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PubMedDate not supplied

[Computer-assisted design in removable partial dentures. Expert system and software for framework tracing].

Stelligraph, a software program for designing the removable partial denture offers many possibilities in computer assisted technology. The design can be realized on any particular case, based on three different concepts. This software program can also provide on individual, manual and personalized design this offening complete freedom in the conception. This software program is an expert system in removable partial denture by providing the general practitioner with a rational design as well as clinical guidance for oral rehabilitation.

Denture Design↗
PubMed2026

Quantum computing-assisted validation of a conserved macrophage suppression module shared by ASFV and PEDV.

BACKGROUND: African swine fever virus (ASFV) and porcine epidemic diarrhea virus (PEDV) differ in viral biology and cellular tropism, yet both pathogens suppress macrophage-mediated immune responses in pigs. OBJECTIVE: To identify a conserved macrophage suppression module shared by ASFV and PEDV and evaluate quantum computing as an independent framework for biological network validation. METHODS: Integrated analysis of publicly available GEO datasets (GSE231435 for ASFV and GSE306895) identified 471 shared downregulated genes. A network- and multi-omics-informed 20-gene core was selected and encoded as a 20-qubit modularity-based Quadratic Unconstrained Binary Optimization (QUBO) problem. Community detection was benchmarked using the Quantum Approximate Optimization Algorithm (QAOA) on both the IBM Quantum Aer simulator and the 156-qubit IBM Fez (Heron r2) quantum processor and compared with brute-force enumeration and simulated annealing. RESULTS: A conserved macrophage suppression module shared by ASFV and PEDV was identified. For the STRING protein-protein interaction network, QAOA at circuit depth p = 3 reproduced the brute-force optimum with an approximation ratio of 1.000. In contrast, performance progressively declined in the denser co-expression network with increasing circuit depth, consistent with noise accumulation under current Noisy Intermediate-Scale Quantum (NISQ) conditions. Multi-run consensus analysis identified stable hub genes, including MMP9 and SLA-DOA, as well as genes exhibiting variable community assignments. CONCLUSION: These findings reveal a conserved macrophage suppression module shared between ASFV and PEDV and demonstrate that quantum computing can serve as an independent validation framework for biologically meaningful host-response networks. Network topology emerged as a key determinant of QAOA performance on real NISQ hardware.

Animals↗
PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗
PubMed2025

scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing.

MOTIVATION: Recent advancements in long-read single-cell RNA sequencing (scRNA-seq) have facilitated the quantification of full-length transcripts and isoforms at the single-cell level. Historically, long-read data would need to be complemented with short-read single-cell data in order to overcome the higher sequencing errors to correctly identify cellular barcodes and unique molecular identifiers. Improvements in Oxford Nanopore sequencing, and development of novel computational methods have removed this requirement. Though these methods now exist, the limited availability of modular and portable workflows remains a challenge. RESULTS: Here, we present, nf-core/scnanoseq, a secondary analysis pipeline for long-read single-cell and single-nuclei RNA that delivers gene and transcript-level quantification. The scnanoseq pipeline is implemented using Nextflow and is built upon the nf-core framework, enabling portability across computational environments, scalability and reproducibility of results across pipeline runs. The nf-core/scnanoseq workflow follows best practices for analyzing single-cell and single-nuclei data, performing barcode detection and correction, genome and transcriptome read alignment, unique molecular identifier deduplication, gene and transcript quantification, and extensive quality control reporting. AVAILABILITY AND IMPLEMENTATION: The source code, and detailed documentation are freely available at https://github.com/nf-core/scnanoseq and https://nf-co.re/scnanoseq under the MIT License. Documentation for the version of nf-core/scnanoseq used for this paper, including default parameters and descriptions of output files are available at https://nf-co.re/scnanoseq/1.1.0.

Single-Cell Analysis↗
PubMed2025

A dataset of estimated heterozygous individual and carrier couple frequencies for pan-ancestry carrier screening.

The data described in this publication supported the development and evaluation of pan-ancestry reproductive carrier screening panels for autosomal recessive (AR) and X-linked (XL) conditions. Raw data included combined sets of DNA variants in 1,350 AR/XL genes obtained from the ClinVar and gnomAD databases. The dataset enabled calculations of positive yield for individuals and couples across both ancestry-specific and pan-ancestry, optimised "Goldilocks"-ranked gene panels, addressing population-specific variations in the frequencies of heterozygous individuals and carrier couples. The positive yield analysis offered a performance metric for carrier screening panels, facilitating the modeling of screening performance for panels of varying sizes and composition and providing resources for optimizing panel content to ensure equity across underrepresented genetic ancestries The dataset can support ongoing research into the equitable application of carrier screening and offers significant reuse potential for refining population genetic screening practices, validating computational models, and developing frameworks to update carrier screening panels in alignment with evolving genomic data, including in underrepresented and minority populations.

