The purpose of this study is to examine whether experiencing a social stressor prior to the collection of neuroimaging measures is associated with differences in brain activation in healthy participants.

Start Date01 Sep 2027

Sponsor / Collaborator

Massachusetts Institute of Technology

[+1]

NCT06853665

/ Not yet recruitingPhase 4IIT

A Pharmaco-Imaging Trial of Mixed Amphetamine Salts for the Treatment of ADHD in Intellectually-intact Youth With Autism Spectrum Disorder

The investigators are conducting a 10-week brain imaging and medication study. They are doing the research to study the response of Attention-Deficit/ Hyperactivity Disorder (ADHD) in youth with Autism Spectrum Disorder (ASD) on extended-release formulation of mixed amphetamine salts (MAS) (also know as Adderall XR). The investigators also want to find out if taking MAS has any effect on the brains of children and adolescents with ADHD and ASD. This study will help researchers better understand how the use of MAS to treat ADHD effects children and adolescents with ASD. The investigators will compare MAS to a placebo. The placebo will look exactly like the MAS capsules but will contain no MAS. During this study, participants may get a placebo instead of MAS. Placebos are used in research studies to see if the study results are due to the study drug or due to other reasons.
Participants with ASD and ADHD will complete 4-weeks of treatment with the study medication or placebo. They will complete bi-weekly study visits virtually via a telemedicine platform with the study doctor and complete questionnaires. On alternating weeks, they will meet with a Massachusetts General Hospital (MGH) study team member to discuss medication adherence and potential side effects. Participants will have the option to attend all study visits in-person if participants prefer. They will also complete baseline and endpoint Magnetic Resonance (MR) scan visits at Massachusetts Institute of Technology (MIT). During the MR Scan visits, they will complete a series of tasks to measure inattention, impulsivity, reward sensitivity, decision-making, and working memory.
Participants without ADHD or ASD will complete eligibility screening with MGH. If eligible, they will be invited to baseline and endpoint MR scan visits at MIT. During the MR Scan visits, they will complete a series of tasks to measure inattention, impulsivity, reward sensitivity, decision-making, and working memory.

Start Date01 Aug 2025

Sponsor / Collaborator

The Massachusetts General Hospital

[+1]

NCT05649059

/ RecruitingPhase 4IIT

Investigating the Effects of Cannabidiol on Social Anxiety Disorder

The purpose of this study is to test whether a single-dose of Epidiolex (cannabidiol) is associated with reduced psychological, physiological, and neuroimaging measures of anxiety in people diagnosed with social anxiety disorder (SAD).

Start Date01 Jun 2025

Sponsor / Collaborator

Massachusetts Institute of Technology

[+1]

100 Clinical Results associated with Massachusetts Institute of Technology

0 Patents (Medical) associated with Massachusetts Institute of Technology

139,484

Literatures (Medical) associated with Massachusetts Institute of Technology

01 Dec 2025·Asia-Pacific Journal of Oncology Nursing

Artificial intelligence-assisted machine learning models for predicting lung cancer survival

Article

Author: Zhang, Guolong ; Xie, Hongxia ; Hao, Sicheng ; Gu, Yuqi ; Yuan, Yue ; Mi, Wei ; Zeng, Yingchun ; Huang, Chen

Objective:

This study aimed to evaluate the feasibility of large language model-Advanced Data Analysis (ADA) in developing and implementing machine learning models to predict survival outcomes for lung cancer patients, with a focus on its implications for nursing practice.

Methods:

A retrospective study design was employed using a dataset of lung cancer patients. Data included sociodemographic, clinical, treatment-specific, and comorbidity variables. Large language model-ADA was used to build and evaluate three machine learning models. Model performance was validated, and results were presented using calibration plots.

Results:

Of 737 patients, the survival rate of this cohort was 73.3%, with a mean age of 59.32 years. Calibration plots indicated robust model reliability across all models. The Random Forest model demonstrated the highest predictive accuracy among the models. Most critical features identified were preoperative white blood cells (2.2%), preoperative lung function of Forced Expiratory Volume in one second (2.1%), preoperative arterial oxygen saturation (1.9%), preoperative partial pressure of oxygen (1.7%), preoperative albumin (1.6%), preoperative preparation time (1.5%), age at admission (1.5%), preoperative partial pressure of carbon dioxide (1.5%), preoperative hospital stay days (1.5%), and postoperative total days of thoracic tube drainage (1.4%).

Conclusions:

Large language model-ADA effectively facilitates the development of machine learning models for lung cancer survival prediction, enabling non-technical health care professionals to harness the power of advanced analytics. The findings underscore the importance of preoperative factors in predicting outcomes, while also highlighting the need for external validation across diverse settings.

