Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease.

Medical imaging has been intensively employed in screening, diagnosis and monitoring during the COVID-19 pandemic. With the improvement of RT-PCR and rapid inspection technologies, the diagnostic references have shifted. Current recommendations tend to limit the application of medical imaging in the acute setting. Nevertheless, efficient and complementary values of medical imaging have been recognized at the beginning of the pandemic when facing unknown infectious diseases and a lack of sufficient diagnostic tools. Optimizing medical imaging for pandemics may still have encouraging implications for future public health, especially for long-lasting post-COVID-19 syndrome theranostics. A critical concern for the application of medical imaging is the increased radiation burden, particularly when medical imaging is used for screening and rapid containment purposes. Emerging artificial intelligence (AI) technology provides the opportunity to reduce the radiation burden while maintaining diagnostic quality. This review summarizes the current AI research on dose reduction for medical imaging, and the retrospective identification of their potential in COVID-19 may still have positive implications for future public health.

The Potential of Edible and Medicinal Resource Polysaccharides for Prevention and Treatment of Neurodegenerative Diseases.

As natural medicines in complementary and alternative medicine, edible and medicinal resources are being gradually recognized throughout the world. According to statistics from the World Health Organization, about 80% of the worldwide population has used edible and medicinal resource products to prevent and treat diseases. Polysaccharides, one of the main effective components in edible and medicinal resources, are considered ideal regulators of various biological responses due to their high effectiveness and low toxicity, and they have a wide range of possible applications for the development of functional foods for the regulation of common, frequently occurring, chronic and severe diseases. Such applications include the development of polysaccharide products for the prevention and treatment of neurodegenerative diseases that are difficult to control by a single treatment, which is of great value to the aging population. Therefore, we evaluated the potential of polysaccharides to prevent neurodegeneration by their regulation of behavioral and major pathologies, including abnormal protein aggregation and neuronal damage caused by neuronal apoptosis, autophagy, oxidative damage, neuroinflammation, unbalanced neurotransmitters, and poor synaptic plasticity. This includes multi-target and multi-pathway regulation involving the mitochondrial pathway, MAPK pathway, NF-κB pathway, Nrf2 pathway, mTOR pathway, PI3K/AKT pathway, P53/P21 pathway, and BDNF/TrkB/CREB pathway. In this paper, research into edible and medicinal resource polysaccharides for neurodegenerative diseases was reviewed in order to provide a basis for the development and application of polysaccharide health products and promote the recognition of functional products of edible and medicinal resources.

Instant Fingerprint Discrimination for Military Explosive Vapors by Dy3+ Doping a La2O3-Based Cataluminescence Sensor System.

With the intensification of explosive-based terrorism attack and environmental concerns, the innovation of high-efficiency and portable sensors for facile, rapid, and reliable monitoring of explosives has become one of the major demands in societies. Herein, a reliable and easy-to-use cataluminescence sensor system based on Dy3+ doping La2O3 nanorod catalysts has been developed for the identification and detection of six types of military explosive vapors, including homologous compounds and even isomers. The efficient discrimination is to make full use of the thermodynamic and kinetic information that can be extracted from the catalytic oxidation process of explosive molecules on various sensing materials, that is, the response signal and response time to generate the fingerprint of each target compound, while the rapid detection of the strategy can be manifested in response toward six military explosive vapors within 2.5 s and recover within 4 s. Meanwhile, the quantitative analysis of the explosives by the sensor system was realized based on 0.8%Dy:La2O3 with optimal catalytic activity, and the detection limits of NB, m-MNT, m-DNB, PNT, DNT, and TNT can reach 0.62, 0.49, 0.63, 0.38, 0.023, and 0.067 µg mL-1. In this research, we also constructed a novel sensor device and detection platform for explosive monitoring, which is of great significance for providing a new sensing principle for the efficient identification of explosives.

Light modulates glucose metabolism by a retina-hypothalamus-brown adipose tissue axis.

