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HistoTME is an AI software that predicts the tumor microenvironment’s molecular makeup from standard pathology images, helping identify lung cancer patients likely to benefit from immunotherapy without needing costly molecular tests. Background:
The tumor microenvironment (TME) plays a crucial role in the progression and treatment response of cancers, particularly non-small cell lung cancer (NSCLC). As immunotherapies such as immune checkpoint inhibitors (ICIs) become increasingly central to cancer care, the ability to accurately characterize the TME is essential for predicting which patients will benefit from these treatments. Traditionally, the assessment of TME relies on molecular assays, such as RNA sequencing or immunohistochemistry, which provide insights into the immune landscape of tumors. However, these tests are often expensive, require specialized equipment and expertise, and may not be accessible in all clinical settings. The growing demand for precision oncology has highlighted the need for more accessible, cost-effective, and scalable methods to evaluate the TME and guide immunotherapy decisions. Current approaches to TME characterization face several significant limitations. Biomarkers like PD-L1 expression, while widely used, are not always reliable predictors of response to ICIs, especially in patients with low PD-L1 levels. Molecular assays, though informative, are resource-intensive and may not be feasible in many healthcare environments due to cost, infrastructure, or insurance coverage constraints. Additionally, these methods often require fresh or high-quality tissue samples, which are not always available, and involve lengthy turnaround times that can delay treatment decisions. Digital pathology offers a potential alternative, but existing computational tools typically depend on expert pathologist annotation or lack the ability to directly infer molecular features from routine histopathology slides, limiting their clinical utility and scalability.Technology Overview:  
HistoTME is a software-based artificial intelligence tool designed to predict the molecular composition of the tumor microenvironment (TME) directly from standard hematoxylin and eosin (H&E)-stained histopathology images. The system operates through a sophisticated two-step pipeline: first, it uses the UNI foundation model to extract detailed image features, which are then processed by a multitask-attention-based multiple instance learning (AB-MIL) framework to generate prediction scores and heatmaps. In the second step, HistoTME applies clustering analysis and a two-step random forest classification algorithm to categorize patients into clinically relevant TME subtypes—immune-desert or immune-inflamed. This approach enables the prediction of patient response to immune checkpoint inhibitor (ICI) therapy, using only digital pathology slides, without the need for expert annotation or costly molecular assays. What differentiates HistoTME is its ability to serve as a digital biomarker, offering a scalable, cost-effective alternative to traditional molecular testing for immunotherapy stratification. Unlike conventional biomarkers such as PD-L1 expression, which often lack predictive power in patients with low expression levels, HistoTME leverages routinely available pathology images and advanced AI to infer molecularly defined TME subtypes. The tool was trained and validated on a large, multi-modal dataset of over 650 lung cancer patients with matched histopathology and RNA sequencing data, ensuring robust performance and clinical relevance. Its design eliminates the need for specialized molecular infrastructure, making it particularly valuable for resource-limited settings. Additionally, the integration of interpretability features, such as spatial heatmaps, enhances clinical trust and usability, positioning HistoTME as a transformative solution in the field of computational pathology and precision oncology. https://suny.technologypublisher.com/files/sites/adobestock_553686090.jpegAdvantages:  
•    Enables prediction of tumor microenvironment molecular composition directly from standard H&E-stained histopathology images without requiring molecular testing.
•    Improves identification of non-small cell lung cancer (NSCLC) patients likely to benefit from immune checkpoint inhibitor (ICI) therapy, enhancing treatment personalization.
•    Provides a cost-effective and accessible alternative to expensive molecular assays, suitable for resource-limited medical centers.
•    Utilizes advanced AI techniques combining foundation models and multiple instance learning for accurate and interpretable predictions with spatial heatmaps.
•    Classifies patients into clinically relevant immune-inflamed or immune-desert TME subtypes, aiding clinical decision-making.
•    Validated on a large multi-modal dataset with matched histopathology and RNA sequencing data, ensuring robust performance and clinical relevance.
•    Does not require expert pathologist annotation, facilitating scalable deployment in diverse clinical settings. Applications:  
•    Immunotherapy response prediction
•    Digital pathology workflow integration
•    Cost-effective biomarker development
•    Resource-limited cancer diagnostics Intellectual Property Summary:
Patent PendingStage of Development:
TRL 5Licensing Status:
This technology is available for licensing.

An easy-to-use, minimally-invasive treatment that improves the efficacy of endoscopic treatments by preventing/reducing scar formation.  
Background:
Urethral strictures cause around 1,000,000 emergency room visits in the US and nearly as many visits to urologist clinics. Treatments include urethral dilations (mechanical stretching) and endoscopic incision of the scar, followed by a temporary (up to seven days) catheter placement. These treatments are considered minimally-invasive, but have a poor success rate (under 40%) due to scar recurrence or progression in response to mechanical injury by the treatment itself. In addition, some centers offer open reconstructive techniques called urethroplasties. These use local flaps, distant grafts, and/or complete resection of the affected scar tissue. These treatments are more effective (up to 90%). However, they are significantly more invasive, require specialized surgical training to perform, and catheter stays of up to 28 days to avoid breaking the suture lines of the repair.
Technology Overview:  
The subject technology is a therapeutic lighted catheter (red and near-infrared) for treatment and prevention of urethral strictures and bladder neck contractures. This offers the minimal-invasiveness of endoscopic, eliminates the need for special training (any urologist can dilate and place the catheter), reduces the time of in-dwelling urethral catheter, and increases the effectiveness of the endoscopic treatment by preventing/reducing scar formation. Preliminary animal experiments with this technology has confirmed the efficacy of this treatment with promising initial results, including in terms of radiographic and endoscopic improvement and resolution of strictures. 
https://suny.technologypublisher.com/files/sites/adobestock_285163387_(1).jpeg
Advantages:  •    Minimally-invasive.
•    Eliminates the need for special training.
•    Reduces the time the catheter must remain in place.
•    Prevents/reduces scar formation.
Applications:  
The primary application for this technology is treatment and prevention of urethral stricture disease, bladder neck stenosis, and anastomotic contractures. 
Intellectual Property Summary:
US Patent application filed on 1/10/23. Application Serial # 18/095,121.
Stage of Development: TRL 3 - Experimental proof of concept
Licensing Status:
This technology is available for licensing.
Licensing Potential:
This technology would be of interest to anyone involved in the treatment of urethral strictures and related diseases, including:
•    Manufacturers of medical devices.
•    Hospitals.
•    Universities.
•    Medical research laboratories.
  

