Comparison of different interventions for the reduction of labor pain: A systematic review and network meta-analysis.

OBJECTIVE: This systematic review and network meta-analysis were performed to compare different interventions for the reduction of labor pain. METHODS: PubMed, Embase, Cochrane Library, Web of Science and ScienceDirect databases were searched for the randomized controlled trials (RCTs) meeting prespecified inclusion criteria up to January, 2023. Interventions including electrical acupoint stimulation (TEAS), epidural analgesia (EA) and control treatments. The primary outcomes included pain scores, failure rate of natural delivery, adverse events and Apgar scores. The methodological quality was assessed by the Cochrane risk of bias tool. Meta-analysis was performed by R software with gemtc package. Surfaces under the cumulative ranking curves (SUCRA) were used to rank the intervention. RESULTS: Twelve studies met the inclusion criteria and were included in the network meta-analysis. TEAS (WMD -3.1, 95% CrI -3.8, -2.5) and EA (WMD -2.1, 95% CrI -2.8, -1.3) was more effective than the control in decreasing VAS. TEAS ranked first (SUCRA, 90.9%), EA ranked second (SUCRA, 74.0%) and control ranked last (SUCRA, 35.0%) for reducing VAS. For patients with labor pain, with respect to the most effective treatment for reducing failure rate of natural delivery, TEAS ranked first (SUCRA, 96.6%), EA ranked second (SUCRA, 50.4%) and control ranked last (SUCRA, 3.0%). With regard to the Apgar scores, there was high probability that TEAS ranked first (SUCRA, 80.7%), compared to control (SUCRA, 41.4%) and EA (SUCRA 27.9%). With regard to the adverse events, there was high probability that TEAS ranked first (SUCRA, 99.9%), compared to control (SUCRA, 33.2%) and EA (SUCRA 17.6%). CONCLUSION: TEAS has the potential to serve as a viable alternative for women in labor, offering a simple, noninvasive, and non-pharmacological intervention that surpasses EA in terms of both analgesic effectiveness and safety for both mothers and neonates.

Ring Repeating Unit: An Upgraded Structure Representation of Linear Condensation Polymers for Property Prediction.

Unique structure representation of polymers plays a crucial role in developing models for polymer property prediction and polymer design by data-centric approaches. Currently, monomer and repeating unit (RU) approximations are widely used to represent polymer structures for generating feature descriptors in the modeling of quantitative structure-property relationships (QSPR). However, such conventional structure representations may not uniquely approximate heterochain polymers due to the diversity of monomer combinations and the potential multi-RUs. In this study, the so-called ring repeating unit (RRU) method that can uniquely represent polymers with a broad range of structure diversity is proposed for the first time. As a proof of concept, an RRU-based QSPR model was developed to predict the associated glass transition temperature (Tg) of polyimides (PIs) with deterministic values. Comprehensive model validations including external, internal, and Y-random validations were performed. Also, an RU-based QSPR model developed based on the same large database of 1321 PIs provides nonunique prediction results, which further prove the necessity of RRU-based structure representation. Promising results obtained by the application of the RRU-based model confirm that the as-developed RRU method provides an effective representation that accurately captures the sequence of repeat units and thus realizes reliable polymer property prediction by data-driven approaches.