Atherosclerosis is a primary cause of cardiovascular and cerebrovascular diseases, with the unpredictable rupture of vulnerable atherosclerotic plaques enriched with lipid-laden macrophages being able to lead to heart attacks and strokes. Activating macrophage autophagy presents itself as a promising strategy for preventing vulnerable plaque formation and reducing the risk of rupture. In this study, we have developed a novel metal-free nanozyme (HCN@DS) that integrates the functions of multimodal imaging-guided therapy for atherosclerosis. HCN@DS has demonstrated high macrophage-targeting abilities due to its affinity toward scavenger receptor A (SR-A), along with excellent photoacoustic and photothermal imaging capabilities for guiding the precise treatment. It combines mild photothermal effects with moderate reactive oxygen species (ROS) generation to treat atherosclerosis. This controlled approach activates autophagy in atherosclerotic macrophages, inhibiting foam cell formation by reducing the uptake of oxidized low-density lipoproteins (oxLDL) and promoting efferocytosis and cholesterol efflux in macrophages. Additionally, it prevents plaque rupture by inhibiting apoptosis and inflammation within the plaque. Therefore, this metal-free nanozyme holds great potential for reducing the risk of atherosclerosis due to its high biosafety, excellent targeting ability, dual-modality imaging capability, and appropriate modulation of autophagy.

Mycoplasma pneumoniae (MP) is one of the most common causes of community-acquired pneumonia in children. Most children have fever. In 2021, we found that the proportion of children without fever increased. The aim of this study is to summarize the differences in the clinical characteristics of children with MP pneumonia who are febrile or not, and to raise awareness of children who are not febrile. Demographic information of the children was collected on admission. Clinical manifestations during the course of the disease and the first laboratory, imaging, and pulmonary function tests before discharge were recorded and compared. From August to December, a total of 542 people were included in the study. We found that older children were more likely to have fever. Inflammatory indicators including procalcitonin (P = 0.030), C-reaction protein (P < 0.001), erythrocyte sedimentation rate (P < 0.001), ferritin (P = 0.040) and the rate of atelectasis (P = 0.049) of febrile children were higher in febrile children. However, the elevated lactate dehydrogenase and pulmonary function impairment (P all > 0.05), especially the small airway function impairment, are no lower in afebrile children than in febrile children. The fever rate is lower in younger children, but wheezing is more common. In afebrile children, the impairment of organ and lung function was no less than in febrile children. Therefore, attention should also be paid to children who are not febrile.

This study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events. The data of 180 patients with carotid atherosclerosis plaques were analysed from January 2017 to December 2021; 110 patients (64.03±9.58 years old, 20 women, 90 men) were allocated to the symptomatic group, and 70 patients (64.70±9.89 years old, 50 women, 20 men) were allocated to the asymptomatic group. Overall, five machine learning models using the XGBoost algorithm, based on different CT and clinical features, were developed in the training cohort. The performances of all five models were assessed in the testing cohort using receiver operating characteristic curves, accuracy, recall rate, and F1 score. The shapley additive explanation (SHAP) value ranking showed fat fraction (FF) as the highest among all CT and clinical features and normalised iodine density (NID) as the 10th. The model based on the top 10 features from the SHAP measurement showed optimal performance (area under the curve [AUC] .885, accuracy .833, recall rate .933, F1 score .861), compared with the other four models based on conventional CT features (AUC .588, accuracy .593, recall rate .767, F1 score .676), DECT features (AUC .685, accuracy .648, recall rate .667, F1 score .678), conventional CT and DECT features (AUC .819, accuracy .740, recall rate .867, F1 score .788), and all CT and clinical features (AUC .878, accuracy .833, recall rate .867, F1 score .852). FF and NID can serve as useful imaging markers of symptomatic carotid plaques. This tree-based machine learning model incorporating both DECT and clinical features could potentially comprise a non-invasive method for identification of symptomatic carotid plaques to guide clinical treatment strategies.

Radiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. A total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. The SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer–Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. Deep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.

Accurate prediction of prognosis and risk stratification in patients with laryngeal cancer can inform appropriate treatment decision-making. This study aims to develop a multi-channel deep learning radiomics model based on contrast-enhanced computed tomography (CECT) for predicting postoperative overall survival (OS) in patients. A total of 272 patients with laryngeal cancer were retrospectively recruited from two hospitals between January 2016 and July 2021. Specifically, 156 patients were enrolled from Center 1 as the training cohort, and 116 patients from Center 2 as the external test cohort. Two imaging signatures, reflecting phenotypes of the radiomics and multi-channel deep learning features, were constructed using pretreatment venous-phase CECT images. Feature selection involved reproducibility evaluation, Spearman correlation coefficient, and least absolute shrinkage and selection operator (LASSO) regression. Ten machine learning algorithms were employed to construct the signatures. An integrated Deep Learning Radiomics Nomogram (DLRN) was subsequently developed to predict OS. Predictive performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Kaplan–Meier survival curves derived from the DLRN were used for patient risk stratification, and subgroup analyses were conducted to validate the model’s robustness. The DLRN demonstrated satisfactory prognostic performance for laryngeal cancer. In the external test cohort, the 1-, 2-, and 3-year AUC values were 0.74, 0.75, and 0.80, respectively, and achieved a C-index of 0.73, outperforming individual models. Calibration curves and DCA indicated excellent calibration and the highest clinical net benefit. Subgroup analyses confirmed consistent DLRN performance across clinical stages, age groups, and surgical modalities. The proposed multi-channel deep learning radiomics model showed promising performance for predicting OS in laryngeal cancer patients. This approach may support individualized risk stratification and assist clinical decision-making in patients with laryngeal carcinoma.

Bow Hunter's syndrome is extremely rare, which is mainly caused by mechanical vertebral artery occlusion or stenosis during head and neck rotation or hyperextension. Herein, we describe the case of a 19-year-old man without a history of trauma who presented with dizziness, binocular blackness, and disturbance of consciousness after looking up when cleaning the classroom. Subsequent imaging findings revealed the blood flow of the C2 segment of the contralateral vertebral artery was interrupted when the patient turned his head to 1 side. Such patients with normal CT angiography of the head and neck scan will show that the head and neck blood vessels are normal, which will affect the prognosis of patients. This case highlights the importance and implications of dynamic CT angiography of the head and neck in the diagnosis of Bow Hunter's syndrome.