Biomedical Engineering and Computational Biology最新文献

Fluorescent protein imaging, a promising tool in biological research, incorporates numerous applications that can be of specific use in the field of regenerative medicine. To enhance tissue regeneration efforts, scientists have been developing new ways to monitor tissue development and maturation in vitro and in vivo. To that end, new imaging tools and novel fluorescent proteins have been developed for the purpose of performing deep-tissue high-resolution imaging. These new methods, such as intra-vital microscopy and Förster resonance energy transfer, are providing new insights into cellular behavior, including cell migration, morphology, and phenotypic changes in a dynamic environment. Such applications, combined with multimodal imaging, significantly expand the utility of fluorescent protein imaging in research and clinical applications of regenerative medicine.

Animal and two-dimensional cell culture models have had a profound impact on not only lung research but also medical research at large, despite inherent flaws and differences when compared with in vivo and clinical observations. Three-dimensional (3D) tissue models are a natural progression and extension of existing techniques that seek to plug the gaps and mitigate the drawbacks of two-dimensional and animal technologies. In this review, we describe the transition of historic models to contemporary 3D cell and organoid models, the varieties of current 3D cell and tissue culture modalities, the common methods for imaging these models, and finally, the applications of these models and imaging techniques to lung research.

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article.

Identifying subsets of genes that jointly mediate cancer etiology, progression, or therapy response remains a challenging problem due to the complexity and heterogeneity in cancer biology, a problem further exacerbated by the relatively small number of cancer samples profiled as compared with the sheer number of potential molecular factors involved. Pure data-driven methods that merely rely on multiomics data have been successful in discovering potentially functional genes but suffer from high false-positive rates and tend to report subsets of genes whose biological interrelationships are unclear. Recently, integrative data-driven models have been developed to integrate multiomics data with signaling pathway networks in order to identify pathways associated with clinical or biological phenotypes. However, these approaches suffer from an important drawback of being restricted to previously discovered pathway structures and miss novel genomic interactions as well as potential crosstalk among the pathways. In this article, we propose a novel coalition-based game-theoretic approach to overcome the challenge of identifying biologically relevant gene subnetworks associated with disease phenotypes. The algorithm starts from a set of seed genes and traverses a protein-protein interaction network to identify modulated subnetworks. The optimal set of modulated subnetworks is identified using Shapley value that accounts for both individual and collective utility of the subnetwork of genes. The algorithm is applied to two illustrative applications, including the identification of subnetworks associated with (i) disease progression risk in response to platinum-based therapy in ovarian cancer and (ii) immune infiltration in triple-negative breast cancer. The results demonstrate an improved predictive power of the proposed method when compared with state-of-the-art feature selection methods, with the added advantage of identifying novel potentially functional gene subnetworks that may provide insights into the mechanisms underlying cancer progression.

Objective: This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics.

Study design: A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted.

Results: The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material.

Conclusions: Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

There are numerous available biodegradable materials that can be used as scaffolds in regenerative medicine. Currently, there is a huge emphasis on the designing phase of the scaffolds. Materials can be designed to have different properties in order to match the specific application. Modifying scaffolds enhances their bioactivity and improves the regeneration capacity. Modifications of the scaffolds can be later characterized using several tissue engineering tools. In addition to the material, cell source is an important component of the regeneration process. Modified materials must be able to support survival and growth of different cell types. Together, cells and modified biomaterials contribute to the remodeling of the engineered tissue, which affects its performance. This review focuses on the recent advancements in the designs of the scaffolds including the physical and chemical modifications. The last part of this review also discusses designing processes that involve viability of cells.