Increasing the Analytical Accessibility of Multishell and Diffusion Spectrum Imaging Data Using Generalized Q-Sampling Conversion

Many diffusion MRI researchers, including the Human Connectome Project (HCP), acquire data using multishell (e.g., WU-Minn consortium) and diffusion spectrum imaging (DSI) schemes (e.g., USC-Harvard consortium). However, these data sets are not readily accessible to high angular resolution diffusion imaging (HARDI) analysis methods that are popular in connectomics analysis. Here we introduce a scheme conversion approach that transforms multishell and DSI data into their corresponding HARDI representations, thereby empowering HARDI-based analytical methods to make use of data acquired using non-HARDI approaches. This method was evaluated on both phantom and in-vivo human data sets by acquiring multishell, DSI, and HARDI data simultaneously, and comparing the converted HARDI, from non-HARDI methods, with the original HARDI data. Analysis on the phantom shows that the converted HARDI from DSI and multishell data strongly predicts the original HARDI (correlation coefficient > 0.9). Our in-vivo study shows that the converted HARDI can be reconstructed by constrained spherical deconvolution, and the fiber orientation distributions are consistent with those from the original HARDI. We further illustrate that our scheme conversion method can be applied to HCP data, and the converted HARDI do not appear to sacrifice angular resolution. Thus this novel approach can benefit all HARDI-based analysis approaches, allowing greater analytical accessibility to non-HARDI data, including data from the HCP

Brain MRI Segmentation using Template-Based Training and Visual Perception Augmentation

Deep learning models usually require sufficient training data to achieve high accuracy, but obtaining labeled data can be time-consuming and labor-intensive. Here we introduce a template-based training method to train a 3D U-Net model from scratch using only one population-averaged brain MRI template and its associated segmentation label. The process incorporated visual perception augmentation to enhance the model's robustness in handling diverse image inputs and mitigating overfitting. Leveraging this approach, we trained 3D U-Net models for mouse, rat, marmoset, rhesus, and human brain MRI to achieve segmentation tasks such as skull-stripping, brain segmentation, and tissue probability mapping. This tool effectively addresses the limited availability of training data and holds significant potential for expanding deep learning applications in image analysis, providing researchers with a unified solution to train deep neural networks with only one image sample

Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output

The Regulation Requirement of Dengue Vaccines

Dengue fever (dengue), a mosquito-borne disease caused by dengue viruses (DENVs), represents severe public health problems in Southeast Asia, Latin America, Africa and other subtropical regions. Many regulatory issues arise along with the development of dengue vaccines. It is required to follow the regulatory pathway for the license application. Dengue vaccines can be approved without local clinical phase III data. The national regulatory authorities (NRAs) must have the information, training and ability to review and approve the application. A novel vaccine product Dengvaxia® for dengue has been approved in many countries. The approval is based on clinical trials that show the vaccine could reduce about 60% dengue, prevented 90% of severe cases and 80% of hospitalizations. Several other DNA, live-attenuated, purified inactivated, subunit, vectored and chimeric vaccine candidates are currently developing in clinical phases. Although there are still some challenges for the development and regulation of vaccine, the prospects of dengue vaccines are promising provided that we can overcome the difficulty

Nanotechnologies Applied in Biomedical Vaccines

Vaccination, one of the most effective strategies to prevent infectious diseases, is the administration of antigenic materials to stimulate an individual’s immune system to develop adaptive immunity to a specific pathogen. Though it is so advantageous for diseases control and prevention, vaccines still have some limitations. Nanotechnology is an approach to prepare a novel biomedicine vaccine with the vaccine consumption and side effects significantly decreased. Regulation is the most important criterion for the development of nanovaccines. All marketing products have to meet the requirement of regulation. The fast-track designation potentially aids in the development and expedites the review of nanovaccines that show promises in an unmet medical need. Here, some successful nanovaccine products are introduced—Inflexal® V, Epaxal®, GardasilTM, and CervarixTM have been widely used for the clinical applications, which are delivered either in the form of virosomes or virus-like particles. Vaccines based on nanotechnology may overcome their original disadvantages and lead to the development of painless, safer, and more effective products

Biotechnologies Applied in Biomedical Vaccines

Vaccination, the administration of an antigenic material (vaccine), is considered to be the most effective method for disease prevention and control. A vaccine usually contains an agent that resembles a diseases‐causing pathogen and is often made from inactivated microbes, live attenuated microbes, its toxins, or part of surface antigens (subunit). However, the modern biotechnological tools and genomics have opened a new era to develop novel vaccines and many products are successfully marketing around the world. It is important to formulate and deliver these vaccines appropriately to maximize the potential advances in prevention, therapy, and vaccinology. New vaccines employing biotechnological innovations are helping us to change the way for illness prevention. The clinical application of vaccines will be diversified along with the development of biotechnologies. In modern society, the outbreak of many infectious diseases has decreased through vaccination, but the burden of noninfectious diseases is growing. The new biotechnologies may result in not only the appreciation of vaccines which are critical in inducing protection against an infectious disease but also the production of therapeutic vaccines which are effective for alldiseases including infectious and noninfectious diseases

Angelica Sinensis promotes myotube hypertrophy through the PI3K/Akt/mTOR pathway

BACKGROUND: Angelica Sinensis (AS), a folk medicine, has long been used in ergogenic aids for athletes, but there is little scientific evidence supporting its effects. We investigated whether AS induces hypertrophy in myotubes through the phosphatidylinositol 3-kinase (PI3K)/Akt (also termed PKB)/mammalian target of the rapamycin (mTOR) pathway. METHODS: An in vitro experiment investigating the induction of hypertrophy in myotubes was conducted. To investigate whether AS promoted the hypertrophy of myotubes, an established in vitro model of myotube hypertrophy with and without AS was used and examined using microscopic images. The role of the PI3K/Akt/mTOR signaling pathway in AS-induced myotube hypertrophy was evaluated. Two inhibitors, wortmannin (an inhibitor of PI3K) and rapamycin (an inhibitor of mTOR), were used. RESULT: The results revealed that the myotube diameters in the AS-treated group were significantly larger than those in the untreated control group (P < 0.05). Wortmannin and rapamycin inhibited AS-induced hypertrophy. Furthermore, AS increased Akt and mTOR phosphorylation through the PI3K pathway and induced myotube hypertrophy. CONCLUSION: The results confirmed that AS induces hypertrophy in myotubes through the PI3K/Akt/mTOR pathway