Public health studies indicate that artificial light is a high-risk factor for metabolic disorders. However, the neural mechanism underlying metabolic modulation by light remains elusive. Here, we found that light can acutely decrease glucose tolerance (GT) in mice by activation of intrinsically photosensitive retinal ganglion cells (ipRGCs) innervating the hypothalamic supraoptic nucleus (SON). Vasopressin neurons in the SON project to the paraventricular nucleus, then to the GABAergic neurons in the solitary tract nucleus, and eventually to brown adipose tissue (BAT). Light activation of this neural circuit directly blocks adaptive thermogenesis in BAT, thereby decreasing GT. In humans, light also modulates GT at the temperature where BAT is active. Thus, our work unveils a retina-SON-BAT axis that mediates the effect of light on glucose metabolism, which may explain the connection between artificial light and metabolic dysregulation, suggesting a potential prevention and treatment strategy for managing glucose metabolic disorders.

Influence of physical education on anxiety, depression, and self-esteem among college students.

BACKGROUND: Physical education is pivotal in our country's education reform. Urban schools have notably enhanced the intensity of physical education in recent years. However, the effects of physical education on students' anxiety, depression, and self-esteem levels, as well as their interrelations, remain unexplored. AIM: To analyze the influence of physical education on students' anxiety, depression, and self-esteem. METHODS: This study employed a cross-sectional design. A stratified cluster sampling method was used to select 478 first-year university students. Self-administered questionnaires were used to investigate the physical education status and basic information of college students. We used the Physical Activity Rank Scale-3 (PARS-3), Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and Self-Esteem Scale (SES) to assess the level of exercise, anxiety, depression, and self-esteem. Multiple Logistic regression was used to analyze the factors influencing anxiety, depression, and low self-esteem. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the predictive ability of PARS-3 scores for anxiety, depression, and low self-esteem. Spearman's correlation was used to analyze the correlations among the PARS-3, SAS, SDS, and SES. RESULTS: Compared with the domestic norms, SAS and SDS scores were higher, and SES scores were lower (P < 0.05). Among the participants, 210 (43.93%) had PARS-3 scores below 20, 94 (19.67%) had scores of 20-42, and 174 (36.40%) had scores above 42. After adjusting for daily sleep time, gender, being an only child, major, father's educational background, mother's educational background, and family residence, PARS-3 scores were independent influencing factors for anxiety, depression, and low self-esteem (P < 0.05). The AUC of PARS-3 scores predicting anxiety, depression, and low self-esteem were 0.805 (0.760-0.849), 0.799 (0.755-0.843), and 0.831 (0.788-0.874), respectively. The sensitivities were 0.799, 0.801, and 0.748, and the specificities were 0.743, 0.716, and 0.814, respectively. PARS-3 was negatively correlated with SAS and SDS scores (r = -0.566, -0.621, both P < 0.001) and positively correlated with SES scores (r = -0.621, P < 0.001). SES scores were negatively correlated with SAS and SDS scores (r = -0.508, r = -0.518, both P < 0.001). CONCLUSION: The amount of physical activity is negatively correlated with anxiety and depression degree and positively correlated with self-esteem degree.

Cataluminescence System Coupled with Vacuum Desorption-Sampling Methodology for Real-Time Ozone Sensing during the Self-Decomposition Process on Functional Boron Nitride.

The treatment and detection of ozone have been widely studied in recent decades with respect to toxicity and contamination, while the measurement method of ozone is relatively toneless. Fortunately, a new concept of the cataluminescence (CTL) sensor provides a scheme of real-time ozone sensing in a tiny system. Here, a novel CTL sensor system was specially developed with silica-hydroxyl functional boron nitride as the sensing material for rapid and sensitive ozone detection. Coupled with the construction of a pulse vacuum static sampling system, ozone on the surface of sensing material can be desorbed rapidly and can step into the next detection circulation in a few seconds. Based on the strong emission initiated by the transient of reactive oxygen species (ROS) including singlet oxygen, a trioxide group, and an oxygen radical, the detection limit of ozone could be optimized to be as low as 51.2 ppb. Besides, the sensor system exhibited remarkable anti-interference performance in which humidity changes and common VOCs do not disturb or weakly disturb ozone sensing, and the CTL mechanism of the multistep degradation process was further discussed on the basis of multiple pieces of experimental evidence and a DFT transient calculation. A real-time degradation-sensing module was further attached to the system to realize the functions of ozone decomposition and real-time monitoring.

Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach are confirmed in tests of various external imaging tracers on different scanners. The robust, generalizable, and transparent DL development may enhance the potential of clinical translation.

Melanopsin retinal ganglion cells mediate light-promoted brain development.

During development, melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs) become light sensitive much earlier than rods and cones. IpRGCs project to many subcortical areas, whereas physiological functions of these projections are yet to be fully elucidated. Here, we found that ipRGC-mediated light sensation promotes synaptogenesis of pyramidal neurons in various cortices and the hippocampus. This phenomenon depends on activation of ipRGCs and is mediated by the release of oxytocin from the supraoptic nucleus (SON) and the paraventricular nucleus (PVN) into cerebral-spinal fluid. We further characterized a direct connection between ipRGCs and oxytocin neurons in the SON and mutual projections between oxytocin neurons in the SON and PVN. Moreover, we showed that the lack of ipRGC-mediated, light-promoted early cortical synaptogenesis compromised learning ability in adult mice. Our results highlight the importance of light sensation early in life on the development of learning ability and therefore call attention to suitable light environment for infant care.

An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET.

PURPOSE: Deep learning is an emerging reconstruction method for positron emission tomography (PET), which can tackle complex PET corrections in an integrated procedure. This paper optimizes the direct PET reconstruction from sinogram on a long axial field of view (LAFOV) PET. METHODS: This paper proposes a novel deep learning architecture to reduce the biases during direct reconstruction from sinograms to images. This architecture is based on an encoder-decoder network, where the perceptual loss is used with pre-trained convolutional layers. It is trained and tested on data of 80 patients acquired from recent Siemens Biograph Vision Quadra long axial FOV (LAFOV) PET/CT. The patients are randomly split into a training dataset of 60 patients, a validation dataset of 10 patients, and a test dataset of 10 patients. The 3D sinograms are converted into 2D sinogram slices and used as input to the network. In addition, the vendor reconstructed images are considered as ground truths. Finally, the proposed method is compared with DeepPET, a benchmark deep learning method for PET reconstruction. RESULTS: Compared with DeepPET, the proposed network significantly reduces the root-mean-squared error (NRMSE) from 0.63 to 0.6 (p < 0.01) and increases the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) from 0.93 to 0.95 (p < 0.01) and from 82.02 to 82.36 (p < 0.01), respectively. The reconstruction time is approximately 10 s per patient, which is shortened by 23 times compared with the conventional method. The errors of mean standardized uptake values (SUVmean) for lesions between ground truth and the predicted result are reduced from 33.5 to 18.7% (p = 0.03). In addition, the error of max SUV is reduced from 32.7 to 21.8% (p = 0.02). CONCLUSION: The results demonstrate the feasibility of using deep learning to reconstruct images with acceptable image quality and short reconstruction time. It is shown that the proposed method can improve the quality of deep learning-based reconstructed images without additional CT images for attenuation and scattering corrections. This study demonstrated the feasibility of deep learning to rapidly reconstruct images without additional CT images for complex corrections from actual clinical measurements on LAFOV PET. Despite improving the current development, AI-based reconstruction does not work appropriately for untrained scenarios due to limited extrapolation capability and cannot completely replace conventional reconstruction currently.

Efficient Photoinduced Thermocatalytic Chemiluminescence System Based on the Z-Scheme Heterojunction Ag3PO4/Ag/Bi4Ti3O12 for H2S Sensing.

