Chemical-PDMS binding kinetics and implications for bioavailability in microfluidic devices.

Microfluidic organ-on-chip devices constructed from polydimethylsiloxane (PDMS) have proven useful in studying both beneficial and adverse effects of drugs, supplements, and potential toxicants. Despite multiple advantages, one clear drawback of PDMS-based devices is binding of hydrophobic chemicals to their exposed surfaces. Chemical binding to PDMS can change the timing and extent of chemical delivery to cells in such devices, potentially altering dose-response curves. Recent efforts have quantified PDMS binding for selected chemicals. Here, we test a wider set of nineteen chemicals using UV-vis or infrared spectroscopy to characterize loss of chemical from solution in two setups with different PDMS-surface-to-solution-volume ratios. We find discernible PDMS binding for eight chemicals and show that PDMS binding is strongest for chemicals with a high octanol-water partition coefficient (log P > 1.85) and low H-bond donor number. Further, by measuring depletion and return of chemical from solution over tens to hundreds of hours and fitting these results to a first order model of binding kinetics, we characterize partitioning into PDMS in terms of binding capacities per unit surface area and both forward and reverse rate constants. These fitted parameters were used to model the impact of PDMS binding on chemical transport and bioavailability under realistic flow conditions and device geometry. The models predict that PDMS binding could alter in-device cellular exposures for both continuous and bolus dosing schemes by up to an order of magnitude compared to nominal input doses.

Conclusions and data analysis: a 6-year study of Raman spectroscopy of solid tumors at a major pediatric institute.

PURPOSE: Create a Raman spectroscopic database with potential to diagnose cancer and investigate two different diagnostic methodologies. Raman spectroscopy measures the energy of photons scattered inelastically by molecules. These molecular signatures form the basis of identifying complex biomolecules and can be used to differentiate normal from neoplastic tissue. METHODS: 1,352 spectra from 55 specimens were collected from fresh or frozen normal brain, kidney and adrenal gland and their malignancies. Spectra were obtained utilizing a Renishaw Raman microscope (RM1000) at 785 nm excitation wavelength with an exposure time of 10 to 20 s/spectrum over three accumulations. Spectra were preprocessed and discriminant function analysis was used to classify spectra based on pathological gold standard. RESULTS: The results of leave 25 % out training/testing validation were as follows: 94.3 % accuracy for training and 91.5 % for testing adrenal, 95.1 % accuracy for training and 88.9 % for testing group of brain, and 100 % accuracy for kidney training/testing groups when tissue origin was assumed. A generalized database not assuming tissue origin provided 88 % training and 85.5 % testing accuracy. CONCLUSION: A database can be made from Raman spectra to classify and grade normal from cancerous tissue. This database has the potential for real time diagnosis of fresh tissue and can potentially be applied to the operating room in vivo.