Subjects: Biology >> Biochemistry submitted time 2024-04-24
Abstract： How to differentiate the diagnosis of depression and bipolar disorder has always been an important problem that needs to be solved urgently in clinical practice. In this study, from the perspective of urine proteomics, urine samples of similar age were collected from two hospitals to investigate the candidate biomarkers for differentiating the diagnosis of depression and bipolar disorder using both group analysis and one-to-many analysis. The experimental results of the paired group analysis showed that 108 differential proteins were identified in the depressed group compared to the bipolar group under strict screening conditions with screening criteria of FC ≥ 2 or ≤ 0.5 and a two-tailed unpaired t-test of P < 0.01, with an average of 3.7 randomly generated differential proteins, and a confidence level of 96.6 % for the correlation between these proteins and the disease difference. In the one-to-many analysis, 24 differential proteins were co-identified by the samples of 13 depressed patients, 16 of which showed a completely consistent trend of expression changes in all depressed patients studied, and 6 of which were associated with immunoglobulins; 41 differential proteins were co-identified by the samples of 12 depressed patients out of 13, and 19 of which showed a completely consistent trend of expression change in the These results reflect the strong consistency of differential proteins between the two groups of patients. 12 or more samples from depressed patients were enriched for differential proteins related to multiple biological processes and signaling pathways associated with the immune system, which is consistent with previous studies: immune mechanisms may be one of the pathogenetic mechanisms of major depression and that drugs with major immune targets can improve depressive symptoms. In the future, it may be possible to observe the immune status of patients with depression to provide direction and basis for the precise treatment of depression. The results of this paper show that urine proteomics can differentiate between depression and bipolar disorder, suggest possible mechanisms and potential targets for the treatment of depression and bipolar disorder, and provide a tool for future differential diagnosis and precision treatment of the diseases.
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Subjects: Psychology >> Statistics in Psychology submitted time 2019-11-08
Abstract： Problem-solving competence is defined as the capacity to engage in cognitive processing to understand and resolve problem scenarios where a solution is not obvious. Computer-based assessments usually provide an interactive environment in which students can solve a problem by choosing among a set of available actions and taking one or more steps to complete a task. All students’ actions are automatically recorded in system logs as coded and time-stamped strings. These strings are called process data. The process data have multi-level structures in which the actions are nested within a single individual and therefore they are logically interconnected. Recently, researches have focused on characterizing process data and analyzing the response strategies to solve the problem. This study proposed an extended MMixIRT model which incorporated the multilevel structure into a mixture IRT model. It can classify latent groups at process level that have different problem solving strategies, and estimate the students’ abilities at the student level simultaneously. This model takes the accumulated response information as the specific steps at the process level and defines a more free matrix to determine the weight information used for ability estimation at the student level. Specifically, in the standard MMixIRT model, the student-level latent variables are generally obtained from the measurement results made by the process-level response variables, while students’ final responses are used to estimate their problem-solving abilities in the extended MMixIRT model. This research applied process data recorded in one of the items (Traffic CP007Q02) of problem solving in PISA 2012. The samples were 3196 students from Canada, Hongkong-China, Shanghai-China, Singapore, and America. Based on the log file of the process record, there were 139,990 records in the final data file. It was found that (1) The model can capture different problem-solving strategies used by students at the process level, as well as provide ability estimates at the student level. (2) The model can also analyze the typical characteristics of students’ strategy in problem-solving across different countries for targeted instructional interventions. It is concluded that the extended MMixIRT model can analyze response data at process and student levels. These analyses not only play an important role in the scoring, but also provide valuable information to psychometricians and test developers, help them to better understand what distinguishes well performing students from the ones that are not, and eventually lead to better test design. "
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