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This study focuses on analyzing the spatial distribution pattern and formation mechanisms of urban industrial clusters and aims to address the mismatch between industrial clusters and resource distribution. Firstly, the spatial distribution pattern of industrial clusters is analyzed using the kernel density estimation approach. Subsequently, a multi-layered model of interactive driving factors is constructed to analyze the functional types within the multi-layered network space. Lastly, a spatial weighted regression analysis model, considering the intensity of flow space, is developed to explore the intrinsic formation mechanisms of industrial agglomeration. The experimental results indicate the following: (1) There is a trend of industrial agglomeration in the Yangtze River Delta region, primarily concentrated in cities such as Shanghai, Nanjing, Hefei, Jinhua, and Taizhou. (2) The impact of spatial interaction factors on industrial agglomeration development is significant, and the analysis of interaction networks reflects the strength of interactive influencing factors to a certain extent. (3) The regression analysis model, which incorporates interactive information considering flow space intensity, better aligns with the study of the actual mechanisms behind industrial agglomeration in physical space. [ABSTRACT FROM AUTHOR]

Background: Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature (BBT) and heart rate (HR), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators. Methods: This was a prospective observational cohort study conducted at the International Peace Maternity and Child Health Hospital in Shanghai, China. Participants were recruited from August 2020 to November 2020 and followed up for at least four menstrual cycles. Participants used an ear thermometer to assess BBT and wore the Huawei Band 5 to record HR. Ovarian ultrasound and serum hormone levels were used to determine the ovulation day. Menstruation was self-reported by women. We used linear mixed models to assess changes in physiological parameters and developed probability function estimation models to predict the fertile window and menses with machine learning. Results: We included data from 305 and 77 qualified cycles with confirmed ovulations from 89 regular menstruators and 25 irregular menstruators, respectively. For regular menstruators, BBT and HR were significantly higher during fertile phase than follicular phase and peaked in the luteal phase (all P < 0.001). The physiological parameters of irregular menstruators followed a similar trend. Based on BBT and HR, we developed algorithms that predicted the fertile window with an accuracy of 87.46%, sensitivity of 69.30%, specificity of 92.00%, and AUC of 0.8993 and menses with an accuracy of 89.60%, sensitivity of 70.70%, and specificity of 94.30%, and AUC of 0.7849 among regular menstruators. For irregular menstruators, the accuracy, sensitivity, specificity and AUC were 72.51%, 21.00%, 82.90%, and 0.5808 respectively, for fertile window prediction and 75.90%, 36.30%, 84.40%, and 0.6759 for menses prediction. Conclusions: By combining BBT and HR recorded by the Huawei Band 5, our algorithms achieved relatively ideal performance for predicting the fertile window and menses among regular menstruators. For irregular menstruators, the algorithms showed potential feasibility but still need further investigation. Trial registration: ChiCTR2000036556. Registered 24 August 2020. [ABSTRACT FROM AUTHOR]

Purpose: COVID-19 (Coronavirus Disease 2019) was first reported in December 2019 and quickly swept across China and around the world. Levels of anxiety and depression were increased among pregnant women during this infectious pandemic. Thyroid function is altered during stressful experiences, and any abnormality during early pregnancy may significantly affect fetal development and pregnancy outcomes. This study aimed to determine whether the COVID-19 pandemic induces thyroid hormone changes in early pregnant women. Methods: This study comprised two groups of pregnant women in Shanghai in their first trimester – those pregnant women before the COVID-19 outbreak from January 20, 2019, to March 31, 2019 (Group 1) and those pregnant during the COVID-19 outbreak from January 20, 2020, to March 31, 2020 (Group 2). All women were included if they had early pregnancy thyrotropin (TSH), free triiodothyronine (FT3), free thyroxine (FT4), total triiodothyronine (TT3), and total thyroxine (TT4) concentrations, thyroid peroxidase (TPO) antibody or thyroglobulin antibody (TgAb) available and did not have a history of thyroid diseases or received thyroid treatment before or during pregnancy. We used propensity score matching to form a cohort in which patients had similar baseline characteristics. Results: Among 3338 eligible pregnant women, 727 women in Group 1 and 727 in Group 2 had similar propensity scores and were included in the analyses. Pregnant women in Group 2 had significantly higher FT3 (5.7 vs. 5.2 pmol/L, P<0.001) and lower FT4 (12.8 vs. 13.2 pmol/L, P<0.001) concentrations compared with those in Group 1. Pregnant women in Group 2 were more likely to develop isolated hypothyroxinemia (11.6% vs. 6.9%, OR, 1.75 [95% CI, 1.20–2.53], P =0.003) than those in Group 1 but had a significantly lower risk of TgAb positivity (12.0% vs. 19.0%, OR, 0.58 [95% CI, 0.43–0.78], P <0.001). Conclusion: Pregnant women in their first trimester in Shanghai during the COVID-19 outbreak were at an increased risk of having higher FT3 concentrations, lower FT4 concentrations, and isolated hypothyroxinemia. The association between thyroid hormones, pregnancy outcomes, and the COVID-19 outbreak should be explored further. [ABSTRACT FROM AUTHOR]

