Optical Filters and PCR Tests The ability to rapidly diagnose diseases and pathogens has never been more prevalent than now, as we navigate through the COVID-19 global pandemic. One of the most rapidly growing diagnostic techniques involves the use of polymerase chain reaction (PCR) instrumentation to provide real-time qualitative and quantitative detection of nucleic acid sequences. Quantitative PCR (qPCR) instruments operating in real time require the most favourable signal-to-noise ratio possible, combined with the utmost sensitivity. This demand for high selectivity and sensitivity drives the need for optimization of the wavelength selective optical filters used to discriminate the fluorescent emission signals from adjacent fluorochrome channels and excitation light, providing more signal with less background. This article will discuss PCR as a key photonics tool for disease diagnosis along with the challenges faced and addressed by the 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 recognized ability to provide unique structural information from endogenous two-photon fluorescence (TPEF) and second harmonic generation (SHG) signals. Researchers at UCI now report a major breakthrough with the demonstration of a compact, fast large area multiphoton exoscope (FLAME) that should facilitate the translation of the technology into the clinical setting. As lead author, Dr. Mihaela Balu, explains, “ Rapid non-invasive in-vivo and ex-vivo multiphoton-based imaging with molecular contrast and high spatial resolution could become an important tool for maximizing diagnostic efficacy and guiding therapy in surgical procedures. We believe the FLAME system represents a big step forward in addressing the limitations of previous multiphoton microscopy platforms by significantly increasing the speed and size of the scanned area without sacrificing the spatial resolution in a very compact setup. Near-Infrared Spectroscopy Near-infrared (NIR) spectroscopy occupies a distinct spot at the frontier of life sciences. Despite dominating several areas of application in science and industry, certain limitations of this technique prevailed. However, recent key advancements in technology and methodology opened the pathway to unlocking the potential of NIR spectroscopy in a spectrum of new roles. Miniaturization of the instrumentation enabled its use as an on-site, flexible and accurate tool for quality control in the natural medicines and food industries. Smartphone-operated sensors can be easily used in remote locations for rapid on-site analysis, e.g. in a direct in-field monitoring of medicinal plants. Fundamental research towards the theoretical simulation of NIR spectra decisively shifted the limits in the spectral data interpretability. Combined with chemometrics and machine learning, optimization of the sensor suite towards a specific application is easier than before. Current developments of multi-sensor designs based on data fusion enabled integrating NIR spectroscopy with complementary techniques. Hyperspectral Imaging Camera and Amyloid Alzheimer’s Disease (AD) is one of the three so-called trillion-dollar diseases in the world, next to cancer and diabetes. It is expected that in the next 40 years, the number of people diagnosed with AD, will triple. Until today, AD diagnosis is made with expensive and invasive techniques such as brain scans and lumbar puncture, and diagnosis is often only made in the late 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 thought 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 researched. An easy-to-use hyperspectral snapshot camera – 16 spectral bands between 460nm and 620nm with 10nm-bandwidth - was used to quantify amyloid accumulation while optical coherence tomography allowed to assess the thickness of the retinal nerve fibre layer. Dedicated image preprocessing and machine learning were instrumental to discriminate between Alzheimer patients and healthy subjects. The best results were obtained when the hyperspectral and OCT data were combined. Within a few years, this new concept could lead to a rapid, user-friendly, and affordable test for diagnosing Alzheimer’s.