Tracking endogenous proteins based on RNA editing-mediated genetic code expansion.

Protein labeling approaches are important to study proteins in living cells, and genome editing tools make it possible to tag endogenous proteins to address the concerns associated with overexpression. Here we established RNA editing-mediated noncanonical amino acids (ncAAs) protein tagging (RENAPT) to site-specifically label endogenous proteins with ncAAs in living cells. RENAPT labels protein in a temporary and nonheritable manner and is not restricted by protospacer adjacent motif sequence. Using a fluorescent ncAA or ncAA with a bio-orthogonal reaction handle for subsequent dye labeling, we demonstrated that a variety of endogenous proteins can be imaged at their specific subcellular locations. In addition, two proteins can be tagged individually and simultaneously using two different ncAAs. Furthermore, endogenous ion channels and neuron-specific proteins can be real-time labeled in primary neurons. Thus, RENAPT presents a promising platform with broad applicability for tagging endogenous proteins in living cells to study their localization and functions.

Investigation of N-Glycan Functions in Receptor for Advanced Glycation End Products V Domain through Chemical Glycoprotein Synthesis.

The receptor for advanced glycation end products (RAGE) plays a crucial role in inflammation-related pathways and various chronic diseases. Despite the recognized significance of N-glycosylation in the ligand-binding V domain (VD) of RAGE, a comprehensive understanding of the site-activity and structure-activity relationships is lacking due to the challenges in obtaining homogeneous glycoprotein samples through biological expression. Here, we combined chemical and chemoenzymatic approaches to synthesize RAGE-VD and its congeners with Asn3-glycosylation by incorporating precise N-glycan structures. Evaluation of these samples revealed that, in comparison to other RAGE-VD forms, α2,6-sialylated N-glycosylation at the Asn3 site results in more potent inhibition of HMGB1-induced nuclear factor-κB (NF-κB) expression in RAGE-overexpressing cells. Hydrogen/deuterium exchange-mass spectrum analysis revealed a sialylated RAGE-VD-induced interaction region within HMGB1. Conversely, Asn3 N-glycosylation in VD has negligible effects on RAGE-VD/S100B interactions. This study established an approach for accessing homogeneously glycosylated RAGE-VD and explored the modulatory effects of N-glycosylation on the interactions between RAGE-VD and its ligand proteins.

Functional analysis of the CTNS gene exonic variants predicted to affect splicing.

Cystinosis is a severe, monogenic systemic disease caused by variants in CTNS gene. Currently, there is growing evidence that exonic variants in many diseases can affect pre-mRNA splicing. The impact of CTNS gene exonic variants on splicing regulation may be underestimated due to the lack of routine studies at the RNA level. Here, we analyzed 59 exonic variants in the CTNS gene using bioinformatics tools and identified candidate variants that may induce splicing alterations by minigene assays. We identified six exonic variants that induce splicing alterations by disrupting the ratio of exonic splicing enhancers/exonic splicing silencers (ESEs/ESSs) or by interfering with the recognition of classical splice sites, or both. Our results help in the correct molecular characterization of variants in cystinosis and inform emerging therapies. Furthermore, our work suggests that the combination of in silico and in vitro assays facilitates to assess the effects of DNA variants driving rare genetic diseases on splicing regulation and will enhance the clinical utility of variant functional annotation.

Identification of seven variants in the col4a1 gene that alter RNA splicing by minigene assay.

Type IV collagen is an integral component of basement membranes. Mutations in COL4A1, one of the key genes encoding Type IV collagen, can result in a variety of diseases. It is clear that a significant proportion of mutations that affect splicing can cause disease directly or contribute to the susceptibility or severity of disease. Here, we analyzed exonic mutations and intronic mutations described in the COL4A1 gene using bioinformatics programs and identified candidate mutations that may alter the normal splicing pattern through a minigene system. We identified seven variants that induce splicing alterations by disrupting normal splice sites, creating new ones, or altering splice regulatory elements. These mutations are predicted to impact protein function. Our results help in the correct molecular characterization of variants in COL4A1 and may help develop more personalized treatment options.

Potential Biocontrol Microorganisms Causing Attenuated Pathogenicity in Plasmopara viticola.

