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Model based noise correction enhances the accuracy of pancreatic CT perfusion blood flow measurements
Abstract A model based noise correction algorithm was developed to improve the accuracy of CT perfusion (CTp) blood flow (BF) measurements affected by image noise. The algorithm used tissue attenuation curves (TACs), generated by convolving an impulse response function (IRF) with an arterial input function (AIF) averaged from 59 patient datasets. Gaussian noise was introduced to simulate noise, and BF was measured using deconvolution. The algorithm iteratively compared BF without added noise against noise-impacted BF to estimate ground-truth BF (GTBF). Performance was evaluated with digital perfusion phantoms (DPPs) for GTBF values of 5–420 ml/100 ml/min and added noise (standard deviation 25 HU), measuring absolute difference from GTBF and contrast-to-noise ratio (CNR). For clinical evaluation, CTp data from 14 pancreatic ductal adenocarcinoma (PDAC) patients was used. For DPPs, noise-impacted and noise-corrected BF were 140 ± 111 ml/100 ml/min and 131 ± 125 ml/100 ml/min, compared to GTBF of 131 ± 127 ml/100 ml/min. Post-correction, the absolute difference reduced from 18.8 to 3.6 ml/100 ml/min, with CNR improving from 2.52 to 2.66. In clinical datasets, BF for parenchyma shifted from 148 ± 50.8 to 84.1 ± 96.9 ml/100 ml/min, and for PDAC, from 45.8 ± 20.3 to 13.3 ± 18.7 ml/100 ml/min. The algorithm reduced noise impact, improving BF accuracy and CNR, with potential for lower-dose CT without compromising diagnostic quality.
A forest fire identification and monitoring model based on improved YOLOv8
Multiple overlapping binding sites determine transcription factor occupancy
A transformer guided multi modal learning framework for predictive and causal assessment of thermal runaway in high energy batteries
Abstract Machine Learning approaches from the present state either use unimodal data, unable to model elegant long spatial-temporal dependencies in warning systems or create early warning response datasets with limited quantitative interpretability sets. To address these shortcomings, this work introduces T-RUNSAFE, a multi-pronged, machine learning-based predictive prototype for thermal runaway assessment. The framework integrates five specialized modules: (1) ST-Former, a spatiotemporal transformer that encodes thermal gradients from thermal images and sensor logs using temporal self-attention over LSTMs, thus is superior to traditional LSTMs for capturing evolving thermal patterns; (2) FUSE-GEN, adversarial trained dual-encoder variational autoencoder, fusing acoustic emission (AE) signals and thermal embeddings into a shared latent space for early-stage internal degradation detection; (3) DEGRA-GNN, a graph attention network that capitalizes on battery electrode topology to model the spatial propagation of thermal faults; (4) CAUS-RUN, a counterfactual simulation engine employing structural causal models to attribute risk to specific spatial zones for interpretability; and (5) SENSOR-RL, a reinforcement learning module optimizing sensor sampling policies on real-time risk levels that cuts down on sensor power while still holding to detection accuracy. The experimental results show great early prediction accuracy (AUC-ROC > 0.96), high spatial degradation localization accuracy (93.5%), and a 37% decrease in power consumption of sensing. T-RUNSAFE predicts, interprets, and optimizes resource utilization for thermal runaway risk assessment. By integrating deep learning, physics-informed modeling, and causal reasoning, it enables real-time battery safety monitoring. Although challenges remain regarding sensor cost, computational overhead, and chemistry generalization, the study demonstrates the feasibility of advanced onboard battery management systems tailored for next-generation energy applications.
