OTX-202 Smartphone App to Reduce Suicidal Ideation Among High-Risk Transition-Age Youth: Open-Label, Single-Arm, Phase 1 Clinical Trial

<strong>Background:</strong> The transition from adolescence to adulthood (18 to 25 years) is associated with an increased risk of suicidal ideation and behaviors. Suicide-focused cognitive behavioral therapies (CBTs) have been shown to significantly reduce suicidal ideation and behaviors but are not widely available to high-risk individuals. Digital therapeutics could improve access to these treatments. <strong>Objective:</strong> This study aimed to evaluate the acceptability, safety, and potential efficacy of OTX-202 among transition-age youth (18 to 25 years) receiving mental health care outside an inpatient hospital setting. <strong>Methods:</strong> In this phase 1 single-arm clinical trial, 59 transition-age youth with recent suicidal ideation or suicide attempts used OTX-202, a smartphone app designed to deliver suicide-focused CBT, concurrently with usual outpatient mental health care. After baseline, eligible patients completed 12 weekly assessments of suicidal ideation, depression, and anxiety. <strong>Results:</strong> From baseline to week 12, participants reported statistically significant, large reductions in suicidal ideation (mean difference –5.1, 95% CI –6.5 to –3.7; <i>d</i>=0.95). In total, 3 (5.1%; 95% CI 0%-11.2%) participants reported suicide attempts. Reductions in suicidal ideation and suicide attempt rates were consistent with results from previously published randomized clinical trials of suicide-focused CBTs. Participants rated OTX-202 in the 97th percentile of usability and completed a mean of 9.0 (SD 3.5) of 12 app modules, supporting the app’s acceptability. There were no patient deaths, device-related events, or severe adverse events, supporting the app’s safety. <strong>Conclusions:</strong> Results support the safety, acceptability, and potential efficacy of OTX-202 for reducing suicide risk among transition-age youth. <strong>Trial Registration:</strong> ClinicalTrials.gov NCT06008132; https://clinicaltrials.gov/study/NCT06008132

Restoring Protein Recycling Reverses T-Cell Exhaustion in Mice

New research published by scientists at the University of California, San Diego (UCSD), describes an unexpected factor underlying T-cell exhaustion. The details of their work in mice are published in a new Cell paper titled “Proteostasis sustains T-cell differentiation potential and tumor-infiltrating lymphocyte function.”

T cells are critical members of the immune system but there are limits to their defensive capabilities. When fighting cancer cells, T cells often burn out and become dysfunctional. A major focus of current cancer immunotherapy efforts is rescuing T cells from this state and getting them back into cancer-fighting shape. The new Cell study led by scientists in the lab of Ananda Goldrath, PhD, a professor of molecular biology at UCSD, and their collaborators elsewhere, suggests that a potential solution to T-cell exhaustion might have to do with protein recycling.

Specifically, their finding has to do with proteostasis, the network of cellular processes that orchestrates the proper construction, movement, and destruction of proteins in cells. A component of this network features a type of recycling function where healthy cells continuously dismantle old and damaged proteins to preserve energy and reuse building blocks to make new proteins. According to the paper, the scientists uncovered an impaired protein recycling function as the surprise culprit in T-cell exhaustion. 

“We found that exhausted T cells’ recycling programs are falling apart, leading to damaged and misfolded proteins that pile up with nowhere to go,” said Nicole Scharping, PhD, a post-doctoral fellow in the Goldrath lab and lead author on the paper. Additionally, the scientists also uncovered a way to reverse the accumulation of misfolded proteins by fixing the broken recycling function and restoring normal proteostasis. As Scharping explained, the issue can be resolved with a “tag and sort” fix. This is accomplished using E3 ligase enzymes which act as labelers at a recycling facility, tagging worn-out proteins so the cell knows to break them down.

“In exhausted T cells, many of these enzymes get switched off, and recycling grinds to a halt,” said Scharping. After examining thousands of proteins, the scientists honed in on NEURL3, RNF149 and WSB1 as the E3 ligases responsible for rescuing T cell recycling functions. “When we restored specific E3 ligases, the buildup cleared, and the T cells regained their function and worked better at clearing tumors.” While the new study was conducted in mice, the researchers indicate that similar strategies could be employed for immunotherapy treatments in human cancer.