A phenomenon of pathogenicity attenuation of Plasmopara viticola was consistently observed during its subculture on grape. To clarify the causes of attenuated pathogenicity of P. viticola, culturable microbes were isolated from the P. viticola mass (mycelia, sporangiophores, and sporangia) in each generation and tested for their biocontrol efficacies on grape downy mildew (GDM). The results showed that the incidence of GDM decreased with the increase in the number of subculture times on both vineyard-collected leaves and grape leaves from in vitro-grown seedlings. The number of culturable microbial taxa on the surface of P. viticola decreased, whereas the population densities of four specific strains (i.e., K2, K7, P1, and P5) increased significantly with the increase in subculture times. Compared with the control, the biocontrol efficacies of the bacterial strain K2 reached 87.5%, and those of both fungal strains P1 and P5 reached 100.0%. Based on morphological characteristics and molecular sequences, strains K2, P1, and P5 were identified as Curtobacterium herbarum, Thecaphora amaranthi, and Acremonium sclerotigenum, respectively, and these three strains survived very well and multiplied on the surface of P. viticola. As the number of times P. viticola was subcultured increased, all three of these strains became the predominant strains, leading to greater P. viticola inhibition, attenuated P. viticola pathogenicity, and effective GDM biological control. To the best of our knowledge, this is the first report of C. herbarum and T. amaranthi having biological control activity against GDM.

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.

BACKGROUND: To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). METHODS: This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. RESULTS: The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001). CONCLUSIONS: DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.

A novel heterozygous variant of the SALL1 gene with atypical Townes-Brocks syndrome phenotypes in Chinese family.

Townes-Brocks syndrome (TBS) is an autosomal dominant disorder characterised by the triad of anorectal, thumb, and ear malformations. It may also be accompanied by defects in kidney, heart, eyes, hearing, and feet. TBS has been demonstrated to result from heterozygous variants in the SALL1 gene, which encodes zinc finger protein believed to function as a transcriptional repressor. The clinical characteristics of an atypical TBS phenotype patient from a Chinese family are described, with predominant manifestations including external ear dysplasia, unilateral renal hypoplasia with mild renal dysfunction, and hearing impairment. A novel heterozygous variant c.3060T>A (p.Tyr1020*) in exon 2 of the SALL1 gene was identified in this proband. Pyrosequencing of the complementary DNA of the proband revealed that the variant transcript accounted for 48% of the total transcripts in peripheral leukocytes, indicating that this variant transcript has not undergone nonsense-mediated mRNA decay. This variant c.3060T > A is located at the terminal end of exon 2, proximal to the 3' end of the SALL1 gene, and exerts a relatively minor impact on protein function. We suggest that the atypical TBS phenotype observed in the proband may be attributed to the truncated protein retaining partial SALL1 function.

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

BACKGROUND: The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. PURPOSE: To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. MATERIAL AND METHODS: The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. RESULTS: The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). CONCLUSION: DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.

Analyzing the Correlation Between Serum Biomarkers and Child-Pugh Score in Liver Cirrhosis.

Objective: This study aimed to analyze the diagnostic efficacy of serum biomarkers in liver cirrhosis patients categorized by Child-Pugh scores. Methods: An observational cross-sectional study design was employed. A total of 110 liver cirrhosis patients, classified according to Child-Pugh scores and 60 healthy individuals were included in this study. Serum levels of adenosine deaminase (ADA), adiponectin (APN), matrix metalloproteinase-2 (MMP-2), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were measured. Results: The levels of ADA, APN, MMP-2, ALP, ALT, and AST were significantly higher in the study group compared to the control group (P < .05). Furthermore, these levels increased with the severity of liver cirrhosis, with higher levels observed in patients with Child-Pugh class C. The positive diagnostic rates for joint detection in Child-Pugh class A, B, and C were 93.75% (30/32), 100% (34/34), and 100% (44/44), respectively. Conclusions: Combined detection of serum biomarkers improves the diagnostic efficacy of liver cirrhosis. The diagnostic rates were higher when considering Child-Pugh scores, with the highest rates observed in class C.

Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios.

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations.

