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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.
OBJECTIVE: The authors evaluated how quantum computing threatens the cryptographic primitives used in Bitcoin and other blockchain systems. These findings were translated into a standards-aligned post-quantum migration profile for healthcare ledgers, including consent, identity, provenance, audit, and encrypted off-chain data exchange. METHODOLOGY: Narrative analysis, theoretical security analysis, and healthcare-oriented deployment mapping of classical public-key and hash primitives used in blockchain protocols were paired with an implementation-oriented migration profile based on finalized National Institute of Standards and Technology (NIST) post-quantum standards. We summarize the mathematical assumptions underlying RSA, Elliptic Curve Cryptography/Elliptic Curve Digital Signature Algorithm (ECC/ECDSA), and Secure Hash Algorithm (SHA-2/SHA-3-family) hash functions; analyze their susceptibility to Shor's and Grover's quantum algorithms; compare Federal Information Processing Standards (FIPS) 203 Module-Lattice-Based Key-Encapsulation Mechanism Standard (ML-KEM), FIPS 204 Module-Lattice-Based Digital Signature Standard (ML-DSA), and FIPS 205 Stateless Hash-Based Digital Signature Standard (SLH-DSA); and map their distinct roles to healthcare-ledger authorization, auditability, identity, and encrypted off-chain exchange. The term Advanced Hybrid Module-LWE & Code-Based (AHMC) is used only as shorthand for a standards-aligned hybrid post-quantum cryptography (PQC) migration profile and does not denote a proprietary product, a novel algorithm, or a new cryptographic primitive. Proposed adoption of hybrid, quantum-resistant cryptographic primitives for cryptocurrency wallets, transaction signatures, and ledger security during an interim migration period (hybrid classical + PQC, followed by PQC-only). Qualitative and implementation-oriented assessment of (1) break feasibility of RSA/ECC under Shor's algorithm, (2) effective security reduction for hash functions under Grover's algorithm, (3) security assumptions and composition requirements of a standards-aligned hybrid migration profile, (4) transaction-size and verification-cost impact, and (5) healthcare-specific implications for long-retention consent, identity, provenance, and audit records. RESULTS: Shor's algorithm reduces integer factorization and discrete logarithms to polynomial time, directly compromising Rivest-Shamir-Adleman (RSA) and ECC/ECDSA once fault-tolerant, large-scale quantum computers exist. Grover's algorithm yields a quadratic speedup for brute-force search, effectively halving the security margin of symmetric keys and hash functions at fixed output sizes. The AHMC-L1/L3/L5 profiles use ML-KEM-512/768/1024 for key establishment and ML-DSA-44/65/87 for transaction authentication, with SLH-DSA as a hash-based fallback. During a hybrid ECDSA+PQC migration, verification requires one classical and one PQC verification per authorization; transaction-size overhead is dominated by PQC signatures, approximately 2.4 KB for ML-DSA-44, 3.3 KB for ML-DSA-65, and 4.6 KB for ML-DSA-87 before script and encoding overhead. For healthcare ledgers, these findings support selective use of post-quantum signatures for:high-value state transitions,one-time public-key registration where possible, andcontinued off-chain storage of protected health information.Deployment suitability remains contingent on workflow-specific latency, storage, availability, key lifecycle, and side-channel testing. CONCLUSIONS: Quantum risk to blockchain signatures has direct implications for healthcare systems that depend on long-lived consent, identity, provenance, and audit records. A staged, standards-aligned migration profile can preserve authorization and ledger verifiability while keeping protected health information off-chain. The AHMC label refers only to this migration profile, not to a new cryptographic primitive; healthcare adoption requires open implementations, empirical benchmarking, crypto-agile key governance, and side-channel-resistant engineering.
Chromosome conformation capture experiments have revealed the underlying spatial interactions that govern three-dimensional (3D) genome organization and topology. Detecting 3D contacts between genomic loci considerably enhances our understanding of fundamental regulatory processes. Modeling 3D structures from experimental contact matrices can further contextualize the relationship between 3D genome organization and regulation. While classical algorithms have been successful in reconstructing genomic conformations, we investigate the prospect of quantum computation to aid in modeling the conformational space. In this context, we propose a novel variational quantum algorithm (VQA) to model the distribution of 3D genomic structures from experimental contact data. Through rigorous evaluations, we demonstrate the capability of our algorithm to sample ensembles of viable 3D conformations that agree well with experimental and simulated contact data. Furthermore, we extend our methodology to model the conformational space of a single cell or a population of cells. In the advent of sufficient quantum utility, the insights gained from this study can serve as a foundation for investigating high-resolution, large-scale ensembles of genomic conformations through generative VQAs.
