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PubMedDate not supplied

Computers and nursing. Possibilities for transforming nursing.

The use of computers is becoming commonplace in the clinical setting. However, the impact of computer use and its implications for nursing have yet to be understood (Birckhead, 1978). The purpose of this article is to explore how computer technology may transform nursing. The discourse is guided by Burch's (1985) thesis that "the use of technology is non-neutral. It transforms experience, whether for better or worse, and ultimately shapes human thinking and being". If one values nursing as a humanizing activity, then most of the potential transformations can be viewed as negative. When viewed from an instrumental framework, however, the computer may have a positive rather than a negative impact because computer use promotes expediency, efficiency, and precision.

Humans↗
PubMed2026

Integrated computational and experimental benchmarking of Bacillus phage endolysins reveals the relationship between peptidoglycan-fragment recognition descriptors and antibacterial performance.

Protein-based antibacterials such as bacteriophage endolysins offer a targeted therapeutic strategy against Gram-positive pathogens. However, prioritizing the most effective candidates from the large sequence diversity available remains a significant challenge. Here we present a standardized computational-experimental benchmarking framework that evaluates seven phage-derived endolysin variants (E1, E2, E3, E7, E10, E12, and E15) identified from Bacillus genomes. We combined molecular docking and residue-level interaction mapping against muramyl dipeptide (MDP), a minimal conserved peptidoglycan motif, with 1000-ns molecular dynamics simulations, MM/PBSA binding free-energy estimation, and matched functional inhibition assays against Staphylococcus aureus and Micrococcus luteus. Computational analyses revealed generally favorable MDP recognition across variants, albeit with notable differences in contact patterns and complex stability profiles. Experimental screening identified E2 as the most potent antibacterial agent against both species, while E7 and E1 performed strongly in selected computational metrics. Integrated analysis showed only modest correlations between computational descriptors of fragment recognition/stability and observed antibacterial performance. This study establishes a practical comparative benchmarking platform for endolysin candidate prioritization, nominates E2 and E7 as promising candidates for further development, and highlights E1 as a potential structural scaffold for rational engineering, while explicitly demonstrating both the utility and the current limitations of using minimal peptidoglycan fragments as proxies for full cell-wall recognition in lysin benchmarking.

Endopeptidases↗
PubMed1992

Preverbal and verbal counting and computation.

We describe the preverbal system of counting and arithmetic reasoning revealed by experiments on numerical representations in animals. In this system, numerosities are represented by magnitudes, which are rapidly but inaccurately generated by the Meck and Church (1983) preverbal counting mechanism. We suggest the following. (1) The preverbal counting mechanism is the source of the implicit principles that guide the acquisition of verbal counting. (2) The preverbal system of arithmetic computation provides the framework for the assimilation of the verbal system. (3) Learning to count involves, in part, learning a mapping from the preverbal numerical magnitudes to the verbal and written number symbols and the inverse mappings from these symbols to the preverbal magnitudes. (4) Subitizing is the use of the preverbal counting process and the mapping from the resulting magnitudes to number words in order to generate rapidly the number words for small numerosities. (5) The retrieval of the number facts, which plays a central role in verbal computation, is mediated via the inverse mappings from verbal and written numbers to the preverbal magnitudes and the use of these magnitudes to find the appropriate cells in tabular arrangements of the answers. (6) This model of the fact retrieval process accounts for the salient features of the reaction time differences and error patterns revealed by experiments on mental arithmetic. (7) The application of verbal and written computational algorithms goes on in parallel with, and is to some extent guided by, preverbal computations, both in the child and in the adult.

Animals↗
PubMed2026

Dynamic Protein Structure Paradox: An Integrative Framework for Endpoint-Conditioned Evidentiary Sufficiency in Structure-to-Function Claims.

