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PubMed2026

Integrative quantum and systems biology of cancer: From molecular fluctuations to ecological outcomes.

This review treats cancer as a multiscale adaptive system, asks what the framework must predict to be worth adopting, and separates at each scale what the evidence establishes from what is proposed. It is an expert narrative synthesis, not a systematic review, and states the limits of that design. Proton transfer and tautomeric shifts contribute to spontaneous mispairing but do not license claims of directed or non-random mutation: replication timing, three-dimensional chromatin organization, sequence context and known mutagenic processes explain most mutational heterogeneity, leaving any quantum contribution as a residual against that baseline. The Waddington quasi-potential is bounded: outside detailed balance the dynamics are not gradient-derivable and require a probability-flux term. Hysteresis, rate-limited bimodality and return to state after perturbation distinguish an attractor from a transcriptomic cluster. Single-cell karyotype and live-imaging evidence supports whole-genome doubling as an unstable intermediate of heterogeneous origin and context-dependent consequence, not a uniform adaptive strategy. Systems and synthetic biology, virtual cells and digital twins are assessed against benchmarks, not promise. Tissue-scale ecology is reported with the spatial measurements now quantifying it, including evidence that stromal niche construction is not uniformly tumor-supporting. RNA modification is a layer in its own right, showing that the interpretation of a regulatory signal, not its magnitude, is biologically decisive. A dedicated section states the framework's commitments, the observable and evidence at each scale, and what would falsify them, asking what this adds to somatic mutation theory with clonal evolution and plasticity.

Neoplasms↗
PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

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.

Humans↗
PubMed2026

The hidden threat from food-derived carbon dots: Formation, biodistribution, and potential health risks.

Food-derived carbon dots (CDs) are a new class of carbon-based nanoparticles generated during the thermal processing of food matrices. These nanomaterials have been extensively studied for their unique fluorescence, good biocompatibility, and tunable surface chemistry in food detection, intelligent packaging, and biomedical applications. However, their nanoscale size and high surface activity have raised safety concerns regarding biological interactions, in vivo biodistribution, and potential long-term health hazards. Although CDs have traditionally been regarded as low-toxicity materials due to their favorable biocompatibility, the potential hidden risks of CDs have not received sufficient attention. CDs exhibit dose-dependent toxicity, not only accumulating in various tissues and organs but also potentially inducing oxidative stress and interfering with cellular metabolic functions. Therefore, this review summarizes the advances in sources, synthetic strategies, and core properties of CDs, with a special focus on in vivo biological interactions, fates, and potential safety challenges. In addition, it is proposed that the standardized detection and risk assessment system should be established to further explore the long-term health effects of CDs under real dietary exposure, thereby ensuring their safety and sustainable application.

Carbon Quantum Dots↗
PubMed2026

Enhancement of secondary organic aerosol formation from isoprene photooxidation by ammonia.

Ammonia (NH3) can participate in atmospheric secondary organic aerosol (SOA) formation by reacting with organic acids and carbonyl compounds in particle phase, but its influence on the gas phase chemistry remains unclear. This study performed a series of smog chamber experiments to investigate the influence of NH3 on the formation of SOA from isoprene photooxidation by OH radicals. Both gas and particle phase products were measured with a series of state-of- art instruments including a nitrate ion chemical ionization mass spectrometer (nitrate-CIMS) and high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS). Our results showed that in the presence of NH3 SOA in the chamber significantly increased, along with an enhanced oxidation of isoprene. CIMS analysis further showed that NH3 in the chamber homogeneously reacts not only with gas-phase organic acids but also with gaseous low volatility oxygenated organic molecules (OOMs) to generate extremely low volatility and ultralow volatility NH3-OOMs clusters. Quantum chemical calculation showed that NH3 can spontaneously interact with OOMs to form NH3-OOMs clusters by forming hydrogen bonds with RCOOH, R-OOH, and R-OH. These clusters can promote new particles formation and particle growth through nucleation and condensation, directly enhancing the isoprene SOA production with a contribution of 78% to the enhanced SOA. Moreover, the formation of NH3-OOMs clusters also results in more isoprene consumed by OH radicals, indirectly increasing the SOA production with a contribution of 22 % to the enhanced SOA. Our work for the first time clarified a synergetic effect of NH3 on isoprene SOA formation, which should be accounted for by models.

