Surface-enhanced Raman scattering (SERS) is an ultra-sensitive vibration spectroscopy technology, with the advantages of multi-index and non-destructive quantitative detection, has attracted much attention in the joint detection of biomarkers. A novel SERS biosensor with multisite capture and interference-free quantification was designed for the joint detection of the sepsis biomarker interleukin-6 (IL-6) and procalcitonin (PCT). This biosensor had two interference-free core-shell SERS probes with highly efficient electromagnetic enhancement and a multisite functionalized magnetic nanomaterial with high adsorption capacity. They formed sandwich structure with the targets through boronic affinity and immunoreaction, and the multi-target quantitative analysis of biomarkers in serum was performed using a portable Raman spectrometer in the Raman-silent region. The SERS biosensor was exhibited highly sensitive with detection limits of 0.584 and 2.99 pg/mL for IL-6 and PCT, respectively. In addition, it exhibited excellent selectivity and specificity even with the interference of other proteins. As this SERS method showed excellent performance in the detection of sepsis, it has great potential for multi-index detection in clinical diagnosis of major diseases.
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http://dx.doi.org/10.1016/j.aca.2023.341523 | DOI Listing |
Urogynecology (Phila)
October 2024
Data Coordinating Center, RTI International, Research Triangle Park, NC.
Importance: This review aimed to describe research initiatives, evolution, and processes of the Eunice Kennedy Shriver National Institute of Child Health and Human Development-supported Pelvic Floor Disorders Network (PFDN). This may be of interest and inform researchers wishing to conduct multisite coordinated research initiatives as well as to provide perspective to all urogynecologists regarding how the PFDN has evolved and functions.
Study Design: Principal investigators of several PFDN clinical sites and Data Coordinating Center describe more than 20 years of development and maturation of the PFDN.
Background: Frontotemporal dementia is the most common form of dementia impacting those under the age of 60. It is estimated that 30% of affected persons have a genetic predisposition to this disease, with mutations in the genes encoding progranulin (GRN), chromosome 9 open reading frame 72(C9orf72), and microtubule associated protein tau (MAPT). Mutations in MAPT were discovered in 1998, yet to date, there have been no therapies or multisite clinical trials available to families.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Research Program on Cognition & Neuromodulation Based Interventions, Ann Arbor, MI, USA.
Background: The multisite SuperAging Research Initiative (SRI) was established in 2021 to identify resilience and resistance factors promoting cognitive healthspan through a harmonized multidisciplinary protocol with prospective data collection. The designation of SuperAger is reserved for individuals age 80+ with episodic memory performance that is at least average for those 2-3 decades younger. Research studies of this relatively uncommon phenotype allow for investigations of fundamental importance to the neurobiology of brain aging, resilience, resistance, and avoidance of cognitive decline related to "average aging" and more severe impairments associated with Alzheimer's and related dementias (ADRD).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Research Consortium, CA, USA.
Background: Autonomic dysfunction has been linked to empathy deficits in symptomatic frontotemporal degeneration (FTD), but less is known about pre-symptomatic FTD mutation carriers (preFTD+). Our prior work found that increasing resting heart rate (RHR) over time predicts decline in emotional empathy in preFTD+. Here, we replicate previous findings in a large, multi-site consortium sample and assess relationships between RHR and empathy loss across disease stages.
View Article and Find Full Text PDFUltrasonics
December 2024
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. Electronic address:
Deep neural networks (DNNs) have remarkable potential to reconstruct ultrasound images. However, this promise can suffer from overfitting to training data, which is typically detected via loss function monitoring during an otherwise time-consuming training process or via access to new sources of test data. We present a method to detect overfitting with associated evaluation approaches that only require knowledge of a network architecture and associated trained weights.
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