Accurate coordinates for a represented protein state do not, by themselves, establish activity or any other condition-specific function. This article defines the Dynamic Protein Structure Paradox (DPSP) as the apparent conflict between structural accuracy and functional underdetermination and develops it as an integrative evidentiary assessment framework rather than a new theory or paradigm. The underlying problem has been longstanding, since structural genomics, function annotation, allostery, and disorder research each established that fold does not determine function and that function does not determine fold. DPSP consolidates those results into one endpoint-conditioned rule. Once a measurable endpoint is defined, it assesses four coupled dimensions: relevant-state completeness, context completeness, ensemble or kinetic dependence, and chemical dependence. A rubric rates each dimension as adequate, uncertain, or missing, and a materiality test determines which gaps influence the stated decision. The outcome is one of three mutually exclusive modes of utilization: geometry-led, conditional, or function-measured. The deliverable is a concise evidence statement delineating what the structure supports, which decisive variable remains unmeasured, and what corroboration is necessary. DPSP complements, rather than replaces, existing structural, ensemble, and computational approaches. The framework remains unvalidated, its thresholds are provisional, and the studies necessary to confirm or refute it are specified.

Proteins↗
PubMed2026

Mining the sHSP20 (small heat-shock protein) gene family in finger millet (Eleusine coracana (L.) Gaertn.): structural, evolutionary and predicted abiotic-stress-responsive insights.

Small heat-shock proteins (sHSPs, the HSP20 family) are ATP-independent molecular chaperones that hold partially unfolded substrates and protect the proteome during heat and other abiotic stresses; every member is defined by a conserved &#x3b1;-crystallin domain (ACD). Finger millet (Eleusine coracana) is a climate-resilient, calcium-rich allotetraploid cereal of the semi-arid tropics whose HSP20 repertoire had not been catalogued. The present study is an entirely computational (in silico) analysis of the chromosome-scale reference genome of finger millet (NCBI GenBank assembly GCA_032690845.1, cultivar KNE 796-S). Mining the predicted proteome with the ACD profile (Pfam PF00011) and confirming every candidate by NCBI CD-search recovered 76 non-redundant ACD-bearing HSP20 genes (EcHSP20-1-EcHSP20-76). Based on phylogeny and TargetP-predicted localization, the members were classified into ten subfamilies: seven cytosolic/nuclear classes (C-I to C-VII, 60 members) together with chloroplastic (11), mitochondrial (3) and endoplasmic-reticulum (2) groups. The proteins ranged from 110 to 355 amino acids (12.1-39.2&#xa0;kDa) with theoretical pI of 4.85-9.69. The 76 loci were distributed over 14 of the 18 chromosomes and were conspicuously absent from chromosomes 8&#xa0;A, 8B, 9&#xa0;A and 9B, with pronounced clustering on chromosomes 1, 2, 3 and 6. Duplication analysis detected 149 paralogous pairs (49 homoeologous, 80 segmental/dispersed and 18 tandem); 147 of 148 pairs for which substitution rates could be calculated returned Ka/Ks&#x2009;<&#x2009;1 (mean 0.20), indicating strong purifying selection consistent with retention after whole-genome/allopolyploid duplication. Promoter analysis (PlantCARE) revealed enrichment of abscisic-acid-responsive (ABRE), MYB/MYC drought-related, STRE, DRE, low-temperature (LTR) and methyl-jasmonate/salicylic-acid elements, whereas canonical heat-shock elements (HSE) were not recovered. Expression profiling against a public drought transcriptome (SRP081350) showed that about half of the genes (39 of 76) are transcribed in leaf tissue, the expressed fraction being dominated by the cytosolic class C-I. This first finger-millet HSP20 catalogue provides a verified, reproducible framework and nominates computationally predicted candidate genes for future functional work on thermotolerance in cereals.

Allotetraploid↗
PubMed1990

A computational theory of the hippocampal cognitive map.

