Differential effect of pre-pregnancy low BMI on fetal macrosomia: a population-based cohort study.

BACKGROUND: The differential effect of pre-pregnancy low BMI on macrosomia has not been fully addressed. Herein, we conducted a city-wide population-based cohort study to illuminate the association between pre-pregnancy low BMI and macrosomia, stratifying by maternal age, parity, and GDM status. METHODS: All pregnant women who paid their first prenatal visit to the hospital in Qingdao during August 1, 2018, to June 30, 2020, were recruited to this study. The interactive effect of maternal age and pre-pregnancy low BMI on macrosomia was evaluated using logistic regression models, followed by strata-specific analyses. RESULTS: A total of 105,768 mother-child pairs were included, and the proportion of fetal macrosomia was 11.66%. The interactive effect of maternal pre-pregnancy BMI and age was statistically significant on macrosomia irrespective of parity (nullipara: Padjusted=0.0265; multipara: Padjusted=0.0356). The protective effect of low BMI on macrosomia was most prominent among nullipara aged 35 years and above (aOR=0.16, 95% CI 0.05-0.49) and multipara aged 25 years and below (aOR=0.17, 95% CI 0.05-0.55). In nullipara without GDM, the risk estimates gradually declined with increasing conception age (20-to-24 years: aOR=0.64, 95% CI 0.51-0.80; 25-to-29 years: aOR=0.43 95% CI 0.36-0.52; 30-to-34 years: aOR=0.40 95% CI 0.29-0.53; and ≥35 years: aOR=0.19, 95% CI 0.06-0.60). A similar pattern could also be observed in nullipara with GDM, where the aOR for low BMI on macrosomia decreased from 0.54 (95% CI 0.32-0.93) in pregnant women aged 25-29 years to 0.30 (95% CI 0.12-0.75) among those aged 30-34 years. However, younger multiparous mothers, especially those aged 25 years and below without GDM (aOR=0.21, 95% CI 0.06-0.68), were more benefited from a lower BMI against the development of macrosomia. CONCLUSIONS: Maternal low BMI is inversely associated with macrosomia irrespective of maternal age and parity. The impact of pre-pregnancy low BMI on macrosomia varied by maternal age and parity. The protective effect of a lower maternal BMI against fetal macrosomia was more prominent in nulliparous mothers aged 35 years and above, whereas multiparous mothers younger than 25 years of age were more benefited.

Incidence and risk factors of congenital heart disease in Qingdao: a prospective cohort study.

BACKGROUND: Many studies have been conducted to assess the incidence of congenital heart disease (CHD). However, results were greatly inconsistent among these studies with a broad range of findings. METHODS: A prospective census-based cohort study was conducted in Qingdao, China, from August 1, 2018 to April 30, 2019. All of the local registered pregnant women were continuously investigated and followed from 15 to 20 weeks of gestation to delivery, tracking the CHD cases in both the fetal and neonatal stages. A logistic regression model was applied to assess the association between CHD and possible risk factors. RESULTS: The positive rate of prenatal CHD screening was 14.36 per 1000 fetuses and the incidence of CHD was 9.38 per 1000 live births. Results from logistic regression indicated that, living in the countryside (odds ratio, (OR): 0.771; 95% confidence interval, (CI): 0.628-0.946) and having a childbearing history (OR: 0.802; 95%CI: 0.676-0.951) were negatively associated with CHD. However, twin pregnancy (OR: 1.957, 95% CI: 1.245-3.076), illness in the first trimester (OR: 1.306; 95% CI: 1.048-1.628), a family history of CHD (OR: 7.156; 95% CI: 3.293-15.552), and having a child with a birth defect (OR: 2.086; 95% CI: 1.167-3.731) were positively associated with CHD. CONCLUSION: CHD is a serious health problem in Qingdao. The CHD incidence found in this study was similar to existing research. The positive rate of prenatal CHD screening was higher than the incidence of neonatal CHD. Moreover, CHD risk factors were identified in our study, and our findings may have great implications for formation CHD intervention strategies.

The characterization of biochars derived from rice straw and swine manure, and their potential and risk in N and P removal from water.

Nowadays, the plant residual derived biochars have been widely applied to remove nitrogen (N) and phosphorus (P) from water. However, the application of animal manure derived biochars in N and P removal was less studied. To compare the different efficiency and risk of plant residual- and animal manure-derived biochar in removing N and P from water, this study chose rice straw and swine manure as representative to produce biochar at 700 °C, and modified the produced biochar by MgCl2. Then, the characteristics, removal efficiency and release of N and P of biochars were investigated. The results showed swine manure-biochars generally had higher ash content and cation exchange capacity (CEC), but lower pH and surface area relative to rice straw-biochars. Besides, MgCl2 modification reduced the ash content and surface area of both raw biochars, whereas the pH, CEC and pore size were enhanced. Furthermore, this work demonstrated that ammonium and nitrate could be removed by all biochars to certain extent, and MgCl2 modified biochars generally had higher removal efficiency. However, none of phosphate removal was achieved by all biochars. Additionally, the release of ammonium, nitrate and phosphate from biochars was observed, suggesting there might be a risk for applying biochars in N and P removal from water. Notably, the MgCl2 modification seemed to accelerate N and P release from biochars. This work provided important information that the production and modification of biochars should be carefully designed for higher removal efficiency of pollutants. Meanwhile, the risk of released pollutants as well as the release mechanisms should be paid more attention in the future.

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification.

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

Connectivity reveals homology between the visual systems of the human and macaque brains.

