Overall, our results provide a critical evaluation of the views of the general public with regard to genetics and genetic testing services in Greece and should serve as a model for replication in other populations.
These findings provide the basis for assessing the views of healthcare professionals in relation to personalized medicine in Greece, and should help to facilitate the integration of genomics into the medical decision-making process.
Background: The pace of discoveries and advances in genomic research is not reflected in the pace of their translation and incorporation into day-to-day clinical medicine to individualize healthcare decision-making processes. One of the main obstacles is the poor understanding of the policies and the key stakeholders involved in these translation processes. Methods: We used the computerized version of the PolicyMaker political mapping tool to collect and organize important information about the pharmacogenomics and genomic medicine policy environment, serving as a database for assessments of the policy's content, the major players, their power and policy positions, their interests, and networks and coalitions that interconnect them. Results and Conclusions: Our findings indicate that the genomic medicine policy environment in Greece seems to be rather positive, as the vast majority of the stakeholders express their medium to high support in the initially set goals of genomic medicine policy environment. The Ministry of Health and public healthcare insurance funds seem to oppose it, most likely due to financial constrains. These findings would contribute in selecting and implementing policy measures that will expedite the adoption of genomics into conventional medical interventions.
It has been reported that inflammation and immune system are related to prostate cancer. The neutrophil-to-lymphocyte ratio (NLR), as well as the platelet-to-lymphocyte ratio (PLR), have already been proposed as new indices to help diagnose prostate cancer (PCa). However, the monocyte-to-lymphocyte ratio (MLR) with regard to PCa has rarely been mentioned.
To investigate the capability of the MLR to predict PCa.
Patients who were pathologically diagnosed with PCa in our hospital and healthy control subjects who conformed to the inclusion criteria were enrolled. Patient data were recorded, including age, complete blood counts, blood biochemistry, and serum prostate-specific antigen (PSA) levels. The differences in these data between the groups were analyzed and the diagnostic value of the MLR was compared with PSA.
Our study included a total of 100 patients with PCa and 103 healthy control subjects. Patients with PCa presented with a significantly higher NLR, MLR, and PLR compared to control subjects. However, the hemoglobin and lymphocyte levels were lower (
P
< .05) in PCa patients. The area under the curve (AUC) of PSA and ratio of free/total serum prostate-specific antigen were 0.899 (95% confidence interval [CI]: 0.857–0.942) and 0.872 (95% CI: 0.818–0.926), respectively, while the AUC of the MLR was 0.852 (95% CI: 0.798–0.906), which was higher than that of the NLR, PLR, and any other blood parameters. Additionally, the optimal cut-off value of the MLR for PCa was 0.264, with a specificity of 87.4% and a sensitivity of 72.0%. An evaluation of the diagnostic value of MLR + PSA gave an AUC of 0.936 (95% CI: 0.902–0.970). However, the AUC of MLR + PSA + f/tPSA was 0.996 (95% CI: 0.991–1.000). The diagnostic value of MLR + NLR + PSA gave an AUC of 0.945 (95% CI: 0.913–0.977), and the specificity is 0.971.
PSA remains the most important diagnostic indicator. MLR combined with PSA and f/tPSA has the higher predictive value than PSA. It suggests that MLR may be another good predictive indicator of PCa. It can help reduce the clinical false positive rate.
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