Browse Articles

Risk factors associated with cytomegalovirus reactivation and disease in critically-ill COVID-19 and non-COVID-19 patients, concomitantly admitted to intensive care

Prevalence of food addiction and sex-specific correlates in a large sample of Iranian adults

Harnessing chaotic bifurcation in positive feedback transistors for secure and scalable random key generation

Comparative analysis of the efficacy and functional recovery of unilateral biportal endoscopy-assisted lumbar interbody fusion for the treatment of lumbar disc herniation

Quantification of plasma tau species containing the proline-rich region as a biomarker in Alzheimer’s disease

Abstract Tau-based blood biomarkers are increasingly recognised as important for the diagnosis of Alzheimer’s disease (AD). More than 60 proteolytic cleavage sites of tau have been identified, and current assays may miss critical information from some of the smaller protein fragments. By capturing a broader range of tau species, a polyclonal approach may offer greater interrogation of this complex “tauosome” and deliver valuable insights into the onset or progression of AD. A sheep was hyper-immunised with 2N4R tau113-251 peptide, encompassing the proline-rich region. An affinity-purified proline region polyclonal antibody (P.pAb) was derived from sheep serum, after four rounds of immunisation. Following characterisation of P.pAb, utility as a plasma biomarker/diagnostic agent for AD was assessed using a single molecular array (Simoa) assay in a selected cohort consisting of clinically diagnosed AD patients and age-matched cognitively unimpaired (CU) individuals. Two assays were considered for this assessment including pairing the P.pAb with itself (P.pAb-P.pAb) to capture and detect multiple tau fragments in plasma, and pairing pTau217 capture mAb with P.pAb (pTau217-P.pAb). The P.pAb showed high affinity towards full-length tau and 113–251 peptide immunogen and bound smaller 13-amino acid (aa) fragments throughout the proline rich region. The selected patient cohort was initially assessed by commercial neurofilament light (NfL) and pTau217 assays, the results of which were consistent with AD-related neurodegeneration in the AD sample and not in the CU group. The P.pAb-P.pAb and the pTau217-P.pAb assays were each able to distinguish between CU and AD groups; values were greater in AD (1.4-fold, p  < 0.0001 and 2.8-fold, p  < 0.001, respectively). By contrast, a commercial total-tau (T-tau) assay did not distinguish between the two groups. We demonstrate the feasibility of an immunodiagnostic approach based on the detection of tau species containing the proline-rich region. The development of an affinity-purified proline region-specific pAb, capable of detecting multiple tau species in plasma, provides the foundation for a novel approach with potential applications in AD diagnosis and monitoring of disease progression.

Biogeography and host interactions of CPR and DPANN viruses in acid mine drainage sediments

Lightweight DETR algorithm for X-ray weld defect detection

Complex genotype-phenotype relationships shape the response to treatment of down syndrome childhood acute lymphoblastic leukaemia

Abstract Extensive genetic and epigenetic variegation has been demonstrated in many malignancies. Importantly, their interplay has the potential to contribute to disease progression and treatment resistance. To shed light on the complex relationships between these different sources of intra-tumour heterogeneity, we explored their relative contributions to the evolutionary dynamics of Acute Lymphoblastic Leukaemia (ALL) in children with Down syndrome, which has particularly poor prognosis. We quantified the tumour propagating potential of genetically distinct sub-clones using serial transplantation assays and SNP-arrays. While most leukaemias were characterized by a single dominant subclone, others were highly heterogeneous. Importantly, we provide clear and direct evidence that genotypes and phenotypes with functional relevance to leukemic progression and treatment resistance can co-segregate within the disease. Hence, individual genetic lesions can be restricted to well-defined cell immunophenotypes, corresponding to different stages of the leukemic differentiation hierarchy and varied proliferation potentials. As a result of this difference in fitness, which can be accurately quantified via competitive transplantation assays, matching diagnostic, post-treatment, and relapse leukaemias can be dominated by different genotypes, including pre-leukemic clones persisting throughout the disease progression and treatment. Intriguingly, plasticity also appears to be a temporally defined property that can segregate with genotype. These results suggest that Down Syndrome ALL should be viewed as a complex matrix of cells exhibiting genetic and epigenetic heterogeneity that foster extensive clonal evolution and competition. Therapeutic intervention reshapes this ‘eco-system’ and may provide the right conditions for the preferential expansion of selected compartments and subsequently relapse.

