Optical resonant cavity sensors are gaining increasing interest as a potential diagnostic method for a range of applications, including medical prognostics and environmental monitoring. However, the majority of detection demonstrations to date have involved identifying a "known" analyte, and the more rigorous double-blind experiment, in which the experimenter must identify unknown solutions, has yet to be performed. This scenario is more representative of a real-world situation. Therefore, before these devices can truly transition, it is necessary to demonstrate this level of robustness. By combining a recently developed surface chemistry with integrated silica optical sensors, we have performed a double-blind experiment to identify four unknown solutions. The four unknown solutions represented a subset or complete set of four known solutions; as such, there were 256 possible combinations. Based on the single molecule detection signal, we correctly identified all solutions. In addition, as part of this work, we developed noise reduction algorithms.
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http://dx.doi.org/10.3390/s150306324 | DOI Listing |
Acc Chem Res
January 2025
Department of Chemistry and Chemistry Institution for Functional Materials, Pusan National University, Busan 46241, Republic of Korea.
ConspectusControlling selectivity through manipulation of reaction intermediates remains one of the most enduring challenges in organic chemistry, providing novel solutions for selective C-C π-bond functionalization. This approach, guided by activation principles, provides an effective method for selective functional group installation, enabling direct synthesis of organic molecules that are inaccessible through conventional pathways. In particular, the selective functionalization of N-conjugated allenes and alkynes has emerged as a promising research focus, driven by advances in controlling reactive intermediates and activation strategies.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
The Jackson Laboratory, Bar Harbor, ME, USA.
Background: Alzheimer's disease (AD) and AD-related dementias (ADRD) are modulated by gene-environment (GxE) interactions across the lifespan. Variants of specific genes increase AD risk and synergize with lifetime exposure to environmental toxicants ("exposome"), including neurotoxic metals (lead, Pb; cadmium, Cd) and metalloid (As). These metal/metalloid toxicants readily enter the body (e.
View Article and Find Full Text PDFBackground: Interest in use of digital technology to advance AD/ADRD research has been growing exponentially over the last few years. This acceleration is fueled in part by growing awareness that both well used research methods as well as newer biomarker approaches are 1) inadequate for clinical symptom detection in the earliest stages of an insidious onset disease and 2) have resulted in inaccurate as well as biased data that is generating treatment and prevention solutions that are insufficiently relevant to some and potentially not relevant to many.
Methods: Sensors embedded in mobile devices such as smartphones and wearables deliver a high penetration, low-cost solution for overcoming previous limitations of early detection sensitivity and limited representative reach.
Alzheimers Dement
December 2024
Division of Psychiatry, University College London, London, United Kingdom.
Background: Psychological factors such as repetitive negative thinking, proneness to experience distress, and perceived stress are associated with increased risk of neurodegeneration and clinical dementia, whereas having a sense of life-purpose, self-reflection, and dispositional mindfulness may be protective. However, whether combinations of these risk and protective factors may inform distinct psychological profiles, which may be differential associated with age-related health outcomes is currently unknown.
Method: We included 742 middle-aged (mean age 51.
BMC Chem
January 2025
Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia.
Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topological indices derived from chemical graph theory is a promising approach to rationally design new drugs for MS.
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