Gravidity modifies the associations of age and spousal age difference with couple's fecundability: a large cohort study from China.

STUDY QUESTION: Do couple's age ranges for optimal fecundability, and the associations with fecundability of couple's age combinations and age differences differ with gravidity? SUMMARY ANSWER: The couple's age range of optimal fecundability and age combinations differed with gravidity, and gravidity might modify the associations of age and spousal age difference with couple's fecundability. WHAT IS KNOWN ALREADY: Age is one of the strongest determinants of fecundability, but the existing studies have certain limitations in study population, couple's extreme age combinations and age differences, and have not explored whether the association between age and fecundability differs with gravidity. STUDY DESIGN, SIZE, DURATION: Retrospective cohort study. 5 407 499 general reproductive-aged couples (not diagnosed with infertility) participated in the National Free Pre-conception Check-up Projects during 2015-2017. They were followed up for pregnancy outcomes through telephone interviews every 3 months until they became pregnant or were followed up for 1 year. PARTICIPANTS/MATERIALS, SETTING, METHODS: The main outcome was time to pregnancy, and the fecundability odds ratios and 95% CIs were estimated using the Cox models for discrete survival time. The associations of age and spousal age difference with fecundability were evaluated by restricted cubic splines. MAIN RESULTS AND THE ROLE OF CHANCE: In this large cohort of general reproductive-aged population, the age of optimal fecundability of multigravida couples was older than that of nulligravida couples, but their subsequent fecundability declined more sharply with age. The decline in female fecundability was more pronounced with age, with fecundability dropping by ∼30% in both nulligravida and multigravida couples whose female partners aged ≥35 years. In the nulligravida group, the fecundability of couples who were both ≤24 years with the same age was the highest, which decreased steadily with the increase of spousal age difference, and younger male partners did not seem to contribute to improving couple's fecundability. In the multigravida group, couples with female partners aged 25-34 years and a spousal age difference of -5 to 5 years showed higher fecundability, and the effect of spousal age difference on couple's fecundability became suddenly apparent when female partners aged around 40 years. Young male partners were unable to change the decisive effect of female partner's age over 40 years on couple's reduced fecundability, regardless of gravidity. LIMITATIONS, REASONS FOR CAUTION: Lacking the time for couples to attempt pregnancy before enrollment, and some couples might suspend pregnancy plans during follow-up because of certain emergencies, which would misestimate the fecundability. Due to the lack of information on sperm quality and sexual frequency of couples, we could not adjust for these factors. Moreover, due to population characteristics, the extrapolation of our results required caution. WIDER IMPLICATIONS OF THE FINDINGS: The couple's age range of optimal fecundability, age combinations, and spousal age difference on fecundability varied with gravidity. Female age-related decline in fecundability was more dominant in couple's fecundability. Targeted fertility guidance should be provided to couples with different age combinations and gravidities. STUDY FUNDING/COMPETING INTEREST(S): This research received funding from the Project of National Research Institute for Family Planning (Grant No. 2018NRIFPJ03), the National Key Research and Development Program of China (Grant No. 2016YFC1000307), and the National Human Genetic Resources Sharing Service Platform (Grant No. 2005DKA21300), People's Republic of China. The funders had no role in study design, analysis, decision to publish, or preparation of the manuscript. The authors report no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

Post-COVID-19 Fetal Cardiac Morphology and Systolic Evaluation in Infected Pregnant Women by Fetal Heart Quantification Technology.

OBJECTIVES: Due to the government's liberalization of epidemic control, the current 2019 novel coronavirus disease (COVID-19) has started to spread widely within China. This study aimed to use the fetal heart quantification (fetal HQ) technique to assess the cardiac function and morphology of the fetuses of pregnant women diagnosed with COVID-19 in the early stages of pregnancy. METHODS: Exactly 86 pregnant women diagnosed with COVID-19 infection in early pregnancy (COVID-19 group) and 90 healthy pregnant women (control group) who underwent fetal echocardiography were prospectively included in this study. The fetal HQ technique was applied to compare the differences in the global sphericity index (GSI), global strain values (GS), fractional area change (FAC), and 24-segment fractional shortening (FS) of the left and right ventricles, between the COVID-19 group and the control group. RESULTS: Compared with the control group, the differences in GS and 24-segment FS of the left ventricle in the COVID-19 group were not statistically significant. However, the COVID-19 group showed lower GSI values compared with the control group (1.24 vs 1.28). FAC (48.12%) of the left ventricle and GS (-23.55%), FAC (41.74%) of the right ventricle in the COVID-19 group were reduced compared with FAC (50.50%) of the left ventricle and GS (-27.63%), FAC (46.01%) of the right ventricle in the control group. Segmental analysis revealed reduced FS in segments 20-24 in the COVID-19 group compared with the control group. Right ventricular GS was an independent predictor of adverse pregnancy outcome with an optimal cutoff value of -18.66%. CONCLUSIONS: The results suggest that COVID-19 infection in early pregnancy may have a negative impact on fetal cardiac morphology and function. Fetal HQ may offer a new assessment method for the early identification of fetal cardiac alterations in pregnant women infected with COVID-19.

