Browse Articles

Combination therapy with HSP90 inhibitors and NanoPulse stimulation synergistically impedes hepatocellular carcinoma and breast cancer growth in mice

Physiologic and molecular response of Fusarium oxysporum f. sp. zingiberi to ginger autotoxins

Long-term monoculture of ginger triggers Fusarium wilt, a disease caused by Fusarium oxysporum f. sp. zingiberi ( Foz ). However, the interaction between autotoxins and pathogens remains poorly understood. This study examined the allelopathic effects of four autotoxins, syringic acid, coumarin, ferulic acid, and 7-hydroxycoumarin, on the growth, reproduction, and virulence traits of Foz . These compounds were previously identified as inhibitors of ginger growth and enzyme activity. The results revealed that the responses of Foz varied, likely because of the structural differences among the autotoxins. Syringic acid significantly inhibited mycelial growth, sporulation, and spore germination, while markedly enhancing the activity of cell wall-degrading enzymes and mycotoxin synthesis, as evidenced by the upregulation of FUB3 and FUB9 . Coumarin demonstrated a pronounced inhibitory effect on biomass production while concurrently stimulating sporulation and mycotoxin synthesis, as indicated by the upregulation of FUB6 and FUB9 . Ferulic acid treatment reduced the activity of cell wall-degrading enzymes and spore germination, while upregulating sporulation and mycotoxin synthesis. A similar pattern was observed with 7-hydroxycoumarin, which exerted a strong inhibitory effect on mycelial growth, biomass production, mycotoxin synthesis, and FUB gene cluster expression, except for FUB1 and FUB3 expression. In conclusion, four autotoxins exhibited diverse defensive roles against Fusarium wilt, whereas the pathogen enhanced its pathogenicity to counter host regulations. This study provides the first evidence of an interaction between ginger autotoxins and Foz .

Global lung cancer burden shifting to middle-income countries

Publisher Correction: Presymptomatic training mitigates functional deficits in a mouse model of Rett syndrome

Long-term stability of the triglyceride-glucose index in cancer patient Cohort

Hemodynamic impact of blood viscosity in intracranial atherosclerotic arteries with varying stenosis severity: A non-newtonian computational fluid dynamics patient specific study

Intracranial atherosclerotic stenosis (ICAS) is a major cause of ischemic stroke, yet geometric stenosis alone may not fully reflect the functional hemodynamic burden of a lesion. This study used computational fluid dynamics (CFD) with a shear-thinning non-Newtonian Carreau viscosity model to quantify the combined effects of stenosis severity and blood viscosity on intracranial hemodynamics. A middle cerebral artery (MCA) stenosis model with an original ~70% narrowing was reconstructed from computed tomography angiography, and additional idealized stenosis variants (30%, 50%, and 90%) were generated on the same anatomical background to enable controlled comparisons. Three viscosity states (below-normal, normal, and high) were simulated under transient, incompressible, laminar flow with rigid walls and identical boundary conditions. Velocity, pressure, and wall shear stress (WSS), oscillatory shear index (OSI) and time-averaged wall shear stress (TAWSS) were evaluated. The results show that flow behavior is governed by the combined influence of geometry and rheology, rather than by stenosis severity alone. Severe stenosis produced a dual pathological shear environment, characterized by elevated WSS within the stenotic region and disturbed low-shear flow downstream. In addition, TAWSS showed a non-monotonic response, increasing up to 70% stenosis and then decreasing at 90% stenosis. OSI also showed viscosity-dependent elevation under severe stenosis, with values ranging from approximately 0.38 to 0.48, indicating enhanced oscillatory and disturbed flow.These findings support integrating non-Newtonian hemorheology and hemodynamic metrics with geometric assessment to improve ICAS risk stratification and inform hemodynamics-guided intervention timing.

