Prior studies report a decline in male twin live births during economically stressful periods, presumably owing to higher selection in utero against frail male gestations, yet no study has examined the natural corollary: whether provision of economic support increases rates of male twin births. We examined whether male twin live births increase following income gains from the Alaska Permanent Fund Dividend (PFD)—the longest running unconditional cash transfer program in the US. We obtained the monthly volume of male (and female) twin and singleton live births, from January 1980 to December 2019, from Alaska’s Department of Health. Data on PFD timing and payment amounts came from Alaska’s Department of Revenue. We used time-series analyses to gauge whether the odds of male twin live births increase within 2−6 months following PFD receipt, controlling for autocorrelation. Results suggest that for every $1000 increase in PFD payments, the odds of male twin live births increase by 0.002 (p < .05) three months following PFD disbursement. This corresponds with 50 additional (individual) male twin live births statistically attributable to the cumulative PFD amount disbursed over our study period. Income gains through the PFD may correspond with reduced male-specific selection in utero in Alaska.

This study explores directional selection on physical and psychosocial phenotypes in Eastern Eurasian populations, utilizing a dataset of 1245 ancient genomes. By analyzing polygenic scores (PGS) for traits including height, educational attainment (EA), IQ, autism, schizophrenia, and others, we observed significant temporal trends spanning the Holocene era. The results suggest positive selection for cognitive-related traits such as IQ, EA and autism spectrum disorder (ASD), alongside negative selection for anxiety and depression. The results for height were mixed and showed nonlinear relationships with Years Before Present (BP). These trends were partially mediated by genetic components linked to distinct ancestral populations. Regression models incorporating admixture, geography, and temporal variables were used to account for biases in population composition over time. Latitude showed a positive effect on ASD PGS, EA and height, while it had a negative effect on skin pigmentation scores. Additionally, latitude exhibited significant nonlinear effects on multiple phenotypes. The observed patterns highlight the influence of climate-mediated selection pressures on trait evolution. Spline regression revealed that several polygenic scores had nonlinear relationships with years BP. The findings provide evidence for complex evolutionary dynamics, with distinct selective pressures shaping phenotypic diversity across different timescales and environments.

The International Society for Twin Studies (ISTS) held its 19th scientific congress in Assisi, Italy, from September 26 to 28, 2024. This prestigious event, which was the seventh joint meeting with the World Congress on Twin Pregnancy, brought together researchers from various fields, including psychology, biology and medicine. Representatives from ICOMBO (the International Consortium of Multiple Birth Organisations), which supports multiple-birth families worldwide, were also in attendance. Many twin researchers consider this event to be the highlight of their professional year, as it brings together experts and parents alike to discuss the latest advancements in twin studies.

This study aimed to create a risk prediction model with artificial intelligence (AI) to identify patients at higher risk of postpartum hemorrhage using perinatal characteristics that may be associated with later postpartum hemorrhage (PPH) in twin pregnancies that underwent cesarean section. The study was planned as a retrospective cohort study at University Hospital. All twin cesarean deliveries were categorized into two groups: those with and without PPH. Using the perinatal characteristics of the cases, four different machine learning classifiers were created: Logistic regression (LR), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). LR, RF, and SVM models were created a second time by including class weights to manage the underlying imbalances in the data. A total of 615 twin pregnancies were included in the study. There were 150 twin pregnancies with PPH and 465 without PPH. Dichorionity, PAS, and placenta previa were significantly higher in the PPH-positive group (p = .045, p = .004, p = .001 respectively). In our model, LR with class weight was the best model with the highest negative predictive value. The AUC in our LR with class weight model was %75.12 with an accuracy of 70.73%, a PPV of 47.92%, and an NPV of 85.33% in our data. Although the application of machine learning to create predictive models using clinical risk factors and our model’s 70% accuracy rate are encouraging, it is not sufficient. Machine learning modeling needs further study and validation before being incorporated into clinical use.