Objective: To find an association between basic characteristics, seasons as well as disease types and 25-Hydroxyvitamin D serum concentrations in Chinese patients. Methods: We randomly selected 5470 Chinese patients with various diseases, who were hospitalized between May 2012 and August 2013 in Shanghai and analyzed their serum 25-Hydroxyvitamin D2 (25 (OH)D2) and 25-Hydroxyvitamin D3 (25(OH)D3) concentrations with liquid chromatography-tandem mass spectrometry (LC-MS/MS) as well as their parathyroid hormone (PTH) and serum creatinine blood levels. The resulting data were analyzed by linear regression and variance analyses or multivariate analysis with covariance. Results: The 25(OH)D serum concentrations were lowest in December. Among the subjects with a median age of 83.0±16.0, the median 25(OH)D2, 25(OH)D3 and total 25(OH)D serum concentrations were 1.00±1.80 ng/ml, 12.20±8.50 ng/ml and 14.80±9.80 respectively, indicating a prevalent 25(OH)D deficiency. According to our multivariate analysis of covariance, the factors affecting 25(OH)D2 and 25(OH)D3 serum concentrations included age, creatinine, PTH, season and type of disease, whereas gender correlated only with 25(OH)D2 and 25(OH)D2 and D3 values correlated negatively with each other. Our results further revealed that 25(OH)D3 levels were low while 25(OH)D2 levels were high among patients with lung diseases, dyskinesia and coronary heart diseases. In addition, participants with diabetes and cerebral infarction had higher 25(OH)D3 serum concentrations compared with lung disease patients. Conclusion: Vitamin D intake particularly during winter and summer seasons is important especially for elderly lung disease, dyskinesia and coronary heart disease patients to improve their quality of life. [ABSTRACT FROM AUTHOR]

While the effects of ambient pollutants on adverse perinatal outcomes have been studied, most studies have focused on preterm birth, stillbirth, and low birthweight. Few studies have examined the effects of ambient pollutants on prelabor rupture of membranes (PROM). This study was designed to explore the acute effects of ambient pollutants on both term PROM (TPROM) and preterm PROM (PPROM). We enrolled pregnant women receiving antenatal care between October 2013 and December 2019 at the International Peace Maternity and Child Health Hospital (IPMCHH). The effects of ambient pollutants (including PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and 8-h O 3) on TPROM and PPROM were estimated using generalized additive models (GAMs). Exposure-response relationship curves were also evaluated using GAMs after adjustment for confounding factors. Potential lagged effects were examined using various lag models. The data of 100,200 pregnant women who delivered at IPMCHH were analyzed. The fitted spline curves for PPROM were similar to the temporal trends of PM 2.5 , PM 10 , SO 2 , CO and NO 2 but not O 3 , while those for TPROM were different from the temporal trends of all six air pollutants. An increased risk of PPROM was associated with increased concentrations of PM 2.5 , PM 10 , SO 2 and CO on lag days 2 and 3, while no association was found between PPROM and daily concentration of O 3. After adjustment for confounding factors, there was a shift in the exposure-response curves, indicating associations between PPROM and PM 2.5 , PM 10 , SO 2 , and CO on lag days 2–3. Interaction effects of PM 2.5 , PM 10 , SO 2 , and CO were also found to increase the risk of PPROM. In conclusion, acute exposures to six critical air pollutants were not associated with an increased risk of TPROM; however, PM 2.5 , PM 10 , SO 2 , and CO were found to interact, increasing the risk for PPROM on lag days 2 and 3. [Display omitted] • Acute exposure to these air pollutants did not increase the risk of TPROM. • PM 2.5 , PM 10 , SO 2 , and CO interacted with each other to increase the risk of PPROM. • No relationship between O 3 and PROM was observed. • Acute effects of 6 critical air pollutants on PROM were first studied in China. [ABSTRACT FROM AUTHOR]

