Prevalence of placenta previa among deliveries: An update systematic review and meta-analysis after the introduction of the two-child policy in Mainland China.

Background: As birth policy can affect maternal and infant health, we sought to identify whether and how the introduction of the two-child policy might have affected the prevalence of placenta previa in pregnant women in mainland China. Methods: In this update meta-analysis and systematic review, we searched PubMed, Web of Science, the Cochrane Library, Weipu, Wanfang, and the China National Knowledge Infrastructure (CNKI) databases for studies evaluating the prevalence of placenta previa in China published between the inception of each database and March 2024, with no restrictions. Two investigators independently extracted the data from each included study. We then combined the prevalence of placenta previa using random-effects models. Results: We included 128 studies in our analysis, 48 more than in our previous review. The prevalence of placenta previa among Chinese pregnant women was 1.44% (95% confidence interval (CI) = 1.32, 1.56). After the implementation of the two-child policy, the prevalence increased significantly, from 1.25% (95% CI = 1.16, 1.34) to 4.12% (95% CI = 3.33, 4.91). Conclusions: The prevalence of placenta previa increased significantly from the one-child policy period to the two-child policy period among mainland Chinese pregnant women, with varying trends across regions. This change requires the attention of health officials and timely adjustment of resource allocation policies. Registration: PROSPERO: CRD42021262309.

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

Guiding post-pancreaticoduodenectomy interventions for pancreatic cancer patients utilizing decision tree models.

Background: Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in advanced stages, necessitating pancreaticoduodenectomy (PD) as a primary therapeutic approach. However, PD surgery can engender intricate complications. Thus, understanding the factors influencing postoperative complications documented in electronic medical records and their impact on survival rates is crucial for improving overall patient outcomes. Methods: A total of 749 patients were divided into two groups: 598 (79.84%) chose the RPD (Robotic pancreaticoduodenectomy) procedure and 151 (20.16%) chose the LPD (Laparoscopic pancreaticoduodenectomy) procedure. We used correlation analysis, survival analysis, and decision tree models to find the similarities and differences about postoperative complications and prognostic survival. Results: Pancreatic cancer, known for its aggressiveness, often requires pancreaticoduodenectomy as an effective treatment. In predictive models, both BMI and surgery duration weigh heavily. Lower BMI correlates with longer survival, while patients with heart disease and diabetes have lower survival rates. Complications like delayed gastric emptying, pancreatic fistula, and infection are closely linked post-surgery, prompting conjectures about their causal mechanisms. Interestingly, we found no significant correlation between nasogastric tube removal timing and delayed gastric emptying, suggesting its prompt removal post-decompression. Conclusion: This study aimed to explore predictive factors for postoperative complications and survival in PD patients. Effective predictive models enable early identification of high-risk individuals, allowing timely interventions. Higher BMI, heart disease, or diabetes significantly reduce survival rates in pancreatic cancer patients post-PD. Additionally, there's no significant correlation between DGE incidence and postoperative extubation time, necessitating further investigation into its interaction with pancreatic fistula and infection.

Maternal fasting glucose levels throughout the pregnancy and risk of adverse birth outcomes in newborns: a birth cohort study in Foshan city, Southern China.

OBJECTIVE: We aimed to investigate the associations between maternal fasting plasma glucose (FPG) levels and glycemic fluctuations during different trimesters and adverse birth outcomes among newborns. METHODS: This cohort study used data from 63 213 pregnant women and their offspring in Foshan city from November 2015 to January 2019. Associations between maternal FPG and glycemic fluctuations during different trimesters and adverse birth outcomes [congenital heart defect (CHD), macrosomia, small/large for gestational age (SGA/LGA), and preterm birth (PTB)] in newborns were estimated using mixed-effects logistic regression models. RESULTS: A total of 45 516 participants accepted at least one FPG test throughout pregnancy, and 7852 of whom had glycemic trajectory data. In the adjusted model, higher maternal FPG throughout the pregnancy was associated with an increased risk of adverse birth outcomes (except for SGA). Each 1 mmol/L increase in maternal FPG during trimester 1 was associated with higher odds of CHD (OR = 1.14 (95% CI: 1.02, 1.26)). The same increase in maternal FPG during trimester 3 was associated with a higher risk of PTB (OR = 1.05 (95% CI: 1.01, 1.10)). Increment of maternal FPG during trimester 2 and trimester 3 was associated with a higher risk of macrosomia and LGA. Increase in FPG throughout the pregnancy was associated with slightly lower odds of SGA. Similar results were observed when analyzing the associations between glycemic fluctuations during different trimesters and adverse birth outcomes. CONCLUSIONS: Our findings indicate higher maternal FPG levels during different trimesters were associated with different adverse birth outcomes, which suggests the importance of glycemic management throughout the pregnancy.

Prediction of vaginal birth after cesarean in China.

OBJECTIVE: This study aimed to develop and validate a prediction model of vaginal birth after cesarean delivery (VBAC) in China. METHODS: A nomogram for effective prediction of VBAC of singleton, cephalic and one previous low-transverse cesarean section deliveries was created by comparing the combinations of ultrasonographic and non-ultrasonographic factors from five hospitals between 2018 and 2019. RESULTS: A total of 1066 women were included. Of the women who underwent trial of labor after cesarean (TOLAC), 854 (80.1%) had a VBAC. Ultrasound factors included reached a higher area under the curve (AUC) combined with non-ultrasonographic factors. Of the three ultrasonographic factors analyzed, the best predictive factor for successful TOLAC was fetal abdominal circumference. A nomogram was generated with eight validated factors, including maternal age, gestational week, height, previous vaginal delivery, Bishop score, dilatation of the cervix at the time of admission, body mass index at delivery, and fetal abdominal circumference by ultrasound. The trained and validated AUC were 0.719 (95% confident interval 0.674-0.764) and 0.774 (95% confident interval 0.712-0.837), respectively. CONCLUSION: Our VBAC nomogram based on obstetric factors and fetal abdominal circumference obtained by ultrasound could be used to counsel women who are considering TOLAC.