There has been significant evolution in advanced therapy (AT) options for moderate-to-severe ulcerative colitis (UC). We assessed shifts in treatment patterns over time using a large claims database with long-term follow-up. This retrospective study utilized the IQVIA PharMetrics Plus claims database (2012-2023) to analyze adults in the United States with UC who initiated an AT. Patients had ≥3 years of continuous follow-up after AT initiation and no evidence of other autoimmune diseases during the six-month baseline period. Treatment patterns including persistence, switching, and/or discontinuation were summarized. Analyses were stratified by year of AT initiation: 2012-2014, 2015-2016, 2017-2018, and 2019-2020. 6726 patients with a mean follow-up of 61.5 months and treatment duration of 22.9 months (SD: 14.2) were included. Vedolizumab usage increased over time in parallel with a reduction in anti-TNF therapy. Around 27%-34% of patients had dose escalation and 40% switched ATs at some point during follow-up. AT treatment persistence rates at 3 years of follow-up were <40% across all time periods, and <10% when limiting to patients who did not require dose escalation and/or steroids. Approximately 57% of patients in routine practice discontinued AT and did not start a subsequent AT. Despite the availability of multiple new AT options for UC, rates of steroid-free persistence without the need for dose escalation were low. High rates of treatment discontinuation across lines of therapy highlight a need for durable treatment options.
We used machine learning to investigate the residual visual field (VF) deficits and macula retinal ganglion cell (RGC) thickness loss patterns in recovered optic neuritis (ON). We applied archetypal analysis (AA) to 377 same-day pairings of 10-2 VF and optical coherence tomography (OCT) macula images from 93 ON eyes and 70 normal fellow eyes ≥ 90 days after acute ON. We correlated archetype (AT) weights (total weight = 100%) of VFs and total retinal thickness (TRT), inner retinal thickness (IRT), and macular ganglion cell-inner plexiform layer (GCIPL) thickness. AA showed most ON eyes had a 10-2 VF pattern like the normal fellow eye VF, despite having markedly thinner GCIPL patterns. AA identified 7 VF and 11 retinal thickness ATs for each OCT model. The normal VF AT constituted 80% of ON eyes and 90% of normal fellow eyes. The most common GCIPL AT consisted of diffuse thinning. We identified significant correlations for the normal AT weights using OCT AT weights of five GCIPL ATs (r = 0.45), four TRT ATs (0.53) and two IRT ATs (0.42). Following acute ON, most eyes had complete 10-2 VF recovery despite significant GCIPL thinning, suggesting compensatory mechanisms for vision.
Once a youth waives their Miranda rights and agrees to talk to police, they increase their risk of a myriad of negative short- and long-term outcomes. Given that a vast majority (over 90%) of interrogated youth waive their rights, it is important to examine their perspectives on waiver decision making. Participants (n = 82) between 13 and 17 years old listened to a vignette in which they imagined they were in police custody and explained how they would respond and why, both when imagining they were guilty and innocent. Responses were coded using framework analysis, revealing that although most guilty and innocent participants believed they would assert their rights, more would waive when innocent than when guilty. Most participants, guilty and innocent, voiced a desire for guidance from an authority figure. Some also expressed beliefs around potential benefits of talking to police officers, and a few identified police officers as potentially harmful. Patterns emerged regarding differences in responses based on race and gender identity, in which minoritized groups expressed more distrust in the legal system. Results underscore the importance of ensuring that youth are provided with legal support during interrogation, which can guide policy reform.
Participation in and enjoying arts and creative activities is a United Nations human right, offering significant benefits, particularly for young people. However, past research, predominantly from Western countries, has shown that many young people do not engage in the arts and that such engagement is socially patterned, yet research gaps remain. It is unclear whether this pattern is also observed in other parts of the world and whether it is persistent across both in-school and out-of-school contexts in different countries. We analyzed data from the OECD Programme for International Study Assessment (PISA), which surveyed 441,183 15-year-olds across 73 countries and found substantial variation in engagement rates. Three key engagement patterns were identified. (1) Countries with higher in-school engagement rates also had higher out-of-school engagement rates. (2) Most students engaged more in the arts in school than out of school. (3) Individual-, school-, and country-related factors may influence engagement, with a strong social gradient, especially for out-of-school engagement. Schools hold the potential to equalize engagement in and outside school and thus reduce cultural, health, and academic inequalities. This aligns with Sustainable Development Goal 3: promoting wellbeing for all, and is relevant across multiple sectors and countries worldwide.