Aerosols↗
PubMed2026

Measurable Residual Disease and the Unresolved Biology of Leukemic Stem Cells.

Measurable residual disease (MRD) testing has transformed the management of hematologic cancers by enabling detection of residual malignant cells after therapy. Current approaches rely on qPCR and next-generation sequencing to monitor leukemia-associated somatic mutations, while multiparameter flow cytometry identifies aberrant leukemic immunophenotypes. Although these methods provide valuable prognostic and therapeutic information, MRD negativity remains an imperfect surrogate for cure. Most MRD platforms evaluate CD45+, rapidly dividing leukemic populations and fail to detect quiescent cells that may survive cytotoxic therapies which efficiently target proliferating hematopoietic cells. Relapse frequently occurs despite deep molecular remission, suggesting persistence of rare leukemic stem cells (LSCs) that are intrinsically resistant to chemotherapy and targeted therapies. The paradox of relapse despite molecular remission could be explained by the presence of very small embryonic-like stem cells (VSELs) which are pluripotent, quiescent stem cells sitting at the top of cellular hierarchy in multiple adult tissues including bone marrow. A pluripotent VSEL divides through asymmetrical cell division to give rise to two cells of different sizes and fates, smaller cell is to self-renew while the bigger is lineage-restricted and tissue-committed progenitor which undergoes extensive epigenetic changes, divides rapidly and undergoes clonal expansion before further differentiation. Dysfunctions of VSELs initiate both solid and hematologic cancers. Based on this view, somatic mutations monitored during MRD assessment possibly represent downstream consequences of clonal expansion rather than the initiating drivers of disease persistence. Thus, exclusive monitoring of somatic mutations and CD45 + leukemic populations possibly overlook rare, small-sized, CD45- VSELs that contribute to therapeutic resistance and relapse.

Humans↗
PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗
PubMed2026

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks↗
PubMed2026

Molecular mechanisms of natural de novo shoot organogenesis and their applications.

Natural de novo shoot organogenesis (DNSO) is the spontaneous regeneration of shoots from wound sites outside the shoot apical region through endogenous developmental programs. This regenerative capacity enables plants to recover from severe tissue damage by re-establishing the shoot-root axis. Here, we review current knowledge about the molecular mechanisms of natural DNSO, focusing on transcriptomic and physiological studies in model plants. Accumulating evidence suggests that natural DNSO proceeds through three sequential phases: (i) early wound responses, characterized by the activation of the WIND1-ESR1 module and the establishment of apical-basal auxin asymmetry; (ii) cellular proliferation driven by metabolic and cell-cycle reprogramming; and (iii) cytokinin-mediated establishment of shoot apical meristem identity. We also discuss how these mechanistic insights have been harnessed for practical applications, including tissue culture-free transformation systems such as the cut-dip-budding (CDB) method, and developmental reprogramming strategies that employ ectopic expression of developmental regulator (DR) genes to induce DNSO in otherwise recalcitrant species. Together, these advances illustrate how understanding natural regeneration can guide the development of simplified, broadly applicable plant transformation technologies.

Plant Shoots↗
PubMed2026

Lipid-mediated activation of BLT2 promotes membrane repair to prevent cell death.

Various pathogenic microorganisms produce toxins that create pores in cell membranes, causing cell damage and disrupting the host epithelial barrier. Recently, we reported that mice lacking the G protein-coupled receptor leukotriene B4 receptor 2 (BLT2), which is expressed in vascular endothelial and alveolar epithelial cells, are highly susceptible to pneumolysin (PLY), a pneumococci-generated toxin. Although we clarified the protective roles of BLT2 in vascular endothelial cells, those in alveolar epithelial cells have not been elucidated. Here, we report that lipid mediator 12-hydroxyheptadecatrienoic acid (12-HHT), which is produced by membrane-damaged epithelial cells, prevents cell death by promoting membrane repair through BLT2. BLT2 promoted the release of PLY-bound plasma membranes as extracellular vesicles in a sphingomyelinase-dependent manner. Additionally, BLT2 activated Rac1 and subsequent actin polymerization, leading to resistance to cell death. Furthermore, inhibition of 12-HHT production by aspirin and treatment with a BLT2 antagonist abolished the protective effect of BLT2. These findings provide a new therapeutic strategy for bacterial infection.

