The Methodology for Evaluating the Operating State of SF6 HVCBs Based on IDDA.

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.

Vehicle-Type Recognition Method for Images Based on Improved Faster R-CNN Model.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

A hydrogel-based ratiometric fluorescent sensor relying on rhodamine B labelled AIE-featured hyperbranched poly(amido amine) for heparin detection.

The fluorescent flexible sensor for point-of-care quantification of clinical anticoagulant drug, Heparin (Hep), is still an urgent need of breakthrough. In this research, a hyperbranched poly(amido amine) (HPA) was decorated with tetraphenylethene (TPE) and Rhodamine B (RhB), constructing a ratiometric fluorescent sensor (TR-HPA) for Hep. When the sensor was exposed to Hep, the TPE units within the probe skeleton would aggregate, resulting in an increasing fluorescent emission at 483 nm. The 580 nm of fluorescence came from RhB enhance, simultaneously, due to the fluorescence resonance energy transfer. As a result, there are two good linear correlation between the fluorescence emission ratio (E483/E580) of TR-HPA and the Hep concentration over a range of 0-1.0 µM, with a low limit of detection of 3.0 nM. Furthermore, we incorporate the TR-HPA probe into a polyvinyl alcohol (PVA) hydrogel matrix to create a flexible fluorescent sensing system platform, denoted as TR-HPA/PVA. This approach offers a straightforward visual detection method by causing a fluorescence color change from pink to blue when trace amounts of Hep are present. The hydrogel-based fluorescent sensor streamlines the detection procedures for Hep in biomedical applications. It shows great potential in rapid and point-of-care human blood clotting condition monitoring, making it suitable for next-generation wearable medical devices.

PI3K/AKT mediated De novo fatty acid synthesis regulates RIG-1/MDA-5-dependent type I IFN responses in BVDV-infected CD8+T cells.

Bovine viral diarrhea virus (BVDV) has caused massive economic losses in the cattle business worldwide. Fatty acid synthase (FASN), a key enzyme of the fatty acid synthesis (FAS) pathway, has been shown to support virus replication. To investigate the role of fatty acids (FAs) in BVDV infection, we infected CD8+T lymphocytes obtained from healthy cattle with BVDV in vitro. During early cytopathic (CP) and noncytopathic (NCP) BVDV infection in CD8+ T cells, there is an increase in de novo lipid biosynthesis, resulting in elevated levels of free fatty acids (FFAs) and triglycerides (TG). BVDV infection promotes de novo lipid biosynthesis in a dose-dependent manner. Treatment with the FASN inhibitor C75 significantly reduces the phosphorylation of PI3K and AKT in BVDV-infected CD8+ T cells, while inhibition of PI3K with LY294002 decreases FASN expression. Both CP and NCP BVDV strains promote de novo fatty acid synthesis by activating the PI3K/AKT pathway. Further investigation shows that pharmacological inhibitors targeting FASN and PI3K concurrently reduce FFAs, TG levels, and ATP production, effectively inhibiting BVDV replication. Conversely, the in vitro supplementation of oleic acid (OA) to replace fatty acids successfully restored BVDV replication, underscoring the impact of abnormal de novo fatty acid metabolism on BVDV replication. Intriguingly, during BVDV infection of CD8+T cells, the use of FASN inhibitors prompted the production of IFN-α and IFN-ß, as well as the expression of interferon-stimulated genes (ISGs). Moreover, FASN inhibitors induce TBK-1 phosphorylation through the activation of RIG-1 and MDA-5, subsequently activating IRF-3 and ultimately enhancing the IFN-1 response. In conclusion, our study demonstrates that BVDV infection activates the PI3K/AKT pathway to boost de novo fatty acid synthesis, and inhibition of FASN suppresses BVDV replication by activating the RIG-1/MDA-5-dependent IFN response.

NIR-II photothermal conversion and imaging based on a cocrystal containing twisted components.

