Second Harmonic Generation Microscopy This feature examines the crucial role of machine learning (ML) in advancing second harmonic generation (SHG) microscopy, focusing on how ML algorithms transform acquisition and analysis. Techniques such as ESRGAN for upscaling and CARE 2D and N2V 2D for denoising significantly enhance image quality and extract valuable data from low-resolution or noisy images. These innovations reduce the acquisition times for polarization-resolved SHG and standard SHG, allowing rapid whole-sample imaging while maintaining the image analysis accuracy. They also enable the use of lower laser power, reducing sample damage and potentially cutting equipment costs. ML further automates and improves SHG image classification, distinguishes between healthy and pathological tissues, and enables the quantitative analysis of collagen structures and fiber alignment essential for understanding tissue microenvironments and remodeling. By addressing the challenges in speed, quality, and analytical depth, ML broadens the utility of SHG microscopy in biomedical research. This feature also discusses the implementation, strengths, and future potential of each method. Key Technologies: Machine learning, SHG microscopy Dynamic Light Scattering "Historically, dynamic light scattering has been used to predict the development of cataracts in rabbits, the development of cataract formation and diabetes mellitus in humans, the effectiveness of treatment for wet age-related macular degeneration6, and the success of retinal stem cell surgery. The results have demonstrated the utility of DLS to noninvasively quantitate subtle changes at the molecular level. DLS captures molecular changes indicative of a particular disease or treatment earlier than they could be captured by imaging methods which detect late changes in structure. A proof-of-concept instrument for making retinal measurements has been developed. The detector is interfaced with a standard clinical fundus camera. Scattered light is analyzed by a digital autocorrelator with an extended delay option for baseline determination. The intensity fluctuations are averaged over 5 s, and the cumulant analysis method is used to analyze the light-scattering data as a function of the sample time." Key Technologies: dynamic light scattering, optical coherence tomography, fundus camera, spectroscopy Fluorescence Microscopy Fluorescence illumination has emerged as a powerful tool in biomedical fields, including medical diagnostics, research, and surgical procedures. Originally developed for microscopy, this technology allows for the excitation of fluorophores in biological samples, either through external dyes, genetically modified fluorescent molecules, or by leveraging intrinsic autofluorescence. The key advantage of fluorescence imaging lies in its exceptional specificity, providing detailed molecular-level information that surpasses traditional widefield imaging techniques. Building on the success of fluorescence microscopy, industrial collaborators have expanded their technological capabilities to offer illumination solutions for a broader range of applications, and with integration of more sophisticated technologies. These advanced systems now cater to analytical instrumentation, medical diagnostics, endoscopy, and surgical procedures. By combining white light illumination with expanded fluorescence excitation capabilities along with advanced control and IoT, a broader range of applications can be targeted in the biomedical arena. Whether it is for slide scanning, PCR, or surgical applications. This article describes the new technologies, their benefits and importance in enabling advances to modernize biomedicine, as well as the continual need to adapt to meet the diverse needs of researchers and medical professionals. Key Technologies: Fluorescence microscopy, illumination Flow Cytometry Multiplexing assays in flow cytometry identify cell types from mixed cell populations using antibodies conjugated to fluorophores. Often, panels are relatively small requiring eight or fewer antibodies to answer the biological question. By selecting optimal fluorophore combinations with minimal spectral overlap, you can create no or low compensation panels of this size tailored to the flow cytometer. This simplifies analysis by omitting the compensation step and improves data quality by reducing data spread. Using specialty dyes allows the design of such panels, enabling quick and reliable data generation. The design and successful use of these panels will be further discussed. Key Technologies: Flow cytometry Download Media Kit hbspt.forms.create({ region: "na1", portalId: "4478512", formId: "cd1ac50f-b2fe-4ef7-a540-91a3e1782991" });