Receptors, Leukotriene B4↗
PubMed2026

Activity shapes large herbivores' ecological influences.

The ecological effects of large herbivores are shaped by their spatial and temporal patterns of activity (i.e. where, when and how intensely they use specific locations). When large herbivores' ecological influences are perceived to be undesirable, the traditional approach has been to reduce their population size. This numbers-first logic assumes that ecological effects scale primarily with abundance. We argue that this framing provides an incomplete understanding of large herbivores' ecological impacts. Using African elephants (Loxodonta africana) as a well-documented case study, we show that ecological effects on plants, animals and ecosystem processes correlate more with spatio-temporal patterns of activity than with population size. In large, open systems characterized by strong gradients of water availability, forage quality, shade and risk, elephants concentrate into predictable hotspots while relaxing activity elsewhere, generating localized impacts and opportunities for recovery. By contrast, in small, fenced or fragmented landscapes, where movements are constrained, and gradients are weak, spatial self-regulation breaks down, producing homogenized use and widespread ecological effects. We contend that understanding where, when and under what constraints herbivores use space provides a more general and mechanistic basis for interpreting ecological influence than abundance alone, with implications that extend beyond elephants to large herbivores globally.

Animals↗
PubMed2026

Investigating telomere length and hTERT-MNS16A VNTR polymorphism in Bipolar disorder: Insights into clinical features.

OBJECTIVE: To compare leukocyte telomere length (LTL; T/S ratio) and hTERT-MNS16A VNTR polymorphism between patients with bipolar disorder (BD) and healthy controls, and to examine their associations with clinical features in BD. METHODS: A total of 179 participants (100 BD patients, 79 healthy controls) were enrolled. Relative LTL was assessed by qPCR-based T/S ratio; hTERT-MNS16A VNTR genotyping by PCR and gel electrophoresis. Clinical variables including episode frequency, illness duration, age at onset, symptom severity scales, first episode polarity, and family history of mood disorder were evaluated. RESULTS: No significant differences were observed between BD patients and healthy controls in T/S ratio or hTERT-MNS16A VNTR genotype distributions (all p > 0.05). Within the BD group, S allele carriers (L/S or S/S) had significantly more depressive episodes than L/L homozygotes (1.45 &#xb1; 2.58 vs. 0.61 &#xb1; 1.52; p = .040). Significant inverse correlations were identified between T/S ratio and depressive episode count (&#x3c1; = -0.220, p = .028) and total mood episodes (&#x3c1; = -0.207, p = .039). Multivariable negative binomial regression revealed four independent predictors of depressive episode frequency: lower T/S ratio (p = 0.005), S allele carriage (L/S or S/S genotypes) (p = 0.001), first depressive episode polarity (p < 0.001), and family history of mood disorder (p = 0.035). CONCLUSION: Although LTL and hTERT-MNS16A VNTR genotype did not differ between BD patients and healthy controls, shorter telomere length and S allele carriage were independently associated with higher depressive episode frequency within the BD group, implicating telomere biology and hTERT genetic variation in the biological substrate of depressive illness burden.

Humans↗
PubMed2026

Genome-Wide Impact of Human DBR1 Depletion on RNA Processing Networks Reveal a Connection Between Pre-mRNA Splicing, mRNA Surveillance and Stress Granule Dynamics.