On account of the scarcity of molecules with a satisfactory second near-infrared (NIR-II) response, the design of high-performance organic NIR photothermal materials has been limited. Herein, we investigate a cocrystal incorporating tetrathiafulvalene (TTF) and tetrachloroperylene dianhydride (TCPDA) components. A stable radical was generated through charge transfer from TTF to TCPDA, which exhibits strong and wide-ranging NIR-II absorption. The metal-free TTF-TCPDA cocrystal in this research shows high photothermal conversion capability under 1064 nm laser irradiation and clear photothermal imaging. The remarkable conversion ability-which is a result of twisted components in the cocrystal-has been demonstrated by analyses of single crystal X-ray diffraction, photoluminescence and femtosecond transient absorption spectroscopy as well as theoretical calculations. We have discovered that space charge separation and the ordered lattice in the TTF-TCPDA cocrystal suppress the radiative decay, while simultaneously strong intermolecular charge transfer enhances the non-radiative decay. The twisted TCPDA component induces rapid charge recombination, while the distorted configuration in TTF-TCPDA favors an internal non-radiative pathway. This research has provided a comprehensive understanding of the photothermal conversion mechanism and opened a new way for the design of advanced organic NIR-II photothermal materials.

Sound Touch Elastography for Noninvasive Assessment of Liver Stiffness in Patients With Chronic Heart Failure.

Heart failure (HF) can damage various organs, including the liver, a phenomenon known as "cardiohepatic syndrome." The latter is characterized by liver congestion and hepatic artery hypoperfusion, which can lead to liver damage. In this study, we aimed to assess liver damage quantitatively in chronic HF (CHF) with sound touch elastography (STE). A total of 150 subjects were enrolled, including HF with reduced ejection fraction (HFrEF) groups (left ventricular ejection fraction ≤40%, n = 45), HF with mildly reduced ejection fraction (HFmrEF) groups (left ventricular ejection fraction between 41% and 49%, n = 40), and right-sided HF (RHF) groups (n = 25); normal groups (n = 40). Liver stiffness measurement (LSM) was performed in all subjects by STE. The other hepatic parameters were also measured. The LSM was 5.4 ± 1.1 kPa in normal subjects and increased slightly to 5.9 ± 0.7 kPa in patients with HFmrEF. However, the HFrEF and RHF groups had significantly higher LSMs of 8.4 ± 2.0 kPa and 10.3 ± 2.7 kPa, respectively. The LSM of HFrEF was significantly higher than that of HFmrEF, whereas the increase in LSM in patients with RHF was significant relative to HFmrEF and HFrEF. In addition, the other parameters showed abnormal values in only RHF and HFrEF. In conclusion, STE is a useful clinical technique for the noninvasive evaluation of liver stiffness associated with CHF, which could help patients with CHF manage their treatment regimens.

The re-emerging role of linoleic acid in paediatric asthma.

Asthma is the most common chronic disease within the paediatric population. Although it is multifactorial, its onset may be linked to early-life exposures with subsequent impact on immune system development. Microbial and dietary metabolic products have been implicated in the development and exacerbation of paediatric asthma. Linoleic acid is the most common omega-6 polyunsaturated fatty acid in the Western diet. In this review, we summarise the literature regarding the involvement of linoleic acid in the development of and its impact on existing paediatric asthma. First, we summarise the existing knowledge surrounding the relationship between human microbial metabolism and allergic diseases in children. Next, we examine cellular or animal model-based mechanistic studies that investigated the impact of dietary- and microbial-derived linoleic acid metabolites on asthma. Finally, we review the literature investigating the impact of linoleic acid metabolites on the development and exacerbation of childhood asthma. While there is conflicting evidence, there is growing support for a role of linoleic acid in the onset and pathophysiology of asthma. We recommend that additional cellular, animal, and longitudinal studies are performed that target linoleic acid and its metabolites.

An image caption model based on attention mechanism and deep reinforcement learning.

Image caption technology aims to convert visual features of images, extracted by computers, into meaningful semantic information. Therefore, the computers can generate text descriptions that resemble human perception, enabling tasks such as image classification, retrieval, and analysis. In recent years, the performance of image caption has been significantly enhanced with the introduction of encoder-decoder architecture in machine translation and the utilization of deep neural networks. However, several challenges still persist in this domain. Therefore, this paper proposes a novel method to address the issue of visual information loss and non-dynamic adjustment of input images during decoding. We introduce a guided decoding network that establishes a connection between the encoding and decoding parts. Through this connection, encoding information can provide guidance to the decoding process, facilitating automatic adjustment of the decoding information. In addition, Dense Convolutional Network (DenseNet) and Multiple Instance Learning (MIL) are adopted in the image encoder, and Nested Long Short-Term Memory (NLSTM) is utilized as the decoder to enhance the extraction and parsing capability of image information during the encoding and decoding process. In order to further improve the performance of our image caption model, this study incorporates an attention mechanism to focus details and constructs a double-layer decoding structure, which facilitates the enhancement of the model in terms of providing more detailed descriptions and enriched semantic information. Furthermore, the Deep Reinforcement Learning (DRL) method is employed to train the model by directly optimizing the identical set of evaluation indexes, which solves the problem of inconsistent training and evaluation standards. Finally, the model is trained and tested on MS COCO and Flickr 30 k datasets, and the results show that the model has improved compared with commonly used models in the evaluation indicators such as BLEU, METEOR and CIDEr.