The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.
BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.
It is proposed that "Quantum Molecular" computer of a neuron consists of the cell cytoskeleton serving as calculating media and input ionic channel sending a hypersound signal to observe these media. The sound spreads through the media travelling along microtubules and microfilaments and switching between those via molecular bridges which serve as elementary switches. The whole system works like a wave guiding net connecting input ionic channels (which generate different sound signals) and output ionic channels (which are controlled by the processed sound signals). Thus the output of such systems depends on the input (controlled by synaptic activity) and on the construction and state of these calculating media. We think that the sound waves spreading through different calculating media solve different physical problems. The construction of the calculating part of the cytoskeleton, according to the hypothesis, is different in different neurons. It is defined by special protein which is produced by DNA, RNA and protein molecular word processor (during brain development and, may be, education). We comment on how the existence of an extremal computer produces an impact on physics and mathematics exemplified by the optimality principle as substitution of physical relativity principle for a complex problem.
We review our research on triose-phosphate isomerase and bacteriophage T4 lysozyme. In our studies over the last ten years we have used electrostatic potentials, computer graphics, quantum mechanics, molecular mechanics, molecular dynamics and free energy calculations to try to understand why triose-phosphate isomerase is such an efficient enzyme and why its efficiency is dramatically decreased by several site-specific mutations. For T4 lysozyme we have used free energy methods to analyse and try to understand why Thr-157----Val and Thr-157----Ala mutations decrease protein stability by about 1-2 kcal/mol.
Quantum-mechanical computations performed by the ab initio self-consistent field molecular orbital method have been used for the determination of the principal hydration sites of acetylcholine in its gauche et trans conformations. The hydration destabilizes the gauche form with respect to the trans one by about 2 kcal/mole. Taking into account, however, that the gauche form is 3 kcal/mole intrinsically more stable, this form still remains the most stable one of acetylcholine in solution.
Natural and synthetic peptides that contain detectable intramolecular alpha-helical structure in aqueous solution have been used to evaluate the helical propensities for the common amino acids. Experimental spectroscopic data must be fit to a model of the helix-coil transition in order to determine quantitative stability constants for each amino acid. We present here a statistical mechanical description of helix formation in peptides or protein fragments that takes into account multiple internal conformations, heterogeneity in the stabilizing effects of different side chains, and specific side-chain-side-chain interactions. The model enables one to calculate values of [theta]222 for a given peptide using the length dependence of the helix signal computed by a quantum mechanical treatment of the n pi * transition that dominates the 222-nm band. In addition, the helical probability at any residue in the chain is readily computed, and should prove useful as nmr spectral data become available. The free energy of specific side-chain interactions, including ion pair formation, can be evaluated. Application of the analysis to experimental data on a pair of isomeric peptides, only one of which contains ion pairs, indicates that forming a single glutamate-lysine ion pair stabilizes the alpha-helix by 0.50 kcal/mole in 10 mM sodium ion and pH 7. A survey of the CD data measured for a variety of model peptides is presented, indicating that a single set of s values and sigma constant can account for some but not all of the available results.
The absorption and emission spectra of slabs of human and bovine dental enamel were determined. The absorption and scattering coefficients and emission quantum yields were computed according to theoretical models. The samples were gradually demineralized. The absorption, scattering, and emission parameters were determined as a function of the demineralization time. Using the theoretical models combined with the experimental values, ratio of the visible and UV luminescence, and the decrease of visible emission intensity upon demineralization are explained.
Computer simulation approaches can provide a powerful tool for correlating the structure of enzymes with their catalytic activity. One of the most effective ways of simulating enzymatic reactions is provided by the empirical valence bond method. The general applicability of this method has been demonstrated in several enzymatic reactions and it is reexamined here in a study of the initial proton-transfer step in the catalytic reaction of carbonic anhydrase. The simulations produce a rate constant which is in agreement with the observed kinetic data and emphasizes the importance of the electrostatic effect associated with the catalytic zinc ion. The calculations are also used to examine the validity of linear free-energy relationships (LFERs) in enzyme catalysis and to evaluate quantum-mechanical corrections of the calculated rate constant. It is found that LFERs are valid in the present case and it is argued that this reflects the fact that the protein responds linearly to the development of electrostatic forces during the reaction. It is concluded that the present approach can be used to augment experimental studies in establishing the general validity of LFERs. It is noted, however, that such relationships are much more valid for transitions between different resonance structures than for transitions between reactants and product states.
