Browsing by Author "Cherukuri, Paul"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Biomedical studies of single -walled carbon nanotubes using near-infrared fluorescence
    (2007) Cherukuri, Paul; Weisman, R. Bruce
    Experimental studies will be described aimed at providing a scientific foundation for the use of single-walled carbon nanotubes (SWNTs) in biomedical applications. SWNTs have been found to be a unique class of nanoscale near-infrared (NIR) fluorescence contrast agents that also exhibit novel therapeutic capabilities. In our first study, we found that cultured macrophage cells readily engulf individual nanotubes. The rate of cellular uptake of SWNTs was measured by monitoring their characteristic NIR emissions. Furthermore, we also found that the NIR emissions of individual SWNTs are persistent in both the extracellular and intracellular environment of macrophage cells. Next, we extended our study from simple in vitro systems to the more complex in vivo mammalian animal model. By quantitatively tracking individual SWNTs, we have determined the rabbit's pharmacokinetic SWNT profile without the aid of additional fluorophores or radiolabels. As a final therapeutic application, we have developed SWNTs as novel pharmaceutical agents that efficiently carry siRNA molecules into cancer cells in order to induce targeted apoptosis of specific tumors.
  • Thumbnail Image
    Item
    Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries
    (IOP Publishing, 2021) Schmid, William; Fan, Yingying; Chi, Taiyun; Golanov, Eugene; Regnier-Golanov, Angelique S.; Austerman, Ryan J.; Podell, Kenneth; Cherukuri, Paul; Bentley, Timothy; Steele, Christopher T.; Schodrof, Sarah; Aazhang, Behnaam; Britz, Gavin W.; Neuroengineering Initiative (NEI)
    Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making ‘go/no-go’ decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute and early-stage mTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.
  • Thumbnail Image
    Item
    Teslaphoresis of Carbon Nanotubes
    (American Chemical Society, 2016) Bornhoeft, Lindsey R.; Castillo, Aida C.; Smalley, Preston R.; Kittrell, Carter; James, Dustin K.; Brinson, Bruce E.; Rybolt, Thomas R.; Johnson, Bruce R.; Cherukuri, Tonya K.; Cherukuri, Paul
    This paper introduces Teslaphoresis, the directed motion and self-assembly of matter by a Tesla coil, and studies this electrokinetic phenomenon using single-walled carbon nanotubes (CNTs). Conventional directed self-assembly of matter using electric fields has been restricted to small scale structures, but with Teslaphoresis, we exceed this limitation by using the Tesla coil’s antenna to create a gradient high-voltage force field that projects into free space. CNTs placed within the Teslaphoretic (TEP) field polarize and self-assemble into wires that span from the nanoscale to the macroscale, the longest thus far being 15 cm. We show that the TEP field not only directs the self-assembly of long nanotube wires at remote distances (>30 cm) but can also wirelessly power nanotube-based LED circuits. Furthermore, individualized CNTs self-organize to form long parallel arrays with high fidelity alignment to the TEP field. Thus, Teslaphoresis is effective for directed self-assembly from the bottom-up to the macroscale.