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PubMed1990

Addiction in adolescents.

Some symptoms seen in adolescents with the disease of chemical dependence are similar to those seen in adults. Because of their age, lack of personality development, dependent family role, immaturity, and acting out of age-related behavioral tendencies, however, symptoms specific to this population occur. These may become exacerbated and telescope--intensify and shorten--the progression of the disease. A plan to solve the problem of adolescent chemical dependence must focus on education, demonstration, cooperation, prevention, intervention, habilitation, treatment, and recovery. The phenomenon of denial in a chemically dependent adolescent yields a more complex delusional system that dictates age-specific intervention approaches. Habilitation is necessary for successful adolescent treatment and recovery because what is needed is an initial process of learning, not relearning or rehabilitation. If specific adolescent issues are addressed through comprehensive, multimodality treatment approaches, then treatment and recovery outcomes for chemically dependent adolescents and their families are substantially improved. Primary care physicians must be alert to the possibility of drug use in their young patients and aware of treatment options.

Adolescent↗
PubMed1991

Biofeedback-assisted relaxation training for the aging chronic pain patient.

The older segments of the U.S. population are expanding rapidly and account for a disproportionate amount of health care, including treatment for pain-related musculoskeletal disorders. In a prospective study with objective measures and one-year follow-up, Middaugh et al. (1988) found that older patients (55-78 yr; N = 17, 76% success) treated in a multidisciplinary chronic pain rehabilitation program enjoyed a success rate equal to that of younger patients (29-48 yr, N = 20, 70% success). The current study presents additional data on these two groups of patients to compare their ability to learn the physiological self-regulation skills taught in the biofeedback/relaxation component of the multimodal program. This component included progressive muscle relaxation training, diaphragmatic breathing instruction, and EMG biofeedback. Repeated measures ANOVA showed significant increases in digital skin temperature (peripheral vasodilation) and decreases in respiration rate both within and across training sessions (p values = .04 to .0001) with no differences between age groups (p greater than .05). EMG measures for the upper trapezius ms in patients with cervical pain showed similar deficits in muscle control at evaluation and similar improvements with biofeedback training for the two age groups. These findings indicate that older pain patients responded well to the biofeedback/relaxation training component of the multimodal pain program.

Adult↗
PubMed1997

Affiliative processes and vocal development.

Affiliative behavior is often expressed through communication, and the nature of affiliative interactions affects the ontogeny of communication. I presented three phenomena that demonstrate the importance of affiliation in vocal development in marmosets and tamarins, but the results have parallels in many other species including birds, dolphins, and humans. Pygmy marmosets use trill-like vocalizations to maintain contact with other group members. Individuals change subtle aspects of call structure when they encounter new social groups or acquire a new mate. This process of vocal accommodation is common in many other species. Infant pygmy marmosets go through a stage of "babbling." producing long sequences of vocalizations that have several similarities to the babbling of human infants. Babbling infants receive more social attention than nonbabbling infants, and these social interactions may shape vocalizations towards more adult forms. In adult cotton-top tamarins, food-associated vocalizations communicate the presence and quality of food. However, reproductively inhibited juveniles and subadults use many other types of calls in feeding situations and display a high proportion of imperfect forms of adult food-associated calls. When subadult monkeys are paired with new mates and change their reproductive status, they rapidly (within 3-6 weeks) display both adult structure and adult usage of food-associated calls, suggesting that affiliative processes can both facilitate and inhibit vocal ontogeny. Three mechanisms of how social interactions affect communication (multimodal stimulation, attentional focus, and reinforcement) were proposed and illustrated through examples of parrots learning English labels for objects and attributes and infant cotton-top tamarins acquiring food-associated vocalizations.

Animals↗
PubMed1989

On cross-modal similarity: the perceptual structure of pitch, loudness, and brightness.

Examined how pitch and loudness correspond to brightness. In the Experiment 1, 16 Ss identified which of 2 lights more resembled each of 16 tones; in Experiment 2, 8 of the same 16 Ss rated the similarity of lights to lights, tones to tones, and lights to tones. (1) Pitch and loudness both contributed to cross-modal similarity, but for most Ss pitch contributed more. (2) Individuals differed as to whether pitch or loudness contributed more; these differences were consistent across matching and similarity scaling. (3) Cross-modal similarity depended largely on relative stimulus values. (4) Multidimensional scaling revealed 2 perceptual dimensions, loudness and pitch, with brightness common to both. A simple quantitative model can describe the cross-modal comparisons, compatible with the view that perceptual similarity may be characterized through a malleable spatial representation that is multimodal as well as multidimensional.

Attention↗
PubMed1977

Unit responses in the frog's caudal thalamus.

Single unit microelectrode recordings were made in the caudal thalamic region of the frog, Rana pipiens, from a sample of multimodal sensory units which included monocular, binocular, tactile, and spontaneous types, and which showed a wide variety of receptive field sizes, response rates, preferred stimulus sizes habituation rates, and velocity and tactile sensitivities. A number of special response properties were occasionally observed, including directional sensitivity, stationary object sensitivity, afterdischarges, and visual and tactile inhibitory fields. Computer analysis of spontaneously active units revealed four types: regular, exponential, bursting, and multimodal.