The ultimate goal of a QSAR analysis is prediction, which depends on the elaboration of the most appropriate set of molecular descriptors. As such, molecular description is the nucleus of QSAR and in the absence of exhaustive molecular description, rational drug design may be greatly impeded. As previously discussed, computational methods such as quantum mechanics and molecular mechanics provide molecular description at a fundamental level which then enhances the descriptive capability and predictive power of a QSAR analysis. In recognition of these capabilities, semi-empirical molecular orbital methods and molecular mechanics now have been incorporated into or interphased with QSAR programs. Such integrated packages are being successfully used in computer-aided molecular modeling. Computer-aided molecular modeling can provide the three-dimensional structure of a molecule, its chemical and physical characteristics, comparisons of structures of different molecules, and visualization of complexes formed between them. From the foregoing, predictions may be made about how related new molecules may function. Thus, the combination of quantum and/or molecular mechanics and QSAR provides a formidable weapon in the chemist's armamentarium. The molecular modeling approaches are certainly more practical to use than physicochemical methods. They also provide electronic and thermodynamic data that are not available from x-ray crystallographic data. Of course, these techniques are not confined to radiopharmaceutical development and they also could aid in the development of contrast agents for radiography or magnetic resonance imaging. We believe that as computational resources and capabilities increase over the next decade, computer-aided drug design will become a standard procedure in all drug development laboratories.
A computer-generated method using quantum mechanics was applied to the calculation and subsequent plotting of nonperspective three-dimensional illustrations of molecules in vacuo. The purpose was to generate isoelectrostatic energy contour spheres for larger molecules and current drugs. The molecules chosen, morphine, meperidine, and alphaprodine, possess similar pharmacological properties. Minor configurational manipulation of meperidine and alphaprodine molecules was made to approximate the spatial configuration of the rigid morphine molecule so that direct comparisons were possible. Common areas of reactivity, potential energy minima, net atomic charges, spatial regions, and near neighbor influences are considered.
Biological plasticity refers to the ability of synapses to strengthen or weaken over time. These adaptive properties play a fundamental role in learning and memory, spanning many orders of magnitude in timescales. Short-term plasticity (STP) arises from rapid correlative activity, while long-term plasticity (LTP) is governed by slower biochemical processes. Here, we investigate electromagnetically driven relaxation dynamics in perovskite nickelate thin films as an analogue of biological learning behaviors. By comparing radio frequency (RF), infrared (IR), visible, and ultraviolet (UV) radiation as stimuli, we find that RF excitation primarily induces STP, while visible and IR illumination lead to reversible relaxation on behavioral timescales. In contrast, UV illumination results in persistent, non-thermal changes in conductivity over extended timescales. Notably, UV-exposed nickelate films exhibit glass-like dynamics, characterized by stretched exponential relaxation and aging phenomena. The films display habituation to repeated stimuli, along with sensitization and spontaneous recovery under controlled environments. A minimal dynamical systems model captures key qualitative features of the UV-induced resistance changes. Our results demonstrate that electromagnetic frequency enables multi-timescale relaxation spanning nearly nine orders of magnitude, suggesting perovskite nickelates as promising platforms for adaptive optoelectronic hardware and for linking computational neuroscience with emerging quantum technologies.
Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.
cis(Z)-Chlorprothixene has antidopaminergic potency, while trans(E)-chlorprothixene is virtually inactive. In order to reveal the structural features causing the difference in activity, the three-dimensional molecular and electronic structures of cis(Z)- and trans(E)-chlorprothixene were examined by computer graphics and molecular mechanical and quantum mechanical calculations. The internal molecular motions of the isomers were studied by molecular dynamics simulations in vacuo and in aqueous solution. The cis(Z)-isomer had lower potential molecular energy than the trans(E)-isomer, mainly due to electrostatic interactions within the side-chain and between the dimethylamino group and the chlorine atom. During molecular dynamics simulations in aqueous solution, the side-chain of the trans(E)-isomer stayed closer to the central S-C axis of the ring system than did the side-chain of the cis(Z)-isomer. The molecular electrostatic potentials were significantly lower in the vicinity of the chlorine atom in the trans(E)- than in the cis(Z)-isomer. Differences in molecular electrostatic potentials and in three-dimensional structure are suggested to be the main reasons for the difference in pharmacological activities of cis(Z)- and trans(E)-chlorprothixene.
The suitability of Dewar's Hamiltonians as a source of bonded force field parameters is explored from the comparison analysis between up to 270 semiempirically derived force field parameters and experimentally derived values reported in some of the most popular force fields. From the statistical analysis of the results, some general conclusions about the semiempirical parametrization are formulated.