Mechanisms of the TGF-ß1/Smad3-signaling pathway in gender differences in alcoholic liver fibrosis.

The TGF-ß1/Smad3-signaling pathway and gender differences were investigated in alcoholic liver fibrosis. Mice were divided into female normal, female model, male normal, and male model groups. Liver injury and fibrosis were assessed using histopathology and serology. Western blotting was performed to analyze the expression of relevant factors. HSC-T6 cells were divided into estradiol + saline, estradiol + ethanol, testosterone + saline, and testosterone + ethanol groups, and similar assessments were conducted in vitro. Compared with the female model group, the male model group exhibited significantly increased GPT, GOT, TNF-α, IL-6, and testosterone levels, fibrosis rate, and TGF-ß1, Smad3, and PCNA expression, and significantly decreased estradiol levels and Caspase-3 expression. The apoptosis rate was higher in the estradiol + ethanol group than in the testosterone + ethanol group, although the testosterone + ethanol group exhibited significantly increased TNF-α, IL-6, Collagen-I, α-SMA, TGF-ß1, Smad3, and PCNA expression, and significantly decreased Caspase-3 expression. Alcoholic liver fibrosis showed significant gender differences associated with the TGF-ß1/Smad3-signaling pathway.

Strategies adopted by Salmonella to survive in host: a review.

Salmonella, a Gram-negative bacterium that infects humans and animals, causes diseases ranging from gastroenteritis to severe systemic infections. Here, we discuss various strategies used by Salmonella against host cell defenses. Epithelial cell invasion largely depends on a Salmonella pathogenicity island (SPI)-1-encoded type 3 secretion system, a molecular syringe for injecting effector proteins directly into host cells. The internalization of Salmonella into macrophages is primarily driven by phagocytosis. After entering the host cell cytoplasm, Salmonella releases many effectors to achieve intracellular survival and replication using several secretion systems, primarily an SPI-2-encoded type 3 secretion system. Salmonella-containing vacuoles protect Salmonella from contacting bactericidal substances in epithelial cells and macrophages. Salmonella modulates the immunity, metabolism, cell cycle, and viability of host cells to expand its survival in the host, and the intracellular environment of Salmonella-infected cells promotes its virulence. This review provides insights into how Salmonella subverts host cell defenses for survival.

LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications.

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.

3D-FVS: construction and application of three-dimensional fundus vascular structure model based on single image features.

BACKGROUND: Fundus microvasculature may be visually observed by ophthalmoscope and has been widely used in clinical practice. Due to the limitations of available equipment and technology, most studies only utilized the two-dimensional planar features of the fundus microvasculature. METHODS: This study proposed a novel method for establishing the three-dimensional fundus vascular structure model and generating hemodynamic characteristics based on a single image. Firstly, the fundus vascular are segmented through our proposed network framework. Then, the length and width of vascular segments and the relationship among the adjacent segments are collected to construct the three-dimensional vascular structure model. Finally, the hemodynamic model is generated based on the vascular structure model, and highly correlated hemodynamic features are selected to diagnose the ophthalmic diseases. RESULTS: In fundus vascular segmentation, the proposed network framework obtained 98.63% and 97.52% on Area Under Curve (AUC) and accuracy respectively. In diagnosis, the high correlation features extracted based on the proposed method achieved 95% on accuracy. CONCLUSIONS: This study demonstrated that hemodynamic features filtered by relevance were essential for diagnosing retinal diseases. Additionally, the method proposed also outperformed the existing models on the levels of retina vessel segmentation. In conclusion, the proposed method may represent a novel way to diagnose retinal related diseases, which can analysis two-dimensional fundus pictures by extracting heterogeneous three-dimensional features.

Protein scaffolds: antibody alternatives for cancer diagnosis and therapy.

Although antibodies are well developed and widely used in cancer therapy and diagnostic fields, some defects remain, such as poor tissue penetration, long in vivo metabolic retention, potential cytotoxicity, patent limitation, and high production cost. These issues have led scientists to explore and develop novel antibody alternatives. Protein scaffolds are small monomeric proteins with stable tertiary structures and mutable residues, which emerged in the 1990s. By combining robust gene engineering and phage display techniques, libraries with sufficient diversity could be established for target binding scaffold selection. Given the properties of small size, high affinity, and excellent specificity and stability, protein scaffolds have been applied in basic research, and preclinical and clinical fields over the past two decades. To date, more than 20 types of protein scaffolds have been developed, with the most frequently used being affibody, adnectin, ANTICALIN®, DARPins, and knottin. In this review, we focus on the protein scaffold applications in cancer therapy and diagnosis in the last 5 years, and discuss the pros and cons, and strategies of optimization and design.