Memory on trial: the new science of when to trust eyewitness testimony

Microbiota awareness and fermented food consumption among university students: evidence of a knowledge-behavior gap

Abstract Fermented foods are traditional functional foods that may beneficially influence gut microbiota composition and contribute to overall health. However, it remains unclear whether microbiota-related awareness is associated with regular consumption of fermented foods. This study aimed to examine the association between microbiota awareness and fermented food consumption among university students. A cross-sectional study was conducted among students enrolled in sports science programmes. Data were collected using a sociodemographic questionnaire, the Microbiota Awareness Scale, and the Fermented Food Consumption Index. Overall microbiota awareness levels were relatively high, particularly in domains related to general microbiota knowledge and probiotic–prebiotic concepts. Despite this, fermented food consumption was generally low among participants. A weak but statistically significant negative correlation was observed between total microbiota awareness scores and fermented food consumption (Spearman’s r = − 0.28, p  < 0.01). In contrast, strong positive correlations were found between microbiota awareness and both general microbiota knowledge (r = 0.85, p  < 0.001) and probiotic–prebiotic knowledge (r = 0.82, p  < 0.001). Students reporting gastrointestinal complaints had significantly higher awareness scores than those without such symptoms ( p  < 0.05). These findings indicate a discrepancy between microbiota-related knowledge and actual dietary behaviour, consistent with the intention–action gap described in behavioural nutrition literature.

Multimerization interactions between protein-inspired single-chain random heteropolymers

Single-chain nanoparticles (SCNPs) are attractive for their ability to interact with or behave akin to native proteins. In this work, we use molecular dynamics simulations to examine how SCNPs which are assembled non-covalently due to the hydrophobic effect and monomers with negative Flory-Huggins interaction parameters ( χ ) in water can interact with other macromolecules. The occurrence of multimerization is characterized for a methacrylate-based random heteropolymer system with heterogeneous surfaces and sequence behaviors. The system shows two primary interaction modes: (1) adsorbing through side-chain interactions similar to protein oligomerization and (2) maintaining their single-chain structures or with only transient interactions. For cases in which adsorption does occur, the small, amphiphilic methyl methacrylate monomers are shown to enrich at the points of contact. Hydrophobic residues are typically present at the interface when adsorption is prolonged, while hydrophilic monomers associate in more transient inter-polymeric interactions. Finally, we demonstrate that polymer conformation of a single polymer sequence plays a large role in multimerization, while the variation among these conformations is statistically indistinguishable from the variation amongst different sequences.

Associations of personal temperature exposure in the preceding days with vital signs, biochemical, and hydration parameters in cystic fibrosis

Combined diagnostic accuracy of two artificial intelligence systems for glaucoma diagnosis using color fundus photography

Purpose To evaluate the diagnostic accuracy of two commercially available artificial intelligence (AI) systems based on color fundus photography (CFP), the Laguna ONhE and VUNO Med-Fundus AI, for glaucoma detection, both independently and in combination. Methods This retrospective cross-sectional study included 370 eyes from 193 patients (248 eyes with primary open-angle glaucoma and 122 healthy eyes). All eyes underwent structural evaluation with swept-source optical coherence tomography (Triton, Topcon) and visual field testing (Octopus 900, Haag-Streit AG). Fundus photographs were analyzed using Laguna ONhE and VUNO Med-Fundus AI systems. Diagnostic accuracy was evaluated. Results Both AI systems demonstrated high diagnostic accuracy. Laguna achieved an AUC of 0.879 using Glaucoma Discriminant Function (GDF), and VUNO showed an AUC of 0.857. When combined, GDF + VUNO achieved an AUC of 0.903. The Global Mean Deviation (GMD) reached the highest diagnostic accuracy (AUC = 0.916), which was not significantly different from GDF + VUNO (p = 0.146). Conclusions Laguna ONhE and VUNO Med-Fundus AI had high diagnostic accuracy in detecting glaucoma using only CFP. Their combined use improved further, achieving accuracy comparable to the GMD. This represents a practical approach for glaucoma screening, particularly in settings without access to OCT or automated perimetry.

