Background: Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental health care can be effectively designed. Objective: This study aimed to investigate, from an end user’s (patient’s) perspective, the potential use scenarios, desired features, and perceived risks of AI psychotherapists in depression treatment, providing design guidelines for their development. Methods: A grounded theory approach was applied to analyze qualitative responses from 452 individuals recruited via Amazon Mechanical Turk. Data were collected through a scenario-based online survey on AI-assisted depression treatment administered between March 2023 and May 2023. Participants responded to 3 open-ended questions regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks, along with demographic, control, and contextual measures. The open-ended responses were inductively coded into themes, with intercoder reliability established (Cohen κ=0.80). In addition, variations in themes were further examined across participant profiles, including social stigma, current depression severity, trust in an AI psychotherapist, and privacy awareness. Results: Participants envisioned AI psychotherapists across 5 primary scenarios: diagnosis, treatment, consultation, self-management, and companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment options, patience, trustworthiness, and basic treatment alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotional engagement. Based on these findings, a general MoSCoW (must have, should have, could have, and won’t have) prioritization framework was proposed to serve as a conceptual starting point for future AI system design and empirical validation in mental health care. Notably, feature prioritization varied across user profiles: individuals with higher stigma placed greater emphasis on privacy protection, those with more severe depression prioritized precision care and timely access, low-trust users de-emphasized remote services, and privacy-sensitive individuals showed reduced preference for features requiring extensive data disclosure. These patterns highlight the need for context-sensitive design. Conclusions: This study provides a patient-centered framework for designing AI psychotherapists and complements the existing literature by highlighting the importance of balancing clinical effectiveness with relational considerations. The findings offer actionable guidelines for designing AI mental health care tools that are aligned with user expectations and sensitive to individual differences.
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An Ecological Momentary Assessment Smartphone App for High-Risk HIV Populations: Development and Usability Study
Background: HIV incidence has continued to increase among men who have sex with men (MSM) in Peru, despite intervention efforts. Addressing stigma, risky behaviors, and low medication adherence is key to reducing incidence rates. Ecological momentary assessment (EMA) allows for collection of discrete, real-time data on stigmatized, risky behaviors while reducing recall bias. Objective: The aim of this study was to develop and assess the usability of an EMA smartphone app among MSM with HIV in Peru, which tracks daily health risk behaviors to determine ease of use, usefulness, and satisfaction with the app. Methods: A mixed-method 3-phase study was conducted with 10 MSM with HIV, which included a usability test, 10-day field testing, and a debriefing focus group. Quantitative survey data and user analytics allowed for assessments of acceptability and user compliance. Qualitative interview and focus group data were thematically analyzed for in-depth assessments of user satisfaction. Results: Acceptability of the EMA app was high, with a mean usability rating of 6.4 of 7.0 (SD 0.62), indicating high user satisfaction, ease of use, and usefulness. A 10-day field test demonstrated a high average compliance rate of 93% (93/100), which suggests high feasibility of the app for daily tracking of health risk behaviors among MSM with HIV. Interview and focus group findings indicated that the app was navigable, time-efficient, and holds promise for long-term use, particularly with the inclusion of daily reminders and incentives for prolonged use. Conclusions: EMA apps can provide valuable real-time data while protecting users’ privacy. This formative work lays the foundation for future larger-scale EMAs of substance use and sexual risk behaviors among high-risk HIV populations, and for the development of just-in-time interventions to address stigma, improve medication adherence, and reduce risky behaviors.