Cataluminescence as a highly efficient gas transduction principle has attracted wide attention among research in environmental monitoring and clinical diagnosis with increasing awareness of human safety. Nowadays, the development of innovation sensing systems and the construction of the sensing mechanism to improve the analytical performance of compounds remain a major challenge. Herein, we construct an advanced photoinduced thermocatalytic chemiluminescence (PI-TC-CL) gas-sensing system via the introduction of a Z-scheme heterojunction Ag3PO4/Ag/Bi4Ti3O12 to achieve higher efficient detection of H2S. The unique electron transport path of the Z-scheme heterojunction and the LSPR effect of Ag nanoparticles fascinate the generation of the photoinduced electron-hole pair on the surface of catalysts when stimulated by LED lamps and slow down the recombination of electron-hole pairs under thermal conditions. Thus, based on the cooperative effect of the Z-scheme heterojunction AgPO/Ag/BTO and PI-TC-CL system, we have successfully established an efficient H2S CTL detection system, which has a response three times higher than that on the traditional CTL system and even 45 times higher than that on BTO and ranges among the best of the state-of-the-art CTL performance in H2S detection with the linear range of 0.095-8.87 µg mL-1 and a limit of detection of 0.0065 µg mL-1. Besides, to explore the gas-sensing mechanism, the synergetic effects of photoinduction and thermal catalysis are investigated thoroughly via conductivity and electrochemical experiments. This research provides a new perspective of engineering highly efficient catalysts and ingenious sensor systems through designing the nanostructure of materials and synergism catalytic mechanism.

Symbolic Transformer Accelerating Machine Learning Screening of Hydrogen and Deuterium Evolution Reaction Catalysts in MA2Z4 Materials.

Two-dimensional (2D) materials have been developed into various catalysts with high performance, but employing them for developing highly stable and active nonprecious hydrogen evolution reaction (HER) catalysts still encounters many challenges. To this end, the machine learning (ML) screening of HER catalysts is accelerated by using genetic programming (GP) of symbolic transformers for various typical 2D MA2Z4 materials. The values of the Gibbs free energy of hydrogen adsorption (ΔGH*) are accurately and rapidly predicted via extreme gradient boosting regression by using only simple GP-processed elemental features, with a low predictive root-mean-square error of 0.14 eV. With the analysis of ML and density functional theory (DFT) methods, it is found that various electronic structural properties of metal atoms and the p-band center of surface atoms play a crucial role in regulating the HER performance. Based on these findings, NbSi2N4 and VSi2N4 are discovered to be active catalysts with thermodynamical and dynamical stability as ΔGH* approaches to zero (-0.041 and 0.024 eV). In addition, DFT calculations reveal that these catalysts also exhibit good deuterium evolution reaction (DER) performance. Overall, a multistep workflow is developed through ML models combined with DFT calculations for efficiently screening the potential HER and DER catalysts from 2D materials with the same crystal prototype, which is believed to have significant contribution to catalyst design and fabrication.

Evaluating the Band Gaps of Semiconductors by Cataluminescence.

A rapid and efficient methodology for the evaluation of band gaps of semiconductors is highly desirable to analyze and assess the intrinsic properties and extending application scopes of semiconductor materials. Here, the negative correlation of the cataluminescence (CTL) signal in the presence of H2S and the band gap of Aurivillius-type perovskite oxide Bi4+nFenTi3O12+3n (n = 1-4) was confirmed, where the H2S-induced CTL signal acts as a probe to evaluate the band gaps of semiconductor materials. The related mechanism shows that the thermal energy obtained by heating makes the electrons in the valence band more easily excite into the conduction band of a narrower band gap material and further promotes electron transfer between the gaseous compounds and semiconductor materials, causing acceleration of the catalytic oxide process. In addition, the extensibility was further verified by exploring the layered perovskite containing other insertion structures, including Bi4+nConTi3O12+3n (n = 1-4), Bi5NiTi3O15, and Bi5MnTi3O15, which was also consistent with the results characterized by UV diffuse reflectance spectroscopy. The established CTL probe for band gap evaluation shows rapid response, is simple to operate, and is of low cost, which is expected to become an innovative alternative to the conventional band gap assessment approach.