[Prenatal diagnosis of a fetus with Rubinstein-Taybi syndrome].

OBJECTIVE: To explore the clinical characteristics and variant of CREBBP gene in a fetus with Rubinstein-Taybi syndrome (RSTS). METHODS: A fetus with RSTS diagnosed at the Third Affiliated Hospital of Zhengzhou University in August 2022 was selected as the study subject. Clinical data, amniotic fluid sample of the fetus and peripheral blood samples of its parents were collected for whole exome sequencing (WES). Candidate variant was verified by Sanger sequencing. RESULTS: Foot malformation, cerebellar vermis agenesis, brain agenesis, polysyndactyly of the big toes and other phenotypes were found by prenatal ultrasound. WES revealed that the fetus has harbored a heterozygous c.4684G>T (p.E1562*) variant in exon 28 of the CREBBP gene (NM_004380.3), which was de novo in origin. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was predicted to be pathogenic (PVS1+PS2_Moderate+PM2_Supporting). After genetic counseling, the couple had opted to terminate the pregnancy and refused autopsy of the fetus. CONCLUSION: The c.4684G>T (p.E1562*) variant of the CREBBP gene probably underlay the RSTS in this fetus. The newly discovered variant has enriched the mutational spectrum of the CREBBP gene and illustrated that WES is an efficient tool for the prenatal diagnosis of RSTS.

Ambient particulate matter, maternal thyroid function, and birth weight: A mediation analysis.

BACKGROUND: Birth weight (BW) is an indicator of fetal growth and development. Previous studies showed inconsistent results on the association of ambient particulate matter (PM) exposure with BW, and the role of maternal thyroid function has not been clarified. METHODS: We recruited 1711 gravidas between 2017 and 2019 in Henan, China. All participants were followed up until delivery. Daily concentrations of PM, including PM2.5 and PM10, were evaluated by using the spatial-temporal model. Maternal thyroid hormone (TH) levels were quantified by electrochemiluminescent microparticle immunoassay. Linear regression models were employed to examine the association among PM, BW, and maternal TH. Mediating effects of maternal TH interrelated with PM exposure on BW were investigated by causal mediation analyses. RESULTS: A total of 1049 gravidas were identified. We found that per 10 µg/m3 increase in PM2.5 and PM10 were associated with a decreased BW of 9.941 g, and 7.758 g (PM2.5: 95 %CI: -18.184, -1.698; PM10: 95 %CI: -14.436, -1.080). An inverse correlation of maternal FT4 levels with BW was found, with the pooled ß of -319.983 g (95 %CI: -483.216, -156.750). We found a prominent positive correlation between gestational FT4 and PM exposure (PM2.5: ß = 0.004, 95 %CI: 0.001, 0.007; PM10: ß = 0.003, 95 %CI: 0.000, 0.006). Mediation analysis found that FT4 levels mediated the relationship between maternal PM exposure and BW, ranging from 5.55 % to 15.86 %. CONCLUSIONS: Maternal PM exposure may induce a reduction in newborn BW by affecting the maternal TH concentrations.

Leveraging text skeleton for de-identification of electronic medical records.

BACKGROUND: De-identification is the first step to use these records for data processing or further medical investigations in electronic medical records. Consequently, a reliable automated de-identification system would be of high value. METHODS: In this paper, a method of combining text skeleton and recurrent neural network is proposed to solve the problem of de-identification. Text skeleton is the general structure of a medical record, which can help neural networks to learn better. RESULTS: We evaluated our method on three datasets involving two English datasets from i2b2 de-identification challenge and a Chinese dataset we annotated. Empirical results show that the text skeleton based method we proposed can help the network to recognize protected health information. CONCLUSIONS: The comparison between our method and state-of-the-art frameworks indicates that our method achieves high performance on the problem of medical record de-identification.

Association between fine particulate matter and fecundability in Henan, China: A prospective cohort study.

