Background: Neutrophil gelatinase-assoicated lipocalin (NGAL) appears to be a promising proximal tubular injury biomarker for early prediction of delayed graft function (DGF) in kidney transplant recipients. However, its predictive values in urine and blood were varied among different studies. Here, we performed the meta-analysis to compare the predictive values of urine NGAL (uNGAL) and blood NGAL (bNGAL) for DGF in adult kidney transplant recipients.
Methods: We systematically searched Medline, Cochrane library and Embase for relevant studies from inception to May 2018. The summary receiver operating characteristic (SROC) curves, the pooled sensitivity, specificity and diagnostic odds ratio (DOR) were used to evaluate the prognostic performance of uNGAL and bNGAL for the identification of DGF.
Results: A total of 1036 patients from 14 eligible studies were included in the analysis. 8 studies focused on NGAL in urine and 6 reported NGAL in serum or plasma. The composite area under the ROC (AUC) for 24 h uNGAL was 0.91 (95% CI, 0.89-0.94) and the overall DOR for 24 h uNGAL was 24.17(95% CI, 9.94-58.75) with a sensitivity of 0.88 (95% CI, 0.75-0.94) and a specificity of 0.81 (95% CI, 0.68-0.89). The composite AUC for 24 h bNGAL was 0.95 (95% CI, 0.93-0.97) and the overall DOR for 24 h bNGAL was 43.11 (95% CI, 16.43-113.12) with a sensitivity of 0.91 (95% CI, 0.81-0.96) and a specificity of 0.86 (95% CI, 0.78-0.92).
Conclusions: Urine and serum/plasma NGAL were valuable biomarkers for early identification of DGF in kidney transplantation. In addition, the bNGAL was superior to uNGAL in early prediction of DGF.
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http://dx.doi.org/10.1186/s12882-019-1491-y | DOI Listing |
J Integr Neurosci
January 2025
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 637000 Nanchong, Sichuan, China.
Background: Volume alterations in the parietal subregion have received less attention in Alzheimer's disease (AD), and their role in predicting conversion of mild cognitive impairment (MCI) to AD and cognitively normal (CN) to MCI remains unclear. In this study, we aimed to assess the volumetric variation of the parietal subregion at different cognitive stages in AD and to determine the role of parietal subregions in CN and MCI conversion.
Methods: We included 662 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 228 CN, 221 early MCI (EMCI), 112 late MCI (LMCI), and 101 AD participants.
Viruses
January 2025
Antiguo Hospital Civil de Guadalajara, "Fray Antonio Alcalde", Guadalajara 44280, Mexico.
This study investigates the relationship between SARS-CoV-2 RT-PCR cycle threshold (Ct) values and key COVID-19 transmission and outcome metrics across five years of the pandemic in Jalisco, Mexico. Utilizing a comprehensive time-series analysis, we evaluated weekly median Ct values as proxies for viral load and their temporal associations with positivity rates, reproduction numbers (Rt), hospitalizations, and mortality. Cross-correlation and lagged regression analyses revealed significant lead-lag relationships, with declining Ct values consistently preceding surges in positivity rates and hospitalizations, particularly during the early phases of the pandemic.
View Article and Find Full Text PDFPharmaceuticals (Basel)
January 2025
Centro de Química Médica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile.
Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model.
View Article and Find Full Text PDFSensors (Basel)
January 2025
United States Department of Agriculture-Agriculture Research Service, Grassland Soil and Water Research Laboratory, Temple, TX 76502, USA.
Efficient and reliable corn ( L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers.
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