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Fall-Prevention Clinical Trial in Rural China Shows Promising Results
The device targets early detection of infection, microbiome disruption, and stress, aiding over 320,000 annual preterm births in Ethiopia where resources are limited.
- Researchers developed and validated a multisensor diagnostic device that integrates sensors monitoring three indicators— infection, vaginal microbiome disruption, and maternal stress— for early PTB detection.
- Globally, over 15 million babies are born prematurely each year, concentrated in Asia and Africa, and PTB accounts for approximately 75% of perinatal deaths, straining families and specialised healthcare services.
- Researchers note that infection and inflammation have a firm causal link to preterm birth, and a `molecular signature of inflammation` may be detectable via transcriptomic analysis before histological evidence appears.
- Designed for community deployment, the device aims to expand early screening access in community health posts and home environments, easing families' emotional and financial burdens in low-resource settings.
- Current diagnostic tools such as transvaginal ultrasonography and tocodynamometry offer limited predictive value and nearly 50% of PTB cases are idiopathic, underscoring the need for improved early detection methods.
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Fall-prevention clinical trial in rural China shows promising results
A fall can have consequences such as life-changing bone fractures and even death. A clinical trial shows success with an approach to reduce the risk of falls occurring. A fall can have consequences such as life-changing bone fractures and even death. A clinical trial shows success with an approach to reduce the risk of falls occurring.
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Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
Gravitational-wave signal detection with unknown source parameters buried in dynamic detector noise remains a formidable computational challenge. Existing approaches face core limitations from restrictive assumptions: traditional methods rely on predefined theoretical priors, while neural network...
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- 34% of the sources lean Left, 33% of the sources are Center, 33% of the sources lean Right
34% Left
L 34%
C 33%
R 33%
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