Rapid and Accurate Detection of (Diptera: Calliphoridae) Using Recombinase Polymerase Amplification Combined with Lateral Flow Dipstick.

Estimating the postmortem interval (PMI) is critical in the field of forensic science, and necrophagous insects play a significant role in this process. (Fabricius) (Diptera: Calliphoridae) is a common necrophagous insect species, making its rapid and accurate identification essential. However, commonly used molecular biology methods, such as DNA barcode, still have some limitations in identifying necrophagous insects as they are often complex, time-consuming, and reliant on laboratory instruments.

Combining spectrum and machine learning algorithms to predict the weathering time of empty puparia of Sarcophaga peregrine (Diptera: Sarcophagidae).

The weathering time of empty puparia could be important in predicting the minimum postmortem interval (PMImin). As corpse decomposition progresses to the skeletal stage, empty puparia often remain the sole evidence of fly activity at the scene. In this study, we used empty puparia of Sarcophaga peregrina (Diptera: Sarcophagidae) collected at ten different time points between January 2019 and February 2023 as our samples.

Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning.

Objective: The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.

Methods: ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms.

Association of Air Pollution with the Number of Common Respiratory Visits in Children in a Heavily Polluted Central City, China.

Children's respiratory health is vulnerable to air pollution. Based on data collected from June 2019 to June 2022 at a children's hospital in Zhengzhou, China, this study utilized Spearman correlation analysis and a generalized additive model (GAM) to examine the relationship between daily visits for common respiratory issues in children and air pollutant concentrations. Results show that the number of upper respiratory tract infection (URTI), pneumonia (PNMN), bronchitis (BCT), and bronchiolitis (BCLT) visits in children showed a positive correlation with PM, PM, NO, SO, and CO while exhibiting a negative correlation with temperature and relative humidity.