Animals↗
PubMed2026

Deep generative models in biological sequence and structure analysis and design.

Deep generative models have transformed biological sequence modeling from predictive analysis toward increasingly controllable design. Early biological applications of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) established latent representation learning and sequence synthesis, while recent advances in transformer-based language models, discrete diffusion, flow-matching, and multimodal generative frameworks have substantially expanded the scope of biological design. This review examines generative models for DNA, RNA, and protein sequence design, emphasizing how different model classes represent biological constraints, operate over discrete and continuous spaces, and integrate sequence, structure, and function. We compare VAEs, GANs, autoregressive and masked language models, diffusion models, and flow-based approaches across genomics, transcriptomics, and proteomics, with particular attention to controllability, long-range dependency modeling, structural grounding, generalization, and experimental utility. We further examine evaluation strategies, out-of-distribution generalization, and closed-loop design-build-test-learn workflows that connect in silico generation with empirical validation. We distinguish fundamental modality-dependent constraints including sequence discreteness, context length, structural coupling, and physical or thermodynamic requirements from architecture-dependent advantages that reflect the current state of the field. Current studies suggest that long-context models are particularly useful for genome-scale representation and sequence modeling, whereas structure-aware diffusion, flow-based, and inverse-folding approaches provide better frameworks for geometry-constrained RNA and protein design. This perspective provides a critical framework for understanding the present capabilities, limitations, and convergence of generative approaches toward reliable and experimentally grounded biological design.

Biological sequence analysis↗
PubMed2025

Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care. OBJECTIVE: While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science. METHODS: We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality. RESULTS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS: We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

Artificial Intelligence↗
PubMedDate not supplied

Attention-deficit hyperactivity disorder in adults.

It has been estimated that 30% to 70% of children who are diagnosed as having attention-deficit hyperactivity disorder (ADHD) will continue to show symptoms of the condition as adults. Since the prevalence of ADHD among school children may be 3% or more, its prevalence among adults may be 1% or 2%. The third revised edition of the Diagnostic and Statistical Manual (1987) of the American Psychiatric Association lists three essential features for the diagnosis of ADHD: "developmentally inappropriate inattention, impulsiveness, and hyperactivity." Other conditions associated with ADHD in adults include learning disabilities (or their sequelae), general anxiety disorder, drug and alcohol abuse, and dysthymic and cyclothymic disorders. Strong correlations have been found between ADHD and oppositional defiant and conduct disorders in children and an increased risk for antisocial disorders in adults. A combination of genetic, biologic, and environmental factors appears to be implicated in the etiology of ADHD. The management of adult ADHD requires a multimodal approach. The patient needs to be informed of the cause of his or her impulsive and often self-destructive behavior. Many patients will have learning difficulties that require evaluation and remediation by specialists in learning disabilities. Psychotherapy can help the patients resolve disturbances in perceptions of self and others and family therapy can address difficulties in the adult's relationships with family members. Pharmacotherapy of adult ADHD includes the use of central nervous system stimulants, such as methylphenidate, dextroamphetamine, and pemoline, of the tricyclic antidepressants imipramine and desimipramine, and of other antihypertensive, analgesic, and antimanic drugs.

Adult↗
PubMed1994

Dissociation between conditioned taste aversion and radial maze learning following seizure-induced multifocal brain damage: quantitative tests of serial vs. parallel circuit models of memory.

Multivariate analyses between conditioned taste aversion (CTA) and radial maze acquisition (RMA) scores and percentages of neuronal dropout within thalamic and telencephalic structures were completed for rats in which overt seizures had been evoked following a single systemic injection of lithium/pilocarpine. Despite multifocal damage, only the amount of damage within the hippocampus (CA1) and the basolateral amygdala was most strongly associated with attenuated CTA, whereas damage within the mediodorsal thalamus was primarily associated with RMA. There was no significant correlation between CTA or RMA. Multiple regression analyses for specific Paxinos and Watson structures and their traditional aggregates supported more precise delineation of neuronal substrates of learning/memory and a multimodal (parallel) model for these processes.

Animals↗
PubMed1994

Group and family treatment of post-traumatic stress disorder.

A central feature of PTSD is its effect on social relationships. Trauma affects groups of people, not just individuals. Family systems, neighborhoods, and even whole generations may feel the results of psychological trauma. Because of the social nature of the effects of trauma, post-trauma treatment must address an individual's relationship to others. Group and family psychotherapy are ideally suited to this and are important components of a multimodal approach to PTSD treatment. Group and family psychotherapies provide superb opportunities for social support, social reintegration, and interpersonal learning. As with any powerful technique, these methods must be carefully applied. Although not all patients are appropriate for exposure-based treatments, improved interpersonal coping skills will likely be beneficial to many PTSD patients. Patients should be carefully evaluated for treatment types and assessed for treatment response. Although group and family therapies currently provide relief and growth for PTSD patients, many considerations remain for the future. For example, how can patients be matched with various treatments for optimal results? How should acute and chronic PTSD treatments be similar and different? What is the effectiveness of group and family therapies for PTSD? What are the social and legal implications of a prolonged course of treatment for a victim whose children meanwhile are being traumatized by the parent's relatively poor parenting skills secondary to their inadequacies and disabilities? Finally, at a global level, how do we improve systems therapy technology to enable us more radically, effectively, and quickly to bring about total systems change? Because families and groups are the "cells" that compose the "vital organs" we call nations, and these nations in turn make the total body of humankind, the answers to these questions may have a significant determining effect on the future survival of us all.

