Spaceflight induces changes in gene expression profiles linked to insulin and estrogen.

Organismal adaptations to spaceflight have been characterized at the molecular level in model organisms, including Drosophila and C. elegans. Here, we extend molecular work to energy metabolism and sex hormone signaling in mice and humans. We found spaceflight induced changes in insulin and estrogen signaling in rodents and humans. Murine changes were most prominent in the liver, where we observed inhibition of insulin and estrogen receptor signaling with concomitant hepatic insulin resistance and steatosis. Based on the metabolic demand, metabolic pathways mediated by insulin and estrogen vary among muscles, specifically between the soleus and extensor digitorum longus. In humans, spaceflight induced changes in insulin and estrogen related genes and pathways. Pathway analysis demonstrated spaceflight induced changes in insulin resistance, estrogen signaling, stress response, and viral infection. These data strongly suggest the need for further research on the metabolic and reproductive endocrinologic effects of space travel, if we are to become a successful interplanetary species.

Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.

We present a deep learning (DL) network-based approach for detecting and semantically segmenting two specific types of tuberculosis (TB) lesions in chest X-ray (CXR) images. In the proposed method, we use a basic U-Net model and its enhanced versions to detect, classify, and segment TB lesions in CXR images. The model architectures used in this study are U-Net, Attention U-Net, U-Net++, Attention U-Net++, and pyramid spatial pooling (PSP) Attention U-Net++, which are optimized and compared based on the test results of each model to find the best parameters. Finally, we use four ensemble approaches which combine the top five models to further improve lesion classification and segmentation results. In the training stage, we use data augmentation and preprocessing methods to increase the number and strength of lesion features in CXR images, respectively. Our dataset consists of 110 training, 14 validation, and 98 test images. The experimental results show that the proposed ensemble model achieves a maximum mean intersection-over-union (MIoU) of 0.70, a mean precision rate of 0.88, a mean recall rate of 0.75, a mean F1-score of 0.81, and an accuracy of 1.0, which are all better than those of only using a single-network model. The proposed method can be used by clinicians as a diagnostic tool assisting in the examination of TB lesions in CXR images.

Nanosized Particles Assembled by a Recombinant Virus Protein Are Able to Encapsulate Negatively Charged Molecules and Structured RNA.

RNA-based molecules have recently become hot candidates to be developed into therapeutic agents. However, successful applications of RNA-based therapeutics might require suitable carriers to protect the RNA from enzymatic degradation by ubiquitous RNases in vivo. Because of their better biocompatibility and biodegradability, protein-based nanoparticles are considered to be alternatives to their synthetic polymer-based counterparts for drug delivery. Hepatitis C virus (HCV) core protein has been suggested to be able to self-assemble into nucleocapsid-like particles in vitro. In this study, the genomic RNA-binding domain of HCV core protein consisting of 116 amino acids (p116) was overexpressed with E. coli for investigation. The recombinant p116 was able to assemble into particles with an average diameter of approximately 27 nm, as visualized by electron microscopy and atomic force microscopy. Measurements with fluorescence spectroscopy, flow cytometry, and fluorescence quenching indicated that the p116-assembled nanoparticles were able to encapsulate small anionic molecules and structured RNA. This study demonstrates methods that exploit the self-assembly nature of a virus-derived protein for nanoparticle production. This study also suggests that the virus-derived protein-assembled particles could possibly be developed into potential carriers for anionic molecular drugs and structured RNA-based therapeutics.

Visible light responsive photocatalyst induces progressive and apical-terminus preferential damages on Escherichia coli surfaces.

BACKGROUND: Recent research shows that visible-light responsive photocatalysts have potential usage in antimicrobial applications. However, the dynamic changes in the damage to photocatalyzed bacteria remain unclear. METHODOLOGY/PRINCIPAL FINDINGS: Facilitated by atomic force microscopy, this study analyzes the visible-light driven photocatalyst-mediated damage of Escherichia coli. Results show that antibacterial properties are associated with the appearance of hole-like structures on the bacteria surfaces. Unexpectedly, these hole-like structures were preferentially induced at the apical terminus of rod shaped E. coli cells. Differentiating the damages into various levels and analyzing the percentage of damage to the cells showed that photocatalysis was likely to elicit sequential damages in E. coli cells. The process began with changing the surface properties on bacterial cells, as indicated in surface roughness measurements using atomic force microscopy, and holes then formed at the apical terminus of the cells. The holes were then subsequently enlarged until the cells were totally transformed into a flattened shape. Parallel experiments indicated that photocatalysis-induced bacterial protein leakage is associated with the progression of hole-like damages, further suggesting pore formation. Control experiments using ultraviolet light responsive titanium-dioxide substrates also obtained similar observations, suggesting that this is a general phenomenon of E. coli in response to photocatalysis. CONCLUSION/SIGNIFICANCE: The photocatalysis-mediated localization-preferential damage to E. coli cells reveals the weak points of the bacteria. This might facilitate the investigation of antibacterial mechanism of the photocatalysis.