BackgroundTumor mutation burden (TMB) as a prognostic marker for immunotherapy has shown prognostic value in many cancers. However, there is no systematic investigation on TMB in papillary thyroid carcinoma (PTC).MethodsBased on the somatic mutation data of 487 PTC patients from The Cancer Genome Atlas (TCGA), TMB was calculated, and we classified the samples into high-TMB (H-TMB) and low-TMB (L-TMB) groups. Bioinformatics methods were used to explore the characteristics and potential mechanism of TMB in PTC.ResultsHigh TMB predicts shorter progression-free survival (PFS) (P < 0.001). TMB was positively correlated with age, stage, tumor size, metastasis, the male sex and tall cell PTC. Compared to the L-TMB group, the H-TMB group presented with lower immune cell infiltration, a higher proportion of tumor-promoting immune cells (M0 macrophages, activated dendritic cells and monocytes) and a lower proportion of antitumor immune cells (M1 macrophages, CD8+ T cells and B cells). Additionally, the characteristics displayed by different TMB groups were not driven by critical driver mutations such as BRAF and RAS.ConclusionsPTC patients with high TMB have a worse prognosis. By stratifying PTC patients according to their TMB, advanced PTC patients who are candidates for immunotherapy could be selected. 相似文献
Objective The present study aims to investigate the concentrations of Hg and its aspects methyl mercury(Me-Hg) and inorganic mercury(I-Hg) in the biological samples(BSs) of fluorescent lamp industries workers(FLIWs).Methodology Different BSs including red blood cells(RBCs),plasma,urine,hair and nails were collected from the workers exposed to Hg and unexposed persons were selected as control group to measure both the T-Hg concentration as well as its species in different biological samples through quantitative analysis.Health data was collected through questionnaire survey.Results The mean concentrations of T-Hg(31.9 μg/L),Me-Hg(27.7 μg/L),and I-Hg(5.36 μg/L) in RBCs were found significantly(P 0.001) higher among the workers(n = 40) as compared to the control group(n = 40).Similarly the mean Hg concentrations in plasma,urine,hair and nails were also significantly higher among the workers than the control group.The statistical relation between Hg concentration and demographic characteristics observed that workers experience and fish consumption has increased the Hg concentration while age,weight and smoking found no significant effect on Hg concentration in the BSs.Conclusion The study observed that the workers were highly exposed to high concentration of Hg and they are at a high health risk. 相似文献
PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. 相似文献
Background: Most theoretical models of self-determination suggest that both environmental and personal factors influence the development of self-determination. The design and implementation of interventions must be conducted with foreknowledge of such mediating and moderating factors if the intervention is to be successful.
Methods: The purpose of this study was to examine the degree to which several personal factors and school characteristics affect and explain students’ self-determination. A total of 232 students with intellectual disability from Spain participated. Their self-determination level was assessed by the ARC-INICO Scale.
Results: Students with moderate levels of intellectual disability obtained significantly lower scores on self-determination than their peers with mild intellectual disability. There were significant differences in relation to the level of support needs and their experience with transition programs. The level of support needs was a significant predictor.
Conclusion: These findings contribute to current research in this field and practical implications were discussed. 相似文献