Non-invasive determination of gene expression in placental tissue using maternal plasma cell-free DNA fragmentation characters.

BACKGROUND: The expression profiles of placental genes are crucial for understanding the pathogenesis of fetal development and placental-origin pregnancy syndromes. However, owing to ethical limitations and the risks of puncture sampling, it is difficult to obtain placental tissue samples repeatedly, continuously, multiple times, or in real time. Establishing a non-invasive method for predicting placental gene expression profiles through maternal plasma cell-free DNA (cfDNA) sequencing, which carries information about the source tissue and gene expression, can potentially solve this problem. METHODS: Peripheral blood and placental samples were collected simultaneously from pregnant women who underwent cesarean section. Deep sequencing was performed on the separated plasma cfDNA and single-cell sequencing was performed on peripheral blood mononuclear cells (PBMC), chorioamniotic membranes (CAM), placental villi (PV), and decidua basalis (DB). The aggregation of corresponding information for each gene was combined with the transcriptome of PBMCs and a differential resolution transcriptome of the placenta. This combined information was then utilized for the construction of gene expression prediction models. After training, all models evaluated the correlation between the predicted and actual gene expression levels using external test set data. RESULTS: From five women, more than 20 million reads were obtained using deep sequencing for plasma cfDNA; PBMCs obtained 32,401 single-cell expression profiles; and placental tissue obtained 156,546 single-cell expression profiles (59,069, 44,921, and 52,556 for CAM, PV, and DB, respectively). The cells in the PBMC and placenta were clustered and annotated into five and eight cell types, respectively. A "DEPICT" gene expression prediction model was successfully constructed using deep neural networks. The predicted correlation coefficients were 0.75 in PBMCs, 0.84 in the placenta, and 0.78, 0.80, and 0.77 in CAM, BP, and PV respectively, and greater than 0.68 in different cell lines in the placenta. CONCLUSION: The DEPICT model, which can noninvasively predict placental gene expression profiles based on maternal plasma cfDNA fragmentation characteristics, was constructed to overcome the limitation of the inability to obtain real-time placental gene expression profiles and to improve research on noninvasive prediction of placental origin pregnancy syndrome.

Evidence of Bariatric Surgery Benefits Cardiac Function in Non-HFpEF Patients with Obesity: a Meta-Analysis.

BACKGROUND/OBJECTIVE: Nowadays, increasing clinical evidence on metabolic and weight-loss effects of bariatric surgery on improving cardiac structure in obese patients, but its application in improving the cardiac function of HF (heart failure) patients remains controversial. The objective of this meta-analysis was to assess the effects of BS on cardiac function by quantifying the changes of LVEF (left ventricular ejection fraction) and NYHA (New York Heart Association classification) after operations in non-HFpEF (heart failure and preserved ejection fraction) patients. METHODS: Articles were searched using PubMed and Embase from inception to December 9, 2022, and the Minors scale was used for quality assessments. The included patients should be non-HFpEF and clinically severely obese, and their pre-operative and post-operative values of LVEF or NYHA should be reported. RESULT: Nine studies involving 146 patients were eventually included with a final result showing that the cardiac functional parameters were improved in non-HFpEF patients. After a weighted mean follow-up time of 15.8 months, the mean NYHA decreased by 0.59 (I2 = 0; 95% CI 0.27 ~ 0.92; p = 0.003), and the mean LVEF increased by 7.49% (I2 = 0; 95% CI - 9.99 ~ - 4.99; p < 0.00001). CONCLUSION: Bariatric surgery offers beneficial cardiac effects on non-HFpEF patients with obesity but failed to show a significant improvement in the pooled analysis for the changes of cardiac parameters. The improving degree may be related to the baseline BMI, the extent of BMI loss, and the baseline age. Future studies should focus on finding out the influencing factors of effectivenesses and defining the suitable crowd.

Ionic nano-convection in anodisation of aluminium plate.

An ionic nano-convection model has been established for elucidating early stage anodisation of aluminium plate and the direct consequence of such convection is an ordered pattern of charge distribution near the oxide/electrolyte interface, guiding the initial ordering of the pore formation.