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At least 19 recordsLinked to original sources
PubMed1992

[Local compressibility of denture foundation--evaluation of denture foundation in relation to impression pressure].

The viscoelastic character of the denture foundation is different in each individual or site. The character of the mucosa and the appropriate impression procedure should be examined. However, few investigations concerning the relation between the compressibility of the denture foundation and the impression pressure have been reported. The purpose of this study was to discuss the objective evaluation of the denture foundation in relation to the impression pressure. In this report, the local compressibility of the denture foundation of 20 upper edentulous patients was measured with the 20 MHz B-Mode ultrasonic diagnostic equipment. The Compressible Index was defined to analyze the compressibility of the denture foundation quantitatively as against the impression pressure. The main results were as follows: 1. The compressible amount of the denture foundation against 100 gw/cm2 pressurization was an average of 0.32-0.61 mm. 2. The compressible rate of the denture foundation against 100 gw/cm2 pressurization was an average of 13.5-20.7%. 3. The Compressible Index was confirmed to represent the compressibility of the denture foundation accurately as against the impression pressure. 4. It was suggested that the Compressible Index was useful for the clinical simple evaluation of the denture foundation.

Aged↗
PubMed1992

Sowing the seeds of neo-imperialism: the Rockefeller Foundation's yellow fever campaign in Mexico.

The Rockefeller Foundation's campaign against yellow fever in Mexico sought to advance the economic and political interests of U.S. capitalism. The campaign was implemented at a time of strong anti-American sentiments on the part of the Mexican people. With no diplomatic relationships between Mexico and the United States, the Rockefeller Foundation presented its campaign as an international commitment. Thus, Foundation doctors became the most salient U.S. diplomats. At the same time they made sure that the Mexican yellow fever would not spread to the United States through the southern border. The by-products of the campaign went beyond the political arena. Special techniques to combat the vectors allowed the Rockefeller Foundation's brigades to change the anti-American sentiments of the people. When the campaign ended, the Foundation had already set in place the foundation for the modern Mexican health care system. Benefits from the campaign also accrued to President Obregón, who used the campaign to strengthen his position of power. Mexican doctors adopting a pro-American attitude also allied with the Rockefeller Foundation to gain reputation and power within the emerging Mexican State.

Cause of Death↗
PubMed1991

Application of beams on elastic foundation and B-spline solution methodologies to parametric analysis of intramedullary implant systems.

A simple numerical technique for parametric evaluation of orthopaedic implant systems, to be used as a screening tool before complex structural analysis (e.g. Finite Element Method), is the subject of this paper. A modified Beams on Elastic Foundation model (with non-constant foundation modulus) is solved using this numerical technique based on B-spline differential equation modelling. A model with variation in the modulus of the foundation, as solved with this spline technique, was compared with a model with constant foundation modulus, solvable with closed form techniques. While deflections were smaller, the reaction force was up to ten times greater for the models with constant modulus of foundation, compared with varying modulus. The model presented in this paper is a refinement of previous models using closed form solution techniques for foundations with constant moduli. It is primarily useful for detecting trends in parametric analyses, or to select specific cases for further analysis by more computationally intensive analytic methods.

Biomechanical Phenomena↗
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↗
PubMed1992

Professional paths chosen by past recipients of ASHP Foundation fellowships.

Past recipients of ASHP Foundation fellowships were surveyed to determine their professional activities and the impact of the fellowships on their careers. Questionnaires were mailed to 92 former fellowship recipients. Questions covered the respondents' education and postgraduate training, employment, job and career satisfaction, opinions on the ASHP Research and Education Foundation Fellowships Program, publications and presentations, achievements, experience, honors, and demographics. Respondents returned 77 usable questionnaires, for an 83.7% response rate. Most respondents worked primarily in academia or teaching; one fourth primarily taught. Most of the respondents indicated a positive attitude about both their professional careers and the ASHP Foundation Fellowships Program. Respondents have published (alone or as coauthors) 477 research articles and 359 professional articles; they have given 548 podium presentations and 438 poster presentations at national or regional scientific or professional meetings. Some respondents commented that the program helped them develop needed professional skills and urged that the program be continued. Some suggested that funding be increased to two years and that scoring and selection criteria be revised. Others suggested that the stipend be increased, the results be disseminated, and certain fellowships be re-established. The former ASHP Foundation fellows surveyed have made substantive contributions to the advancement of pharmacy and pharmaceutical science. ASHP Foundation fellowships seem to have favorably affected the professional careers of these fellowship recipients.

Career Choice↗
PubMed1992

Caring, virtue theory, and a foundation for nursing ethics.