01 Dec 2025·Energy and climate change

Policy implications of net-zero emissions: A multi-model analysis of United States emissions and energy system impacts

Article

Author: Muratori, Matteo ; Iyer, Gokul ; Sarmiento, Luis ; Rossmann, Charles ; Orvis, Robbie ; Fawcett, Allen A ; Edmonds, Jae ; Yuan, Mei ; Fuhrman, Jay ; Hoehne, Christopher ; Binsted, Matthew ; Vaillancourt, Kathleen ; Prabhu, Amogh ; McJeon, Haewon ; Bistline, John E T ; Blanford, Geoffrey ; Johnson, Jeremiah X ; Harris, Chioke ; Wood, Frances ; Mahajan, Megan ; Boyd, Gale ; Melaina, Marc ; Mousavi, Seyed Shahabeddin ; Browning, Morgan ; McFarland, James R ; Mai, Trieu ; Weyant, John ; Sinha, Aditya ; Showalter, Sharon ; Starke, Emma ; Gao, Ruying ; Kaplan, P Ozge ; Stewart, Eric ; Cai, Yongxia ; Sands, Ronald D ; Loughlin, Dan

Many countries, subnational jurisdictions, and companies are setting net-zero emissions goals; however, questions remain about strategies to reach these targets, policy measures, technology gaps, and economic impacts. We investigate the potential policy implications of reaching economy-wide net-zero CO₂ emissions across the United States by 2050 using results from a multi-model comparison with 14 energy-economic models. Model results suggest that achieving net-zero CO₂ targets depends on policies that accelerate deployment of zero- and low-emitting technologies that have seen rapid cost reductions in recent years (including wind, solar, battery storage, and electric vehicles) as well as relatively nascent options (including carbon capture and storage, advanced biofuels, low-carbon hydrogen, advanced nuclear, and long-duration energy storage). While net-zero policies are likely to lower fossil fuel consumption, including considerable coal and petroleum reductions, achieving net-zero emissions does not necessarily mean phasing out all fossil fuels. Model results indicate that the Inflation Reduction Act's energy and climate provisions amplify near-term decarbonization but that net-zero policies have larger impacts on long-run outcomes. Stringent climate policy can have large fiscal impacts on tax revenue and government spending-revenues from carbon pricing and subsidies for carbon removal range from 0.1% to 3.7% of GDP in 2050 across models. Each dollar per metric ton carbon price leads to a 0.06% to 0.31% reduction in economy-wide CO₂ emissions relative to a reference scenario with current policies. Spending on energy across the economy decreases relative to today for many models under reference and net-zero policies, especially as a share of GDP, due primarily to end-use electrification and energy efficiency.

01 Dec 2025·JOURNAL OF EXPERIMENTAL CHILD PSYCHOLOGY

Children’s and adults’ understanding of how punishment shapes social relationships

Article

Author: Lee, Young-Eun ; Solomon, Larisa Heiphetz

Punishment regulates selfish behaviors and maintains cooperation. However, because punishment imposes costs on another person, it could also harm relationships. The current work asked how punishment shapes 5- to 10-year-olds' (Study 1; n = 128) and adults' (Study 2; n = 159) attitudes toward punishers and those who receive punishment as well as their inferences about relationships between punishers and targets. We reasoned that the motives underlying punishment might shape evaluations; punishments motivated by prosocial desires may elicit more positive responses than punishments motivated by antisocial desires. We tested both motives that were external to the punisher (the behavior that elicited the punishment) as well as internal motives (the desire to harm versus rehabilitate transgressors). The main result is that we found negative social relationships among punishers, targets, and observers. Both children and adults preferred punishers who inflicted punishment for behaviors that violated (versus did not violate) norms, preferred targets of punishment who had not (versus had) violated norms, and expected punishers and targets to dislike each other. External motives, but not internal motives, consistently influenced participants' own social preferences. In contrast, neither external nor internal motives consistently shaped participants' inferences about social relationships between punishers and their targets. Our work contributes to social cognitive development by clarifying how motives shape children's and adults' understanding of social relationships.

