The latest medical research on Obstetrics And Gynecology

To evaluate whether antepartum hospitalization was associated with differences in sleep duration or disrupted sleep patterns.

This was a prospective cohort study with enrollment of pregnant people aged 18-55 years with singleton gestations at 16 weeks of gestation or more between 2021 and 2022. Each enrolled antepartum patient was matched by gestational age to outpatients recruited from obstetric clinics at the same institution. Participants responded to the ISI (Insomnia Severity Index) and wore actigraph accelerometer watches for up to 7 days. The primary outcome was total sleep duration per 24 hours. Secondary outcomes included sleep efficiency (time asleep/time in bed), ISI score, clinical insomnia (ISI score higher than 15), short sleep duration (less than 300 minutes/24 hours), wakefulness after sleep onset, number of awakenings, and sleep fragmentation index. Outcomes were evaluated with multivariable generalized estimating equations adjusted for body mass index (BMI), sleep aid use, and insurance type, accounting for gestational age correlations. An interaction term assessed the joint effects of time and inpatient status.

Overall 58 participants were included: 18 inpatients and 40 outpatients. Inpatients had significantly lower total sleep duration than outpatients (mean 4.4 hours [SD 1.6 hours] inpatient vs 5.2 hours [SD 1.5 hours] outpatient, adjusted β=-1.1, 95% CI, -1.8 to -0.3, P=.01). Awakenings (10.1 inpatient vs 13.8, P=.01) and wakefulness after sleep onset (28.3 inpatient vs 35.5 outpatient, P=.03) were lower among inpatients. There were no differences in the other sleep outcomes, and no interaction was detected for time in the study and inpatient status. Inpatients were more likely to use sleep aids (39.9% vs 12.5%, P=.03).

Hospitalized pregnant patients slept about 1 hour/day less than outpatients. Fewer awakenings and reduced wakefulness after sleep onset among inpatients may reflect increased use of sleep aids in hospitalized patients.

Pacific Islander (PI) women in Australia have an increased risk of gestational diabetes (GDM); however, their perinatal outcomes are poorly understood.

The aim was to determine the maternal characteristics and perinatal outcomes of PI women with and without GDM compared to Australian/European (AE)-born women.

A retrospective analysis of perinatal outcomes of singleton deliveries >20 weeks' gestation between 1 January 2011 and 31 December 2020 was conducted at a tertiary provider (Melbourne, Australia). Antenatal details and birth outcomes were extracted from the Birth Outcome Systems database. t-Tests and χ2, univariate and multivariable logistic regression analyses assessed the relationship between ethnicity and outcomes.

Of 52,795 consecutive births, 24,860 AE women (13.3% with GDM) and 1207 PI-born women (20.1% with GDM) were compared. PI women had significantly greater pre-pregnancy body mass index (BMI) and significantly lower rates of smoking and nulliparity. PI women with GDM had higher rates of pre-eclampsia (P < 0.001), large-for-gestational age (LGA) neonates (P = 0.037) and neonatal hypoglycaemia (P = 0.017) but lower rates of small-for-gestational age neonates (P = 0.034). Neonatal intensive care unit (NICU)/special care nursery requirements did not increase. After having adjusted for covariates, PI women's risk of LGA neonates (adjusted odds ratio (aOR): 1.06, 95% confidence interval (CI): 0.86-1.31) was attenuated; however, risk of pre-eclampsia (aOR: 1.49, 95% CI: 1.01-2.21) and neonatal hypoglycaemia (aOR: 1.40, 95% CI: 1.01-1.96) still increased. They were less likely to require a primary caesarean section (aOR: 0.86, 95% CI: 0.73-0.99).

PI women have higher BMI and GDM rates, contributing to an increased likelihood of adverse perinatal outcomes. BMI is a modifiable risk factor that could be addressed prenatally.

Allergic rhinitis constitutes a widespread health concern, with traditional treatments often proving to be painful and ineffective. Acupuncture targeting the pterygopalatine fossa proves effective but is complicated due to the intricate nearby anatomy.

To enhance the safety and precision in targeting the pterygopalatine fossa, we introduce a deep learning-based model to refine the segmentation of the pterygopalatine fossa. Our model expands the U-Net framework with DenseASPP and integrates an attention mechanism for enhanced precision in the localisation and segmentation of the pterygopalatine fossa.

The model achieves Dice Similarity Coefficient of 93.89% and 95% Hausdorff Distance of 2.53 mm with significant precision. Remarkably, it only uses 1.98 M parameters.

Our deep learning approach yields significant advancements in localising and segmenting the pterygopalatine fossa, providing a reliable basis for guiding pterygopalatine fossa-assisted punctures.