Mixed effects of ambient air pollutants on oocyte-related outcomes: A novel insight from women undergoing assisted reproductive technology.

Air pollution is widely acknowledged as a significant risk factor for human health, especially reproductive health. Nevertheless, many studies have disregarded the potentially mixed effects of air pollutants on reproductive outcomes. We performed a retrospective cohort study involving 8048 women with 9445 cycles undergoing In Vitro Fertilization (IVF) and Intracytoplasmic Sperm Injection (ICSI) in China, from 2017 to 2021. A land-use random forest model was applied to estimate daily residential exposure to air pollutants, including sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and fine particulate matter (PM2.5). Individual and joint associations between air pollutants and oocyte-related outcomes of ART were evaluated. In 90 days prior to oocyte pick-up to oocyte pick-up (period A), NO2, O3 and CO was negatively associated with total oocyte yield. In the 90 days prior to oocyte pick-up to start of gonadotropin medication (Gn start, period B), there was a negative dose-dependent association of exposure to five air pollutants with total oocyte yield and mature oocyte yield. In Qgcomp analysis, increasing the multiple air pollutants mixtures by one quartile was related to reducing the number of oocyte pick-ups by -2.00 % (95 %CI: -2.78 %, -1.22 %) in period A, -2.62 % (95 %CI: -3.40 %, -1.84 %) in period B, and -0.98 % (95 %CI: -1.75 %, -0.21 %) in period C. During period B, a 1-unit increase in the WQS index of multiple air pollutants exposure was associated with fewer number of total oocyte (-1.27 %, 95 %CI: -2.16 %, -0.36 %) and mature oocyte (-1.42 %, 95 %CI: -2.41 %, -0.43 %). O3 and NO2 were major contributors with adverse effects on the mixed associations. Additionally, period B appears to be the susceptible window. Our study implies that exposure to air pollution adversely affects oocyte-related outcomes, which raises concerns about the potential adverse impact of air pollution on women's reproductive health.

Fast Fourier ptychographic quantitative phase microscopy for in vitro label-free imaging.

Quantitative phase microscopy (QPM) is indispensable in biomedical research due to its advantages in unlabeled transparent sample thickness quantification and obtaining refractive index information. Fourier ptychographic microscopy (FPM) is among the most promising QPM methods, incorporating multi-angle illumination and iterative phase recovery for high-resolution quantitative phase imaging (QPI) of large cell populations over a wide field of-view (FOV) in a single pass. However, FPM is limited by data redundancy and sequential acquisition strategies, resulting in low imaging efficiency, which in turn limits its real-time application in in vitro label-free imaging. Here, we report a fast QPM based on Fourier ptychography (FQP-FPM), which uses an optimized annular downsampling and parallel acquisition strategy to minimize the amount of data required in the front end and reduce the iteration time of the back-end algorithm (3.3% and 4.4% of conventional FPM, respectively). Theoretical and data redundancy analyses show that FQP-FPM can realize high-throughput quantitative phase reconstruction at thrice the resolution of the coherent diffraction limit by acquiring only ten raw images, providing a precondition for in vitro label-free real-time imaging. The FQP-FPM application was validated for various in vitro label-free live-cell imaging. Cell morphology and subcellular phenomena in different periods were observed with a synthetic aperture of 0.75 NA at a 10× FOV, demonstrating its advantages and application potential for fast high-throughput QPI.

Fast digital refocusing Fourier ptychographic microscopy method based on convolutional neural network.

Fourier ptychographic microscopy (FPM) is used to achieve high resolution and a large field of view. However, traditional FPM image reconstruction methods often yield poor image quality when encountering out-of-focus issues during reconstruction. Therefore, this study proposes a defocus-distance regression network based on convolutional neural networks. In an experimental validation, the root-mean-square error calculated from 1000 sets of predicted and true values was approximately 6.2 µm. The experimental results suggest that the proposed method has good generalization, maintains high accuracy in predicting defocus distances even for different biological samples, and extends the imaging depth-of-field of the FPM system by a factor of more than 3.

Fast and robust Fourier ptychographic microscopy with position misalignment correction.

Significance: Fourier ptychographic microscopy (FPM) is a new, developing computational imaging technology. It can realize the quantitative phase imaging of a wide field of view and high-resolution (HR) simultaneously by means of multi-angle illumination via a light emitting diode (LED) array, combined with a phase recovery algorithm and the synthetic aperture principle. However, in the FPM reconstruction process, LED position misalignment affects the quality of the reconstructed image, and the reconstruction efficiency of the existing LED position correction algorithms needs to be improved. Aim: This study aims to improve the FPM correction method based on simulated annealing (SA) and proposes a position misalignment correction method (AA-C algorithm) using an improved phase recovery strategy. Approach: The spectrum function update strategy was optimized by adding an adaptive control factor, and the reconstruction efficiency of the algorithm was improved. Results: The experimental results show that the proposed method is effective and robust for position misalignment correction of LED arrays in FPM, and the convergence speed can be improved by 21.2% and 54.9% compared with SC-FPM and PC-FPM, respectively. Conclusions: These results can reduce the requirement of the FPM system for LED array accuracy and improve robustness.

Spermatogenic cell-specific SPACA4 is essential for efficient sperm-zona pellucida binding in vitro.

Fertilization is a complex and highly regulated process that involves a series of molecular interactions between sperm and oocytes. However, the mechanisms of proteins involved in human fertilization, such as that of testis-specific SPACA4, remain poorly understood. Here we demonstrated that SPACA4 is a spermatogenic cell-specific protein. SPACA4 is expressed during spermatogenesis, upregulated in early-stage spermatids, and downregulated in elongating spermatids. SPACA4 is an intracellular protein that locates in the acrosome and is lost during the acrosome reaction. Incubation with antibodies against SPACA4 inhibited the binding of spermatozoa to zona pellucida. SPACA4 protein expression levels across different semen parameters were similar but varied significantly among patients. A prospective clinical study found no association between SPACA4 protein levels and fertilization or cleavage rates. Thus, the study suggests a novel function for SPACA4 in human fertilization in a non-dose-dependent manner. However, a larger clinical trial is required to evaluate the potential use of sperm SPACA4 protein levels to predict fertilization potential.

Adaptive correction method of hybrid aberrations in Fourier ptychographic microscopy.

Significance: Fourier ptychographic microscopy (FPM) enables quantitative phase imaging with a large field-of-view and high resolution by acquiring a series of low-resolution intensity images corresponding to different spatial frequencies stitched together in the Fourier domain. However, the presence of various aberrations in an imaging system can significantly degrade the quality of reconstruction results. The imaging performance and efficiency of the existing embedded optical pupil function recovery (EPRY-FPM) aberration correction algorithm are low due to the optimization strategy. Aim: An aberration correction method (AA-P algorithm) based on an improved phase recovery strategy is proposed to improve the reconstruction image quality. Approach: This algorithm uses adaptive modulation factors, which are added while updating iterations to optimize the spectral function and optical pupil function updates of the samples, respectively. The effectiveness of the proposed algorithm is verified through simulations and experiments using an open-source biological sample dataset. Results: Experimental results show that the proposed AA-P algorithm in an optical system with hybrid aberrations, recovered complex amplitude images with clearer contours and higher phase contrast. The image reconstruction quality was improved by 82.6% when compared with the EPRY-FPM algorithm. Conclusions: The proposed AA-P algorithm can reconstruct better results with faster convergence, and the recovered optical pupil function can better characterize the aberration of the imaging system. Thus, our method is expected to reduce the strict requirements of wavefront aberration for the current FPM.