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A Bilingual AI-Based Chatbot for Nutrition Education in a Food Is Medicine Intervention for High-Risk Pregnant Women: Design and Development Study
Background: Conversational agents (artificial intelligence [AI]–based chatbots) offer a novel approach to health interventions by providing personalized, adaptive interactions that improve over time based on user engagement. In nutrition education, given the wide variation in knowledge, skills, and abilities across participants, AI-based chatbots have the potential to enhance accessibility, engagement, and behavior change. Food is Medicine (FIM) interventions, which aim to improve food security and diet quality among multicultural, at-risk populations, often face challenges related to sustained engagement and use. Objective: This paper describes the design, development, and iterative refinement of a bilingual AI-driven nutrition chatbot integrated into an FIM intervention for high-risk pregnant women receiving care at obstetric clinics in Houston, Texas. Methods: The chatbot was developed using an iterative process informed by behavioral theory, human-centered design (HCD), and plan-do-study-act (PDSA) quality improvement cycles. The conversational agent was embedded within an ongoing 2-arm randomized controlled trial (N=200) comparing standard FIM nutrition education to FIM plus AI-driven nutrition chatbot support. HCD activities took place prior to deployment and involved community advisory group members and implementation stakeholders. Postdeployment refinements were guided by 2 PDSA cycles and informal question-and-answer sessions conducted with intervention arm participants. Qualitative feedback was collected using structured scripts to identify facilitators of and barriers to chatbot engagement. Results: The chatbot was developed using the GPT-3.5 Turbo application programming interface. An initial prototype built in Python using Gradio enabled rapid testing but lacked flexibility for modifications. To improve scalability and logging capabilities, the system was rebuilt using PHP, HTML, JavaScript, and SQL. To further understand usage patterns, participants who interacted with the chatbot at least once or not at all (classified as low users; n=32) were engaged in question-and-answer sessions. Of these participants, all were female (32/32, 100%), 88% (28/32) identified as Hispanic or Latino, and 90% (29/32) preferred Spanish. Two PDSA cycles guided iterative refinements. Cycle 1 identified low initial engagement, whereas cycle 2 focused on improving content clarity and cultural relevance through physical reminder prompts. Qualitative findings identified key barriers to engagement, including high cooking self-efficacy with perceived lack of need for support, low technology self-efficacy, and low urgency due to competing priorities. Conclusions: Embedding a bilingual AI-driven nutrition chatbot within an FIM intervention was feasible and featured critical design and implementation considerations for engaging high-risk pregnant populations. Findings show the importance of HCD and iterative refinement to address engagement barriers. This work provides actionable guidance for integrating conversational agents into FIM programs, with implications for future evaluation of clinical outcomes, long-term engagement, and scalability. Trial Registration: ClinicalTrials.gov NCT07165990; https://clinicaltrials.gov/study/NCT07165990
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DNA-Containing Extracellular Vesicles Boost Antitumor Responses in Mice
A study led by investigators at Weill Cornell Medicine has found that activated T cells secrete extracellular vesicles (EVs) containing DNA, which can enter other immune and tumor cells to stimulate the body’s defense systems. Preclinical experiments showed that this vesicle-associated DNA could be useful therapeutically, boosting T cell attacks against tumors that otherwise evoke little or no immune response.
Studies in live mice showed that these activated T cell-derived-EVs (AT-EVs) enhanced antigen processing and presentation (APP) in tumor cells and dendritic cells (DCs) across different immunologically cold tumors. The ATEVs also synergized with immune checkpoint inhibitors (ICIs) to trigger antitumor immunity and hold back tumor growth.
The discovery extends the scientific understanding of the immune system, identifies a new strategy for boosting immunity against cancers, and potentially offers a new tool for delivering genetic payloads to other cells. “These findings reveal a natural mechanism for treating immunologically silent tumors and other diseases that stem from insufficient immune surveillance,” said David Lyden, MD, PhD, the Stavros S. Niarchos professor in pediatric cardiology and a member of the Gale and Ira Drukier Institute for Children’s Health and the Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine.
Lyden is co-senior author of the researchers’ published paper in Cancer Cell, titled “Activated T cell extracellular vesicle DNA transfer enhances antigen presentation and anti-tumor immunity,” in which they stated, “We uncover a mechanism whereby activated T cell-derived extracellular vesicles (ATEVs) drive a positive feedback loop that enhances antigen presentation and immune responses in normal physiology and cancer … Notably, ATEVs hold promise as an acellular immunotherapy, restoring APP and synergizing with checkpoint blockade in immunotherapy-refractory tumors.”
Most animal cells secrete extracellular vesicles which can contain cargo including proteins, snippets of DNA, and other molecules. “Extracellular vesicles (EVs) are nanoparticles naturally released by all living cells, containing proteins, lipids, and genetic material, that facilitate intercellular communication,” the investigators wrote.
