First Report of Agroathelia rolfsii Causing Southern Blight on Lippia (Phyla canescens) in Guangzhou, China.

Lippia (Phyla canescens) is a fast-growing, mat-forming, and prostrate perennial plant well adapted to infertile, high-saline, and drought environments (Leigh, et al. 2004). It arrived in China from Japan as a flowering ground cover in 2001 (Cai, et al. 2004). In June 2022, southern blight appeared in our nursery of the Floriculture Research Institute of Guangdong Academy of Agricultural Sciences. High temperature and damp environment are major factors for this disease. The symptoms of top-layer plants were not easily detected, but they were slightly yellowed. A yellowish-brown water-soak lesion appeared on the stems and lowest leaves exposed to soil. White mycelium appeared in the middle stage. Finally, the surface plants showed water-soak decay, and a mass of beige to black-brown rapeseed-shaped sclerotia appeared on the residue and surrounding soil; these plants died. Sclerotia and mycelia were collected from disease tissue, and after surface sterilization, sclerotia was cultured on potato dextrose agar (PDA) at 28±2°C in an incubator without light. Eight fungal isolates with similar colony morphologies were consistently isolated by purifying from different sampling areas. The isolates exhibited obvious septa and a clamp connection structure within the white mycelium. The average growth rate was 26.86±0.06 mm/day. Numerous white granular sclerotia were produced on the mycelium 6 days later. The sclerotia with a diameter of 1.24±0.07mm (n=189) gradually changed from diage to yellow to brown. A typical strain B1 was selected for further identification, targeting its 18S rRNA and LSU rRNA sequences (Yang, et al. 2011; Xue, et al. 2019). Its 18S rRNA sequence (GenBank Accession No. OR517233, 1626 bp) is 99.63% and 99.57% identical to Athelia rolfsii (AY665774, 1179bp; KC670714, 1775bp; JF819726, 1781bp). Its LSU rRNA sequence (OR539570, 757 bp) is 99.87% identical to Agroathelia rolfsii (OR526537, 904 bp). For Athelia rolfsii, a synonym of Agroathelia rolfsii, by combining the morphological characteristics and molecular identification, the isolate pathogen B1 was confirmed to be Agroathelia rolfsii (the teleomorph of Sclerotium rolfsii). To fullfill Koch's postulates, we inoculated the mycelial plugs to healthy lippia stems and leaves which has grown for one year, with PDA plugs free of mycelium as the control. All the plants were kept in a greenhouse at 28±2°C with a 14-h photoperiod and 80% relative humidity. Each treatment was repeated thrice and vaccinated with 6 points. At 7 d following inoculation, all plants inoculated with B1 showed typical symptoms, but the control group was asymptomatic, and sclerotia appeared 17d after inoculation. Using the same protocol mentioned above, pathogenic fungal was reisolated only from treated groups, but not from the control group. Chose three of the pathogens for 18S rRNA and LSU rRNA sequencing, the results showed 100% identity to B1, the same as its microstructure. There are few reports about the disease on P. canescens. Sosa (2007) investigated the pathogens on P. canescens in Argentina, 16 fungi were found but no A. rolfsii. Sclerotium rolfsii were identified on P. nodiflora or P. lanceolata (Michaux) Greene in America (Farr, et al. 1989). To our knowledge, this is the first report in China. Because this pathogen has wide-ranging hosts and causes serious damage, the results from this study will offer guidance for the prevention and treatment of this disease.

Using rice straw-augmented ecological floating beds to enhance nitrogen removal in carbon-limited wastewater.

Agricultural biomass used as solid carbon substrates in ecological floating beds (EFBs) has been proven to be applicable in nitrogen removal for carbon-limited wastewater treatment. However, the subtle interactions among plants, rhizosphere microorganisms, and supplementary carbon sources have not been thoroughly studied. This study combined rice straw mats with different aquatic macrophytes in EFBs to investigate denitrification efficiency in carbon-limited eutrophic waters. Results showed that rice straw significantly enhanced the nitrogen removal efficiency of EFBs, while enriching nitrogen-fixing and denitrifying bacteria (such as Rhizobium, Rubrivivax, and Rhodobacter, etc.). Additionally, during the denitrification process in EFBs, rice straw can release humic acid-like fraction as electron donors to support the metabolic activities of microorganisms, while aquatic macrophytes provide a more diverse range of dissolved organic matters, facilitating a sustainable denitrification process. These findings help to understand the synergistic effect of denitrification processes within wetland ecosystems using agricultural biomass.

Pangenome analysis reveals transposon-driven genome evolution in cotton.

BACKGROUND: Transposable elements (TEs) have a profound influence on the trajectory of plant evolution, driving genome expansion and catalyzing phenotypic diversification. The pangenome, a comprehensive genetic pool encompassing all variations within a species, serves as an invaluable tool, unaffected by the confounding factors of intraspecific diversity. This allows for a more nuanced exploration of plant TE evolution. RESULTS: Here, we constructed a pangenome for diploid A-genome cotton using 344 accessions from representative geographical regions, including 223 from China as the main component. We found 511 Mb of non-reference sequences (NRSs) and revealed the presence of 5479 previously undiscovered protein-coding genes. Our comprehensive approach enabled us to decipher the genetic underpinnings of the distinct geographic distributions of cotton. Notably, we identified 3301 presence-absence variations (PAVs) that are closely tied to gene expression patterns within the pangenome, among which 2342 novel expression quantitative trait loci (eQTLs) were found residing in NRSs. Our investigation also unveiled contrasting patterns of transposon proliferation between diploid and tetraploid cotton, with long terminal repeat (LTR) retrotransposons exhibiting a synchronized surge in polyploids. Furthermore, the invasion of LTR retrotransposons from the A subgenome to the D subgenome triggered a substantial expansion of the latter following polyploidization. In addition, we found that TE insertions were responsible for the loss of 36.2% of species-specific genes, as well as the generation of entirely new species-specific genes. CONCLUSIONS: Our pangenome analyses provide new insights into cotton genomics and subgenome dynamics after polyploidization and demonstrate the power of pangenome approaches for elucidating transposon impacts and genome evolution.

Chidamide plus envafolimab as subsequent treatment in advanced non-small cell lung cancer patients resistant to anti-PD-1 therapy: A multicohort, open-label, phase II trial with biomarker analysis.

BACKGROUND: Combination of chidamide and anti-PD-L1 inhibitor produce synergistic anti-tumor effect in advanced NSCLC patients resistant to anti-PD-1 treatment. However, the effect of chidamide plus envafolimab has not been reported. AIMS: This study aimed to evaluate the efficacy of chidamide plus envafolimab in advanced NSCLC patients resistant toanti-PD-1 treatment. MATERIALS AND METHODS: Eligible advanced NSCLC patients after resistant to anti-PD-1 therapy received chidamide and envafolimab. The primary endpoint was objective response rate (ORR). The secondary end points included disease control rate (DCR), progression-free survival (PFS), and safety. The expression of histone deacetylase 2 (HDAC2), PD-L1, and blood TMB (bTMB) was also analyzed. RESULTS: After a median follow-up of 8.1 (range: 7.6-9.2) months, only two patients achieved partial response. The ORR was 6.7% (2/30), DCR was 50% (15/30), and median PFS (mPFS) was 3.5 (95% confidence interval: 1.9-5.5) months. Biomarker analysis revealed that patients with high-level HDAC2 expression had numerically superior ORR (4.3% vs. 0), DCR (52.2% vs. 0) and mPFS (3.7 vs. 1.4m). Patients with negative PD-L1 had numerically superior DCR (52.2% vs. 33.3%) and mPFS (3.7m vs. 1.8m), so were those with low-level bTMB (DCR: 59.1% vs. 16.7%, mPFS: 3.8 vs.1.9m). Overall safety was controllable. DISCUSSION: High HDAC2patients showed better ORR, DCR, and PFS. In addition, patient with negative PD-L1 and low-level bTMB had better DCR and PFS. This may be related to the epigenetic function of chidamide. However, the sample size was not big enough, so it is necessary to increase sample size to confirm the conclusion. CONCLUSION: Combination of chidamide and envafolimab showed efficacy signals in certain NSCLC patients. But further identification of beneficial population is necessary for precision treatment.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Background: Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). Methods: In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. Results: In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. Conclusions: The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Largely Improved Creep Resistance and Thermal-Aging Stability of Eco-Friendly Polypropylene High-Voltage Insulation by Long-Chain Branch-Induced Interfacial Constraints.