The wide availability of biomedical data, coupled with advanced analytics, holds unprecedented promise for scientific discovery and improved patient care; yet, heterogeneity across datasets remains a major barrier. Given the inherent diversity of biomedical domains, one-size-fits-all solutions are impractical. Despite decades of active research and numerous methods for automating data integration, there is a scarcity of open-source tools capable of handling this complexity. To address these challenges, we introduce Biomedical Data Integration and Harmonization Toolkit (BDI-Kit), an extensible toolkit designed for human-AI collaboration that provides a diverse suite of harmonization methods. It offers two complementary interfaces: a Python API that supports the creation of computational pipelines for harmonization and an AI-assisted chat interface that enables domain experts to perform harmonization using natural language. In this paper, we describe BDI-Kit and demonstrate its capabilities through real-world use cases. By simplifying data harmonization, BDI-Kit empowers researchers and practitioners, facilitating effective exploration and accelerating scientific discovery and clinical research.
Rhythm organizes many human motor activities from before birth and continues to shape development throughout infancy. In this review, we examine the role of rhythmic processes in early vocal development, drawing on research from motor control, physiology, speech, and language acquisition. We propose that respiration functions as a crucial core of early rhythmic coordination, linking vocalizations and bodily movements into an integrated system. At present, we have an imprecise understanding of how infant breathing for speech develops during the first year of life. However, respiration, an inherently flexible and adaptive system, may provide a temporal framework within which speech articulation and motor actions become progressively aligned. During canonical babbling, a key milestone in language acquisition, repetitive adult-like syllables emerge from rhythmic motor actions. The advent of this behavior presumably reflects developing coordination among motor, respiratory, and vocal subsystems. This three-way coordination creates the multimodal foundation of language. In this perspective, the respiratory rhythm is fundamental to early vocal development. Along with reviewing past work and its limitations, we suggest directions for future work to better address how the respiratory rhythm subserves developing linguistic and nonlinguistic actions in infant development.
It remains unclear whether specific blood pressure components-systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), or mean arterial pressure (MAP)-are similarly associated with white matter hyperintensity (WMH) burden across sexes within racial and ethnic groups. Participants were drawn from the Health and Aging Brain Study-Health Disparities (HABS-HD) and included non-Hispanic White (NHW; n = 1318), non-Hispanic Black (NHB; n = 1009), and Hispanic (n = 1307) adults. Linear regression models examined associations between SBP, DBP, PP, and MAP with log-transformed WMH volume normalized to intracranial volume. Analyses were stratified by race and ethnic group and included sex × blood pressure interaction terms. Models adjusted for age, education, neuroimaging scanner, diabetes, dyslipidemia, obesity, and tobacco use. Among NHW participants, higher SBP and PP were more strongly associated with greater WMH volume in females compared to males. Among NHB participants, blood pressure-WMH associations did not differ by sex. Among Hispanic participants, females exhibited greater WMH volume in DBP- and MAP-adjusted models, although blood pressure-WMH associations were stronger in males. Blood pressure-WMH associations differ by sex within racial and ethnic groups. These findings highlight intersectional heterogeneity in cerebrovascular vulnerability and suggest that sex-specific blood pressure-related pathways to small-vessel injury vary across racial and ethnic contexts. Non-Hispanic White (NHW) females showed stronger systolic blood pressure (SBP)- and pulse pressure (PP)-white matter hyperintensity (WMH) associations than NHW males.Blood pressure (BP)-WMH associations did not differ by sex among non-Hispanic Black participants.Among Hispanic adults, BP-WMH associations were stronger in males than females.Antihypertensive use was associated with higher WMHs across race and ethnic groups.BP-related WMH vulnerability varies by sex and race and ethnic context.