The visual systems of humans and nonhuman primates share many similarities in both anatomical and functional organization. Understanding the homology and differences between the two systems can provide important insights into the neural basis of visual perception and cognition. This research aims to investigate the homology between human and macaque visual systems based on connectivity, using diffusion tensor imaging and resting-state functional magnetic resonance imaging to construct structural and functional connectivity fingerprints of the visual systems in humans and macaques, and quantitatively analyze the connectivity patterns. By integrating multimodal magnetic resonance imaging, this research explored the homology and differences between the two systems. The results showed that 9 brain regions in the macaque visual system formed highly homologous mapping relationships with 11 brain regions in the human visual system, and the related brain regions between the two species showed highly structure homologous, with their functional organization being essentially conserved across species. Finally, this research generated a homology information map of the visual system for humans and macaques, providing a new perspective for subsequent cross-species analysis.

Does recurrent gestational diabetes mellitus increase the risk of preterm birth? A population-based cohort study.

AIMS: To investigate whether recurrent gestational diabetes mellitus (GDM) is associated with an increased risk of preterm birth. METHODS: We conducted a prospective population-based cohort study covering all live singleton births born to nulliparous and multiparous mothers aged 20 years and older in Qingdao, from 2018 to 2020 (n = 105,528). Preterm birth (<37 gestational weeks) was classified into moderate preterm birth (32-36 weeks of gestation) and very preterm birth (<32 weeks). Logistic regression analysis was performed to estimate the risk and severity of prematurity in relation to parity among mothers with previous GDM, current GDM, and recurrent GDM (previous and current GDM), using mothers without GDM as the reference group. Z-test and ratio of odds ratios (ROR) were used to determine subgroup differences. RESULTS: Maternal GDM increased the risk of preterm birth in both nullipara (ORadj = 1.28, 95 %CI: 1.14-1.45) and multipara (ORadj = 1.26, 95 %CI: 1.14-1.40). However, the risk of premature delivery in multiparous mothers with recurrent GDM and those with current GDM did not differ significantly, with a ROR of 0.89 (95 %CI: 0.71-1.12). The risk of recurrent GDM on preterm birth was most pronounced among multiparous mothers with pre-pregnancy BMI above 30 kg/m2 (ORadj = 2.18, 95 %CI: 1.25-3.82) as compared with those with current GDM alone (ROR = 2.20, 95 %CI: 1.07-4.52). The risk of GDM for moderate preterm birth was similar to that of overall preterm birth. In contrast, GDM was not associated with very preterm birth irrespective of parity (all P values > 0.05). CONCLUSIONS: Maternal GDM increased the risk of preterm birth in nullipara and multipara, whereas recurrent GDM was not associated with a further increase in the risk of prematurity in multiparous mothers. Maternal GDM did not contribute to very preterm birth irrespective of parity. Our findings can be useful for facilitating more targeted preventive strategies for adverse pregnancy outcomes.

[Spatial Distribution, Risk, and Influencing Factors of River Water-Sediment Heavy Metals in the Lower Reaches of the Qianhe River].

The spatial distribution of heavy metals in rivers is affected by human activities and the natural environment, posing a risk to human health related to heavy metal pollution. In order to study the characteristics, health risk levels, and influencing factors of heavy metal distribution and pollution in the lower reaches of the Qianhe River, 19 surface sediments and 20 water samples were collected, and the contents of As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn were measured by inductively coupled plasma mass spectrometry(ICP-MS). Using the DEM, air temperature, precipitation and other 11 factors as independent variables, the spatial differentiation of heavy metal pollution in sediments were explored based on geo-detector and geo-weighted regression model. The results showed that the eight heavy metal contents in the lower reaches of the Qianhe River did not exceed the "Surface Water Environmental Quality Standards" Ⅱ for water-like bodies, in which the variation coefficient of Pb element was 3.11, and the high content areas were mainly concentrated around Dongling Smelting Company and Fengxiang Railway Station. The average Rc value of adult carcinogens in water bodies was 7.72 E-06, showing a low risk level, and the children's carcinogens average Rc value was 1.17 E-04, showing a strong risk degree. The non-carcinogen risks for adults and children were both tolerable. The total high R value for children was mainly concentrated around Fengxiang Railway Station, posing a high risk. Sedimentation of heavy metals, except As and Mn, exceeded the soil background value in Shaanxi Province. The average content of Cd element was 1.12 mg·kg-1, which was 12 times the soil background value in Shaanxi Province. The pollution of Cd, Zn, and Pb was high, and distributed mainly in Changqing Village, Nanwan Village and Niujiatan Village, Gaozhuang, and Dongling Smelting Company. PLIzone of heavy metals in the sediments in the study area was 1.71, which was light pollution. DEM, temperature and precipitation were the main natural factors influencing the spatial pattern of heavy metal pollution load index(PLI) in sediments, and their nonlinear interactions were enhanced, which may play a role in the spread of heavy metals in sediments. This research can provide a scientific basis for urban planning and human health risks prevention in the Qianhe River.

Optimization of Fenton process on removing antibiotic resistance genes from excess sludge by single-factor experiment and response surface methodology.

Excess sludge contains large amounts of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs), posing a risk for human health. However, most current studies usually ignored their abundance and removal in excess sludge. Therefore, this study aimed to reduce ARGs/MGEs in sludge by Fenton process, and applied single-factor experiment (SFE) and response surface methodology (RSM) to optimize the Fenton reaction condition for higher removal rates of ARGs/MGEs. The results demonstrated that the removal rates of target genes by SFE optimized condition ranged from 10.91% to 66.86%, while the removal rates caused by RSM optimized condition were 48.02% - 76.36%, indicating RSM was a useful tool to improve the removal rates of ARGs in excess sludge. Additionally, the scanning electron microscope and cell apoptosis results suggested that the Fenton treatment altered the structure of sludge and reduced the numbers of normal cells, thus causing the reductions of target genes.