Two Hematological Markers Predicting the Efficacy and Prognosis of Neoadjuvant Chemotherapy Using Lobaplatin Against Triple-Negative Breast Cancer.

BACKGROUND: Our previous work indicated that the addition of lobaplatin to combined therapy with taxane and anthracycline can improve the pathological complete response rate of neoadjuvant therapy for triple-negative breast cancer (TNBC) and lengthen long-term survival significantly, but the therapeutic markers of this regimen are unclear. METHODS: Eighty-three patients who met the inclusion criteria were included in this post hoc analysis. We analyzed the association between platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) before neoadjuvant chemotherapy with the efficacy and prognosis after treatment with docetaxel, epirubicin, and lobaplatin neoadjuvant chemotherapy regimen. χ2 test and Cox regression were used to analyze the association between PLR and NLR with total pathologic complete response (tpCR), as well as the association between PLR and NLR with event-free survival (EFS) and overall survival (OS), respectively. RESULTS: The tpCR rate in the PLR- group was 49.0% (25/51), which was significantly higher than that in the PLR+ group (25.0% [8/32], P = .032). The tpCR rate in the NLR- group was 49.1% (26/53), which was significantly higher than that in the NLR+ group (23.3% [7/30], P = .024). The tpCR rate of the PLR-NLR- (PLR- and NLR-) group was 53.7% (22/41), which was significantly higher than that of the PLR+/NLR+ (PLR+ or/and NLR+) group (26.1% [11/42]; P = .012). EFS and OS in the NLR+ group were significantly shorter than those in the NLR- group (P = .028 for EFS; P = .047 for OS). Patients in the PLR-NLR- group had a longer EFS than those in the PLR+/NLR+ group (P = .002). CONCLUSION: PLR and NLR could be used to predict the efficacy of neoadjuvant therapy with the taxane, anthracycline, and lobaplatin regimen for patients with TNBC, as patients who had lower PLR and NLR values had a higher tpCR rate and a better long-term prognosis.

Taxane combined with lobaplatin or anthracycline for neoadjuvant chemotherapy of triple-negative breast cancer: a randomized, controlled, phase II study.

BACKGROUND: Previous studies have shown that the addition of platinum to neoadjuvant chemotherapy (NAC) improved outcomes for patients with triple-negative breast cancer (TNBC). However, no studies have assessed the efficacy and safety of the combination of taxane and lobaplatin. In this study, we conducted a randomized controlled phase II clinical study to compare the efficacy and safety of taxane combined with lobaplatin or anthracycline. METHODS: We randomly allocated patients with stage I-III TNBC into Arm A and Arm B. Arm A received six cycles of taxane combined with lobaplatin (TL). Arm B received six cycles of taxane combined with anthracycline and cyclophosphamide (TEC) or eight cycles of anthracycline combined with cyclophosphamide and sequential use of taxane (EC-T). Both Arms underwent surgery after NAC. The primary endpoint was the pathologic complete response (pCR). Secondary endpoints were event-free survival (EFS), overall survival (OS), and safety. RESULTS: A total of 103 patients (51 in Arm A and 52 in Arm B) were assessed. The pCR rate of Arm A was significantly higher than that of Arm B (41.2% vs. 21.2%, P = 0.028). Patients with positive lymph nodes and low neutrophil-to-lymphocyte ratio (NLR) benefited significantly more from Arm A than those with negative lymph nodes and high NLR (Pinteraction = 0.001, Pinteraction = 0.012, respectively). There was no significant difference in EFS (P = 0.895) or OS (P = 0.633) between the two arms. The prevalence of grade-3/4 anemia was higher in Arm A (P = 0.015), and the prevalence of grade-3/4 neutropenia was higher in Arm B (P = 0.044). CONCLUSIONS: Neoadjuvant taxane plus lobaplatin has shown better efficacy than taxane plus anthracycline, and both regimens have similar toxicity profiles. This trial may provide a reference for a better combination strategy of immunotherapy in NAC for TNBC in the future.