Evidence from single unit and lesion studies suggests that the hippocampal formation acts as a spatial or cognitive map (O'Keefe and Nadel, 1978). In this chapter, I summarise some of the unit recording data and then outline the most recent computational version of the cognitive map theory. The novel aspects of the present version of the theory are that it identifies two allocentric parameters, the centroid and the eccentricity, which can be calculated from the array of cues in an environment and which can serve as the bases for an allocentric polar co-ordinate system. Computations within this framework enable the animal to identify its location within an environment, to predict the location which will be reached as a result of any specific movement from that location, and conversely, to calculate the spatial transformation necessary to go from the current location to a desired location. Aspects of the model are identified with the information provided by cells in the hippocampus and dorsal presubiculum. The hippocampal place cells are involved in the calculation of the centroid and the presubicular direction cells in the calculation of the eccentricity.

Animals↗
PubMed2026

Scaling linear-model breeding values to the liability scale: an application to pig binary traits.

In commercial pig production, many important traits are recorded as binary phenotypes. For such traits, threshold models offer an appropriate framework but are computationally intensive. Thus, linear models are widely used to obtain genomic estimated breeding values (GEBV); however, these are on the observed scale (phenotypic). This creates the need for a robust method to approximate GEBV from linear models to the liability scale. A recently proposed approximation showed good concordance for low-prevalence traits (<5%) but has not yet been tested for a wider range of prevalence values and for models with more than one random effect. We aimed to evaluate the performance of this approximation for pig binary traits with prevalences ranging from <5% to >86%, in both animal and maternal animal models. Data were available for five fitness traits (FT1-FT5), with up to 233k animals with phenotypes, of which 204k animals were genotyped with a 25k SNP array. Variance component estimates were obtained using threshold models. Classical animal models were used for FT1-FT3, and maternal animal models for FT4 and FT5. Variance components on the observed scale were then obtained by multiplying estimates from a threshold model by the square of the height of the standard normal density evaluated at the threshold. GEBV were predicted using single-step genomic best linear unbiased prediction under both linear and threshold models. The approximation tested involved scaling the GEBV using the height of the ordinate of the standard normal distribution evaluated at the threshold as a scaling factor. The agreement between GEBV from the scaled linear model and the threshold model on the probability scale was evaluated using Pearson and Spearman correlations, mean squared error (MSE), regression parameters, overlapping coefficient (OVL), distribution overlap, and classification accuracy (CACC). Correlations between linear and threshold GEBV ranged from 0.94 (low-prevalence traits) to 0.99 (high-prevalence traits) for the direct GEBV and were 0.99 for the maternal GEBV. MSE were close to zero. The OVL exceeded 0.83 for all traits. CACC ranged from 95.10% to 98.33% for the direct GEBV and from 92.54% to 97.42% for the maternal GEBV. Regardless of model and trait prevalence, this approximation yielded GEBV that are highly consistent with threshold model GEBV, providing a reliable, practical approach for large-scale pig genetic evaluations for binary traits using linear models.

Animals↗
PubMed1988

Cohort parity analysis: statistical estimates of the extent of fertility control.

Cohort parity analysis (CPA) is a method for indirect measurement of the extent and timing of the adoption of fertility control within marriage. It uses information on the parity distribution of a cohort of women of specified marriage ages and durations. A multinomial model of parity provides a convenient framework for the computation of distributional parameters describing the extent to which marital fertility control has been accepted and characterizing the way control has been used within specific durations of marriage. This leads to a pair of easily implemented formulas for upper- and lower-bound estimates of the expected proportion of the population ever controlling and the distribution of controllers by parity. The power of CPA is illustrated, using census data for currently married couples in Dublin, Belfast, and other county boroughs of Ireland in 1911.

Adult↗
PubMed2025

Automating candidate gene prioritization with large language models: from naive scoring to literature-grounded validation.

