Effect of percutaneous electrical stimulation at the Baliao point on preventing postpartum urinary retention after labor analgesia.

BACKGROUND: Anesthetic drugs used in labor analgesia also paralyze the bladder muscle by blocking the sacral plexus, thereby affecting maternal postpartum spontaneous urination and increasing the risk of postpartum urinary retention (PUR). AIM: To analyze the effect of percutaneous electrical stimulation at the Baliao point combined with biofeedback therapy for PUR prevention. METHODS: We selected 182 pregnant women who received labor analgesia in obstetrics between June 2022 and December 2023. They were divided into the combined therapy group (transcutaneous electrical stimulation of the Baliao point combined with biofeedback therapy) and the control group (biofeedback therapy alone). The first spontaneous urination time, first postpartum urine volume, bladder residual urine volume, postpartum hemorrhage volume, pre-urination waiting time, PUR incidence, adverse reactions, and the intervention's clinical efficacy were compared between the two groups. RESULTS: The first spontaneous urination time after delivery was more delayed (2.92 ± 1.04 h vs 3.61 ± 1.13 h, P < 0.001), with fewer initial postpartum urine (163.54 ± 24.67 mL vs 143.72 ± 23.95 mL, P < 0.001), more residual bladder urine (54.81 ± 10.78 mL vs 65.25 ± 13.52 mL, P < 0.001), more postpartum bleeding (323.15 ± 46.95 mL vs 348.12 ± 45.03 mL, P = 0.001), and longer waiting time for urination (0.94 ± 0.31 min vs 1.29 ± 0.42 min, P < 0.001), in the control group than in the combined therapy group. The control group also had higher PUR incidence (4.65% vs 15.85%, P = 0.016). Both groups had no adverse reactions, but the clinical total efficacy rate of the intervention was significantly higher in the combined therapy group than in the control group (95.35% vs 84.15%, P = 0.016). CONCLUSION: Percutaneous electrical stimulation of the Baliao point combined with biofeedback can significantly promote postpartum micturition of parturients with labor analgesia, thereby effectively preventing PUR occurrence.

Impact of perinatal factors on meconium aspiration syndrome in full-term newborns and the construction of a column chart prediction model: An observational study.

To explore the influence of perinatal-related factors on meconium aspiration syndrome (MAS) in full-term neonates and construct a nomogram prediction model for risk stratification of neonatal MAS and adoption of preventive measures. A total of 424 newborns and their mothers who were regularly examined at our hospital between January 2020 and December 2023 who had meconium-contaminated amniotic fluid during delivery were retrospectively selected as participants. Neonates were divided into MAS and non-MAS groups based on whether MAS occurred within 3 days after birth. Data from the 2 groups were analyzed, and factors influencing MAS were screened using multivariate logistic regression analysis. The R3.4.3 software was used to construct a nomogram prediction model for neonatal MAS risk. Receiver operating characteristic (ROC) curve analysis and the Hosmer-Lemeshow goodness-of-fit test were used to evaluate the performance of the model, and its clinical effectiveness was evaluated using a decision curve. Among the 424 neonates with meconium-stained amniotic fluid, 51 developed MAS within 3 days of birth (12.03%). Multivariate logistic regression analysis showed that a low amniotic fluid index before delivery (OR = 2.862, P = .019), advanced gestational age (OR = 0.526, P = .034), cesarean section (OR = 2.650, P = .013), severe amniotic fluid contamination (OR = 4.199, P = .002), low umbilical cord blood pH (OR = 2.938, P = .011), and low neonatal Apgar 1-min score (OR = 3.133, P = .006) were influencing factors of MAS in full-term neonates. Based on the above indicators, a nomogram prediction model for MAS risk of full-term newborns was constructed. The area under the ROC curve of the model was 0.931. The model was also tested for goodness-of-fit deviation (χ2 = 3.465, P = .903). Decision curve analysis found that the model was clinically effective in predicting the net benefit of MAS risk in neonates with meconium-stained amniotic fluid. The construction of a column chart prediction model for neonatal MAS risk based on prenatal amniotic fluid index, gestational age, delivery method, amniotic fluid contamination level, newborn umbilical blood pH value, and Apgar 1-min score has a certain application value.

