Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors

[EN] Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.This research was funded by the National Key R&D Program, grant number 2019YFC0119700, and the National Natural Science Foundation of China, grant number U20A20388.Zhang, Y.; Hao, D.; Yang, L.; Zhou, X.; Ye Lin, Y.; Yang, Y. (2022). Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors. Sensors. 22(9):1-18. https://doi.org/10.3390/s2209335211822

Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram

Uterine contraction (UC) activity is commonly used to monitor the approach of labour and delivery. Electrohysterograms (EHGs) have recently been used to monitor UC and distinguish between efficient and inefficient contractions. In this study, we aimed to identify UC in EHG signals using a convolutional neural network (CNN). An open-access database (Icelandic 16-electrode EHG database from 45 pregnant women with 122 recordings, DB1) was used to develop a CNN model, and 14000 segments with a length of 45 s (7000 from UCs and 7000 from non-UCs, which were determined with reference to the simultaneously recorded tocography signals) were manually extracted from the 122 EHG recordings. Five-fold cross-validation was applied to evaluate the ability of the CNN to identify UC based on its sensitivity (SE), specificity (SP), accuracy (ACC), and area under the receiver operating characteristic curve (AUC). The CNN model developed using DB1 was then applied to an independent clinical database (DB2) to further test its generalisation for recognizing UCs. The EHG signals in DB2 were recorded from 20 pregnant women using our multi-channel system, and 308 segments (154 from UCs and 154 from non-UCs) were extracted. The CNN model from five-fold cross-validation achieved average SE, SP, ACC, and AUC of 0.87, 0.98, 0.93, and 0.92 for DB1, and 0.88, 0.97, 0.93, and 0.87 for DB2, respectively. In summary, we demonstrated that CNN could effectively identify UCs using EHG signals and could be used as a tool for monitoring maternal and foetal health

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Background. Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). Materials and Methods. In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. Results. The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak. Conclusion. The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN

Quercetin Pretreatment Attenuates Hepatic Ischemia Reperfusion-Induced Apoptosis and Autophagy by Inhibiting ERK/NF- κ

Background. Hepatic ischemia reperfusion (IR) injury is a common phenomenon in transplantation or trauma. The aim of the present study was to determine the protective effect of quercetin (QE) on hepatic IR injury via the ERK/NF-κB pathway. Methods. Mice were randomized into the sham, IR, QE100 + IR, and QE200 + IR groups. Quercetin was administered intragastrically daily at two doses (100 mg/kg and 200 mg/kg) for 5 days prior to IR injury. The expression levels of liver enzymes, inflammatory cytokines, and other marker proteins were determined at 2, 8, and 24 hours after IR. And they were compared among these groups. Results. Compared with the IR group, the treatment of QE reduced the release of cytokines, leading to inhibition of apoptosis and autophagy via downregulation of the ERK/NF-κB pathway in this model of hepatic IR injury. Conclusion. Apoptosis and autophagy caused by hepatic IR injury were inhibited by QE following a reduction in the release of inflammatory cytokines, and the relationship between the two may be associated with inactivation of the ERK/NF-κB pathway

A Rapid PCR-Free Next-Generation Sequencing Method for the Detection of Copy Number Variations in Prenatal Samples

Next-generation sequencing (NGS) is emerging as a new method for the detection of clinically significant copy number variants (CNVs). In this study, we developed and validated rapid CNV-sequencing (rCNV-seq) for clinical application in prenatal diagnosis. Low-pass whole-genome sequencing was performed on PCR libraries prepared from amniocyte genomic DNA. From 10–40 ng of input DNA, PCR-free libraries consistently produced sequencing data with high unique read mapping ratios, low read redundancy, low coefficient of variation for all chromosomes and high genomic coverage. In validation studies, reliable and accurate CNV detection using PCR-free-based rCNV-seq was demonstrated for a range of common trisomies and sex chromosome aneuploidies as well as microdeletion and duplication syndromes. In reproducibility studies, CNV copy number and genomic intervals closely matched those defined by chromosome microarray analysis. Clinical testing of genomic DNA samples from 217 women referred for prenatal diagnosis identified eight samples (3.7%) with known chromosome disorders. We conclude that PCR-free-based rCNV-seq is a sensitive, specific, reproducible and efficient method that can be used in any NGS-based diagnostic laboratory for detection of clinically significant CNVs

In-service training of librarians under the network environment [in Chinese]

Text in Chinese, with English abstractIn view of the reality that traditional libraries are changing to modern ones and the printed information environment is challenged by the networked environment, the evolution of librarian positions and required corresponding qualifications in American academic libraries is reviewed. The librarian positions and qualifications of Chinese academic libraries are examined. The structure of librarians of Chinese academic libraries is analysed. The practice of in-service training of librarians in Qian Xuesen Library is introduced and a discussion is given on the needs and means of in-service training of librarians for academic libraries

A rare de novo duplication of chromosome 21q22.12 → q22.3 with other concomitant deletion and duplication of small fragments in 21q associated with Down syndrome: Prenatal diagnosis, molecular cytogenetic characterization

BACKGROUND: Karyotyping is considered the gold standard for the genome-wide detection of genomic imbalances in prenatal diagnosis, but it has a number of inherent limitations, namely the time required to culture cell and the limited resolution(5 ~ 10 Mb). Although fluorescence in situ hybridization (FISH) can also be used as a rapid prenatal diagnosis for common aneuploidies, it is labor intensive, requires prior knowledge of the regions of interest, and can only be used to diagnose one or a few genomic regions simultaneously. Array comparative genomic hybridization (aCGH) can overcome the resolution, the locus-specific, and the time limitations of the karyotyping and FISH techniques and is currently the most powerful method for detecting chromosomal alterations in pre and postnatal clinical cases. Several investigations have suggested that the aCGH testing should be considered a first-tier test for the diagnosis of cytogenetic aberrations in the fetus. RESULTS: This study used karyotyping, FISH, sequence-tagged site (STS) analysis and aCGH to diagnose a case of de novo duplication of chromosome 21q22.12 → q22.3 with other concomitant deletion and duplication of small fragments in 21q associated with Down syndrome prenatally. CONCLUSIONS: FISH, aCGH and STS analysis are useful in prenatal investigation of the nature of de novo alterations of small fragments of the chromosome