With new advances in infectious disease, antifouling surfaces, and environmental microbiology research comes the need to understand and control the accumulation and attachment of bacterial cells on a surface. Thus,...
BackgroundThe use of CGP in guiding treatment decisions in aNSCLC with acquired resistance to ALK TKIs is questionable.MethodsWe prospectively assessed the impact of CGP on the decision-making process in ALK-rearranged aNSCLC patients following progression on 2nd/3rd-generation ALK TKIs. Physician’s choice of the most recommended next-line systemic treatment (NLST) was captured before and after receival of CGP results; the percentage of cases in which the NLST recommendation has changed was assessed along with the CGP turnaround time (TAT). Patients were divided into groups: patients in whom the NLST was initiated after (group 1) and before (group 2) receival of the CGP results. Time-to-treatment discontinuation (TTD) and overall survival (OS) with NLST were compared between the groups.ResultsIn 20 eligible patients (median [m]age 63 years [range, 40-89], females 75%, adenocarcinoma 100%, failure of alectinib 90%, FoundationOne Liquid CDx 80%), CGP has altered NLST recommendation in 30% of cases. CGP findings were as follows: ALK mutations 30% (l1171X 10%, G1202R, L1196M, G1269A, G1202R+l1171N+E1210K 5% each), CDKN2A/B mutation/loss 10%, c-met amplification 5%. CGP mTAT was 2.9 weeks [IQR, 2.4-4.4]. mTTD was 11.3 months (95% CI, 2.1-not reached [NR]) and 5.4 months (95% CI, 2.0-NR) in groups 1 and 2, respectively (p-0.34). mOS was 13.2 months (95% CI, 2.9-NR) and 13.0 months (95% CI, 6.0-NR) in groups 1 and 2, respectively (p-0.86).ConclusionCGP has a significant impact on the decision-making process in ALK-rearranged aNSCLC following progression on 2nd/3rd-generation ALK TKIs.
The peculiarities of surface-bound bacterial cells are often overshadowed by the study of planktonic cells in clinical microbiology. Thus, we employ phase-shift reflectometric interference spectroscopic measurements to observe the interactions between bacterial cells and abiotic, microstructured material surfaces in a label-free, real-time manner. Both material characteristics (i.e., substrate surface charge and wettability) and characteristics of the bacterial cells (i.e., motility, cell charge, biofilm formation, and physiology) drive bacteria to adhere to a particular surface. We conclude that the attachment of bacterial cells to a surface is determined by the culmination of numerous factors. When specific characteristics of the bacteria are met with factors of the surface, enhanced cell attachment and biofilm formation occur. Such knowledge can be exploited to predict antibiotic efficacy, biofilm development, enhance biosensor development, as well as prevent biofouling.
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