Transcriptome analysis reveals miRNA expression profiles in hypothalamus tissues during the sexual development of Jining grey goats.

BACKGROUND: Exploring the physiological and molecular mechanisms underlying goat sexual maturation can enhance breeding practices and optimize reproductive efficiency and is therefore substantially important for practical breeding purposes. As an essential neuroendocrine organ in animals, the hypothalamus is involved in sexual development and other reproductive processes in female animals. Although microRNAs (miRNAs) have been identified as significant regulators of goat reproduction, there is a lack of research on the molecular regulatory mechanisms of hypothalamic miRNAs that are involved in the sexual development of goats. Therefore, we examined the dynamic changes in serum hormone profiles and hypothalamic miRNA expression profiles at four developmental stages (1 day (neonatal, D1, n = 5), 2 months (prepubertal, M2, n = 5), 4 months (sexual maturity, M4, n = 5), and 6 months (breeding period, M6, n = 5)) during sexual development in Jining grey goats. RESULTS: Transcriptome analysis revealed 95 differentially expressed miRNAs (DEMs) in the hypothalamus of goats across the four developmental stages. The target genes of these miRNAs were significantly enriched in the GnRH signalling pathway, the PI3K-Akt signalling pathway, and the Ras signalling pathway (P < 0.05). Additionally, 16 DEMs are common among the M2 vs. D1, M4 vs. D1, and M6 vs. D1 comparisons, indicating that the transition from D1 to M2 represents a potentially critical period for sexual development in Jining grey goats. The bioinformatics analysis results indicate that miR-193a/miR-193b-3p-Annexin A7 (ANXA7), miR-324-5p-Adhesion G protein-coupled receptor A1 (ADGRA1), miR-324-3p-Erbb2 receptor tyrosine kinase 2 (ERBB2), and miR-324-3p-Rap guanine nucleotide exchange factor 3 (RAPGEF3) are potentially involved in biological processes such as hormone secretion, energy metabolism, and signal transduction. In addition, we further confirmed that miR-324-3p targets the regulatory gene RAPGEF3. CONCLUSION: These results further enrich the expression profile of hypothalamic miRNAs in goats and provide important insights for studying the regulatory effects of hypothalamic miRNAs on the sexual development of goats after birth.

Study on the Influence of Label Image Accuracy on the Performance of Concrete Crack Segmentation Network Models.

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices.

Blending Aryl Ketone in Covalent Organic Frameworks to Promote Photoinduced Electron Transfer.

Aryl-ketone derivatives have been acknowledged as promising organic photocatalysts for photosynthesis. However, they are limited by their photostability and have been less explored for photoinduced electron transfer (PET) applications. Herein we demonstrate a novel strategy to cover the shortage of aryl-ketone photocatalysts and control the photoreactivity by implanting symmetric aryl ketones into the conjugated covalent organic frameworks (COFs). To prove the concept, three comparative materials with the same topology and varied electronic structures were built, adopting truxenone knot and functionalized terephthalaldehyde linkers. Spectroscopic investigation and excited carrier dynamics analysis disclosed improvements in the photostability and electronic transfer efficiency as well as the structure-performance relationships toward N-aryl tetrahydroisoquinoline oxidation. This system provides a robust rule of thumb for designing new-generation aryl-ketone photocatalysts.

Study on the correlation between bioelectrical impedance analysis index and protein energy consumption in maintenance dialysis patients.

