Diseases - Mr Validity

We note several strengths. Firstly, the comparative use of two large, independent, multicentre patient populations ensured a high degree of internal validity, with similar patient demographics between cohorts. Nevertheless, future studies would greatly benefit from the inclusion of further EDs from alternative geographies, different income-settings and hospitals with significantly different triage procedures, in order to rule-out any significant influence on results and increase the reproducibility of the findings. Secondly, the use of optimised derivation MR-proADM cut-offs resulted in similar findings across both cohorts with regard to the identification of disease severity and out-patient treatment, strengthening its potential use in both areas.

Diseases - Mr Validity

Hepatic fibrosis is a common result of different chronic liverdiseases caused by viral infections, alcohol abuse, nonalcoholic fattyliver disease (NAFLD), autoimmune disease, and metabolic/geneticdisorders. Hepatic fibrosis is a dynamic process which can, in somecases, be reversed with effective treatment.1,2 Otherwise, itcan progress to more advanced stages, including cirrhosis, where liverfunction is impaired and severe complications, such as varicealbleeding, ascites, portal hypertension, hepatocellular carcinoma, anddeath can occur.3 In 2009, chronic liver disease andcirrhosis were responsible for 143,000 hospitalizations, 11,000in-hospital deaths (7.7% death rate), and $6.7 billion in hospitalcharges in toto.4 Accurately detecting and staging fibrosisare important in the treatment of chronic liver disease and monitoringits effects on the liver. Chronic viral hepatitis C (HCV) infection is asignificant cause of hepatic disease and its treatment may depend onthe presence of substantial fibrosis.5,6

Hepaticfibrosis is an accumulation of the extracellular matrix that resultsfrom hepatic stellate cell transdifferentiation to myofibroblaststrigged by necroinflammation of hepatocytes. The necroinflammation is awound-healing response to liver-cell injury that may be due to differentcauses of liver disease.48 Accumulated data have shown thatliver stiffness measured by MRE is highly correlated and increases withfibrosis stage identified by liver histology.3,21,26,27,29-31 In some cases, an early increase of liver stiffness can be found even before the onset of fibrosis due to liver cell injury.32,49-53One possible explanation of the strong relationship between liverdisease and liver stiffness could be that early liver cell injury leadsto changes in the extracellular matrix that increase the stiffness ofhepatic tissue, which through a process known as mechanotransductionpromotes the activation of stellate cells and the subsequent developmentof fibrosis,32,49,50 which in turn further increases liverstiffness. The persistent elevation in stiffness of the mechanicalenvironment is then believed to accelerate fibrosis progression to moreadvanced stages. To date, the data have shown that, in chronic liverdiseases with different causes, MRE has high diagnostic accuracy (AUROC =92%-100%) for detecting and staging hepatic fibrosis, as seen in Table1. Within our institution, we use a liver stiffness value of 2.93 kPa(kilopascals) as the threshold for detecting nonfibrotic liver tissue,where an abnormal liver stiffness is > 2.93 kPa. The diagnosticaccuracy, sensitivity, specificity, positive- and negative-predictivevalues of this cut-off value are no lower than 97%.3

Hepatic fibrosis is a common result of different chronic liver\ndiseases caused by viral infections, alcohol abuse, nonalcoholic fatty\nliver disease (NAFLD), autoimmune disease, and metabolic/genetic\ndisorders. Hepatic fibrosis is a dynamic process which can, in some\ncases, be reversed with effective treatment.1,2 Otherwise, it\ncan progress to more advanced stages, including cirrhosis, where liver\nfunction is impaired and severe complications, such as variceal\nbleeding, ascites, portal hypertension, hepatocellular carcinoma, and\ndeath can occur.3 In 2009, chronic liver disease and\ncirrhosis were responsible for 143,000 hospitalizations, 11,000\nin-hospital deaths (7.7% death rate), and $6.7 billion in hospital\ncharges in toto.4 Accurately detecting and staging fibrosis\nare important in the treatment of chronic liver disease and monitoring\nits effects on the liver. Chronic viral hepatitis C (HCV) infection is a\nsignificant cause of hepatic disease and its treatment may depend on\nthe presence of substantial fibrosis.5,6