Identified eleven exon variants in PKD1 and PKD2 genes that altered RNA splicing by minigene assay.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is a common monogenic multisystem disease caused primarily by mutations in the PKD1 gene or PKD2 gene. There is increasing evidence that some of these variants, which are described as missense, synonymous or nonsense mutations in the literature or databases, may be deleterious by affecting the pre-mRNA splicing process. RESULTS: This study aimed to determine the effect of these PKD1 and PKD2 variants on exon splicing combined with predictive bioinformatics tools and minigene assay. As a result, among the 19 candidate single nucleotide alterations, 11 variants distributed in PKD1 (c.7866C > A, c.7960A > G, c.7979A > T, c.7987C > T, c.11248C > G, c.11251C > T, c.11257C > G, c.11257C > T, c.11346C > T, and c.11393C > G) and PKD2 (c.1480G > T) were identified to result in exon skipping. CONCLUSIONS: We confirmed that 11 variants in the gene of PKD1 and PKD2 affect normal splicing by interfering the recognition of classical splicing sites or by disrupting exon splicing enhancers and generating exon splicing silencers. This is the most comprehensive study to date on pre-mRNA splicing of exonic variants in ADPKD-associated disease-causing genes in consideration of the increasing number of identified variants in PKD1 and PKD2 gene in recent years. These results emphasize the significance of assessing the effect of exon single nucleotide variants in ADPKD at the mRNA level.

Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.

OBJECTIVES: To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness. METHODS: A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified. RESULTS: DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively. CONCLUSIONS: DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified. KEY POINTS: • DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

OBJECTIVES: To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. METHODS: The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. RESULTS: DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). CONCLUSIONS: DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. KEY POINTS: • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT.

Background Ultra-low-dose (ULD) CT could facilitate the clinical implementation of large-scale lung cancer screening while minimizing the radiation dose. However, traditional image reconstruction methods are associated with image noise in low-dose acquisitions. Purpose To compare the image quality and lung nodule detectability of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) in ULD CT. Materials and Methods Patients who underwent noncontrast ULD CT (performed at 0.07 or 0.14 mSv, similar to a single chest radiograph) and contrast-enhanced chest CT (CECT) from April to June 2020 were included in this prospective study. ULD CT images were reconstructed with filtered back projection (FBP), ASIR-V, and DLIR. Three-dimensional segmentation of lung tissue was performed to evaluate image noise. Radiologists detected and measured nodules with use of a deep learning-based nodule assessment system and recognized malignancy-related imaging features. Bland-Altman analysis and repeated-measures analysis of variance were used to evaluate the differences between ULD CT images and CECT images. Results A total of 203 participants (mean age ± standard deviation, 61 years ± 12; 129 men) with 1066 nodules were included, with 100 scans at 0.07 mSv and 103 scans at 0.14 mSv. The mean lung tissue noise ± standard deviation was 46 HU ± 4 for CECT and 59 HU ± 4, 56 HU ± 4, 53 HU ± 4, 54 HU ± 4, and 51 HU ± 4 in FBP, ASIR-V level 40%, ASIR-V level 80% (ASIR-V-80%), medium-strength DLIR, and high-strength DLIR (DLIR-H), respectively, of ULD CT scans (P < .001). The nodule detection rates of FBP reconstruction, ASIR-V-80%, and DLIR-H were 62.5% (666 of 1066 nodules), 73.3% (781 of 1066 nodules), and 75.8% (808 of 1066 nodules), respectively (P < .001). Bland-Altman analysis showed the percentage difference in long diameter from that of CECT was 9.3% (95% CI of the mean: 8.0, 10.6), 9.2% (95% CI of the mean: 8.0, 10.4), and 6.2% (95% CI of the mean: 5.0, 7.4) in FBP reconstruction, ASIR-V-80%, and DLIR-H, respectively (P < .001). Conclusion Compared with adaptive statistical iterative reconstruction-V, deep learning image reconstruction reduced image noise, increased nodule detection rate, and improved measurement accuracy on ultra-low-dose chest CT images. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee in this issue.

Deoxyhypusine hydroxylase as a novel pharmacological target for ischemic stroke via inducing a unique post-translational hypusination modification.