A serotonin release assay that requires no special training or expensive equipment, and produces results within hours.  Background:
Platelet serotonin release assay (SRA) is a widely-used clinical assay for diagnosing heparin-induced thrombocytopenia (HIT), a life-threatening complication of heparin treatment. The current SRA procedure entails sending patient serum to a facility for testing by radioactive serotonin uptake/release or by mass spectrometry. Both assay methods require specialized equipment and trained personnel, with a turnaround time typically between three to seven days. Unfortunately, the mortality of HIT increases significantly with each passing day. As a result, quicker SRA turnaround time could save lives. Ideally, point-of-care facilities would obtain SRA results within hours, using inexpensive equipment and no special training to perform.Technology Overview:  
This technology is a luminescent biosensor that changes from green to blue in the presence of a target analyte. The biosensor consists of two molecules. The first is a specially designed green-to-blue color-changing luminescent protein (nLuc-AFF). The second is one or more short (20-50 nucleotide) DNA hairpins or DNA aptamers of novel design. These bind the target DNA sequence, RNA sequence, small molecule, or protein. Upon binding, the AP-1 sequence becomes exposed and activates the biosensor. The biosensor can be readily adapted to recognize different targets by modifying the DNA component, using existing online DNA design tools. The color change is visible and detection/quantification is via cell phone camera. https://suny.technologypublisher.com/files/sites/adobestock_446068375.jpegAdvantages:  
•    Produces results within hours.
•    Requires no special training or expensive equipment.
•    Can be easily adapted to recognize a variety of targets.
 Applications:  
•    Improved platelet SRA for diagnosing HIT.
•    Rapid detection of virus or other pathogen infection, such as coronavirus or cytomegalovirus.
•    Rapid detection of disease biomarkers such as microRNA, metabolites, or aberrant proteins.
 Intellectual Property Summary:
Know-how basedStage of Development:
TRL 3 - Experimental proof of concept Licensing Status:
This technology is available for licensing.Licensing Potential:
This technology would be of interest to anyone involved in the development of methods for detection of HIT and other conditions. These include:
•    Pharmaceutics companies.
•    Hospitals.
•    Medical research laboratories.
•    Universities.

An easy-to-use method for rapidly, accurately, and non-invasively assessing flavivirus immunity by measuring various antibody classes present in saliva.  Background:
Flaviviruses (including dengue virus) are the most common vector-borne human viral pathogens in the world. Nearly 40% of the world’s population are at risk of infection with dengue virus alone, with nearly 400 million dengue virus infections occurring each year resulting in an estimated 20,000 deaths. There is currently an unmet need for a non-invasive, sensitive, and accurate point-of care assay capable of quantifying flavivirus-specific antibody immunity. In addition, the only currently available dengue virus vaccine is only recommended for use in individuals with preexisting dengue virus antibodies. Therefore, a rapid and non-invasive antibody test for assessing flavivirus immunity would allow for safer and more accurate vaccine administration in settings where venipuncture is not convenient.Technology Overview:  
This technology is a saliva-based assay to rapidly, accurately, and non-invasively assess flavivirus immunity by measuring various antibody classes present in saliva. Saliva is collected via an absorbent swab and separate proprietary protein-stabilization solution. The collection method is uncomplicated and can be self-performed. Once collected and placed in the stabilizer solution, the saliva is stable at room temperature for several days, for weeks at 4oC, or indefinitely below -20oC. Testing the saliva sample uses a diagnostics instrument and a custom-designed flavivirus antigen panel. The assay can differentiate the specificity of homotypic humoral immune responses to the dengue viruses (1-4), zika virus, and JE virus, as well as differentiate individuals with polytypic dengue virus immunity and those who previously experienced both dengue virus and zika virus infections. https://suny.technologypublisher.com/files/sites/110-2173adobestock_890015932.jpeg Advantages:  
•    Easy to use, can be self-administered without advanced training.
•    Non-invasive.
•    Rapid.
•    Accurate.
•    Can differentiate a variety of factors.  Applications:  
The primary application for this technology is the treatment of flaviviruses such as dengue virus. Intellectual Property Summary:
Know-How Based.Stage of Development:
TRL 3 - Experimental proof of concept Licensing Status:
This technology is available for licensing.Licensing Potential:
This technology would be of interest to anyone involved in the development of therapeutics for flaviviruses, including:
•    Pharmaceutics companies.
•    Hospitals.
•    Medical research laboratories.
•    Universities.
•    Humanitarian organizations.