Polypropylene (PP)-based composites have attracted numerous attention as a replacement of prevailing cross-linked polyethylene (XLPE) for high-voltage insulation due to their ease of processing, recyclability, and excellent electrical performance. However, the poor resistances against high-temperature creep and thermal aging are obstacles to practical applications of PP-based thermoplastic high-voltage insulation. To address these problems, in this Letter, we synthesized an impact polypropylene copolymer (IPC) containing multifold long-chain branched (LCB) structures in phases, especially the interfaces between the PP matrix and the rubber phase. The results indicated that the structural stability of LCBIPC was significantly enhanced under extreme conditions. In comparison to IPC (without LCB structures), 24.1% less creep strain and 75.2% less unrecoverable deformation are achieved in LCBIPC at 90 °C. In addition, the thermal aging experiments were performed at 135 °C for 48 and 88 days for IPC and LCBIPC, respectively. The results show that the resistance against thermal aging was also enhanced in LCBIPC, which showed a 133% longer thermal aging life compared to IPC. Further results revealed that the interfacial layer between the PP matrix and the rubber phase was constructed in LCBIPC. The two phases are tightly linked by chemical bonds in LCB structures, leading to enforced constraints of the rubber phase at the micro level and better resistance performance against creep and thermal aging at the macro level. Evidently, the reported eco-friendly LCBIPC thermoplastic insulation shows great potential for applications in high-voltage cable insulation.

Cardiac GR Mediates the Diurnal Rhythm in Ventricular Arrhythmia Susceptibility.

BACKGROUND: Ventricular arrhythmias (VAs) demonstrate a prominent day-night rhythm, commonly presenting in the morning. Transcriptional rhythms in cardiac ion channels accompany this phenomenon, but their role in the morning vulnerability to VAs and the underlying mechanisms are not understood. We investigated the recruitment of transcription factors that underpins transcriptional rhythms in ion channels and assessed whether this mechanism was pertinent to the heart's intrinsic diurnal susceptibility to VA. METHODS AND RESULTS: Assay for transposase-accessible chromatin with sequencing performed in mouse ventricular myocyte nuclei at the beginning of the animals' inactive (ZT0) and active (ZT12) periods revealed differentially accessible chromatin sites annotating to rhythmically transcribed ion channels and distinct transcription factor binding motifs in these regions. Notably, motif enrichment for the glucocorticoid receptor (GR; transcriptional effector of corticosteroid signaling) in open chromatin profiles at ZT12 was observed, in line with the well-recognized ZT12 peak in circulating corticosteroids. Molecular, electrophysiological, and in silico biophysically-detailed modeling approaches demonstrated GR-mediated transcriptional control of ion channels (including Scn5a underlying the cardiac Na+ current, Kcnh2 underlying the rapid delayed rectifier K+ current, and Gja1 responsible for electrical coupling) and their contribution to the day-night rhythm in the vulnerability to VA. Strikingly, both pharmacological block of GR and cardiomyocyte-specific genetic knockout of GR blunted or abolished ion channel expression rhythms and abolished the ZT12 susceptibility to pacing-induced VA in isolated hearts. CONCLUSIONS: Our study registers a day-night rhythm in chromatin accessibility that accompanies diurnal cycles in ventricular myocytes. Our approaches directly implicate the cardiac GR in the myocyte excitability rhythm and mechanistically link the ZT12 surge in glucocorticoids to intrinsic VA propensity at this time.

Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study.

BACKGROUND AND PURPOSE: To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification. MATERIALS AND METHODS: This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature. RESULTS: Within the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk. CONCLUSIONS: The CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.

High expression of RTEL1 predicates worse progression in gliomas and promotes tumorigenesis through JNK/ELK1 cascade.

Gliomas are the most common primary intracranial tumor worldwide. The maintenance of telomeres serves as an important biomarker of some subtypes of glioma. In order to investigate the biological role of RTEL1 in glioma. Relative telomere length (RTL) and RTEL1 mRNA was explored and regression analysis was performed to further examine the relationship of the RTL and the expression of RTEL1 with clinicopathological characteristics of glioma patients. We observed that high expression of RTEL1 is positively correlated with telomere length in glioma tissue, and serve as a poor prognostic factor in TERT wild-type patients. Further in vitro studies demonstrate that RTEL1 promoted proliferation, formation, migration and invasion ability of glioma cells. In addition, in vivo studies also revealed the oncogene role of RTEL1 in glioma. Further study using RNA sequence and phospho-specific antibody microarray assays identified JNK/ELK1 signaling was up-regulated by RTEL1 in glioma cells through ROS. In conclusion, our results suggested that RTEL1 promotes glioma tumorigenesis through JNK/ELK1 cascade and indicate that RTEL1 may be a prognostic biomarker in gliomas.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Deep Learning Image Reconstruction for Transcatheter Aortic Valve Implantation Planning: Image Quality, Diagnostic Performance, Contrast volume and Radiation Dose Assessment.

