parameters at early stage of software project planning is one the challenging issued in software project management, for the last decade. Software cost and time estimation supports the project planning and tracking, and it controls the expenses of software development. Software effort estimation refers to the estimates of the likely amount of cost, schedule, and manpower required to develop a software. Accurate effort estimate at the early phase of software development can help project managers to efficiently control project progress and improve the project success rate. This paper proposes a novel artificial neural network (ANN) prediction model incorporates Constructive Cost Model (COCOMO), ANN-COCOMO II, to provide more accurate software estimates at the early phase of software development. This model uses the advantages of artificial neural networks such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. The ANN is utilised to calibrate the software attributes using past project data, in order to produce accurate software estimates. The proposed model is evaluated using 156 sets of project data from two data sets, COCOMO I and NASA93. The analysis of the obtained results shows 8.36% improvement in estimation accuracy in the ANN-COCOMO II model, when compared with the original COCOMO II.
