Bento is an open-source software toolkit that uses single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. The toolkit is compatible with datasets produced by commercial and academic platforms. Bento is integrated with the open-source single-cell analysis software ecosystem.

Researchers from UC San Diego and Oregon Health Science Univeristy developed a microelectrode grid for continuous interoperative neuromonitoring. The microelectrode grid includes a flexible substrate with low impedance electrochemical interface materials on conducting metal pads. The metal pads are connectable to stimulation/acquisition electronics through metal lead interconnects forming stimulation and recording channels and eventually to bonding pads. A flap within the substrate is movable away from the remainder of the substrate while at least some of the metal pads on the remainder of the substrate can remain in contact with an organ when the flap is moved away from the remainder of the substrate.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious threat to human health without effective treatment. There is an urgent need for both real-time tracking and precise treatment of the SARS-CoV-2 infected cells to mitigate and ultimately prevent viral transmission. However, selective and responsive triggering and tracking of the therapeutic processin infected cells remains challenging.

Brief description not available

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. Researchers from UC San Diego developed a new method using a pair of femtosecond mid-infrared and visible excitation pulses to distinguish chromophores, including molecules and quantum dots, that possess nearly identical emission spectra using multiplexed conditions in a three-dimensional space. 

High-frequency photoacoustic tomography (> 20 MHz) is becoming increasingly important in biomedical applications. However, it requires data acquisition (DAQ) to have commensurately high sampling rate, which imposes challenges to hardwires and increases the cost of building a PA imaging system. For example, the sampling rate should be higher than 80 MHz to cover 100% bandwidth of a 26-MHz transducer (Nuquist limit). A commercial PA imaging system such as Vevo LAZR X (Fujifilm VISUALSONICS Inc. ON, Canada) with 80-MHz sampling rate can cost more than 990,000$ in the United States.Many PA groups use clinical ultrasound DAQs, which are low cost but also have a low sampling rate, e.g., the iu22 system’s sampling rate is 32 MHz.

As an important tool for electrophysiological recordings, neural electrodes implanted on the brain surface have been instrumental in basic neuroscience research to study large-scale neural dynamics in various cognitive processes, such as sensorimotor processing as well as learning and memory. In clinical settings, neural recordings have been adopted as a standard tool to monitor the brain activity in epilepsy patients before surgery for detection and localization of epileptogenic zones initiating seizures and functional cortical mapping. Neural activity recorded from the brain surface exhibits rich information content about the collective neural activities reflecting the cognitive states and brain functions. For the interpretation of surface potentials in terms of their neural correlates, most research has focused on local neural activities.   From basic neuroscience research to clinical treatments and neural engineering, electrocorticography (ECoG) has been widely used to record surface potentials to evaluate brain function and develop neuroprosthetic devices. However, the requirement of invasive surgeries for implanting ECoG arrays significantly limits the coverage of different cortical regions, preventing simultaneous recordings from spatially distributed cortical networks. However, this rich information content of surface potentials encoded for the large-scale cortical activity remains unexploited and little is known on how local surface potentials are correlated with the spontaneous neural activities of distributed large-scale cortical networks. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Cardiac MRI is the clinical reference standard for visual and quantitative assessment of heart function. Specifically, cine balanced steady-state free precession (SSFP) can yield cardiac images with high myocardium–blood pool contrast for evaluation of left ventricular (LV) function. However, MRI suffers from long acquisition times, often requiring averaging across multiple heartbeats, and necessitates a trade-off among spatial resolution, temporal resolution, and scan time. Clinically, radiologists are forced to balance acquisition time with resolution to fit clinical needs, and certain applications such as real-time imaging may require small acquisition matrices. Image scaling is typically performed by using conventional upscaling methods, such as Fourier domain zero padding and bicubic interpolation. These methods, however, do not readily recover spatial detail, such as the myocardium–blood pool interface or delineation of papillary muscles.

4D Flow MRI has become increasingly valuable for the qualitative and quantitative assessment of cardiovascular disease. Since all measurements can be obtained following image acquisition without the need for targeted ultrasonographic windows or placement of 2D phase contrast planes at the time of the exam, 4D Flow provides versatility that can be essential in the diagnostic process.However, the correction of magnetic eddy current-related background phase error remains a critical bottleneck in abdominal applications.

Time-resolved 3D phase-contrast MRI with three-dimensional velocity encoding (4D Flow MRI) has become increasingly valuable for the evaluation of cardiovascular disease. While cardiothoracic and neurovascular applications have grown rapidly, a limiting factor for abdominal applications is the correction of magnetic eddy current-related background phase error, which can be more challenging to reliably correct in abdominopelvic regions due to complex vascular and soft tissue geometry. Phase-error correction is essential for both quantification of blood flow as well as for visualization.

Currently, there are no automated solutions for phase‐error correction that are effective for brain imaging.

There is a clinical need for improved visual inspection for ENT diagnosis and surgeries. Endoscopy is required to access locations of ENT conditions. However, the assessment and identification of ENT abnormalities and pathologies remain challenging due to the difficult-to- reach ENT locations and the complex nature of the related pathologies. An imaging technique that could provide additional information, high contrast, and quantitative data about the patient condition will be useful, especially to assist ENT clinicians in diagnosis and surgeries and to avoid the need to resort to more expensive imaging techniques (e.g., CT scans, ultrasound imaging,MRI).

Cardiovascular disease (CVD) is a tremendous burden on the population in terms of morbidity and mortality, as well as on the healthcare system in terms of cost. Various forms of CVD including atherosclerosis, valve and ventricular dysfunction, aneurysms, and thrombogenesis can be identified by measuring localized abnormalities in blood flow. Accordingly, the ability to noninvasively interrogate physiological flows enables identification and diagnosis of disease, monitoring of the effects of therapy, and research on the hemodynamic nature of CVD and its associated interventions. In the clinic, blood flow measurements are primarily made using phase contrast magnetic resonance imaging (PC-MRI) and ultrasonic color Doppler imaging. Certain limitations of these techniques for patients who have contraindications or suffer from arrhythmias, as well as the desire for volumetric flow information necessitate the development of a new modality for blood flow velocimetry.

The majority of state‐of‐the‐art lung segmentation algorithms in the literature do not simultaneously segment lungs, lung lobes, and airway in a single algorithm. Additionally, automated algorithms typically perform the segmentation task on a series of 2D slices, which can reduce segmentation accuracy of anatomical structures (i.e. lung lobes) that may require contextual information across all three spatial dimensions. Many existing algorithms also have not been validated on chest CTs across a wide variety of conditions to evaluate algorithm generalizability. Currently, quantification of respiratory measurements requires a radiologist, trained analyst, or technician to recognize, identify, and manually annotate anatomical landmarks such as the lung lobes or airway in the chest. A fully‐automated deep learning system may eliminate the need for manual analysis, thereby improving efficiency and expanding applicability to a large number of CTs.

Sarcopenia  is defined as an age associated decline in or loss of lean skeletal muscle mass. The pathophysiology can be multifactorial and the change in body composition may be difficult to detect due to obesity, changes in fat mass, or edema. Changes in weight, limb or waist circumference are not reliable indicators of muscle mass changes. Sarcopenia may also cause reduced strength, functional decline and increased risk of falling. Sarcopenia is otherwise asymptomatic and is often unrecognized.  

Fluorescent proteins (FP) have been widely used as research tools in both academia and pharma for many years.  Naturally occurring FP have been mutated to either be brighter, be monomers, and/or for easier folding and expression in cells.  The most common FP to date has been the green fluorescent protein (GFP) of the jelly fish Aequorea victoria which can be expressed in cells and fused with proteins of interest, and has proven to be an excellent tool to study protein localization, expression, signaling, etc. in real time via microscopy and other techniques. 

Electroencephalographic source imaging (a.k.a. magnetic/electric or M/EEG source imaging, ESI, or brain electrical tomography) usually depends upon sophisticated signal processing algorithms for data cleaning, source separation and imaging. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust brain source images on a wide range of applications.

Magnetic resonance imaging (MRI) has been noted for its excellent soft tissue imaging capability with zero radiation dose. It has repeatedly been touted as the imaging modality of the future, but due to its complexity, long exam times and high cost, its growth has been severely limited. This especially has been the case for cardiac MRI, which only accounts for about I percent of all MRI exams in the United States. Delayed enhancement (DE) imaging is an essential component of cardiac MRI, widely used for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI), known as the myocardial null point (TINP), to suppress the background myocardial signal is required to optimize image contrast in myocardial delayed enhancement (MDE) acquisitions. Incorrect selection of TINP can impair diagnostic quality. In certain diffuse myocardial diseases such as amyloidosis, it may be difficult to identify a single optimal null point. Further, it is known that TINP varies after intravenous contrast administration, and is therefore time-sensitive. In practice, selection of myocardial inversion time is generally performed through visual inspection and selection of TINP from an inversion recovery scout acquisition. This is dependent on the skill of a technologist or physician to select the optimal inversion time, which may not be readily available outside of specialized centers. However, such methods still rely on visual inspection of an image series by a trained human observer to select an optimal myocardial inversion time. A way to overcome these deficiencies is to embrace Deep learning approaches, including convolutional neural networks (CNNs),     which have the potential to automate selection of inversion time, and are the current state-of-the-art technology for image classification, segmentation, localization, and Spatial Temporal Ensemble Myocardium Inversion NETwork (STEMI-NET) prediction. However, these static CNN models have some drawbacks which could be overcome via the use of dynamic temporal activities for object recognition.

