Home

This technology uses lung-targeting lipid nanoparticles to deliver a combination of anti-inflammatory and immune-modulating drugs directly to the lungs, offering a more effective and targeted treatment for acute lung injury and acute respiratory distress syndrome. Background:
Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are severe, life-threatening conditions characterized by widespread inflammation and increased permeability in the lungs, often resulting from infection, trauma, or other critical illnesses. These syndromes lead to impaired gas exchange, hypoxemia, and respiratory failure, frequently requiring intensive care and mechanical ventilation. Despite advances in supportive care, mortality rates for ALI/ARDS remain high, underscoring the urgent need for more effective therapeutic interventions. The complexity of these conditions, which involve dysregulated immune responses and extensive lung tissue damage, has driven ongoing research into targeted therapies that can modulate inflammation and promote tissue repair directly within the lungs. Current treatment strategies for ALI/ARDS are largely supportive, focusing on mechanical ventilation and fluid management, with pharmacological interventions offering only modest benefits. Conventional drugs such as corticosteroids, neuromuscular blockers, and inhaled nitric oxide have shown limited efficacy in improving patient outcomes, and many promising agents—including antioxidants, statins, surfactant therapy, and cytokine inhibitors—have failed to demonstrate consistent clinical benefit. One major limitation of existing approaches is the lack of targeted delivery to lung tissue, resulting in suboptimal drug concentrations at the site of injury and increased risk of systemic side effects. Furthermore, most therapies address only a single aspect of the disease process, rather than the multifaceted immune and inflammatory pathways involved in ALI/ARDS, leaving a significant gap in effective, comprehensive treatment options.Technology Overview:  
This technology utilizes specialized lung-targeting lipid nanoparticles (LNPs) designed for the intravenous delivery of multiple therapeutic agents to treat acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). The LNPs are engineered to transport a combination of an anti-inflammatory drug and immune modulators directly to lung tissue. By leveraging the unique properties of lipid nanoparticles, this approach enables precise targeting of the lung, ensuring that the therapeutic agents are delivered efficiently to the site of injury. Preclinical studies in mouse models have demonstrated that this multi-agent delivery system can enhance localized therapeutic effects, potentially offering a more effective treatment for ALI/ARDS compared to conventional therapies. What differentiates this technology is its multi-modal, lung-specific delivery strategy, which addresses several key limitations of current ALI/ARDS treatments. Traditional therapies often suffer from limited efficacy and significant systemic side effects due to non-specific drug distribution. In contrast, the LNP system’s ability to co-deliver  synergistic agents directly to the lungs allows for simultaneous suppression of inflammation, modulation of immune responses, and targeted inhibition of specific inflammatory pathways. This integrated approach not only maximizes therapeutic efficacy but also minimizes off-target effects, representing a significant advancement over existing non-targeted therapies. The innovation lies in the combination of targeted delivery, multi-agent synergy, and the potential for improved patient outcomes, positioning this technology as a transformative solution for severe lung injuries.                                                   https://suny.technologypublisher.com/files/sites/adobestock_221990236.jpegAdvantages:  
•    Targeted delivery of therapeutic agents specifically to lung tissue enhances treatment efficacy for ALI/ARDS.
•    Combination of anti-inflammatory, immunomodulatory, and pathway-specific inhibitors provides a multi-modal therapeutic approach.
•    Intravenous administration of lipid nanoparticles enables efficient and localized drug delivery.
•    Potential to reduce systemic side effects compared to conventional treatments.
•    Demonstrated promising efficacy in preclinical mouse models of lung injury.
•    Addresses significant unmet medical needs in treating acute lung injury and respiratory distress syndrome.
•    Innovative use of proprietary lung-targeting lipid nanoparticles as a delivery platform for multiple complementary agents in one system. Applications:  
•    ALI/ARDS hospital treatment enhancement
•    Targeted drug delivery for lungs
•    Acute respiratory failure emergency care Intellectual Property Summary:
Patent application: 63/813,654, filed on 05/29/2025Stage of Development:
TRL 3Licensing Status:
This technology is available for licensing.

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.

This technology is a computer vision-based software platform that enables real-time human movement analysis, providing physical therapists and trainers with objective feedback and progress tracking through a standard webcam. Background:
Traditional physical therapy and fitness training often rely on subjective visual assessments, which can limit the accuracy and consistency of movement analysis. Recognizing this challenge, the invention was developed to introduce an objective, data-driven solution that enhances motor learning and rehabilitation outcomes by leveraging advancements in computer vision and pose estimation technologies.Technology Overview:  
This innovative software platform uses computer vision algorithms to analyze human movement in real time via a standard webcam. At its core, the system employs advanced pose estimation techniques, such as those found in open-source libraries like MediaPipe, to track joint angles and assess the quality of movements. By integrating theories of motor control and motor learning, the platform delivers precise feedback designed to optimize movement retraining, crucial for both rehabilitation and fitness improvement. The technology features automated repetition counting, detailed feedback on individual performance, and comprehensive summaries after each session, enabling users and professionals to monitor progress effectively. It supports use in both clinical environments and remote settings, offering accessibility and flexibility to a broad range of users. The platform's architecture primarily consists of newly developed code complemented by established video processing tools like OpenCV, ensuring robust performance and accuracy. Designed to address significant gaps in current physical therapy and fitness markets, this solution transforms subjective observations into actionable, evidence-based insights. Future enhancements include cloud-based scaling options and compatibility with health record systems and wearable devices, extending its applicability and integration within modern healthcare ecosystems. https://suny.technologypublisher.com/files/sites/adobestock_1666031996.jpegAdvantages:  
•    Provides objective and precise movement analysis compared to subjective visual assessments.
•    Real-time feedback facilitates immediate correction and more effective motor learning.
•    Uses accessible hardware—a standard webcam—allowing broad adoption without specialized equipment.
•    Supports both clinical and remote use cases, enhancing flexibility and convenience.
•    Automated features such as repetition counting reduce manual tracking efforts.
•    Comprehensive post-session data assists in monitoring long-term progress and rehabilitation outcomes.
•    Incorporates scientifically grounded motor control theories to optimize movement retraining.
•    Future cloud integration promises scalability and seamless interoperability with health technologies. Applications:  
•    Physical therapy clinics for precise movement assessment and rehabilitation monitoring.
•    Fitness training environments where coaches can provide objective, data-driven feedback.
•    Remote rehabilitation programs enabling patients to perform exercises at home with professional oversight.
•    Sports performance analysis to optimize athletes' movement techniques and reduce injury risks.
•    Integration with wearable devices and electronic health records for comprehensive health management.
•    Research settings studying motor control and learning through consistent and repeatable movement data. Intellectual Property Summary:
Copyright, patents availableStage of Development:
TRL 6Licensing Status:
This technology is available for licensing.

This technology uses drugs that stabilize β-catenin to boost protective immune cells in the lungs, reducing damage from pulmonary hemorrhage and inflammatory lung diseases by activating a novel immunomodulatory pathway. Background:
Pulmonary hemorrhage and other inflammatory lung diseases represent significant clinical challenges due to their high morbidity and mortality rates. These conditions are characterized by excessive inflammation and immune dysregulation within the lung tissue, leading to tissue damage, impaired gas exchange, and life-threatening complications. Current therapeutic strategies primarily focus on symptomatic management, such as corticosteroids and supportive care, but these approaches often fail to address the underlying immune mechanisms driving disease progression. As a result, there is a pressing need for innovative therapies that can modulate the immune response more precisely, reduce inflammation, and promote tissue repair, thereby improving outcomes for patients affected by these severe pulmonary conditions. Despite ongoing research, existing treatments for inflammatory lung diseases and pulmonary hemorrhage remain inadequate. Corticosteroids and broad-spectrum immunosuppressants, while effective at dampening inflammation, carry significant risks of systemic side effects and increased susceptibility to infections. Moreover, these therapies do not selectively target the specific immune pathways implicated in lung injury, leading to suboptimal efficacy and frequent relapses. Attempts to modulate regulatory T cell (Treg) populations have been limited by challenges in achieving tissue specificity and sustained functional enhancement. Consequently, there is a critical gap in the development of targeted immunomodulatory therapies that can provide durable protection against lung inflammation and hemorrhage without compromising overall immune competence.Technology Overview:  
This technology utilizes β-catenin agonists to pharmacologically stabilize β-catenin, offering a novel therapeutic approach for protecting against pulmonary hemorrhage and other inflammatory lung diseases. The treatment works by inducing a specialized phenotype in tissue-resident regulatory T cells (Tregs) within the lung. In preclinical studies using a mouse model of lung injury, administration of β-catenin agonists led to increased lung Treg populations and significantly reduced lung damage, as evidenced by pathology and histological analysis. This method demonstrates the potential for a targeted, immune-based intervention in the management of inflammatory lung conditions, with applications for clinicians, pharmaceutical developers, and researchers in immunology and pulmonary medicine. What differentiates this technology is its ability to pharmacologically mimic the protective effects seen in genetic models of β-catenin stabilization, offering a practical and scalable therapeutic strategy. Unlike conventional anti-inflammatory treatments that broadly suppress immune responses, this approach specifically enhances a beneficial immunoregulatory pathway thereby promoting tissue protection without compromising overall immune function. The elucidation of this pathway provides a unique target for drug development, setting the technology apart from existing therapies by focusing on the modulation of tissue-resident Tregs and their role in lung repair. This targeted mechanism not only addresses a critical unmet need in pulmonary hemorrhage treatment but also opens avenues for broader applications in inflammatory disease management. https://suny.technologypublisher.com/files/sites/adobestock_282277137.jpegAdvantages:  
•    Provides pharmacological protection against pulmonary hemorrhage and inflammatory lung diseases.
•    Induces a specialized tissue-resident regulatory T cell (Treg) phenotype to modulate immune response.
•    Recapitulates protective effects observed in genetic β-catenin stabilization models without genetic modification.
•    Demonstrated efficacy in preclinical mouse models with significant reduction of lung damage.
•    Potentially applicable to broader inflammatory disease management beyond pulmonary conditions.
•    Offers a new therapeutic approach for clinicians and pharmaceutical developers targeting lung inflammation. Applications:  
•    Pulmonary hemorrhage therapeutic development
•    Inflammatory lung disease treatment
•    Immunomodulatory drug discovery
•    Acute lung injury intervention Intellectual Property Summary:
Patent application 63/922,548 filed on 11/21/2025Stage of Development:  
TRL 3. The technology is currently at a preclinical development stage, with proof of concept demonstrated through pharmacologic β-catenin stabilization and validation in relevant in vitro and in vivo lung injury modelsLicensing Status:
This technology is available for licensing.