Ischemic stroke remains one of the leading causes of death worldwide, thereby highlighting the urgent necessary to identify new therapeutic targets. Deoxyhypusine hydroxylase (DOHH) is a fundamental enzyme catalyzing a unique posttranslational hypusination modification of eukaryotic translation initiation factor 5A (eIF5A) and is highly involved in the progression of several human diseases, including HIV-1 infection, cancer, malaria, and diabetes. However, the potential therapeutic role of pharmacological regulation of DOHH in ischemic stroke is still poorly understood. Our study first discovered a natural small-molecule brazilin (BZ) with an obvious neuroprotective effect against oxygen-glucose deprivation/reperfusion insult. Then, DOHH was identified as a crucial cellular target of BZ using HuProt™ human proteome microarray. By selectively binding to the Cys232 residue, BZ induced a previously undisclosed allosteric effect to significantly increase DOHH catalytic activity. Furthermore, BZ-mediated DOHH activation amplified mitophagy for mitochondrial function and morphology maintenance via DOHH/eIF5A hypusination signaling pathway, thereby protecting against ischemic neuronal injury in vitro and in vivo. Collectively, our study first identified DOHH as a previously unreported therapeutic target for ischemic stroke, and provided a future drug design direction for DOHH allosteric activators using BZ as a novel molecular template.

Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT.

OBJECTIVES: To evaluate inter- and intra- scan mode and scanner repeatability and reproducibility of radiomics features within and between single-energy CT (SECT) and dual-energy CT (DECT). METHODS: A standardized phantom with sixteen rods of clinical-relevant densities was scanned on seven DECT-capable scanners and three SECT-only scanners. The acquisition parameters were selected to present typical abdomen-pelvic examinations with the same voxel size. Images of SECT at 120 kVp and corresponding 120 kVp-like virtual monochromatic images (VMIs) in DECT which were generated according to scanners were analyzed. Regions of interest were drawn with rigid registrations to avoid variations due to segmentation. Radiomics features were extracted via Pyradiomics platform. Test-retest repeatability was evaluated by Bland-Altman analysis for repeated scans. Intra-scanner reproducibility for different scan modes was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility among different scanners for same scan mode was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). RESULTS: The test-retest analysis presented that 92.91% and 87.02% of the 94 assessed features were repeatable for SECT 120kVp and DECT 120 kVp-like VMIs, respectively. The intra-scanner analysis for SECT 120kVp vs DECT 120 kVp-like VMIs demonstrated that 10.76% and 10.28% of features were with ICC > 0.90 and CCC > 0.90, respectively. The inter-scanner analysis showed that 17.09% and 27.73% of features for SECT 120kVp were with CV < 10% and QCD < 10%, and 15.16% and 32.78% for DECT 120 kVp-like VMIs, respectively. CONCLUSIONS: The majority of radiomics features were non-reproducible within and between SECT and DECT. KEY POINTS: • Although the test-retest analysis showed high repeatability for radiomics features, the overall reproducibility of radiomics features within and between SECT and DECT was low. • Only about one-tenth of radiomics features extracted from SECT images and corresponding DECT images did match each other, even their average photon energy levels were considered alike, indicating that the scan mode potentially altered the radiomics features. • Less than one-fifth of radiomics features were reproducible among multiple SECT and DECT scanners, regardless of their fixed acquisition and reconstruction parameters, suggesting the necessity of scanning protocol adjustment and post-scan harmonization process.

Multivendor Comparison of Quantification Accuracy of Iodine Concentration and Attenuation Measurements by Dual-Energy CT: A Phantom Study.