1,730

News (Medical) associated with Massachusetts Institute of Technology

02 Jul 2025

·www.prnewswire.com

The Jane Coffin Childs Fund for Medical Research Launches Partnership with Hope Funds for Cancer Research

NEW HAVEN, Conn., July 2, 2025 /PRNewswire/ -- The Jane Coffin Childs Memorial Fund for Medical Research (Jane Coffin Childs Fund) announces the launch of a collaboration with the Hope Funds for Cancer Research to provide funding support for outstanding postdoctoral Fellows in biomedical research. This unique initiative will expand the number of Fellows awarded by each organization, at a time when federal funding cuts are diminishing resources for the scientific workforce. After a rigorous joint review process, Dr. David Klawon, in the Laboratory of Tyler Jacks, Ph.D. at the Koch Institute for Integrative Cancer Research of Massachusetts Institute of Technology, was selected as the inaugural recipient of the Hope Funds for Cancer Research-Jane Coffin Childs Fund Fellowship. This three-year investment will serve to advance cancer research while further supporting the workforce pipeline. Dr. Klawon is fascinated with the role that our immune system plays in resolving or mediating different diseases. His current research focuses on immune responses in lung cancer and seeks to understand the mechanisms used to furnish tumors with cancer-fighting or tumor-promoting immune cells. "Hope Funds is enthusiastic to partner with an organization of the Jane Coffin Childs Fund's stature and to support the work of Dr. David Klawon, an exceptional candidate whose work reflects our commitment to promoting scientific innovation," said Professor Bryan R.G. Williams, Chair, Hope Funds Board of Trustees. Hope Funds for Cancer Research was established in 2006 to encourage investigation of innovative treatment and detection for the most difficult-to-treat cancers. The organization supports research for highly innovative projects that challenge traditional paradigms associated with understanding the causes, mechanisms, progression, disease markers, or risk factors of the most understudied and difficult-to-treat cancers. Hope Funds emphasizes a creative approach to research and awards grants to young scientists based on the following criteria: project innovation and originality; the significance and direct relevance of the research proposal; the project's approach and conceptual framework; the researcher's qualifications and those of his or her mentors; and the quality of the researcher's overall working environment. Since 2008, Hope Funds has funded research projects by 61 fellows deemed to have the highest probability of success, as determined by rigorous scientific and economic analysis. "When visionary organizations come together, the impact is exponential," said Dr. Anita Pepper, Executive Director of the Jane Coffin Childs Fund. "This partnership exemplifies the power of collaborative philanthropy—amplifying bold ideas, accelerating scientific discovery, and empowering the next generation of leaders to transform cancer research." The Jane Coffin Childs Memorial Fund for Medical Research was established by the Childs Family in 1937 to honor the memory of Jane Coffin Childs. Inspired by the founding purpose to support research into the causes and treatment of cancer, the Fund's mission has broadened to support fundamental scientific research that advances our understanding of the causes, treatments, and cures for human disease. This year, the Board of Scientific Advisors selected twenty-nine new fellows from the largest applicant pool in the fund's history based on their doctoral research accomplishments, innovative research plans, and their chosen scientific mentors. More information about the funds and the fellows they support can be found on their websites: and . SOURCE The Jane Coffin Childs Fund For Medical Research WANT YOUR COMPANY'S NEWS FEATURED ON PRNEWSWIRE.COM? 440k+ Newsrooms & Influencers 9k+ Digital Media Outlets 270k+ Journalists Opted In GET STARTED

30 Jun 2025

·www.fiercebiotech.com

Pfizer expands AI-powered small-molecule discovery collab with XtalPi

Pfizer and XtalPi’s previous work has focused on crystal structure prediction and screening—as well as an artificial-intelligence-powered platform to model small-molecule drugs.\n Pfizer has added another chapter to its long-running drug discovery collaboration with Chinese tech startup XtalPi, this time upgrading their molecular modeling work.The aim is to help “grasp Pfizer\'s rapidly expanding small molecule chemical space, with the goal of delivering accurate predictive tools with significantly improved throughput,” XtalPi explained in a June 30 release.XtalPi was founded in 2014 by a group of quantum physicists from the Massachusetts Institute of Technology, and the company has since expanded to encompass locations in Cambridge, Massachusetts, and Beijing and Shenzhen in China.Pfizer and XtalPi’s previous work together has included collaborating in crystal structure prediction and screening—using computer models to determine the potential molecular stability of an organic compound—followed by a 2018 agreement to develop an artificial-intelligence-powered platform to model small-molecule drugs.The two companies are now fine-tuning this small-molecule work by “developing more accurate predictive models to Pfizer\'s proprietary chemical space, with the aim of further empowering small molecule drug discovery and development across a broader range of research applications,” XtalPi explained.This involves XtalPi deploying its XFEP AI-driven platform for Pfizer’s drug discovery efforts. XtalPi is touting the platform as being able to support Pfizer’s scientists “across diverse drug design and development scenarios.”“Pfizer\'s leadership in pharmaceutical innovation and their deep scientific expertise have been invaluable in shaping the evolution of our AI-driven platform,” XtalPi CEO Jian Ma, Ph.D., said in the release. “This collaboration demonstrates the transformative potential of XtalPi\'s computational platform combining physics-based insights with advanced AI technology, and propels us to develop even more powerful predictive tools,” the CEO added. “Together, we are building up a new platform for drug discovery, potentially unlocking possibilities that were once out of reach and accelerating the delivery of life-changing therapies for patients worldwide.”The release didn’t divulge the financial details of the latest Pfizer add-on pact.XtalPi has accrued a number of Big Pharma partners over the years, including Eli Lilly, which struck a $250 million deal in 2023 to use XtalPi’s tech to identify potential drug candidates that Lilly will then usher through the clinical and commercial development processes.Johnson & Johnson’s Janssen Pharmaceutical arm also turned to XtalPi’s automated tech to identify potential therapeutic candidates for a target that will be decided by Janssen.