Cascaded terahertz hollow-core Bragg waveguide: numerical design and experimental demonstration.

A THz hollow-core Bragg waveguide with discontinuous support bridges in both radial and axial directions is proposed. The influence of the support bridges on the transmission loss of the waveguide is demonstrated numerically. The proposed waveguide shows confinement loss two orders of magnitude lower than that of the Bragg waveguide with conventional support bridges. A waveguide sample is fabricated by 3D printing technology, and the experimental results show that the transmission loss is in agreement with that of the simulation results. It is also demonstrated that the transmission loss of the fabricated waveguide is mainly determined by the large absorption loss of the waveguide material used in the experiment.

Abnormal hemispheric specialization and inter-hemispheric functional cooperation in generalized anxiety disorder.

Abnormal hemispheric specialization and inter-hemispheric interactions may contribute to the pathogenesis of general anxiety disorder (GAD). The current study investigated these abnormalities in GAD patients based on the two analytic approaches and examined whether such abnormalities are correlated with anxiety symptom severity. Seventy-three patients with GAD and 60 matched healthy controls were recruited. All participants completed anxiety symptoms assessment and resting-state functional magnetic resonance imaging (rs-fMRI). The autonomy index (AI) and Connectivity between Functionally Homotopic voxels (CFH) were applied to measure and compared between groups. Compared to controls, patients showed stronger AI in the right middle temporal gyrus (MTG). Seed-based analysis revealed stronger functional connectivity (FC) of the right MTG with both right precuneus and right dorsolateral prefrontal cortex (dlPFC) in patients. Patients also exhibited greater CFH in right anterior cingulate cortex (ACC) but decreased CFH in bilateral postcentral gyrus (PCG) and superior occipital gyrus (SOG). Further there were significant correlations between these regional CFH and anxiety symptoms severity. GAD patients demonstrate right hemispheric specialization and aberrant inter-hemispheric functional cooperation, and abnormal inter-hemispheric coordination is associated with anxiety symptom severity. These findings provide a clue to understanding the neuropathological mechanisms of GAD.

A data security scheme based on EEG characteristics for body area networks.

Body area network (BAN) is a body-centered network of wireless wearable devices. As the basic technology of telemedicine service, BAN has aroused an immense interest in academia and the industry and provides a new technical method to solve the problems that exist in the field of medicine. However, guaranteeing full proof security of BAN during practical applications has become a technical issue that hinders the further development of BAN technology. In this article, we propose a data encryption method based on electroencephalogram (EEG) characteristic values and linear feedback shift register (LFSR) to solve the problem of data security in BAN. First, the characteristics of human EEG signals were extracted based on the wavelet packet transform method and as the MD5 input data to ensure its randomness. Then, an LFSR stream key generator was adopted. The 128-bit initial key obtained through the message-digest algorithm 5 (MD5) was used to generate the stream key for BAN data encryption. Finally, the effectiveness of the proposed security scheme was verified by various experimental evaluations. The experimental results showed that the correlation coefficient of data before and after encryption was very low, and it was difficult for the attacker to obtain the statistical features of the plaintext. Therefore, the EEG-based security scheme proposed in this article presents the advantages of high randomness and low computational complexity for BAN systems.

Antihyperlipidemic effect of Vaccinium dunalianum buds based on biological activity screening and LC-MS.