BACKGROUND: Protein-energy wasting (PEW) has been reported to be pretty common in maintenance dialysis patients. However, the existing PEW diagnostic standard is limited in clinical use due to the complexity of it. Bioelectrical impedance analysis (BIA), as a non-invasive nutritional assessment method, can objectively and quantitatively analyze the changes of body tissue components under different nutritional states. We aim to explore the association between PEW and BIA and establish a reliable diagnostic model of PEW. METHODS: We collected cross-sectional data of 609 maintenance dialysis patients at the First Affiliated Hospital, College of Medicine, Zhejiang University. PEW was diagnosed according to International Society of Renal Nutrition and Metabolism (ISRNM) criteria. Among them, 448 consecutive patients were included in the training set for the establishment of a diagnostic nomogram. 161 consecutive patients were included for internal validation. 52 patients from Zhejiang Hospital were included for external validation of the diagnostic model. Correlation analysis of BIA indexes with other nutritional indicators was performed. Logistic regression was used to examine the association of BIA indexes with PEW. 12 diagnostic models of PEW in maintenance dialysis patients were developed and the performance of them in terms of discrimination and calibration was evaluated using C statistics and Hosmer-Lemeshow-type χ2 statistics. After comparing to existing diagnostic models, and performing both internal and external validation, we finally established a simple but reliable PEW diagnostic model which may have great value of clinical application. RESULTS: A total of 609 individuals from First Affiliated Hospital, College of Medicine, Zhejiang University and 52 individuals from Zhejiang Hospital were included. After full adjustment, age, peritoneal dialysis (compared to hemodialysis), subjective global assessment (SGA, compared to non-SGA) and water ratio were independent risk factors, while triglyceride, urea nitrogen, calcium, ferritin, BCM, VFA and phase angle were independent protective factors of PEW. The model incorporated water ratio, VFA, BCM, phase angle and cholesterol revealed best performance. A nomogram was developed according to the results of model performance. The model achieved high C-indexes of 0.843 in the training set, 0.841 and 0.829 in the internal and external validation sets, respectively, and had a well-fitted calibration curve. The net reclassification improvement (NRI) showed 8%, 13%, 2%, 38%, 36% improvement of diagnostic accuracy of our model compared with "PEW score model", "modified PEW score model", "3-index model", "SGA model" and "BIA decision tree model", respectively. CONCLUSIONS: BIA can be used as an auxiliary tool to evaluate PEW risk and may have certain clinical application value.

Research on the Efficiency of Bridge Crack Detection by Coupling Deep Learning Frameworks with Convolutional Neural Networks.

Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.

Succinic anhydride-based chemical modification making laccase@Cu3(PO4)2 hybrid nanoflowers robust in removing bisphenol A in wastewater.

To prepare a robust biocatalyst and enhance the removal of bisphenol A in wastewater, succinic anhydride was reacted with laccase to obtain succinic anhydride-modified laccase (SA-laccase) and then co-crystallized with Cu3(PO4)2 to form SA-laccase@Cu3(PO4)2 hybrid nanoflowers (hNFs). The activity of SA-laccase@Cu3(PO4)2 reached 5.27 U/mg, 1.86-, 2.88- and 2.15-fold those of bare laccase@Cu3(PO4)2, laccase@Ca3(PO4)2 and laccase@epoxy resin, respectively. Compared with free laccase, the obtained hNFs present enhanced activity and tolerance to pH and high temperature in the removal of BPA. Under the optimum conditions of pH 6.0 and 35 °C, BPA removal reached 93.2% using SA-laccase@Cu3(PO4)2 hNFs, which was 1.21-fold of that using free laccase. In addition, the obtained SA-laccase@Cu3(PO4)2 hNFs retained nearly 90% of their initial catalytic activity for BPA removal after 8 consecutive batch cycles. This efficient method for preparing immobilized laccase can also be further developed and improved to acquire green biocatalysts for removing persistent organic pollutants in wastewater.

The role of agricultural green production technologies in improving low-carbon efficiency in China: Necessary but not effective.