30 Jun 2025

·www.prnewswire.com

XtalPi and Pfizer Expand Strategic Collaboration to Advance AI-Driven Drug Discovery and Materials Science Simulations

CAMBRIDGE, Mass., June 29, 2025 /PRNewswire/ -- XtalPi (2228.HK), a leading global technology company in integrating artificial intelligence (AI) and robotics to advance the discovery of groundbreaking medicine and innovative materials, announced an expansion of its research collaboration with Pfizer to pioneer a next-generation molecular modeling platform for drug discovery. This initiative is focused on improving the accuracy of advanced physics-based methods with the speed, scalability, and expansive coverage of cutting-edge AI models to optimize the discovery and development of small molecule medicines. Building on the success of their existing strategic collaboration, Pfizer and XtalPi will now collaborate on the development of an enhanced platform to grasp Pfizer's rapidly expanding small molecule chemical space, with the goal of delivering accurate predictive tools with significantly improved throughput. In a joint research paper published in 2024, XtalPi and Pfizer unveiled the development and validation of the first-generation of XtalPi Force Field (XFF), which demonstrated superior performance in predicting small molecules geometry at molecular quantum mechanics (QM) level, as well as accurate Free Energy Perturbation (FEP) calculations for binding affinity predictions. These capabilities are critical for computing and predicting small molecules' key properties such as efficacy and selectivity, enabling more precise drug screening and rational design. Under this expansion of their collaboration, the two teams will focus on developing more accurate predictive models to Pfizer's proprietary chemical space, with the aim of further empowering small molecule drug discovery and development across a broader range of research applications. In the collaboration, XtalPi will deploy its XFEP platform, from parameter customization to FEP calculations, for Pfizer to use in its drug discovery efforts. With the goals of improved accuracy, high-throughput speed, and user-friendly interface, the platform will be designed to support Pfizer scientists across diverse drug design and development scenarios. "Pfizer's leadership in pharmaceutical innovation and their deep scientific expertise have been invaluable in shaping the evolution of our AI-driven platform," said Dr. Jian Ma, CEO of XtalPi. "This collaboration demonstrates the transformative potential of XtalPi's computational platform combining physics-based insights with advanced AI technology, and propels us to develop even more powerful predictive tools. Together, we are building up a new platform for drug discovery, potentially unlocking possibilities that were once out of reach and accelerating the delivery of life-changing therapies for patients worldwide." About XtalPi XtalPi Holdings Limited (XtalPi, 2228.HK) was founded in 2015 by three physicists from the Massachusetts Institute of Technology (MIT). It is an innovative R&D platform powered by quantum physics, artificial intelligence, and robotics. By integrating first-principles calculations, AI algorithms, high-performance cloud computing, and standardized automation systems, XtalPi provides digital and intelligent R&D solutions for companies in the pharmaceutical, materials science, agricultural technology, energy, new chemicals, and cosmetics industries. SOURCE XtalPi Inc. WANT YOUR COMPANY'S NEWS FEATURED ON PRNEWSWIRE.COM? 440k+ Newsrooms & Influencers 9k+ Digital Media Outlets 270k+ Journalists Opted In GET STARTED

100 Deals associated with Massachusetts Institute of Technology

100 Translational Medicine associated with Massachusetts Institute of Technology

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Pipeline

Pipeline Snapshot as of 14 Jul 2025

The statistics for drugs in the Pipeline is the current organization and its subsidiaries are counted as organizations,Early Phase 1 is incorporated into Phase 1, Phase 1/2 is incorporated into phase 2, and phase 2/3 is incorporated into phase 3

Discovery

Preclinical

Phase 1

Other

Current Projects

Drug(Targets)	Indications	Global Highest Phase

P53-SADA-BsAb ( GD2 )	Sarcoma More	Phase 1
PRECISE-SHANK3/HTT(MIT) ( HTT x SHANK3 )	Huntington Disease More	Preclinical
FuOXP/siXkr8 NPs ( XKR8 )	Colonic Cancer More	Preclinical
BTP-7 ( BCAN )	Glioma More	Preclinical
Cytokine fusion proteins(MIT)	Neoplasms More	Preclinical

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