9 results about "Inner cell mass" patented technology

This invention relates to the field of bioengineering technology, and in particular to a method for simultaneously collecting the inner cell mass and trophectoderm of mammalian blastocysts. The method includes the following steps: (1) after removing the zona pellucida of the blastocyst, the blastocyst is cut into an inner cell mass and a trophectoderm portion, and the cut trophectoderm portion is transferred to a culture medium for collection; (2) the inner cell mass portion obtained in step (1) is sequentially placed in an antibody treatment solution and a complement treatment solution for incubation; (3) the inner cell mass portion treated in step (2) is pipetted to obtain a purified inner cell mass. This method achieves simultaneous and efficient separation of two key cell lines from the blastocyst, fundamentally changing the traditional method's "resource waste" model of only obtaining the inner cell mass while discarding the trophectoderm, and providing more comprehensive experimental materials for embryonic development, placental biology, and stem cell research.

This invention discloses a method for predicting the outcome of in vitro fertilization-embryo transfer (IVF-ET) based on static embryo images, used to distinguish between biochemical pregnancy and clinical pregnancy. It belongs to the field of medical artificial intelligence and assisted reproductive technology and is applicable to single embryo transfer (SET) and dual embryo transfer (DET) scenarios. The method includes: acquiring a static image of at least one embryo to be transferred; preprocessing and standardizing the embryo image, and then inputting it into a feature extraction model based on a convolutional neural network. The model uses DenseNet-169 as the backbone network and combines it with a Feature Pyramid Network (FPN) for multi-scale feature fusion. When the input is a dual embryo image, a Trophectoderm–Inner Cell Attention (TEIC-Attn) attention module is introduced to adaptively weight and fuse the features of the two embryos to characterize the contribution of different embryos to the final transfer outcome. When the input is a single embryo image, the prediction is completed based on the corresponding embryo features. The model ultimately outputs a probability prediction result of whether the IVF treatment outcome is a biochemical pregnancy or a clinical pregnancy. Furthermore, this invention combines a gradient-based class activation mapping method to generate interpretable visual heatmaps, highlighting key morphological regions closely related to embryo transfer outcomes, such as the inner cell mass and trophectoderm, thereby providing clinicians and patients with intuitive and reliable decision-making support. This invention enables unified prediction and interpretation of single and double embryo transfer outcomes without relying on time-series data, improving the accuracy and interpretability of in-vitro fertilization treatment decisions and demonstrating promising clinical application prospects.

The application provides a blastocyst three-dimensional fitting reconstruction method and device, electronic equipment and storage medium. The method comprises the following steps: acquiring a plurality of focal plane images of a blastocyst; identifying a trophoblast cell mask and an inner cell mass mask in the blastocyst based on the plurality of focal plane images; constructing a trophoblast cell grid model and an inner cell mass grid model according to the trophoblast cell mask and the inner cell mass mask; constructing a depth map of the blastocyst according to the plurality of focal plane images; performing image fusion on the plurality of focal plane images to obtain a texture image; performing plane mapping according to the texture image and the depth map to generate an internal three-dimensional point cloud model of the blastocyst; constructing a three-dimensional geometric model of the blastocyst according to the trophoblast cell grid model, the inner cell mass grid model and the internal three-dimensional point cloud model; and mapping the texture image to the surface of the three-dimensional geometric model to generate a blastocyst three-dimensional model. The reconstruction based on the method does not affect the development of the embryo, and is low in cost and safe and reliable.

This disclosure provides ungulate embryonic stem cells (ESCs) derived from the inner cell mass of pre-implantation blastocysts or pluripotent cells from embryos. From an agricultural and biomedical perspectives, the derivation of stable ESCs from domestic ungulates is important for genomic testing and selection, genetic engineering, and providing an experimental tool for studying human diseases. Cattle are one of the most important domestic ungulates that are commonly used for food and bioreactors.

Disclosed in the present invention are a composition and method for increasing the number and proportion of inner cell mass cells of an in vitro embryo and use. According to the present invention, in view of the problems of in vitro fertilized embryos such as low blastocyst rate during early development, low inner cell mass cell number in the blastocyst stage, low proportion of the inner cell mass cells, and poor subsequent embryonic development potential, by adding one or two of regulators folic acid and laxiflorin B into the culture medium for early embryo in vitro culturing, the blastocyst rate, the number and proportion of the inner cell mass cells in the blastocyst, the survival rate of blastocysts after extended culturing, and the diameter of blastocysts are significantly improved, effectively improving the developmental potential of in vitro embryos. In addition, the present invention also effectively improves the ratio of in vitro fertilized embryos that can be subsequently transplanted. The present invention is low in cost, safe, and efficient, provides a new strategy for in vitro embryo production of livestock and development of human-assisted reproductive technology, and exhibits broad application prospects.

