g-C3N4-Based Photocatalytic Materials for Converting CO2 Into Energy: A Review.

Environmental pollution management and renewable energy development are humanity's biggest issues in the 21st century. The rise in atmospheric CO2, which has surpassed 400 parts per million, has stimulated research on CO2 reduction and conversion methods. Presently, photocatalytic conversion of CO2 to valuable hydrocarbons enables the transformation of solar energy into chemical energy and offers a novel avenue for energy conversion while regulating the greenhouse effect. This is an ideal strategy for simultaneously addressing environmental issues and the energy crisis. Photocatalysts are essential to photocatalytic processes. Photocatalyst is the core of photocatalytic technology, and graphite carbon nitride (g-C3N4) has attracted much attention because of its nonmetallic characteristics, and it has the characteristics of low cost, tunable electronic structure, easy manufacture and strong reducibility. However, its activity is not only affected by external reaction conditions, but also by the band gap structure, physical and chemical stability, surface morphology and specific surface area of the photocatalyst it. In this paper, the application progress of g-C3N4-based photocatalytic materials in CO2 reduction is reviewed, and the modification strategies of g-C3N4-based catalysts to obtain better catalytic efficiency and selectivity in CO2 photocatalytic reduction are summarized, and the future development of this material is prospected.

Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy.

BACKGROUND: Biopsy Gleason score (GS) is crucial for prostate cancer (PCa) treatment decision-making. Upgrading in GS from biopsy to radical prostatectomy (RP) puts a proportion of patients at risk of undertreatment. PURPOSE: To develop and validate a radiomics model based on multiparametric magnetic resonance imaging (mp-MRI) to predict PCa upgrading. STUDY TYPE: Retrospective, radiomics. POPULATION: A total of 166 RP-confirmed PCa patients (training cohort, n = 116; validation cohort, n = 50) were included. FIELD STRENGTH/SEQUENCE: 3.0T/T2 -weighted (T2 W), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) sequences. ASSESSMENT: PI-RADSv2 score for each tumor was recorded. Radiomic features were extracted from T2 W, ADC, and DCE sequences and Mutual Information Maximization criterion was used to identify the optimal features on each sequence. Multivariate logistic regression analysis was used to develop predictive models and a radiomics nomogram and their performance was evaluated. STATISTICAL TESTS: Student's t or chi-square were used to assess the differences in clinicopathologic data between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated. RESULTS: In PI-RADSv2 assessment, 67 lesions scored 5, 70 lesions scored 4, and 29 lesions scored 3. For each sequence, 4404 features were extracted and the top 20 best features were selected. The radiomics model incorporating signatures from the three sequences achieved better performance than any single sequence (AUC: radiomics model 0.868, T2 W 0.700, ADC 0.759, DCE 0.726). The combined mode incorporating radiomics signature, clinical stage, and time from biopsy to RP outperformed the clinical model and radiomics model (AUC: combined model 0.910, clinical model 0.646, radiomics model 0.868). The nomogram showed good performance (AUC 0.910) and calibration (P-values: training cohort 0.624, validation cohort 0.294). DATA CONCLUSION: Radiomics based on mp-MRI has potential to predict upgrading of PCa from biopsy to RP. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2020;52:1239-1248.

Radiomics in liver diseases: Current progress and future opportunities.

Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.

Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method.

OBJECTIVES: To predict histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLMs) with a noninvasive radiomics model. METHODS: Patients with chemotherapy-naive CRLMs who underwent abdominal contrast-enhanced multidetector CT (MDCT) followed by partial hepatectomy between January 2007 and January 2019 from two institutions were included in this retrospective study. Hematoxylin- and eosin-stained histopathologic sections of CRLMs were reviewed, with HGPs defined according to international consensus. Lesions were divided into training and validation datasets based on patients' sources. Radiomic features were extracted from pre- and post-contrast (arterial and portal venous) phase MDCT images, with review focusing on the segmented tumor-liver interface zones of CRLMs. Minimum redundancy maximum relevance and decision tree methods were used for radiomics modeling. Multivariable logistic regression analyses and ROC curves were used to assess the predictive performance of these models in predicting HGP types. RESULTS: A total of 126 CRLMs with histopathologic-demonstrated desmoplastic (n = 68) or replacement (n = 58) HGPs were assessed. The radiomics signature consisted of 20 features of each phase selected. The 3 phases fused radiomics signature demonstrated the best predictive performance in distinguishing between replacement and desmoplastic HGPs (AUCs of 0.926 and 0.939 in the training and external validation cohorts, respectively). The clinical-radiomics combined model showed good discrimination (C-indices of 0.941 and 0.833 in the training and external validation cohorts, respectively). CONCLUSIONS: A radiomics model derived from MDCT images may effectively predict the HGP of CRLMs, thus providing a basis for prognostic stratification and therapeutic decision-making.