Rectal Tumor Stiffness Quantified by In Vivo Tomoelastography and Collagen Content Estimated by Histopathology Predict Tumor Aggressiveness.

PURPOSE: To investigate the significance of collagen in predicting the aggressiveness of rectal tumors in patients, examined in vivo based on tomoelastography quantified stiffness and ex vivo by histologically measured collagen volume fraction (CVF). EXPERIMENTAL DESIGN: 170 patients with suspected rectal cancer were prospectively enrolled and underwent preoperative magnetic resonance imaging (MRI) and rectal tomoelastography, a technique based on multifrequency magnetic resonance elastography. Histopathologic analysis identified eighty patients with rectal cancer who were divided into subgroups by tumor-node (TN) stage, prognostic stage, and risk level. Rectal tumor stiffness was correlated with histopathologic CVF. Area-under-the-curve (AUC) and contingency analysis were used to evaluate the performance of rectal stiffness in distinguishing tumor stages which was compared to standard clinical MRI. RESULTS: In vivo tomoelastography revealed that rectal tumor stiffened significantly with increased TN stage (p<0.05). Tumors with poorly differentiated status, perineural and lymphovascular invasion also displayed higher stiffness than well-to-moderately differentiated, noninvasive tumors (all p<0.05). Similar to in vivo stiffness, CVF indicated an abnormally high collagen content in tumors with perineural invasion and poor differentiation status. CVF was also positively correlated with stiffness (p<0.05). Most importantly, both stiffness (AUROC: 0.82) and CVF (AUROC: 0.89) demonstrated very good diagnostic accuracy in detecting rectal tumors that have high risk for progressing to an aggressive state with poorer prognosis. CONCLUSION: In human rectal carcinomas, overexpression of collagen is correlated with increased tissue stiffness and high risk for tumor advancing more aggressively. In vivo tomoelastography quantifies rectal tumor stiffness which improves the diagnostic performance of standard MRI in the assessment of lymph nodes metastasis. Therefore, in vivo stiffness mapping by tomoelastography can predict rectal tumor aggressiveness and add diagnostic value to MRI.

Role of heparanase 2 (Hpa2) in gastric cancer.

Heparanase is highly implicated in tumor metastasis due to its capacity to cleave heparan sulfate and, consequently, remodel the extracellular matrix underlying epithelial and endothelial cells. In striking contrast, only little attention was given to its close homolog, heparanase 2 (Hpa2), possibly because it lacks heparan sulfate-degrading activity typical of heparanase. We subjected sections of gastric carcinoma to immunostaining and correlated Hpa2 immunoreactivity with clinical records, including tumor grade, stage and patients' status. We over-expressed Hpa2 in gastric carcinoma cell lines and examined their tumorigenic properties in vitro and in vivo. We also evaluated the expression of Hpa2 by gastric carcinoma cells following inhibition of the proteasome, leading to proteotoxic stress, and the resulting signaling responsible for Hpa2 gene regulation. Here, we report that gastric cancer patients exhibiting high levels of Hpa2 survive longer. Similarly, mice administrated with gastric carcinoma cells engineered to over-express Hpa2 produced smaller tumors and survived longer than mice administrated with control cells. This was associated with increased phosphorylation of AMP-activated protein kinase (AMPK), a kinase that is situated at the center of a tumor suppressor network. We also found that MG132, an inhibitor of the proteasome that results in proteotoxic stress, prominently enhances Hpa2 expression. Notably, Hpa2 induction by MG132 appeared to be mediated by AMPK, and AMPK was found to induce the expression of Hpa2, thus establishing a loop that feeds itself where Hpa2 enhances AMPK phosphorylation that, in turn, induces Hpa2 expression, leading to attenuation of gastric tumorigenesis. These results indicate that high levels of Hpa2 in some tumors are due to stress conditions that tumors often experience due to their high rates of cell proliferation and high metabolic demands. This increase in Hpa2 levels by the stressed tumors appears critically important for patient outcomes.

Ratiometric Cataluminescence Sensor of Amine Vapors for Discriminating Meat Spoilage.

