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At least 73 records · Page 4Linked to original sources
PubMed2026

Factors influencing the enhancement of the new iron triangle in healthcare organisations.

PURPOSE: A new paradigm, "healthcare's new iron triangle," has been developed to emphasise the technological perspective of healthcare delivery, focusing on automation, value and empathy. The study aims to build a conceptual model and to identify factors for the enhancement of the new iron triangle in healthcare organisations. DESIGN/METHODOLOGY/APPROACH: The healthcare organisation is the primary focus point of the current study. To determine the factors, a survey of the literature and healthcare experts' opinions was conducted. The healthcare professionals validated the identified factors. Data for this study were gathered using a closed-ended questionnaire and scheduled interviews. The study employed "Total Interpretive Structural Modeling methodology and Matriced' Impacts Croise´s Multiplication Appliqué´ a UN Classement/Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis" to address the "why" and "how" the factors interact and prioritise the identified factors. FINDINGS: The study found that organisational structure (F8), artificial intelligence (F1), innovation (F2) and human resources (F5) are the driving or key factors of the study. RESEARCH LIMITATIONS/IMPLICATIONS: The study primarily focused on identifying factors for the enhancement of a new iron triangle in healthcare organisations. The scope could eventually be expanded to explore more areas. PRACTICAL IMPLICATIONS: Academics and other stakeholders will have a better understanding of the key drivers for the enhancement of the new iron triangle in healthcare organisations. ORIGINALITY/VALUE: In this study, total interpretive structural modeling and cross-impact MICMAC analysis are proposed as an innovative approach to address the new iron triangle in healthcare organisations.

Humans↗
PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗
PubMed1979

Metabolism of totally ischemic excised dog heart. II. Interpretation of a computer model.

Analysis of the ischemic dog heart preparation described in the preceding paper indicates that it is an analogue in slow motion of the tissue in the center of a cardiac infarct. It is respiring very slowly and not capable of performing mechanical work. Glycolysis starts up with both glucose and glycogen as inputs. Later hexokinase and to some extent phosphofructokinase become limiting owing to inhibitor accumulation or acidosis. Metabolism then results primarily from cAMP-driven glycogenolysis, largely limited by the glycogen debranching enzymes at later times, with accumultion not only of lactate and alpha-glycerophosphate but of glucose as well. Amino acid levels oscillate with time while fatty acids accumulate at late times. The elevation of cAMP at later times may involve disturbances in its metabolism as well as mechanisms such as adenosine accumulation that are more important in cardiac ischemia than in normal heart. The clinical implications of this behavior are discussed.

Amino Acids↗
PubMed1977

[Electronic measuring and calculating devices for arcogrammetric model diagnosis and for the interpretation of teleradiographs].

A newly developed method to mesure different parameters from plaster models of the teeth, from dental radiographs and from cephalometric X-rays by means of a 4-K minicomputer and on-line linear transducers are described. Programs in connection to Arcogrammetrics [Herren] are presented. The electronic devices allow storage of these parameters in order to make drawings of the actual dental arches and of predicted arches, as well as to trace growth and/or progress in orthodontic treatment.

Cephalometry↗
PubMed2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy.

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Databases, Protein↗
PubMed2026

Predicting food taste with bound-driven optimization.

The prediction of sensory attributes from ingredient-level formulations is an emerging challenge at the intersection of food science and artificial intelligence. We address the fundamental question of whether the taste of a food can be predicted from its ingredients by treating recipes as composite materials. We apply Hashin-Shtrikman (HS) and Reuss-Voigt (RV) bounds, techniques originally developed for elastic moduli, as a null-hypothesis additive baseline for five taste dimensions (sweetness, sourness, bitterness, umami, saltiness) on a curated dataset of 70 recipes decomposed into 115 distinct ingredients scored against a library of 209 ingredient-level taste references with trained-panel ground truth. This baseline systematically under-predicts perceived taste: 77% of actual taste values exceeded the HS upper bound, with the exceedance rate ranging from 26% (bitterness) to 97% (saltiness). We traced this gap to specific processing chemistry (Maillard reactions, caramelization, evaporative concentration, protein hydrolysis, and nucleotide synergy) and introduced a hybrid model that augments the HS baseline with eight chemistry-proxy features encoding these mechanisms. Our results show that our interpretable hybrid model eliminates the systematic bias and reduces mean absolute error by 27%-62% for sweetness, sourness, umami, and saltiness while using only 10 interpretable features, achieving performance comparable to a black-box Lasso regression on 115 per-ingredient features. We further demonstrate constrained inverse design via Differential Evolution, recovering ingredient formulations that match target taste profiles subject to compositional bounds. Our work demonstrates how key chemical processes during food preparation can inform and augment physics-based and machine learning models, providing a quantitative fingerprint of processing chemistry's contribution to taste perception and paving the way for model-driven food formulation with targeted sensory characteristics.

