Ya-Cheng Pan

TL;DR: In this paper, the authors compared three unsupervised machine learning methods and 10 supervised machine learning algorithms, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphilicococcus species in order to test the capacity of different machine learning method for bacterial rapid differentiation and accurate prediction.

TL;DR: In this article, a review of recent studies of Raman spectroscopy in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

TL;DR: Wang et al. as discussed by the authors used surface-enhanced Raman spectroscopy (SERS) to detect carbapenem-sensitive Klebsiella pneumoniae (CSKP) from clinical samples.

TL;DR: Machine learning methods can be potentially applied to classify and predict bacterial pathogens via Raman spectra at general level through machine learning algorithms in order to discriminate bacterial pathogens quickly and accurately.

TL;DR: Examination of ERα, Erβ, and GPER1 expressions aims to examine their roles in the mechanism of EGFR-TKIs resistance in lung adenocarcinoma and reports an enhanced cytoplasmic expression of GP ER1 in tissue samples.