Detecting Bacteria with Ultralow Concentrations by Enzymatic Cascade Reaction-Amplifying Strategy.

Bacteria can cause infectious diseases even at ultralow concentrations (<1 CFU/mL). It is important to rapidly identify bacterial contamination at ultralow concentrations. Herein, FITC-labeled gelatinase-sensitive nanoparticles (GNPs@FITCs) and NFM@GNP@FITCs are designed and fabricated as ultralow concentration bacteria detection platforms based on an enzymatic cascade reaction-amplifying strategy. Bacterial secretions could trigger the dissociation of GNPs@FITCs to release FITC, with gelatinase used as the model secretion. The detectable signal of ultralow concentration bacteria could be amplified effectively by the gelatinase-triggered cascade reaction. Bacterial concentration was evaluated by the change in fluorescence density. The results showed that the GNPs@FITCs and NFM@GNP@FITCs could be used for identifying bacterial contamination qualitatively, even when the bacterial contamination is lower than 1 CFU/mL. Moreover, the method has better timeliness and convenience, when compared with national standards. As solid films, NFM@GNP@FITCs have better long-term storage stability than GNPs@FITCs. The potential applications of GNPs@FITC and NFM@GNP@FITCs were proved by detecting pathogenic bacteria in food. All the results showed that the method has great potential for screening pathogenic bacterial contamination qualitatively.

Bioinformatic analysis of defective viral genomes in SARS-CoV-2 and its impact on population infection characteristics.

DVGs (Defective Viral Genomes) are prevalent in RNA virus infections. In this investigation, we conducted an analysis of high-throughput sequencing data and observed widespread presence of DVGs in SARS-CoV-2. Comparative analysis between SARS-CoV-2 and diverse DNA viruses revealed heightened susceptibility to damage and increased sequencing sample heterogeneity within the SARS-CoV-2 genome. Whole-genome sequencing depth variability analysis exhibited a higher coefficient of variation for SARS-CoV-2, while DVG analysis indicated a significant proportion of recombination sites, signifying notable genome heterogeneity and suggesting that a large proportion of assembled virus particles contain incomplete RNA sequences. Moreover, our investigation explored the sequencing depth and DVG content differences among various strains. Our findings revealed that as the virus evolves, there is a notable increase in the proportion of intact genomes within virus particles, as evidenced by third-generation sequencing data. Specifically, the proportion of intact genome in the Omicron strain surpassed that of the Delta and Alpha strains. This observation effectively elucidates the heightened infectiousness of the Omicron strain compared to the Delta and Alpha strains. We also postulate that this improvement in completeness stems from enhanced virus assembly capacity, as the Omicron strain can promptly facilitate the binding of RNA and capsid protein, thereby reducing the exposure time of vulnerable virus RNA in the host environment and significantly mitigating its degradation. Finally, employing mathematical modeling, we simulated the impact of DVG effects under varying environmental factors on infection characteristics and population evolution. Our findings provide an explanation for the close association between symptom severity and the extent of virus invasion, as well as the substantial disparity in population infection characteristics caused by the same strain under distinct environmental conditions. This study presents a novel approach for future virus research and vaccine development.

A Metasurface Plasmonic Analysis Platform Combined with Gold Nanoparticles for Ultrasensitive Quantitative Detection of Small Molecules.

Food safety related to drug residues in food has become a widespread public concern. Small-molecule drug residue analysis often relies on mass spectrometry, thin-layer chromatography, or enzyme-linked immunosorbent assays (ELISA). Some of these techniques have limited sensitivity and accuracy, while others are time-consuming, costly, and rely on specialized equipment that requires skilled operation. Therefore, the development of a sensitive, fast, and easy-to-operate biosensor could provide an accessible alternative to conventional small-molecule analysis. Here, we developed a nanocup array-enhanced metasurface plasmon resonance (MetaSPR) chip coupled with gold nanoparticles (AuNPs) (MSPRAN) to detect small molecules. As sulfamethazine drug residues in poultry eggs may cause health issues, we selected this as a model to evaluate the feasibility of using MSPRAN for small-molecule detection. The MSPRAN biosensor employed competitive immunoassay technology for sulfamethazine detection. The limit of detection was calculated as 73 pg/mL, with sensitivity approximately twice that of previously reported detection methods. Additionally, the recovery rate of the biosensor, tested in egg samples, was similar to that measured using ELISA. Overall, this newly developed MSPRAN biosensor platform for small-molecule detection provides fast and reliable results, facile operation, and is relatively cost-effective for application in food safety testing, environmental monitoring, or clinical diagnostics.

Antibody Dynamics Simulation-A Mathematical Exploration of Clonal Deletion and Somatic Hypermutation.

We have employed mathematical modeling techniques to construct a comprehensive framework for elucidating the intricate response mechanisms of the immune system, facilitating a deeper understanding of B-cell clonal deletion and somatic hypermutation. Our improved model introduces innovative mechanisms that shed light on positive and negative selection processes during T-cell and B-cell development. Notably, clonal deletion is attributed to the attenuated immune stimulation exerted by self-antigens with high binding affinities, rendering them less effective in eliciting subsequent B-cell maturation and differentiation. Secondly, our refined model places particular emphasis on the crucial role played by somatic hypermutation in modulating the immune system's functionality. Through extensive investigation, we have determined that somatic hypermutation not only expedites the production of highly specific antibodies pivotal in combating microbial infections but also serves as a regulatory mechanism to dampen autoimmunity and enhance self-tolerance within the organism. Lastly, our model advances the understanding of the implications of antibody in vivo evolution in the overall process of organismal aging. With the progression of time, the age-associated amplification of autoimmune activity becomes apparent. While somatic hypermutation effectively delays this process, mitigating the levels of autoimmune response, it falls short of reversing this trajectory entirely. In conclusion, our advanced mathematical model offers a comprehensive and scholarly approach to comprehend the intricacies of the immune system. By encompassing novel mechanisms for selection, emphasizing the functional role of somatic hypermutation, and illuminating the consequences of in vivo antibody evolution, our model expands the current understanding of immune responses and their implications in aging.

Identification of triacylglycerol using automated annotation of high resolution multistage mass spectral trees.

High complexity of identification for non-target triacylglycerols (TAGs) is a major challenge in lipidomics analysis. To identify non-target TAGs, a powerful tool named accurate MS(n) spectrometry generating so-called ion trees is used. In this paper, we presented a technique for efficient structural elucidation of TAGs on MS(n) spectral trees produced by LTQ Orbitrap MS(n), which was implemented as an open source software package, or TIT. The TIT software was used to support automatic annotation of non-target TAGs on MS(n) ion trees from a self-built fragment ion database. This database includes 19108 simulate TAG molecules from a random combination of fatty acids and corresponding 500582 self-built multistage fragment ions (MS ≤ 3). Our software can identify TAGs using a "stage-by-stage elimination" strategy. By utilizing the MS(1) accurate mass and referenced RKMD, the TIT software can discriminate unique elemental composition candidates. The regiospecific isomers of fatty acyl chains will be distinguished using MS(2) and MS(3) fragment spectra. We applied the algorithm to the selection of 45 TAG standards and demonstrated that the molecular ions could be 100% correctly assigned. Therefore, the TIT software could be applied to TAG identification in complex biological samples such as mouse plasma extracts.