Adult↗
PubMed2026

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer.

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor-stromal-immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.

Humans↗
PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗
PubMed1997

Future directions in lung cancer research and therapeutics.

Although immune and genetic approaches do not yet have substantial clinical exposure in NSCLC, it is hoped that the future will bring less toxic, more specific, and more effective methods to prevent the development of end-stage NSCLC. The principles of multitargeted and multimodality therapy to overcome tumor heterogeneity and resistance, and drug delivery issues have been developed over years of effort using chemotherapy. The lessons learned should not be forgotten, because they will remain applicable to newer biologic and genetic modalities.

Antineoplastic Agents↗
PubMed2026

Beyond antibiotics: artificial intelligence-enabled anti-infective ecosystems for next-generation precision therapeutics against antimicrobial resistance.

The rapid global expansion of antimicrobial resistance (AMR) threatens to undermine decades of progress in infectious disease management and highlights the limitations of conventional antibiotic-centered therapeutic strategies. Although emerging technologies-including antimicrobial peptides, bacteriophage therapy, CRISPR-based antimicrobials, microbiome therapeutics, anti-virulence approaches, nanotechnology-enabled drug delivery, and artificial intelligence (AI)-have individually demonstrated considerable promise, they are predominantly being developed as independent interventions rather than as coordinated components of an integrated therapeutic strategy. This Perspective proposes the Intelligent Anti-Infective Ecosystem (IAIE) as a conceptual systems-level framework that computationally integrates multimodal diagnostics, pathogen genomics, microbiome profiling, AI-assisted decision support, programmable precision therapeutics, ecological monitoring, and longitudinal clinical feedback within a continuously learning dynamically optimized workflow. Unlike existing paradigms that primarily optimize individual technologies or therapeutic decisions, IAIE emphasizes closed-loop coordination among complementary antimicrobial approaches to support precision-guided infection management while preserving microbiome integrity and mitigating resistance selection pressure. We further outline the core components, operational principles, translational challenges, and technology readiness of the major therapeutic platforms that could contribute to such an ecosystem, while distinguishing clinically established interventions from emerging experimental strategies. Importantly, IAIE should be interpreted as a prospective conceptual architecture rather than an existing clinical platform. Its proposed clinical value remains to be established through sequential computational, preclinical, and prospective clinical investigations using standardized microbiological, ecological, and patient-centered outcome measures. By framing antimicrobial innovation within an responsive systems perspective, IAIE provides a roadmap for future multidisciplinary research aimed at integrating artificial intelligence and systems microbiology to enable sustainable management of antimicrobial resistance.

Humans↗
PubMed1992

Evaluation of coma and vegetative states.

The Coma/Near-Coma (CNC) scale was designed to measure small clinical changes in patients with severe traumatic and nontraumatic brain injuries who were functioning at very low levels characteristic of near-vegetative and vegetative states. In 20 patients followed for 16 weeks the scale identified 25% who ultimately showed modest improvement. Interrater reliability was high (r = .95); validity was supported by significant correlations between CNC- and brain-multimodality evoked potential abnormality scores as well as between scores on the CNC and the Disability Rating Scale. The CNC scale was easily learned and it could be completed quickly and cost effectively. Staff found it useful in recognizing among relatively homogeneous low-level patients those most likely to respond to further rehabilitation care. The CNC appears to be useful for justifying ongoing intensive rehabilitation and for preventing premature transfer to lower levels of care.

Adolescent↗
PubMed2026

Biological Foundation Models for Complex Disease Research and Clinical Translation.

Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this challenge by treating nucleotides and genes as tokens and learning representations that can be transferred to downstream biomedical and clinical tasks. In this review, we examine two major model classes, genomic sequence foundation models and cell foundation models, and compare their tokenization strategies, model architectures, pretraining objectives, and adaptation methods. We summarize their emerging applications in regulatory variant interpretation, disease-associated cell-state analysis, drug-response prediction, and therapeutic target discovery across complex diseases. We distinguish applications supported by experimental or retrospective validation from those that remain primarily computational or conceptual. We further discuss key challenges to clinical translation, including multimodal data integration, model interpretability, benchmarking, patient-specific prediction, and privacy protection. We highlight future opportunities to integrate biological foundation models with emerging frameworks of medical digital twins, agentic AI, and federated learning. By linking model design to translational goals, this review provides a practical framework for evaluating biological foundation models and their readiness for complex disease research and clinical use.

biological foundation model↗