Preliminary application of three-dimensional printing in congenital uterine anomalies based on three-dimensional transvaginal ultrasonographic data.

BACKGROUND: The three-dimensional (3D) printing technology has remarkable potential as an auxiliary tool for representing anatomical structures, facilitating diagnosis and therapy, and enhancing training and teaching in the medical field. As the most available diagnostic tool and it is routinely used as the first approach in diagnosis of the uterine anomalies, 3D transvaginal ultrasonography (3D-TVS) has been proposed as non-invasive "gold standard" approach for these malformations due to high diagnostic accuracy. Despite holding promise of manufacturing 3D printed models based on 3D-TVS data, relevant reports about 3D-TVS derived gynecological 3D printing haven't been reported to the best of our knowledge. We found an opportunity to explore the feasibility of building 3D printed models for the abnormal uterus based on the data acquired by 3D-TVS. METHODS: The women suspected with congenital uterine anomalies (CUAs) were enrolled in the study. The diagnose of CUAs were made by 3D-TVS scanning and further confirmed under the hysteroscopy examination. One volunteer with normal uterus was enrolled as control. All subjects underwent 3D-TVS scanning for 3D printing data collection. Acquired images were stored and extracted as DICOM files, then processed by professional software to portray and model the boundary of the uterine inner and outer walls separately. After the computer 3D models were constructed, the data were saved and output as STL files for further surface restoration and smoothing. The colors of endometrium and uterine body were specified, respectively, in the print preview mode. Then the uncured photosensitive resin was cleaned and polished to obtain a smooth and transparent solid model after printed models were cooled down. RESULTS: 3D printing models of normal uterus, incomplete septate uterus, complete septate uterus, uterus didelphys and unicornuate uterus were produced on ultrasonographic data of 3D-TVS. CONCLUSIONS: Our research and practice made the first try in modeling CUAs successfully based on ultrasonographic data entirely, verifying that it's a feasible way to build 3D printed models of high-quality through 3D-TVS scanning.

Association between MC1R gene and coat color segregation in Shanxia long black pig and Lulai black pig.

BACKGROUND: Coat color, as a distinct phenotypic characteristic of pigs, is often subject to preference and selection, such as in the breeding process of new breed. Shanxia long black pig was derived from an intercross between Berkshire boars and Licha black pig sows, and it was bred as a paternal strain with high-quality meat and black coat color. Although the coat color was black in the F1 generation of the intercross, it segregated in the subsequent generations. This study aims to decode the genetic basis of coat color segregation and develop a method to distinct black pigs from the spotted in Shanxia long black pig. RESULTS: Only a QTL was mapped at the proximal end of chromosome 6, and MC1R gene was picked out as functional candidate gene. A total of 11 polymorphic loci were identified in MC1R gene, and only the c.67_68insCC variant was co-segregating with coat color. This locus isn't recognized by any restriction endonuclease, so it can't be genotyped by PCR-RFLP. The c.370G > A polymorphic locus was also significantly associated with coat color, and has been in tightly linkage disequilibrium with the c.67_68insCC. Furthermore, it is recognized by BspHI. Therefore, a PCR-RFLP method was set up to genotype this locus. Besides the 175 sequenced individuals, another more 1,391 pigs were genotyped with PCR-RFLP, and all of pigs with GG (one band) were black. CONCLUSION: MC1R gene (c.67_68insCC) is the causative gene (mutation) for the coat color segregation, and the PCR-RFLP of c.370G > A could be used in the breeding program of Shanxia long black pig.

A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation.

Industrial quality detection is one of the important fields in machine vision. Big data analysis, the Internet of Things, edge computing, and other technologies are widely used in industrial quality detection. Studying an industrial detection algorithm that can be organically combined with the Internet of Things and edge computing is imminent. Deep learning methods in industrial quality detection have been widely proposed recently. However, due to the particularity of industrial scenarios, the existing deep learning-based general object detection methods have shortcomings in industrial applications. This study designs two isomorphic industrial detection models to solve these problems: T-model and S-model. Both proposed models combine swin-transformer with convolution in the backbone and design a residual fusion path. In the neck, this study designs a dual attention module to improve feature fusion. Second, this study presents a knowledge distiller based on the dual attention module to improve the detection accuracy of the lightweight S-model. According to the analysis of the experimental results on four public industrial defect detection datasets, the model in this study is more advantageous in industrial defect detection.