Polyvinyl Alcohol Phantoms With Heterogeneous Plaques: Estimation of Pulse Wave Velocity at the Stenotic Region Using Pulse Wave Imaging.

OBJECTIVE: Plaque characterization is essential for stroke prevention. In the study reported herein, we describe a heterogeneous phantom manufacturing technique with varying plaque compositions of different stiffness using polyvinyl alcohol (PVA) to emulate stenotic arteries and evaluated the use of pulse wave imaging (PWI) to assess plaque stiffness by comparing derived pulse wave velocities, with the goal of assessing plaque vulnerability and identifying high-risk patients for stroke. METHODS: Five stenotic phantoms (50% stenosis) were fabricated by pouring PVA solutions into 3-D-printed molds. Two of the phantoms had heterogeneous plaque compositions of soft (E0 = 13 kPa) and intermediate (E0 = 40 kPa) materials and of stiff (E0 = 54 kPa) and intermediate materials. Ultrasound sequences were acquired as the arterial phantoms were connected to a pulsating pump, and PWI was performed on the ultrasound acquisition using normalized cross-correlation to track the pulse-induced phantom wall distension propagations. Pulse wave velocities were estimated by fitting a linear regression line between the arrival time of the peak acceleration of the wall distension waveform and the corresponding location. RESULTS: Arterial phantoms with heterogeneous plaque stiffness were successfully fabricated. Pulse wave velocities of 2.06, 2.21, 2.49, 2.67 and 3.31 m/s were found in the phantom experiments using PWI for homogeneous soft plaque, the heterogeneous soft and intermediate plaque, homogeneous intermediate plaque, the heterogeneous stiff and intermediate plaque and homogeneous stiff plaque, respectively. CONCLUSION: A novel arterial phantom building technique was reported with varying heterogenous plaque compositions of different stiffness. The feasibility of using PWI to evaluate plaque stiffness in stenotic arteries was determined and found that PWI can distinguish between plaques of distinct stiffness and composition.

Deep learning-based lesion detection and severity grading of small-bowel Crohn's disease ulcers on double-balloon endoscopy images.

BACKGROUND AND AIMS: Double-balloon endoscopy (DBE) is widely used in diagnosing small-bowel Crohn's disease (CD). However, CD misdiagnosis frequently occurs if inexperienced endoscopists cannot accurately detect the lesions. The CD evaluation may also be inaccurate owing to the subjectivity of endoscopists. This study aimed to use artificial intelligence (AI) to accurately detect and objectively assess small-bowel CD for more refined disease management. METHODS: We collected 28,155 small-bowel DBE images from 628 patients from January 2018 to December 2022. Four expert gastroenterologists labeled the images, and at least 2 endoscopists made the final decision with agreement. A state-of-the-art deep learning model, EfficientNet-b5, was trained to detect CD lesions and evaluate CD ulcers. The detection included lesions of ulcer, noninflammatory stenosis, and inflammatory stenosis. Ulcer grading included ulcerated surface, ulcer size, and ulcer depth. A comparison of AI model performance with endoscopists was performed. RESULTS: The EfficientNet-b5 achieved high accuracies of 96.3% (95% confidence interval [CI], 95.7%-96.7%), 95.7% (95% CI, 95.1%-96.2%), and 96.7% (95% CI, 96.2%-97.2%) for the detection of ulcers, noninflammatory stenosis, and inflammatory stenosis, respectively. In ulcer grading, the EfficientNet-b5 exhibited average accuracies of 87.3% (95% CI, 84.6%-89.6%) for grading the ulcerated surface, 87.8% (95% CI, 85.0%-90.2%) for grading the size of ulcers, and 85.2% (95% CI, 83.2%-87.0%) for ulcer depth assessment. CONCLUSIONS: The EfficientNet-b5 achieved high accuracy in detecting CD lesions and grading CD ulcers. The AI model can provide expert-level accuracy and objective evaluation of small-bowel CD to optimize the clinical treatment plans.

