Prevalence and characteristics of coronary artery fistulas among 20 259 patients undergoing invasive coronary angiography.

BACKGROUND: Coronary artery fistula (CAF) is a rare coronary anomaly. This study aimed to investigate the prevalence, clinical features, and imaging characteristics of CAF among patients undergoing coronary angiography (CAG). METHOD: This was a retrospective study including 20 259 consecutive patients (12 458 were male) who underwent CAG at our institution from September 2018 to March 2023. Electronic angiography records were reviewed, and a total of 86 (0.42%) CAF patients were enrolled and analyzed. RESULT: Of the 86 CAF patients, 42 (49%) were male. Thus, the prevalence of CAF for males and females was 0.34% and 0.56%, respectively. Arrhythmia, left ventricular (LV) hypertrophy, LV dilation, and LV systolic dysfunction were observed in 38, 25, 10 and 5 cases, respectively. Among the 86 CAF patients, a total of 117 CAFs were detected. 61 (71%) patients had a single CAF, and the remaining 25 (29%) patients had multiple CAFs. Of the 117 CAFs, the most common origins and terminations were the left anterior descending artery (n = 50) and the pulmonary artery (n = 73), respectively. The CAF diameters were greatly varied, ranging from unmeasurable to 7.8 mm, and 22 (18%) CAFs were larger than 3 mm. CONCLUSION: In the present study, the prevalence of CAF was 0.42% with a female predilection. Arrhythmia, LV remodeling and dysfunction were common. Seventy-one percent of patients had a single CAF. The left anterior descending artery and the pulmonary artery were the most common origin and termination of CAFs, respectively. Most CAFs were small, and 18% of CAFs were larger than 3 mm.

Development and Validation of an Automated Classification System for Osteonecrosis of the Femoral Head Using Deep Learning Approach: A Multicenter Study.

BACKGROUND: Accurate classification can facilitate the selection of appropriate interventions to delay the progression of osteonecrosis of the femoral head (ONFH). This study aimed to perform the classification of ONFH through a deep learning approach. METHODS: We retrospectively sampled 1,806 midcoronal magnetic resonance images (MRIs) of 1,337 hips from 4 institutions. Of these, 1,472 midcoronal MRIs of 1,155 hips were divided into training, validation, and test datasets with a ratio of 7:1:2 to develop a convolutional neural network model (CNN). An additional 334 midcoronal MRIs of 182 hips were used to perform external validation. The predictive performance of the CNN and the review panel was also compared. RESULTS: A multiclass CNN model was successfully developed. In internal validation, the overall accuracy of the CNN for predicting the severity of ONFH based on the Japanese Investigation Committee classification was 87.8%. The macroaverage values of area under the curve (AUC), precision, recall, and F-value were 0.90, 84.8, 84.8, and 84.6%, respectively. In external validation, the overall accuracy of the CNN was 83.8%. The macroaverage values of area under the curve, precision, recall, and F-value were 0.87, 79.5, 80.5, and 79.9%, respectively. In a human-machine comparison study, the CNN outperformed or was comparable to that of the deputy chief orthopaedic surgeons. CONCLUSION: The CNN is feasible and robust for classifying ONFH and correctly locating the necrotic area. These findings suggest that classifying ONFH using deep learning with high accuracy and generalizability may aid in predicting femoral head collapse and clinical decision-making.

Automatic detection of early osteonecrosis of the femoral head from various hip pathologies using deep convolutional neural network: a multi-centre study.

PURPOSE: The aim of this study was to develop a deep convolutional neural network (DCNN) for detecting early osteonecrosis of the femoral head (ONFH) from various hip pathologies and evaluate the feasibility of its application. METHODS: We retrospectively reviewed and annotated hip magnetic resonance imaging (MRI) of ONFH patients from four participated institutions and constructed a multi-centre dataset to develop the DCNN system. The diagnostic performance of the DCNN in the internal and external test datasets was calculated, including area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score, and gradient-weighted class activation mapping (Grad-CAM) technique was used to visualize its decision-making process. In addition, a human-machine comparison trial was performed. RESULTS: Overall, 11,730 hip MRI segments from 794 participants were used to develop and optimize the DCNN system. The AUROC, accuracy, and precision of the DCNN in internal test dataset were 0.97 (95% CI, 0.93-1.00), 96.6% (95% CI: 93.0-100%), and 97.6% (95% CI: 94.6-100%), and in external test dataset, they were 0.95 (95% CI, 0.91- 0.99), 95.2% (95% CI, 91.1-99.4%), and 95.7% (95% CI, 91.7-99.7%). Compared with attending orthopaedic surgeons, the DCNN showed superior diagnostic performance. The Grad-CAM demonstrated that the DCNN placed focus on the necrotic region. CONCLUSION: Compared with clinician-led diagnoses, the developed DCNN system is more accurate in diagnosing early ONFH, avoiding empirical dependence and inter-reader variability. Our findings support the integration of deep learning systems into real clinical settings to assist orthopaedic surgeons in diagnosing early ONFH.

