Tailoring the best treatments to cancer patients remains a highly important endeavor in the oncology field. However, personalized treatment courses are challenging to determine, and technologies or methods that can successfully be employed for precision oncology are lacking.

Multi-parameter flow cytometry has been extensively used in multiple disciplines of biological discoveries, including immunology and cancer research. However, the disadvantage of traditional flow cytometry platforms using excitation lasers and fluorescence detectors is spectral overlap when using multiple dyes on the same biological sample. Metaethical compensation of spectral overlap could only be effective to a certain degree. Mass cytometry is advantageous compared to flow cytometry but is pricey and requires highly skilled operators. 

Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that are released from cells. EVs may contain proteins derived from their cells of origin with the potential as diagnostic biomarkers indicating the state of the cells when released. However, due to their small size (50-1000nm), the methods currently used to phenotype EVs have limited sensitivity and scale. A need exists for development of novel technologies improving EV detection and phenotyping.

Vaccines have become one of the most important tools in the fight against cancers and infectious diseases. However, some vaccines have shown limitations due to their high cost and low immune responses. To overcome these limitations, bacteriophages were proposed for the development of more cost-effective, immunogenic vaccines. Phages have shown a strong ability to activate induced and adaptive immune systems. The genome of these viral particles can be engineered, and their surface proteins can be exploited for antigen display.

Computer and imaging technologies led to the development of digital pathology and the capture and storage of pathological specimens as digitally formatted images. The use of artificial intelligence (AI) in digital pathology, such as in three-dimensional (3D) reconstruction, requires analyses of high volumes of data. This results in increased demands for processing and acquisition of digital images of pathology samples. Increased usage cannot be met by the time-consuming, manual, and laborious methods currently used.

Computer and imaging technologies led to the development of digital pathology and the capture and storage of pathological specimens as digitally formatted images. The use of artificial intelligence (AI) in digital pathology, such as in three-dimensional (3D) reconstruction, requires analyses of high volumes of data. This resulted in increased demands for processing and acquisition of digital images of pathology samples. Increased usage cannot be met by the time-consuming, manual, and laborious methods currently used.

The Multichannel Individualized Stimulation Therapy (MIST) device is a multichannel electrical stimulation system that can be used for targeted, individualized electroconvulsive therapy (ECT), especially for treatment-resistant depression (TRD). Millions of individuals suffer from TRD, for which ECT is often the most efficacious and rapidly acting treatment option.

This technology is a device and system for expediting the thawing frozen specimens (e.g., cryopreserved cells) contained in cryo-vials, offering a breakthrough solution for researchers seeking efficiency and precision in their workflows. The device is equipped with a small elongated tubular adaptor that suspends a cryo-vial of frozen cells over a centrifuge tube containing culture medium in an inverted position. With a focus on speed, efficiency and automation, the adaptor dramatically expedites the process of recovering viable cells from frozen specimens.