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Di(2-ethylhexyl) phthalate (DEHP) is a widely recognized environmental endocrine disruptor that potentially impacts female reproductive function, although the specific mechanisms leading to such impairment remain unclear. A growing body of research has revealed that the endoplasmic reticulum and mitochondrial function significantly influence oocyte quality. The structure of mitochondria-associated endoplasmic reticulum membranes (MAMs) is crucial for facilitating the exchange of Ca2+, lipids, and metabolites. This study aimed to investigate the alterations in the composition and function of MAMs after DEHP exposure and to elucidate the underlying mechanisms of ovarian toxicity. The female mice were exposed to DEHP at doses of 5 and 500 mg/kg/day for one month. The results revealed that DEHP exposure led to reduced serum anti-Müllerian hormone levels and increased atretic follicles in mice. DEHP induced endoplasmic reticulum stress and disrupted calcium homeostasis in oocytes. Furthermore, DEHP impaired the mitochondrial function of oocytes and reduced their membrane potential, and promoting apoptosis. Similar results were observed in human granulosa cells after exposure to mono-(2-ethylhexyl) phthalate (MEHP, metabolites of DEHP) in vitro. Proteomic analysis and transmission electron microscopy revealed modifications in the functional proteins and structure of the MAMs, and the suppression of oxidative phosphorylation pathways. The findings of this investigation provide a new perspective on the mechanism underlying the reproductive toxicity of DEHP in females.

BackgroundPhthalates are ubiquitously used in a variety of products and have an adverse effect on folliculogenesis. However, previous epidemiological studies on the associations between phthalate exposure and antral follicle count (AFC) produced conflicting results. The present study aimed to evaluate the associations between urinary phthalate metabolite concentrations and AFC among women undergoing in vitro fertilization (IVF).MethodsWe collected 525 urine samples and measured 8 phthalate metabolites from IVF patients. Poisson regression models were conducted to evaluate the associations between phthalate metabolite concentrations and AFC. In addition, participants were stratified into a younger group (< 35 years) and an older group (≥ 35 years) to explore the potential effect modification by age. We also performed sensitivity analyses by restricting our analyses to only infertile women diagnosed with tubal factor infertility to test the robustness of the results.ResultsSignificant positive associations were observed among urinary MBP, MEOHP and ∑PAEs concentrations and AFC after adjusting for age, BMI, year of study and infertility diagnosis. Compared with women in the first tertile, women in the third tertile of MBP and MEOHP had 7.02% (95% CI: 1.18%, 12.9%) and 8.84% (95% CI: 2.83%, 14.9%) higher AFC, respectively, and women in the second and third tertiles of ∑PAEs had 6.19% (95% CI: 0.37%, 12.0%) and 9.09% (95% CI: 3.22%, 15.0%) higher AFC, respectively. In addition, MBP, MEOHP and ∑PAEs also had significant positive associations with AFC in trend tests for dose-response. In the age-stratified analysis, we found a stronger relationship between phthalate metabolite concentrations and AFC among older women and an inverse association among younger women. We observed similar results in the sensitivity analyses.ConclusionWe found positive associations between phthalate exposure and AFC, which support the idea that phthalate exposure may accelerate primordial follicle recruitment and lead to higher AFC in women undergoing IVF. More studies are needed to better understand their relationships.

Background: Phthalates have been shown to disrupt the estrous cycle in animal studies. However, epidemiological research investigating their associations with menstrual cycle characteristics is limited. Objective: To explore the relationships between phthalate exposure and menstrual cycle characteristics among women seeking fertility assistance. Methods: We determined the levels of eight phthalate metabolites in both follicular fluid (FF) and urine specimens collected from 441 women in the Tongji Reproductive and Environmental (TREE) cohort, using high-performance liquid chromatography and tandem mass spectrometry. Information about menstrual cycle parameters was obtained through a questionnaire. The impacts of individual and joint exposure to phthalates on menstrual cycle characteristics were assessed using multivariable linear regression, Poisson regression, and quantile g-computation approaches. Results: After adjusting for relevant covariates, we found that per log10-unit increase in mono(2-ethylhexyl) phthalate (MEHP) level in urine specimens was associated with a decrease of 0.20 days (95 % CI: −0.37, −0.03) in bleeding duration. We also observed that mono(2-ethyl-5-carboxypentyl) phthalate (MECPP) and the sum of di(2-ethylhexyl) phthalate (DEHP) metabolites (∑DEHP) concentrations in FF samples were inversely related to cycle length [β = −1.92 (95 % CI: −3.10, −0.75) and −1.87 (95 % CI: −3.56, −0.19), respectively]. However, we generally observed null associations between phthalate metabolites and irregular cycle, dysmenorrhea, hypomenorrhea, or cycle length variation. Furthermore, we also found that phthalate metabolite mixtures in FF and urine were generally unrelated to menstrual cycle characteristics. Conclusion: Our findings suggest that some DEHP metabolites in FF and urine are inversely associated with menstrual cycle length and menstrual bleeding duration in women attending a fertility center.

The beam position is the most important reference basis for the operation of synchrotron radiation light sources, particularly for commissioning the first-turn injection of fourth-generation light sources. To improve the accuracy of the beam position measurement, we analyzed methods for calculating the beam position, and a finite element calculation and the stretched wire calibration system were used to demonstrate the procedure. We proved the relationship between the coverage range, fitting order, scanning step size, and accuracy both theoretically and experimentally, which can provide a basis for selecting the appropriate fitting order for different operation stages of the accelerator. It was proved that the accuracy of beam position calculations using simplified polynomial coefficients is comparable to those without a simplified one, which can save resources for reading electronic processing. The testing results of a batch of beam position monitors (BPMs) were in good agreement with the finite element calculation results, and the small difference between the manufactured BPMs also proved that quality control was performed well, and it benefited from button sorting.

To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the detection capability for small targets. Finally, this paper applies L1 regularization to train the improved network, followed by pruning and fine-tuning operations to remove redundant channels, reducing computational and parameter complexity and enhancing the detection efficiency of the network. Training is conducted on the VisDrone2019-DET dataset. The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%, respectively. The average detection accuracy improves by 5.15%, and the detection speed reaches 47 images per second, satisfying real-time requirements. Compared with existing approaches, including YOLOv5m and classical vehicle detection algorithms, our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing. [ABSTRACT FROM AUTHOR]