ETHNOPHARMACOLOGICAL RELEVANCE: The buds of Vaccinium dunalianum Wight are used as folk medicine in the Yi settlement of the Yunnan Province, China. It has long been used as herbal tea in the local area owing to its effects of lowering blood lipids and body weight. However, there are only a few studies on its antihyperlipidemic effects, effective substances and mechanisms, especially its effectiveness in diet-induced hyperlipidemia. AIM OF THE STUDY: This study aimed to elucidate the therapeutic effects, pharmacodynamic material bases, and mechanisms of V. dunalianum buds on diet-induced hyperlipidemia. MATERIALS AND METHODS: A high-fat diet-induced hyperlipidemic Sprague-Dawley (SD) rat model was established. Rats were gavaged with different doses of aqueous extract of V. dunalianum(VDW) for 8 weeks and their sera and organ samples were collected. The antihyperlipidemic effect of VDW on SD rats was evaluated based on the biochemical indices and histopathological outcomes. Liquid chromatography-mass spectrometry(LC-MS) was used to determine the main components in VDW, which were separated and purified using sequential chromatographic methods. Their chemical structures were determined using high-resolution electrospray ionization mass spectroscopy and nuclear magnetic resonance spectroscopy. 6'-O-caffeoyl-arbutin, as the principal component of VDW, was also evaluated for its antihyperlipidemic activity using an approach similar to that used for VDW. Lastly, the potential targets of VDW and 6'-O-caffeoyl-arbutin in lowering blood lipids were screened out using network pharmacology, and the selected targets were docked with arbutin derivatives. The expression of target proteins was determined using western blotting to illustrate the antihyperlipidemic mechanisms of VDW and 6'-O-caffeoyl-arbutin. RESULTS: VDW reduced triglyceride, total cholesterol, low-density lipoprotein, alanine transaminase, and aspartate transaminase levels in the serum of modeled rats, and increased high-density lipoprotein levels. There was an improvement in steatoses, and lipid droplet accumulation decreased in vivo after VDW intervention. LC-MS revealed that VDW mainly contained arbutin and chlorogenic acid derivatives. Sixteen compounds were isolated and identified. 6'-O-caffeoyl-arbutin was the main compound of VDW (>21.67%) that showed obvious antihyperlipidemic effect with low hepatic damage at different doses. PTGS2, ADH1C, and MAOB were screened out using network pharmacology and they showed strong correlations with arbutin derivative through molecular docking. Results from WB showed that VDW and 6'-O-caffeoyl-arbutin could reduce blood lipid levels by reducing the protein expression of PTGS2, ADH1C, and MAOB. CONCLUSIONS: 6'-O-caffeoyl-arbutin was the main component of V. dunalianum buds. VDW and 6'-O-caffeoyl-arbutin could regulate blood lipid levels in the high-fat diet-induced rat model of hyperlipidemia without damaging their vital organs. Furthermore, they could regulate the expression of PTGS2, ADH1C, and MAOB proteins and play a role in lowering blood lipids. The findings of this study lay a foundation for the further development of V. dunalianum and 6'-O-caffeoyl-arbutin as health supplements or drugs for the management of hyperlipidemia.

Allergen Content and Protease Activity in Milk Feeds from Mothers of Preterm Infants.

Rationale: There is little information regarding the allergen content of milk feeds in the preterm population. Previous studies have not performed a broad analysis of the allergenic peptide content and protease activity of milk feeds in this population. Methods: To evaluate feasibility, we initially performed mass spectrometry on 4 human milk (HM) samples (2 term and 2 preterm) from the Mommy's Milk Human Milk Biorepository (HMB) and analyzed the results against the University of Nebraska FASTA database and UniProt for a total of 2,211 protein sequences. We then further analyzed five samples from the Microbiome, Atopy, and Prematurity (MAP) study including peptidomic and protease activity analysis. Results: Each HMB sample had between 806 and 1,007 proteins, with 37-44 nonhuman proteins/sample encompassing 26 plant and animal species. In the preterm MAP samples, 784 digested nonhuman proteins were identified, 30 were nonbovine in origin. Proteins from 23 different species including aeroallergens, food, and contact allergens were identified. Protease activity was highest in HM samples without human milk fortifier and lowest in preterm formula. Conclusions: These findings represent the first preterm milk feed mass spectrometry and protease analysis with identification of known allergenic proteins to food, contact, and aeroallergens. These results raise questions of whether the composition of milk feeds in the neonatal intensive care unit impact the development of atopic disease in the preterm population and whether the complex interaction between allergens, proteases, and other HM components can serve to induce sensitization or tolerance to allergens in infants. Clinical Trial Registration Number: NCT04835935.

Further sesquiterpenoids from Pittosporum qinlingense and their anti-inflammatory activity.

