Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms

Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens.To unravel the relationship between periodontitis and systemic diseases,it is very important to correctly discriminate major periodontal pathogens.To realize convenient,effcient,and high-accuracy bacterial species classification,the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis(Pg),Fusobacterium nucleatum(Fn),and Aggregatibacter actinomycetemcomitans(Aa).The result shows that this novel method can successfully discriminate the three abovementioned periodontal pathogens.Moreover,the classification accuracies for the three categories of the original data were 94.7%at the sample level and 93.9%at the spectrum level by the machine learning algorithm extra trees.This study provides a fast,simple,and accurate method which is very beneficial to differentiate periodontal pathogens.

Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions

Advances in microfluidic technologies rely on engineered 3D flow patterns to manipulate samples at the microscale.However,current methods for mapping flows only provide limited 3D and temporal resolutions or require highly specialized optical set-ups.Here,we present a simple defocusing approach based on brightfield microscopy and open-source software to map micro-flows in 3D at high spatial and temporal resolution.Our workflow is both integrated in ImageJ and modular.We track seed particles in 2D before classifying their Z-position using a reference library.We compare the performance of a traditional cross-correlation method and a deep learning model in performing the classification step.We validate our method on three highly relevant microfluidic examples:a channel step expansion and displacement structures as single-phase flow examples,and droplet microfluidics as a twophase flow example.First,we elucidate how displacement structures efficiently shift large particles across streamlines.Second,we reveal novel recirculation structures and folding patterns in the internal flow of microfluidic droplets.Our simple and widely accessible brightfield technique generates high-resolution flow maps and it will address the increasing demand for controlling fluids at the microscale by supporting the efficient design of novel microfluidic structures.