Importantly, the findings may have implications in other diseases as impaired protein processing is not unique to exhausted T cells. “We think this loss of proteostasis resembles what occurs in neurons in other protein aggregate diseases such as Parkinson’s and Alzheimer’s,” said Goldrath. “Rescuing these cells from exhaustion could improve the ability of T cells to respond to both chronic infection as well as tumors.”

The post Restoring Protein Recycling Reverses T-Cell Exhaustion in Mice appeared first on GEN – Genetic Engineering and Biotechnology News.

Data-Driven Tool Identifies Individuals at Highest Risk of Obesity-Related Disease

A new clinical risk model may transform how obesity is managed, by identifying which individuals are most likely to develop serious complications, regardless of their body mass index (BMI).

Developed by researchers at Queen Mary University of London and the Berlin Institute of Health, the tool, called OBSCORE, uses just 20 routinely collected clinical variables to predict the future risk of 18 obesity-related conditions, ranging from type 2 diabetes to cardiovascular disease.

Published in Nature Medicine, the study challenges the long-standing reliance on BMI as the primary metric for assessing obesity-related health risk.

Moving beyond BMI

BMI has long served as a simple proxy for obesity, but it fails to capture the biological heterogeneity of patients. Two individuals with similar BMI can have vastly different risks of developing complications.

The new model addresses this limitation directly. As described in the study, it “provides information beyond BMI” by integrating multiple dimensions of health into a unified risk score.

These include demographic data, clinical biomarkers, disease history, and lifestyle factors, variables already commonly available in healthcare settings.

The findings show that BMI alone is a poor discriminator of risk. The model consistently outperformed BMI-based approaches across all tested outcomes.

Large-scale data enables precision risk prediction

To build the model, researchers analyzed health data from nearly 200,000 individuals with overweight or obesity from the UK Biobank.

Using an interpretable machine learning framework, they screened more than 2,000 potential predictors and distilled them into a core set of 20 features that best predicted long-term health outcomes.

The resulting OBSCORE model estimates the 10-year risk of developing 18 conditions, including cardiovascular disease, kidney disease, sleep apnea, and metabolic disorders.

The model demonstrated strong predictive performance, with median concordance indices around 0.75 across outcomes, indicating robust discrimination between high- and low-risk individuals.

Hidden high-risk individuals

One of the most striking findings is that high-risk individuals are not always those with the highest BMI.

A substantial proportion of individuals classified as high risk fell into the “overweight” category (BMI 27–30 kg/m²), rather than obesity. In some outcomes, up to ~40% of those in the highest risk group had BMI below the obesity threshold.

This reveals a critical gap in current clinical practice: individuals who may benefit from intervention could be overlooked simply because they do not meet BMI-based criteria.

On the other hand, some individuals with obesity may have relatively low risk and may not require intensive intervention.

Strong risk stratification across diseases

Beyond prediction, the scientists believe that OBSCORE enables meaningful risk stratification. Individuals in the highest risk group showed dramatically higher rates of disease compared to those in the lowest group.

For example, the study reports:

  • Up to 89-fold higher risk for chronic kidney disease
  • 42-fold higher risk for type 2 diabetes
  • 47-fold higher risk for cardiovascular mortality

These differences exceed those observed when comparing individuals based solely on BMI categories, underscoring the added value of multidimensional risk assessment.

Clinical and healthcare implications

The implications of these findings are significant, particularly in the context of emerging obesity therapies.

Highly effective drugs such as GLP-1 receptor agonists and dual incretin therapies have transformed treatment options, but their high cost and limited availability make patient prioritization essential.

As the authors note, current systems lack robust frameworks to identify which patients should receive treatment.

OBSCORE offers a potential solution by enabling risk-based allocation of interventions, ensuring that treatment is directed toward those most likely to benefit.

This could improve clinical outcomes while optimizing healthcare resource use.

Toward implementation in clinical practice

One of the key strengths of OBSCORE is its practicality. Unlike many predictive models, it relies on a small number of variables that are already routinely collected, making it suitable for integration into electronic health records.

The researchers envision the model being used as a decision-support tool in clinical settings, complementing rather than replacing existing frameworks.

