This technology includes an IgA antibody, specifically designed to target the receptor binding domain of SARS-CoV-2, the virus causing COVID-19. Administered intranasally, this antibody has potential neutralizing activity, aiming to prevent COVID-19. IgA, an antibody class present in mucosal areas, plays a crucial role in immune defense at the initial site of viral infection. The primary application of this technology is envisioned as a therapeutic nasal spray, intended to prevent SARS-CoV-2 infection, particularly in high-risk populations.

This technology includes an approach that dramatically lessens the effects of depth-dependent degradation in fluorescence microscopy images. First, we develop realistic ‘forward models’ of the depth dependent degradation and apply these forward models to shallow imaging planes that are expected to be relatively free of such degradation. In doing so, we create synthetic image planes that resemble the degradation found in deeper imaging planes. Second, we train neural networks to remove the effect of such degradation, using the shallow images as ground truth.

This technology includes anti-PSMA antibody labeled with 177Lu, which has shown to be an effective treatment for prostate cancer. Several small molecules targeting PSMA were also evaluated in prostate cancer patients labeled with betta emitters such as 177Lu. The most common one is 177Lu-PSMA-617 which is under clinical evaluation in many countries. Usual treatment in patients in most clinical trials was composed of up to 3 cycles of 177Lu-PSMA-617.

This technology includes a method that extends fluorescence polarization imaging so that the dipole moment of a fluorescent dye may be excited regardless of its 3D orientation. By exciting the dipole from multiple directions, we ensure that excitation may occur even if the dipole is unfavorably oriented along the axial (propagation) axis. If the dye can be rigidly attached to the structure of interest, our method also enables the 3D orientation of the structure to be estimated accurately.

This technology includes improvements in the fluorescence scanner to increase efficiency. This method works by eliminating the need to radially slide the optical assembly during scanning, instead using a galvanometric mirror deflecting a laser beam to different positions in the sample. This allows the scanner to be incorporated into existing commercial analytical ultracentrifugation (AUC) systems with minimal modifications.

This technology includes an illuminator and reflector that enables flexible standing wave illumination on an inverted microscope stand, and procedures for using such illumination to improve axial resolution in confocal or instant SIM imaging systems. The axial resolution in conventional fluorescence microscopy is typically limited by diffraction to ~700 nm. This method that improves axial resolution ~7-fold over the diffraction limit, and that can be applied to any fluorescence microscope.

This technology includes a newly designed, truncated Evans Blue (EB) form which allows labeling with metal isotopes for nuclear imaging and radiotherapy. Unlike previous designs, this new form of truncated EB confers site specific mono-labeling of desired molecules. The newly designed truncated EB form can be conjugated to various molecules including small molecules, peptides, proteins and aptamers to improve blood half-life and tumor uptake, and confer better imaging, therapy and radiotherapy.

This technology includes algorithms and software that improve the speed of iterative deconvolution, a common method for improving spatial resolution and contrast in fluorescence microscopy images. These algorithms also improve the registration of multiview datasets, and apply deep learning to accelerate spatially varying deconvolution.

This technology includes a microscopy technique that produces super-resolution images from diffraction-limited images obtained from a line scanning confocal microscope. First, the operation of the confocal microscope is modified so that images with sparse line excitation are recorded. Second, these images are processed to increase resolution in one dimension. Third, by taking a series of such super-resolved images from a given sample type, a neural network may be trained to produce images with 1D super-resolution from new diffraction-limited images.

This technology includes a number of dimeric RGD peptides which been developed and labeled with various PET isotopes (1BF, 68Ga, and 64Cu) for imaging integrin expression in cancer, inflammation, rheumatoid arthritis, myocardial infarct, stroke and traumatic injury. A number of these peptides have been translated into clinic for diagnosis and therapy response monitoring.