Abstract :In CAST-R-HP, automated acquisition and machine learning of single-cell Raman spectra (SCRS) enable distinguishing individual H. pylori cells directly from a biopsy sample, with 98.5 ± 0.27% accuracy in ID. Moreover, by adding a 48- to72-h D2O feeding and drug exposure step prior to SCRS acquisition, CAST-R-HP reports AST for levofloxacin and clarithromycin with 100% accuracy, based on metabolic inhibition level.

Abstract : Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry (CAST-R), based on D2O-probed Raman microspectroscopy. Featuring a liquid robot for sample pretreatment and a machine learning-based control scheme for data acquisition and quality control, the 3-h, automated CAST-R process accelerates AST by >10-fold, processes 96 paralleled antibiotic-exposure reactions, and produces high-quality Raman spectra.

Abstract : Ramanome-based technique can catch pathogens (Candida ablicans as a model organism for fugal pathogens) that are still present and metabolically active in a tooth about to undergo root canal treatment even after they appear to have stopped growing. The technique could limit re-infections while reducing the amount of disinfectant required, in turn dialling down the chance of side-effects.

Abstract : Ramanome-based technique can catch pathogens (Enterococcus faecalis as a model organism for bacterial pathogens) that are still present and metabolically active in a tooth about to undergo root canal treatment even after they appear to have stopped growing. The technique could limit re-infections while reducing the amount of disinfectant required, in turn dialling down the chance of side-effects.

Abstract : Intra-Ramanome Correlation Analysis (IRCA) algorithm was introduced to construct a metabolite conversion network from a single instance of just one sample of isogenic cells.

Abstract : The Addressable Dynamic Droplet Array (aDDA) was invented for paralleled single-cell sample preparation and sequencing in a single, streamlined workflow, using yeast cells as an example. In aDDA, all the sample preparation steps, including single-cell isolation, cell lysis, amplification and product retrieval, are performed in sequence within one very small droplet.