Courtship is often viewed as a linear process where males display and female assessment of this display shapes her mating decisions. However, communication can be far more dynamic and interactive, particularly in species that develop long-term relationships. Interactional complexity is not well captured by traditional models of animal communication. Here, we tested whether interactional elements shape female preferences in the monogamous zebra finch. We used selective calling as a measure of female preference. First, we asked whether females' most-preferred song (based on passive song playback) matched her most-preferred male (based on a live interaction). We found a mismatch in the preferences for song versus live males, and female preferences for a live male did not appear to be linked to how much he sang. Next, to experimentally manipulate male responsiveness, we habituated females to the song of their most-preferred or least-preferred male. This song habituation caused females to change their preferences differently depending on whether they were habituated to their most- or least-preferred male. Together, these results highlight that additional interactional elements, beyond male song, impact female social preferences. More broadly, our results contribute to growing evidence that models of communication should incorporate interactional and distributed elements.
Recent studies have demonstrated that high-quality annotated data are crucial for segmentation performance. However, incomplete or corrupted mask annotations remain common, limiting supervised learning. To address this, we introduce a mask-reconstruction task, referred to as masked segmentation label modeling (MSLM), which refines partially occluded labels by leveraging visible regions without manual annotations. We further propose the label masked autoencoder (L-MAE), which identifies erroneous regions and reconstructs them through contextual inference. The L-MAE fuses incomplete labels and corresponding images into a unified map for reconstruction, and an image patch supplement (IPS) algorithm restores missing image information, improving the average mean intersection over union (mIoU) by 4.1%. To validate the L-MAE, we train segmentation models on a degraded and L-MAE-enhanced Pascal VOC dataset, with the latter achieving a 13.5% mIoU improvement. The L-MAE attains predict area (PA)-mIoU scores of 91.0% on Pascal VOC 2012 and 86.4% on Cityscapes, outperforming state-of-the-art supervised segmentation models.
Multiplexed spatial proteomics profiling platforms expose the intricate geometric structure of cells in the tumor microenvironment (TME). The spatial arrangement of cells has been shown to have important clinical implications, correlating with disease prognosis and treatment response. These datasets require new statistical methods to test whether cell-level images are associated with patient-level outcomes. We propose the topological kernel association test (TopKAT), which combines persistent homology with kernel testing to determine whether geometric structures created by cells predict continuous, binary, or survival outcomes. TopKAT quantifies the topological structure of cells in each image using persistence diagrams and compares the similarities between persistence diagrams on the basis of the number and lifespan of the detected homologies among cells. We show that TopKAT can be more powerful than existing approaches, particularly when cells arise along the boundary of a ring and demonstrate its utility in breast cancer and colorectal cancer applications.
Mentoring programs are a widely used strategy for both the prevention of problem behavior and the promotion of healthy development and resilience among disadvantaged youth. The largest and longest-standing of these programs in the United States is the community-based mentoring (CBM) program of Big Brothers Big Sisters of America. This research reports findings from a randomized controlled trial of the CBM program that followed 1353 youth ages 10 and older for 4 years. Outcomes were assessed through youth and parent surveys and administrative records of arrest, with program effects examined through intent-to-treat analyses on hypothesized primary and secondary outcomes as assessed at study endpoint. For primary outcomes, the treatment group had significantly lower rates of violence-related delinquent behavior and recurring substance use and nonsignificantly lower rates of property-related delinquent behavior and arrest. For secondary outcomes, there were significant effects favoring the treatment group on measures of risk factors for problem behavior (e.g., negative peer associations), personal resources (e.g., self-control, social skills, coping efficacy), mental health (e.g., positive affect, depressive symptoms), academic performance, and the parenting behavior of the youth's caregiver; there were also numerous outcomes for which effects were nonsignificant, albeit in nearly all cases in a direction favoring the treatment group.