Combined analysis of the microbiome, metabolome and transcriptome of silkie chickens in response to avian pathogenic E. coli (APEC).

Avian colibacillosis is a bacterial disease caused by avian pathogenic Escherichia coli (APEC) that results in great losses in the poultry industry every year. Individual Silkie chickens of the same breed that are given the same feed in the same feeding conditions have different levels of resistance or susceptibility to APEC. Differences in gut microbes, gut metabolites, and gene expression in the spleen of APEC-resistant and APEC-susceptible chickens were compared, and multiple omics associations were analyzed to explore the mechanism of resistance to APEC in Silkie chickens. Compared with those in the APEC-susceptible group, the APEC-resistant group showed significantly increased abundances of many gut microorganisms, including Bacillus, Thermoactinomyces, Arthrobacter, and Ureibacillus, which were positively correlated with norvaline, l-arginine, and valyl-glycine levels. Intestinal tryptophan, indole, and indole derivative-related differentially abundant metabolites played an active role in combatting APEC infection. In the spleen, "response to stimulus" was the most significantly enriched GO term, and "cytokine‒cytokine receptor interaction" was the most significantly enriched KEGG pathway. The arginine biosynthesis and PPAR signaling pathways were the KEGG pathways that were significantly enriched with differentially abundant metabolites and differentially expressed genes. This study provides new insight into the prevention and treatment of APEC infection in Silkie chickens and lays a foundation to study the mechanism of APEC infection in poultry.

Artificial Intelligence Iterative Reconstruction in Computed Tomography Angiography: An Evaluation on Pulmonary Arteries and Aorta With Routine Dose Settings.

OBJECTIVE: The objective of this study is to investigate whether a newly introduced deep learning-based iterative reconstruction algorithm, namely, the artificial intelligence iterative reconstruction (AIIR), has a clinical value in computed tomography angiography (CTA), especially for visualizing vascular structures and related lesions, with routine dose settings. METHODS: A total of 63 patients were retrospectively collected from the triple rule-out CTA examinations, where both pulmonary and aortic data were available for each patient and were taken as the example for investigation. The images were reconstructed using the filtered back projection (FBP), hybrid iterative reconstruction (HIR), and the AIIR. The visibility of vasculature and pulmonary emboli and the general image quality were assessed. RESULTS: Artificial intelligence iterative reconstruction resulted in significantly ( P < 0.001) lower noise as well as higher signal-to-noise ratio and contrast-to-noise ratio compared with FBP and HIR. Besides, AIIR achieved the highest subjective scores on general image quality ( P < 0.05). For the vasculature visibility, AIIR offered the best vessel conspicuity, especially for the small vessels ( P < 0.05). Also, >90% of emboli on the AIIR images were graded as sharp (score 5), whereas <15% of emboli on FBP and HIR images were scored 5. CONCLUSION: As demonstrated for pulmonary and aortic CTAs, AIIR improves the image quality and offers a better depiction for vascular structures compared with FBP and HIR. The visibility of the pulmonary emboli was also increased by AIIR.

Improving intracranial aneurysms image quality and diagnostic confidence with deep learning reconstruction in craniocervical CT angiography.