BACKGROUND. Studies comparing accuracy of quantification by dual-energy CT (DECT) scanners have been limited by small numbers of scanners evaluated and narrow ranges of scanning conditions. OBJECTIVE. The purpose of this study was to compare DECT scanners of varying vendors, technologies, and generations in terms of the accuracy of iodine concentration and attenuation measurements. METHODS. A DECT quality-control phantom was designed to contain seven inserts of varying iodine concentrations as well as soft-tissue and fat inserts. The phantom underwent DECT using 12 different scanner configurations based on seven different DECT scanners from three vendors, with additional variation in tube voltage settings. Technologies included rapid-switching, dual-source, and dual-layer detector DECT. Scans also used three radiation dose levels (10, 20, and 30 mGy) and multiple reconstruction algorithms (filtered back projection, medium and high iterative reconstruction, and deep learning image reconstruction [DLIR]). The mean absolute percentage error (MAPE, representing the absolute ratio of measured error to nominal values on average; lower values indicate better accuracy) was calculated for iodine concentration on iodine maps (MAPEiodine) and attenuation on virtual monochromatic images (VMIs) using 40, 70, 100, and 140 keV (MAPEHU). Linear mixed models were used to explore factors affecting quantification accuracy. RESULTS. MAPEiodine and MAPEHU ranged 4.62-28.55% and 10.21-26.33%, respectively, across scanner configurations. Accuracies of iodine concentration and attenuation measurements were higher for third-generation rapid-switching and dual-source scanners in comparison with respective earlier-generation scanners and the single evaluated dual-layer detector scanner. Among all configurations, the third-generation rapid-switching scanner using DLIR had the highest quantification accuracy for iodine concentration (MAPEiodine, 4.62% ± 3.87%) and attenuation (MAPEHU, 10.21% ± 11.43%). Overall, MAPEiodine was significantly affected by scanner configuration (F = 450.0, p < .001) and iodine concentration (F = 211.0, p < .001). Overall, MAPEHU was significantly affected by scanner configuration (F = 233.5, p < .001), radiation dose (F = 14.9, p < .001), VMI energy level (F = 1959.4, p < .001), and material density (F = 411.5, p < .001); radiation dose was significantly associated with MAPEHU for five of 12 individual configurations. CONCLUSION. Quantification accuracy varied among DECT configurations of varying vendors, platforms, and generations and was affected by acquisition and reconstruction parameters. DLIR may improve quantification accuracy. CLINICAL IMPACT. The interscanner differences in DECT-based measurements should be recognized when quantitative evaluation is performed by DECT in clinical practice.

Novel and simple analytical method for simultaneous determination of sulfonamide, quinolone, tetracycline, macrolide, and chloramphenicol antibiotics in soil.

The multiclass determination of antibiotic residues in the soil is challenging because of its complex physicochemical properties. In this study, a simple analytical method was developed to simultaneously extract and determine 58 antibiotics from the soil. A novel acidity-regulated extraction-partition-concentration protocol was established for the simultaneous extraction of five classes (23 sulfonamides, 18 quinolones, five tetracyclines, eight macrolides, and four chloramphenicols) of antibiotics from the soil. Compared to traditional methods, the sample preparation efficiency was significantly improved by four times (45 min vs. 230 min) by optimizing the extraction method and omitting the time-consuming solid-phase extraction (SPE) procedure. The ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was optimized to determine the 58 antibiotics in a single run by applying positive/negative switching acquisition mode in less than 10 min with the baseline separation of sulfameter and sulfamethoxypyridazine. Suitable recoveries, ranging between 60 and 120%, were obtained for most antibiotics, with RSD <20%. The limits of quantification (LOQ) of the method were 2 µg/kg and 5 µg/kg. Thus, this study provides a simple, reliable, and economical method for accurately and rapidly determining a multiclass of antibiotics in the soil.

Two novel variants in a Bardet-Biedl syndrome type 5 patient with severe renal phenotype.

Bardet-Biedl syndrome type 5 (BBS5) has never been reported in Chinese populations. The aim of this study is to report the first BBS5 case in China, explore the phenotype and genotype correlation. The case was male, Han nationality, born with polydactyly and gained weight after birth, accompanied by polydipsia, polyuria and nocturia. He was found to have low vision at the age of 7 years, and having insufficient renal function at the age of 20 years. After hospitalization, he was found to have suffered from atrophy of the whole layer of macular retina, and end stage of kidney disease, presenting with shrinking and cyst-like changes of bilateral kidneys. Whole-exome sequencing was performed among the proband and his parents (Trios), further validated using Sanger sequencing and quantitative polymerase chain reaction. Two novel compound heterozygous variants of BBS5 gene [a missense variant NC_000002.12, NM_152384.3:c.1A>G(p.Met1?) & a large deletion c.(?_-60)_(386 + 1_387-1)del] were detected. BBS is rare, whereas BBS5 is rarer. Herein, we reported a Chinese BBS5 patient with severe renal phenotype and identified two novel BBS5 variants.