The freshness of meat has always been the focus of attention from consumers and suppliers for health and economic reasons. Usually, amine vapors, as one of the main components of the gas produced in the process of meat spoilage, can be used to monitor meat spoilage. Here, a new ratiometric cataluminescence (CTL) sensor based on energy transfer was developed to identify amine vapors and monitor meat freshness. After Tb doping, amine vapors exhibit a dual-wavelength (490 and 555 nm) property of CTL signals when reacted on the surface of Tb-doped La2O2CO3, and the ratio of I555 to I490 (R555/490) is a unique value for a given analyte within a wide range of concentrations. To illustrate the new sensor, 15 amine vapors were successfully identified using R555/490, including homologues and isomers. Besides, this sensor was used to monitor four meats, and the freshness of meats can be distinguished by cluster analysis successfully. Moreover, further discussion of energy-transfer phenomena and influence factors has facilitating effects on exploring the mechanism of energy transfer at the gas-solid interface.

Tomoelastography Based on Multifrequency MR Elastography for Prostate Cancer Detection: Comparison with Multiparametric MRI.

Background Multiparametric MRI is used for depiction of prostate cancer (PCa) but without consideration of the mechanical alteration of prostatic tissue by cancer. Purpose To investigate the diagnostic performance of stiffness and fluidity quantified with tomoelastography, a multifrequency MR elastography technique, for depiction of PCa compared with multiparametric MRI with Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Materials and Methods Prospective participants suspected to have PCa and healthy controls (HCs) underwent multiparametric MRI and tomoelastography between March 2019 and July 2020. Tomoelastography maps of shear-wave speed (c) and loss angle (φ) quantified stiffness and fluidity, respectively, for PCa and benign prostatic disease and for the peripheral and transition zones in HCs. Differences between entities and regions were analyzed by using analysis of variance or Kruskal-Wallis test. Diagnostic performance was assessed with area under the receiver operating characteristic curve (AUC) analysis. Results There were 73 participants with PCa (mean age, 72 years ± 7 [standard deviation]), 82 with benign prostatic disease (66 years ± 7), and 53 HCs (41 years ± 14). Mean ± standard deviation of c and φ were higher in PCa (3.4 m/sec ± 0.6 and 1.3 radian ± 0.2, respectively) than in benign prostatic disease (2.6 m/sec ± 0.3 and 1.0 radian ± 0.2, respectively; P < .001) and age-matched HCs (2.2 m/sec ± 0.1 and 0.8 radian ± 0.1, respectively; P < .001). Incorporating c and φ (AUC, 0.95; 95% CI: 0.92, 0.98) improved the diagnostic performance of PI-RADS version 2.1 (AUC, 0.85; 95% CI: 0.80, 0.91; P < .001). Multiparametric MRI combined with c and φ enabled detection of PCa with 95% (78 of 82 non-PCa) specificity, which was significantly higher than with use of multiparametric MRI alone (77% [63 of 82 non-PCa]; P < .001). In regional analysis, c combined with φ enabled differentiation of transition zone PCa from benign prostatic hyperplasia (AUC, 0.91; 95% CI: 0.83, 0.98) and peripheral zone PCa from chronic prostatitis (AUC, 0.94; 95% CI: 0.88, 1.00). Conclusion Use of tomoelastography-quantified stiffness and fluidity improved the diagnostic performance of multiparametric MRI with Prostate Imaging Reporting and Data System version 2.1 in detecting cancer in both the peripheral and transition zones. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Hectors and Lewis in this issue. An earlier incorrect version of this article appeared online. This article was corrected on March 24, 2021.

Abdominal perivascular epithelioid cell tumor (PEComa) without visible fat: a clinicopathologic and radiological analysis of 16 cases.