Carrier screening↗
PubMed1992

[Acute abdominal pain--standardized findings as diagnostic support. Results of a prospective multicenter intervention study and testing of a computer-assisted diagnosis system].

Despite powerful diagnostic tools (e.g. ultrasound, special laboratory investigations), the diagnosis of acute abdominal pain is still a considerable problem. Several studies in the UK have shown that the diagnostic accuracy can be improved by structured and standardized history taking and clinical examination and by computer-aided diagnosis. In the framework of a concerted action of the European Community we have conducted a prospective multicenter interventional trial comparing two consecutive phases: a) a baseline phase in clinical routine without additional intervention, b) a test phase with structured and standardized history and clinical examination (questionnaire, documentation programme). In addition, a computer-aided diagnostic system developed in the UK was applied to the cases in the test phase. Outcome criteria were the diagnostic accuracy of the initial and the final examiner, the perforated appendix rate, the negative appendectomy rate, the negative laparotomy rate and the rates of diagnostic errors with missing indication to operation and of delayed urgent operations. No differences could be found between the phases with respect to the outcome criteria. In the baseline phase (test phase) diagnostic accuracy was 59% (59%), diagnostic accuracy after investigation (senior examiner) was 77% (78%), perforated appendix rate was 11% (16%), negative appendectomy rate was 13% (15%), negative laparotomy rate was 7% (8%), the rate of missed urgent indications to operation was 1.1% (1.9%) and the rate of delayed urgent operations was 3.4% (2.4%). Major differences between the centers were recorded. Computer-aided diagnosis resulted in a diagnostic accuracy of 51%. The introduction of structured and standardized history taking and clinical examination has not brought any improvement of the good results in clinical routine. It is doubtful, whether existing systems of computer-aided diagnosis are able to significantly decrease the still remaining error rate of 20%.

Abdomen, Acute↗
PubMed2026

Endothelial cell-specific DNA methylation alterations in breast cancer.

DNA methylation alterations are well-established contributors to carcinogenesis, yet, in the tumor microenvironment (TME), patterns of lineage and cell-specific methylation alterations are not well understood. Single-cell DNA methylation profiling in the TME is limited by technical challenges and high costs. Here, we use bulk DNA methylation, cell type deconvolution (HiTIMED), and an interaction testing framework (CellDMC) to identify reproducible, computationally inferred lineage-specific epigenetic alterations in the TME supported by orthogonal data sources. Tumor endothelial cells (TECs), critical regulators of angiogenesis, vascular permeability, and immune cell trafficking, acquire structural and functional abnormalities that promote tumor growth. We hypothesize that TECs have altered DNA methylation compared with endothelial cells in non-tumor tissues. In genome-scale methylation data from discovery and validation datasets (tumor n = 1071; non-tumor n = 415), we identify and validate >4500 TEC-specific CpGs with altered methylation, many mapping to genes involved in angiogenesis and endothelial function. Integration with gene expression data indicates that TEC-specific methylation alterations may reprogram transcriptional networks controlling angiogenesis. High-resolution, cell lineage-specific epigenetic landscapes can be inferred from bulk methylation data, implicating TEC-specific DNA methylation alterations as potential drivers of cancer angiogenesis and vascular dysfunction and providing a framework for future mechanistic and translational studies of the tumor vasculature.

DNA Methylation↗
PubMed1992

A computational model of the amplitude and implicit time of the b-wave of the human ERG.

