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: Digital Publishing >> New Media submitted time 2023-10-08 Cooperative journals: 《中国传媒科技》
Abstract：信息时代下为新闻记者的工作发展带来的优势与便利,随着我国社会主义市场经济的发展和改革开放的推进,我国经济迅速发展,媒体对我国经济生活和经济现象的报道也越来越多,这一现实提出了对新闻记者的素质方面的新要求。高质量、高水平的经济新闻记者成为了经济类新闻媒体生存发展的重要力量。同时也要求新闻记者要极大地提升自己的工作成效。
Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Besides affecting vision, light exerts powerful non-visual effects on a wide range of biological functions and behaviors, such as modulating circadian rhythm, melatonin suppression, and acute alertness. Comprehensive studies on the effect of light on human circadian rhythm now lead more researchers to pay attention to the effects of light on acute alertness. Here, we summarized (1) different measures of alertness, (2) factors that may influence the effect of light on alertness like illuminance, exposure, timing, wavelength and color temperature, (3) promising applications of the alerting effect of light in some affective disorders, circadian rhythm, and office lighting, and (4) future directions focusing on investigation of neural mechanisms, optimal lighting characteristics, and potential confounds.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-05-10 Cooperative journals: 《计算机应用研究》
Abstract： The traditional way of internal attack for smartphone is easy to detected and prevented by the user. As a common audio signal, DTMF signal plays a very important role in mobile communication, but also faces severe security risk. This paper proposed an external attack method for smartphone based on DTMF signal, which could attack effectively without the user being aware and without interaction with the smartphone. Firstly, it recorded some important keystroke operation of user. Secondly, performed double-threshold endpoint detection in time domain to extract the effective area of the signal. Thirdly, converted the effective area to frequency domain by Goertzel algorithm for digital classification. Finally, all the keystroke data of the user were obtained by comparing the DTMF coding table. The experimental results show that the method can decipher more than 80% of the keystroke data under the condition of 10db signal-to-noise ratio and no interaction with the smartphone.
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