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Integrative transcriptomic and single-cell analysis reveals mitochondrial-related gene biomarkers in heart failure with preserved ejection fraction
Machine learning-assisted event classification in cadmium zinc telluride positron emission tomography detectors leveraging entanglement-informed angular correlations
Abstract Gamma–positron imaging with tracers that emit a prompt $$\gamma$$ (> 511 keV) is vulnerable to Compton down-scatter leaking into the 511-keV window and mimicking true annihilation pairs. Conventional Positron Emission Tomography (PET) systems reconstruct annihilation events without leveraging that the two 511-keV photons are not only orthogonally polarized but also produced in a Bell-entangled state. The polarization correlations of this entanglement imprint themselves in Compton scattering kinematics, particularly the relative azimuthal scattering angle ( $$\Delta \phi$$ ), offering a physics-informed handle for event discrimination. We present a machine-learning framework that exploits these quantum-encoded features to resolve true lines of response (LORs) and reject random coincidences in a dual-panel cadmium zinc telluride (CZT) system. Detected events were categorized into one-photoelectric (1P) and Compton (1C) interaction patterns, yielding four candidate interaction sequences per event. Each event was represented as a 4 $$\times$$ 21 feature matrix comprising spatial coordinates, energy deposits, and angular descriptors, including $$\Delta \phi$$ and polar scattering angle $$\theta$$ . Feature ablation with five-fold cross-validation revealed that the combination of energy and $$\Delta \phi$$ provided the highest discriminative power (Area Under the Receiver Operating Characteristic Curve (ROC–AUC) 0.87–0.95), followed by energy alone (ROC–AUC 0.85–0.95), while inclusion of spatial coordinates with energy and $$\Delta \phi$$ ranked third, achieving consistent performance across folds (ROC–AUC 0.81–0.91). These results demonstrate that incorporating entanglement-sensitive angular features into learning pipelines can suppress prompt contamination while preserving true LORs in a gamma-positron imaging system.
Influence of posture on prepulse inhibition and its link to postural control in healthy subjects
Abstract An unexpected loud sound typically elicits a reflexive eye-blink. This response is attenuated when preceded by a weaker non-startling stimulus, a phenomenon known as prepulse inhibition (PPI), which reflects sensory gating. PPI has been reported to differ between sitting and standing, suggesting a relationship with postural control. Sensory gating may dynamically suppress irrelevant inputs while preserving responsiveness to salient cues, a process likely supporting postural stability in complex environments. In this study, we investigated how PPI is modulated by postural demands and whether these changes relate to postural sway. Forty-five healthy participants underwent PPI testing while supine, standing on a hard surface, soft surface, and in tandem stance. We examined both auditory and somatosensory PPI, with and without visual input. Consistent with the hypothesis of dynamic gating, PPI was strongest during quiet standing on a firm surface, but weakened during more challenging postural conditions. While both somatosensory and auditory PPI were modulated, no significant modality-specific differences emerged. Removing visual input slightly reduced PPI but did not alter the overall pattern. These findings suggest that PPI reflects a flexible inhibitory mechanism tuned to postural context. While no causal relationship can be inferred, the results indicate a potential functional link between sensory gating and postural control, supporting future investigations into whether PPI could serve as a physiological marker of balance regulation.
