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Here we report on the first ultrabright fluorescent nanothermometers, ∼50 nm-size particles, capable of measuring temperature in 3D and down to the nanoscale. The temperature is measured through the recording of the ratio of fluorescence intensities of fluorescent dyes encapsulated inside the nanochannels of the silica matrix of each nanothermometer. The brightness of each particle excited at 488 nm is equivalent to the fluorescence coming from 150 molecules of rhodamine 6G and 1700 molecules of rhodamine B dyes. The fluorescence of both dyes is excited with a single wavelength due to the Förster resonance energy transfer (FRET). We demonstrate repeatable measurements of temperature with the uncertainty down to 0.4 K and a constant sensitivity of ∼1%/K in the range of 20–50 °C, which is of particular interest for biomedical applications. Due to the high fluorescence brightness, we demonstrate the possibility of measurement of accurate 3D temperature distributions in a hydrogel. The accuracy of the measurements is confirmed by numerical simulations. We further demonstrate the use of single nanothermometers to measure temperature. As an example, 5–8 nanothermometers are sufficient to measure temperature with an error of 2 K (with the measurement time of >0.7 s). 

We propose an innovative machine learning paradigm enabling precision medicine for AD biomarker discovery. The paradigm tailors the imaging biomarker discovery process to individual characteristics of a given patient. We implement this paradigm using a newly developed learning-to-rank method 𝙿𝙻𝚃𝚁 . The 𝙿𝙻𝚃𝚁 model seamlessly integrates two objectives for joint optimization: pushing up relevant biomarkers and ranking among relevant biomarkers. The empirical study of 𝙿𝙻𝚃𝚁 conducted on the ADNI data yields promising results to identify and prioritize individual-specific amyloid imaging biomarkers based on the individual’s structural MRI data. The resulting top ranked imaging biomarker has the potential to aid personalized diagnosis and disease subtyping.