BACKGROUND: The diagnostic impact of deep learning computed tomography (CT) reconstruction on intracranial aneurysm (IA) remains unclear. PURPOSE: To quantify the image quality and diagnostic confidence on IA in craniocervical CT angiography (CTA) reconstructed with DEep Learning Trained Algorithm (DELTA) compared to the routine hybrid iterative reconstruction (HIR). MATERIAL AND METHODS: A total of 60 patients who underwent craniocervical CTA and were diagnosed with IA were retrospectively enrolled. Images were reconstructed with DELTA and HIR, where the image quality was first compared in noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Next, two radiologists independently graded the noise appearance, arterial sharpness, small vessel visibility, conspicuity of calcifications that may present in arteries, and overall image quality, each with a 5-point Likert scale. The diagnostic confidence on IAs of various sizes was also graded. RESULTS: Significantly lower noise and higher SNR and CNR were found on DELTA than on HIR images (all P < 0.05). All five subjective metrics were scored higher by both readers on the DELTA images (all P < 0.05), with good to excellent inter-observer agreement (κ = 0.77-0.93). DELTA images were rated with higher diagnostic confidence on IAs compared to HIR (P < 0.001), particularly for those with size ≤3 mm, which were scored 4.5 ± 0.6 versus 3.4 ± 0.8 and 4.4 ± 0.7 versus 3.5 ± 0.8 by two readers, respectively. CONCLUSION: The DELTA shows potential for improving the image quality and the associated confidence in diagnosing IA that may be worth consideration for routine craniocervical CTA applications.

Artificial intelligence iterative reconstruction in abdominal CT of patients with irregular arm positioning: a case-by-case evaluation.

BACKGROUND: Streak artifacts induced by irregular arm positioning have been an issue in diagnosing the abdomen. PURPOSE: To illustrate the risk of misdiagnosis in abdominal computed tomography (CT) of patients with irregular arm positioning through a case-by-case evaluation and to test if it can be solved by the artificial intelligence iterative reconstruction (AIIR) algorithm. MATERIAL AND METHODS: By reviewing 5220 cases of chest and thoracoabdominal CT, 64 patients with irregular arm positioning were enrolled, whose image data were reconstructed using AIIR in addition to routine hybrid iterative reconstruction (HIR). Lesion detection for livers, spleens, kidneys, gallbladders, and pancreas on AIIR images, performed by two radiologists, was compared with those on HIR images. Discrepancies arising from AIIR images included both cases with additional abnormalities and those with corrections made on previous detections. For cases with discrepancies, artifact scores for organs where discrepancies were found, and contrast-to-noise ratios (CNRs) of cysts with discrepancies were compared between two image sets. RESULTS: Additional abnormalities were detected for 15 cases: additional liver cirrhosis (n=2); additional gallbladder stone (n=1); additional cholecystitis (n=1), additional spleen nodule (n=1); additional kidney cysts (n=8); additional liver cysts (3); and additional spleen cyst (n=1). A spleen contusion was corrected for one case. All involved artifact scores were improved on AIIR images. CNRs of involved liver, kidney, and spleen cysts were improved by up to 539.7%, 538.5%, and 245.5%, respectively. CONCLUSION: Irregular arm positioning may induce a variety of misdiagnoses in abdominal CT, which is almost totally avoidable by the AIIR algorithm.

Research on Miniaturized UHF Sensing Technology for PD Detection in Power Equipment Based on Symmetric Cut Theory.

In answer to the demand for high sensitivity and miniaturization of ultra-high frequency (UHF) sensors for partial discharge (PD) detection in power equipment, this paper proposes research on miniaturized UHF-sensing technology for PD detection in power equipment based on symmetric cut theory. The symmetric cut theory is applied for the first time to the miniaturization of PD UHF sensors for power equipment. A planar monopole UHF sensor with a size of only 70 mm × 70 mm × 1.6 mm is developed using an exponential asymptotic feed line approach, which is a 50% size reduction. The frequency-response characteristics of the sensor are simulated, optimized and tested; the results show that the standing wave ratio of the sensor developed in this paper is less than 2 in the frequency band from 427 MHz to 1.54 GHz, and less than 5 in the frequency band from 300 MHz to 1.95 GHz; in the 300 MHz~1.5 GHz band; the maximum and average gains of the sensor E-plane are 4.76 dB and 1.02 dB, respectively. Finally, the PD simulation experiment platform for power equipment is built to test the sensor's sensing performance; the results show that the sensor can effectively detect the PD signals; the sensing sensitivity is improved by about 95% relative to an elliptical monopole UHF sensor.

