The recent outbreak of human infections caused by SARS-CoV-2, the third zoonotic coronavirus has raised great public health concern globally. Rapid and accurate diagnosis of this novel pathogen posts great challenges not only clinically but also technologically. Metagenomic next-generation sequencing (mNGS) and reverse-transcription PCR (RT-PCR) have been the most commonly used molecular methodologies. However, each has their own limitations. In this study, we developed an isothermal, CRISPR-based diagnostic for COVID-19 with near single-copy sensitivity. The diagnostic performances of all three technology platforms were also compared. Our study aimed to provide more insights into the molecular detection of SARS-CoV-2, and also to present a novel diagnostic option for this new emerging virus. The recently discovered betacoronavirus severe acute respiratory syndrome (SARS)-CoV-2 has caused an outbreak of Coronavirus Disease 2019 (COVID-19) that rapidly developed into a global pandemic. The surging demand for rapid screening and identification of COVID-19 posts great diagnostic challenges. A lack of rapid and accurate molecular tools has hampered efficient public health responses to the viral threat. Here, we harnessed the unique collateral activity of programmable CRISPR/Cas13a to develop CRISPR-COVID, a rapid and sensitive diagnostic for SARS-CoV-2 infection, and compared it to sequencing-based metagenomic and RT-PCR-based assays in a clinical cohort. CRISPR-COVID demonstrated a sensitivity level of near single copy and was highly specific without cross reacting to related pathogens. CRISPR-COVID takes only 40 mins and requires no sophiscated thermo-cyclers, providing a valuable alternative to the conventional RT–PCR assay to circumvent the bottlenecks in assay turnaround time, equipment and reagent supplies for COVID-19 testing.

Lateral flow immunoassay (LFA) strips are extensively used for rapid tests of various biochemical molecules, but these strips still have some limitations in bacterial detection due to their low sensitivity and poor stability in complex samples. In this study, we reported a highly sensitive and quantitative fluorescent LFA strip for bacterial detection by using novel magnetic-core@dual quantum dot (QD)-shell nanoparticles (Fe₃O₄@DQDs) as multifunctional fluorescent labels. The Fe₃O₄@DQDs were prepared through a polyethyleneimine (PEI)-mediated layer-by-layer (LBL) assembly method, and they possess monodispersity, high magnetic responsiveness, good stability, and superior fluorescence properties. Based on these merits, the Fe₃O₄@DQDs were used to capture and enrich bacteria from complex samples and then used as advanced fluorescent labels of LFA strips for the quantitative detection of bacteria. Under optimal conditions, the assay ultra-sensitively detected Streptococcus pneumoniae with a low limit of detection of 8 cells per mL and a wide dynamic linear range of 10 cells per mL to 10⁷ cells per mL. Systematic comparison revealed that the fluorescence detection limit of the Fe₃O₄@DQD-based strip was 55 and 1000 times higher than those of Fe₃O₄-core@QD-shell nanocomposite (Fe₃O₄-QD)-based and conventional QD microsphere-based strips, respectively. The proposed method also exhibited high specificity and selectivity for biological samples (human whole blood and sputum) and is thus a promising tool for real bacterial sample testing.

Background: Emerging evidence has suggested that dysbiosis of the lung microbiota may be associated with the development of lung diseases. However, the interplay between the lung microbiome and lung cancer remains unclear. The aim of the present study was to evaluate and compare differences in taxonomic and derived functional profiles in the lung microbiota between lung cancer and benign pulmonary diseases. Methods: Bronchoalveolar lavage fluid (BALF) samples were collected from 32 patients with lung cancer and 22 patients with benign pulmonary diseases, and further analyzed by 16S rRNA amplicon sequencing. The obtained sequence data were deeply analyzed by bioinformatics methods. Results: A significant differentiation trend was observed between the lung cancer and control groups based on principal coordinate analysis (PCoA), while richness and evenness in the lung microbiome of lung cancer patients generally resembled those of patients with benign pulmonary diseases. Phylum TM7 and six genera (c:TM7-3, Capnocytophaga, Sediminibacterium, Gemmiger, Blautia and Oscillospira) were enriched in the lung cancer group compared with the control group (adjust P<0.05). The area under the curve (AUC) combining the microbiome with clinical tumor markers to predict lung cancer was 84.52% (95% CI: 74.06–94.97%). In addition, predicted KEGG pathways showed that the functional differences in metabolic pathways of microbiome varied with groups. Conclusions: The results indicated that differences existed in the lung microbiome of patients with lung cancer and those with benign pulmonary diseases, and some certain bacteria may have potential to predict lung cancer, though future larger-sample studies are required to validate this supposition.