Fabrication of Wettability Mesh with Quasi-Rectangular-Restraining Capacity to Water.

A water droplet placed on a surface is usually round owing to surface tension. Restraining a droplet to a rectangle shape has been rarely reported. Herein, we fabricated three meshes with diverse wettability including ordinary mesh, superhydropilic mesh, and quasi-rectangular-restraining mesh. The profiles of water droplets on these three meshes were entirely different from the top view, especially for the quasi-rectangular-restraining mesh, which enables the water droplet on it to achieve the rectangular shape. The surface morphologies and chemical compositions of the meshes were characterized by scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffraction. Moreover, the influences of processing parameters of the quasi-rectangular-restraining mesh on the quasi-rectangular quality of the water droplet on it were investigated to obtain the relatively optimum processing parameters. The dynamic properties of water droplets on the three meshes were compared, and forces acting on the water droplets during the spreading and shrinking processes on the three meshes were qualitatively analyzed. Additionally, we studied the influences of falling height and water volume on the quasi-rectangular quality of the water droplet on the quasi-rectangular-restraining mesh. Water droplets on the quasi-rectangular-restraining mesh demonstrated good stability under vibration and the droplet could maintain the quasi-rectangular quality on the quasi-rectangular-restraining mesh for about 7 days, revealing a good durability. Further, the large-scaled fabrication of the quasi-rectangular-restraining mesh was realized.

Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas.

PURPOSE: To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas. METHODS: This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93). RESULTS: The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction. CONCLUSION: Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

A Reliable and Real-Time Tracking Method with Color Distribution.

Occlusion is a challenging problem in visual tracking. Therefore, in recent years, many trackers have been explored to solve this problem, but most of them cannot track the target in real time because of the heavy computational cost. A spatio-temporal context (STC) tracker was proposed to accelerate the task by calculating context information in the Fourier domain, alleviating the performance in handling occlusion. In this paper, we take advantage of the high efficiency of the STC tracker and employ salient prior model information based on color distribution to improve the robustness. Furthermore, we exploit a scale pyramid for accurate scale estimation. In particular, a new high-confidence update strategy and a re-searching mechanism are used to avoid the model corruption and handle occlusion. Extensive experimental results demonstrate our algorithm outperforms several state-of-the-art algorithms on the OTB2015 dataset.

All-optically reconfigurable and tunable fiber surface grating for in-fiber devices: a wideband tunable filter.

A fiber surface grating (FSG) formed from a photosensitive liquid crystal hybrid (PLCH) film overlaid on a side-polished fiber (SPF) is studied and has been experimentally shown to be able to function as an all-optically reconfigurable and tunable fiber device. The device is all-optically configured to be a short period fiber surface grating (SPFSG) when a phase mask is used, and then reconfigured to be a long period FSG (LPFSG) when an amplitude mask is used. Experimental results show that both the short and long period FSGs can function as an optically tunable band-rejection filter and have different performances with different pump power and different configured period of the FSG. When configured as a SPFSG, the device can achieve a high extinction ratio (ER) of 21.5dB and a wideband tunability of 31nm are achieved. When configured as a LPFSG, the device can achieve an even higher ER of 23.4dB and a wider tunable bandwidth of 60nm. Besides these tunable performances of the device, its full width at half maximum (FWHM) can also be optically tuned. The reconfigurability and tunability of the fiber device open up possibilities for other all-optically programmable and tunable fiber devices.

