Suicide-relevant information processing in unipolar and bipolar depression: An eye-tracking study.

Suicide-relevant attentional biases are found in suicide attempters (SAs) with depression. Wenzel and Beck provide a theoretical framework that suggests suicide-related attention biases confer vulnerability to suicide. In this study, we integrated eye-tracking dynamics of suicide-related attentional biases with self-report measures to test their model. A free-viewing eye-tracking paradigm, which simultaneously presented four images with different valences (suicide-related, negative, positive, neutral), was examined in 76 SAs with unipolar or bipolar depression, 66 nonsuicidal depressive participants (ND), and 105 healthy never-depressed healthy control participants (HC). Structural equation modeling (SEM) was used for the theory testing. SA gazed more at suicide-relevant stimuli throughout the 25-s trial compared with ND. SA and ND initially detected suicide-related stimuli faster than HC. Groups did not differ on how often they initially gazed at suicide images or how fast they disengaged away from them. Eye-tracking indices of attentional biases, together with self-reported hopelessness, adequately fit an SEM consistent with Wenzel and Beck's cognitive theory of suicide-related information processing. Potentially, suicide-related attention biases could increase vulnerability to suicidal ideation and eventual suicidal behaviors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Mutation load estimation model as a predictor of the response to cancer immunotherapy.

The determination of the mutation load, a total number of nonsynonymous point mutations, by whole-exome sequencing was shown to be useful in predicting the treatment responses to cancer immunotherapy. However, this technique is expensive and time-consuming, which hampers its application in clinical practice. Therefore, the objective of this study was to construct a mutation load estimation model for lung adenocarcinoma, using a small set of genes, as a predictor of the immunotherapy treatment response. Using the somatic mutation data downloaded from The Cancer Genome Atlas (TCGA) database, a computational framework was developed. The estimation model consisted of only 24 genes, used to estimate the mutation load in the independent validation cohort precisely (R2 = 0.7626). Additionally, the estimated mutation load can be used to identify the patients with durable clinical benefits, with 85% sensitivity, 93% specificity, and 89% accuracy, indicating that the model can serve as a predictive biomarker for cancer immunotherapy treatment response. Furthermore, our analyses demonstrated the necessity of the cancer-specific models by the constructed melanoma and colorectal models. Since most genes in the lung adenocarcinoma model are not currently included in the sequencing panels, a customized targeted sequencing panel can be designed with the selected model genes to assess the mutation load, instead of whole-exome sequencing or the currently used panel-based methods. Consequently, the cost and time required for the assessment of mutation load may be considerably decreased, which indicates that the presented model is a more cost-effective approach to cancer immunotherapy response prediction in clinical practice.