The RNA lariat debranching enzyme DBR1 is essential for intron turnover and RNA metabolism, yet its broader impact on transcriptome regulation remains incompletely defined. To elucidate the consequences of DBR1 depletion, we performed transcriptome-wide RNA sequencing of DBR1-knockdown and wild-type HEK293 cells. Differential expression analysis revealed widespread perturbations in pathways linked to RNA splicing, mRNA surveillance, translational control, and stress-granule biology. Many of the most significantly altered transcripts encode splicing factors and RNA quality-control components, underscoring DBR1's influence on post-transcriptional regulation. Alternative splicing analysis showed changes across multiple event types, with exon skipping accounting for >50% of events, followed by mutually exclusive exons, alternative 5' and 3' splice sites, and retained introns, indicating that DBR1 depletion induces pervasive splicing defects. Direct spliceosome inhibition using isoginkgetin (blocks tri-snRNP recruitment) and pladienolide B (targets SF3B1) reproduced the DBR1-KD mis-splicing patterns of cell signaling genes and factors involved in RNA metabolism, supporting a functional link between DBR1 activity and alternative splicing. Notably, DBR1 knockdown revealed a subset of transcripts that are both NMD-sensitive and enriched within stress granules. Consistent with this observation, G3BP1 immunopurification and confocal microscopy further support a role for DBR1 and UPF1 in stress-granule dynamics, suggesting that these factors may participate at distinct stages to influence mRNA fate under stress conditions. Together, these findings indicate that DBR1 functions beyond lariat RNA turnover as a common regulator of RNA processing, transcriptome stability, and stress granule homeostasis, revealing intricate crosstalk between RNA splicing and RNA quality control pathways in human cells.

Humans↗
PubMed2026

Targeted sequencing reveals a distinct genetic alteration landscape in oral multiple primary squamous cell carcinomas.

OBJECTIVE: Oral multiple primary cancers (MPCs) are associated with poor clinical outcomes, yet their genomic characteristics remain insufficiently understood. DESIGN: Fifty-four formalin-fixed paraffin-embedded (FFPE) tumor samples from 30 patients with oral MPCs were analyzed using high-depth targeted sequencing of a customized 14-gene panel derived from prior whole-exome sequencing data. Detected alterations were analyzed after removal of synonymous mutations. RESULTS: Non-silent genomic alterations were identified in 59.3% (32/54) of samples, involving 19 patients. A total of 70 variant loci across 13 genes were detected. AKAP13 was the most frequently mutated gene at both the sample (22.2%, 12/54), with recurrent mutations observed across multiple patients. In contrast, TP53 mutations occurred at a substantially lower frequency (11.1%, 6/54). Marked inter- and intra-patient mutational heterogeneity was observed. CONCLUSIONS: FFPE-based targeted sequencing enabled an initial characterization of genomic alterations in oral MPCs. Recurrent alterations in AKAP13, GLI2, JMJD1C, and DNAH8, together with the relatively low frequency of TP53 alterations, identify candidate genomic features for further investigation and provide a basis for future studies of the molecular basis of oral MPCs.

Humans↗
PubMed2026

Functional characterization of the MdFLZ2 gene in drought and salt stress tolerance in apple.

Drought and salt stress are significant environmental limitations that severely constrain plant growth and productivity, therefore, enhancing stress tolerance is a key goal in crop improvement. The plant-specific FCS-like zinc finger (FLZ) proteins have been identified as important regulators of stress adaptation. In this study, we conducted a genome-wide characterization of the FLZ gene family in apple and functionally characterized MdFLZ2. qRT-PCR analysis revealed that MdFLZ2 was differentially expressed across various tissues and transcriptionally induced by both drought and salt stress. Subcellular localization assays demonstrated that the MdFLZ2 protein is localized to both the nucleus and the cytoplasm. The overexpression of MdFLZ2 in apple calli, Arabidopsis and tomato conferred increased resistance to drought and salt stress. In addition, yeast two-hybrid (Y2H) assays confirmed that MdFLZ2 interacted with MdSnRK1.1, and similar interactions were also detected between other MdFLZ family members and MdSnRK1.1. Collectively, our findings suggest MdFLZ2 as a positive regulator of drought and salt tolerance and highlight its potential to serve as a genetic resource for abiotic stress improvement.

Malus↗
PubMed2026

Molecular Determinants and Therapeutic Targeting of Stop Codon Readthrough in Eukaryotic Translation.

