Building a machine learning-based risk prediction model for second-trimester miscarriage.

Background: Second-trimester miscarriage is a common adverse pregnancy outcome that imposes substantial economic and psychological pressures on both the physical and mental well-being of patients and their families. Currently, there is a scarcity of research on predictive models for the risk of second-trimester miscarriage.

Methods: Clinical data were retrospectively collected from patients who were in the second trimester of pregnancy (between 14+0 and 27+6 weeks gestation), whose main diagnosis was "threatened abortion" and who were hospitalized at the Women and Children's Hospital of Ningbo University from January 2020 to October 2023.

Rapid point-of-care pathogen sensing in the post-pandemic era.

In the post-pandemic era, interest in on-site technologies capable of rapidly and accurately diagnosing viral or bacterial pathogens has significantly increased. Advances in functional nanomaterials and bioengineering have propelled the progress of point-of-care (POC) sensors, enhancing their speed, specificity, sensitivity, affordability, ease of use, and accuracy. Notably, biosensors that utilize surface-enhanced Raman scattering (SERS) technology have revolutionized the rapid and sensitive diagnosis of biomarkers in pathogenic infections.

SERS-based microdevices for use as diagnostic biosensors.

Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status.

A strategy to enhance SERS detection sensitivity through the use of SiO beads in a 1536-well plate.

The development of rapid and accurate assays is crucial to prevent the rapid spread of highly contagious respiratory infections such as coronavirus (COVID-19). Here, we developed a surface-enhanced Raman scattering (SERS)-enzyme-linked immunosorbent assay (ELISA) method that allows for the screening of multiple patient samples with high sensitivity on a 1536-well plate. As the well number on the ELISA well plate increases from 96 to 1536, the throughput of the assay increases but the sensitivity decreases due to the low number of biomarkers and the increase in non-specific binding species.

Dual-mode SERS-based lateral flow assay strips for simultaneous diagnosis of SARS-CoV-2 and influenza a virus.

Since COVID-19 and flu have similar symptoms, they are difficult to distinguish without an accurate diagnosis. Therefore, it is critical to quickly and accurately determine which virus was infected and take appropriate treatments when a person has an infection. This study developed a dual-mode surface-enhanced Raman scattering (SERS)-based LFA strip that can diagnose SARS-CoV-2 and influenza A virus with high accuracy to reduce the false-negative problem of the commercial colorimetric LFA strip.