OBJECTIVE: To investigate the relationship between ambient fine particulate matter (PM2.5) exposure and fecundability. METHODS: This study included 751,270 female residents from Henan Province who participated in the National Free Pre-conception Check-up Projects during 2015-2017. Ambient cycle-specific PM2.5 exposure was assessed at the county level for each participant using satellite-based PM2.5 concentration data at 1-km resolution. Cox proportional hazards models with time-varying exposure were used to estimate the association between fecundability and PM2.5 exposure, adjusted for potential individual risk factors. RESULTS: During the study period, 568,713 participants were pregnant, monthly mean PM2.5 concentrations varied from 25.5 to 114.0 µg/m3 across study areas. For each 10 µg/m3 increase in cycle-specific PM2.5, the hazard ratio for fecundability was 0.951 (95 % confidence interval: 0.950-0.953). The association was more pronounced in women who were older, with urban household registration, history of pregnancy, higher body mass index (BMI), hypertension, without exposure to tobacco, or whose male partners were older, with higher BMI, or hypertension. CONCLUSION: In this population-based prospective cohort, ambient cycle-specific PM2.5 exposure was associated with reduced fecundability. These findings may support the adverse implications of severe air pollution on reproductive health.

Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation.

BACKGROUND: Data-driven medical health information processing has become a new development trend in obstetrics. Electronic medical records (EMRs) are the basis of evidence-based medicine and an important information source for intelligent diagnosis. To obtain diagnostic results, doctors combine clinical experience and medical knowledge in their diagnosis process. External medical knowledge provides strong support for diagnosis. Therefore, it is worth studying how to make full use of EMRs and medical knowledge in intelligent diagnosis. OBJECTIVE: This study aims to improve the performance of intelligent diagnosis in EMRs by combining medical knowledge. METHODS: As an EMR usually contains multiple types of diagnostic results, the intelligent diagnosis can be treated as a multilabel classification task. We propose a novel neural network knowledge-aware hierarchical diagnosis model (KHDM) in which Chinese obstetric EMRs and external medical knowledge can be synchronously and effectively used for intelligent diagnostics. In KHDM, EMRs and external knowledge documents are integrated by the attention mechanism contained in the hierarchical deep learning framework. In this way, we enrich the language model with curated knowledge documents, combining the advantages of both to make a knowledge-aware diagnosis. RESULTS: We evaluate our model on a real-world Chinese obstetric EMR dataset and showed that KHDM achieves an accuracy of 0.8929, which exceeds that of the most advanced classification benchmark methods. We also verified the model's interpretability advantage. CONCLUSIONS: In this paper, an improved model combining medical knowledge and an attention mechanism is proposed, based on the problem of diversity of diagnostic results in Chinese EMRs. KHDM can effectively integrate domain knowledge to greatly improve the accuracy of diagnosis.

RNF213 gene silencing upregulates transforming growth factor ß1 expression in bone marrow-derived mesenchymal stem cells and is involved in the onset of Moyamoya disease.

Moyamoya disease (MMD) is a chronic and progressive cerebrovascular occlusion disease, the precise etiology of which is poorly understood. Ring finger protein 213 (RNF213) has been previously identified as a susceptibility gene that serves an important role in angiogenesis, where it has been shown to be closely associated with the onset of MMD. Patients with MMD exhibit increased expression levels of various pro-inflammatory molecules and angiogenic factors. Under certain conditions, bone marrow mesenchymal stem cells (BMSCs) have the ability to differentiate to form neuron-like and microglia-like cells. In the present study, a total of 40 MMD patients and 40 healthy individuals were enrolled. ELISA assays revealed that the expression of serum vascular endothelial growth factor (VEGF) and transforming growth factor ß1 (TGF-ß1) were higher than that in healthy controls. Furthermore, rat BMSCs (rBMSCs) were isolated and cultured using the whole bone marrow adherence method, which were then phenotyped using flow cytometry. Osteogenic and adipogenic differentiation were determined by using Alizarin red and oil red O staining, respectively. RNF213 was knocked-down using a lentivirus-mediated short hairpin RNA system in passage three rBMSCs, and successful transfection of the RNF213 was confirmed by RT-qPCR and fluorescence imaging. The expression levels of VEGF and TGF-ß1 in these rBMSCs were measured on days 7 and 14, respectively. The results demonstrated that RNF213 knockdown upregulated TGF-ß1 at both protein and mRNA levels, but did not exert any effect on VEGF gene expression. In conclusion, these findings suggested that that RNF213 knockdown may contribute to aberrant TGF-ß1 expression via a pathway that remains to be unidentified, indicating that quantitative changes in RNF213 gene expression may serve an important role in the pathogenesis of MMD.

Mortality rates of children aged under five in Henan province, China, 2004-2008.

