(1) Background: This study aimed to evaluate the role of cervical pessary for preventing preterm birth in singleton pregnant women with a cervical length (CL) ≤25 mm and a uterocervical angle (UCA) ≥95o and ≥105o. (2) Methods: A quasi-experimental study was conducted on 225 singleton pregnant women with a gestational age of 16+o-23+6 weeks and transvaginal ultrasound CL measurements ≤25 mm, whose pregnancy was man-aged at the Haiphong Hospital of Obstetrics and Gynecology, Vietnam, from September 2020 to September 2022. Participants were allocated into two groups: prophylactic treatment with progesterone (123 pregnant women) and combined with a cervical pessary (102 pregnant women). The UCAs measured via transvaginal ultrasound by a single sonographer were divided into groups with UCA measurements ≥95o and ≥105o. The gestational age at birth was classified into full-term and preterm <37 weeks and <34 weeks. A binary regression model assessed the association between CL, UCA, and pre-term birth outcomes. The difference was statistically significant, with p<0.05. (3) Results: There was no statistically significant difference in the rate of preterm birth <37 weeks and <34 weeks between the two groups (16.3% vs. 11.8% and 6.5% vs. 4.9%, p>0.05). The probability of preterm birth <37 weeks in the group of pregnant women with a UCA ≥95o treated via cervical pessary plus progesterone decreased by 60.6%, OR (95% CI) of 0.394 (0.165-0.941), p<0.05. In the group of pregnant women with a UCA ≥105o, there was a significant association in the group of progesterone treatment combined with a cervical pessary, with the lower rate of PTB <37 weeks and <34 weeks, with OR (95% CI) of 0.028 (0.005-0.146), p<0.001, and 0.141 (0.030-0.664), p<0.05, respectively. (4) Conclusion: The results of this study suggest that cervical pessary is beneficial to the prevention of preterm birth in singleton pregnant women with an obtuse uterocervical angle. Further randomized studies among pregnant women with obtuse uterocervical angles will be needed to clarify the role of a cervical pessary in preventing preterm birth.

Species richness is a widely used measure for assessing the diversity of a particular area. However, observed richness often underestimates the true richness due to resource limitations, particularly in the small-sized sample or highly heterogeneous assemblage. Estimating species richness in a large-scale region typically involves an integrated data set consisting of subsamples collected independently from different subregions. However, the pooled sample of integrated data is no longer a random sample from the entire region, and the use of different sampling schemes results in variations in data formats. Consequently, employing a single sampling distribution to model the pooled sample becomes impractical, rendering existing richness estimators inadequate. This study theoretically explains the applicability of Chao's lower bound estimators in estimating species richness for large-scale areas using the pooled sample. Additionally, a new nonparametric estimator is introduced, which adjusts the bias of Chao's lower bound estimator by leveraging the Good-Turing frequency formula. This proposed estimator only utilizes the pooled sample's singleton, doubleton, and tripleton richness. Simulated data sets across various models are employed to demonstrate the statistical performance of the estimator, showcasing its ability to reduce bias and provide accurate 95% confidence intervals. Real data sets are also utilized to illustrate the practical application of the proposed approach.