The effects of agricultural green production technologies (AGPTs) on agricultural productivity and the environment have received increasing attention. With the panel data of agricultural production of mainland China from 2000 to 2017, this study investigates the role of AGPTs adoption rates in improving low-carbon efficiency by adopting a random-effects panel Tobit model. Results indicate that average adoption rates of AGPTs are less than 20% and unbalanced adoptions vary between the main and non-main grain-producing areas, as well as the northern and southern main grain-producing areas. Furthermore, AGPTs adoption reduces low-carbon efficiency at nationwide and main grain-producing areas. In the northern main grain-producing areas, water-saving irrigation and no-tillage seeding reduce low-carbon efficiency, while mechanized returning straw crushing promotes it. In the southern main grain-producing areas, deep tillage with fertilizer application and no-tillage seeding decrease low-carbon efficiency, while mechanized deep ploughing and scarification increases it. We also find that AGPTs can promote low-carbon efficiency through comprehensive mechanization level and mechanical input density. To improve low-carbon efficiency, we suggest that the improvement and diffusion of applicable AGPTs should be adapted to the local production conditions, and the agricultural machinery service, research and development system should be improved as well.

A Lacunary Polyoxovanadate Precursor and Transition-Metal-Sandwiched Derivatives for Catalytic Oxidation of Sulfides.

A SeO3 -centered lacunary Keggin-type heteropolyoxovanadate (hetero-POV) K6 H2 [SeV10 O28 (SeO3 )3 ]⋅14 H2 O (1) was isolated by one-pot reaction of KVO3 and SeO2 under acidic conditions. X-ray studies revealed that it comprised a single {VO5 }-capped trivacant B-α-type Keggin ion [SeV9 O33 (VO)]14- with its lacunary sites decorated by three {SeO3 } pyramids. Interestingly, this new basic hetero-POV building block was further used as a precursor to assemble with different transition-metal (TM) ions, yielding a series of TM-sandwiched POVs K6 H8 [(SeV10 O28 (SeO3 )3 )2 (M(H2 O)4 )]⋅24 H2 O (M2+ =Mn2+ (2), Co2+ (3), Zn2+ (4)). All four compounds were characterized by single-crystal X-ray structure analysis, IR, X-ray photoelectron spectroscopy (XPS), EPR, and 51 V NMR spectroscopy. Importantly, three TM-sandwiched derivatives exhibited effective catalytic activity for the heterogeneous oxidative desulfurization of sulfides at room temperature.

36-Nuclearity Organophosphonate-Functionalized Polyoxomolybdates: Synthesis, Characterization and Selective Catalytic Oxidation of Sulfides.

The crown-shaped 36-molybdate cluster organophosphonate-functionalized polyoxomolybdates with the highest nuclearity in organophosphonate-based polyoxometalate chemistry, (NH4 )19 Na7 H10 [Cu(H2 O)TeMo6 O21 {N(CH2 PO3 )3 }]6 ⋅31 H2 O, has been reported for the first time. The synthesized 36-molybdate cluster was characterized by routine techniques and tested as a heterogeneous catalyst for selective oxidation of sulfides with impressive catalytic and selective performances after heat treatment. High efficiency (TON=15333) was achieved in the selective oxidation of sulfides to sulfoxides, caused by the synergic effect between copper and polyoxomolybdates and the generation of the cuprous species during the heat treatment.

Synthesis and Characterization of a Crown-Shaped 36-Molybdate Cluster and Application in Catalyzing Knoevenagel Condensation.

A novel crown-shaped 36-molybdate cluster with organophosphonate-functionalized polyoxomolybdates, (NH4)17Na7H12[Co(H2O)TeMo6O21{N(CH2PO3)3}]6·42H2O, has been successfully synthesized and well-characterized. It owns the highest nuclearity in the family of organophosphonate-based polyoxometalates reported so far. Furthermore, for the first time in the field, we illustrated that polyoxomolybdate could work as an effective heterogeneous catalyst for the Knoevenagel condensation reaction with high TOF (7714 h-1) and good recyclability. Impressively, the catalytic performance of 1 was also tested successfully in a large scale (∼10 g) reaction, where 89% of reaction yield and 3216 of TON were afforded.

Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS.

Automatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and height differences, which is a novel sensor combination mode for terrestrial laser scanning. The approximate distance between two neighboring scan positions is firstly calculated with smartphone GPS coordinates. Then, 2D distribution entropy is used to measure the distribution coherence between the two scans and search for the optimal initial transformation parameters. To this end, we propose a method called Iterative Minimum Entropy (IME) to correct initial transformation parameters based on two criteria: the difference between the average and minimum entropy and the deviation from the minimum entropy to the expected entropy. Finally, the presented method is evaluated using two data sets that contain tens of millions of points from panoramic and non-panoramic, vegetation-dominated and building-dominated cases and can achieve high accuracy and efficiency.

[Automatic houses detection with color aerial images based on image segmentation].

In order to achieve housing automatic detection from high-resolution aerial imagery, the present paper utilized the color information and spectral characteristics of the roofing material, with the image segmentation theory, to study the housing automatic detection method. Firstly, This method proposed in this paper converts the RGB color space to HIS color space, uses the characteristics of each component of the HIS color space and the spectral characteristics of the roofing material for image segmentation to isolate red tiled roofs and gray cement roof areas, and gets the initial segmentation housing areas by using the marked watershed algorithm. Then, region growing is conducted in the hue component with the seed segment sample by calculating the average hue in the marked region. Finally through the elimination of small spots and rectangular fitting process to obtain a clear outline of the housing area. Compared with the traditional pixel-based region segmentation algorithm, the improved method proposed in this paper based on segment growing is in a one-dimensional color space to reduce the computation without human intervention, and can cater to the geometry information of the neighborhood pixels so that the speed and accuracy of the algorithm has been significantly improved. A case study was conducted to apply the method proposed in this paper to high resolution aerial images, and the experimental results demonstrate that this method has a high precision and rational robustness.

Altered resting-state brain functional activities and networks in Crohn's disease: a systematic review.

Background: Crohn's disease (CD) is a non-specific chronic inflammatory disease of the gastrointestinal tract and is a phenotype of inflammatory bowel disease (IBD). The current study sought to compile the resting-state functional differences in the brain between CD patients and healthy controls. Methods: The online databases PubMed, Web of Science Core, and EMBASE were used to find the published neuroimage studies. The search period was from the beginning through December 15, 2023. The predetermined inclusion and exclusion criteria allowed for the identification of the studies. The studies were assembled by two impartial reviewers, who also assessed their quality and bias. Results: This review comprised 16 resting-state fMRI studies in total. The included studies generally had modest levels of bias. According to the research, emotional processing and pain processing were largely linked to increased or decreased brain activity in patients with CD. The DMN, CEN, and limbic systems may have abnormalities in patients with CD, according to research on brain networks. Several brain regions showed functional changes in the active CD group compared to the inactive CD group and the healthy control group, respectively. The abnormalities in brain areas were linked to changes in mood fluctuations (anxiety, melancholy) in patients with CD. Conclusion: Functional neuroimaging helps provide a better understanding of the underlying neuropathological processes in patients with CD. In this review, we summarize as follows: First, these findings indicate alterations in brain function in patients with CD, specifically affecting brain regions associated with pain, emotion, cognition, and visceral sensation; second, disease activity may have an impact on brain functions in patients with CD; and third, psychological factors may be associated with altered brain functions in patients with CD.

Density of tertiary lymphoid structures and their correlation with prognosis in non-small cell lung cancer.

