Samples containing low-copy numbers of DNA are routinely encountered in casework. The signal acquired from these sample types can be difficult to interpret as they do not always contain all of the genotypic information from each contributor, where the loss of genetic information is associated with sampling and detection effects. The present work focuses on developing a validation scheme to aid in mitigating the effects of the latter. We establish a scheme designed to simultaneously improve signal resolution and detection rates without costly large-scale experimental validation studies by applying a combined simulation and experimental based approach. Specifically, we parameterize an in silico DNA pipeline with experimental data acquired from the laboratory and use this to evaluate multifarious scenarios in a cost-effective manner. Metrics such as signal-to-noise resolution, false positive and false negative signal detection rates are used to select tenable laboratory parameters that result in high-fidelity signal in the single-copy regime. We demonstrate that the metrics acquired from simulation are consistent with experimental data obtained from two capillary electrophoresis platforms and various injection parameters. Once good resolution is obtained, analytical thresholds can be determined using detection error tradeoff analysis, if necessary. Decreasing the limit of detection of the forensic process to one copy of DNA is a powerful mechanism by which to increase the information content on minor components of a mixture, which is particularly important for probabilistic system inference. If the forensic pipeline is engineered such that high-fidelity electropherogram signal is obtained, then the likelihood ratio (LR) of a true contributor increases and the probability that the LR of a randomly chosen person is greater than one decreases. This is, potentially, the first step towards standardization of the analytical pipeline across operational laboratories.
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http://dx.doi.org/10.1016/j.fsigen.2017.09.005 | DOI Listing |
J Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
View Article and Find Full Text PDFPLoS One
January 2025
School of Emergency Management, Institute of Disaster Prevention, Sanhe, Hebei, China.
With the increasing number of patients with Alzheimer's Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer's disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However, these methods have limitations in the early detection of Alzheimer's disease, such as strong subjectivity in diagnostic criteria, high detection costs, and high misdiagnosis rates.
View Article and Find Full Text PDFPLoS One
January 2025
Escuela de Odontología, Universidad Internacional del Ecuador, Quito, Ecuador.
Background: Monitoring hospitalization rates associated with oral health conditions is an important part of epidemiological surveillance, especially when these conditions have increased significantly in low-and middle-income countries. This study aimed to evaluate the temporal trends in hospital discharges associated with oral health-related conditions in Ecuador from 2000 to 2023 and identify the leading diagnoses groups.
Methods: An ecological time-series study was conducted based on annual data from the National Institute of Statistics and Censuses of Ecuador.
In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning.
View Article and Find Full Text PDFProstate
January 2025
VUI Center for Outcomes Research, Analysis, and Evaluation, Henry Ford Health System, Detroit, Michigan, USA.
Introduction: PSA screening remains a pivotal tool for early prostate cancer (PCa) detection. International guidelines rely on evidence from three major randomized clinical trials: ERSPC, PLCO, and CAP. We aim to examine the percentage of patients in real-world practice who get PSA screening as defined by each of the aforementioned trials.
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