To improve the usefulness of the ERG in identifying the sites and mechanisms of adaptation, development, and disease processes, a theoretical framework based upon Granit's analysis of the ERG was evaluated. The framework assumes that the ERG is the sum of two potentials, one, P3, generated by the receptors and the other, P2, generated by the cells of the INL. Hood and Birch (1990a, b) demonstrated that the leading edge of the a-wave can be quantitatively described by a model used to describe the response from single rod receptors. This model provides P3(t), a theoretical receptor response as a function of time, for any given flash intensity. The ERGs from normal observers and patients with retinal diseases were analyzed in this framework, first by deriving P2 by computer subtracting the predicted P3(t) responses. This analysis was successful and a computational model of the ERG was then derived. The model of P2(t) was constructed with linear filters and a static nonlinearity and using P3(t) as the input. The ERG for any given flash intensity is then P3(t) + P2(t). The model describes (1) the change both in implicit times and in trough-to-peak b-wave amplitudes with flash intensity for the normal, dark-adapted observers; and (2) the changes in b-wave implicit times and amplitudes for three patients with retinal diseases. Among the implications drawn from these analyses were as follows: (1) The fits of the Naka-Rushton equation to trough-to-peak b-wave amplitudes must be interpreted with great care. (2) When the INL is affected by retinal disease, the b-wave may be a very poor reflection of INL activity. (3) The implicit time of the b-wave can provide a measure of receptor sensitivity.

Adult↗
PubMed1992

An approach to evaluating heuristics in abduction: a case study using RedSoar--an abductive system for red blood cell antibody identification.

Abduction, or inference to a best explanation, is a ubiquitous type of inference that is frequently used by humans in a wide range of tasks. However, many realistic domains have properties that make abduction computationally intractable (i.e., where the time to reach a solution increases exponentially with the number of possible explanations). We present a domain task analysis and performance evaluation of RedSoar, a plausible cognitive computational model of abduction, that accomplishes the antibody identification task in immunohematology. The task analysis reveals how a computationally intractable abductive problem, where one is seeking optimal solutions, can be reformulated to be a computationally tractable abductive problem, by seeking satisfactory rather then optimal solutions. From the satisfactory perspective, our evaluation framework of RedSoar's performance explores the computational benefits and costs of having directly available abstract hypothesis formation knowledge, and how a strong causal constraint between hypotheses and data reduces the combinatorial explosion of constructing a best explanation.

Antibodies↗
PubMed2025

Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms.

MOTIVATION: Modern single-cell omics data are key to unraveling the complex mechanisms underlying risk for complex diseases revealed by genome-wide association studies (GWAS). Phenotypic screens in model organisms have several important parallels to GWAS which the author explores in this essay. RESULTS: The author provides the historical context of such screens, comparing and contrasting similarities to association studies, and how these screens in model organisms can teach us what to look for. Then the author considers how the results of GWAS might be exhaustively interrogated to interpret the biological mechanisms underpinning disease processes. Finally, the author proposes a general framework for tackling this problem computationally, and explore the data, mechanisms, and technology (both existing and yet to be invented) that are necessary to complete the task. AVAILABILITY AND IMPLEMENTATION: There are no data or code associated with this article.

Genome-Wide Association Study↗
PubMed1992

Chimeric and humanized antibodies with specificity for the CD33 antigen.

L and H chain cDNAs of M195, a murine mAb that binds to the CD33 Ag on normal and leukemic myeloid cells, were cloned. The cDNAs were used in the construction of mouse/human IgG1 and IgG3 chimeric antibodies. In addition, humanized antibodies were constructed which combined the complementarity-determining regions of the M195 antibody with human framework and constant regions. The human framework was chosen to maximize homology with the M195 V domain sequence. Moreover, a computer model of M195 was used to identify several framework amino acids that are likely to interact with the complementarity-determining regions, and these residues were also retained in the humanized antibodies. Unexpectedly, the humanized IgG1 and IgG3 M195 antibodies, which have reshaped V regions, have higher apparent binding affinity for the CD33 Ag than the chimeric or mouse antibodies.

Amino Acid Sequence↗
PubMedDate not supplied

A computer-based AIDS education program for nursing students.

A multidisciplinary team has developed a computer-based software program for educating nursing students and other college students about six sexually transmitted diseases (STDs). The program for educating about Acquired Immune Deficiency Syndrome (AIDS) is the first of the six "Smartbooks" to be completed, using the Hypercard Expert System shell on the Macintosh computer. Information is organized within a framework known as a concept map, and the student user progresses through the program in a self-directed way by "clicking" on the portion of the concept map that is of interest. Thus, the knowledge base can be accessed in many different ways, and is not restricted to a linear format. Comparative quantitative analysis using a control program shows consistently better student retention of information presented within the program that features concept maps. Qualitative results indicate that students find the conceptually-arranged knowledge base implicitly easy to understand, "fun" to use, and interesting to explore. Educational theory suggests that the success of such a format may be related to the schematic nature of memory.