Four new sesquiterpenoid glycoside esters, Pitqinlingoside N-Q (1-4), together with eleven known metabolites (5-15), were isolated from 95% EtOH extract of the twigs, fruits and leaves of P. qinlingense. The structures of new compounds were elucidated on the basis of extensive spectroscopic analyses, including IR, UV, HRMS, NMR and electronic circular dichroism spectra. Unusal glycoside esters are characterized by the presence of polyacylated ß-D-fucopyranosyl and ß-d-glucopyranosyl units. Pitqinlingoside N (1), O (2), P (3), boscialin (5) and arvoside C (6) showed significant nitric oxide production inhibition in lipopolysaccharide (LPS)-induced BV-2 microglial cells with IC50 values ranging from 1.58 to 28.74 µM. Structure-activity relationships of the isolated compounds are discussed.

Development and verification of radiomics framework for computed tomography image segmentation.

BACKGROUND: Radiomics has been considered an imaging marker for capturing quantitative image information (QII). The introduction of radiomics to image segmentation is desirable but challenging. PURPOSE: This study aims to develop and validate a radiomics-based framework for image segmentation (RFIS). METHODS: RFIS is designed using features extracted from volume (svfeatures) created by sliding window (swvolume). The 53 svfeatures are extracted from 11 phantom series. Outliers in the svfeature datasets are detected by isolation forest (iForest) and specified as the mean value. The percentage coefficient of variation (%COV) is calculated to evaluate the reproducibility of svfeatures. RFIS is constructed and applied to the gross target volume (GTV) segmentation from the peritumoral region (GTV with a 10 mm margin) to assess its feasibility. The 127 lung cancer images are enrolled. The test-retest method, correlation matrix, and Mann-Whitney U test (p < 0.05) are used to select non-redundant svfeatures of statistical significance from the reproducible svfeatures. The synthetic minority over-sampling technique is utilized to balance the minority group in the training sets. The support vector machine is employed for RFIS construction, which is tuned in the training set using 10-fold stratified cross-validation and then evaluated in the test sets. The swvolumes with the consistent classification results are grouped and merged. Mode filtering is performed to remove very small subvolumes and create relatively large regions of completely uniform character. In addition, RFIS performance is evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and Dice similarity coefficient (DSC). RESULTS: 30249 phantom and 145008 patient image swvolumes were analyzed. Forty-nine (92.45% of 53) svfeatures represented excellent reproducibility(%COV<15). Forty-five features (91.84% of 49) included five categories that passed test-retest analysis. Thirteen svfeatures (28.89% of 45) svfeatures were selected for RFIS construction. RFIS showed an average (95% confidence interval) sensitivity of 0.848 (95% CI:0.844-0.883), a specificity of 0.821 (95% CI: 0.818-0.825), an accuracy of 83.48% (95% CI: 83.27%-83.70%), and an AUC of 0.906 (95% CI: 0.904-0.908) with cross-validation. The sensitivity, specificity, accuracy, and AUC were equal to 0.762 (95% CI: 0.754-0.770), 0.840 (95% CI: 0.837-0.844), 82.29% (95% CI: 81.90%-82.60%), and 0.877 (95% CI: 0.873-0.881) in the test set, respectively. GTV was segmented by grouping and merging swvolume with identical classification results. The mean DSC after mode filtering was 0.707 ± 0.093 in the training sets and 0.688 ± 0.072 in the test sets. CONCLUSION: Reproducible svfeatures can capture the differences in QII among swvolumes. RFIS can be applied to swvolume classification, which achieves image segmentation by grouping and merging the swvolume with similar QII.

Reduced nucleus accumbens functional connectivity in reward network and default mode network in patients with recurrent major depressive disorder.

The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

The impact of maternal asthma on the preterm infants' gut metabolome and microbiome (MAP study).

Preterm infants are at a greater risk for the development of asthma and atopic disease, which can lead to lifelong negative health consequences. This may be due, in part, to alterations that occur in the gut microbiome and metabolome during their stay in the Neonatal Intensive Care Unit (NICU). To explore the differential roles of family history (i.e., predisposition due to maternal asthma diagnosis) and hospital-related environmental and clinical factors that alter microbial exposures early in life, we considered a unique cohort of preterm infants born ≤ 34 weeks gestational age from two local level III NICUs, as part of the MAP (Microbiome, Atopic disease, and Prematurity) Study. From MAP participants, we chose a sub-cohort of infants whose mothers had a history of asthma and matched gestational age and sex to infants of mothers without a history of asthma diagnosis (control). We performed a prospective, paired metagenomic and metabolomic analysis of stool and milk feed samples collected at birth, 2 weeks, and 6 weeks postnatal age. Although there were clinical factors associated with shifts in the diversity and composition of stool-associated bacterial communities, maternal asthma diagnosis did not play an observable role in shaping the infant gut microbiome during the study period. There were significant differences, however, in the metabolite profile between the maternal asthma and control groups at 6 weeks postnatal age. The most notable changes occurred in the linoleic acid spectral network, which plays a role in inflammatory and immune pathways, suggesting early metabolomic changes in the gut of preterm infants born to mothers with a history of asthma. Our pilot study suggests that a history of maternal asthma alters a preterm infants' metabolomic pathways in the gut, as early as the first 6 weeks of life.