Knowledge enhancement and full utilization of document information for document-level biomedical relation extraction
VENturing into machine learning for the morphological analysis of von Economo neurons
Abstract Von Economo neurons (VENs) are a specialized type of large, highly elongated projection neurons located in specific cortical regions. Despite their implication in higher-order cognitive functions and psychiatric disorders in humans, consistent and objective identification criteria for VENs remain lacking. We analyzed 761 digitally reconstructed neurons from the NeuroMorpho.Org database. We applied six supervised machine learning algorithms and a convolutional neural network with Grad-CAM visualization to classify the reconstructions into VENs and pyramidal neurons. Variable importance was evaluated using information-driven and expert-based selection. We compared the classifications made by machine learning algorithms to the reconstructions’ original labels. Reconstructions misclassified by the classifier models were further examined by a neuroanatomy expert. Machine learning models generally achieved high classification accuracy. Morphometric features such as dendritic length and number of stems emerged as some of the key discriminators. Expert ratings only partially aligned with machine findings, and there was low agreement between experts. Most misclassifications made by the classifier models were attributable to reconstruction artifacts or ambiguous morphology rather than model limitations. Our findings demonstrate the utility of combining machine learning with expert insight for distinguishing VENs from pyramidal neurons. While soma shape remains important for the characterization of VENs, classifier models revealed that dendritic architecture may be equally as specific and could help distinguish between borderline cases. This framework offers a replicable, data-driven method for studying VENs and can be utilized for future research on their distribution and function.
Tensile properties of sheet-based triply periodic minimal surface lattices fabricated using vat polymerization
Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation
Integrative analysis of scRNA-seq and RNA-seq to investigate the prognostic value of lactylation and fibroblast-related genes in intrahepatic cholangiocarcinoma
Graph transformer for link prediction on N-ary facts
Integrated optical and radar remote sensing for litho-structural mapping of the Kerdous and Ait Abdellah inliers, western Anti-Atlas, Morocco
Evaluation of longitudinal changes in choroidal thickness during pregnancy and postpartum using widefield swept-source optical coherence tomography
Altered PTPN13–β-catenin interaction by pathogenic mutations and involvement of this axis in B-cell receptor signalling
Low estimated glucose disposal rate (eGDR) predicts decreased all-cause mortality in critically ill patients with cardiorenal syndrome (CRS): analysis of the MIMIC-IV database
Risk stratification for metachronous metastasis in colon cancer based on landscape ecological analysis
Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication
Fluorescence-based bioassay for the detection of changes in mitochondrial physiology during artemisinin-induced growth arrest of blood-stage Plasmodium falciparum
The coronaviral landscape across diverse mammalian species in the Northeastern United States
MTAKD: multi-teacher agreement knowledge distillation for edge AI skin disease diagnosis
Cluster-specific genetic associations of CDKAL1, CDKN2A, CDKN2B, HHEX, KCNQ1, MTNR1B, PAX4, SLC30A8, TCF7L2, and UBE2E2 variants in new onset type 2 diabetes
Abstract Type 2 diabetes (T2D) is a heterogeneous metabolic disorder. Recent cluster-based classifications offer insights into distinct pathophysiological subtypes. The objective of the study is to investigate the association of genetic variants in T2D-related genes with defined T2D clusters. We analyzed 678 single nucleotide polymorphisms (SNPs) from ten genes ( CDKAL1 , CDKN2A , CDKN2B , HHEX , KCNQ1 , MTNR1B , PAX4 , SLC30A8 , TCF7L2 , and UBE2E2 ) in 471 T2D patients classified into four clusters: Severe Insulin-Deficient Diabetes (SIDD), Mild Obesity-related Diabetes (MOD), Mild Age-related Diabetes (MARD), and Metabolic Syndrome-related Diabetes (MSD). Genotyping was performed using the Axiom PDMRAv2 array. Following Hardy–Weinberg Equilibrium filtering, 376 SNPs were analysed. The association between T2D clusters and SNPs was assessed by multinomial logistic regression. Nineteen SNPs showed significant differences in genotypic frequencies among clusters ( p < 0.05). Eight SNPs (rs61875103 in TCF7L2 ; rs12576156, rs2283220, rs2074197, and rs163165 KCNQ1 ; rs4710943, rs9368248, and rs6456379 in CDKAL1 ) significantly associated with cluster assignment. Cluster-specific effects were most notable in SIDD and MOD subgroups. Our findings support genetic heterogeneity of TCF7L2 , KCNQ1 , and CDKAL1 in T2D clusters and underscore the potential for genetically informed precision therapy strategies.