Puromycin is an aminonucleoside antibiotic produced by the bacterium Streptomyces alboniger. Its mode of action is to inhibit protein synthesis by disrupting peptide transfer on ribosomes, leading to premature chain termination during protein translation. Puromycin blocks protein synthesis in both eukaryotes and prokaryotes and is routinely used as a research tool in cell culture. The native Puromycin is also used assays such as mRNA display. As such, derivatives have been synthesized in which the amino acid of the 3' end of adenosine based antibiotics is altered to change the compound's antibiotic activity. Other compounds have been synthesized with differing amino acids and functionalities to examine the effect it has on bacterial viability. The majority do not show useful absorption or emission profiles. What is needed is a method to track the compounds in biological systems.

Routine clinical evaluation of osteoporosis (OP) has been focused on dual energy X-ray absorptiometry(DEXA) and/or computed tomography (CT), which provides qualitative analysis of bone mineral (~45% of bone by volume). The majority of bone which is the organic matrix and water (~55% of bone by volume) plays an important role in bone viscosity and strength. Bone mineral density (BMD) by itself only predicts fractures with an accuracy of 30-50%. The overall fracture risk increases 13-fold from ages 60 to 80, but BMD alone only predicts a doubling of the fracture risk. A recent study of over 7806 patients found that only 44% of all non-vertebral fractures occurred in women with a T-score below -2.5 (WHO definition of OP). This percentage dropped to 21% in men. There is a clear need for more sensitive risk assessment tools which not only use BMD, but other determinants of risk such as bone microstructure, porosity, organic matrix and bone water. The organic matrix and water are undetectable with any of the current non-invasive imaging and/or quantification techniques. Magnetic resonance imaging (MRI) detects signals from water in tissues, thus potential for detecting the collagen matrix (bound water) and bone porosity (bulk water). However, bone water has very short transverse relaxation time (T2*) and is undetectable using conventional MR sequences on clinical MR systems.

Metastasis is a complex process in which cancer cells migrate from the primary tumor, invade into the vasculature, and travel to distant parts of the body to establish secondary tumors. Cells with a greater metastatic potential have a proclivity for leading migration away from the primary tumor. Progress in identifying cells primed to metastasize and in assessing metastatic risk has been slow. This may be due in part to the lack of consistent molecular prognostic markers between cancer types and significant heterogeneity in metastatic potential within the tumor. Furthermore, not all tumors are metastatic and determining the metastatic proclivity of single tumor cells remains a major challenge. Another looming scientific question is estimating the metastatic “potential” because conventional techniques, e.g., Immunohistochemistry (IHC) are not capable of this and only molecular imaging can resolve these issues. So far, improved imaging platforms have helped detect established metastases and assessed tumor cell properties such as surrogate markers of metastatic potential. However, single cell-based assays to measure the dynamic pro-metastatic signaling programs that contribute to the 'potential' for metastasis remains a Holy Grail.

Near infrared (NIR) fluorescence imaging (FI) utilizing the fluorophore indocyanine green (ICG) has become more popular for use in medical diagnostics. It is useful for assessing tissue perfusion in a number of surgeries, particularly abdominal, heart, plastic, hepatic as well as other areas of medicine. The light needed for the excitation of the fluorescence is generated by a near infrared light source which is attached directly to a camera. A digital video camera allows the absorption of the ICG fluorescence to be recorded in real time, which means that perfusion can be assessed and documented. Currently, ICG provides a visual representation of tissue perfusion as a global view. Although some efforts have been put into density analysis, no device or software currently performs dynamic evaluation of blood flow for a surgeon. Without objective dynamic measurements, practitioners are only limited to snap shot view of the static environment. This is a problem because it is the dynamics of blood flow that determines tissue perfusion, not how much blood present at a stationary point in time. Furthermore, because there are no numerical evaluations out on the market that can capture this dynamic aspect of blood flow, practitioners are forced to use the naked eye to make a clinical decision that is not only subjective, but is difficult to assess between cases.

Researchers at University of California, San Diego invented and patented a technology that establishes RCas9 as a means to track RNA in living cells in a programmable manner without genetically encoded tags, and may open doors to new treatments for many conditions, from cancer to autism.Presently, UCSD is offering to license patent rights in the United States (US 11,667,903) and other countries listed below:  

In conventional vessel encoded pseudo-continuous arterial spin labeling (PASL), the temporal signal to noise (tSNR) is improved by repeatedly applying pulsed labeling pulses in between Look-Locker readouts.  This works optimally when the temporal width of the tagged boluses matches the inter-pulse spacing. However, because the feeding arteries generally have different velocities and geometries, the conventional labeling slab fails to achieve desirable tSNR.  

Imaging machines used for radiology studies often export data (such as vascular velocities, bone densitometry, radiation dose, etc.) as characters stored in image format. Radiologists are expected to interpret this data and also store it in their text-based reports of the studies. This is usually accomplished by dictating the data into the text report or copying it by typing it. However, these methods are error-prone and time-intensive.