Advancing Wound Filling Extraction on 3D Faces: A Auto-Segmentation and Wound Face Regeneration Approach

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study. Our method achieved a remarkable accuracy of 0.9999986\% on the test suite, surpassing the performance of the previous method. From this result, we use 3D printing technology to illustrate the shape of the wound filling. The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design. By automating facial wound segmentation and improving the accuracy of wound-filling extraction, our approach can assist in carefully assessing and optimizing interventions, leading to enhanced patient outcomes. Additionally, it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants. Our source code is available at \url{https://github.com/SIMOGroup/WoundFilling3D}

Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures

In this study, we emphasize the integration of a pre-trained MICA model with an imperfect face dataset, employing a self-supervised learning approach. We present an innovative method for regenerating flawed facial structures, yielding 3D printable outputs that effectively support physicians in their patient treatment process. Our results highlight the model's capacity for concealing scars and achieving comprehensive facial reconstructions without discernible scarring. By capitalizing on pre-trained models and necessitating only a few hours of supplementary training, our methodology adeptly devises an optimal model for reconstructing damaged and imperfect facial features. Harnessing contemporary 3D printing technology, we institute a standardized protocol for fabricating realistic, camouflaging mask models for patients in a laboratory environment

ViFi: accurate detection of viral integration and mRNA fusion reveals indiscriminate and unregulated transcription in proximal genomic regions in cervical cancer.

The integration of viral sequences into the host genome is an important driver of tumorigenesis in many viral mediated cancers, notably cervical cancer and hepatocellular carcinoma. We present ViFi, a computational method that combines phylogenetic methods with reference-based read mapping to detect viral integrations. In contrast with read-based reference mapping approaches, ViFi is faster, and shows high precision and sensitivity on both simulated and biological data, even when the integrated virus is a novel strain or highly mutated. We applied ViFi to matched genomic and mRNA data from 68 cervical cancer samples from TCGA and found high concordance between the two. Surprisingly, viral integration resulted in a dramatic transcriptional upregulation in all proximal elements, including LINEs and LTRs that are not normally transcribed. This upregulation is highly correlated with the presence of a viral gene fused with a downstream human element. Moreover, genomic rearrangements suggest the formation of apparent circular extrachromosomal (ecDNA) human-viral structures. Our results suggest the presence of apparent small circular fusion viral/human ecDNA, which correlates with indiscriminate and unregulated expression of proximal genomic elements, potentially contributing to the pathogenesis of HPV-associated cervical cancers. ViFi is available at https://github.com/namphuon/ViFi

Design of a genetic muller C-element

Journal ArticleSynthetic biology uses engineering principles to design circuits out of genetic materials that are inserted into bacteria to perform various tasks. While synthetic combinational Boolean logic gates have been constructed, there are many open issues in the design of sequential logic gates. One such gate common in most asynchronous circuits is the Muller C-element, which is used to synchronize multiple independent processes. This paper proposes a novel design for a genetic Muller C-element using transcriptional regulatory elements. The design of a genetic Muller C-element enables the construction of virtually any asynchronous circuit from genetic material. There are, however, many issues that complicate designs with genetic materials. These complications result in modifications being required to the normal digital design procedure. This paper presents two designs that are logically equivalent to a Muller C-element. Mathematical analysis and stochastic simulation, however, show that only one functions reliably

Determinant of HIV-1 mutational escape from cytotoxic T lymphocytes.

CD8+ class I-restricted cytotoxic T lymphocytes (CTLs) usually incompletely suppress HIV-1 in vivo, and while analogous partial suppression induces antiretroviral drug-resistance mutations, epitope escape mutations are inconsistently observed. However, escape mutation depends on the net balance of selective pressure and mutational fitness costs, which are poorly understood and difficult to study in vivo. Here we used a controlled in vitro system to evaluate the ability of HIV-1 to escape from CTL clones, finding that virus replicating under selective pressure rapidly can develop phenotypic resistance associated with genotypic changes. Escape varied between clones recognizing the same Gag epitope or different Gag and RT epitopes, indicating the influence of the T cell receptor on pressure and fitness costs. Gag and RT escape mutations were monoclonal intra-epitope substitutions, indicating limitation by fitness constraints in structural proteins. In contrast, escape from Nef-specific CTL was more rapid and consistent, marked by a polyclonal mixture of epitope point mutations and upstream frameshifts. We conclude that incomplete viral suppression by CTL can result in rapid emergence of immune escape, but the likelihood is strongly determined by factors influencing the fitness costs of the particular epitope targeted and the ability of responding CTL to recognize specific epitope variants