Background: Bacterial meningitis remains often etiologically unconfirmed, especially in resource-poor settings. We tested the potential of real-time polymerase chain reaction to identify Streptococcus pneumoniae (Pnc) and Haemophilus influenzae type b (Hib) from cerebrospinal fluid impregnated on filter paper strips.
Methods: Pnc and Hib genome equivalents were blindly quantified by polymerase chain reaction from 129 liquid cerebrospinal fluid (CSF) samples-the standard-and strips stored at room temperature for months. Genome counts were compared by simple regression.
Results: The strips showed a sensitivity and specificity of 92% and 99% for Pnc, and of 70% and 100% for Hib, respectively. The positive and negative predictive values were 94% and 97% for Pnc, and 100% and 89% for Hib, respectively. For Pnc, the positive and negative likelihood ratio was 92 and 0.08, and the overall accuracy 98%, whereas for Hib they were 70 and 0.30, and 91%, respectively. Genome counting showed good correlation between the filter paper and liquid CSF samples, r(2) being 0.87 for Pnc and 0.68 for Hib (P < 0.0001 for both).
Conclusion: Although not replacing bacterial culture, filter paper strips offer an easy way to collect and store CSF samples for later bacteriology. They can also be transported in standard envelops by regular mail.
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http://dx.doi.org/10.1097/inf.0b013e3181b4f041 | DOI Listing |
JACC Cardiovasc Interv
December 2024
Harrington Heart and Vascular Institute, University Hospitals, Cleveland, Ohio, USA.
Sensors (Basel)
January 2025
Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva blvd 1, Beer-Sheva 84105, Israel.
Algorithms for detecting point targets in hyperspectral imaging commonly employ the spectral inverse covariance matrix to whiten inherent image noise. Since data cubes often lack stationarity, segmentation appears to be an attractive preprocessing operation. Surprisingly, the literature reports both successful and unsuccessful segmentation cases, with no clear explanations for these divergent outcomes.
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January 2025
College of Information Science and Engineering, Hohai University, Changzhou 213200, China.
Fast Fourier Transform-based Space-Time Image Velocimetry (FFT-STIV) has gained considerable attention due to its accuracy and efficiency. However, issues such as false detection of MOT and blind areas lead to significant errors in complex environments. This paper analyzes the causes of FFT-STIV gross errors and then proposes a method for validity identification and rectification of FFT-STIV results.
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January 2025
Instituto de Telecomunicações (IT), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.
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January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
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