Accurate translation termination is essential for proteome integrity and in eukaryotes is primarily governed by the release factors eRF1 and eRF3, which ensure precise recognition of stop codons and efficient release of nascent polypeptides. However, proteome integrity is challenged by mutations that generate premature termination codons (PTCs), leading to truncated, nonfunctional proteins and degradation of the aberrant transcript via nonsense-mediated mRNA decay (NMD). Collectively, these events account for &#x223c;1800 human genetic diseases. Translational readthrough, the process by which near-cognate tRNAs decode stop codons and allow ribosomes to continue elongation beyond the stop codon, represents a possibility to suppress PTCs and restore full-length protein synthesis. Initially discovered in viruses as a mechanism to expand coding capacity, readthrough is now recognized as a regulated feature of eukaryotic gene expression influenced by both cis-acting sequence elements and trans-acting factors. Recent evidence highlights the remarkable context dependence of readthrough, revealing variation across transcripts, tissues, and developmental stages. In this review, we examine the molecular determinants that define stop codon recognition and readthrough efficiency, with particular emphasis on nucleotide context. We further discuss the mechanisms and binding sites of small molecules that promote PTC readthrough, and summarize the clinical development landscape of readthrough-inducing compounds for the treatment of diseases caused by nonsense mutations.

Humans↗
PubMed2026

Comparative genomic and proteomic analysis reveals orthogroup structured evolution of tick protease inhibitors.

Protease inhibitors (PIs) play central roles in regulating endogenous proteolysis and host-parasite interactions in ticks. However, the evolutionary architecture underlying their diversification across tick lineages remains insufficiently resolved. Here, we performed a genome-wide comparative analysis of predicted proteomes from 14 tick species to systematically characterize PI repertoires. In total, 4931 putative PIs were identified and grouped into 20 families using the MEROPS classification system. Further, PI families such as Antistasin, WAP-type, and Pacifastin, which have not previously been systematically reported in tick genomes, were classified. Orthogroup inference demonstrated that PI expansion is structured at the level of evolutionary lineages rather than uniformly across families. By stratifying orthogroups according to duplication burden and taxonomic conservation, we identified a broadly conserved single-copy core under strong purifying selection. Motif level analysis of serpin reactive center loops further revealed conservation of inhibitory specificity within single copy orthogroups and diversification of key functional residues in duplication-associated lineages. Integration of secretion prediction and tissue-resolved proteomics from Hyalomma anatolicum and Rhipicephalus microplus demonstrated that evolutionary stratification is reflected at the protein level. Together, these findings provide an orthogroup-resolved evolutionary framework linking duplication dynamics, molecular evolution, and tissue-level protein deployment. This integrative approach offers a systematic basis for prioritizing conserved and diversified PI lineages for future functional and anti-tick intervention studies.

Animals↗
PubMed2026

In Vitro comparison of herbal and conventional antifungals against Candida strains in Oral candidiasis: A systematic review and meta-analysis.

OBJECTIVE: This study aimed to systematically review and meta-analyze the in vitro antifungal activity of herbal and conventional antifungals against Candida strains. DESIGN: In vitro studies were identified through PubMed, Embase, Scopus, and Web of Science up until May 2026. This review is registered with Prospero (CRD420251128404). Eligibility was determined using the Population, Intervention, Comparison, and Outcome (PICO) framework, with specific inclusion and exclusion criteria focused on in vitro antifungal investigations comparing herbal antifungals with conventional antifungals. The risk of bias was assessed using the modified Quality Assessment Tool for In Vitro Studies (QUIN Tool). A meta-analysis was performed, with the primary outcome measure being the ratio of means (RoM). RESULTS: The systematic review included twenty-five articles. Most studies showed different results in inhibition zones or minimum inhibitory concentrations between herbal and conventional agents. The meta-analysis indicates that certain herbal antifungals are equally effective as or more effective than conventional antifungals against Candida dubliniensis, Candida lusitaniae, and Candida tropicalis. While the efficacy of herbal antifungals for Candida albicans and Candida glabrata was modest, Piper betle L. demonstrated significant inhibitory potential. In contrast, conventional antifungals outperformed herbal antifungals against Candida krusei and Candida parapsilosis. CONCLUSIONS: This systematic review and meta-analysis highlight herbal medicine as a potential antifungal therapy for oral candidiasis, emphasizing the need for new strategies due to resistance to conventional antifungals.

Humans↗