Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer.

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. In this study, multivariable datasets were collected from 751 college students who could be easily affected by pandemics based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g. decision-making in uncertain situations) were more important in post-pandemic conditions. Although the results should be validated using other mental health variables or national datasets, this study will serve as a reference for researchers regarding the use of the quantum annealing approach in factor analysis with validation through real-world survey dataset analysis.

Serum insulin-like growth factor-I level is an independent predictor of recurrence and survival in early hepatocellular carcinoma: a prospective cohort study.

PURPOSE: Insulin-like growth factor-I (IGF-I) reflects hepatic synthetic function and plays an important role in the development and progression of various cancers. In this study, we investigated whether pretreatment serum IGF-I levels predict time-to-recurrence (TTR) and overall survival (OS) in patients with early-stage hepatocellular carcinoma after curative treatment. EXPERIMENTAL DESIGN: Consecutive patients with hepatocellular carcinoma who had undergone surgical resection, radiofrequency ablation, or percutaneous ethanol injection as curative treatments of early hepatocellular carcinoma were included from two prospective cohorts and the training set (n = 101) and the validation set (n = 91) were established. Serum samples were collected before treatment and the levels of IGF-I and IGF-binding protein-3 (IGFBP-3) were analyzed with regard to their associations with recurrence and survival. RESULTS: In the training set, patients with low IGF-I levels showed significantly shorter TTR [median, 14.6 months; 95% confidence interval (CI), 1.8-27.5] than patients with high IGF-I levels (median, 50.8 months; 95% CI, 36.9-64.7; P < 0.001) during a median follow-up period of 52.4 months. In the multivariate analysis, low levels of IGF-I were an independent predictor of recurrence (HR, 2.49; 95% CI, 1.52-4.08; P < 0.001). Furthermore, together with high-serum α-fetoprotein and multiple tumors, low levels of IGF-I remained an independent predictor of poorer survival (HR, 8.00; 95% CI, 1.94-33.01; P = 0.004). Applied to the independent validation set, low-serum IGF-I levels maintained their prognostic value for shorter TTR and OS. CONCLUSIONS: Low-baseline IGF-I levels independently correlated with shorter TTR and poorer survival in patients with early-stage hepatocellular carcinoma after curative treatment.

Recent History Functional Linear Models for Sparse Longitudinal Data.

We consider the recent history functional linear models, relating a longitudinal response to a longitudinal predictor where the predictor process only in a sliding window into the recent past has an effect on the response value at the current time. We propose an estimation procedure for recent history functional linear models that is geared towards sparse longitudinal data, where the observation times across subjects are irregular and total number of measurements per subject is small. The proposed estimation procedure builds upon recent developments in literature for estimation of functional linear models with sparse data and utilizes connections between the recent history functional linear models and varying coefficient models. We establish uniform consistency of the proposed estimators, propose prediction of the response trajectories and derive their asymptotic distribution leading to asymptotic point-wise confidence bands. We include a real data application and simulation studies to demonstrate the efficacy of the proposed methodology.