Dynamical freezing for magnetometry in an interacting spin ensemble

Evaluating large language models for accuracy incentivizes hallucinations

Abstract Large language models sometimes produce confident, plausible falsehoods (‘hallucinations’), limiting their reliability 1,2 . Previous work has offered numerous explanations and effective mitigations such as retrieval and tool use 3 , consistency-based self-verification 4 and reinforcement learning from human feedback 5 . Nonetheless, the problem persists even in state-of-the-art language models 6,7 . Here we show how next-word prediction and accuracy-based evaluations inadvertently reward unwarranted guessing. Initially, next-word pretraining creates statistical pressure towards hallucination even with idealized error-free data: using learning theory 8,9 , we show that facts lacking repeated support in training data (such as one-off details) yield unavoidable errors, whereas recurring regularities (such as grammar) do not. Subsequent training stages aim to correct such errors. However, dominant headline metrics such as accuracy systematically reward guessing over admitting uncertainty. To align incentives, we suggest two additions to the classic approach of adding error penalties to evaluations to control abstention 10,11 . First, we propose ‘open rubric’ evaluations that explicitly state how errors are penalized (if at all), which test whether a model modulates its abstentions to stated stakes while optimizing accuracy. Second, as hallucination-specific benchmarks rarely make leaderboards 12 , we suggest using open-rubric variants of existing evaluations, to reverse their guessing incentives. Reframing hallucination as an incentive problem opens a practical path towards more reliable language models.

DupyliCate: mining, classifying, and characterizing gene duplications

Abstract Paralogs, copies of a gene, form an important basis for novelty during evolution. Analysis of such gene duplications is important to understand the emergence of novel traits during evolution. DupyliCate is a Python tool that has been developed for this purpose. With the ability to process multiple datasets concurrently, flexible features, and parameters to set species-specific thresholds, DupyliCate offers a high-throughput method for gene copy identification and analysis. The different available parameters and modes are explored in detail based on Arabidopsis thaliana datasets. Proof of concept for the tool is presented by characterizing well known duplications in different plants, and its broad applicability is demonstrated by running it on diverse datasets including complex plant genome sequences with high heterozygosity. Further, two case studies involving the evolution of FLAVONOL SYNTHASE ( FLS ) genes in Brassicales, and the evolution of flavonol synthesis regulating myeloblastosis (MYB) transcription factors— MYB12 and MYB111 across a large number of plant species, are presented as exemplar use cases. The tool’s applicability beyond plants is demonstrated on Escherichia coli, Saccharomyces cerevisiae, and Caenorhabditis elegans datasets. DupyliCate is available at: https://github.com/ShakNat/DupyliCate .

Taxonomic characterizations of the genus Commicarpus Standl. (Nyctaginaceae) in Saudi Arabia

The genus Commicarpus Standl. is a member of the family Nyctaginaceae. The genus includes about 30–35 species distributed across tropical and subtropical regions worldwide, including Saudi Arabia. Five species of Commicarpus are found through, the field survey, which are primarily concentrated in the western and southwestern regions of Saudi Arabia. The collected species are C. grandiflorus , C. helenae , C. mistus , C. plumbagineus , and C. sinuatus . The aim of this study is to do morphological, anatomical, and palynological analyses of these species. Morphologically, growth habit, stem texture, leaf characteristics, floral structure, and fruit morphology were evaluated, these characters are significant to distinguish Commicarpus species. Anatomical studies of the stems, leaves, and petioles show some important characteristics that can help to separate Commicarpus species, including variations in collenchyma and chlorenchyma layers, vascular bundle arrangement, and mesophyll structure. Petiole anatomy, particularly the shape and arrangement of ground tissue and vascular bundles, provides additional taxonomic markers. Also, the study of pollen grains of the species using light microscopes (LM) and scanning electron microscopes (SEM) provides significant character that can be used for species differentiation, including differences in pollen size, shape, polar and equatorial axis dimensions, tubuliferous density, pore diameter, and spinule length. Pollen grains are very large in C. grandiflorus , C. plumbagineus , and C. sinuatus and large in C. helenae and C. mistus ; their shapes range from oblate-spheroidal in C. grandiflorus to prolate-spheroidal in the other species. Two keys are constructed, one utilizing morphological characteristics and the other employing anatomical features of the petioles to aid in species identification. These results contribute valuable taxonomic information for the genus Commicarpus in Saudi Arabia.