Ultrasound-enhanced covalent reaction of gliadin: the inhibition of antigenicity and its potential mechanisms.

BACKGROUND: Wheat proteins can be divided into water/salt-soluble protein (albumin/globulin) and water/salt-insoluble protein (gliadins and glutenins (Glu)) according to solubility. Gliadins (Glia) are one of the major allergens in wheat. The inhibition of Glia antigenicity by conventional processing techniques was not satisfactory. RESULTS: In this study, free radical oxidation was used to induce covalent reactions. The effects of covalent reactions by high-intensity ultrasound (HIU) of different powers was compared. The enhancement of covalent grafting effectiveness between gliadin and (-)-epigallo-catechin 3-gallate (EGCG) was confirmed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, matrix-assisted laser desorption/ionization-time-of-flight-mass spectrometry and Folin-Ciocalteu tests. HIU caused protein deconvolution and disrupted the intrastrand disulfide bonds that maintain the tertiary structure, causing a shift in the side chain structure, as proved by Fourier, fluorescence and Raman spectroscopic analysis. Comparatively, the antigenic response of the conjugates formed in the sonication environment was significantly weaker, while these conjugates were more readily hydrolyzed and less antigenic during simulated gastrointestinal fluid digestion. CONCLUSION: HIU-enhanced free radical oxidation caused further transformation of the spatial structure of Glia, which hid or destroyed the antigenic epitope, effectively inhibiting protein antigenicity. This study widened the application of polyphenol modification in the inhibition of wheat allergens. © 2024 Society of Chemical Industry.

Identifying Membrane Protein-Lipid Interactions with Lipidomic Lipid Exchange-Mass Spectrometry.

Lipids can play important roles in modulating membrane protein structure and function. However, it is challenging to identify natural lipids bound to membrane proteins in complex bilayers. Here, we developed lipidomic lipid exchange-mass spectrometry (LX-MS) to study the lipid affinity for membrane proteins on a lipidomic scale. We first mix membrane protein nanodiscs with empty nanodiscs that have no embedded membrane proteins. After allowing lipids to passively exchange between the two populations, we separate the two types of nanodiscs and perform lipidomic analysis on each with liquid chromatography and MS. Enrichment of lipids in the membrane protein nanodiscs reveals the affinity of individual lipids for binding the target membrane protein. We apply this approach to study three membrane proteins. With the Escherichia coli ammonium transporter AmtB and aquaporin AqpZ in nanodiscs with E. coli polar lipid extracts, we detected binding of cardiolipin and phosphatidyl-glycerol lipids to the proteins. With the acetylcholine receptor in nanodiscs with brain polar lipid extracts, we discovered a complex set of lipid interactions that depended on the head group and tail composition. Overall, lipidomic LX-MS provides a detailed understanding of the lipid-binding affinity and thermodynamics for membrane proteins in complex bilayers and provides a unique perspective on the chemical environment surrounding membrane proteins.

Dramatic Response to Pyrotinib and T-DM1 in HER2-Negative Metastatic Breast Cancer With 2 Activating HER2 Mutations.

HER2 signaling is activated in response to somatic HER2 mutations, which are often found in invasive lobular breast cancer (ILC) and are associated with poor prognosis. Tyrosine kinase inhibitors (TKIs) have demonstrated considerable antitumor activity in patients with HER2-mutated advanced breast cancer (BC). Further, several clinical trials have indicated that HER2-targeted antibody-drug conjugates (ADCs) exhibit promising efficacy in lung cancer with HER2 mutations, and the efficacy of ADCs against HER2-mutated BC is currently being evaluated. Several preclinical studies have demonstrated that the therapeutic efficacy of ADCs in HER2-mutated cancer can be enhanced by the addition of irreversible TKIs, but the potential of such a combined treatment regimen for the treatment of HER2-mutated BC has not been reported. Herein, we describe a case in which a patient with estrogen receptor-positive/HER2-negative metastatic ILC with 2 activating HER2 mutations (D769H and V777L) exhibited a significant and durable response to anti-HER2 treatment with pyrotinib (an irreversible TKI) in combination with ado-trastuzumab emtansine, which was administered after multiple lines of therapy that had resulted in disease progression. Further, based on the evidence from the present case, TKI plus ADC seems to be a promising combination anti-HER2 regimen for patients with HER2-negative/HER2-mutated advanced BC, although further rigorous studies are warranted to confirm these findings.