Recent advances in the diagnosis and management of reflux disease were the central focus of the inaugural Gatherings in Esophagology (GiE), which convened experts across gastroenterology, surgery, otolaryngology, pulmonology, and basic research. The sessions highlighted innovations in reflux monitoring-including high-resolution manometry, wireless pH monitoring, and novel salivary biomarkers-while critically evaluating their diagnostic accuracy and clinical utility. Presentations explored the limitations of traditional proton-pump inhibitor therapy, the emergence of potassium-competitive acid blockers as a new class of acid suppressants, and the evolving role of adjunctive treatments such as mucosal protectants, reflux reducers, and neuromodulators for refractory symptoms. The discourse extended to advanced interventional procedures, including transoral incisionless fundoplication, magnetic sphincter augmentation, and the RefluxStop device, with discussion of patient selection, efficacy, and complication management. Discussants emphasized the pathophysiology and management of extraesophageal manifestations of reflux, the interplay between reflux and pulmonary disease, and the diagnostic challenges in pediatric populations. The meeting also addressed the integration of behavioral therapies, the role of the microbiome, and the application of artificial intelligence in reflux diagnostics. Collectively, these insights underscore a shift toward precision medicine in reflux disease, emphasizing individualized diagnostic strategies and tailored therapeutic approaches to improve patient outcomes.
Microalgal genomes contain a vast "dark proteome"-sequences lacking detectable homology that evade conventional classification tools. We developed LA4SR (language modeling with AI for algal amino acid sequence representation), a framework using transformer- and state-space models to classify translated ORFeomes across ten algal phyla. Training on ∼77 million sequences, LA4SR achieves near-complete recall, accelerates classification by ∼10,701× relative to BLASTP+, and generalizes robustly to unseen sequences using less than 2% of available data. Models trained on synthetic, chimeric (terminal information [TI]-free) sequences maintained high accuracy, demonstrating that internal sequence features alone can drive robust classification. Inference speed and scalability were further enhanced under TI-free settings, supporting rapid annotation of large proteomic datasets. Custom explainability tools revealed interpretable amino acid patterns linked to evolutionary and biophysical features. Designed for accessibility across disciplines, LA4SR integrates biological context and computational innovation in parallel, enabling both biologists and data scientists to interrogate the microbial dark proteome.
The Consensus on subdomains and measures of Affective and Social cognition for research on Bipolar Disorder (CAS-BD) project aimed to formulate preliminary consensus-based recommendations for assessing affective and social cognition in BD. Three sequential surveys administered to experts on affective and social cognition in BD were conducted using the Delphi process. Experts responded to questions regarding affective and social cognition subdomains and rated their importance to research on BD. Experts also nominated measures, rated them for suitability, and ranked them by preference for use. Consensus was defined as ≥ 80% agreement. 31 experts completed the initial survey, with 20-23 completing subsequent surveys. Consensus was obtained for the subdomain structure of both affective cognition and social cognition, and the definition of each subdomain within. Explicit emotion regulation was ranked as being of highest priority for further research on affective cognition, and theory of mind as highest priority for further research on social cognition. The top-preferenced measures of all affective cognition subdomains were considered by consensus to be suitable for use in BD research. Agreement that the top-preferenced measures of social cognition were suitable ranged from 71.5% to 95.3%. Expert consensus on subdomains and measures of affective and social cognition for research on BD was obtained via a staged approach. Prior familiarity may have influenced some experts' rankings, but generally there was a notable lack of consistency in the use of available measures by BD experts. This reaffirms the need for more specific guidance and validated batteries of social and affective cognition to direct the field and allow for more consistency and replication of research in the future.
Conventional analgesics often provide limited relief for chronic pain and can cause systemic side effects. This scoping review aims to analyze mechanistic and bibliometric trends in nanoparticle-engineered delivery systems designed to selectively modulate transient receptor potential vanilloid 1 (TRPV1) receptors for precision chronic pain therapy; following PRISMA 2018 guidelines, the first 100 top-cited records from the Web of Science (WoS) Core Collection were organized in Microsoft Excel (Microsoft® Corp., Redmond, WA) and BibTeX (Oren Patashnik, Stanford University, Stanford, CA) for bibliometric analysis, with data also being qualitatively synthesized. Citation patterns were concentrated among a few leading researchers and institutions, highlighting the value of aligning with established funding bodies. Advanced polymeric and magnetic nanoparticles demonstrated the ability to cross the blood-brain barrier and selectively modulate TRPV1-mediated pain pathways. Nanoparticles carrying charged capsaicinoids improved bioavailability and reduced neuroinflammation relative to free capsaicin. Dose-dependent effects were consistently observed, as sustained low-dose release produced receptor desensitization and analgesia, while burst or high-dose delivery caused neuronal ablation. Surface-functionalized nanoparticles, particularly those with TRPV1-binding ligands or redox-responsive coatings, enhanced receptor specificity and reduced transient receptor potential ankyrin 1 (TRPA1) co-activation. Rationally engineered nanoparticles optimized for size, charge, ligand density, and release kinetics present a promising avenue for safer, more effective chronic pain therapies. By selectively modulating TRPV1 while mitigating thermoregulatory disruption, researchers can achieve long-lasting analgesia by prioritizing targeting precision to advance sustainable chronic pain treatments.