Composite material bounds↗
PubMed1978

General theory of critical periods and development of obesity.

The general systems theory (GST), the general theory of organization (GTO), and the general theory of critical periods (GTCP) have been applied to some nutritional problems. This theoretical approach seems to be in good agreement with most of the data of the literature and with the personal experience, pointing at the possibility to use a simple general model to interpret the complex problem of obesity.

Critical Period, Psychological↗
PubMed1975

Intrauterine pressure wave form characteristics in hypocontractile labor before and after oxytocin administration.

The data demonstrate that the contractions of hypocontractile active labor and normal spontaneous labor are different in several measures in addition to maximal amplitude. Furthermore, when the pathophysiology is corrected by the use of oxytocin, the contractions resemble those of normal spontaneous labor except in the maximal rate of tension development. Our data tend to support the subcellular model of uterine contractility, although the incompleteness of these models limits interpretation.

Adrenocorticotropic Hormone↗
PubMed2026

Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.

Although machine learning models can predict antimicrobial susceptibility from bacterial whole genome sequencing (WGS), state-of-the-art approaches are computationally demanding or dependent on knowledge of genetic resistance determinants. Here, we describe an efficient data-driven approach to predicting minimum inhibitory concentration (MIC) by progressively extending and refining predictive genome segments, independent of prior knowledge of resistance determinants. Resultant models had high interpretability - known and potentially novel resistance determinants were captured. Using 762 clinical E. coli strains, 71.6% of predictions were within one dilution of the measured MIC. Models trained with this algorithm generalised better onto external data (F1 score = 0.85) compared with alternative models trained on annotated resistance determinants (F1 = 0.82) or k-mer counts (F1 = 0.74). Computational demands were low (RAM usage 23.6GB vs 38.8GB for k-mer model). These advantages represent an important advance in predicting antimicrobial susceptibility from WGS, with potential applications for clinical diagnostics, drug development, and surveillance.

Journal Article↗
PubMed1992

A coupled pacemaker-slave model for the insect photoperiodic clock: interpretation of ovarian diapause data in Drosophila melanogaster.

A coupled circadian oscillator model for the insect photoperiodic clock is described which consists of a hierarchically arranged pacemaker and slave. The pacemaker is self-sustained, temperature compensated, and entrainable by the light cycle; the slave is a damping oscillation receiving entrainment from two sources, from the pacemaker via a coupling factor, and also directly from the light. The damping slave oscillation is seen as the "photoperiodic oscillator", equivalent to that proposed earlier by Lewis and Saunders (1987). The present simulations describe the effect of the strength of the coupling factor between hypothetical short- and long-period pacemaker oscillations (modelled on the "clock" mutants perS and perL2 in Drosophila melanogaster) and a slave oscillation with a period of about 24 hours. The output is presented in terms of photoperiodic response curves and Nanda-Hamner, or resonance, plots. With a high coupling strength, the pacemakers strongly entrain the slave, but with a low coupling strength the slave's properties are more evident. The model is presented as a possible explanation for recent ovarian diapause data in D. melanogaster "clock" mutants (Saunders 1990), but also as a more general model for the role of the insect circadian system in seasonal time measurement.

Animals↗
PubMed2026

Evaluating the pathogenic significance of unique chromosomal variants in craniosynostosis using patient-derived induced pluripotent stem cells and mouse modelling.

PURPOSE: Unravelling causal links between unique structural/copy-number variants (SV/CNV) and associated phenotypes is essential for correct genetic counselling. We investigated two families in which patients with craniosynostosis had SV/CNV potentially dysregulating a fibroblast growth factor (FGF)-encoding gene; a 730 kb dup(4)(q21.21) including FGF5; and a complex 568 kb interspersed 13q12.11 duplication, located 841 kb from FGF9. METHODS: We combined bioinformatic predictions of altered topologically-associating domain (TAD) structure, with experimental analysis (RNA- and ATAC- [assay for transposase-accessible chromatin] sequencing) of patient induced pluripotent stem cell lines (iPSCs) differentiated to neural crest (NCC) and osteoprogenitor (OPC) identities. For the dup(4)(q21.21) we generated a mouse bearing an equivalent rearrangement using CRISPR-Cas9 targeting. RESULTS: TAD analysis suggested potential dysregulation of the FGF5/FGF9 gene by bringing it into a novel genomic milieu. The RNA- and ATAC-seq assays demonstrated FGF5/FGF9 upregulation (2.7-18x) and local opening of chromatin, in 3/4 cell lines. For the dup(4)(q21.21), a causal role was supported by the mouse model, whereas interpretation of the 13q12.11 SV is confounded by a co-existing FOXP2 pathogenic variant. CONCLUSION: Patient iPSC-differentiated NCC and OPC lines, combined with TAD-based modelling to generate testable functional hypotheses, provide valuable functional evidence when evaluating causation of unique SV/CNV in craniosynostosis.