An event-oriented diffusion-refinement method for sparse events completion.

Event cameras or dynamic vision sensors (DVS) record asynchronous response to brightness changes instead of conventional intensity frames, and feature ultra-high sensitivity at low bandwidth. The new mechanism demonstrates great advantages in challenging scenarios with fast motion and large dynamic range. However, the recorded events might be highly sparse due to either limited hardware bandwidth or extreme photon starvation in harsh environments. To unlock the full potential of event cameras, we propose an inventive event sequence completion approach conforming to the unique characteristics of event data in both the processing stage and the output form. Specifically, we treat event streams as 3D event clouds in the spatiotemporal domain, develop a diffusion-based generative model to generate dense clouds in a coarse-to-fine manner, and recover exact timestamps to maintain the temporal resolution of raw data successfully. To validate the effectiveness of our method comprehensively, we perform extensive experiments on three widely used public datasets with different spatial resolutions, and additionally collect a novel event dataset covering diverse scenarios with highly dynamic motions and under harsh illumination. Besides generating high-quality dense events, our method can benefit downstream applications such as object classification and intensity frame reconstruction.

ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light Field Reconstruction.

This paper proposes a hybrid radiance field representation for unbounded immersive light field reconstruction which supports high-quality rendering and aggressive view extrapolation. The key idea is to first formally separate the foreground and the background and then adaptively balance learning of them during the training process. To fulfill this goal, we represent the foreground and background as two separate radiance fields with two different spatial mapping strategies. We further propose an adaptive sampling strategy and a segmentation regularizer for more clear segmentation and robust convergence. Finally, we contribute a novel immersive light field dataset, named THUImmersive, with the potential to achieve much larger space 6DoF immersive rendering effects compared with existing datasets, by capturing multiple neighboring viewpoints for the same scene, to stimulate the research and AR/VR applications in the immersive light field domain. Extensive experiments demonstrate the strong performance of our method for unbounded immersive light field reconstruction.

The impact of mandibular partial edentulous distal extension on virtual occlusal record accuracy when using two different intraoral scanners: An in vitro analysis.

OBJECTIVES: This in vitro study was formulated to clarify how mandibular partial edentulous distal extension involving three missing teeth affects the virtual occlusal record (VOR) accuracy, in terms of both trueness and precision, when using two different intraoral scanners (IOSs) - the Primescan (PS) and Trios 4 (TR) scanners. METHODS: A typodont model missing the left mandibular second premolar, first molar, and second molar as well as the right mandibular first molar was mounted on a semi-adjustable articulator. Four implants were placed at the sites of the missing mandibular teeth. Six pairs of stainless steel markers (diameter: 0.5 mm) were affixed to the maxillary and mandibular casts in the buccal gingiva adjacent to the implants and on the facial surfaces of teeth as reference positions for measurements. The model was digitized with an inEox X5 laboratory scanner to create a reference dataset. Intraoral scans were performed with the PS and TR scanners, with each scan duplicated 10 times to generate 20 paired IOS files. Automatic VOR generation followed the bilateral buccal scan protocol, divided into PS and TR groups (n = 10). Six subgroups of linear distances between interarch markers were assessed with Geomagic Control software, comparing deviations from the reference scan. Data normality was confirmed with the Shapiro-Wilk test. Trueness was evaluated with two-way ANOVAs and pairwise comparisons performed with Tukey's test, whereas precision was assessed with the Levene test (α=0.05). RESULTS: The mean linear deviation (Δd) and standard deviation (SD) of VOR were both significantly affected by marker position (P < 0.001), and a significant position × scanner interaction was detected (P < 0.001). Negative mean deviations were observed for the distally extended edentulous areas in both groups. PS scans exhibited trueness that was significantly better than that for TR scans in the D16-46, D13-43, D23-33, and D27-37 subgroups (P < 0.05), whereas there were no significant differences in the D25-35 or D26-36 subgroups. PS scanning was associated with significantly better precision than TR scanning (P < 0.001), and worse precision was observed at D27-37 for both tested IOSs. CONCLUSIONS: Mandibular partial edentulous distal extension can significantly affect VOR accuracy. The type of IOS could also affect VOR accuracy depending on the area being scanned, with better overall performance observed for the Primescan device as compared to the Trios 4 scanner. Both of these IOSs tended to underestimate VOR occlusal dimensions in mandibular distally extended edentulous areas. CLINICAL SIGNIFICANCE: Mandibular distal edentulous areas can contribute to occlusal dimensions that are underestimated in digital workflows, which may result in infraocclusal discrepancies that arise when performing restorations. IOSs and VOR scanning protocols should thus be carefully considered in order to minimize these risks.

OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation.

Light field disparity estimation is an essential task in computer vision. Currently, supervised learning-based methods have achieved better performance than both unsupervised and optimization-based methods. However, the generalization capacity of supervised methods on real-world data, where no ground truth is available for training, remains limited. In this paper, we argue that unsupervised methods can achieve not only much stronger generalization capacity on real-world data but also more accurate disparity estimation results on synthetic datasets. To fulfill this goal, we present the Occlusion Pattern Aware Loss, named OPAL, which successfully extracts and encodes general occlusion patterns inherent in the light field for calculating the disparity loss. OPAL enables: i) accurate and robust disparity estimation by teaching the network how to handle occlusions effectively and ii) significantly reduced network parameters required for accurate and efficient estimation. We further propose an EPI transformer and a gradient-based refinement module for achieving more accurate and pixel-aligned disparity estimation results. Extensive experiments demonstrate our method not only significantly improves the accuracy compared with SOTA unsupervised methods, but also possesses stronger generalization capacity on real-world data compared with SOTA supervised methods. Last but not least, the network training and inference efficiency are much higher than existing learning-based methods. Our code will be made publicly available.

Application of benzothiadiazole to Cabernet Gernischt grapes (Vitis vinifera L.) for quality improvement: Effects on aroma metabolism precursors and related genes expression.

Pre-harvest spraying of benzothiadiazole (BTH) can improve the winemaking properties of grapes, especially their aroma compounds and phenolics. Limited research has explored the molecular mechanisms by which BTH influences the accumulation of grape aroma precursors during early grape development. This study investigated the effects and putative molecular mechanisms of applying 0.37 mM BTH through whole-plant spraying on the accumulation of aroma metabolism precursors and gene expression in Cabernet Gernischt grapes during ripening. The results showed that BTH treatment increased the levels of fructose, alanine, aspartate, threonine, myristic acid, myristoleic acid, palmitic acid, ß-cryptoxanthin, norisoprenoids and methoxypyrazines. Contrarily, it decreased the levels of glucose, sucrose, phenylalanine, tyrosine, leucine, valine, glycine, arginine, histidine, total unsaturated fatty acids (particularly linoleic acid), zeaxanthin, lutein, and organic acids. Additionally, BTH upregulated the expression of genes associated with the production and degradation of amino acids, fatty acids, and carotenoids while decreasing the expression of genes involved in the synthesis and degradation of soluble sugars and organic acids. Ten different metabolites, including fumaric acid, were identified as potential biological markers for distinguishing BTH-treated grapes from control grapes. The study demonstrates that BTH treatment had a substantial impact on the concentration and developmental patterns of aroma metabolism precursors. Furthermore, it altered the winemaking characteristics of Cabernet Gernischt grapes by modulating genes associated with the production and breakdown of metabolites.

Influence of Benzothiadiazole on the Accumulation and Metabolism of C6 Compounds in Cabernet Gernischt Grapes (Vitis vinifera L.).