External validation in independent cohorts—including populations of different ancestry, demonstrated strong generalizability, further supporting its potential for real-world deployment.

Limitations and next steps

Despite its promise, the model requires further validation in broader populations, including younger individuals and more diverse healthcare settings.

Additionally, while OBSCORE effectively stratifies risk, translating these predictions into actionable treatment thresholds will require clinical consensus and cost-effectiveness analyses.

The authors also emphasize that the model identifies predictive, not necessarily causal, factors, and should be interpreted accordingly.

Taken together, the findings mark a shift toward precision medicine in obesity, moving from simplistic metrics like BMI to data-driven, individualized risk assessment.

By capturing the complex interplay of metabolic, clinical, and behavioral factors, OBSCORE could enable earlier intervention, better targeting of therapies, and improved long-term outcomes for patients living with overweight and obesity.

The post Data-Driven Tool Identifies Individuals at Highest Risk of Obesity-Related Disease appeared first on Inside Precision Medicine.

Machine Learning Tool Helps Improve Type 1 Diabetes Prediction

A machine learning model can improve genetic prediction of type 1 diabetes by as much as 10%, show results from a University of California, San Diego study.

The researchers used the machine‑learning model T1GRS to improve on a gold standard polygenic genetic risk score used to predict who is likely to develop the condition called GRS2.

Type 1 diabetes is an autoimmune condition that impacts around 2 million people in the U.S. While it is a multifactorial condition, genetics plays a big role and around 50% of a person’s susceptibility comes from genetics.

“The natural history of type 1 diabetes suggests that the disease occurs in genetically susceptible individuals exposed to environmental triggers, leading to the development of islet-specific autoantibodies and autoreactive T cells and progressive loss of insulin secretory function, although the underlying etiology is not fully understood,” write lead author Kyle Gaulton, PhD, associate professor of pediatrics at UC San Diego School of Medicine, and colleagues in Nature Genetics.

The GRS2 polygenic risk score has been widely tested and can be used to predict newborns who are at high risk of developing type 1 diabetes. While early prediction can’t necessarily stop the disease it can help to prevent emergencies like diabetic ketoacidosis at diagnosis, allow families time to prepare and could allow use of therapies to delay onset of the condition.

In this study, Gaulton and colleagues carried out a genome‑wide association study in 20,355 people with type 1 diabetes and 797,363 non‑diabetic Europeans, as well as a further analysis around the MHC region in 10,107 diabetic and 19,639 nondiabetic individuals.

“The MHC has ‘blocks’ of co-inherited genetic information that are very highly enriched in individuals with type 1 diabetes,” said co-first author Emily Griffin, PhD, a postdoctoral fellow in Gaulton’s lab. “If you have them, it doesn’t mean that you’re going to get diabetes, but if you don’t have them, it means you have a very low chance of getting diabetes.”

Overall 160 risk signals were identified, and the team trained their T1GRS model to predict who was likely to develop type 1 diabetes based on their genetics. The model was able to improve on the GRS2 model predictions by up to 10% in both populations of European and African American ancestry.

Overall the new score correctly flagged about 89 of 100 people with type 1 diabetes while correctly reassuring about 84 of 100 people without the disease.

“Our results highlight the value of combining the results of genetic association studies with machine learning methods to improve the prediction of complex diseases,” conclude the authors.

The post Machine Learning Tool Helps Improve Type 1 Diabetes Prediction appeared first on Inside Precision Medicine.

Patient-Derived Lab-on-a-Chip Improves Precision Modeling of Pancreatic Cancer

Researchers at UTHealth Houston have developed a patient-derived “tumor-on-a-chip” model designed to more precisely study pancreatic ductal adenocarcinoma (PDAC). The study, published in Advanced Science, details how the investigators designed the chip to integrate three-dimensional tumor organoids with components of the tumor microenvironment inside a microfluidic system to recreate interactions between cancer cells, stromal tissue, blood vessels, and immune cells.

“Our goal was to build a model that looks and behaves much more like a real pancreatic tumor than traditional lab models,” said Faraz Bishehsari, MD, PhD, professor and director of the Gastroenterology Research Center at McGovern Medical School at UTHealth Houston. “By recreating the tumor’s environment, we can better understand the disease and test treatments in a patient-specific way.”