copy-number variant↗
PubMed2026

Causal circuit tracing reveals distinct computational architectures in single-cell foundation models: inhibitory dominance, biological coherence, and cross-model convergence.

MOTIVATION: Sparse autoencoders (SAEs) decompose foundation-model activations into interpretable features, but the model-internal causal interactions between those features (i.e. what ablating one feature does to the others, as distinct from the biological causal structure of the underlying cells)-and how those model-internal relationships relate to biological structure-are uncharacterized in single-cell foundation models. RESULTS: We introduce model-internal causal circuit tracing-zeroing one SAE feature at a source layer and measuring the resulting change in all downstream SAE features, for each of 120 source features-and apply it to Geneformer V2-316M and scGPT whole-human across four conditions (96&#xa0;892 ablation-derived edges, 80&#xa0;191 forward passes). On annotation-selected source features, edges share GO/KEGG/Reactome/STRING/TRRUST ontology terms at 50.9%-68.5%, a 2.9-6.2&#xd7; enrichment over a configuration-preserving permutation null (P<.002); on 20 randomly sampled source features this attenuates to 21.5%-26.3%-still 2.5-3.1&#xd7; above null-quantifying the annotation-selection contribution. Inhibitory dominance (fraction of ablation edges with d<0, i.e. source activation supports downstream target) is 65.5%-89.4%. scGPT produces larger raw per-edge effects (mean |d|=1.40 versus 1.05); after feature-share normalization, Geneformer is stronger (paired gene-pair ratio 0.64 on 33&#xa0;301 shared pairs). Cross-model consensus yields 1142 architecture-invariant domain pairs (ordered pairs of GO biological-process categories "A&#x2192;B" each connected by at least one ablation edge in both models; 10.6&#xd7; enrichment over permutation null; P<.001). Circuit edge magnitude explains <1% of the variance in marginal driver-gene coexpression on the same cells (R2=0.010, n=31&#xa0;176): the graph encodes structure beyond bivariate correlation. Against a matched-cell-type ENCODE ChIP-seq prior, circuit-predicted transcription factor (TF)&#x2192;target pairs are enriched 2.06&#xd7; (Fisher OR 5.84), markedly higher than 1.12&#xd7; against TRRUST; direct ChIP-seq-supported target pairs show 10-30&#xd7; larger CRISPRi sign-bias-corrected excess than indirect pairs. Gene-level CRISPRi validation on Replogle K562 and the noncancer RPE1 arm (and a true primary-T-cell control from Shifrut E, Carnevale J, Tobin V et&#xa0;al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 2018; 175: 1958-71.e15) after sign-bias correction shows excess over baseline of +0.03 and +0.35 percentage points on K562 and RPE1, respectively (baseline already 52%-56% from sign marginals); effect-magnitude Spearman correlations &#x3c1;&#x2248;0. Bootstrap and per-cell-type stability (N&#x2208;{50,100,200}; B cell, CD4&#xa0;+ T, macrophage) give Pearson r&#x2265;0.97 on shared edges with 100% sign agreement; edge Jaccard grows monotonically with sample size. The circuit graph is therefore highly reproducible as an effect-size map, cell type specific in edge identity, consistent with coexpression encoding, and weakly but detectably enriched for ChIP-seq-supported direct regulatory edges. AVAILABILITY AND IMPLEMENTATION: https://github.com/Biodyn-AI/bio-sae-circuits (Python). Archival DOI: 10.5281/zenodo.19,633,166 (Zenodo).

Humans↗
PubMed1975

Mathematical modelling of cell cycle and chronobiology: preliminary results.

A mathematical model taking into account the observed diurnal variations in cell kinetics is presented. The principle of the method is to divide each phase of the cell cycle in a definite number of compartments and to assume that the fluxes into and out of the compartments corresponding to the G1 phase are the only varying parameters through the day. Theoretical evolutions of percentages of cells in M and S phase, theoretical curves for percentage labelled mitosis experiment are derived. Preliminary results of the applications of the model to interpretation of published experimental data obtained in hamster cheek pouch epithelium are shown.

