sCD14-ST and Related Osteoimmunological Biomarkers: A New Diagnostic Approach to Osteomyelitis.

Osteomyelitis (OM) is a major challenge in orthopedic surgery. The diagnosis of OM is based on imaging and laboratory tests, but it still presents some limitations. Therefore, a deeper comprehension of the pathogenetic mechanisms could enhance diagnostic and treatment approaches. OM pathogenesis is based on an inflammatory response to pathogen infection, leading to bone loss. The present study aims to investigate the potential diagnostic role of a panel of osteoimmunological serum biomarkers in the clinical approach to OM. The focus is on the emerging infection biomarker sCD14-ST, along with osteoimmunological and inflammatory serum biomarkers, to define a comprehensive biomarker panel for a multifaced approach to OM. The results, to our knowledge, demonstrate for the first time the diagnostic and early prognostic role of sCD14-ST in OM patients, suggesting that this biomarker could address the limitations of current laboratory tests, such as traditional inflammatory markers, in diagnosing OM. In addition, the study highlights a relevant diagnostic role of SuPAR, the chemokine CCL2, the anti-inflammatory cytokine IL-10, the Wnt inhibitors DKK-1 and Sclerostin, and the RANKL/OPG ratio. Moreover, CCL2 and SuPAR also exhibited early prognostic value.

Segmentation of coronary artery based on discriminative frequency learning and coronary-geometric refinement.

Coronary artery segmentation is crucial for physicians to identify and locate plaques and stenosis using coronary computed tomography angiography (CCTA). However, the low contrast of CCTA images and the intricate structures of coronary arteries make this task challenging. To address these difficulties, we propose a novel model, the DFS-PDS network. This network comprises two subnetworks: a discriminative frequency segment subnetwork (DFS) and a position domain scales subnetwork (PDS). DFS introduced a gated mechanism within the feed-forward network, leveraging the Joint Photographic Experts Group (JPEG) compression algorithm, to discriminatively determine which low- and high-frequency information of the features should be preserved for latent image segmentation. The PDS aims to learn the shape prototype by predicting the radius. Additionally, our model has the consistent ability to guarantee region and boundary features through boundary consistency loss. During training, both subnetworks are optimized jointly, and in the testing stage, the coarse segmentation and radius prediction are produced. A coronary-geometric refinement method refines the segmentation masks by leveraging the shape prior to being reconstructed from the radius map, reducing the difficulty of segmenting coronary artery structures from complex surrounding structures. The DFS-PDS network is compared with state-of-the-art (SOTA) methods on two coronary artery datasets to evaluate its performance. The experimental results demonstrate that the DFS-PDS network performs better than the SOTA models, including Vnet, nnUnet, DDT, CS2-Net, Unetr, and CAS-Net, in terms of Dice or connectivity evaluation metrics.

Deep learning for patient-specific quality assurance of volumetric modulated arc therapy: Prediction accuracy and cost-sensitive classification performance.

PURPOSE: To evaluate a deep learning model's performance in predicting and classifying patient-specific quality assurance (PSQA) results for volumetric modulated arc therapy (VMAT), aiming to streamline PSQA workflows and reduce the onsite measurement workload. METHODS: A total of 761 VMAT plans were analyzed using 3D-MResNet to process multileaf collimator images and monitor unit data, with the gamma passing rate (GPR) as the output. Thresholds for the predicted GPR (Th-p) and measured GPR (Th-m) were established to aid in PSQA decision-making, using cost curves and error rates to assess classification performance. RESULTS: The mean absolute errors of the model for the test set were 1.63 % and 2.38 % at 3 %/2 mm and 2 %/2 mm, respectively. For the classification of the PSQA results, Th-m was 88.3 % at 2 %/2 mm and 93.3 % at 3 %/2 mm. The lowest cost-sensitive error rates of 0.0127 and 0.0925 were obtained when Th-p was set as 91.2 % at 2 %/2 mm and 96.4 % at 3 %/2 mm, respectively. Additionally, the 2 %/2 mm classifier also achieved a lower total expected cost of 0.069 compared with 0.110 for the 3 %/2 mm classifier. The deep learning classifier under the 2 %/2 mm gamma criterion had a sensitivity and specificity of 100 % (10/10) and 83.5 % (167/200), respectively, for the test set. CONCLUSIONS: The developed 3D-MResNet model can accurately predict and classify PSQA results based on VMAT plans. The introduction of a deep learning model into the PSQA workflow has considerable potential for improving the VMAT PSQA process and reducing workloads.