Impact of chronic khat chewing on liver function tests and fasting blood glucose levels among adult male khat chewers

Abstract Khat ( Catha edulis ) has been widely chewed in the Middle East and Eastern Africa, particularly in Ethiopia. However, its potential adverse effects are not investigated in well-controlled, community-based studies. Therefore, this study assessed the impact of khat chewing on blood glucose and liver function tests. A community-based comparative cross-sectional study was conducted among 100 adult male chronic khat chewers and 100 non-khat chewers in Dilla Town, Southern Ethiopia, from June to September 2023. Serum fasting blood glucose and liver function parameters, including aminotransferases, alkaline phosphatase, total protein and total and direct bilirubin, were analysed using an automated clinical chemistry analyser (EXL 200 Siemens, Germany). Fisher’s exact test, Mann–Whitney U test, Kruskal–Wallis test, and spearman correlation analyses were performed as appropriate. A p -value < 0.05 was considered statistically significant. Khat chewers had higher aminotransferase enzyme levels (aspartate aminotransferase and alanine aminotransferase) and fasting blood glucose than non-khat chewers ( p  = 0.007, p  =  < 0.001, and p  = 0.002, respectively). Khat chewers’ alanine aminotransferase levels were positively correlated with chewing frequency (days/week), duration (in years), and dose, with Spearman’s rho ( p ) values of 0.488 (< 0.001), 0.679 (< 0.001), and 0.323 (< 0.001) respectively, and duration of khat chewing had a positive correlation with fasting blood glucose with Spearman’s rho ( p ) values of 0.282 (0.004). The present findings indicated that aminotransferase enzymes and fasting blood glucose levels were significantly higher among khat chewers than non khat chewers. Dose, duration, and frequency of khat chewing also had a positive correlation with aminotransferases and fasting blood glucose levels.

Retraction: Towards sustainable management: Exploring the role of internal monitoring in pollution prevention

Experimental randomness amplification

Associations of body mass index and metabolic health with stroke risk in a large prospective cohort with time updated covariates

Abstract The global prevalence of overweight and obesity is rising, and recent studies have established an independent contribution of adiposity to stroke risk. How the increased risk associated with adiposity relates to other factors including metabolic health is not fully understood. We analyzed 132,045 participants from the Northern Sweden Health and Disease Study with repeated health examinations (1985–2022). Stroke events were identified via national registers. Body mass index (BMI) was modeled as continuous (splines) and categorical (WHO definitions). Cox models with time-updated covariates estimated hazard ratios (HRs), and machine learning (XGBoost-AFT) assessed complex relationships. Over a median 20.2-year follow-up (2.67 million person-years), 7,493 strokes occurred. In fully adjusted models, overweight (HR: 1.14; 95% CI: 1.08–1.20) and obesity (HR: 1.36; 95% CI: 1.27–1.45) independently increased stroke risk versus normal weight. Poor metabolic health was also strongly associated (HR: 1.41 ; 95% CI: 1.34–1.49) with increased stroke risk. Combined obesity and poor metabolic health conferred the highest risk (HR: 1.79; 95% CI: 1.67–1.93). Age modified the association between higher BMI and stroke risk (p-interaction = 0.007), with stronger associations at younger ages. Machine learning confirmed the BMI-stroke risk pattern. In conclusion, overweight and obesity are associated with an independent stroke risk, even among younger and metabolically healthy individuals.

Semigroup-theoretic analysis of supply-chain disruptions and resilience

This paper develops a rigorous framework using finite transformation semigroups to model supply-chain state evolution under cascading disruptions and resilience interventions. Disruptions and interventions are represented as non-invertible transformations on finite configuration spaces, generating a semigroup whose structure encodes collapse conditions, equilibria, minimal collapse-inducing sets, and redundancy. We establish explicit semigroup-theoretic criteria for synchronizing collapse, idempotent stabilisation, and redundancy identification via basis-pruning algorithms. The framework is illustrated with examples spanning manufacturing, agricultural, and e-commerce logistics systems. Analytical results, algorithmic procedures, and case studies demonstrate how semigroup properties map to measurable resilience indicators, providing interpretable, computationally tractable tools for assessing shock containment strategies in real-world networks.