Online Buffer Exchange Enables Automated Membrane Protein Analysis by Native Mass Spectrometry.

Membrane proteins represent the majority of clinical drug targets and are actively involved in a range of cellular processes. However, the complexity of membrane mimetics for membrane protein solubilization poses challenges for native mass spectrometry (MS) analyses. The most common approach for native MS analyses of membrane proteins remains offline buffer exchange into native MS-compatible buffers prior to manual sample loading into static nano-ESI emitters. This laborious process requires relatively high sample consumption and optimization for the individual proteins. Here, we developed online buffer exchange coupled to native mass spectrometry (OBE-nMS) for analyzing membrane proteins in different membrane mimetics, including detergent micelles and nanodiscs. Detergent screening for OBE-nMS reveals that mobile phases containing ammonium acetate with lauryl-dimethylamine oxide are most universal for characterizing both bacterial and mammalian membrane proteins in detergent. Membrane proteins in nanodiscs simply require ammonium acetate as the mobile phase. To preserve the intact nanodiscs, a novel switching electrospray approach was used to capture the high-flow separation on the column with a low-flow injection to MS. Rapid OBE-nMS completes each membrane protein measurement within minutes and thus enables higher-throughput assessment of membrane protein integrity prior to its structural elucidation.

One-stop combined CT angiography of coronary and craniocervical arteries: recommended as the first examination for patients suspected of coronary or craniocervical artery disease.

OBJECTIVES: To investigate the potential diagnostic value of one-stop combined CT angiography (CTA) as the first examination for patients suspected of coronary artery disease (CAD) or craniocervical artery disease (CCAD), and compare its clinical performance with two consecutive CTA scans. METHODS: Patients with suspected but unconfirmed CAD or CCAD were prospectively enrolled and grouped randomly to undergo coronary and craniocervical CTA using the combined protocol (group 1) or the consecutive protocol (group 2). Diagnostic findings were evaluated for both the targeted and non-targeted regions. The objective image quality, overall scan time, radiation dose, and contrast medium dosage were compared between the two groups. RESULTS: Each group enrolled 65 patients. A substantial number of lesions were found in non-targeted regions, which was 44/65 (67.7%) by patients for group 1 and 41/65 (63.1%) for group 2, reiterating the necessity of extending the scan coverage. Specifically, lesions in non-targeted regions were detected more often for patients suspected of CCAD than for those suspected of CAD (71.4% vs 61.7%). With 21.5% (~51.1 s) reduction of scan time and 21.8% (~20.8 mL) less contrast medium as compared to the consecutive protocol, high-quality images were obtained by the combined protocol. CONCLUSIONS: One-stop combined CTA enables effective detection of lesions in non-targeted regions at a lower cost of scan time and contrast medium than two separate examinations and is thus worth taking as the first examination for patients suspected of CAD or CCAD. KEY POINTS: • Extending the scan range for coronary or craniocervical CTA has the potential to reveal lesions in non-targeted regions. • One-stop combined CTA as enabled on high-speed wide-detector CT delivers high-quality images at a lower cost of contrast medium and operational time than two consecutive CTA scans. • Patients with suspected but unconfirmed CAD or CCAD may benefit from the one-stop combined CTA in the first examination.

Deep Learning-Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation.

