Copying the two-step-annealing procedure in self-aligned silicide technology, Ti/Si samples deposited under ultra-high vacuum were annealed at low temperatures, selective etched, and again annealed at high temperatures in our "RHT-6000 rapid heat treatment equipment for VLSI" with nitrogen as the protective ambient. The experimental results showed that titanium silicide and nitride were formed simultaneously in the thin film. The formation of titanium nitride was studied in detail. In addition, we studied the electrical properties of the final product TiN-TiSi/sub 2/ and its diffusion barrier function in the Al/TiN-TiSi/sub 2//Si(n/sup +/p) diode structure.
Background: Given that 2019 novel coronavirus (COVID-19) spreads rapidly, it is critical to make rapid and accurate detection of COVID-19 patients towards containment of SARS-CoV-2 virus. At present, COVID-19 patients are mainly identified through viral nuclear acid testing (NAT). However, factors such as time for patients being tested, experience of test operators, and specimen's preparation, might affect the accuracy of testing results. The purpose of this study was to use different classification and feature selection methods to improve the diagnostic accuracy of COVID-19 patients. Methods: We utilized seven machine learning algorithms for assisting diagnosis of COVID-19 by developing a non-NAT algorithm. In order to reduce the number of input features while maintaining the models' performance so as to decrease the cost and time consumption, we adopted three algorithms, such as Chi-square test, variance analysis, and feature importance tests to identify the optimal feature sets. Findings: The XGBoost and RF models displayed the best performance for COVID-19 detection, with the highest accuracy rate more than 0·96. The accuracy of RF model was 0·968 when using only ten hematological features and body temperature. Interpretation: Ten blood features and body temperature can fairly accurately determine whether a suspected patient is infected with COVID-19. Our model can improve the diagnostic accuracy of COVID-19 and reduce the spread. Funding: This work is supported by grants from the National Key Research and Development Program of China under Grant 2017YFE0123600, the Natural Science Foundation of China (81873931, 81974382 and 81773104), the Frontier Exploration Program of Huazhong University of Science and Technology (2015TS153), and the Major Scientific and Technological Innovation Projects in Hubei Province (2018ACA136).Declaration of Interests: All the authors stated that the paper had never been published elsewhere, and that there were no competing economic interests.Ethics Approval Statement: The collection, use, and retrospective analysis of chest CT images, CFs and SARS-CoV-2 nucleic acid PCR results of patients were approved by the institutional ethical committees of HUST-UH (IRB ID: [2020] IEC(A001)).
Cortical morphometry is an intermediate phenotype that is closely related to the genetics and onset of major depressive disorder (MDD), and cortical morphometric networks are considered more relevant to disease mechanisms than brain regions. We sought to investigate changes in cortical morphometric networks in MDD and their relationship with genetic risk in healthy controls.
Methods:
We recruited healthy controls and patients with MDD of Han Chinese descent. Participants underwent DNA extraction and magnetic resonance imaging, including T1-weighted and diffusion tensor imaging. We calculated polygenic risk scores (PRS) based on previous summary statistics from a genome-wide association study of the Chinese Han population. We used a novel method based on Kullback–Leibler divergence to construct the morphometric inverse divergence (MIND) network, and we included the classic morphometric similarity network (MSN) as a complementary approach. Considering the relationship between cortical and white matter networks, we also constructed a streamlined density network. We conducted group comparison and PRS correlation analyses at both the regional and network level.
Results:
We included 130 healthy controls and 195 patients with MDD. The results indicated enhanced connectivity in the MIND network among patients with MDD and people with high genetic risk, particularly in the somatomotor (SMN) and default mode networks (DMN). We did not observe significant findings in the MSN. The white matter network showed disruption among people with high genetic risk, also primarily in the SMN and DMN. The MIND network outperformed the MSN network in distinguishing MDD status.
Limitations:
Our study was cross-sectional and could not explore the causal relationships between cortical morphological changes, white matter connectivity, and disease states. Some patients had received antidepressant treatment, which may have influenced brain morphology and white matter network structure.
Conclusion:
The genetic mechanisms of depression may be related to white matter disintegration, which could also be associated with decoupling of the SMN and DMN. These findings provide new insights into the genetic mechanisms and potential biomarkers of MDD.
Abstract Abnormal gut microbiota is associated with the occurrence of depression, but the specific pathophysiological role of gut microbiota in the pathogenesis of depression is still unknown. We found that the levels of serum steroid hormone testosterone in male patients with depression were lower than in healthy controls. Using testosterone as the only carbon source, the testosterone‐degrading bacteria Arthrobacter koreensis was isolated from fecal of low testosterone male patients with depression. We found that A. koreensis administration in mice led to reduced serum testosterone levels and depression‐like behaviors, which were improved by antibiotic treatment. Using whole genome sequencing, the gene mediating testosterone degradation in A. koreensis was identified and annotated as 3α‐hydroxysteroid dehydrogenase (3α‐HSD). Escherichia coli heterologously expressing 3α‐HSD obtained the capacity to degrade testosterone, causing depression‐like behaviors after gavage to mice. Testosterone supplementation improves depression‐like behavior in mice induced by gavage of Escherichia coli heterologously expressing 3α‐HSD. Finally, the universality of 3α‐HSD in gut of male patients with depression was higher than that of healthy controls. Overall, our results revealed a new pathway that potentially links testosterone degradation by gut microbes harboring 3α‐HSD enzymes to the pathogenesis of depression. Gut microbial 3α‐HSD can induce depression in mice via testosterone degradation. This means that 3α‐HSD expressed by gut bacteria may be a potential target for depression in men.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)