Adaptive Annotation Correlation Based Multi-Annotation Learning for Calibrated Medical Image Segmentation.

Medical image segmentation is a fundamental task in many clinical applications, yet current automated segmentation methods rely heavily on manual annotations, which are inherently subjective and prone to annotation bias. Recently, modeling annotator preference has garnered great interest, and several methods have been proposed in the past two years. However, the existing methods completely ignore the potential correlation between annotations, such as complementary and discriminative information. In this work, the Adaptive annotation CorrelaTion based multI-annOtation LearNing (ACTION) method is proposed for calibrated medical image segmentation. ACTION employs consensus feature learning and dynamic adaptive weighting to leverage complementary information across annotations and emphasize discriminative information within each annotation based on their correlations, respectively. Meanwhile, memory accumulation-replay is proposed to accumulate the prior knowledge and integrate it into the model to enable the model to accommodate the multi-annotation setting. Two medical image benchmarks with different modalities are utilized to evaluate the performance of ACTION, and extensive experimental results demonstrate that it achieves superior performance compared to several state-of-the-art methods.

Q-ADER: An Effective Q-Learning for Recommendation With Diminishing Action Space.

Deep reinforcement learning (RL) has been widely applied to personalized recommender systems (PRSs) as they can capture user preferences progressively. Among RL-based techniques, deep Q-network (DQN) stands out as the most popular choice due to its simple update strategy and superior performance. Typically, many recommendation scenarios are accompanied by the diminishing action space setting, where the available action space will gradually decrease to avoid recommending duplicate items. However, existing DQN-based recommender systems inherently grapple with a discrepancy between the fixed full action space inherent in the Q-network and the diminishing available action space during recommendation. This article elucidates how this discrepancy induces an issue termed action diminishing error in the vanilla temporal difference (TD) operator. Due to this discrepancy, standard DQN methods prove impractical for learning accurate value estimates, rendering them ineffective in the context of diminishing action space. To mitigate this issue, we propose the Q-learning-based action diminishing error reduction (Q-ADER) algorithm to modify the value estimate error at each step. In practice, Q-ADER augments the standard TD learning with an error reduction term which is straightforward to implement on top of the existing DQN algorithms. Experiments are conducted on four real-world datasets to verify the effectiveness of our proposed algorithm.

Precise Localization for Anatomo-Physiological Hallmarks of the Cervical Spine by Using Neural Memory Ordinary Differential Equation.

In the evaluation of cervical spine disorders, precise positioning of anatomo-physiological hallmarks is fundamental for calculating diverse measurement metrics. Despite the fact that deep learning has achieved impressive results in the field of keypoint localization, there are still many limitations when facing medical image. First, these methods often encounter limitations when faced with the inherent variability in cervical spine datasets, arising from imaging factors. Second, predicting keypoints for only 4% of the entire X-ray image surface area poses a significant challenge. To tackle these issues, we propose a deep neural network architecture, NF-DEKR, specifically tailored for predicting keypoints in cervical spine physiological anatomy. Leveraging neural memory ordinary differential equation with its distinctive memory learning separation and convergence to a singular global attractor characteristic, our design effectively mitigates inherent data variability. Simultaneously, we introduce a Multi-Resolution Focus module to preprocess feature maps before entering the disentangled regression branch and the heatmap branch. Employing a differentiated strategy for feature maps of varying scales, this approach yields more accurate predictions of densely localized keypoints. We construct a medical dataset, SCUSpineXray, comprising X-ray images annotated by orthopedic specialists and conduct similar experiments on the publicly available UWSpineCT dataset. Experimental results demonstrate that compared to the baseline DEKR network, our proposed method enhances average precision by 2% to 3%, accompanied by a marginal increase in model parameters and the floating-point operations (FLOPs). The code (https://github.com/Zhxyi/NF-DEKR) is available.

Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning.

The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment.

A Forward Learning Algorithm for Neural Memory Ordinary Differential Equations.

The deep neural network, based on the backpropagation learning algorithm, has achieved tremendous success. However, the backpropagation algorithm is consistently considered biologically implausible. Many efforts have recently been made to address these biological implausibility issues, nevertheless, these methods are tailored to discrete neural network structures. Continuous neural networks are crucial for investigating novel neural network models with more biologically dynamic characteristics and for interpretability of large language models. The neural memory ordinary differential equation (nmODE) is a recently proposed continuous neural network model that exhibits several intriguing properties. In this study, we present a forward-learning algorithm, called nmForwardLA, for nmODE. This algorithm boasts lower computational dimensions and greater efficiency. Compared with the other learning algorithms, experimental results on MNIST, CIFAR10, and CIFAR100 demonstrate its potency.

Prototype Bayesian Meta-Learning for Few-Shot Image Classification.

Meta-learning aims to leverage prior knowledge from related tasks to enable a base learner to quickly adapt to new tasks with limited labeled samples. However, traditional meta-learning methods have limitations as they provide an optimal initialization for all new tasks, disregarding the inherent uncertainty induced by few-shot tasks and impeding task-specific self-adaptation initialization. In response to this challenge, this article proposes a novel probabilistic meta-learning approach called prototype Bayesian meta-learning (PBML). PBML focuses on meta-learning variational posteriors within a Bayesian framework, guided by prototype-conditioned prior information. Specifically, to capture model uncertainty, PBML treats both meta-and task-specific parameters as random variables and integrates their posterior estimates into hierarchical Bayesian modeling through variational inference (VI). During model inference, PBML employs Laplacian estimation to approximate the integral term over the likelihood loss, deriving a rigorous upper-bound for generalization errors. To enhance the model's expressiveness and enable task-specific adaptive initialization, PBML proposes a data-driven approach to model the task-specific variational posteriors. This is achieved by designing a generative model structure that incorporates prototype-conditioned task-dependent priors into the random generation of task-specific variational posteriors. Additionally, by performing latent embedding optimization, PBML decouples the gradient-based meta-learning from the high-dimensional variational parameter space. Experimental results on benchmark datasets for few-shot image classification illustrate that PBML attains state-of-the-art or competitive performance when compared to other related works. Versatility studies demonstrate the adaptability and applicability of PBML in addressing diverse and challenging few-shot tasks. Furthermore, ablation studies validate the performance gains attributed to the inference and model components.

ICNoduleNet: Enhancing Pulmonary Nodule Detection Performance on Sharp Kernel CT Imaging.

Thoracic computed tomography (CT) currently plays the primary role in pulmonary nodule detection, where the reconstruction kernel significantly impacts performance in computer-aided pulmonary nodule detectors. The issue of kernel selection affecting performance has been overlooked in pulmonary nodule detection. This paper first introduces a novel pulmonary nodule detection dataset named Reconstruction Kernel Imaging for Pulmonary Nodule Detection (RKPN) for quantifying algorithm differences between the two imaging types. The dataset contains pairs of images taken from the same patient on the same date, featuring both smooth (B31f) and sharp kernel (B60f) reconstructions. All other imaging parameters and pulmonary nodule labels remain entirely consistent across these pairs. Extensive quantification reveals mainstream detectors perform better on smooth kernel imaging than on sharp kernel imaging. To address suboptimal detection on the sharp kernel imaging, we further propose an image conversion-based pulmonary nodule detector called ICNoduleNet. A lightweight 3D slice-channel converter (LSCC) module is introduced to convert sharp kernel images into smooth kernel images, which can sufficiently learn inter-slice and inter-channel feature information while avoiding introducing excessive parameters. We conduct thorough experiments that validate the effectiveness of ICNoduleNet, it takes sharp kernel images as input and can achieve comparable or even superior detection performance to the baseline that uses the smooth kernel images. The evaluation shows promising results and proves the effectiveness of ICNoduleNet.

Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network.

Aims: Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis. This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using pre-procedural 12-lead electrocardiogram (ECG) images. Methods and results: We collected pre-procedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using five-fold cross-validation and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features. After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an area under the curve (AUC) of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG images. The performance was better than the Emory score (AUC = 0.704), as well as the logistic (AUC = 0.574) and XGBoost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score of 0.776, sensitivity of 0.794, and specificity of 0.752. Conclusion: Artificial intelligence-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.

Mitochondrial GPX4 acetylation is involved in cadmium-induced renal cell ferroptosis.

Increasing evidences demonstrate that environmental stressors are important inducers of acute kidney injury (AKI). This study aimed to investigate the impact of exposure to Cd, an environmental stressor, on renal cell ferroptosis. Transcriptomics analyses showed that arachidonic acid (ARA) metabolic pathway was disrupted in Cd-exposed mouse kidneys. Targeted metabolomics showed that renal oxidized ARA metabolites were increased in Cd-exposed mice. Renal 4-HNE, MDA, and ACSL4, were upregulated in Cd-exposed mouse kidneys. Consistent with animal experiments, the in vitro experiments showed that mitochondrial oxidized lipids were elevated in Cd-exposed HK-2 cells. Ultrastructure showed mitochondrial membrane rupture in Cd-exposed mouse kidneys. Mitochondrial cristae were accordingly reduced in Cd-exposed mouse kidneys. Mitochondrial SIRT3, an NAD+-dependent deacetylase that regulates mitochondrial protein stability, was reduced in Cd-exposed mouse kidneys. Subsequently, mitochondrial GPX4 acetylation was elevated and mitochondrial GPX4 protein was reduced in Cd-exposed mouse kidneys. Interestingly, Cd-induced mitochondrial GPX4 acetylation and renal cell ferroptosis were exacerbated in Sirt3-/- mice. Conversely, Cd-induced mitochondrial oxidized lipids were attenuated in nicotinamide mononucleotide (NMN)-pretreated HK-2 cells. Moreover, Cd-evoked mitochondrial GPX4 acetylation and renal cell ferroptosis were alleviated in NMN-pretreated mouse kidneys. These results suggest that mitochondrial GPX4 acetylation, probably caused by SIRT3 downregulation, is involved in Cd-evoked renal cell ferroptosis.

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy.

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

Coexistence of Cyclic Sequential Pattern Recognition and Associative Memory in Neural Networks by Attractor Mechanisms.

Neural networks are developed to model the behavior of the brain. One crucial question in this field pertains to when and how a neural network can memorize a given set of patterns. There are two mechanisms to store information: associative memory and sequential pattern recognition. In the case of associative memory, the neural network operates with dynamical attractors that are point attractors, each corresponding to one of the patterns to be stored within the network. In contrast, sequential pattern recognition involves the network memorizing a set of patterns and subsequently retrieving them in a specific order over time. From a dynamical perspective, this corresponds to the presence of a continuous attractor or a cyclic attractor composed of the sequence of patterns stored within the network in a given order. Evidence suggests that the brain is capable of simultaneously performing both associative memory and sequential pattern recognition. Therefore, these types of attractors coexist within the neural network, signifying that some patterns are stored as point attractors, while others are stored as continuous or cyclic attractors. This article investigates the coexistence of cyclic attractors and continuous or point attractors in certain nonlinear neural networks, enabling the simultaneous emergence of various memory mechanisms. By selectively grouping neurons, conditions are established for the existence of cyclic attractors, continuous attractors, and point attractors, respectively. Furthermore, each attractor is explicitly represented, and a competitive dynamic emerges among these coexisting attractors, primarily regulated by adjustments to external inputs.

