A few studies have examined the association between ambient temperature and preterm birth (PTB), and the results have been inconsistent. This study explored the association between ambient temperature and PTB in Shenzhen, China. Data of daily singleton PTB, air pollution and meteorological variables from 2005 to 2011 were collected in Shenzhen. A distributed lag non-linear model (DLNM) was used to investigate the association of the low and high temperatures (1st, 5th, 95th, and 99th percentiles) with PTB. The median temperature was 24.5 °C and the 1st, 5th, 95th, and 99th percentiles of daily mean temperatures were 9, 12.5, 29.9 and 30.7 °C, respectively. The prevalence of singleton PTB was 5.61 % in Shenzhen. The association between temperature and PTB was not linear. There was an immediate positive association of low temperature (1st and 5th percentiles) and a negative association of high temperature (95th and 99th percentiles) with PTB. The effect of low temperature 9 °C (1st) on PTB on the current day was stronger than that of 12.5 °C (5th), with a relative risk (RR) of 1.54 (95 % CI: 1.36–1.75) and 1.49 (95 % CI: 1.35–1.63), respectively. The cumulative RR (up to 30 days) of 9 and 12.5 °C was 1.72 (95 % CI: 1.28–2.33) and 1.96 (95 % CI: 1.60–2.39), respectively. The cumulative effects (up to 30 days) of high temperature (95th and 99th percentiles) on PTB were 0.69 (95 % CI: 0.60–0.80) and 0.62 (95 % CI: 0.52–0.74), respectively. The cumulative effect (up to 30 days) of low temperatures on vaginal delivery PTB was lower than that of the cesarean section PTB with an RR of 1.58 (95 % CI: 1.12–2.22) and 1.93 (95 % CI: 1.21–3.08), respectively. This study suggests that low temperature might be a risk factor, while high temperature might be a protective factor of PTB in Shenzhen.

Over the last decade, a few studies have investigated the possible adverse effects of ambient air pollution on preterm birth. However, the correlation between them still remains unclear, due to insufficient evidences.

The aim of the present study was to identify potential serum biomarkers for insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS) by comparing the differences in serum protein expression levels between PCOS patients with and without IR. PCOS patients aged from 18 to 35 years were recruited at Guangdong Women and Children's Hospital from January, 2013 to February, 2014. A total of 218 PCOS patients were enrolled and divided into the insulin resistance (PCOS‑IR) and non‑insulin resistance (PCOS‑NIR) groups according to their homeostasis model assessment of insulin resistance. Two‑dimensional difference gel electrophoresis (2D‑DIGE) and matrix‑assisted laser desorption/ionization time‑of‑flight mass spectrometry (MALDI‑TOF‑MS/MS) techniques were used to identify differences in protein expression levels between the PCOS‑IR and PCOS‑NIR groups. The present study demonstrated that the total cholesterol (TCH), triglycerides (TG), low‑density lipoprotein (LDL), fasting plasma glucose (FPG), 3‑h blood glucose (3hBG) and uric acid (UA) levels in the PCOS‑IR group were higher than those in the PCOS‑NIR group (P<0.05). Between the PCOS‑IR and PCOS‑NIR groups, a total of 20 differentially expressed protein spots were detected by 2D‑DIGE. Among these, 4 proteins, namely afamin, serotransferrin, complement C3 and apolipoprotein C3 (APOC3), were also identified by MALDI‑TOF‑MS/MS. The alteration of APOC3 was further confirmed by western blot analysis and enzyme‑linked immunosorbent assay (ELISA). The present study also confirmed that the expression level of APOC3 was positively associated with the homeostasis model assessment of insulin resistance (HOMA‑IR). On the whole, the data indicate that APOC3 may be a potential diagnostic marker for PCOS‑IR patients.

Preterm birth is the leading cause of child mortality under 5 years of age. Temporal trends in preterm birth rates are highly heterogeneous among countries and little information exists for China. To address this data gap, we investigated annual changes in preterm birth incidence rate and explored potential determinants of these changes in Shenzhen, China. A total of 1.4 million live births, during 2003-2012, were included from the Shenzhen birth registry. Negative-binominal regression models were used to estimate the annual percent changes in incidence. To identify the potential determinants behind temporal trends, we estimated the contribution of each changing risk factor to changes in rate by calculating the difference in population-attributable risk fraction. Annual preterm birth incidence rates increased by 0.94% (95% CI 0.30%, 1.58%) overall, 3.60% (95% CI 2.73%, 4.48%) for medically induced, and 3.13% (95% CI 1.01%, 5.31%) for preterm premature rupture of membranes, but decreased by 2.34% (95% CI 1.62%, 3.06%) for spontaneous preterm labor. Higher maternal educational attainment (0.20 rate increase), lower proportion of inadequate prenatal care (0.15 rate reduction), more multipara (0.08 rate reduction), decreased proportion of preeclampsia or eclampsia (0.05 rate reduction), and larger proportion of young and older pregnant women (0.04 rate increase) were significant contributors to the overall change over time. Contributions of changing risk factors were different between preterm birth subtypes. Preterm birth rate in Shenzhen, China increased overall during 2003-2012, although trends varied across three preterm birth subtypes. The rising rates were associated with changes in maternal education and age.

Environmental pollution plays a major role in the development of prostate cancer (PCA). However, there has been no research on machine learning (ML) modelling between multiple heavy metal exposures and PCA risk. Based on the 8022 samples from the 2003-2018 National Health and Nutrition Examination Survey (NHANES) database, we utilized the information pertaining to the concentrations of 18 blood and urinary heavy metals and minerals as well as 14 covariates. Among the eight ML models evaluated, the random forest (RF) algorithm showed superior performance, achieving an accuracy of 72.835 %, an area under the receiver operating characteristic curve (AUC) of 0.869, an F₁ score of 0.145, a G-mean of 0.749, and a Youden index of 0.498 in the test set. Four interpretable methods were integrated into the ML model. RF found that specific levels of blood lead (Pb) (0.449-29.964 µg/dL), urinary cesium (Cs) (1.822-270.426 µg/L), and urinary antimony (Sb) (0.015-4.953 µg/L) were positively associated with the PCA risk, while blood cadmium (Cd) (0.247-9.025 µg/L) showed a negative association. Notably, urinary Cs and Sb emerged as novel risk-related metals for the PCA in our study. The synergistic effect analysis further identified blood Pb, urinary Sb, and urinary Cs as the major contributing factors. The predictive model established in this study can provide valuable strategies for the prevention and the control of PCA.