To analyse the mortality rates, causes of death and trends over time in children aged under five years in Henan province and to provide preventive advice to attempt to decrease the mortality rates of these children, a retrospective study was conducted, analysing the vital statistics data from 2004 to 2008 from the annual report of maternal and child health in Henan. The early neonatal (<7 days), neonatal (<28 days), infant (<1 year) and children under-five mortality rates were calculated for each year. The child mortality rates decreased in all age groups (early neonatal, neonatal, infants and children <5 years) from 2004 (7.44 per thousand, 9.81 per thousand, 12.08 per thousand and 14.04 per thousand, respectively) to 2008 (4.86 per thousand, 5.50 per thousand, 7.08 per thousand and 8.81 per thousand, respectively). The changes were more pronounced in the rural areas. However, child mortality remained higher in the rural areas (5.00 per thousand, 5.62 per thousand, 7.22 per thousand and 9.06 per thousand) than urban areas (3.98 per thousand, 4.74 per thousand, 6.21 per thousand and 7.30 per thousand). Infants and neonates accounted for a large proportion of deaths in the under-fives (84.5% and 67.9%). The first five leading causes of death in the under-fives were: disorders relating to short gestation and low birthweight, birth asphyxia, congenital anomalies, accidents and pneumonia. The leading causes of death in different age groups varied between rural and urban areas. Reducing early newborn death, especially in the rural areas, is a key step to further decreasing mortality in the under-fives. Attention should be paid to perinatal care to prevent preterm birth and congenital anomalies.

Analysis of Cyclooxygenase 2, Programmed Cell Death Ligand 1, and Arginase 1 Expression in Human Pituitary Adenoma.

BACKGROUND: Cyclooxygenase 2 (COX-2) is a key enzyme in the synthesis of prostaglandins. Recent studies have shown that overexpression of COX-2 can reduce the antitumor effect of the immune system by inhibiting the proliferation of B and T lymphocytes. Programmed cell death ligand 1 (PD-L1) was the first functionally characterized ligand of programmed cell death protein 1. It plays an important role in maintaining peripheral and central immune tolerance by combining with programmed cell death protein 1. Arginase 1 (ARG1) can process L-arginine in the local microenvironment and affect the function of T cells, resulting in immune escape. In this study, COX-2, PD-L1, and ARG1 expression in human pituitary adenoma (PA) and their relationship were investigated, which provided an initial theoretic basis for further study of the immune escape mechanism in PA in cellular and animal experiments. METHODS: The protein expression of COX-2, PD-L1, and ARG1 in 55 PA samples was detected by immunohistochemistry, with 10 normal brain tissues as the control group. The location of COX-2, PD-L1, and ARG1 in PA cells was studied by double immunofluorescence colocalization. The results of immunohistochemistry were further verified by Western blot. RESULTS: The expression of COX-2, PD-L1, and ARG1 in PA was significantly higher than that in normal brain tissue. In functional PA (FPA) and nonfunctional PA (NFPA), there was no significant difference in the expression of COX-2 and PD-L1, whereas ARG1 was higher in NFPA. Moreover, the protein expression level of COX-2 was positively correlated with that of PD-L1 and ARG1, and the expression of PD-L1 was positively correlated with that of ARG1. Immunofluorescence confocal imaging showed that COX-2, PD-L1, and ARG1 were all expressed in the cytoplasm of PA cells, and the physical positions of COX-2, PD-L1, and ARG1 were partially coincident. CONCLUSIONS: These findings indicate that overexpression of COX-2, PD-L1, and ARG1 may be involved in the pathogenesis of PA. ARG1 plays a more important role in the development of NFPA. By upregulating the expression of PD-L1, COX-2 may promote the expression of ARG1, forming the COX-2/PD-L1/ARG1 signal pathway in promoting the occurrence and development of PA. Perhaps further study of the pathogenesis of PA can start with the mechanism of immune escape.

The Comparative Experimental Study of Multilabel Classification for Diagnosis Assistant Based on Chinese Obstetric EMRs.

Obstetric electronic medical records (EMRs) contain massive amounts of medical data and health information. The information extraction and diagnosis assistants of obstetric EMRs are of great significance in improving the fertility level of the population. The admitting diagnosis in the first course record of the EMR is reasoned from various sources, such as chief complaints, auxiliary examinations, and physical examinations. This paper treats the diagnosis assistant as a multilabel classification task based on the analyses of obstetric EMRs. The latent Dirichlet allocation (LDA) topic and the word vector are used as features and the four multilabel classification methods, BP-MLL (backpropagation multilabel learning), RAkEL (RAndom k labELsets), MLkNN (multilabel k-nearest neighbor), and CC (chain classifier), are utilized to build the diagnosis assistant models. Experimental results conducted on real cases show that the BP-MLL achieves the best performance with an average precision up to 0.7413 ± 0.0100 when the number of label sets and the word dimensions are 71 and 100, respectively. The result of the diagnosis assistant can be introduced as a supplementary learning method for medical students. Additionally, the method can be used not only for obstetric EMRs but also for other medical records.