Evaluating adaptive management frameworks for data-limited crustacean fisheries.

Crustacean fisheries represent an increasingly important contribution to global landings, food security and economic growth, especially in developing countries. However, many productive and valuable crustacean fisheries in Asian countries are characterized by limited data availability, scientific capacity, and fisheries management. Adaptive management frameworks, which use past and emerging information to provide stock status information and management advice, have been touted as particularly applicable for managing capacity- and data-limited fisheries because they employ methods that can improve data collection and result in evaluations of stock and ecosystem status with varying levels of data and capacity. Here, we examined the application of three adaptive fisheries management frameworks (FISHE, FishPath, and DLMtool) to three typical Asian crustacean fisheries that offered contrasting data types and availability, governance, and management and socio-economic contexts. Our aim was to evaluate their suitability for crustacean fisheries and identify particular data and modeling needs and management gaps in these fisheries. We found that while each of the frameworks could effectively recommend suitable monitoring, assessment, and management options given particular contextual factors, there were also limitations with each approach. For example, FISHE took a more wholistic view of ecosystem and fisheries heath, while the other frameworks were more focused on particular aspects of management such as stock assessment (FishPath) and management strategy evaluation (MSE; DLMtool). Applications of each approach also highlighted particular challenges in collecting commercial catch data due to limited monetary investment and poorly designed monitoring programs, which further hindered the implementation of catch and effort limits. The three frameworks also shared common challenges when applied to crustacean species, mainly associated with misalignment with the unique life-histories of crustaceans compared to finfish. By comparing the outputs of the three frameworks, we highlighted their respective strengths and weaknesses and propose an integrated framework that incorporates elements of each of the three frameworks. This integration offers a more comprehensive adaptive roadmap tailored to crustacean fisheries, which involves a mix of qualitative and quantitative approaches that could be applied depending on contextual factors and capacities. To further improve the applicability of adaptive frameworks to crustacean fisheries, we suggest considering crustacean's unique life history and the effects of climate change and other environmental factors, strengthening participatory processes, and balancing socio-economic and ecological objectives.

Is seafloor litter contributing to sea anemone blooms?

Increasing marine litter have become a global environmental disaster. The accumulation of seafloor litter (generally includes anthropogenic litter and natural debris) could change the habitat of benthic organisms and thereby affecting their population dynamics including spatial distribution. Metridium senile fimbriatum (i.e., M. senile), a fast-growing sea anemone, has become a dominant species of benthic community in the north Yellow Sea in recent years. In this study, we tested the hypothesis that the distribution of M. senile is positively correlated with that of seafloor litter, using data collected on seafloor litter and M. senile from three fisheries-independent bottom trawl surveys in the Yellow Sea in May, August and November 2019. Gradient Forest Model (GFM) was used to select appropriate response variables for characterizing the distribution of M. senile, and evaluate the influences of potential environmental factors on M. senile distribution. Surface area of anthropogenic litter (represented as 'Anthropogenic litter'), surface area of natural debris (represented as 'Natural debris') and latitude (Lat) were identified as the most significant variables influencing the distribution of M. senile. Furthermore, Generalized Additive Mixed Model (GAMM) was applied to model the abundance distribution of M. senile in terms of significant environmental variables, and evaluate its correlations with 'Anthropogenic litter' and 'Natural debris'. The best fitting GAMM showed that the abundance of M. senile has a significantly positive association with 'Anthropogenic litter' and 'Natural debris'. We therefore speculated that accumulation of seafloor litter might contribute to the bloom of M. senile, given that seafloor litter could serve as "vectors" for M. senile dispersal and provide with a preferable "natural habitat" for their settlement.