A pan-cancer agent for screening, resection and wound monitoring via NIR and SWIR imaging.

Fluorescence guided surgery (FGS) facilitates real time tumor delineation and is being rapidly established clinically. FGS efficacy is tied to the utilized dye and provided tumor contrast over healthy tissue. Apoptosis, a cancer hallmark, is a desirable target for tumor delineation. Here, we preclinically in vitro and in vivo, validate an apoptosis sensitive commercial carbocyanine dye (CJ215), with absorption and emission spectra suitable for near infrared (NIR, 650-900nm) and shortwave infrared (SWIR, 900-1700nm) fluorescence imaging (NIRFI, SWIRFI). High contrast SWIRFI for solid tumor delineation is demonstrated in multiple murine and human models including breast, prostate, colon, fibrosarcoma and intraperitoneal colorectal metastasis. Organ necropsy and imaging highlighted renal clearance of CJ215. SWIRFI and CJ215 delineated all tumors under ambient lighting with a tumor-to-muscle ratio up to 100 and tumor-to-liver ratio up to 18, from 24 to 168 h post intravenous injection with minimal uptake in healthy organs. Additionally, SWIRFI and CJ215 achieved non-contact quantifiable wound monitoring through commercial bandages. CJ215 provides tumor screening, guided resection, and wound healing assessment compatible with existing and emerging clinical solutions.

Merging data curation and machine learning to improve nanomedicines.

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.

A perception-based nanosensor platform to detect cancer biomarkers.

Conventional molecular recognition elements, such as antibodies, present issues for developing biomolecular assays for use in certain technologies, such as implantable devices. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, often diagnosed at advanced stages, leading to low survival rates. We investigated the detection of protein biomarkers in uterine lavage samples, which are enriched with certain cancer markers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from patients with cancer. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.

Ice Growth Acceleration by Antifreeze Proteins Leads to Higher Thermal Hysteresis Activity.

Since some antifreeze proteins and glycoproteins (AF(G)Ps) cannot directly bind to all ice crystal planes, they change ice crystal morphology by minimizing the area of the crystal planes to which they cannot bind until crystal growth is halted. Previous studies found that growth along the c-axis (perpendicular to the basal plane, the crystal plane to which these AF(G)Ps cannot bind) is accelerated by some AF(G)Ps, while growth of other planes is inhibited. The effects of this growth acceleration on crystal morphology and on the thermal hysteresis activity are unknown to date. Understanding these effects will elucidate the mechanism of ice growth inhibition by AF(G)Ps. Using cold stages and an infrared laser, ice growth velocities and crystal morphologies in AF(G)P solutions were measured. Three types of effects on growth velocity were found: concentration-dependent acceleration, concentration-independent acceleration, and concentration-dependent deceleration. Quantitative crystal morphology measurements in AF(G)P solutions demonstrated that the adsorption rate of the proteins to ice plays a major role in determining the morphology of the bipyramidal crystal. These results demonstrate that faster adsorption rates generate bipyramidal crystals with diminished basal surfaces at higher temperatures compared to slower adsorption rates. The acceleration of growth along the c-axis generates crystals with smaller basal surfaces at higher temperatures leading to increased growth inhibition of the entire crystal.