Gritstone Description
The foundational aspect of our immunotherapy approach lies in our comprehension of antigens and neoantigens, particularly in identifying which variations will be transcribed, translated, processed, and subsequently displayed on the surface of cells via Human leukocyte antigen (HLA) molecules, thus making them recognizable to T cells. We achieve this by employing Gritstone EDGETM, a unique platform powered by machine learning. Creating cancer immunotherapies that incorporate tumor-specific neoantigens proves challenging, mainly because tumors consist of numerous mutations, yet only a fraction of these lead to genuine tumor-specific neoantigens. To tackle this complexity, we have developed EDGE's cutting-edge integrated neural network model, trained with millions of data points gathered from a diverse range of tumor and normal tissue samples across various patient ancestries. This extensive training allows us to enhance the accuracy of neoantigen identification and improve the effectiveness of our immunotherapy strategies.
Gritstone Alternatives
QBench allows you to keep track of all your samples and where they are located in the workflow using a single system. QBench eliminates the need for spreadsheets, shared folders in the network, and paper-based tracking systems. You can view hundreds of PDF reports/COAs before publishing or emailing. You can generate barcodes and create labels that you can customize for your samples. Compatible with standard printers and scanners. QBench's billing module allows you to create and send invoices right from the system. You can see counts and latencies for different data types in QBench. This includes metrics like turnaround time, sample counts per test, sample delay, and many others. QBench makes it easy for you to gather the data your lab needs for the assays you perform.
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An API powered by Google's AI technology allows you to accurately convert speech into text. You can accurately caption your content, provide a better user experience with products using voice commands, and gain insight from customer interactions to improve your service. Google's deep learning neural network algorithms are the most advanced in automatic speech recognition (ASR). Speech-to-Text allows for experimentation, creation, management, and customization of custom resources. You can deploy speech recognition wherever you need it, whether it's in the cloud using the API or on-premises using Speech-to-Text O-Prem. You can customize speech recognition to translate domain-specific terms or rare words. Automated conversion of spoken numbers into addresses, years and currencies. Our user interface makes it easy to experiment with your speech audio.
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Elements Spine SRS
Brainlab's Elements Spine Stereotactic Radiosurgery (SRS) represents a cutting-edge software solution aimed at refining the management of spinal metastases. This innovative workflow features automation at each phase, encompassing anatomical mapping, curvature adjustments, and target identification, which guarantees precision and uniformity down to submillimetric levels. A distinctive algorithm addresses variations in spinal curvature, thereby improving the accuracy of image fusion. The software employs automatic segmentation of spinal anatomy through a patented synthetic tissue model that effectively identifies and labels different spine levels for accurate dose calculations. Tools for outlining spinal tumors are included for Gross Tumor Volume (GTV) contouring, along with automatic suggestions for the Clinical Target Volume (CTV) and a cropped spinal canal object in alignment with International Spine Consortium Guidelines. Furthermore, the integration of AI-driven contouring solutions allows for the swift and dependable delineation of over 200 structures, including lymph nodes. This advancement not only streamlines the treatment process but also enhances overall patient outcomes in spinal cancer management.
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Elements Contrast Clearance Analysis
Brainlab's Elements Contrast Clearance Analysis is an MRI-based technique aimed at distinguishing areas of contrast clearance and accumulation within brain tumor imaging datasets. This advanced high-resolution analysis enhances the understanding required for ongoing evaluations and decision-making across various clinical fields, including radiosurgery, radiation oncology, neurosurgery, neuro-oncology, and neuroradiology. The methodology entails capturing two standard 3D T1-weighted MRIs; the first scan is taken around 5 minutes after administering a standard dose of contrast agent, while the second scan occurs 60 to 105 minutes later. By subtracting the initial series from the subsequent one, volumetric maps are created that clearly identify zones of contrast clearance (shown in blue) against those of contrast accumulation (illustrated in red). These findings empower clinicians to better evaluate the effects of radiation treatment in contrast to potential tumor regrowth, allowing for more educated decisions regarding both initial and subsequent treatment plans. As a result, this analysis not only aids in clinical assessments but also enhances the overall management of patient care in complex cases.
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Company Details
Company:
Gritstone bio
Year Founded:
2015
Headquarters:
United States
Website:
gritstonebio.com/scientific-platform/
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Web-Based
Customer Support
Online Support
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