An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules.

Background US-based diagnosis of thyroid nodules is subjective and influenced by radiologists' experience levels. Purpose To develop an artificial intelligence model based on American College of Radiology Thyroid Imaging Reporting and Data System characteristics for diagnosing thyroid nodules and identifying nodule characteristics (hereafter, MTI-RADS) and to compare the performance of MTI-RADS, radiologists, and a model trained on benign and malignant status based on surgical histopathologic analysis (hereafter, MDiag). Materials and Methods In this retrospective study, 1588 surgically proven nodules from 636 consecutive patients (mean age, 49 years ± 14 [SD]; 485 women) were included. MTI-RADS and MDiag were trained on US images of 1345 nodules (January 2018 to December 2019). The performance of MTI-RADS was compared with that of MDiag and radiologists with different experience levels on the test data set (243 nodules, January 2019 to December 2019) with the DeLong method and McNemar test. Results The area under the receiver operating characteristic curve (AUC) and sensitivity of MTI-RADS were 0.91 and 83% (55 of 66 nodules), respectively, which were not significantly different from those of experienced radiologists (0.93 [P = .45] and 92% [61 of 66 nodules; P = .07]) and exceeded those of junior radiologists (0.78 [P < .001] and 70% [46 of 66 nodules; P = .04]). The specificity of MTI-RADS (87% [154 of 177 nodules]) was higher than that of both experienced and junior radiologists (80% [141 of 177 nodules; P = .02] and 75% [133 of 177 nodules; P = .001], respectively). The AUC of MTI-RADS was higher than that of MDiag (0.91 vs 0.84, respectively; P = .001). In the test set of 243 nodules, the consistency rates between MTI-RADS and the experienced group were higher than those between MTI-RADS and the junior group for composition (79% [n = 193] vs 73% [n = 178], respectively; P = .02), echogenicity (75% [n = 183] vs 68% [n = 166]; P = .04), shape (93% [n = 227] vs 88% [n = 215]; P = .04), and smooth or ill-defined margin (72% [n = 174] vs 63% [n = 152]; P = .002). Conclusion The area under the receiver operating characteristic curve (AUC) of an artificial intelligence model based on the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) was higher than that of a model trained on benign and malignant status based on surgical histopathologic analysis. The AUC and sensitivity of the model based on TI-RADS exceeded those of junior radiologists; the specificity of the model was higher than that of both experienced and junior radiologists. © RSNA, 2022.

Multi-modal feature-fusion for CT metal artifact reduction using edge-enhanced generative adversarial networks.

Computed Tomography (CT) imaging is one of the most widely-used and cost-effective technology for organ screening and diseases diagnosis. Because of existence of metallic implants in some patients, the CT images acquired from these patients are often corrupted by undesirable metal artifacts, which causes severe problem of metal artifact. Although there have been proposed many methods to reduce metal artifact, reduction is still challenging and inadequate, and results are suffering from symptom variance, second artifact and poor subjective evaluation. To address these problems, we propose a novel metal artifact reduction method based on generative adversarial networks to simultaneously reduce metal artifacts and enhance texture structure of corrected CT images. Specifically, we firstly incorporate interactive information (text) and imaging CT (image) into a comprehensive feature to yield multi-modal feature-fusion representation, which overcomes the representative ability limitation of single-modal data. The incorporation of interaction information constrains the feature generation to ensure symptom consistency between corrected and target CT. Then, we design an edge-enhance sub-network to avoid second artifact and suppress noise. Besides, we invite three professional physicians to evaluate corrected CT image subjectively. In this paper, We achieved average increment of 11.3% PSNR and 12.1% SSIM on DeepLesion dataset. The subjective evaluations by physicians show that ours outperforms over 6.3%, 7.1%, 5.50% and 6.9% in term of sharpness, resolution, invariance and acceptability, respectively. Our proposed method can achieve high-quality metal artifact reduction results.