The genetic susceptibility analysis of TAAR1 rs8192620 to methamphetamine and heroin abuse and its role in impulsivity

Abnormal genetic polymorphism of trace amine-associated receptor 1 (TAAR1) rs8192620 site has been confirmed to induce methamphetamine (MA) use and drug craving. However, the genetic susceptibility difference between MA addicts and heroin addicts is unknown. This study evaluated genetic heterogeneity of TAAR1 rs8192620 between MA and heroin addicts and elucidated whether rs8192620 genotypes associated with discrepancy in emotional impulsivity, which would help to instruct individualized treatment in addiction via acting on TAAR1 and evaluate risk of varied drug addiction. Participants consisting of gender-matched 63 MA and 71 heroin abusers were enrolled in the study. Due to mixed drug usage in some MA addicts, MA users were further subdivided into 41 only-MA (only MA taking) and 22 mixed-drug (Magu composed of about 20% MA and 70% caffeine) abusers. Via inter-individual single nucleotide polymorphism (SNP) analysis and two-sample t tests, respectively, the genotypic and Barratt Impulsiveness Scale-11 (BIS-11) scores differences between groups were completed. With following genotypic stratification, the differences in BIS-11 scores between groups were analyzed through two-sample t test. Individual SNP analysis showed significant differences in alleles distribution of rs8192620 between MA and heroin subjects (p = 0.019), even after Bonferroni correction. The TT homozygotes of rs8192620 dominated in MA participants, while C-containing genotypes in heroin (p = 0.026). There was no association of genotypes of TAAR1 rs8192620 with addicts' impulsivity. Our research indicates that the TAAR1 gene polymorphism might mediate the susceptibility discrepancy between MA and heroin abuse.

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma

OBJECTIVE

To investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients.

MATERIALS AND METHODS

A retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC).

RESULTS

399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR-). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively).

CONCLUSIONS

Radiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.