Methodological heterogeneity in dementia with Lewy bodies (DLB) trials contributes to publication bias and makes evidence synthesis and meta-analysis challenging. We aimed to develop a core outcome set for DLB (DLB COS) trials to improve consistency and comparability in DLB research. We conducted a systematic review to identify outcomes and administered a two-stage Delphi survey to a diverse panel of lay and professional stakeholders. We asked respondents which outcomes should be prioritized and included in DLB COS. Forty-nine outcomes were presented to survey respondents. Consensus was reached regarding eight outcomes for the final DLB COS: delusions/paranoia; fluctuations in cognition, attention, and arousal; functioning; global cognition; hallucinations; quality of life; motor parkinsonism; and rapid eye movement sleep behavior disorder. If adopted, DLB COS can enhance the comparability of research findings and facilitate standardization and harmonization. A systematic review revealed heterogeneity in dementia with Lewy bodies (DLB) study outcomes.Our study produced a DLB Core Outcome Set (DLB COS) comprising eight outcomes.DLB COS sets the minimum reporting standards for future trials.DLB-specific rating scales incorporating these outcomes are needed.Addressing this gap is a strategic priority in DLB research.
Alcohol use and alcohol use disorder (AUD) among adults age 50 and older are an expanding public health challenge, requiring effective alcohol prevention interventions. Empirical literature on prevention interventions among older adults is limited by design issues, lack of publication, and misconceptions of aging. To enhance scientific rigor, prior reviews of prevention interventions among older adults excluded pre-to-posttest studies and studies with subgroups, such as veterans, racial minorities, and individuals who seek out digital interventions. The current narrative review aims to understand with whom prevention interventions for older adults are tested; describe barriers and facilitators of successful interventions; and include perspectives of both older adults and intervention providers. Unlike prior reviews, it includes a range of study designs, including digital interventions, and examines decade of age, periods in which studies took place, and generational factors associated with prevention intervention success. In December 2024, Boolean search terms, such as "alcohol*," "older adults," and "intervention," were used across medical and social science databases, including PubMed, World of Science, PsycInfo, Social Sciences Citation Index, Cochrane Database, and other sources. The searches identified 983 articles published between 1999 and 2024, 582 of which were duplicates. Of the 401 abstracts reviewed, 231 did not mention older adults and/or alcohol. Thus, 170 full texts were reviewed. To be included, studies had to be peer-reviewed; have a mean participant age of 55 and older or a labeled subsample of individuals age 50 and older; focus on a nonpharmacological intervention; and reported alcohol or alcohol-related outcomes or older adult and/or provider perspectives of interventions. Studies set in a formal substance use treatment program were excluded. Overall, 84 records describing 51 interventions and 16 articles of consumer and provider perspectives were synthesized. Studies were categorized into primary prevention, secondary prevention of AUD, and tertiary prevention of worsening AUD. Most interventions were delivered in person, in primary care, with individuals born from 1901 to 1923 (Greatest Generation) and 1924 to 1945 (The Silent Generation), and yielded significant reductions in alcohol use and related consequences. Only The Silent Generation consistently responded to interventions, demonstrating large effects. Additionally, two out of 18 randomized controlled trials found that individuals born from 1946 to 1964 (Baby Boomers) significantly responded to prevention interventions. Digital interventions were successful across generations. Barriers to successful interventions occur at the organizational, provider, and older adult levels. Prevention intervention facilitators include drink tracking, agreement with another person, and aligning tone of the intervention to older adult perspectives of their drinking and perceived need to change. Adapting prevention interventions to older adults could include tailoring to an individual's identity, culture, and meaning behind their drinking, which is often defined by generation, rather than only by age.
