Context: Neonatal mass screening program for congenital hypothyroidism provides the best tool for prevention of its devastating effects on mental development. Despite the overall success of the screening programs in detecting congenital hypothyroidism and eliminating its sequelae and new developments made in the program design, high recall rate and false positive results impose a great challenge worldwide. Lower recall rate and false positive results may properly organize project expenses by reducing the unnecessary repeated laboratory tests, increase physicians and parents' assurance and cooperation, as well as reduce the psychological effects in families.
Evidence Acquisition: In this review, we assessed the recall rate in different programs and its risk factors worldwide.
Methods: Publications reporting the results of the CH screening program from 1997 to 2016 focusing on the recall rate have been searched.
Results: Recall rates vary from 0.01% to 13.3% in different programs; this wide range may be due to different protocols of screening (use of T4 or TSH or both), different laboratory techniques, site of sample collection, recall cutoff, iodine status, human error, and even CH incidence as affected by social, cultural, and regional factors of the population.
Conclusions: It is suggested to implement suitable interventions to reduce the contributing factors by improving the quality of laboratory tests, selecting conservative cut off points, control iodine deficiency, use of iodine free antiseptic during delivery, and use of more specific markers or molecular tests. Applying an age dependent criteria for thyrotropin levels can be helpful in regions with a varied time of discharge after delivery or for preterm babies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702453 | PMC |
http://dx.doi.org/10.5812/ijem.55451 | DOI Listing |
Int J Telemed Appl
January 2025
Medical Familiar Unit, Instituto de Seguridad y Servicios Sociales de Los Trabajadores del Estado, Torreón, Coahuila, Mexico.
This study proposes an automated system for assessing lung damage severity in coronavirus disease 2019 (COVID-19) patients using computed tomography (CT) images. These preprocessed CT images identify the extent of pulmonary parenchyma (PP) and ground-glass opacity and pulmonary infiltrates (GGO-PIs). Two types of images-saliency () image and discrete cosine transform (DCT) energy image-were generated from these images.
View Article and Find Full Text PDFSci Prog
January 2025
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China.
This study presents a novel integration of two advanced deep learning models, U-Net and EfficientNetV2, to achieve high-precision segmentation and rapid classification of pathological images. A key innovation is the development of a new heatmap generation algorithm, which leverages meticulous image preprocessing, data enhancement strategies, ensemble learning, attention mechanisms, and deep feature fusion techniques. This algorithm not only produces highly accurate and interpretatively rich heatmaps but also significantly improves the accuracy and efficiency of pathological image analysis.
View Article and Find Full Text PDFBMC Pediatr
January 2025
Department of Medical Laboratory Sciences, School of Allied Health Sciences, Kampala International University Western Campus, P. O. Box 71, Bushenyi, Uganda.
In spite of the commendable global Pneumococcal Conjugate Vaccine (PCV) coverage in the last two decades, completion and timeliness of receipt of all the required doses are still below target. In Uganda, the 3 + 0 PCV regimen has been reported to have a steady decline in the completion rate and the reasons for the delayed completion are unidentified. This study aimed at assessing the influence of socio-demographic factors on delayed PCV completion among young children.
View Article and Find Full Text PDFPoult Sci
January 2025
College of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China; Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China; Guangdong Engineering Research Center of Agricultural Big Data, Guangzhou 510642, China. Electronic address:
Accurate individual egg-laying detection is crucial for eliminating low-yielding breeder ducks and improving production efficiency. However, existing methods are often expensive and require strict environmental conditions. This study proposes a data processing method based on wearable sensors and joint time-frequency representation (TFR), aimed at accurately identifying egg-laying in ducks.
View Article and Find Full Text PDFBackground: Diabetic kidney disease (DKD) is one of the typical complications of type 2 diabetes (T2D), with approximately 10 % of DKD patients experiencing a Rapid decline (RD) in kidney function. RD leads to an increased risk of poor outcomes such as the need for dialysis. Albuminuria is a known kidney damage biomarker for DKD, yet RD cases do not always show changes in albuminuria, and the exact mechanism of RD remains unclear.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!