Pre-harvest application of elicitors improves grape quality, specifically the phenolic compounds and color characteristics. Limited research has been conducted on the impact of elicitors on the C6 compounds found in grapes. This is due to lack of comprehensive studies examining the combined effects of bound aroma compounds, enzyme activity, and substrate availability. This study aimed to assess the impact of benzothiadiazole (BTH) on the physicochemical properties and C6 compounds of Cabernet Gernischt grapes during ripening. Compared with the control group (CK), BTH treatment significantly increased the 100-berry weight, skin/berry ratio, pH, total phenolic content, and total flavonoid content in ripe grapes. Additionally, BTH treatment led to significant reductions in reducing sugar, total soluble solids, titratable acidity, linoleic acid, linolenic acid, and free C6 aldehydes. Furthermore, BTH treatment significantly decreased the contents of free C6 alcohols and increased the levels of free and bound C6 esters. BTH treatment also increased the activities of lipoxygenase, alcohol dehydrogenase, and alcohol acetyltransferase enzymes, while it decreased the activity of hydroperoxide lyase enzyme. The application of BTH resulted in changes to the physicochemical properties and levels of C6 compounds in Cabernet Gernischt grapes by up-regulating enzyme activity and down-regulating precursors.

In situ preparation of a multifunctional adsorbent by optimizing the Fe2+/Fe3+/Mn2+/HA ratio for simultaneous and efficient removal of Cd(II), Pb(II), Cu(II), Zn(II), As(III), Sb(III), As(V) and Sb(V) from aqueous environment: Behaviors and mechanisms.

Multiple potentially toxic elements (PTEs) often coexist in practical wastewater environment, which poses serious risks to the ecological environment and human health. However, few of the reported adsorbents are capable of simultaneously and effectively removing multiple PTEs from wastewater due to the unique properties of each element. In this work, a multifunctional adsorbent FMHs was developed by optimizing Fe2+/Fe3+/Mn2+/HA ratio, and applied to remove Cd(II), Pb(II), Cu(II), Zn(II), As(III), Sb(III), As(V) and Sb(V) from aqueous solution. Results revealed that the adsorption data obeyed the Elovich, Sips and Redlich-Peterson models in the mono-component system, and the maximum adsorption capacity of FMHs was superior to most adsorbents reported in the literatures. In addition, FMHs retained considerable removal capacity after four cycles, and maintained excellent adsorption performance under the interference of different environmental factors (including pH, ionic strength, co-existing ions and humic acid). In the multi-component system, FMHs also presented high adsorption capacity for all the selected PTEs, especially for Sb(III/V) and Pb(II). Characterization results confirmed that various removal mechanisms, such as precipitation, surface complexation, ion exchange, electrostatic attraction and redox, were responsible for the capture of PTEs by FMHs.

Winter madness: Melatonin as a neuroendocrine regulator of seasonal aggression.

Individuals of virtually all vertebrate species are exposed to annual fluctuations in the deterioration and renewal of their environments. As such, organisms have evolved to restrict energetically expensive processes and activities to a specific time of the year. Thus, the precise timing of physiology and behavior is critical for individual reproductive success and subsequent fitness. Although the majority of research on seasonality has focused on seasonal reproduction, pronounced fluctuations in other non-reproductive social behaviors, including agonistic behaviors (e.g., aggression), also occur. To date, most studies that have investigated the neuroendocrine mechanisms underlying seasonal aggression have focused on the role of photoperiod (i.e., day length); prior findings have demonstrated that some seasonally breeding species housed in short "winter-like" photoperiods display increased aggression compared with those housed in long "summer-like" photoperiods, despite inhibited reproduction and low gonadal steroid levels. While fewer studies have examined how the hormonal correlates of environmental cues regulate seasonal aggression, our previous work suggests that the pineal hormone melatonin acts to increase non-breeding aggression in Siberian hamsters (Phodopus sungorus) by altering steroid hormone secretion. This review addresses the physiological and cellular mechanisms underlying seasonal plasticity in aggressive and non-aggressive social behaviors, including a key role for melatonin in facilitating a "neuroendocrine switch" to alternative physiological mechanisms of aggression across the annual cycle. Collectively, these studies highlight novel and important mechanisms by which melatonin regulates aggressive behavior in vertebrates and provide a more comprehensive understanding of the neuroendocrine bases of seasonal social behaviors broadly.

The predictive value of impulse oscillometry for asthma exacerbations in childhood: A systematic review and meta-analyses.

