Hasil untuk "Medicine"

C. Bailey, C. Day

J. Keyes

K. Radcliffe

M. Peters-Golden, W. Henderson

J. Bilezikian, J. Potts, G. E. Fuleihan et al.

F. Montorsi, G. Adaikan, E. Becher et al.

J. Jarow, I. Sharlip, A. Belker et al.

A. Egan, E. Blind, K. Dunder et al.

D. Lawrence, Z. Shahrokh, S. Marsters et al.

M. Weir

S. Ancoli-Israel, Jennifer L Martin, T. Blackwell et al.

A. Bisno, M. Gerber, J. Gwaltney et al.

M. Luster, S. Clarke, M. Dietlein et al.

H. Vlassara, M. Palace

A. Ahuja, Bryce W. Polascik, Divyesh Doddapaneni et al.

a Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, United States of America b Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America c Department of Internal Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America d Department of Internal Medicine, Orlando Regional Medical Center, Orlando, Florida, United States of America e Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miller School of Medicine, Miami, Florida, United States of America

D. Skuse, N. Sartorius

Doi Thi Lan, Quan T. Ngo, Luong Vuong Nguyen et al.

Abstract In cognitive radio (CR) systems, the accurate detection of spectrum holes is a cornerstone for efficient spectrum utilization. However, the increasing complexity of CR environments, particularly those with multiple primary users (PUs), has made precise spectrum sensing a paramount challenge. To address this challenge, this study introduces the ATC model, a novel deep learning architecture that integrates a parallel combination of attention mechanism-based networks and a Convolutional Neural Network (CNN). This hybrid design enables the model to capture both spatial and temporal features from the distinct statistics of sensing signals, thereby enhancing the accuracy of spectrum state detection. The model employs a Graph Attention Network (GAT) to extract complex topological features from graph-structured data derived from received signal strength, dynamically highlighting the most relevant information. To complement this, a CNN processes the sample covariance matrix of sensing signals, unlocking localized statistical correlations and hierarchical feature representations by treating the matrix as an image. Temporal dynamics, such as PU activity patterns, are modeled using a Transformer encoder, which leverages a self-attention mechanism to learn sequential features effectively. The proposed model is evaluated using both simulated and real-world datasets. For the simulated datasets, the model is assessed and compared with baseline methods under multi-PU scenarios across different channel models. For the real-world dataset, the experimental setup is configured for a single-PU scenario due to practical data collection limitations. In both cases, the ATC model demonstrates improved performance over the benchmarked spectrum sensing methods, exhibiting higher accuracy and robustness within the respective evaluation settings.

Lucie Marisa Bucci, Smaragda Lamprianou, Francesco Gesualdo et al.

Duy Nguyen Anh Tran, Bao Tu Thai Nguyen, Hoan Le Nguyen et al.

Background: Preoperative cognitive function (PCF) is gaining attention as a predictor of surgical outcomes due to its association with muscle function and recovery. Its role in postoperative recovery following transforaminal lumbar interbody fusion (TLIF), however, remains unclear. Objectives: This study aimed to evaluate the impact of PCF on functional and quality-of-life outcomes after TLIF surgery. Materials and Methods: A prospective study of 89 patients undergoing TLIF assessed PCF preoperatively using the Short Portable Mental Status Questionnaire. Outcomes, including Japanese Orthopaedic Association (JOA) and EuroQol 5-Dimensions 3-Level (EQ-5D-3L) scores, were measured at baseline, 3, 6, and 12 months postsurgery. Generalized estimating equations analyzed the associations between PCF and recovery. Results: JOA and EQ-5D-3L scores improved significantly at all postoperative time points, reflecting enhanced functional and quality-of-life outcomes after TLIF. PCF showed a weak-to-moderate negative correlation with JOA and EQ-5D-3L scores across all time points. Greater PCF impairment was associated with lower postoperative JOA (β = −1.432, P = 0.036) and EQ-5D-3L (β = −0.065, P = 0.016) scores. Conclusions: PCF significantly affects postoperative outcomes following TLIF, particularly in early recovery. Assessing and addressing PCF preoperatively could enhance recovery trajectories.

Nafiseh Hajian Afarani, Fatemeh Keshavarzi, Kahin Shahanipour et al.

Abstract Tenofovir is an antiviral drug that prevents the replication of AIDS and hepatitis B viruses. To minimize the harmful side effects of the drug on the body, it is essential to determine its precise concentration in the blood. Fluorescence spectroscopy is a cost-effective and highly sensitive method for detecting drugs like tenofovir. In this study, carbon nitride (g-C3N4), a unique form of carbon known for its exceptional stability, high quantum efficiency and specific surface area, was used to create a C3N4/Fe/Cu sensor. The g-C3N4/Fe/Cu sensor was characterized using SEM, UV-VIS, PL, EDX, mapping and FT-IR methods. The influencing factors of C3N4/Fe/Cu response, such as pH, analyte concentration and temperature, were optimized during the experiments. Various concentrations of tenofovir were prepared in water and added to g-C3N4/Fe/Cu. The fluorescence intensity of the resulting solutions was analyzed using PL analysis at a wavelength of 463 nm under optimal conditions. The results confirmed the identification of tenofovir in pharmaceutical capsules within the concentration range of 5.0-700.0 µM using g-C3N4/Cu/Fe. A strong linear relationship with an R2 = 0.9304 and a LOD = 3.2 µM (signal-to-noise = 3) was achieved. Therefore, g-C3N4/Fe/Cu is recommended for tenofovir drug assay.