Acquired Immunodeficiency Syndrome↗
PubMed2025

SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.

Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL's effectiveness and highlight its potential in advancing spatial genomics research.

Genomics↗
PubMed1992

Modelling of intersegmental coordination in the lamprey central pattern generator for locomotion.

Rhythmic motor activity requires coordination of different muscles or muscle groups so that they are all active with the same cycle duration and appropriate phase relationships. The neural mechanisms for such phase coupling in vertebrate locomotion are not known. Swimming in the lamprey is accomplished by the generation of a travelling wave of body curvature in which the phase coupling between segments is so controlled as to give approximately one full wavelength on the body at any swimming speed. This article reviews work that has combined mathematical analysis, biological experimentation and computer simulation to provide a conceptual framework within which intersegmental coordination can be investigated. Evidence is provided to suggest that in the lamprey, ascending coupling is dominant over descending coupling and controls the intersegmental phase lag during locomotion. The significance of long-range intersegmental coupling is also discussed.

Animals↗
PubMed2026

Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.

MOTIVATION: Large-scale bioinformatics analyses increasingly require collaboration across multiple cohorts and institutions, yet existing workflows often rely on data co-localization, which is slow, difficult to scale, and raises privacy concerns. We present a privacy-preserving federated analytics framework that enables secure statistical analysis across distributed datasets without transferring raw data, by performing all computations on encrypted data via cryptographic methods. RESULTS: We evaluate the framework by validating polygenic risk scores and conducting meta-analyses on two real-world cohorts. The proposed solution achieves over 99.9% accuracy relative to plaintext analyses, while maintaining scalable runtime performance with increasing data size and number of participating sites. These results demonstrate the feasibility of secure federated analytics for practical bioinformatics applications involving sensitive data.

Computational Biology↗
PubMed1992

Continuum electrostatics of the C-peptide: anatomy of the problem.

A computational study of the role of all ionizable groups of the C-peptide in its helix-coil transition is performed within the framework of continuum electrostatics. The method employed in our computations involves a numeric solution of the Poisson equation with the Boundary Element Method. Our calculations correctly predict the experimentally observed trends in the helix-coil equilibrium of the C-peptide, and suggest that the mechanisms involved are more complex than usually presumed in the literature. Our results suggest that electrostatic interactions in the unfolded conformation are often more important than in the helix, total electrostatic contribution to the helix-coil transition due to the side chains of the C-peptide destabilizes the helix, changes in the helix stability produced by the changes in the ionization state of the side chains are dominated by side chain effects, the effect of the helix dipole on the energetics of the helix-coil transition of the C-peptide is either minor or similar to other contributions in magnitude; while the formation of a salt bridge is electrostatically favorable, formation of the hydrogen bond between a charged and a polar side chains is not. Factors limiting the accuracy of the computations are discussed.

C-Peptide↗
PubMed2026

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection,  imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.

Cancer Vaccines↗
PubMed2026

Transfer Learning across Material Properties Using Center-Environment Features: From Energetics to Mechanical Properties in Multicomponent Mo Alloys.

Transfer learning (TL) provides a viable approach to mitigate data scarcity in materials informatics. While conventional TL focuses on predicting identical properties across different systems, this work demonstrates a cross-property extension of TL from energy to mechanical properties via end-to-end model weight pre-training and fine-tuning: knowledge learned from predicting substitution energies is transferred to predict distinctly different mechanical properties, substantially improving computational efficiency given the typically higher cost of acquiring target-domain data. To accelerate computational alloy design, machine learning models using center-environment (CE) features were first developed to predict substitution energies of alloying elements in molybdenum (Mo)-based alloys. The Random Forest models achieved the optimal performance and transferability-R2 = 0.97, 〈MAE〉 = 0.11 eV, and 〈RMSE〉 = 0.16 eV-against the density functional theory (DFT) benchmark. The model dependency of feature selection and importance analysis was discussed. The transferability of the energy models was validated on unknown systems with new elements. Subsequently, the energy models were fine-tuned using limited mechanical property data to construct energy-to-property (E2P) TL models capable of predicting elastic properties, including bulk modulus, Young's modulus, shear modulus, and elastic constants, achieving an improved accuracy over the non-transferred ML by ∼10-30%, with its transferability verified by additional DFT calculations. This cross-property E2P transfer learning framework opens a new avenue for accelerating computational materials discovery and may be extended to other multiproperty predictions governed by similar physical principles.

center-environment feature↗