OBJECTIVE: This study aimed to evaluate the clinical performance of a deep learning-based motion correction algorithm (MCA) in projection domain for coronary computed tomography angiography (CCTA). METHODS: A total of 192 patients who underwent CCTA examinations were included and divided into 2 groups based on the average heart rate (HR): group 1, 82 patients with HR of <75 beats per minute; group 2, 110 patients with HR of ≥75 beats per minute. The CCTA images were reconstructed with and without MCA. The subjective image quality was graded in terms of vessel visualization, sharpness, diagnostic confidence, and overall image quality using a 5-point scale, where cases with all scores of ≥3 were deemed interpretable. Objective image quality was measured through signal-to-noise ratio and contrast-to-noise ratio in regions relative to the vessels. The image quality scores for 2 reconstructions and effective dose between 2 groups were compared. RESULTS: The mean effective dose was similar between 2 groups. Neither group showed significant difference on objective image quality for 2 reconstructions. Images reconstructed with and without MCA were both found interpretable for group 1, whereas the subjective image quality was significantly improved by the MCA for all 4 metrics in group 2, with the interpretability increased from 80.91% to 99.09%. Compared with group 1, group 2 showed similar interpretability and diagnostic confidence, despite inferior overall image quality. CONCLUSIONS: In CCTA examinations, the deep learning-based MCA is capable of improving the image quality and diagnostic confidence for patients with increased HR to a similar level as for those with low HR.

Refining the radiation and contrast medium dose in weight-grouped scanning protocols for coronary CT angiography.

PURPOSE: To refine the currently used, weight-grouped protocol for coronary computed tomography angiography (CCTA), in terms of the radiation and contrast medium dose, through clinical evaluation. METHODS: Following the current routine setting that varies between three weight groups (group A: 55-65 kg, group B: 66-75 kg, group C: 76-85 kg), three additional reduction protocols were proposed to each group, with different combinations of lowered tube voltage (70-100 kVp), tube current (100-220 mAs), and iodine delivery rate (0.8-1.5 gI/s). A total of 321 patients scheduled for CCTA due to suspected coronary artery disease were enrolled, who were randomly assigned to one of the four subgroups of settings under the corresponding weight group. The resulting objective image quality was compared by measuring the contrast-to-noise ratio and signal-to-noise ratio. Subjective image quality was graded by two radiologists using a 4-point Likert scale, on a total of 3848 segments. The optimal protocol for each weight group was determined with respect to the image quality and the applied radiation dose. RESULTS: For all three groups, no significant difference was noticed in objective images quality between subgroups of dose settings (all p > 0.05). The average score on subjective image quality was ≥3 for every subgroup, while the percentage of score 4 showed greater dependence on the setting, ranging from 83.2% to 91.5%, and was chosen to be the determining factor. The optimal dose settings were found to be 80 kVp, 150 mAs, and 1.0 gI/s for patients of 55-75 kg in weight, and 100 kVp, 170 mAs, and 1.5 gI/s for those of 76-85 kg. CONCLUSION: It is feasible to refine the currently used, weight-grouped protocol for CCTA in terms of radiation and contrast medium dose, by use of an optimization strategy where the balance between dose and image quality can be improved in a routine clinical setting.

Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

PURPOSE: To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological and functional evaluation. MATERIALS AND METHODS: The acquired image data of 53 CCTA cases, where the patient heart rate (HR) was ≥75 bpm, were reconstructed at 0, ±2, ±4, ±6, and ±8% deviations from each optimal systolic phase, with and without the MCA, yielding a total of 954 images (53 cases × 9 phases × 2 reconstructions). The overall image quality and diagnostic confidence were graded by two radiologists using a 5-point scale, with scores ≥3 being deemed clinically interpretable. Signal-to-noise ratio, contrast-to-noise ratio, vessel sharpness, and circularity were measured. The CCTA-derived fractional flow reserve (CT-FFR) was calculated in 38 vessels on 24 patients to identify functionally significant stenosis, using the invasive fractional flow reserve (FFR) as reference. All metrics were compared between two reconstructions at various phases. RESULTS: Inferior image quality was observed as the phase deviation was enlarged. However, MCA significantly improved the image quality at nonoptimal phases and the optimal phase. Coronary artery evaluation was feasible within 4% phase deviation using MCA, with interpretable overall image quality and high diagnostic confidence. With MCA, the performance of identifying functionally significant stenosis via CT-FFR was increased for images at various phase deviations. However, obvious decrease in accuracy, as compared to the image at the optimal phase, was found on those with deviations >4%. CONCLUSION: The deep learning-based MCA allows up to 4% phase deviation in acquiring CCTA for reliable morphological and functional evaluation on patients with high HRs.

Preclinical validation of a novel deep learning-based metal artifact correction algorithm for orthopedic CT imaging.

PURPOSE: To validate a novel deep learning-based metal artifact correction (MAC) algorithm for CT, namely, AI-MAC, in preclinical setting with comparison to conventional MAC and virtual monochromatic imaging (VMI) technique. MATERIALS AND METHODS: An experimental phantom was designed by consecutively inserting two sets of pedicle screws (size Φ 6.5 × 30-mm and Φ 7.5 × 40-mm) into a vertebral specimen to simulate the clinical scenario of metal implantation. The resulting MAC, VMI, and AI-MAC images were compared with respect to the metal-free reference image by subjective scoring, as well as by CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and correction accuracy via adaptive segmentation of the paraspinal muscle and vertebral body. RESULTS: The AI-MAC and VMI images showed significantly higher subjective scores than the MAC image (all p < 0.05). The SNRs and CNRs on the AI-MAC image were comparable to the reference (all p > 0.05), whereas those on the VMI were significantly lower (all p < 0.05). The paraspinal muscle segmented on the AI-MAC image was 4.6% and 5.1% more complete to the VMI and MAC images for the Φ 6.5 × 30-mm screws, and 5.0% and 5.1% for the Φ 7.5 × 40-mm screws, respectively. The vertebral body segmented on the VMI was closest to the reference, with only 3.2% and 7.4% overestimation for Φ 6.5 × 30-mm and Φ 7.5 × 40-mm screws, respectively. CONCLUSIONS: Using metal-free reference as the ground truth for comparison, the AI-MAC outperforms VMI in characterizing soft tissue, while VMI is useful in skeletal depiction.

Design of High-Sensitivity Flexible Low-Profile Spiral Antenna Sensor for GIS Built-in PD Detection.

Spiral antenna sensors are commonly used in partial discharge (PD) ultra-high frequency (UHF) detection in gas-insulated switchgears (GISs). However, most of the existing UHF spiral antenna sensors are based on a rigid base and balun, such as FR-4. The safe built-in installation of antenna sensors requires the complex structural transformation of GISs. To solve this problem, a low-profile spiral antenna sensor is designed based on a polyimide (PI) flexible base, and its performance is optimized by improving the clearance ratio. The simulation and measurement results show that the profile height and diameter of the designed antenna sensor is 0.3 mm and 137 mm, which is 99.7% and 25.4% smaller than the traditional spiral antenna. Under a different bending radius, the antenna sensor can maintain VSWR ≤ 5 in 650 MHz~3 GHz, and its maximum gre is up to 6.1 dB. Finally, the PD detection performance of the antenna sensor is carried out on a real 220 kV GIS. The results show that, after being built in, the PD with a weak discharge magnitude of 4.5 pC can be effectively detected by the antenna sensor, and the antenna sensor has the ability to quantify the severity of PD. In addition, through the simulation, the antenna sensor has potential for the detection of micro water in GISs.

Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research.