Hepatic\nfibrosis is an accumulation of the extracellular matrix that results\nfrom hepatic stellate cell transdifferentiation to myofibroblasts\ntrigged by necroinflammation of hepatocytes. The necroinflammation is a\nwound-healing response to liver-cell injury that may be due to different\ncauses of liver disease.48 Accumulated data have shown that\nliver stiffness measured by MRE is highly correlated and increases with\nfibrosis stage identified by liver histology.3,21,26,27,29-31 In some cases, an early increase of liver stiffness can be found even before the onset of fibrosis due to liver cell injury.32,49-53\nOne possible explanation of the strong relationship between liver\ndisease and liver stiffness could be that early liver cell injury leads\nto changes in the extracellular matrix that increase the stiffness of\nhepatic tissue, which through a process known as mechanotransduction\npromotes the activation of stellate cells and the subsequent development\nof fibrosis,32,49,50 which in turn further increases liver\nstiffness. The persistent elevation in stiffness of the mechanical\nenvironment is then believed to accelerate fibrosis progression to more\nadvanced stages. To date, the data have shown that, in chronic liver\ndiseases with different causes, MRE has high diagnostic accuracy (AUROC =\n92%-100%) for detecting and staging hepatic fibrosis, as seen in Table\n1. Within our institution, we use a liver stiffness value of 2.93 kPa\n(kilopascals) as the threshold for detecting nonfibrotic liver tissue,\nwhere an abnormal liver stiffness is > 2.93 kPa. The diagnostic\naccuracy, sensitivity, specificity, positive- and negative-predictive\nvalues of this cut-off value are no lower than 97%.3

Based on the experiment results, the parameter is set to 1, and the accuracy rates of the classification AD and NC using optimal feature subset with the method proposed in this paper is shown in Figure 6. It is still the mean accuracy rate for training set, as well as optimized accuracy rate for test set. There are 292-dimensional features that are extracted from the experiment in the feature extracting process. It can be seen from the figure that when the feature subset is initially selected by SFS, both classification accuracies have a significant upward trend. The reason behind this situation is that the feature set is mainly constituted by the most discriminatory features and there is no redundancy in the feature subset. When the feature dimension is 9, both accuracy rates of two-sample set are highest as shown in Figure 6, so we obtain the optimal feature subset at this time. After this, with the growing of number of selected features, the accuracy rates of two-sample set have declined. That is to say, the most discriminative feature combination can be efficiently selected at the beginning, and it also proves the validity of the improved SVM-RFE method proposed in this paper. As shown in the experiment, the robustness of the proposed method can be proved without employing the parameter optimization in the process of feature order and cross validation on training set.

(2) MCI-NC (Mild Cognitive Impairment Comparing with Normal Controls). The experimental result of comparing MCI and NC are shown in Table 6, and it is the same as the experiment of comparing AD and NC that value represents the number of features that need to be selected in the SFS process having the minimum redundancy with the previous highest ranking feature. When the value of is 1, each time a feature with the lowest redundancy of the highest ranking feature is selected, both the mean accuracy of training set and the optimized accuracy rate of test set are highest using the selected optimal feature subset. Furthermore, both accuracy rates exceed 95%. We can see that, from comparing the result of to , both the accuracy rates of training set and test set have sharply increased about 6%. So this can prove the importance of taking the correlation into consideration and the validity of our method.

For this reason, the parameter is set to 1; the experiment setting and classification result of MCI and NC are the same as the AD and NC. It is also the mean accuracy rate for training set and optimized accuracy rate for test set. When the feature dimension is 36, both accuracy rates of two-sample set are highest as shown in Figure 7, so we obtain the optimal feature subset at this time. After this, with the growing of number of selected features, the accuracy rates of training set have declined. That is to say, the most discriminative feature combination can be efficiently selected at the beginning, and it also proves the validity of the improved SVM-RFE method proposed in this paper.

Olfactory Dysfunction (OD) may affect 1 to 20% of the general population [1]. The primary causes of OD are sinonasal disorders, post-viral olfactory dysfunction, neurological diseases and post-traumatic lesions of the olfactory nerve [1]. The prevalence of OD has substantially increased since the onset of the coronavirus disease 2019 (COVID-19) pandemic, reaching 30 to 86% of patients infected by Alpha, Delta or Omicron variants [2,3,4]. The OD may include anosmia, hyposmia or parosmia throughout the clinical course of the disease. According to several studies, the OD may persist in a significant number of patients more than 6-month post-infection and may affect their quality of life (QoL) [4, 5]. The evaluation of OD has to involve psychophysical tests and patient-reported outcome questionnaires that provide additional insight into the impact of OD on patients QoL [6]. 041b061a72