Phosphorylation-coupled autoregulation of TANGO1 and Sec16A maintains functional ER exit sites

Few-shot cross-domain fault diagnosis via adversarial meta-learning

Continuous blood glucose monitoring prediction for diabetes using evolving neural network

Abstract This research presents a new evolving neural network approach to forecast blood glucose for people with diabetes. The accuracy of forecasting using the proposed evolving neural network is demonstrated to outperform a conventional back propagation neural network. People with diabetes need to control their blood sugar levels. High blood sugar over long term leads to many other health complications. To avoid high blood sugar, it is important for people to be able to predict what will happen to blood sugar so that they can do something to prevent hypo or hyper glycaemia. However, many external uncontrollable factors can make blood glucose difficult to predict, such as meals which increase carbs and glucose goes up. Exercise also affects blood glucose, but exercise can be aerobic or anaerobic and these affect blood glucose in opposite ways. There has been research aiming to predict blood glucose by analysing previous recorded data from continuous glucose monitoring devices. This research applies a new approach with evolutionary computation to evolve a neural network, using neuro evolution, and the optimised neural network is then applied to predict and forecast blood glucose changes. In the comparison of accuracy, the results show that evolved neural network outperformed a back-propagation neural network in this task on forecasting CGM data. This can help people with diabetes to have a better idea about how their blood glucose is going to change before it occurs, so that hypo and hyper can be avoided. This can reduce diabetes complications and costs for the health service.

Ultrabroadband, achromatic, and non-diffracting perfect optical vortex generation via radial momentum control in dielectric metasurfaces

Spatiotemporal patterns, source apportionment, and ecological risk of major and trace elements in sediment cores from Anzali International Wetland

Comprehensive analysis of Guanfacine treatment in autism spectrum disorder with comorbid attention deficit hyperactivity disorder

Gut mucin fucosylation dictates the entry of botulinum toxin complexes

Abstract Botulinum toxins (BoNTs) produced by Clostridium botulinum are the most potent known bacterial toxins. The BoNT complex from serotype B-Okra (LPTC/B Okra ) exerts at least 80-fold higher oral toxicity in mice compared with that from serotype A1 (L-PTC/A 62A ). Here, we show that L-PTC/B Okra is predominantly absorbed through enterocytes, whereas LPTC/A 62A targets intestinal microfold cells. Furthermore, α1,2-fucosylation of intestinal mucin determines the oral toxicity of L-PTCs as well as their entry routes, due to differential carbohydrate-binding spectrum of one of the L-PTC components, the hemagglutinin (HA) complex. Fucosylation-deficient mice display reduced intestinal mucin penetration of L-PTC/B Okra via HA, and lower susceptibility to oral intoxication with this toxin. Thus, our results shed light on the molecular mechanisms by which the oral toxicity of BoNTs is increased after crossing intestinal mucus layers

Safety design of aviation propulsion lithium-ion battery systems based on thermal runaway explosion index

Rapid self-recognition ability in the cleaner fish

Abstract Whether animals are self-aware has important implications for our approaches to both animal cognition and animal welfare. A landmark moment in animal cognition research was when great apes passed the mark-test and demonstrated mirror self-recognition (MSR). Animals that pass the mark-test are capable of visually self-recognising and considered to be self-aware. Other taxa, including a fish, the cleaner wrasse (cleaner fish: Labroides dimidiatus ) have also now passed the mark-test, forcing a rethink of the mental and neurological requirements for MSR. Previous research has largely focused on which species can pass the mark-test, rather than the processes underlying MSR. Here, we marked mirror-naïve cleaner fish with an ecologically relevant mark resembling an ectoparasite and then undertook detailed behavioural observations after exposure to a mirror. We found that cleaner fish achieve MSR rapidly, implying self-awareness prior to mirror exposure. By observing the exact timing of MSR in individuals, we could also report previously undocumented differences in pre- and post-MSR behaviours, including post-MSR exploratory behaviour of the mirror’s reflective properties. We find remarkable parallels between the processing of MSR in humans and cleaner fish, suggesting that some aspects of self-awareness are conserved across animal taxa.

Engineering yeast peroxisome assembly enables the increased production of acetyl-CoA and its derived 5-deoxyflavonoids

Performance of oat under different cutting scheduling and integrated nutrient management in Eastern Himalayan agroclimatic conditions of India

Accurate and efficient P-values for rank-based independence tests with clustered data using a saddlepoint approximation

Abstract Accurate statistical inference for clustered data—common in multi-center clinical trials and longitudinal studies—poses significant challenges due to within-cluster correlation. Rank-based tests like the logrank, Wilcoxon, and Datta-Satten are valued for robustness but often suffer inflated Type I error rates under standard asymptotic approximations. While exact permutation tests offer theoretical accuracy, they are computationally impractical for large datasets, highlighting a methodological gap. This paper proposes a double saddlepoint approximation framework to deliver accurate p-values and confidence intervals for a wide class of rank-based tests. The method is built on a novel permutation distribution reformulation via block urn design, which preserves cluster integrity. This reformulation enables the test statistic’s distribution to be represented as a sum of independent conditional random variables, from which a joint cumulant generating function can be derived for saddlepoint computation. The approach supports analyses with right-censored survival data and tied ranks. Extensive simulations confirm that the saddlepoint method accurately controls Type I error rates, performing identically to permutation-based benchmarks but with a vast reduction in computational cost. A case study on clinical trial data demonstrates the practical importance of this accuracy, showing how our approach avoids a potential false-positive conclusion reported by the standard asymptotic method. Ultimately, this research provides biostatisticians with a tool that is at once practical, efficient, and statistically rigorous for analyzing clustered data.