Alterations in cellular metabolism under different grades of glioma staging identified based on a multi-omics analysis strategy.

Glioma is a type of brain tumor closely related to abnormal cell metabolism. Firstly, multiple combinatorial sequencing studies have revealed this relationship. Genomic studies have identified gene mutations and gene expression disorders related to the development of gliomas, which affect cell metabolic pathways. In addition, transcriptome studies have revealed the genes and regulatory networks that regulate cell metabolism in glioma tissues. Metabonomics studies have shown that the metabolic pathway of glioma cells has changed, indicating their distinct energy and nutritional requirements. This paper focuses on the retrospective analysis of multiple groups combined with sequencing to analyze the changes in various metabolites during metabolism in patients with glioma. Finally, the changes in genes, regulatory networks, and metabolic pathways regulating cell metabolism in patients with glioma under different metabolic conditions were discussed. It is also proposed that multi-group metabolic analysis is expected to better understand the mechanism of abnormal metabolism of gliomas and provide more personalized methods and guidance for early diagnosis, treatment, and prognosis evaluation of gliomas.

A colorimetric sensor with dual-ratio and dual-mode for detection of nicotine in tobacco samples.

Nicotine (NIC) is a harmful substance, drug, pesticide and chemical that is widely found in tobacco. It has carcinogenic, teratogenic and neurotoxic effects that have raised serious concerns. Herein, a colorimetric sensor with dual-ratio and dual-mode for the detection of NIC in tobacco samples was reported. The localized surface plasmon resonance signals of gold nanoparticles (AuNPs) and AuNPs-NIC are used as dual-ratio signals. The absorbance ratio of NIC to AuNPs or the absorbance ratio of NIC to AuNPs-NIC and the wavelength shift value of AuNPs-NIC are applied as dual-mode. Transmission electron microscopy, energy dispersive spectroscopy, dynamic light scattering spectroscopy, ultraviolet-visible spectrophotometry, cyclic voltammetry, and potentiostatic methods were used to characterize the sensor. Further analysis of NIC was conducted through morphological fitting and theoretical calculations. Under optimal conditions, the sensor shows a wide linear range of 5-500 µM. The detection limits for NIC are 2.48 µM, 1.63 µM and 1.34 µM, respectively. The experimental result shows that the dual-ratio signal of AuNPs and AuNPs-NIC has good selectivity and sensitivity, and can effectively reduce the interference of impurities on NIC detection. And the dual-mode of detection for NIC improves the accuracy and comparability of the result significantly. In addition, the proposed sensor was also applied to test NIC in tobacco samples with satisfactory recovery.

Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.

Objective. Arterial wall stiffness can provide valuable information on the proper function of the cardiovascular system. Ultrasound elasticity imaging techniques have shown great promise as a low-cost and non-invasive tool to enable localized maps of arterial wall stiffness. Such techniques rely upon motion detection algorithms that provide arterial wall displacement estimation.Approach. In this study, we propose an unsupervised deep learning-based approach, originally proposed for image registration, in order to enable improved quality arterial wall displacement estimation at high temporal and spatial resolutions. The performance of the proposed network was assessed through phantom experiments, where various models were trained by using ultrasound RF signals, or B-mode images, as well as different loss functions.Main results. Using the mean square error (MSE) for the training process provided the highest signal-to-noise ratio when training on the B-modes images (30.36 ± 1.14 dB) and highest contrast-to-noise ratio when training on the RF signals (32.84 ± 1.89 dB). In addition, training the model on RF signals demonstrated the capability of providing accurate localized pulse wave velocity (PWV) maps, with a mean relative error (MREPWV) of 3.32 ± 1.80% and anR2 of 0.97 ± 0.03. Finally, the developed model was tested in human common carotid arteriesin vivo, providing accurate tracking of the distension pulse wave propagation, with an MREPWV= 3.86 ± 2.69% andR2 = 0.95 ± 0.03.Significance. In conclusion, a novel displacement estimation approach was presented, showing promise in improving vascular elasticity imaging techniques.

