3,588 research outputs found
Histocompatibility and Long-Term Results of the Follicular Unit-Like Wigs after Xenogeneic Hair Transplantation: An Experimental Study in Rabbits
Objective. This study was designed to observe the histocompatibility and long-term results of wigs after xenogeneic hair transplantation and to explore the possibility of industrial products in clinical application. Methods. The human hair and melted medical polypropylene were preceded into the follicular unit-like wigs according to the natural follicular unit by extrusion molding. 12 New Zealand rabbits were used as experimental animals for wigs transplantation. The histocompatibility of polypropylene and human hair was observed by H&E staining and scanning electron microscope. The loss rate of wigs was calculated to evaluate the long-term result after transplantation. Results. Mild infiltration by inflammatory cells around the polypropylene and human hair were seen during the early period after transplantation, accompanied with local epithelial cell proliferation. The inflammatory cells were decreased after 30 days with increased collagen fibers around the polypropylene and human hair. The follicular unit-like wigs maintained a good histocompatibility in one year. The degradation of hair was not significant. The loss rate of wigs was 4.1 Ā± 4.0% in one year. The appearance of hair was satisfactory. Conclusions. We successfully developed a follicular unit-like wigs, which were made of xenogeneic human hair with medical polypropylene, showing a good histocompatibility, a low loss rate, and satisfactory appearance in a year after transplantation. The follicular unit-like wigs may have prospective industrial products in clinical application
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment
Anomaly segmentation plays a crucial role in identifying anomalous objects
within images, which facilitates the detection of road anomalies for autonomous
driving. Although existing methods have shown impressive results in anomaly
segmentation using synthetic training data, the domain discrepancies between
synthetic training data and real test data are often neglected. To address this
issue, the Multi-Granularity Cross-Domain Alignment (MGCDA) framework is
proposed for anomaly segmentation in complex driving environments. It uniquely
combines a new Multi-source Domain Adversarial Training (MDAT) module and a
novel Cross-domain Anomaly-aware Contrastive Learning (CACL) method to boost
the generality of the model, seamlessly integrating multi-domain data at both
scene and sample levels. Multi-source domain adversarial loss and a dynamic
label smoothing strategy are integrated into the MDAT module to facilitate the
acquisition of domain-invariant features at the scene level, through
adversarial training across multiple stages. CACL aligns sample-level
representations with contrastive loss on cross-domain data, which utilizes an
anomaly-aware sampling strategy to efficiently sample hard samples and anchors.
The proposed framework has decent properties of parameter-free during the
inference stage and is compatible with other anomaly segmentation networks.
Experimental conducted on Fishyscapes and RoadAnomaly datasets demonstrate that
the proposed framework achieves state-of-the-art performance.Comment: Accepted to ACM Multimedia 202
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