Pancreatic cancer is difficult to treat because tumors exist within a dense and complex microenvironment that influences both tumor growth and drug response. Current in vitro methods to study the disease, such as two-dimensional cell cultures, as well as pancreatic cancer organoids, often fail to replicate these dynamics.

Ex vivo models that replicate the tumor and its microenvironment can advance precision medicine in PDAC,” the researchers wrote, but noted that organoids alone “fall short in replicating the tumor microenvironment (TME), which includes various stromal and immune cells influencing tumor growth and chemoresistance.”

To address this, the UTHealth team combined patient-derived organoids with fibroblasts, endothelial cells, and immune cells in a microfluidic chip. The model was created using tumor and blood samples donated by consenting patients, which were used to grow organoids that retained the functional features of the original tumor. The organoids were then incorporated into a chip containing microfluidic channels that mimic blood flow and circulation to create a more dynamic interaction between cells types than current models.

The significance this new lab-on-a-chip lies in its ability to more closely replicate the tumor microenvironment as it would exist in humans more accurately than existing approaches. The design of the chip allows researchers to observe how tumors evolve over time, how stromal and immune components influence cancer behavior, and, perhaps most importantly, how potential drugs and therapies perform under conditions that more closely resemble human disease.

The researchers wrote that their chip “successfully recapitulated the in vivo cancer-stroma interaction of PDAC.” This included the formation of desmoplastic stroma, a dense, scar-like tissue known to limit drug effectiveness. This feature is difficult to reproduce in current PDAC models, but is known to be a major contributor to treatment resistance.

The chip allowed the team to test both chemotherapy and immunotherapies targeting PDAC. They showed that when stromal components were targeted in the model, the effectiveness of standard chemotherapy increased. For immune response, the team studied the effects of pembrolizumab to see how immune cells interacted with the tumor and showed that the drug enhanced T cell infiltration and tumor cell kill. Their observations that lower doses were less effective mirror patterns that have emerged in other clinical studies.

Based on these findings, the researchers noted that chip could serve as a tool for testing new drugs, studying mechanisms of resistance, and evaluating combination therapies tailored to individual patients. Because of its ability to closely recreate the way a tumor would react in vivo, the chip could serve as an important tool to identify the preclinical candidates most likely to effectively treat PDAC.

The implications for developing more precise PDAC therapies are significant. By incorporating organoids and tissues collected directly from individual patients, the chip could allow testing of individualized treatments to account for tumor heterogeneity. Further, it could help find ways to overcome drug resistance driven by stromal interactions and immune suppression.

Next steps for the research include improving the platform’s scalability and reproducibility to support broader use. Future work will also focus on incorporating additional immune components and refining the model to better reflect patient-specific tumor biology.

“This study shows that we can faithfully recreate key features of human pancreatic tumors, including interactions with stromal and immune cells,” Bishehsari said. “The next step is making these systems more practical so they can be widely used in research and drug development.”

The post Patient-Derived Lab-on-a-Chip Improves Precision Modeling of Pancreatic Cancer appeared first on Inside Precision Medicine.

Citraconate Enhances Antitumor Activity and Reduces Exhaustion in T Cells

Researchers have found a novel therapeutic target to enhance the effects of cancer immunotherapy. A study published today in Science Immunology reveals how a metabolite known as citraconate can reduce T cell exhaustion and enhance the ability of these immune cells to live longer, multiply, and effectively fight tumors. 

Despite the widespread success of checkpoint inhibitor immunotherapies, a substantial proportion of patients still do not respond to these treatments. One contributing factor is metabolic dysregulation within the tumor microenvironment (TME), which compromises the antitumor activity of tumor-infiltrating T cells and limits their proliferation, reducing the efficacy of immunotherapy.  

“Emerging evidence highlights the TME as a formidable metabolic barrier to immune cell function, attributable, in part, to the accumulation of immunosuppressive metabolites, which collectively promote T cell exhaustion and resistance to immunotherapy,” writes Lianjun Zhang, PhD, professor at the Suzhou Institute of Systems Medicine and senior author of the study. “Although tumor-derived metabolites are increasingly recognized as key modulators of T cell dysfunction and antitumor immunity, the critical metabolic circuits and specific metabolites that shape and sustain T cell phenotypes remain incompletely characterized.”