RATIONALE AND OBJECTIVES: To assess image quality, contrast volume and radiation dose reduction potential and diagnostic performance with the use of high-strength deep learning image reconstruction (DLIR-H) in transcatheter aortic valve implantation (TAVI) planning CT. METHODS: We prospectively enrolled 128 patients referred to TAVI-planning CT. Patients were randomly divided into two groups: DLIR-H group (n = 64) and conventional group (n = 64). The DLIR-H group was scanned with tube voltage of 80kVp and body weighted-dependent contrast injection rate of 28mgI/kg/s, images reconstructed using DLIR-H; the conventional group was scanned with 100kVp and contrast injection rate of 40mgI/kg/s, and images reconstructed using adaptive statistical iterative reconstruction-V at 50% (ASIR-V 50%). Radiation dose, contrast volume, contrast injection rate, and image quality were compared between the two groups. The diagnostic performance of TAVI planning CT for coronary stenosis in 115 patients were calculated using invasive coronary angiography as golden standard. RESULTS: DLIR-H group significantly reduced radiation dose (4.94 ± 0.39mSv vs. 7.93 ± 1.20mSv, p < 0.001), contrast dose (45.28 ± 5.38 mL vs. 63.26 ± 9.88 mL, p < 0.001), and contrast injection rate (3.1 ± 0.31 mL/s vs. 4.9 ± 0.2 mL/s, p < 0.001) compared to the conventional group. Images in DLIR-H group had significantly higher SNR and CNR (all p < 0.001). For the diagnostic performance on a per-patient basis, TAVI planning CT in the DLIR-H group provided 100% sensitivity, 92.1% specificity, 100% negative predictive value (NPV), and 84.2% positive predictive value for the detection of > 50% stenosis. In the conventional group, the corresponding results were 94.7%, 95.3%, 97.6%, and 90.0%, respectively. CONCLUSION: DLIR-H in TAVI-planning CT provides improved image quality with reduced radiation and contrast doses, and enables satisfactory diagnostic performance for coronary arteries stenosis.

A dominant negative mutation of GhMYB25-like alters cotton fiber initiation, reducing lint and fuzz.

Cotton (Gossypium hirsutum) fibers, vital natural textile materials, are single-cell trichomes that differentiate from the ovule epidermis. These fibers are categorized as lint (longer fibers useful for spinning) or fuzz (shorter, less useful fibers). Currently, developing cotton varieties with high lint yield but without fuzz remains challenging due to our limited knowledge of the molecular mechanisms underlying fiber initiation. This study presents the identification and characterization of a naturally occurring dominant negative mutation GhMYB25-like_AthapT, which results in a reduced lint and fuzzless phenotype. The GhMYB25-like_AthapT protein exerts its dominant negative effect by suppressing the activity of GhMYB25-like during lint and fuzz initiation. Intriguingly, the negative effect of GhMYB25-like_AthapT could be alleviated by high expression levels of GhMYB25-like. We also uncovered the role of GhMYB25-like in regulating the expression of key genes such as GhPDF2 (PROTODERMAL FACTOR 2), CYCD3; 1 (CYCLIN D3; 1) and PLD (Phospholipase D), establishing its significance as a pivotal transcription factor in fiber initiation. We identified other genes within this regulatory network, expanding our understanding of the determinants of fiber cell fate. These findings offer valuable insights for cotton breeding and contribute to our fundamental understanding of fiber development.

CLDN6 inhibited cellular biological function of nonsmall cell lung cancer cells through suppressing aerobic glycolysis via the RIP1/ASK1/JNK axis.

Claudin-6 (CLDN6) has been extensively studied in different tumors to date. However, in the case of nonsmall cell lung cancer (NSCLC), CLDN6 has a largely unknown role and molecular mechanism. We detected the expression of CLDN6 in NSCLC tissues and cells using reverse transcription-quantitative polymerase chain reaction (PCR) and western blot assays. A gain-of-function experiment was performed to evaluate the biological effects of CLDN6 on NSCLC cell behaviors. Methylation-specific PCR was utilized to detect the DNA methylation of CLDN6 gene promoter region. The interaction of CLDN6 and receptor interacting protein 1 (RIP1) was determined by coimmunoprecipitation assay. Furthermore, the modulation of CLDN6 on RIP1/apoptosis signal-regulating kinase 1 (ASK1)/c-Jun N-terminal kinase (JNK) axis was confirmed. The results showed that in NSCLC tissues and cells, CLDN6 expression level was declined, and was associated with a high level of DNA methylation. CLDN6 overexpression suppressed the viability, invasion, migration, and promoted cell apoptosis. Besides, the enhanced expression of CLDN6 reduced the glycolysis and the dysfunction of mitochondrial respiration of NSCLC cells. Mechanistic investigation confirmed that CLDN6 interacted with RIP1 and inhibited cellular biological function of NSCLC cells via RIP1/ASK1/JNK axis. Besides, CLDN6 overexpression inhibited tumor growth in vivo. In conclusion, CLDN6 inhibited NSCLC cell proliferation through inactivating aerobic glycolysis via the RIP1/ASK1/JNK axis.