Objectives: Sleep disturbances are common in preschoolers, and profoundly affected by parenting. Grandparents are increasingly involved in childcare and have attracted worldwide attention for their potential impacts on child well-being. However, no studies have explored child sleep health within the context of grandparental care. We aimed to explore the association between grandparents as primary caregivers and children's sleep disturbances.Methods: Participants included 20,324 preschoolers from the Shanghai Children's Health, Education and Lifestyle Evaluation-Preschool (SCHEDULE-P) in Shanghai, China. They were initially assessed in November 2016 and were reassessed in April 2018 and April 2019. Children's primary caregivers were collected at each survey. Sleep disturbances were assessed by the Children's Sleep Habits Questionnaire (CSHQ).Results: At baseline, 84.4% of children had global sleep disturbances (CSHQ total score >41). Compared with parents, random-effects model indicated that grandparents as primary caregivers increased the adjusted odds ratio (OR) for children's global sleep disturbances by 30% (OR [95%CI]: 1.30 [1.21, 1.40]). To the specific domains of CSHQ, two-way repeated-measures ANOVA showed significant "caregiver transition" group × wave interaction in the behavioral sleep disturbances, such as "Bedtime Resistance" (p < 0.001). At each survey, children with grandparents as primary caregivers demonstrated significant higher behavioral sleep disturbances than their counterparts with parental care.Conclusions: Grandparental care is associated with increased sleep disturbances, particularly behavioral sleep disturbances, in preschoolers. Future studies should explore the underlying mechanisms and whether sleep programs targeting grandparents can decrease children's sleep disturbances. [ABSTRACT FROM AUTHOR]

Despite the longstanding evidence on the effect of air pollutants on preterm birth (PTB), few studies have focused on its subtypes, including spontaneous preterm birth (sPTB) and medically indicated preterm birth (miPTB). Most studies evaluated only the short-term or long-term effects of particulate matter (PM) on PTB. Thus, we designed this study, based on a cohort of 179,385 women, to evaluate both short- and long-term effects of PM with diameters ≤2.5 μm and ≤10 μm (PM 2.5 and PM 10) on PTB, sPTB and miPTB in Shanghai. Generalized additive models (GAMs) were applied to evaluate short-term effects. Lagged effects were identified using different lag structures. Exposure-response correlation curves were plotted using GAMs after adjustment for confounders. ORs and 95% CIs were calculated using logistic regression to estimate the long-term effect after adjustment for confounders. There was 5.67%, 3.70% and 1.98% daily incidence of PTB, sPTB, and miPTB on average. Every 10 μg/m3 increase in PM 2.5 and PM 10 was positively associated with PTB and sPTB at lag 2 day. The exposure-response curves (lag 2 day) indicated a rapid increase in sPTB for PM 2.5 and a linear increase for PM 10 , in PTB for PM 2.5 and PM 10 at concentrations over 100 μg/m3. Regarding long-term exposure, positive associations were found between 10 μg/m3 increases in PM 2.5 and PM 10 in 3rd trimester and greater odds of sPTB (aOR: 1.042, 95% CI: 1.018–1.065, and 1.018, 95% CI:1.002–1.034), and during the 3 months before conception and miPTB (aOR: 1.023, 95% CI: 1.003–1.042, and 1.017, 95% CI: 1.000–1.036). Acute exposure to PM 2.5 and PM 10 at lag 2 day and chronic exposure in 3rd trimester was significantly associated with sPTB, while miPTB was related to chronic exposure during the 3 months before pregnancy. These findings indicate that susceptibility windows of PM exposure can be influenced by different underlying etiologies of PTB. [Display omitted] • Short-term exposure to PM 2.5 and PM 10 at lag 2 day was significantly associated with an increased risk of sPTB. • At lag 2 day, the exposure-response curve was steeper in sPTB for PM 2.5 and linear for PM 10 at concentrations > 100 μg/m3. • Long-term exposure to PM 2.5 and PM 10 in the third trimester during pregnancy adversely affected sPTB. • An increased risk of miPTB was related to long-term exposure to PM 2.5 and PM 10 during the 3 months before pregnancy. [ABSTRACT FROM AUTHOR]