Background: Vaginal Lactobacillus predominance is associated with improved vaginal health and reduced pregnancy complications. Little is known about how dietary quality may improve vaginal microbial composition or about dietary interventions that may promote Lactobacillus abundance. To understand the host factors affecting vaginal microbiota during pregnancy in a multi-ethnic cohort in Hawai`i. We hypothesize that better diet quality improves vaginal microbial composition, as represented by Lactobacillus abundance and depletion of anaerobic organisms. Methods: We compared comprehensive diet quality, as measured by the Healthy Eating Index-2015 (HEI-2015), to taxonomic classifications of bacteria present within the vagina. Participants of the four predominant ethnic groups in Hawai’i (Japanese, Filipino, Non-Hispanic White and Native Hawaiian) completed Quantitative Food Frequency Questionnaires (QFFQs) and collected vaginal swabs during each trimester. 16s rRNA amplicon sequencing (V2–V9 regions) was performed on vaginal samples. HEI-2015 scores and macro- and micronutrient intake were compared with the predominant species present using the Mann–Whitney-U test, PERMANOVA, and the Pearson correlation coefficient. A mixed-effects logistics regression model was used to predict the depletion of Lactobacillus species while accounting for confounding demographic factors. Results: Matched, longitudinal data for 40 participants demonstrated three predominant Lactobacillus species: L. crispatus, L. iners, and L. gasseri, with another subset of samples with anaerobic abundance. (Primarily, Atopobium vaginae, Prevotella, and Gardnerella vaginalis.) Non-Hispanic White participants had lower amounts of Lactobacillus iners compared to other racial and ethnic groups. HEI scores correlated with the chao index and observed species number primarily in the first trimester (r = 0.25, p < 0.05). Greater carbohydrate intake was associated with a higher abundance of L. crispatus, while lower carbohydrate intake trended towards more L. iners (0.056) and anaerobic species. Conclusions: Increased carbohydrate consumption and improved diet quality may be associated with beneficial vaginal microbial composition in pregnancy.

Introduction Mechanical power (MP) is a comprehensive measure that captures the energy delivered to the lungs during mechanical ventilation. By incorporating driving pressure (DP), tidal volume (VT), positive end-expiratory pressure (PEEP), and respiratory rate (RR), MP helps quantify the risk of ventilator-induced lung injury (VILI). The minute ventilation–mean airway pressure (mM) equation has been proposed as a simplified method for estimating MP, and while it has shown good accuracy in passive patients, its validity in those with spontaneous breathing effort remains unclear. This study evaluates the accuracy and reliability of the mM equation in ventilated patients exhibiting spontaneous respiratory effort. Methods and Statistics This study used the online SIVA simulator to model a broad range of ventilatory scenarios, with varying respiratory compliance (10–80 mL/cmH₂O), airway resistance (5–30 cmH₂O/L/s), and key ventilator settings: RR (5–40 breaths/min), VT (150–700 mL), PEEP (0–15 cmH₂O), and DP (5–30 cmH₂O). Simulations were performed in both the volume-controlled (VCV) and pressure-controlled (PCV) modes. To simulate spontaneous breathing, inspiratory muscle pressure (Pmus) was adjusted from 1 to 20 cmH₂O. A total of 1,500 simulations per mode were generated in each mode. The gold standard method of geometrically deriving the area under the Pressure-Volume curve was used to calculate MP with the SIVA simulator (Total, Ventilator, Muscle powers). The mM estimates were derived from corresponding minute ventilation and mean airway pressure values. Pearson correlation coefficients were calculated to compare the relationship of the mM equation to all the measured MP, and linear regressions were used for predicting the three different MP derived from the mM equation in each mode separately. T-test for equal variance and Bland Altmann plot were used to compare the reference MP measured (MP-R) from the simulator to the ones derived from the Mm formula (MP-D). Results Pearson correlation showed a very strong correlation between mM and ventilator power (R 0.969), very strong correlation between mM and total power (R 0.963), and strong correlation between mM and Pmus power (R 0.771) within the pressure-controlled mode, with all demonstrating non-significant differences between the measured and estimated ones. For the volume-controlled mode, Pearson correlation showed a very strong correlation between mM and ventilator power (R 0.963) and strong correlation between mM and total power (R 0.888), but weak correlation between mM and Pmus power (R 0.339). Conclusion During spontaneous efforts, the new mM equation accurately predicted the total, ventilator, muscle power in the pressure-controlled mode. However, in volume-controlled ventilation, it accurately predicted the total and ventilator power only, the weak correlation between mM-derived values and patient-generated (Pmus) power suggests that spontaneous breathing may reduce its accuracy. These findings highlight the need for further research into alternative methods for calculating mechanical power in patients with active respiratory effort.