Cognitive-Affective Verbal Learning Test: An integrated measure of affective and neutral words

Systematic evaluation of urinary formic acid as a new potential biomarker for Alzheimer's disease

INTRODUCTION

The accumulation of endogenous formaldehyde is considered a pathogenic factor in Alzheimer's disease (AD). The purpose of this study was to investigate the relationship between urinary formic acid and plasma biomarkers in AD.

MATERIALS AND METHODS

Five hundred and seventy-four participants were divided into five groups according to their diagnosis: 71 with normal cognitive (NC), 101 with subjective cognitive decline (SCD), 131 with cognitive impairment without mild cognitive impairment (CINM), 158 with mild cognitive impairment (MCI), and 113 with AD.

RESULTS

With the progression of the disease, urinary formic acid levels showed an overall upward trend. Urinary formic acid was significantly correlated with Mini-Mental State Examination (MMSE) scores, the Chinese version of Addenbrooke's Cognitive Examination III (ACE-III) scores, and Montreal Cognitive Assessment-Basic (MoCA-B) time. The areas under the receiver operating characteristic curves (AUC) of urinary formic acid in distinguishing NC from AD was 0.797, which was similar to that of plasma neurofilament light chain (NfL; AUC = 0.768) and better than other plasma biomarkers (Aβ40, Aβ42, Aβ42/Aβ40, T-tau, P-tau181, and P-tau181/T-tau). We also found that using urinary formic acid and formaldehyde levels could improve the accuracy of using plasma biomarkers to determine AD disease stage.

DISCUSSION

Our study revealed the possibility of urinary formic acid as a potential novel biomarker for the early diagnosis of AD.

Psychometric Properties of the Verbal Affective Memory Test-26 and Evaluation of Affective Biases in Major Depressive Disorder

We developed the Verbal Affective Memory Test-26 (VAMT-26), a computerized test to assess verbal memory, as an improvement of the Verbal Affective Memory Test-24 (VAMT-24). Here, we psychometrically evaluate the VAMT-26 in 182 healthy controls, examine 1-month test–retest stability in 48 healthy controls, and examine whether 87 antidepressant-free patients diagnosed with Major Depressive Disorder (MDD) tested with VAMT-26 differed in affective memory biases from 335 healthy controls tested with VAMT24/26. We also examine whether affective memory biases are associated with depressive symptoms across the patients and healthy controls. VAMT-26 showed good psychometric properties. Age, sex, and IQ, but not education, influenced VAMT-26 scores. VAMT-26 scores converged satisfactorily with scores on a test associated with non-affective verbal memory. Test–retest analyses showed a learning effect and a r ≥ 0.0.8, corresponding to a typical variation of 10% in recalled words from first to second test. Patients tended to remember more negative words relative to positive words compared to healthy controls at borderline significance (p = 0.06), and affective memory biases were negatively associated with depressive symptoms across the two groups at borderline significance (p = 0.07), however, the effect sizes were small. Future studies are needed to address whether VAMT-26 can be used to distinguish between depression subtypes in patients with MDD. As a verbal memory test, VAMT-26 is a well validated neuropsychological test and we recommend it to be used in Danish and international studies on affective memory.

Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words

In recent years, with the rapid development and wide application of the Internet, it has become the main place for the generation and dissemination of public opinion. To grasp the information of network public opinion in a timely and comprehensive way can not only effectively prevent sudden network malignant events but also provide a reference for the scientific and democratic decision-making of government departments. Therefore, in view of the practical application needs, this article studies the emotional characteristics and the evolution of public opinion over time based on the emotional feature words of network public opinion participants. Firstly, the positive and negative emotional lexicon of HowNet emotional dictionary is used, and the commonly used emotional lexicon and expression symbols are added to the lexicon. At the same time, the polarity annotation method of Chinese emotional lexicon ontology is used to construct the emotional lexicon of this article. Secondly, considering other emotional polarity characteristics in the dictionary, an emotional tendency analysis model is proposed. In this article, emotional analysis is applied to the evolution analysis of network public opinion, and the change of network public opinion characteristics with time series is obtained. The simulation results show that the emotional dictionary constructed in this article and the proposed model of emotional orientation analysis can effectively analyze the emotional characteristics of network public opinion participants and apply emotional analysis to the evolution analysis of network public opinion, which can get the change of emotional characteristics of public opinion participants with time series.