Citraconate is known to have antioxidative and antiviral properties, as well as being involved in T cell exhaustion. However, the exact signaling pathways it activates and immunological functions it plays in the context of cancer still remain poorly understood. 

Zhang’s team uncovered a previously unreported role for this metabolite in antitumor immunity, by reducing T cell exhaustion and preserving their ability to replicate. In tumor tissue samples from patients, the researchers found that citraconate was depleted within exhausted T cells. In cultured human cells and mouse models, supplementation with citraconate increased the activation of tumor-infiltrating T cells, promoted their division, and reduced exhaustion, boosting their antitumor activity. 

Further examination revealed that citraconate triggers these effects by increasing intracellular levels of cAMP, which in turn represses the ALOX5 enzyme involved in the oxidation of fatty acids such as arachidonic acid. This signaling cascade reduces the vulnerability of T cells to ferroptosis, a form of cell death driven by the accumulation of oxidized lipids on the cell membrane. 

Genetic and pharmacologic inhibition of ALOX5 enhanced antitumor immunity mediated by T cells, confirming these findings. In mouse models of cancer, supplementation with citraconate was shown to boost the effects of immune checkpoint therapy

Taken together, these findings unveil a critical metabolic checkpoint regulating the performance of tumor-infiltrating T cells, presenting a clinically actionable target to enhance the efficacy of immune checkpoint inhibitors. Going forward, the team plans to dive deeper into the signaling pathways that citraconate employs to modulate T cell activity, its role in metabolic regulation, and the potential contributions of epigenetics to the whole process. 

The post Citraconate Enhances Antitumor Activity and Reduces Exhaustion in T Cells appeared first on Inside Precision Medicine.

Figurate SCADA System Launched to Overcome Digital Bottlenecks During Biopharma Manufacturing

Cytiva and Rockwell Automation launched the figurate supervisory control and data acquisition (SCADA) system designed to remove digital bottlenecks during biopharmaceutical manufacturing. Working across multiple instrument vendors and modalities, Figurate SCADA provides the connectivity needed to enable digital integration to advance modern bioprocessing, according to Matt Weaver, vice president of global industry life sciences at Rockwell.

“Biopharma teams are under pressure to move more quickly, but their systems are often not built to keep up,” says Weaver. “This collaboration with Cytiva marks a pivotal step in our mission to democratize digital manufacturing, enabling biopharma innovators to deploy SCADA faster, smarter and more affordably.”

Many biopharma teams have long juggled proprietary systems that cannot communicate with one another, creating operational silos, manual workarounds, and data integrity risks. The new system directly addresses this roadblock by having an open architecture, allowing for third-party instrument integration, and real-time oversight of integration capable unit operations from a single interface, notes a Cytiva spokesperson, who explains that the platform features include:

  • Native interoperability: The platform is natively integrated with Cytiva bioprocessing equipment and Rockwell Automation’s FactoryTalk software suite, enabling seamless interoperability across systems.
  • Scalable growth: A single platform expands from process development to commercial manufacturing without system redesign.
  • Cost-effective compliance: A streamlined digital manufacturing system reduces capital and operational costs and enables cGMP compliance.
  • Rapid implementation: Pre-engineered templates and modular design shorten deployment and validation timelines.
  • Enhanced operational insight: Centralized alarms, real-time monitoring, process intensification and batch reporting tailored to bioprocess workflows.

“This collaboration is designed to empower the next generation of biomanufacturers,” says Nicolas Pivet, manufacturing and digital solutions at Cytiva.

Industry data shows increasing demand for next generation process control systems as organizations transition toward data driven process intensification and continuous manufacturing. Equipment fragmentation remains one of the top pain points cited by biomanufacturers, particularly those advancing programs from R&D to clinical scale. By giving teams a unified digital control layer, the Figurate SCADA reduces the risk of human error, accelerates tech transfer, and supports reliable scaleup as workloads grow in complexity, points out the Cytiva spokesperson.

 

The post Figurate SCADA System Launched to Overcome Digital Bottlenecks During Biopharma Manufacturing appeared first on GEN – Genetic Engineering and Biotechnology News.