The purpose of this paper is to critically examine the use of a framework of virtue theory as a foundation for a nursing ethic embodied in the caring ideal. The first section summarizes the problems identified with traditional moral theory as a foundation for a nursing ethic based on the caring ideal. From these problems, adequacy conditions that a foundation must meet are identified. In section two, the basic ideas of virtue theory are sketched and an account of how virtue theory can be used as a foundation for a nursing ethic based on the caring ideal is discussed. Finally, virtue theory as a foundation for a nursing ethic à la caring is assessed against the adequacy conditions delineated in section one. The conclusion of this paper is that virtue theory does not offer a viable alternative to duty-based theories. While virtue theory provides promise in meeting the identified adequacy conditions, serious secondary issues arise that can not be immediately nor easily resolved.

Caregivers↗
PubMed2025

Orthrus: Towards Evolutionary and Functional RNA Foundation Models.

In the face of rapidly accumulating genomic data, our ability to accurately predict key mature RNA properties that underlie transcript function and regulation remains limited. Pre-trained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks. However, existing genomic foundation models are trained using strategies borrowed from textual domains that do not leverage biological domain knowledge. Here, we introduce Orthrus, a Mamba-based mature RNA foundation model pre-trained using a novel self-supervised contrastive learning objective with biological augmentations. Orthrus is trained by maximizing embedding similarity between curated pairs of RNA transcripts, where pairs are formed from splice isoforms of 10 model organisms and transcripts from orthologous genes in 400+ mammalian species from the Zoonomia Project. This training objective results in a latent representation that clusters RNA sequences with functional and evolutionary similarities. We find that the generalized mature RNA isoform representations learned by Orthrus significantly outperform genomic foundation models on mRNA property prediction tasks, and requires only a fraction of fine-tuning data to do so. Finally, we show that Orthrus is capable of capturing divergent biological function of individual transcript isoforms.

Journal Article↗
PubMed2026

A Foundation Model Based CT Biomarker for Non-Invasive Prediction of Response to Neoadjuvant Immunochemotherapy in Non-Small Cell Lung Cancer.

Predicting pathological complete response (pCR) to neoadjuvant immunochemotherapy in non-small cell lung cancer (NSCLC) is clinically important yet remains challenging. Here, we introduce a foundation model-derived computed tomography (CT) imaging biomarker established from a multi-center cohort of 702 patients. Specifically, we developed and validated a non-invasive baseline CT-based model for risk stratification of pathological response. To address scanner and protocol heterogeneity, we first built a 3D Vision Mamba-based CT super-resolution model trained on 2494 cases for image standardization. We then fine-tuned a lung cancer-specific CT foundation model from a pretrained 3D model (VoCo) using 6643 chest CT scans. Finally, we constructed a multi-task Swin Transformer that jointly performs risk stratification and segments tumors to generate the imaging biomarker. Across five centers, the model achieved consistently strong generalization (AUC: 0.75-0.87) for pCR prediction. Genomic analysis revealed that the biomarker was independent of tumor mutational burden but significantly associated with TP53 mutations, suggesting an association with a radiogenomic phenotype related to this alteration. Together, these results demonstrate a generalizable and biologically meaningful foundation model-based biomarker for non-invasive risk stratification of pathological response in NSCLC.

Female↗
PubMed2025

MutBERT: probabilistic genome representation improves genomics foundation models.

MOTIVATION: Understanding the genomic foundation of human diversity and disease requires models that effectively capture sequence variation, such as single nucleotide polymorphisms (SNPs). While recent genomic foundation models have scaled to larger datasets and multi-species inputs, they often fail to account for the sparsity and redundancy inherent in human population data, such as those in the 1000 Genomes Project. SNPs are rare in humans, and current masked language models (MLMs) trained directly on whole-genome sequences may struggle to efficiently learn these variations. Additionally, training on the entire dataset without prioritizing regions of genetic variation results in inefficiencies and negligible gains in performance. RESULTS: We present MutBERT, a probabilistic genome-based masked language model that efficiently utilizes SNP information from population-scale genomic data. By representing the entire genome as a probabilistic distribution over observed allele frequencies, MutBERT focuses on informative genomic variations while maintaining computational efficiency. We evaluated MutBERT against DNABERT-2, various versions of Nucleotide Transformer, and modified versions of MutBERT across multiple downstream prediction tasks. MutBERT consistently ranked as one of the top-performing models, demonstrating that this novel representation strategy enables better utilization of biobank-scale genomic data in building pretrained genomic foundation models. AVAILABILITY AND IMPLEMENTATION: https://github.com/ai4nucleome/mutBERT.

Humans↗
PubMed1976

Foundations of health education.