Synthesis of a Magnetic Carnation-like Hydroxyapatite/Basic Calcium Carbonate Nanocomposite and Its Adsorption Behaviors for Lead Ions in Water.

Calcium-enriched compounds have great potential in the treatment of heavy-metal contaminated wastewater. Preparing stable basic calcium carbonate (BCC), which is a calcium-enriched compound, and applying it in practice is a great challenge. This work investigated the formation process of hierarchical hydroxyapatite (HAP)/BCC nanocomposites and their adsorption behaviors regarding lead ions (Pb2+). The morphology of the HAP/BCC nanocomposite was controlled by the addition of monododecyl phosphate (MDP). The carnation-like HAP/BCC nanocomposite was achieved with the addition of 30 g of MDP. The carnation-like HAP/BCC nanocomposite had a high Pb2+ adsorption capacity of 860 mg g-1. The pseudo-second-order and Freundlich model simulation results indicated that the adsorptions of Pb2+ on the nanocomposites belonged to the chemisorption and multilayer adsorption processes. The main effective adsorption components for the nanocomposites were calcium-enriched HAP and BCC. Through the Ca2+ ions exchanging with Pb2+, the HAP and BCC phases were converted to hydroxyl-pyromorphite (Pb-HAP) and hydrocerussite (Pb3(CO3)2(OH)2), respectively. The carnation-like HAP/BCC nanocomposite has great potential in the treatment of heavy metal ions. This facile method provides a new method for preparing a stable HAP/BCC nanocomposite and applying it in practice.

Recent advances in applications of power ultrasound for petroleum industry.

Power ultrasound, as an emerging green technology has received increasing attention of the petroleum industry. The physical and chemical effects of the periodic oscillation and implosion of acoustic cavitation bubbles can be employed to perform a variety of functions. Herein, the mechanisms and effects of acoustic cavitation are presented. In addition, the applications of power ultrasound in the petroleum industry are discussed in detail, including enhanced oil recovery, oil sand extraction, demulsification, viscosity reduction, oily wastewater treatment and oily sludge treatment. From the perspective of industrial background, key issue and resolution mechanism, current applications and future development of power ultrasound are discussed. In addition, the effects of acoustic parameters on treatment efficiency, such as frequency, acoustic intensity and treatment time are analyzed. Finally, the challenges and outlook for industrial application of power ultrasound are discussed.

Research on mechanism and characteristics of oil recovery from oily sludge in ultrasonic fields.

The acoustic parameters and operating conditions that determine efficiency of oil recovery from oily sludge are studied. Based on this, the mechanism of ultrasonic disintegration of oily sludge is analyzed. The results show that lower frequency ultrasound results in larger and more energetic cavitation bubbles that are more effective in the desorption of oil from solid particles. Moreover, acoustic intensity and treatment time that correspond to maximal oil recovery are found. Increasing the ratio of water to sludge and pH can reduce the slurry viscosity and facilitate the formation of HSiO3-, respectively, which improves the oil recovery efficiency. Moreover, Triton X-100 has better oil solubilizing effects than SDBS. After ultrasonic treatment, small amounts of asphaltenes are more stable on solid particles than other components. The heteroatoms such as S, N, and O in asphaltenes form hydrogen bonds with hydroxyl groups on the surface of the particles, impeding the desorption of oil. Mechanical effects such as shock waves and micro jets due to acoustic cavitation can break the hydrogen bonds between asphaltenes and solid particles, thereby facilitating oil recovery from oily sludge.

Optimization of acoustic parameters for ultrasonic separation of emulsions with different physical properties.

Ultrasound is an emerging and promising method for demulsification, which is highly affected by acoustic parameters and emulsion properties. Herein, a series of microscopic and dehydration experiments are carried out to investigate the parameter optimization of ultrasonic separation. The results show that the optimal acoustic parameters highly depend on the emulsion properties. For low frequency ultrasonic standing waves (USWs), mechanical vibrations not only facilitate droplet collision and coalescence, but also disperse the surfactant absorbed on the interface to decrease the interfacial strength. Therefore, low frequency ultrasound is suitable for separating emulsions with high viscosity and high interfacial strength. Increasing the energy density to produce moderate cavitation can increase demulsification efficiency. However, excessive cavitation results in secondary emulsification. In high frequency USWs, the droplets migrate directionally and form bandings, thereby promoting droplet coalescence. Therefore, high frequency ultrasound is favorable for separating emulsions with low dispersed phase content and small droplet size. Increasing the energy density can accelerate the aggregation of droplets, however, excessive energy density causes acoustic streaming that disturbs the aggregated droplets, resulting in reduced demulsification efficiency. This work presents rules for acoustic parameter optimization, further advancing industrial applications of ultrasonic separation.