The Lyden lab in recent years has made seminal discoveries about extracellular vesicles and their functions, finding for example that vesicles secreted by tumor cells can influence the immune system’s anti-tumor response. Their findings, they noted, “… raised the possibility that EVDNA from immune cells, such as T cells, may also have immune-related functions.” For their new study the team examined the roles of vesicles secreted by immune cells, and specifically T cells, which are the immune system’s principal tumor-fighters.
In their initial experiments, the scientists found that under physiological conditions, T cell-secreted vesicles tend to home to lymph nodes, spleen and other centers of immune activity. There the vesicles are preferentially taken up by antigen-presenting immune cells, including dendritic cells, which assist in T cell activation, a critical process in the immune response. The researchers found that the overall effect of these vesicles released by activated T cells is to boost the antigen-presenting process, thus promoting T cell priming and broader immune activation. The key payloads in these immune-boosting vesicles turned out to be snippets of T cell DNA.
“These surprisingly abundant DNA fragments are mostly on the surfaces of the vesicles, and are not just random—they are enriched for immune-related genes, including genes that help cells display antigens to the immune system,” said co-senior author Haiying Zhang, PhD, an assistant professor of cell and developmental biology in pediatrics and member of the Lyden lab. “We also found that these vesicles have, attached to their surfaces, a special enzyme that acts as a molecular drill, enabling the transfer of vesicle-carried DNA into the nucleus of the recipient cell where they can be expressed transiently,” added study co-first author Diao Liu, PhD, a postdoctoral research associate in the Lyden Lab.
Infusing DNA-carrying vesicles from activated T cells into mice with tumors, the researchers found that the vesicles were taken up not only by antigen-presenting cells but also by tumor cells themselves. The treated tumors grew more slowly and were better infiltrated by T cells and other immune cells, indicating that the vesicles induced a stronger anti-tumor response. “Our work reveals an EV-mediated mechanism through which activated T cells enhance APP across diverse recipient cells—from DCs in physiological conditions to cancer cells across tumor types,” the authors noted. Although cancers—and viruses—frequently suppress the antigen-presenting process to make malignant or infected cells “invisible” to the immune system, the main effect of the extracellular vesicular DNA was to reverse this process, restoring tumor cells’ visibility.
The team demonstrated the effectiveness of this approach, alone and in combination with existing immunotherapy, in preclinical models of three different immunologically silent cancers: glioblastoma, pancreatic and triple-negative breast cancer. “By boosting APP machinery, ATEVs enhance tumor immunogenicity and elicit robust anti-tumor responses, particularly when combined with ICIs in otherwise resistant tumors, including pancreatic, breast, and brain cancers,” they stated. “These findings reveal the translational potential of activated T cell-derived extracellular vesicles (ATEVs) by exploiting a naturally occurring immune-boosting process to overcome immune evasion, particularly in immunologically silent cancers.”
Co-senior author Irina Matei, PhD, an assistant professor of immunology research in pediatrics and member of the Lyden lab, stated, “There seems to be a positive-feedback loop, in which the DNA-carrying vesicles from activated T cells amplify the immune response by acting on both antigen-presenting cells, which increase expression of the machinery processing tumor antigens, and tumor cells, promoting their recognition by the immune system as well as their own production of DNA-laden vesicles.”
The researchers are now working to translate their findings into a new, vesicle-based cancer treatment, which could be used on its own or in conjunction with standard immunotherapies or other cancer treatments. “The surprising ability of these vesicles to transfer DNA from donor T cells into the nuclei of recipient cells suggests their potential as a natural, non-viral platform for transient gene delivery,” said co-first author Mengying Hu, PhD, a postdoctoral research associate in the Lyden Lab who led the research and is now an assistant professor of pharmaceutical sciences at the Ohio State University. “The results point to a broadly applicable gene-transfer strategy that may offer improved safety and efficiency compared with current gene therapy approaches.”
In their paper the authors concluded, “Overall, ATEVs emerge as an acellular immunotherapy and delivery modality that can prime antitumor immunity, synergize with existing therapies, and serve as a vaccine adjuvant,” they concluded. “Our findings provide a foundation for the therapeutic application of ATEVs through a deeper understanding of the biological role of AT-EVDNA.”
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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.”
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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.
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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.”
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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.
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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, 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.