MOTIVATION: Identifying promising therapeutic targets from thousands of genes in transcriptomic studies remains a major bottleneck in biomedical research. While large language models (LLMs) show potential for gene prioritization, they suffer from hallucination and lack systematic validation against expert knowledge. RESULTS: The framework identified 609 sepsis-relevant genes with >94% filtering efficiency, demonstrating strong enrichment for inflammatory pathways including TNF-&#x3b1; signaling, complement activation, and interferon responses. Literature validation yielded 30 ultra-high confidence therapeutic candidates, including both established sepsis genes (IL10, TREM1, S100A9, NLRP3) and novel targets warranting investigation. Benchmark validation against expert-curated databases achieved 71.2% recall, with systematic correlation between computational confidence and evidence quality. The final candidate set balanced discovery (11 novel genes) with validation (19 known genes), maintaining biological coherence throughout the filtering process. This framework demonstrates that rigorous methodology can transform unreliable LLM outputs into systematically validated biological insights. By combining computational efficiency with literature grounding, the approach provides a practical tool for prioritizing experimental validation efforts. The modular design enables adaptation to other diseases through knowledge base substitution, offering a systematic approach to literature-guided biomarker discovery. AVAILABILITY AND IMPLEMENTATION: We developed a two-stage computational framework that combines LLM-based screening with literature validation for systematic gene prioritization. Starting with 10&#xa0;824 genes from the BloodGen3 repertoire, we applied multi-criteria evaluation for sepsis relevance, followed by retrieval-augmented generation using 6346 curated sepsis publications. A novel faithfulness evaluation system verified that LLM predictions aligned with retrieved literature evidence. Source code and implementation details are available at https://github.com/taushifkhan/llm-geneprioritization-framework, vector database at https://doi.org/10.5281/zenodo.15802241, and Interactive demonstration at https://llm-geneprioritization.streamlit.app/.

Humans↗
PubMed2026

Recent Advances in Multi-Omics of Systemic Lupus Erythematosus.

This comprehensive narrative review examines recent advances in multi-omics research for Systemic Lupus Erythematosus (SLE), emphasizing integrated approaches over single-omics studies. The review critically evaluates technological advancements, methodological innovations, and clinical applications while identifying current limitations and future research directions. We conducted a comprehensive narrative review following SANRA guidelines, searching PubMed, Web of Science, Scopus, and Embase, covering publications from January 2018 to June 2025. The review focuses on studies integrating two or more omics layers in SLE research, with emphasis on computational methods, biomarker validation, and clinical applications. Multi-omics integration has revealed critical insights into SLE pathogenesis, including immune cell heterogeneity, gene-environment interactions, and metabolic dysregulation. However, significant challenges remain in data integration methodologies, small sample sizes, and biomarker reproducibility. Current computational approaches include early integration (concatenation), intermediate integration (joint dimensionality reduction), and late integration (ensemble methods). While multi-omics approaches offer unprecedented insights into SLE complexity, standardized integration protocols and robust validation frameworks are urgently needed. Small sample sizes and heterogeneity issues limit reproducibility, particularly affecting biomarker discovery and clinical translation. Multi-omics integration represents a paradigm shift toward precision medicine in SLE, but realizing this potential requires addressing current methodological limitations, standardizing validation processes, and developing robust computational frameworks for reliable clinical applications.

Humans↗
PubMed1986

A theoretical study of the comparative binding affinities of daunomycin derivatives to a double-stranded oligomeric DNA. Proposal for new high affinity derivatives.