A Bidirectional Feedforward Neural Network Architecture Using the Discretized Neural Memory Ordinary Differential Equation.

Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), which utilizes skip connections to aggregate features between its forward and backward paths. The BiFNN accepts any FNN as a plugin that can incorporate any general FNN model into its forward path, introducing only a few additional parameters in the cross-path connections. The backward path is implemented as a nonparameter layer, utilizing a discretized form of the neural memory Ordinary Differential Equation (nmODE), which is named [Formula: see text]-net. We provide a proof of convergence for the [Formula: see text]-net and evaluate its initial value problem. Our proposed architecture is evaluated on diverse image recognition datasets, including Fashion-MNIST, SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet. The results demonstrate that BiFNNs offer significant improvements compared to embedded models such as ConvMixer, ResNet, ResNeXt, and Vision Transformer. Furthermore, BiFNNs can be fine-tuned to achieve comparable performance with embedded models on Tiny-ImageNet and ImageNet-1K datasets by loading the same pretrained parameters.

Analysis of relevant factors influencing size of breast abscess cavity during lactation: a cross-sectional study.

OBJECTIVE: The aim of this study was to investigate risk factors for the severity of breast abscess during lactation. METHODS: A cross-sectional study was conducted using data from the Questionnaire survey of breast abscess patients. According to whether the maximum abscess diameter > 5 cm, the patients were divided into two groups for univariate and multivariate regression analysis. RESULTS: 1805 valid questionnaires were included. Univariate and Binary logistic regression analysis demonstrated that low education (OR = 1.5, 95% CI 1.1-2.0, P = 0.005), non-exclusive breastfeeding (OR = 0.7, 95% CI 0.6-0.9, P = 0.004), fever > 37.5 â„ƒ (OR = 0.7, 95% CI 0.6-0.9, P = 0.003), flat or inverted nipples (OR = 0.7, 95% CI 0.6-0.9, P = 0.005), antibiotic used (OR = 0.7, 95% CI 0.6-0.9, P = 0.006), and non-medical massage (OR = 0.3, 95% CI 0.2-0.4, P < 0.001) were the effective independent influencing factors for the maximum breast abscess diameter > 5 cm. CONCLUSION: Low education, non-exclusive breastfeeding, fever > 37.5 â„ƒ, inverted or flat nipples, antibiotic used, and non-medical massage history have adverse effects on the severity of breast abscess during lactation.

Efficacy of Tounongsan decoction on pyogenic liver abscess: network pharmacology and clinical trial validation.

OBJECTIVE: To elucidate the molecular mechanisms governing the effect of Tounongsan decoction (, TNS) on the pyogenic liver abscess. METHODS: Based on oral bioavailability and drug-likeness, the main active components of TNS were screened using the Traditional Chinese Medicine Systems Pharmacology platform. The GeneCard and UniProt databases were used to establish a database of pyogenic liver abscess targets. The interactive network map of drug-ingredients-target-disease was constructed using Cytoscape software (Version 3.7.2). A protein-protein interaction network was constructed using the STRING database, and the related protein interaction relationships were analyzed. biological process of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for the core targets. Finally, a clinical trial was performed to verify the reliability of the network pharmacology. RESULTS: Forty active components of TNS decoction were obtained, and 61 potential targets and 11 proteins were identified. Pathways involved in the treatment of pyogenic liver abscess include the phosphatidylinositide 3-kinases-protein kinase B (PI3K-AKT), advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE), and tumor necrosis factor (TNF) signaling pathways. The results of network pharmacology analysis combined with clinical trials validated that TNS decoction could alleviate the inflammatory response of pyogenic liver abscesses by decreasing interleukin 6 (IL-6) levels. CONCLUSIONS: TNS decoction has the characteristics of being multi-system, multi-component, and multi-target. Active ingredients in TNS, such as quercetin, kaempferol, fisetin, and ß-sitosterol, have strong potential to be candidate drugs for treating pyogenic liver abscesses. The possible mechanism of TSN decoction includes regulating immune and inflammatory responses and reducing IL-6 production to control inflammatory development.