RESUMEN
Chronic fluorosis has been widely investigated for its adverse effects on skeletal and neurological health; however, its impact on reproductive health, especially in females, remains underexplored. In this study, female Sprague-Dawley rats were exposed to different fluoride concentrations (0.75, 50, and 100 mg/L) in their drinking water for six months. Dental fluorosis and increased urinary fluoride content were observed in fluoride-exposed rats, reflecting fluoride accumulation and exposure levels. Chronic fluorosis resulted in reduced ovary organ coefficient, indicating harmful effects on ovarian tissue. Additionally, the number of ovarian primordial and primary/secondary follicles decreased, while the number of atresia follicles increased. Furthermore, chronic fluorosis led to disrupted estrous cycles. Hormonal analysis revealed altered secretion of estrogen, progesterone, anti-Müllerian hormone, luteinizing hormone, follicular stimulating hormone, and inhibin B in response to fluoride exposure. Ultrastructural observation of ovarian granulosa cells showed evidence of apoptosis, which was further confirmed by flow cytometry. Caspase-3 activity was increased, and ATP levels were decreased, suggesting mitochondrial impairment and apoptosis induction. The mRNA and protein expression of BMP15 and GDF9, essential regulators of ovarian function, significantly decreased with increasing fluoride concentration. Furthermore, gene expression analysis identified a panel of premature ovarian failure-related genes that were downregulated in fluoride-exposed rat ovaries. These findings suggest that chronic fluoride exposure may contribute to ovarian dysfunction and possibly the pathogenesis of premature ovarian failure. Understanding the toxicological effects of chronic fluoride exposure on ovarian function is essential for identifying potential environmental risk factors affecting female reproductive health.
RESUMEN
OBJECTIVE: To heterologously produce the Shigella dysenteriae serotype 1 O-polysaccharide (O-PS, O-antigen) in Escherichia coli by transferring the minimum number of genes instead of the entire O-PS gene cluster. RESULTS: The three glycosyltransferase genes (rfbR, rfbQ and rfp) responsible for the formation of the O-repeat unit were introduced into E. coli K-12 W3110 to synthesize S. dysenteriae 1 O-PS. The specific O-antigen ladder type with different chain lengths of O-repeat units was observed in the recombinant E. coli strain by SDS-PAGE silver staining and western blotting using S. dysenteriae 1 lipopolysaccharide antiserum. Analysis by mass spectrometry and ion chromatography suggested generation of the specific S. dysenteriae 1 O-repeat unit structure with an extra glucose residue attached. CONCLUSIONS: Recombinant E. coli expressing specific glycosyltransferase genes can generate the O-PS of S. dysenteriae 1 and might be able to synthesize heterologous O-antigens of various pathogenic bacteria for vaccine preparation.
Asunto(s)
Escherichia coli K12/genética , Escherichia coli K12/metabolismo , Expresión Génica , Ingeniería Metabólica/métodos , Antígenos O/biosíntesis , Shigella dysenteriae/genética , Glicosiltransferasas/genética , Glicosiltransferasas/metabolismo , Antígenos O/genética , Proteínas Recombinantes/genética , Proteínas Recombinantes/metabolismoRESUMEN
BACKGROUND: Exopolysaccharides with structural diversity have shown wide applications in biomaterial, food, and pharmaceutical industries. Herein, we isolated an endophytic strain, 14-DS-1, from the traditional medicinal plant Codonopsis pilosula to elucidate the characteristics and anti-cancer activities of purified exopolysaccharides. METHODS: HPLC and GC-MS were conducted to purify and characterize the exopolysaccharides isolated from 14-DS-1. Quantitative RT-PCR, cell migration assays, immunofluorescence staining, and flow cytometry analysis were conducted to investighate the biological activity of DSPS. RESULTS: We demonstrated that exopolysaccharides isolated from 14-DS-1 (DSPS), which were predominately composed of six monosaccharides, showed anti-cancer activities. Biological activity analysis revealed that exposure to DSPS induced macrophage activation and polarization by promoting the production of TNF-α and nitric oxide. Further analysis revealed that DSPS treatment promoted macrophage infiltration, whereas cancer cell migration was suppressed. In addition, DSPS exposure led to S-phase arrest and apoptosis in cancer cells. Immunofluorescence staining revealed that treatment with DSPS resulted in defects in spindle orientation and positioning. CONCLUSION: These findings thus suggest that DSPS may have promising potential in cancer therapy.
Asunto(s)
Proliferación Celular/efectos de los fármacos , Codonopsis/química , Neoplasias/tratamiento farmacológico , Polisacáridos/farmacología , Apoptosis/efectos de los fármacos , Puntos de Control del Ciclo Celular/efectos de los fármacos , Movimiento Celular/efectos de los fármacos , Cromatografía Líquida de Alta Presión , Endófitos/química , Células HeLa , Humanos , Macrófagos/efectos de los fármacos , Neoplasias/genética , Neoplasias/patología , Óxido Nítrico/biosíntesis , Óxido Nítrico/genética , Plantas Medicinales/química , Polisacáridos/química , Polisacáridos/aislamiento & purificaciónRESUMEN
This paper is reports an extension of our previous investigations on adding transparency to neural networks. We focus on a class of linear priors (LPs), such as symmetry, ranking list, boundary, monotonicity, etc., which represent either linear-equality or linear-inequality priors. A generalized constraint neural network-LPs (GCNN-LPs) model is studied. Unlike other existing modeling approaches, the GCNN-LP model exhibits its advantages. First, any LP is embedded by an explicitly structural mode, which may add a higher degree of transparency than using a pure algorithm mode. Second, a direct elimination and least squares approach is adopted to study the model, which produces better performances in both accuracy and computational cost over the Lagrange multiplier techniques in experiments. Specific attention is paid to both "hard (strictly satisfied)" and "soft (weakly satisfied)" constraints for regression problems. Numerical investigations are made on synthetic examples as well as on the real-world datasets. Simulation results demonstrate the effectiveness of the proposed modeling approach in comparison with other existing approaches.