Application of multimodal integration to develop preoperative diagnostic models for borderline and malignant ovarian tumors
First proposed blood test for chronic fatigue syndrome: what scientists think
Machine learning-based prediction of compressive energy absorption in shoe soles with different features
More than 30% of this century’s science Nobel prizewinners immigrated: see their journeys
Author Correction: Clonal dynamics and somatic evolution of haematopoiesis in mouse
A study of the pressureless sintering process of silver nanoparticles for electromagnetic interference shielding applications
Mark Norell obituary: palaeontologist who showed that dinosaurs still walk among us — as birds
Climate change mitigation potential of rural households in Chattogram District of Bangladesh
Clues to why the weight-loss condition cachexia arises when cancer occurs
Publisher Correction: Functional synapses between neurons and small cell lung cancer
Association between TG/HDL-C ratio or triglyceride-glucose index and mean arterial pressure in patients with myocardial infarction
Enhancing bioactivity and bioavailability of Limonium bellidifolium via cyclodextrin-based inclusion complexes: a new strategy in drug discovery from halophyte source
Knowledge, attitude, and practice among nurses regarding the prevention of pressure ulcers in a tertiary care hospital: a cross-sectional study
Abstract This study aimed to evaluate nurses’ knowledge, attitudes, and practices (KAP) regarding pressure ulcer prevention in a tertiary care hospital. A cross-sectional design was employed to assess nurses’ KAP regarding pressure ulcer (PU) prevention. The study was conducted at the Government Hospital of Faisalabad, Pakistan. A purposive sampling method selected 200 registered nurses currently employed at the hospital. The average age was 30.20 ± 5.61 years, with 75% ( n = 150) married and 92% ( n = 182) holding a diploma in nursing. Knowledge regarding pressure ulcers was high, with 49.5% ( n = 98) strongly agreeing and 31.8% ( n = 61) agreeing that pressure ulcers cause severe illnesses, yielding a Likert score of 4.15. Awareness of the Braden Scale was also high, with 50% ( n = 99) strongly agreeing and 31.3% ( n = 62) agreeing, resulting in a score of 4.18. The practice of turning patients every two hours was well-received, with 47.5% ( n = 94) strongly agreeing, leading to a score of 4.10. Age ( p = .134), marital status ( p = .571), and level of education ( p = .072) were not significant predictors of knowledge scores. However, higher knowledge scores significantly predicted more positive attitudes ( p < .001) and better practices ( p < .001) in pressure ulcer prevention. The study highlights significant gaps in nurses’ knowledge, attitudes, and practices regarding PU prevention. The findings underscore the need for continuous education and training to enhance nurses’ competence in PU prevention. Addressing these gaps through targeted interventions can improve patient outcomes and reduce the prevalence of PUs in healthcare settings.
Longer duration of intact hypothalamic–pituitary–gonadal (HPG) axis buffers the adverse impact of late-life frailty in male dogs
High-resolution native electrophoresis in-gel activity assay reveals biological insights of medium-chain fatty acyl-CoA dehydrogenase deficiency
Abstract Medium-chain specific acyl-CoA dehydrogenase (MCAD) is a mitochondrial homotetrameric flavoprotein that catalyzes the first step in fatty acid beta-oxidation. MCAD deficiency arises from variants that either impair enzymatic activity or destabilize interactions between subunits, leading to protein aggregation. Standard enzymatic assays measure the overall MCAD activity but cannot differentiate between tetramers and other protein forms—critical for understanding the impact of pathogenic variants on structure destabilization. In this study, we adapted a native gel colorimetric assay to quantify the activity of MCAD tetramers separately from other protein forms, providing novel insights into how pathogenic variants affect MCAD structure and function. The assay showed a linear correlation between protein amount and enzymatic activity for octanoyl-CoA, a physiological MCAD substrate. Applying this method to clinically relevant MCAD variants allowed us to distinguish subtle differences in protein shape, enzymatic activity, and FAD content, offering profound implications for understanding the molecular basis of MCADD. This methodology can be extended to analyze variants in other acyl-CoA dehydrogenase family members—such as glutaryl-CoA, isovaleryl-CoA or short-chain fatty acyl-CoA dehydrogenases—that are implicated in disorders of fatty acid and amino acid metabolism.