LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data.

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Research on the MEG of Depression Patients Based on Multivariate Transfer Entropy.

The pathogenesis of depression is complex, and the current means of medical diagnosis is single. Patients with severe depression may even have great physical pain and suicidal tendencies. Magnetoencephalography (MEG) has the characteristics of ultrahigh spatiotemporal resolution and safety. It is a good medical means for the diagnosis of depression. In this paper, multivariate transfer entropy algorithm is used to study MEG of depression. In this paper, the subjects are divided into the same brain region and the multichannel combination between different brain regions, and the multivariate transfer entropy of patients with depression and healthy controls under different EEG signal frequency bands is calculated. Finally, the significant difference between the two groups of experimental samples is verified by the results of independent sample t-test. The experimental results show that for the same combination of brain channels, the multivariate transfer entropy in the depression group is generally lower than that in the healthy control group, and the difference is the best in γ frequency band and the largest in the frontal region.

Longitudinal observation of viral load in patients infected with Omicron variant and its relationship with clinical symptoms.

Objective: In 2022, a new coronavirus variant (Omicron) infection epidemic broke out in Shanghai, China. However, it is unclear whether the duration of this omicron variant is different from that of the prototype strain. Methods: We retrospectively analyzed 157 cases of Omicron variant infection in Taizhou Public Health Center from March 29, 2022, to April 18, 2022, and observed the dynamics of nucleic acid Ct values during the admission and discharge of patients. Clinical and laboratory indicators of these patients were also obtained. Results: Compared to the prototype strain, the Omicron variant showed a broad population susceptibility in infected individuals (regardless of age and presence of underlying disease) and had slight damage to the immune system and renal function; the viral loads peaked was 2-3 days from disease onset; the median duration of omicron variant was 15-18 days; the nucleic acid Ct value of nasopharyngeal swabs of infected patients is lower than that of throat swabs, and the Ct value of oropharyngeal swabs is unstable during the recovery period. Conclusion: Therefore, we found that the time to peak viral load of this Omicron variant was 2-3 days after the onset of the disease, and the duration was 15-18 days; symptomatic patients had higher viral load and longer hospitalization time. This finding will provide a basis for understanding omicron variants and formulating the national prevention and control strategy.

Relationship between Contrast-Enhanced CT and Clinicopathological Characteristics and Prognosis of Non-Small Cell Lung Cancer.

BACKGROUND: This study investigated the relationship between contrast-enhanced computed tomography (CECT) and clinicopathological characteristics and prognosis of non-small cell lung cancer (NSCLC). METHODS: A total of 198 NSCLC patients admitted to Enze Hospital from February 2009 to July 2012 underwent pre-surgical CECT to investigate parameters such as tumor size, CECT enhancement, lymph node enlargement, and lymph node size. Chi-square and log-rank tests were used to analyze associations between CECT parameters and pathological features as well as correlations of CECT parameters with prognosis. A Cox proportional hazard model and logistic regression analysis were applied to identify independent risk factors for prognosis. RESULTS: Tumor size, CECT enhancement, and lymph node enlargement and size were related to degree of differentiation, TNM stage, and lymph node metastasis. Tumor size, lymph node enlargement and metastasis, lymph node size, and CECT enhancement were independent risk factors for NSCLC prognosis. Large tumors and lymph nodes, tumor enhancement, and enlarged and metastatic lymph nodes indicated a poor prognosis. CONCLUSION: Our study indicates that CECT features can be associated with clinicopathological characteristics and can predict the prognosis of patients with NSCLC.