The invention provides a trophoblast cell feature extraction method and device, electronic equipment and a storage medium, and relates to the technical field of assisted reproduction. The method comprises the following steps: determining a mask image of an inner cell mass in a target blastocyst, a first trophoblast cell mask image and a second trophoblast cell mask image; according to the first trophoblast cell mask image, the second trophoblast cell mask image and the mask image of the inner cell cluster, determining marking information of trophoblast cells in the target blastocyst; performing curved surface mapping on the mask image of the inner cell cluster, the mask image of the first trophoblast cell and the mask image of the second trophoblast cell according to marking information of the trophoblast cells to generate a curved surface mapping result; and according to a curved surface mapping result, carrying out feature extraction on the trophoblast cell region in the target blastocyst to obtain multiple three-dimensional features of the trophoblast cell region. According to the method, more core three-dimensional characteristics of trophoblast cells can be fully excavated, and the blastocyst development potential evaluation accuracy is improved.

The invention discloses an embryo transplantation outcome prediction method based on single blastocyst image data, belongs to the field of medical artificial intelligence, and is suitable for single blastocyst transplantation. Firstly, a high-resolution embryo image of a patient is obtained. The image resolution is unified by fixing the aspect ratio of an original image and properly cutting and filling. A standard picture enhancement technology is adopted for model training, and adaptability to variable imaging conditions is improved. During prediction, a preprocessed image is input into a model which takes ResNet-50 as a trunk and combines SimCLR comparative learning to perform self-supervised pre-training, the model is used for extracting morphological features of embryos, and the recognition capability of the model for specific tasks is further optimized through supervised fine tuning. The extracted features are averagely pooled and then connected with a newly defined full-connection network, then pregnancy probability is output through a Sigmoid activation function, and finally a prediction result of single blastocyst transplantation is obtained through threshold mapping. Besides, in order to enhance clinical interpretability, gradient weighted class activation mapping (Grad-CAM) is further introduced, and a key region concerned by the model is visualized so as to verify whether the model is judged based on structures with biological significance, such as an internal cell mass and a trophoderm. According to the method, the transplantation outcome can be predicted under the condition of only depending on a single blastocyst image, and the method can be used for assisting clinical embryo selection and decision making.

The invention relates to the technical field of biotechnology, and discloses a construction method of a bovine originating embryonic stem cell line, which comprises the following steps: (1) obtaining in vitro fertilized bovine early blastocyst; (2) trophoblast cells of the blastocyst are removed, an inner cell cluster is inoculated into a culture system containing a feeder layer, and the culture system is a basic culture medium added with FGF2 and WNT pathway inhibitors; and (3) culturing until an inner cell cluster forms a cell set drop, and carrying out digestion passage to obtain the stably proliferated bovine originated embryonic stem cell line. According to the construction method and the application of the bovine embryonic stem cell line, a specific initial induction combination of 'FGF2 + WNT inhibitor IWR1' is adopted, and a cell mass in an early blastocyst is used for establishing the line, so that the bovine embryonic stem cell line which is uniform in form, can be stably passaged for more than 26 generations and still keeps typical embryonic characteristics is successfully obtained; the problems of unstable state and easy differentiation of the bovine stem cell line in the prior art are solved.

First, a three-dimensional image (D2) of a blastocyst embryo is acquired by optical coherence tomography. Secondly, a parameter (P) representing at least one of a volume of an inner cell mass, a thickness of an inner cell mass, a thickness distribution of an inner cell mass, a volume of a trophectoderm, a thickness of a trophectoderm, a thickness distribution of a trophectoderm, or the number of cells in a trophectoderm is calculated on the basis of the three-dimensional image (D2). Then, the parameter (P) of a blastocyst embryo to be classified is input to a trained machine learning model (M1, M2, M3) that has the parameter (P) as an input variable and has a classification result of the blastocyst embryo as an output variable, and the blastocyst embryo is classified on the basis of a classification result output from the machine learning model. Thus, classification of the blastocyst embryo can be performed quantitatively on the basis of the three-dimensional image (D2).