Research News Roundup: September 3, 2026

Journal: NEJM Evidence, 2026, doi: 10.1056/EVIDpha2600137

Authors: Varvara Schoute, Alannah Kittle, Elisha Crane, Fatma Romeh M. Ali, Margaret Mahoney, & Kristy Marynak

Abstract:

The rapidly changing U.S. e-cigarette marketplace is dominated by unauthorized products. We used retail scanner data from Circana, a market research company that collects point-of-sale scanner data from a large retail sample, to estimate the proportion of unauthorized e-cigarette product sales in U.S. brick-and-mortar stores. As of December 2025, 69.4% of e-cigarette product sales in convenience and grocery channels were estimated to be unauthorized. Most unauthorized products were disposable devices in flavors that may appeal to young people. More complete data on products and sales in untracked channels such as vape shops could better inform monitoring and enforcement efforts.

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Journal: Drug and Alcohol Review, 2026, doi: 10.1111/dar.70239

Authors: Steven Curto, Tasha Bulgin, Jordan A. Gette, Margaret Swarbrick, Angela Gonnella, Denise Hien, Sonya B. Norman, & Shannon M. Kehle-Forbes

Abstract:

Introduction: Post-traumatic stress disorder (PTSD) commonly co-occurs with substance use disorders (SUD), yet few community-based SUD programs incorporate evidence-based trauma-focused. Prolonged exposure (PE), including its massed format (M-PE) with session frequency of 3-4 times per week, is a gold standard intervention for PTSD; however, concerns about client readiness, logistical demands and relapse risk have limited its adoption within SUD settings. This study examined staff perspectives on the feasibility and acceptability of integrating M-PE into a community-based SUD program.

Methods: Prior to launching a Hybrid Type 1 effectiveness-implementation trial (Project COMET), we conducted semi-structured virtual interviews with 15 community clinic staff: providers (n = 8), administrators (n = 2) and peer specialists (n = 5). Interviews were recorded, transcribed and analysed using a rapid qualitative analysis framework with matrix techniques to compare themes across roles.

Results: Four overarching themes captured staff perspectives on integrating M-PE: (Theme 1) Prior Knowledge and Experiences: Most staff were familiar with EMDR, while direct knowledge of PE/M-PE was limited. (Theme 2) Perceptions of M-PE: M-PE was widely viewed as a promising, structured intervention that fits the pacing and duration of SUD care. (Theme 3) Symptom Reduction and Client Impact: Staff anticipated improvements in PTSD and SUD symptoms through trauma-focused treatment. (Theme 4) Barriers and Constraints: Participants identified several potential implementation challenges, including logistical barriers and client readiness.

Discussion and conclusions: Findings suggest that staff generally viewed M-PE favourably but emphasised the importance of ensuring client readiness and organisational support. Enhancing feasibility and long-term sustainability may require expanded psychoeducation, targeted provider training and flexible delivery models.

Trial registration: ClinicalTrials.gov identifier: NCT06968832.

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Journal: Nature, 2026, doi: 10.1038/s41586-026-10910-z

Authors: Devon Stoliker, Leonardo Novelli, Moein Khajehnejad, Mana Biabani, Matthew D. Greaves, Tamrin Barta, … Adeel Razi

Abstract:

Psychedelics can profoundly alter consciousness by reorganizing brain connectivity, producing acute experiences that shape lasting psychological change. Psychedelic dynamics are commonly described as desynchronized or entropically disordered, yet the brain organization underlying self-dissolving and boundary-dissolving experiences that participants often report, and how context shapes that organization, remain unresolved. To address this, we acquired the largest single-site psychedelic neuroimaging dataset to date. Sixty-two adults underwent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) during rest and naturalistic stimuli (meditation, music and movie), before and on the day of psilocybin administration (fMRI ~ 80 min post-dose; EEG ~ 150 min post-dose). Half ranked the experience among the most meaningful of their lives. Here, using machine learning to represent the brain dynamics of each individual as low-dimensional trajectories, we show that psilocybin reorganizes brain activity into structured, context-sensitive patterns that co-vary with the quality of subjective experience, revealing a latent order missed by time-averaged measures. Networks that ordinarily segregate internal and external processing integrated, producing cohesive context-aligned trajectories in participants reporting the felt experience of being continuous with, rather than separate from, the environment, a state we refer to as embeddedness. The strength of this context alignment scaled with both the depth of self-dissolving and boundary-dissolving experience and the next-day mindset change. Our findings recast apparent disorder as latent organization aligned with context, linking neurobiology to subjective experience and behavioural change.

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Journal: PLoS One, 2026, doi: 10.1371/journal.pone.0355396

Authors: Kendra Wyant, Jiachen Yu, & John J. Curtin

Abstract:

Alcohol Use Disorder (AUD) is a chronic, relapsing condition, and identifying periods of elevated lapse risk remains a major challenge in supporting recovery. An automated recovery monitoring and support system using personal sensing and machine learning may help detect when individuals are at heightened risk. Cellular communication sensing may be a promising approach for passively capturing risk-relevant information about social interactions, particularly when these data are contextualized with participant-specific meaning. We evaluated a machine learning model predicting next-day alcohol lapse among individuals in early recovery from AUD using contextualized cellular communication data and baseline alcohol use, demographic, and psychiatric and personality characteristics. A total of 144 participants (49% male; mean age = 40; 87% non-Hispanic White) with a goal of abstinence provided cellular communication data and alcohol use reports via a 4x daily EMA for up to three months. Models were trained and evaluated using repeated k-fold cross-validation. The best-performing full model used an elastic net algorithm and retained 10 features (median posterior auro C = 0.67, 95% Bayesian credible interval (CI; [0.64, 0.71]). A comparison model including only baseline features demonstrated comparable performance (median auROC = 0.69, 95% CI [0.65, 0.72]). Cellular communication features on their own performed poorly, but still above chance performance (median auROC = 0.59, 95% CI [0.55, 0.62]). These findings demonstrate that cellular communication data capture some risk-relevant signal for alcohol lapse but do not provide incremental predictive value beyond baseline measures. Nevertheless, several communication features were retained in the full model with moderately sized coefficients, suggesting that aspects of social communication may still be clinically relevant for understanding lapse risk. However, limitations inherent to cellular communication sensing may outweigh its added utility in lapse prediction models.

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