The crosstalk between the gut microbiota and immune state of the host is an essential focus in academia and clinics. To explore the dynamic role of the microbiota in response to immune deficiency, we comprehensively assessed the microbiome of 90 mouse fecal samples, across three time points including two immunodeficiency models, namely severe combined immunodeficient (SCID) mice and non-obese diabetic SCID (NOD/SCID) mice, with BALB/cA as a control strain. Metagenomic analysis revealed a decrease in alpha diversity and the existence of a clear structural separation in the microbiota of immunodeficient mice. Although nuances exist between SCID and NOD/SCID mice, an increase in the protective microbiota, in particular Lactobacillus, contributed the most to the discrimination of immunodeficient and control mice. Further data regarding the red blood cell (RBC) concentration and serum IgA level during different stages of development support the concept of the microbiota alleviating the advancement of immune deficiency, which is called microbial compensation. Taken together, these results demonstrate the dynamic impact of immunodeficiency on the gut microbiota and the adaptive alteration of the microbiota that may influence the host state.

Multidrug-resistant gonorrhea has become an urgent issue for global public health. As the causative agent of gonorrhea, Neisseria gonorrhoeae, has been progressively developing resistance to nearly all prescribed antimicrobial drugs, monitoring its antimicrobial resistance on a broader scale has become a crucial agenda for effective antibiotic stewardship. Unfortunately, gold standard antimicrobial susceptibility testing (AST) relies on time and labor-intensive phenotypic assays, which lag behind the current diagnostic workflow for N. gonorrhoeae identification based on nucleic acid amplification tests (NAAT). Newer assay technologies based on NAAT can rapidly identify N. gonorrhoeae from clinical specimen but fundamentally lack the capacity to provide phenotypic AST information. Herein, we propose a direct-quantitative PCR (direct-qPCR) assay that enables pathogen-specific identification and phenotypic AST via quantitative measurement of N. gonorrhoeae growth directly from a liquid medium without any sample preprocessing. The assay has an analytical sensitivity of 102 CFU/mL and is highly specific to N. gonorrhoeae in the presence of urogenital flora and clinical swab eluent. We tested seven N. gonorrhoeae strains against three antibiotic agents, penicillin, tetracycline, and ciprofloxacin, and achieved 95.2% category agreement and 85.7% essential agreement with the FDA-approved E-test. The assay presented in this work has the unique ability to identify N. gonorrhoeae and provide phenotypic AST directly from the liquid medium with cell densities as low as 102 CFU/mL, demonstrating an accelerated, sensitive, and scalable workflow for performing both identification and AST of N. gonorrhoeae.

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The two‐dimensional estimating signal parameter via rotational invariance techniques (2D‐ESPRIT) algorithm is a classical method to estimate parameters of the two‐dimensional geometric theory of diffraction (2D‐GTD) model. While as signal‐to‐noise‐ratio (SNR) decreases, the parameter estimation performance of 2D‐ESPRIT algorithm is severely influenced. To solve this problem, a performance‐enhanced 2D‐ESPRIT algorithm is proposed in this article. The improved 2D‐ESPRIT algorithm combines the conjugate data with the original back‐scattered data and obtains a novel covariance matrix by squaring the original total covariance matrix. Simulation results indicate that the improved algorithm has a better noise robustness and a more stable parameter estimation performance than the classical ESPRIT algorithm and the classical TLS‐2D‐ESPRIT algorithm. To further validate the superiority of the improved 2D‐ESPRIT algorithm, reconstructed radar cross section (RCS) is presented in this article. Compared with the classical 2D‐ESPRIT algorithm, the proposed algorithm presents higher RCS fitting precision. Furthermore, the impacts of other factors on parameter estimation, such as matrix pencil parameters and paring parameters, are also studied in this article.

Non–small-cell lung cancer (NSCLC) is one of the most fatal malignant tumors harmful to human health. Previous studies report that Platycodin D (PD) exhibits anti-tumor effects in multiple human cancers, including NSCLC, but the underlying mechanisms are largely unknown. Accumulating evidence indicates that non-coding RNAs (ncRNAs) participate in NSCLC disease progression, but the link between PD and the ncRNAs in NSCLC is poorly elucidated. Here, we used whole transcriptome sequencing to systematically investigate the RNAs-associated regulatory network in the PD treating NSCLC cell lines. A total of 942 significantly dysregulated RNAs were obtained. Among those, five circRNAs and six IncRNAs were rigorously selected via database and in vitro validation. In addition, the functional enrichment study of differentially expressed mRNAs, single nucleotide polymorphisms (SNPs) within PD-related mRNA structures, and the interaction between PD and mRNA-related proteins were analyzed through gene set enrichment analysis (GSEA), structural variant analysis, and molecular docking, respectively. With further in vitro validation, the results show that PD inhibits cell proliferation, arrests the cell cycle, and induces cell apoptosis through targeting BCL2-related proteins. We hope these data can provide a full concept of PD-related molecular changes, leading to a new treatment for NSCLC.