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.

OBJECTIVE: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images. METHODS: In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases. RESULTS: Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05]. CONCLUSIONS: DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.

Evaluation of Mucosal Healing in Crohn's Disease: Radiomics Models of Intestinal Wall and Mesenteric Fat Based on Dual-Energy CT.

This study aims to assess the effectiveness of radiomics signatures obtained from dual-energy computed tomography enterography (DECTE) in the evaluation of mucosal healing (MH) in patients diagnosed with Crohn's disease (CD). In this study, 106 CD patients with a total of 221 diseased intestinal segments (79 with MH and 142 non-MH) from two medical centers were included and randomly divided into training and testing cohorts at a ratio of 7:3. Radiomics features were extracted from the enteric phase iodine maps and 40-kev and 70-kev virtual monoenergetic images (VMIs) of the diseased intestinal segments, as well as from mesenteric fat. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression. Radiomics models were subsequently established, and the accuracy of these models in identifying MH in CD was assessed by calculating the area under the receiver operating characteristic curve (AUC). The combined-iodine model formulated by integrating the intestinal and mesenteric fat radiomics features of iodine maps exhibited the most favorable performance in evaluating MH, with AUCs of 0.989 (95% confidence interval (CI) 0.977-1.000) in the training cohort and 0.947 (95% CI 0.884-1.000) in the testing cohort. Patients categorized as high risk by the combined-iodine model displayed a greater probability of experiencing disease progression when contrasted with low-risk patients. The combined-iodine radiomics model, which is built upon iodine maps of diseased intestinal segments and mesenteric fat, has demonstrated promising performance in evaluating MH in CD patients.

Improving image quality consistency and diagnostic accuracy in lower extremity CT angiography using a split-bolus contrast injection protocol.

OBJECTIVES: To evaluate the clinical value of using a split-bolus contrast injection protocol in improving image quality consistency and diagnostic accuracy in lower extremity CT angiography (CTA). METHODS: Fifty (mean age, 66 ± 12 years) and 39 (mean age, 66 ± 11 years) patients underwent CTA in the lower extremity arteries using split-bolus and fixed-bolus injection schemes, respectively. The objective and subjective image quality of the 2 groups were compared and the diagnostic efficacy for the degree of vessel stenosis was compared using digital subtraction angiography as the gold standard. A P < .05 was considered statistically significant. RESULTS: In comparison with the fixed-bolus scheme, the split-bolus scheme greatly improved the consistency of image quality of the low extremities by significantly increasing the arterial enhancement (337.87 ± 64.67HU vs. 254.74 ± 71.58HU, P < .001), signal-to-noise ratio (22.58 ± 11.64 vs. 7.14 ± 1.98, P < .001), and contrast-to-noise ratio (37.21 ± 10.46 vs. 31.10 ± 15.40, P = .041) in the infrapopliteal segment. The subjective image quality was better (P < .001) and the diagnostic accuracy was higher in the split-bolus group than in the fixed-bolus group (96.00% vs. 91.67%, P < .05, for diagnosing >50% stenosis, and 97.00% vs. 89.10%, P < .05, for diagnosing occlusion) for the infrapopliteal segment arteries. CONCLUSIONS: Compared with the fixed-bolus injection scheme, the split-bolus injection scheme improves the image quality consistency and diagnostic accuracy especially for the infrapopliteal segment arteries in lower extremity CTA. ADVANCES IN KNOWLEDGE: (1) The split-bolus injection scheme of CTA of the lower extremity arteries improves the overall image quality, uniformity of contrast enhancement. (2) Compared with the fixed-bolus injection scheme, the split-bolus injection scheme especially improves the infrapopliteal segment arteries image quality and diagnostic efficacy.

Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels.