Theoretical computations were performed on the comparative binding affinities of daunomycin (DM, 1) and seven derivatives related to the double-stranded oligonucleotide d(CGATCG)2. The compounds investigated were 4-demethoxy DM (2), and its beta-anomer (3), 4-demethoxy-7,9-bis-epi DM (4) and its beta anomer (5), a derivative with glucosamine instead of daunosamine (6), and two additional hypothetical DM derivatives in which the cationic NH3+ group of the daunosamine moiety is replaced by either a CH2--NH3+ group (7) or a CH2CH2NH3+ group (8), so as to indicate the effect on the binding affinity of interposing one- or two-methylene groups between the sugar and the cationic charge. The conformational angles of the hexanucleotide are fixed in values found in the representative crystal structure of the d(CGTACG)2-DM complex. The intermolecular drug-hexanucleotide interaction energies and the conformational energy changes of the drug upon binding are computed and optimized in the framework of the SIBFA procedure (sum of interactions between fragments computed ab initio), which uses empirical formulas based on ab initio SCF computations. The overall binding affinity ordering of compounds 1-6 compares satisfactorily with the ordering of available experimental affinity constants. The binding affinities of compounds 7 and 8, for which no experimental results seem to be available yet, are predicted to be significantly higher than those of the parent compound DM, with the greatest affinity found for 7. Because of the overall correlation between binding affinity of anthracyclines to DNA and their antitumor activity, these last two compounds deserve an exploration of their chemotherapeutic efficiency.

DNA↗
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&#x2009;=&#x2009;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↗
PubMed1986

A theoretical study of the binding of phenothiazine derivatives to residues 82-93 of calmodulin.

A theoretical study was performed of the interaction of four phenothiazine derivatives, promethazine, promazine, trifluopromazine, and trifluoperazine, with a fragment (82-93) of calmodulin, held in the alpha-helical conformation. The computations were performed in the framework of the SIBFA 2 procedure (sum of interactions between fragments computed ab initio), which uses analytical formulas based on ab initio self-consistent field computations. The interaction energy is the sum of the intermolecular phenothiazine-oligopeptide interaction energy and of the separate intramolecular energy variations of the phenothiazine and of the side chains of the oligopeptide upon complex formation. The ordering of interaction energies of the four investigated phenothiazines parallels the ordering of their experimentally measured affinities for calmodulin, with a maximum affinity for trifluoperazine. The principal features of the trifluoperazine complex are a short hydrogen bond between the piperazinium proton and one anionic oxygen of Glu 87, and hydrophobic interactions between the piperazinium ring and Val 91 and between the methylene chain and Ala 88, together with partial insertion of the phenothiazine ring and the--CF3 substituent between Phe 89 and Phe 92.

Calmodulin↗
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↗
PubMed1985

A theoretical investigation on the sequence selective binding of daunomycin to double-stranded polynucleotides.

Theoretical computations are performed on the structural and energetical factors involved in the sequence selective binding of daunomycin (DNM) to six representative self-complementary double-stranded hexanucleotides: d(CGTACG)2,d(CGATCG)2,d(CITACI)2, d(TATATA)2, d(CGCGCG)2 and d(TACGTA)2. The conformational angles of the hexanucleotides are fixed in values found in the representative crystal structure of the d(CGTACG)2-DNM complex. The intermolecular DNM-hexanucleotide interaction energies and the conformational energy changes of DNM upon binding are computed and optimized in the framework of the SIBFA procedure, which uses empirical formulas based on ab initio SCF computations. Among the two regularly alternating hexanucleotides, d(TATATA)2 and d(CGCGCG)2, a stronger binding is predicted for the former, in agreement with experimental results obtained with poly(dA-dT).poly(dA-dT) and poly(dG-dC).poly(dG-dC). Altogether, however, among the six investigated sequences, the strongest complexes are computed for the mixed hexanucleotides d(CGATCG)2 and d(CGTACG)2, containing the intercalation site between two CG base pairs and an adjacent TA base pair. This situation may be related to the increased affinity of DNM for GC rich DNA's and to the situation in the crystal structure of the DNM-d(CGTACG)2 complex. Analysis of the intrinsic base sequence preferences expressed by the individual constituents of DNM, namely the daunosamine side chain, the chromophore ring and its two 9-hydroxyl and 9-acetoxy substituents, reveals that the overall sequence preference found is the result of a rather intricate interplay of intrinsic sequence preferences, in particular at the level of daunosamine and the 9-hydroxyl substituent.(ABSTRACT TRUNCATED AT 250 WORDS)

Base Sequence↗
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↗