Cyber-Insecurity in the AI Era


Cybersecurity was already under strain before AI entered the stack. Now, as AI expands the attack surface and adds new complexity, the limits of legacy approaches are becoming harder to ignore. This session from MIT Technology Review’s EmTech AI conference explores why security must be rethought with AI at its core, not layered on after the fact.


About the speaker

Tarique Mustafa, GC Cybersecurity

Tarique Mustafa, Cofounder, CEO, and CTO, GC Cybersecurity

Tarique Mustafa is Cofounder and CEO/CTO of two AI-powered cybersecurity companies: GCCybersecurity, Inc. and its data compliance spinout, Chorology, Inc. A prolific inventor and internationally recognized authority in knowledge representation, inference calculus, and AI planning, Tarique has spent his career applying autonomously collaborative AI to solve complex, ultra-high-scale challenges across cybersecurity, data security, and compliance — with deep expertise spanning Data Classification, DLP, and DSPM industries. His groundbreaking innovations and multiple USPTO patents have earned him global recognition, including frequent invitations to deliver keynote addresses at prestigious international security conferences and forums.

At GCCybersecurity, Tarique architected the core AI algorithms powering the company’s 4th and 5th generation fully autonomous data leak protection and exfiltration platform — among the most advanced platform of its kind. Prior to founding GCCybersecurity and Chorology, he served as founding CEO/CTO of NexTier Networks, a Silicon Valley provider of award-winning Data Leak Prevention solutions. With over 20 years of technical leadership experience, Tarique has held senior roles at Symantec, DHL Airways IT, MCI WorldCom, EDS, Andes Networks, and Nevis Networks, where he served as Principal Architect and built industry-leading security products leveraging next-generation security monitoring, event correlation, IDS/IPS, and SSL/IPSec technologies.

Tarique holds multiple approved and pending patents with the USPTO and has authored numerous research publications spanning Information & Data Security, Computer & Network Security, Software Architecture, Database Technologies, and Artificial Intelligence. A recipient of the prestigious Rotary International Scholarship for doctoral studies in Computer Science at the University of Southern California (USC), Tarique also holds master’s degrees in engineering and computer science from USC, and a bachelor’s degree in mechanical engineering from NED University of Engineering & Technology.

Operationalizing AI for Scale and Sovereignty


Companies are taking control of their own data to tailor AI for their needs. The challenge lies in balancing ownership with the safe, trusted flow of high‑quality data needed to power reliable insights. This conversation from MIT Technology Review’s EmTech AI conference examines how AI factories unlock new levels of scale, sustainability, and governance—positioning data control as a strategic imperative for governments and enterprises.


About the speakers

Chris Davidson, HPE

Chris Davidson, Vice President, HPC & AI Customer Solutions, HPE

Chris Davidson is Vice President of HPC & AI Customer Solutions at Hewlett Packard Enterprise. He leads HPE’s global strategy for AI Factory solutions and Sovereign AI, working with governments, enterprises, and research institutions to build secure, scalable national- and enterprise-grade AI capabilities.

He also directs Product Management and Performance Engineering across HPE’s HPC and AI portfolio, including large-model training platforms and Cray exascale systems. His teams define product strategy, performance architecture, and deployment models that position HPE at the forefront of high-performance and AI computing.

During his nine years at HPE, Chris has led key initiatives across Performance Engineering, AI Cloud, and Professional Services, shaping how HPE delivers optimized, cloud-native, and globally deployed high-performance systems. He previously held technical and leadership roles in the biotech and medical diagnostics sectors.

Chris holds an M.B.A. in Entrepreneurship and Finance and a B.S. in Biology from Loyola University Chicago.

Arjun Shankar, Oak Ridge National Laboratory

Arjun Shankar, Division Director, National Center for Computational Science, Oak Ridge National Laboratory

Mallikarjun (Arjun) Shankar is the Division Director for the National Center for Computational Science at the Oak Ridge National Laboratory. His research focuses on the interdisciplinary bridge between computer science and large-scale scientific discovery campaigns that rely on scalable computing and data science. He is a joint faculty appointee at the University of Tennessee’s Bredesen Center, a senior member of the IEEE and a senior member of the ACM.

<![CDATA[The SPAN survey underscores gaps in schizophrenia care access as new treatments emerge, citing coverage and continuity challenges.]]>