OBJECTIVE: To retrospectively review the clinicopathological features and computed tomography (CT) and magnetic resonance imaging (MRI) findings of abdominal perivascular epithelioid cell tumor without visible fat (PEComawvf). MATERIALS AND METHODS: Sixteen patients with surgically and pathologically confirmed perivascular epithelioid cell tumor without visible fat were enrolled. Their clinicopathological data and imaging findings were retrospectively reviewed. The CT and MRI features, including location, size, shape, margin, density, calcification, cystic necrosis and enhancement pattern, were analyzed. RESULTS: There were 4 males and 12 females (median age, 46 years; range, 21-65 years) in this study. All 16 patients were diagnostic asymptomatic unenhanced CT or MRI and revealed a well-defined (n = 13), oval (n = 10), mass with heterogeneous (n = 6) or homogeneous density/signal intensity (n = 7), calcification and hemorrhage was no found in any cases. On enhanced CT/MRI, markedly enhancement patterns (n = 14) were observed. The "peripheral enhancement" sign was observed in 13 cases. One in 16 cases recurrence was discovered during the follow-up period. CONCLUSIONS: Dynamic CT, MRI and pathology of PEComawvf had some characteristics of non-aggressive pattern of performance, and MRI would provide beneficial detection of microscopic fat. Enhanced imaging showed PEComawvf is characterized by a "peripheral enhancement" with a marked enhancement pattern. Knowing these characteristics could contribute to improving the understanding abdominal PEComawvf and related palliative care.

Recent advances in methodologies and applications of cataluminescence sensing.

Cataluminescence (CTL), a kind of chemiluminescence emitted at the gas-solid interface during catalytic oxidation reactions, has been developed for many decades as a novel and promising gas sensing technique. In this review, we introduce the origin, basic principles, and mechanisms of CTL sensing systems and summarize the recent advances in CTL sensing, focusing on methodologies and extended applications such as in gas selective monitoring, recognition of complex mixture, evaluation for catalytic property and use in high-performance liquid chromatography, capillary electrophoresis and gas chromatography detectors. In addition, development prospects and some challenges facing CTL-based sensing are also discussed.

Evaluation of simultaneous-multislice diffusion-weighted imaging of liver at 3.0 T with different breathing schemes.

PURPOSE: To obtain the optimal simultaneous-multislice (SMS)-accelerated diffusion-weighted imaging (DWI) of the liver at 3.0 T MRI by systematically estimating the repeatability of apparent diffusion coefficient (ADC), signal-to-noise ratio (SNR) and image quality of different breathing schemes in comparison to standard DWI (STD) and other SMS sequences. METHODS: In this institutional review board-approved prospective study, hepatic DWIs (b = 50, 300, 600 s/mm2) were performed in 23 volunteers on 3.0 T MRI using SMS and STD with breath-hold (BH-SMS, BH-STD), free-breathing (FB-SMS, FB-STD) and respiratory-triggered (RT-SMS, RT-STD). Reduction of scan time with SMS-acceleration was calculated. ADC and SNR were measured in nine anatomic locations and image quality was assessed on all SMS and STD sequences. An optimal SMS-DWI was decided by systematically comparing the ADC repeatability, SNR and image quality among above DWIs. RESULTS: SMS-DWI reduced scan time significantly by comparison with corresponding STD-DWI (27 vs. 42 s for BH, 54 vs. 78 s for FB and 42 vs. 97 s for RT). In all DWIs, BH-SMS had the greatest intraobserver agreement (intraclass correlation coefficient (ICC): 0.920-0.944) and good interobserver agreement (ICC: 0.831-0.886) for ADC measurements, and had the best ADC repeatability (mean ADC absolute differences: 0.046-0.058 × 10-3mm2/s, limits of agreement (LOA): 0.010-0.013 × 10-3mm2/s) in nine locations. BH-SMS had the highest SNR in three representative sections except for RT-STD. There were no significant differences in image quality between BH-SMS and other DWI sequences (median BH-SMS: 4.75, other DWI: 4.5-5.0; P > 0.0.5). CONCLUSION: BH-SMS provides considerable scan time reduction with good image quality, sufficient SNR and highest ADC repeatability on 3.0 T MRI, which is thus recommended as the optimal hepatic DWI sequence for those subjects with adequate breath-holding capability.