Browse Articles

Molecular mechanism of action of small molecule SMT-738 on bacterial lipoprotein transporter LolCDE

COVID-19-related inflammation of the placenta impedes fetal development in pregnant hamsters

CD38 degrades MAVS through mitophagy to inhibit type I interferon secretion in nasopharyngeal carcinoma cells and impairs CD8+T cell-mediated anti-tumor immunity

Latitudinal divergence in runoff responses to global forestation due to forest-atmosphere feedbacks

Molecular mechanisms of flotillin complexes in organizing membrane microdomains

Recombinant zoster vaccine is associated with a reduced risk of dementia

Abstract Vaccines to prevent herpes zoster have been associated with reduced dementia risk. We conducted a retrospective matched cohort study of Kaiser Permanente Southern California members aged ≥65 years who received two doses of RZV 4 weeks–6 months apart between 01 April 2018 and 31 December 2020, with no dementia diagnoses or dementia medications prior to or within 6 months of their second RZV dose. Cox regression with inverse probability of treatment weighting was used to estimate adjusted hazard ratios (aHRs). The study included 65,800 RZV-vaccinated individuals and 263,200 unvaccinated matches. Vaccination with two doses of RZV was associated with a 51% lower risk of dementia (aHR: 0.49 [95% confidence interval (CI): 0.46–0.51]); aHRs were comparable across age, and racial and ethnic groups, but the risk reduction was stronger in females compared to males. In an evaluation of potential healthy vaccinee bias, the aHR of dementia for RZV compared to Tdap was 0.73 (95% CI: 0.67–0.79). Vaccination with two doses of RZV is associated with a statistically significant reduction in the risk of dementia in adults aged ≥65 years. After accounting for healthy vaccinee bias, RZV vaccination remains associated with a statistically significant lower risk of dementia.

Converse flexoelectric two-dimensional MoS2 actuator

Abstract Converse flexoelectric actuators offer significant potential for diverse applications due to their rapid response, resilience in extreme environments, and broad material compatibility. However, their development has been limited, primarily because flexoelectric effects are negligible in bulk materials. At the nanoscale, the converse flexoelectric effect becomes much more promising, as the electric field gradient scales quadratically with decreasing material thickness. Here, we report a converse flexoelectric actuator based on two-dimensional molybdenum disulfide. Under alternating current excitation near 20 kHz, the actuator exhibits resonant displacements up to ~45 nm, approximately two orders of magnitude larger than the thickness of the molybdenum disulfide active layer. This performance exceeds that of existing flexoelectric actuators by more than an order of magnitude when normalized by active layer thickness. Moreover, the actuator maintains strong flexoelectric responses under extreme conditions, including vacuum, cryogenic temperatures, and repeated cycling, highlighting the robustness and broad applicability of two-dimensional material-based converse flexoelectric systems.

Structural basis of TACO1-mediated efficient mitochondrial translation

Explainable AI-based analysis of human pancreas sections identifies traits of type 2 diabetes

Abstract Type 2 diabetes (T2D) is a chronic disease currently affecting around 500 million people worldwide with often severe health consequences. Yet, histopathological analyses are still inadequate to infer the glycaemic state of a person based on morphological alterations linked to impaired insulin secretion and β -cell failure in T2D. Giga-pixel microscopy can capture subtle morphological changes, but data complexity exceeds human analysis capabilities. In response, we generate a dataset of pancreas whole-slide images from living donors with multiple chromogenic and multiplex immunofluorescence stainings and train deep learning models to predict the T2D status. Using explainable AI, we make the learned relationships interpretable, quantify them as biomarkers, and assess their association with T2D. Remarkably, the highest prediction performance is achieved by simultaneously focusing on islet α - and δ -cells and neuronal axons, alongside subtle pancreatic alterations in T2D donors such as larger adipocyte clusters, altered islet-adipocyte proximity and smaller islets. This data-driven approach provides a foundation for future research into relevant diagnostic and therapeutic targets, refining several hypotheses regarding tissue alterations associated with T2D.

Photoinduced polarity-mismatched transformations of isoquinolines into naphthalenes

Phytic acid (InsP6) activates HDAC3 epigenetic axis to maintain intestinal barrier function

Strain-stabilized interfacial polarization tunes work function over 1 eV in RuO2/TiO2 heterostructures

A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision

Abstract Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are enabling models of the brain with performances beyond what was possible just a decade ago. However, because the stimuli of these datasets typically live within a common naturalistic visual distribution, they make it challenging to implement out-of-distribution (OOD) generalization tests crucial for the development of robust brain models. Here, we address this by releasing NSD-synthetic, a dataset of 7T fMRI responses from the same eight NSD participants for 284 synthetic images. We show that NSD-synthetic’s fMRI responses reliably encode stimulus-related information and are OOD with respect to NSD; that OOD generalization tests on NSD-synthetic reveal differences between brain models that are not detected in-distribution; and that the degree of OOD (quantified as the test data distance from the training data) is predictive of the magnitude of model failures. Together, NSD-synthetic enables OOD generalization tests that facilitate the development of more robust models of visual processing.

Robotic leaping enhanced by thrust-induced hypogravity, achieving precise, predictable, and extended jumps

Inhibition of TGF-β signaling in microglia stimulates hippocampal adult neurogenesis and reduces anxiety-like behavior in adult mice

Structural basis for activation and conformational plasticity of the GluA4 AMPA receptor

Integrative single-cell and machine learning framework reveals prognostic fibroblast subtypes and constructs a fibroblast-related risk signature in lung adenocarcinoma

Rapid dissemination of Staphylococcus aureus in the neonatal intensive care unit is associated with invasive infection

Correlation of chronological, skeletal and dental (Demirjian method) ages in an Iranian growing orthodontic population – a cross-sectional study

Abstract Growth modification treatments are a type of orthodontic treatment used for patients who are still growing. The treatment plan and timing for these interventions are generally based on the chronological age of patients; however, achieving the most desirable outcome requires a thorough understanding of the physical development of children and adolescents. The aim of this study is to examine the correlation between chronological age and physical development in children and adolescents requiring orthodontic treatment. To assess physical development, both dental age and skeletal age were evaluated. Panoramic and lateral cephalometric radiographs of 194 orthodontic patients aged 7 to 17 years were randomly selected from a dental clinic in Gachsaran. Dental maturity was assessed using the Demirjian method, while skeletal maturity was evaluated using the cervical vertebral maturation stage (CVMS) method described by Baccetti. Data analysis was conducted using SPSS software version 27. The correlation between variables was calculated using Pearson’s correlation coefficient and Spearman’s rank correlation. The mean dental age of patients was found to be higher than their chronological age. Notably, the strongest correlation between dental age and chronological age for both males and females were observed at the first cervical vertebral maturation stage (CVMS1) highlighting its potential clinical relevance for early growth assessment. The positive Pearson correlation between dental age and chronological age was 0.894 in males and 0.876 in females. Moreover, a strong and significant positive correlation was found between dental age and skeletal age, with a Spearman’s rank correlation coefficient of 0.779 in males and 0.785 in females. Additionally, a strong and significant positive correlation was observed between chronological age and skeletal age, with a Spearman’s rank correlation coefficient of 0.783 in males and 0.707 in females. The results of this study indicate a significant positive correlation between chronological age and both methods of assessing physical development (dental age and skeletal age). The Demirjian method appears to overestimate dental age in the study population, suggesting a need to reassess its validity in Iranian population. Furthermore, the strongest correlation between chronological age and dental age was observed at the CVMS1 for both genders. These insights should be taken into account when planning growth modification treatments.

A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis