Association between age at first birth and postpartum depression: A two-sample mendelian randomization analysis

Background: Previous observational research has documented an association between age at first childbirth (AFB) and postpartum depression (PPD). However, the causal relationship remains unclear. This study aimed to assess the causal effects of AFB on PPD using a two-sample Mendelian randomization (MR) analysis. Methods: Three sets of instrumental variables were obtained from the United Kingdom Biobank (UK Biobank), Neale Lab consortium and a meta-analysis of genome-wide association studies (GWAS). Single-nucleotide polymorphisms (SNPs) associated with the PPD phenotype were obtained from the Finngen consortium, which included 13,657 cases and 236,178 controls. Inverse variance weighted (IVW), weighted median, weighted mode, and MR-Egger methods to evaluate causal effects. Heterogeneity was assessed using Cochran's Q test and funnel plots. Horizontal pleiotropy and sensitivity were assessed using the MR-Egger intercept test and “leave-one-out” analysis, respectively. Further meta-analysis was performed to validate the robustness of this relationship. Additionally, the potential mediating effects of risk factors associated with PPD were analyzed. Results: Strong causal effects between AFB and PPD was found in both IVW and weighted median methods, which was further supported by meta-analysis (IVW, odds ratio [OR] 0.59 [95% confidence interval (CI) 0.36–0.96, p = 0.03]; weighted median, OR 0.59 [95% CI 0.37–0.95, p = 0.03]). The power of the MR supports the robustness of the findings. Heterogeneity or horizontal pleiotropy was not observed. Major depressive disorders, family income levels, and marital stress were identified as potential mediating factors in the causal relationships. Conclusion: Results of MR analysis supported the causal effect of increased AFB in reducing the risk for PPD

Multi-Label Symptom Analysis and Modeling of TCM Diagnosis of Hypertension

Traditional Chinese Medicine (TCM) has been used for diagnosis of hypertension and has significant advantages. Symptom analysis and modeling of TCM provides a way for the clinician to produce a service to users to accurately and efficiently diagnose hypertension. In this study, an ensemble learning framework based on network clustering analysis with information fusion is proposed. We first analyze the frequency distribution and cluster heat map of TCM hypertension clinical cases, and establish a network based on the syndrome and symptom of cases. Through the analysis of community networks, we get the dominant and subordinate syndrome and construct a sub-classifier to co-train and improve the performance of the classifier. Then we use ML-KNN and RAkEL-SVM multi-label classifiers to train and test the cases. Considering the result of 10-fold cross validation, we discover that ML-KNN and RAkEL-SVM with information fusion have better performance than traditional learning methods without information fusion. For all evaluation criteria, the average precision of ML-KNN is higher, and the F-Measure does not vary substantially. But the averaged recall of RAkEL-SVM is significantly higher

Multiple approaches for massively parallel sequencing of SARS-CoV-2 genomes directly from clinical samples

BACKGROUND: COVID-19 (coronavirus disease 2019) has caused a major epidemic worldwide; however, much is yet to be known about the epidemiology and evolution of the virus partly due to the scarcity of full-length SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) genomes reported. One reason is that the challenges underneath sequencing SARS-CoV-2 directly from clinical samples have not been completely tackled, i.e., sequencing samples with low viral load often results in insufficient viral reads for analyses. METHODS: We applied a novel multiplex PCR amplicon (amplicon)-based and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of SARS-CoV-2 from serials dilutions of a cultured isolate, and eight clinical samples covering a range of sample types and viral loads. We also examined and compared the sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. RESULTS: We demonstrated that both amplicon and capture methods efficiently enriched SARS-CoV-2 content from clinical samples, while the enrichment efficiency of amplicon outran that of capture in more challenging samples. We found that capture was not as accurate as meta and amplicon in identifying between-sample variations, whereas amplicon method was not as accurate as the other two in investigating within-sample variations, suggesting amplicon sequencing was not suitable for studying virus-host interactions and viral transmission that heavily rely on intra-host dynamics. We illustrated that meta uncovered rich genetic information in the clinical samples besides SARS-CoV-2, providing references for clinical diagnostics and therapeutics. Taken all factors above and cost-effectiveness into consideration, we proposed guidance for how to choose sequencing strategy for SARS-CoV-2 under different situations. CONCLUSIONS: This is, to the best of our knowledge, the first work systematically investigating inter- and intra-individual variations of SARS-CoV-2 using amplicon- and capture-based whole-genome sequencing, as well as the first comparative study among multiple approaches. Our work offers practical solutions for genome sequencing and analyses of SARS-CoV-2 and other emerging viruses