This study provides the first evidence of an association between polymorphisms in the COL1A2 gene with dental fluorosis in high fluoride exposed populations. Future studies are needed to confirm the association.

Metabolism of β-amyloid peptide (Aβ) is closely associated with the pathology and etiology of Alzheimer’s disease (AD). Our previous studies on aging primates and rodents have revealed that early life lead exposure increases the expression of the β-amyloid precursor protein (AβPP), elevates Aβ levels, and promotes neurodegeneration in old age. These effects were attributed to de novo synthetic pathways; however, the impact on Aβ degradation was not explored. Neprilysin (NEP), a rate-limiting catabolic peptidase is involved in Aβ metabolism in vivo. In the present study we sought to investigate whether accumulation of Aβ induced by Pb exposure is partially due to its ability to subdue NEP expression and consequently NEP activity. SH-SY5Y cells were exposed to Pb concentrations of 0, 5, 10, 20, and 50 μM for 48 h and AβPP, NEP protein and mRNA levels were measured. Additionally, NEP enzymatic activity and Aβ levels were also assessed. Western blot and RT-PCR analysis indicated significant increases in the protein and mRNA expression of AβPP, which appeared to be concentration and time-dependent, while the protein and mRNA expression of NEP as well as NEP activity declined. These actions of Pb were specific and were not observed when substituted by another metal. These results suggest that Pb causes both the overexpression of AβPP and repression of NEP resulting in the buildup of Aβ.

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Toxic cyanobacterial blooms in freshwater have been considered as threats to human health. Microcystins are a family of cyclic polypeptides produced by cyanobacteria and are toxic to plants and animals. Microcystin-LR (MC-LR) is the most toxic variant among the microcystin family and could cause oxidative stress in various organs, including the reproduction system. The aim of this study was to investigate the effect of MC-LR on apoptosis of Sertoli cells that play an essential role in the development and maturation of sperm cells. Sertoli cells were isolated from healthy immature rats and cultured with MC-LR. The viability of Sertoli cells was decreased after treatment with MC-LR at 10 μg/ml for 24 h (P < 0.05). Moreover, the MC-LR-treated cells exhibited condensed chromatin and fragmented nuclei, features of apoptosis, as judged by Hoechst 33258 staining. We also analyzed the mRNA and protein levels of three apoptosis-related genes, p53, bax and bcl-2, using reverse transcription-polymerase chain reaction and Western blot analyses, respectively. Both p53 and bax function as promoters of apoptosis, while bcl-2 is an apoptotic suppressor. The mRNA and protein expression levels of p53 and bax were increased in Sertoli cells treated with MC-LR at 10 μg/ml compared with the control group (P < 0.05), while the bcl-2 protein levels were decreased in cells treated with MC-LR at 10 μg/ml (P < 0.05). Moreover, caspase-3 activity that is involved in the induction of apoptosis was significantly increased in Sertoli cells treated with MC-LR. These results indicate that MC-LR induces apoptosis of Sertoli cells.

The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.