This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10 mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. Ninety-four features were extracted via Pyradiomics. Reproducibility of features was calculated between standard and low dose levels, between reconstruction algorithms in reference to FBP images, and within scan mode, using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The average percentage of features with ICC > 0.90 and CCC > 0.90 between the two dose levels was 21.28% and 20.75% in AV-40 images, and 39.90% and 35.11% in AV-100 images, respectively, and increased from 15.43 to 45.22% and from 15.43 to 44.15% with an increasing strength level of DLIR. The average percentage of features with ICC > 0.90 and CCC > 0.90 in reference to FBP images was 26.07% and 25.80% in AV-40 images, and 18.88% and 18.62% in AV-100 images, respectively, and decreased from 27.93 to 17.82% and from 27.66 to 17.29% with an increasing strength level of DLIR. DLIR and ASIR-V algorithms showed low reproducibility in reference to FBP images, while the high-strength DLIR algorithm provides an opportunity for minimizing radiomics variability due to dose reduction.

Artificial cavernosa-like tissue based on multibubble Matrigel and a human corpus cavernous fibroblast scaffold.

Ex vivo tissue culture of the human corpus cavernosum (CC) can be used to explore the tissue structural changes and complex signaling networks. At present, artificial CC-like tissues based on acellular or three-dimensional (3D)-printed scaffolds are used to solve the scarcity of primary penis tissue samples. However, inconvenience and high costs limit the wide application of such methods. Here, we describe a simple, fast, and economical method of constructing artificial CC-like tissue. Human CC fibroblasts (FBs), endothelial cells (ECs), and smooth muscle cells (SMCs) were expanded in vitro and mixed with Matrigel in specific proportions. A large number of bubbles were formed in the mixture by vortexing combined with pipette blowing, creating a porous, spongy, and spatial structure. The CC FBs produced a variety of signaling factors, showed multidirectional differentiation potential, and grew in a 3D grid in Matrigel, which is necessary for CC-like tissue to maintain a porous structure as a cell scaffold. Within the CC-like tissue, ECs covered the surface of the lumen, and SMCs were located inside the trabeculae, similar to the structure of the primary CC. Various cell components remained stable for 3 days in vitro, but the EC content decreased on the 7th day. Wingless/integrated (WNT) signaling activation led to lumen atrophy and increased tissue fibrosis in CC-like tissue, inducing the same changes in characteristics as in the primary CC. This study describes a preparation method for human artificial CC-like tissue that may provide an improved experimental platform for exploring the function and structure of the CC and conducting drug screening for erectile dysfunction therapy.

Targeting metabolic sensing switch GPR84 on macrophages for cancer immunotherapy.

INTRODUCTION: As one of the major components of the tumor microenvironment, tumor-associated macrophages (TAMs) possess profound inhibitory activity against T cells and facilitate tumor escape from immune checkpoint blockade therapy. Converting this pro-tumorigenic toward the anti-tumorigenic phenotype thus is an important strategy for enhancing adaptive immunity against cancer. However, a plethora of mechanisms have been described for pro-tumorigenic differentiation in cancer, metabolic switches to program the anti-tumorigenic property of TAMs are elusive. MATERIALS AND METHODS: From an unbiased analysis of single-cell transcriptome data from multiple tumor models, we discovered that anti-tumorigenic TAMs uniquely express elevated levels of a specific fatty acid receptor, G-protein-coupled receptor 84 (GPR84). Genetic ablation of GPR84 in mice leads to impaired pro-inflammatory polarization of macrophages, while enhancing their anti-inflammatory phenotype. By contrast, GPR84 activation by its agonist, 6-n-octylaminouracil (6-OAU), potentiates pro-inflammatory phenotype via the enhanced STAT1 pathway. Moreover, 6-OAU treatment significantly retards tumor growth and increases the anti-tumor efficacy of anti-PD-1 therapy. CONCLUSION: Overall, we report a previously unappreciated fatty acid receptor, GPR84, that serves as an important metabolic sensing switch for orchestrating anti-tumorigenic macrophage polarization. Pharmacological agonists of GPR84 hold promise to reshape and reverse the immunosuppressive TME, and thereby restore responsiveness of cancer to overcome resistance to immune checkpoint blockade.

Ultra-rapid and highly selective colorimetric detection of hydrochloric acid via an aggregation to dispersion change of gold nanoparticles.

A rapid and highly selective naked-eye detection of hydrochloric acid (HCl) in an aqueous medium was established using HCl-triggered redispersion of gold nanoparticle aggregates.