OBJECTIVE: Several studies have explored the predictive value of impulse oscillometry (IOS) for asthma exacerbations in childhood, but its specific parameters are still unclear. Therefore, we designed this meta-analysis to determine the related indicators of acute asthma attacks. METHODS: A comprehensive literature search was performed on July 9, 2020 based on PubMed, Embase, and Web of Science database. Weighted mean differences (WMDs) were calculated using fixed- or random-effects models. RESULTS: A total of 615 patients from six trials were included in this analysis. IOS may be a useful tool to predict asthma exacerbations. And the results showed that R5 (WMD = -1.21, 95% CI: -1.55 to -0.87, p < .001), Fres (WMD = -1.34, 95% CI: -2.03 to -0.65, p = .018), and AX (WMD = -7.35, 95% CI: -9.94 to -4.76, p < .001) had significant correlation with asthma exacerbations. In addition, X5 may also predict the acute attack of asthma (WMD = 0.81, 95% CI: 0.56 to 1.01, p < .001). CONCLUSIONS: R5, AX, Fres, and X5 may be able to identify the risk of an acute attack of asthma. Besides, our research further demonstrated that peripheral airway injury may play an important role in the acute attack of asthma.

Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics.

BACKGROUND: Differentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features. PURPOSE: To differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses. METHODS AND MATERIALS: A total of 350 patients were collected from two institutions, including 182 patients with GBM and 168 patients with MET, which were all proven by pathology. The ROI of the tumor was obtained on axial postcontrast MRI which was performed before operation. Seven radiomic feature selection methods and four classification algorithms constituted 28 classifiers in two classification strategies, with the best classifier serving as the final radiomics model. The clinical and combination models were constructed using the nomograms developed. The performance of the nomograms was evaluated in terms of calibration, discrimination, and clinical usefulness. Student's t-test or the chi-square test was used to assess the differences in the clinical and radiological characteristics between the training and internal validation cohorts. Receiver operating characteristic curve analysis was performed to assess the performance of developed models with the area under the curve (AUC). RESULTS: The classifier fisher_decision tree (fisher_DT) showed the best performance (AUC: 0.696, 95% CI:0.608-0.783) for distinguishing between GBM and MET in internal validation cohorts; the classifier reliefF_random forest (reliefF_RF) showed the best performance (AUC: 0.759, 95% CI: 0.613-0.904) for distinguishing between MET-lung and MET-other in internal validation cohorts. The combination models incorporating the radiomics signature and clinical-radiological characteristics were superior to the clinical-radiological models in the two classification strategies (AUC: 0.764 for differentiation between GBM in internal validation cohorts and MET and 0.759 or differentiation between MET-lung and MET-other in internal validation cohorts). The nomograms showed satisfactory performance and calibration and were considered clinically useful, as revealed in the decision curve analysis. DATA CONCLUSION: The combination of radiomic and non-radiomic features is helpful for the differentiation among GBM, MET-lung, and MET-other.

A Radiomics-based Approach for Predicting Early Recurrence in Intrahepatic Cholangiocarcinoma after Surgical Resection: A Multicenter Study.

This work aimed to develop a noninvasive and reliable computed tomography (CT)-based imaging biomarker to predict early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) via radiomics analysis. In this retrospective study, a total of 177 ICC patients were enrolled from three independent hospitals. Radiomic features were extracted on CT images, then 11 feature selection algorithms and 4 classifiers were to conduct a multi-strategy radiomics modeling. Six established radiomics models were selected as stable ones by robustness-based rule. Among those models, Max-Relevance Min-Redundancy (MRMR) combined with Gradient Boosting Machine (GBM) yielded the highest areas under the receiver operating characteristics curve (AUCs) of 0.802 (95% confidence interval [CI]: 0.727-0.876) and 0.781 (95% CI: 0.655-0.907) in the training and test cohorts, respectively. To evaluate the generalization of the developed radiomics model, stratification analysis was performed regarding different centers. The MRMR-GBM-based model manifested good generalization with comparable AUCs in each hospital (p > 0.05 for paired comparison). Thus, the MRMR-GBM-based model could offer a potential imaging biomarker to assist the prediction of ER in ICC in a noninvasive manner.Clinical Relevance-The proposed radiomics model achieved satisfactory accuracy and good generalization ability in predicting ER in ICC, which might assist personalized surveillance and clinical treatment strategy making.