Health education is "the process of providing or utilizing experiences for favorably influencing understanding, attitudes, and practices relating to individual, family, and community health. As an applied science it draws its content from a variety of sources. The paradigm which depicts the foundations of health education can be visualized as five vertical pillars consisting of sociocultural, educational, psycho-behavioral, legal, and scientific foundations supporting the work of the health educator. While the components within each pillar may be altered with new developments and advances over time, the model is broad enough in scope to incorporate the changes without altering its purpose. The foundations of health viewed in this light can easily display the depth of the health education profession and will serve to orient the novice and future health educators of the underpinnings of their profession. This framework could easily be adapted for study on the college level and should serve as an orientation to those students planning to major in health.

Behavior↗
PubMed2025

NextVir: Enabling classification of tumor-causing viruses with genomic foundation models.

MOTIVATION: Oncoviruses, pathogens known to cause or increase the risk of cancer, include both common viruses such as human papillomaviruses and rarer pathogens such as human T-lymphotropic viruses. Computational methods for detecting viral DNA from data acquired by modern DNA sequencing technologies have enabled studies of the association between oncoviruses and cancers. Those studies are rendered particularly challenging when multiple species of oncovirus are present in a tumor sample. In such scenarios, merely detecting the presence of a sequencing read of viral origin is insufficiently informative-instead, a more precise characterization of the viral content in the sample is required. RESULTS: We address this need with NextVir, to our knowledge the first multi-class viral classification framework that adapts genomic foundation models to detecting and classifying sequencing reads of oncoviral origin. Specifically, NextVir explores several foundation models-DNABERT-S, Nucelotide Transformer, and HyenaDNA-and efficiently fine-tunes them to enable accurate identification of the sequencing reads' origin. The results demonstrate superior performance of the proposed framework over existing deep learning methods and suggest downstream potential for foundational models in genomics.

Humans↗
PubMed1976

Foreign intervention in medical education: a case study of the Rockefeller Foundation's involvement in a Thai medical school.

A case study of the process of foreign intervention in medical education in the developing world is presented. Material collected from the Rockefeller Foundation Archives on a Foundation program in Thailand is used to analyze the conditions under which foreign agencies and their personnel intervene in the development of medical professionals in the Third World and to study the problems that may occur as a result of such intervention. The importance of value consensus and the competitive advantage foreigners have in the marketing of professional models are highlighted as reasons for the diffusion of Western models of medical education throughout the developing world.

Community Health Services↗
PubMed2026

Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases.

Plant diseases destroy 20-40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.

convolutional neural networks↗
PubMed2026

Bridging Ancestry Gaps in Genomic Risk Prediction with Tabular Foundation Models.

MOTIVATION: Models deployed for genomic prediction of diseases perform unevenly across populations, limiting clinical utility. Two factors drive this limitation: large imbalances in sample availability across ancestry groups and non-stationarity of genotype-phenotype effect sizes across the ancestry continuum. While tabular foundation models with in-context learning (ICL) have shown strong sample efficiency in other domains, their effectiveness for genotype-to-phenotype prediction and their robustness to ancestry-driven effect heterogeneity remain unclear. RESULTS: Using large, ancestrally diverse biobank data, we show that ICL-capable tabular foundation models reduce performance degradation in under-sampled ancestry groups compared to conventional supervised approaches. However, we find that prevailing models trained on existing synthetic tabular tasks fail when allele effect sizes vary across ancestry space. Treating genetic ancestry as a continuous variable, we introduce an instruction-tuning framework that exposes models to synthetic tasks with ancestry-dependent non-stationary effects. Instruction-tuned models achieve improved and more stable predictive performance across the genetic ancestry continuum, including for individuals distant from in-context exemplars in ancestry space. AVAILABILITY AND IMPLEMENTATION: All code for instruction-tuning models, synthetic task generation, data wrangling, and model evaluation, is publicly available at https://github.com/ai4pm/Bridging-Ancestry-Gaps-in-Genomic-Risk-Prediction-with-Tabular-Foundation-Models. The final instruction-tuned model (ICL-NS-G2P-proto) is also released in this repository. Detailed documentation is provided, including environment setup instructions and guidelines for running various parts. The instruction-tuning task datasets are available at https://zenodo.org/records/18309187.

Ancestry Continuum↗
PubMed1975

[The Dutch foundation "Voet en Schoeisel" (foot and shoe)].

The author gives information about the foundation "Voet en Schoeisel" (foot and shoe). He describes in brief the origin, the compostion, the aims and also the works. He hopes to give publicicty to the foundation what possible leads to more international contacts.

Foot Deformities, Acquired↗