Pulse wave imaging of a stenotic artery model with plaque constituents of different stiffnesses: Experimental demonstration in phantoms and fluid-structure interaction simulation.

Vulnerable plaques associated with softer components may rupture, releasing thrombotic emboli to smaller vessels in the brain, thus causing an ischemic stroke. Pulse Wave Imaging (PWI) is an ultrasound-based method that allows for pulse wave visualization while the regional pulse wave velocity (PWV) is mapped along the arterial wall to infer the underlying wall compliance. One potential application of PWI is the non-invasive estimation of plaque's mechanical properties for investigating its vulnerability. In this study, the accuracy of PWV estimation in stenotic vessels was investigated by computational simulation and PWI in validation phantoms to evaluate this modality for assessing future stroke risk. Polyvinyl alcohol (PVA) phantoms with plaque constituents of different stiffnesses were designed and constructed to emulate stenotic arteries in the experiment, and the novel fabrication process was described. Finite-element fluid-structure interaction simulations were performed in a stenotic phantom model that matched the geometry and parameters of the experiment in phantoms. The peak distension acceleration of the phantom wall was tracked to estimate PWV. PWVs of 2.57 ms-1, 3.41 ms-1, and 4.48 ms-1 were respectively obtained in the soft, intermediate, and stiff plaque material in phantoms during the experiment using PWI. PWVs of 2.10 ms-1, 3.33 ms-1, and 4.02 ms-1 were respectively found in the soft, intermediate, and stiff plaque material in the computational simulation. These results demonstrate that PWI can effectively distinguish the mechanical properties of plaque in phantoms as compared to computational simulation.

Analysis of Extended Resection of Limb Soft Tissue Leiomyosarcoma.

Leiomyosarcoma is an uncommon soft tissue sarcoma that composed of malignant mesenchymal cells with distinct features of the smooth muscle lineage. Typically affects the uterus and gastrointestinal tract, it can rarely be seen in large blood vessels, lymphatic and glandular duts, the mesentery, the omentum, retroperitoneum, and limbs. Occurrence is particularly rare in the limb region. Retrospective study based on patient records and postoperative pathological histological features. Four patients with limb leiomyosarcoma that were operated between 2016 and 2020 were included, three of them arising in the subcutis of the thigh region and one in cubitus. Extend resection with satisfactory outcomes is reported. Pathological examination showed that masses were composed of a fascicular arrangement of hyperchromatic spindle-shaped cells, characterized by the proliferation of epithelioid cells with eosinophilic cytoplasm for epithelioid leiomyosarcoma. Leiomyosarcomas that arise in the soft tissue, although rare, should be differentiated from other lesions, such as neurilemoma, neurofibroma, liomyoma,lipomyoma, synoviosarcoma, rhabdomyosarcoma, malignant fibrous histiotoma, and malignant neurinoma.

Organic Cathode Materials for Rechargeable Zinc Batteries: Mechanisms, Challenges, and Perspectives.

Energy and environmental issues have given rise to the development of advanced energy-storage devices worldwide. Electrochemical energy technologies, such as rechargeable batteries, are considered to be the most reliable and efficient candidates. Compared with other batteries, zinc-based batteries seem promising due to their advantages, including inherent safety, cost-effectiveness, and environmentally friendliness. As potential alternatives to conventional inorganic cathodes, organic cathodes for Zn-organic batteries have become a hot topic for research, owing to their favorable characteristics, such as easy structure design, controllable synthesis, and environmental benignancy. Herein, a systematic overview on the fundamentals of organic cathode materials for zinc batteries, including material design, electrochemical mechanisms, technical advances, and challenging analysis, is provided. Furthermore, perspectives and corresponding research directions are offered to facilitate the future development of organic cathode materials for zinc batteries toward practical applications.