Cell Division↗
PubMed1975

[Psychoanalytic interpretation of sleep-disturbances. Model of a structural-theoretical classification (author's transl)].

In psychoanalytic literature anxiety, guilt-feelings and unconscious hostility are assumed to be the most common causes of spleeplessness. The author attempts to ascribe these emotions to conflicts between the instances of the psychoanalytic personality model as a structure-theoretical classification of sleep-disturbances. Three large groups emerge: Neurotic disturbances of sleep with internalized conflicts (these correspond essentially to what are generally understood to be neurotic sleep-disturbances), neurotic disturbances of sleep with externalized conflicts (these are more common in childhood) and non-neurotic sleep disturbances. Within these groups the relative parts of effectiveness of the instances of the psychoanalytic personality-model - ego, super-ego, id and reality - are discussed.

Aggression↗
PubMed1975

Reaction mechanism and structure of the active site of proline racemase.

Proline racemase catalyzes the interconversion of D- and L-proline. Previous studies in this laboratory have established that the reaction proceeds by means of a two-base mechanism in which one base on the enzyme removes the substrate alpha-hydrogen as a proton and the conjugate acid of another base donates a proton to the opposite side of the alpha-carbon (Cardinale, G.J., and Abeles, R.H., (1968), Biochemistry 7, 3970. An assumption of the proposed mechanism was that no proton exchange occurs from the enzyme-substrate complex. In the present study, we have shown that the rate of 3H release from DL-[alpha-3H]proline, in the presence of proline racemase, decreases with increasing proline concentrations. These results establish that release of the substrate derived proton from the enzyme occurs largely, possibly exclusively, after release of the product. Under initial velocity conditions, the rate of 3H release from L-[alpha-3H]proline is not reduced with increasing L-proline concentrations. Thus, the enzyme-bound proton derived from one isomer can only be "captured" by the other isomer. We conclude that there are two forms of the enzyme; one binds L-proline and the other D-proline. Release of the substrate derived proton from enzyme is more rapid than the interconversion of these two forms. These results are consistent with the previously proposed mechanism. Proline racemase is composed of similar subunits of mol wt 38,000 as determined by gel electrophoresis in the presence of sodium dodecyl sulfate. Equilibrium dialysis experiments detect only one substrate binding site for every two subunits. When the oxidized form of the enzyme, which is inactive and cannot bind substrate, is reduced by thiol to yield active enzyme, two cysteine sulfhydryl groups per dimer become available to react with iodoacetate. Inactivation of the enzyme occurs upon modification of one of these cysteines. All iodoacetate incorporation occurs at the same point in the primary sequence of the enzyme, and can be prevented by the presence of proline or pyrrole-2-carboxylate, a substrate analog. A model is proposed in which a single active site is formed by elements of two identical subunits. Although the data are consistent with this model, another interpretation, in which half of the subunits are nonfunctional, cannot be ruled out.

Amino Acid Isomerases↗
PubMed1992

A coupled-oscillator model of ovarian-cycle synchrony among female rats.

The ovarian cycles of female rats become synchronized when they live together, as do the cycles of many other mammals. Ovarian cycles also become synchronized when rats live apart if they share a common air supply, indicating that ovarian-cycle synchrony is mediated by pheromones. We developed a coupled-oscillator model of ovarian-cycle synchrony to test several hypotheses about its pheromonal and neuroendocrine mechanisms and to guide our experimental research. The model spans three levels of organization: the group, the rat, and the neuroendocrine components of the ovarian system. The ovarian system (not the ovaries themselves) are modeled as an oscillating system. Coupling among ovarian systems is mediated by the exchange of two pheromones, one that delays the phase of the ovarian system and one that advances it. Computer simulation experiments showed that this coupled-oscillator model can explain the levels of ovarian-cycle synchrony observed in groups of female rats while, at the same time, matching an empirical distribution of ovarian-cycle lengths. By successfully matching computer simulation data with empirical data, we were able to infer theoretical predictions in a number of areas: (1) effect of initial conditions on the probability that a group will change to different synchrony level and phase relationships, i.e. the transition probability between all synchrony levels and phase relationships; (2) effects of individual differences in pheromone sensitivity on ovarian-cycle synchrony; (3) the timing of pheromone sensitivity during the ovarian cycle; and (4) the existence of partial luteinizing hormone surges, which may cause the "spontaneous" prolonged ovarian cycles associated with ovarian-cycle synchrony. The paper concludes by discussing the integrative role of this model for experimental research. In particular, we focus on the role of this model in interpreting theoretical aspects of ovarian-cycle synchrony as well as for guiding future experimental research into its mechanisms and functions.

Animals↗