Optical filters and PCR tests

The ability to quickly diagnose disease and pathogens has never been more prevalent than it is today, as we navigate the global COVID-19 pandemic. One of the fastest growing diagnostic techniques involves the use of polymerase chain reaction (PCR) instruments to provide real-time qualitative and quantitative detection of nucleic acid sequences. Real-time quantitative PCR (qPCR) instruments require the most favorable signal-to-noise ratio possible, coupled with the highest sensitivity. This demand for high selectivity and sensitivity results in the need to optimize the wavelength-selective optical filters used to discriminate fluorescent emission signals from adjacent fluorochromatic channels and excitation light, providing more signal with less. background noise. This article will discuss PCR as a key photonic tool for disease diagnosis as well as the challenges encountered and addressed by optical filter components that enable its function, both technically and commercially.

Multiphoton microscopy

MPM has struggled to gain traction in the clinical setting despite its proven ability to provide unique structural information from endogenous two-photon fluorescence (TPEF) and second harmonic generation (SHG) signals. UCI researchers are now reporting a major breakthrough with the demonstration of a Compact and Rapid Large Area Multiphoton Exoscope (FLAME) which is expected to facilitate the application of the technology in clinical settings. As lead author, Dr Mihaela Balu, explains: “Rapid non-invasive multiphoton imaging in vivo and ex vivo with molecular contrast and high spatial resolution could become an important tool to maximize diagnostic efficiency and guide therapy. in surgical procedures. We believe that the FLAME system represents a big step forward in overcoming the limitations of previous multiphoton microscopy platforms by dramatically increasing the speed and size of the scanned area without sacrificing spatial resolution in a very compact configuration.

Near infrared spectroscopy

Near infrared spectroscopy (NIR) occupies a distinct place at the frontier of life sciences. Despite the dominance of several fields of application in science and industry, certain limitations of this technique have prevailed. However, recent key advances in technology and methodology have paved the way for unleashing the potential of NIR spectroscopy in an array of new roles. The miniaturization of the instrumentation has enabled its use as an on-site, flexible and precise tool for quality control in natural medicine and food industries. The smartphone-actuated sensors can be easily used in remote locations for rapid on-site analysis, for example in direct field monitoring of medicinal plants. Basic research towards the theoretical simulation of NIR spectra has radically pushed the limits of the interpretability of spectral data. Combined with chemometrics and machine learning, optimizing the sensor suite to a specific application is easier than before. Current developments in multi-sensor designs based on data fusion have made it possible to integrate NIR spectroscopy with complementary techniques.

Hyperspectral and amyloid imaging camera

Alzheimer’s disease (AD) is one of the three trillion dollar diseases in the world, after cancer and diabetes. The number of people diagnosed with AD is expected to triple over the next 40 years. Until today, the diagnosis of AD has been made using expensive and invasive techniques such as brain scans and lumbar puncture, and the diagnosis is often made only at an advanced stage of the disease. , when patients already have memory loss. Therefore, there is a need for non-invasive and affordable diagnostic tests that can detect AD in the pre-symptomatic phase of the disease, when the chances of effective treatments are believed to be much higher. In a recent multidisciplinary study involving 39 patients, the potential of retinal imaging techniques for the diagnosis of Alzheimer’s disease was investigated. An easy-to-use hyperspectral snapshot camera – 16 spectral bands between 460 nm and 620 nm with a bandwidth of 10 nm – was used to quantify amyloid accumulation while optical coherence tomography was used to assess the thickness of the layer of retinal nerve fibers. Dedicated image preprocessing and machine learning have been instrumental in distinguishing between patients with Alzheimer’s disease and healthy subjects. The best results were obtained when hyperspectral and OCT data were combined. Within a few years, this new concept could lead to a rapid, user-friendly and affordable test for the diagnosis of Alzheimer’s disease.