To meet the real demand for broadband full-band high-gain antenna sensors in the process of partial discharge (PD) Ultra-High frequency (UHF) detection test and online monitoring of power equipment, this paper builds a resonant cavity monopole UHF antenna sensor based on Fabry-Perot resonant cavity antenna technology, conducts the sensor Voltage Standing Wave Ratio (VSWR) optimization study using curved flow technology, conducts the sensor gain optimization study using slot dual resonant structure, and, finally, tests the sensor performance using the built PD detection test platform. The resonant cavity monopole antenna exhibits outstanding VSWR performance in the frequency range of 0.37 GHz-3 GHz, according to simulation and test data: the average gain in the frequency range of 0.3 GHz-3 GHz is 4.92 dBi, and the highest gain at the primary resonant frequency of 1.0 GHz is 7.16 dBi, with good radiation performance over the whole frequency spectrum. The electromagnetic pulse signal sensed by the UHF sensor developed in this paper can demonstrate the energy spectrum distribution characteristics of PD radiation electromagnetic wave signal more comprehensively, laying a firm technical foundation for thoroughly understanding the electromagnetic wave radiation characteristics of various types of PD insulation defects of various power equipment and the selection of a specific direction for its supporting optimization.

Detection of Soybean-Derived Components in Dairy Products Using Proofreading Enzyme-Mediated Probe Cleavage Coupled with Ladder-Shape Melting Temperature Isothermal Amplification (Proofman-LMTIA).

Food adulteration is a serious problem all over the world. Establishing an accurate, sensitive and fast detection method is an important part of identifying food adulteration. Herein, a sequence-specific ladder-shape melting temperature isothermal amplification (LMTIA) assay was reported to detect soybean-derived components using proofreading enzyme-mediated probe cleavage (named Proofman), which could realize real-time and visual detection without uncapping. The results showed that, under the optimal temperature of 57 °C, the established Proofman-LMTIA method for the detection of soybean-derived components in dairy products was sensitive to 1 pg/µL, with strong specificity, and could distinguish soybean genes from those of beef, mutton, sunflower, corn, walnut, etc. The established Proofman-LMTIA detection method was applied to the detection of actual samples of cow milk and goat milk. The results showed that the method was accurate, stable and reliable, and the detection results were not affected by a complex matrix without false positives or false negatives. It was proved that the method could be used for the detection and identification of soybean-derived components in actual dairy products samples.

Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT.

OBJECTIVES: To evaluate the clinical performance of an artificial intelligence (AI)-based motion correction (MC) reconstruction algorithm for cerebral CT. METHODS: A total of 53 cases, where motion artifacts were found in the first scan so that an immediate rescan was taken, were retrospectively enrolled. While the rescanned images were reconstructed with a hybrid iterative reconstruction (IR) algorithm (reference group), images of the first scan were reconstructed with both the hybrid IR (motion group) and the MC algorithm (MC group). Image quality was compared in terms of standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI), as well as subjective scores. The diagnostic performance for each case was evaluated accordingly by lesion detectability or the Alberta Stroke Program Early CT Score (ASPECTS) assessment. RESULTS: Compared with the motion group, the SNR and CNR of the MC group were significantly increased. The MSE, PSNR, SSIM, and MI with respect to the reference group were improved by 44.1%, 15.8%, 7.4%, and 18.3%, respectively (all p < 0.001). Subjective image quality indicators were scored higher for the MC than the motion group (p < 0.05). Improved lesion detectability and higher AUC (0.817 vs 0.614) in the ASPECTS assessment were found for the MC to the motion group. CONCLUSIONS: The AI-based MC reconstruction algorithm has been clinically validated for reducing motion artifacts and improving diagnostic performance of cerebral CT. KEY POINTS: • An artificial intelligence-based motion correction (MC) reconstruction algorithm has been clinically validated in both qualitative and quantitative manner. • The MC algorithm reduces motion artifacts in cerebral CT and increases the diagnostic confidence for brain lesions. • The MC algorithm can help avoiding rescans caused by motion and improving the efficiency of cerebral CT in the emergency department.