Remote patient monitoring (RPM) has emerged as a valuable complement to traditional in-person clinical assessments, particularly since the COVID-19 pandemic. RPM encompasses a wide range of tools and technologies that enable the longitudinal collection of biometric, behavioral, and biochemical data outside the conventional clinic setting. These programs have been successfully integrated across several medical subspecialties, such as cardiology, endocrinology, and psychiatry, where the availability of real-time data has facilitated timely evaluation and management of chronic conditions. Building on these successes, the field of inflammatory bowel disease (IBD) has begun adopting RPM and connected health technologies to enhance both access to and quality of care. These innovations include the integration of point-of-care testing for conventional biomarkers, the development of novel biomarkers from other biospecimens (eg, mucus and sweat), and the advent of passive physiologic monitoring aimed at predicting and preventing disease relapses. This article examines current literature on RPM across chronic diseases, explores its emerging applications in IBD, and presents key barriers hindering its broader implementation.
The relationships between 24-h time-use composition (i.e., sleep, sedentary behavior, light physical activity, and moderate-to-vigorous physical activity [MVPA]) and brain morphology in older adulthood remain poorly understood. We examined associations between 24-h time-use composition and brain age using compositional data analysis, predicting that 24-h time use would be associated with brain age and that a greater amount of time engaged in MVPA would drive associations with younger brain age. Baseline data from the Investigating Gains in Neurocognition in an Intervention Trial of Exercise (IGNITE; n = 648) were analyzed. Brain age was estimated using T1-weighted magnetic resonance imaging data. Time-use composition was derived from wrist-worn triaxial accelerometers. Regression models examined associations between 24-h time-use composition (expressed as isometric log ratios) and brain age, adjusting for age, sex, apolipoprotein E4 (APOE4) carriage, education, body mass index, image quality, and site. Compositional isotemporal substitution evaluated how hypothetical reallocations of time between behaviors related to brain age. The final sample included 573 adults (69.8±3.7 years, 407 females). It was found that 24-h time-use composition was associated with brain age (F = 2.72, p = 0.004). Post hoc modeling indicated that time spent in MVPA primarily drove these associations, such that less MVPA was associated with greater brain age, irrespective of whether time was taken from sleep, sedentary behavior, or light physical activity. These results suggest that 24-h time use, especially time spent in MVPA, relates to structural brain age in late adulthood. Maintaining or increasing MVPA may help preserve younger brain age, irrespective of which behaviors this time was reallocated from. Future research should examine whether systematically shifting 24-h time use toward MVPA alters brain aging trajectories.Clinical Trial Registration Number and Name of Trial Registry: ClinicalTrial.gov: NCT02875301. Time use relates to brain age in older adults.More time spent in MVPA may contribute to younger brain age.Associations between time use and brain age are independent of demographic variation or genetic risk for AD.
Particle colliders produce data at extraordinary rates, posing major challenges for transmission and storage. High-throughput compression algorithms are therefore essential. In the sPHENIX experiment taking data at the Relativistic Heavy Ion Collider, a time projection chamber records three-dimensional (3D) particle trajectories that are highly sparse, making conventional learning-free lossy compression ineffective. Convolutional neural networks have surpassed traditional methods in compression ratio and accuracy. However, they fail to exploit sparsity for efficiency. To address these gaps, we present BCAE-VS, a bicephalous convolutional autoencoder with variable compression ratio for sparse data, which adapts compression to input complexity through key-point identification and sparse convolution. BCAE-VS achieves higher accuracy and compression ratios than prior neural approaches while being orders of magnitude smaller. Moreover, its throughput increases with sparsity-a property not observed in other methods. Although it was developed for collider experiments, BCAE-VS readily extends to other sparse data domains, such as light detection and ranging (LiDAR) sensing and 3D microscopy.