Background: Tertiary lymphoid structures (TLS), ordered structure of tumor-infiltrating immune cells in tumor immune microenvironment (TIME), play an important role in the development and anti-tumor immunity of various cancers, including liver, colon, and gastric cancers. Previous studies have demonstrated that the presence of TLS in intra-tumoral (IT), invasive margin (IM), and peri-tumoral (PT) regions of the tumors at various maturity statuses. However, the density of TLS in different regions of non-small cell lung cancer (NSCLC) has not been extensively studied. Methods: TLS and tumor-infiltrating immune cells were assessed using immunohistochemistry (IHC) staining in 82 NSCLC patients. Tumor samples were divided into three subregions as IT, IM and PT regions, and TLS were identified as early/primary TLS (E-TLS) or secondary/follicular TLS (F-TLS). The distribution of TLS in different maturity statuses, along with their correlation with clinicopathological characteristics and prognostic value, was assessed. Nomograms were used to predict the probability of 1-, 3-, and 5-year overall survival (OS) in patients with NSCLC. Results: The density of TLS and proportion of F-TLS in the IT region (90.2%, 0.45/mm2, and 61.0%, respectively) were significantly higher than those in the IM region (72.0%, 0.18/mm2, and 39.0%, respectively) and PT region (67.1%, 0.16/mm2, and 40.2%, respectively). A lower density of TLS, especially E-TLS in the IM region, was correlated with better prognosis in NSCLC patients. CD20+ B cells, CD3+ T cells, CD8+ cytotoxic T cells, and CD68+ macrophages were significantly overexpressed in the IM region. CD20+ B cells and CD3+ T cells in the IM region were significantly correlated with the density of E-TLS, while no statistically significant correlation was found with F-TLS. The E-TLS density in the IM region and TNM stage were independent prognostic factors for NSCLC patients. The nomogram showed good prognostic ability. Conclusions: A higher density of E-TLS in the IM region was associated with a worse prognosis in NSCLC patients, potentially due to the inhibition of TLS maturation caused by the increased density of suppressive immune cells at the tumor invasion front.

miR-18a-5p promotes osteogenic differentiation of BMSC by inhibiting Notch2.

Postmenopausal osteoporosis (PMOP) is a metabolic disorder characterized by the loss of bone density, which increases the risk of developing complications such as fractures. A pivotal factor contributing to the onset of PMOP is the diminished osteogenic differentiation capacity of bone marrow mesenchymal stem cells (BMSCs). MicroRNAs (miRNAs) play a substantial role in this process; however, their specific impact on regulating BMSCs osteogenesis remains unclear. Studies have evidenced a reduced expression of miR-18a-5p in PMOP, and concomitantly, our observations indicate an augmented expression of miR-18a-5p during the osteogenic differentiation of BMSCs. This investigation seeks to elucidate the regulatory influence of miR-18a-5p on BMSC osteogenic differentiation and the underlying mechanisms. In vitro experiments demonstrated that the overexpression of miR-18a-5p facilitated the osteogenic differentiation of BMSCs, while the downregulation of miR-18a-5p yielded converse outcomes. Mechanistically, We employed bioinformatics techniques to screen out the target gene Notch2 of miR-18a-5p. Subsequently, dual-luciferase reporter gene assays and rescue experiments substantiated that miR-18a-5p promotes BMSC osteogenic differentiation by suppressing Notch2. Finally, miR-18a-5p was overexpressed via adenovirus injection into the femoral bone marrow cavity, with results demonstrating its capability to enhance osteogenic differentiation and alleviate PMOP symptoms. Our findings disclose that miR-18a-5p fosters osteogenic differentiation of BMSC by inhibiting Notch2, thereby offering novel targets and strategies for PMOP treatment.

Comparative metabolomics reveals serum metabolites changes in goats during different developmental stages.

Goats can provide meat, milk and skins for humans and are livestock with high economic benefits. However, despite their economic significance, the comprehensive analysis of goats' serum metabolic profile and its intricate alterations throughout their developmental journey remains conspicuously absent. To investigate the stage-specificity and dynamic change characteristics of metabolites during the growth and development of goats, this study compared the alterations in serum hormone levels and serum biochemical markers across different developmental stages of female goats (1, 60, 120 and 180 days old; n = 5). Additionally, a serum untargeted LC-MS metabolomics analysis was conducted. A total of 504 DAMs were identified with age. The results indicated that PE, PC, Lyso-PE, Lyso-PC and FAFHA may play important roles in lipid metabolism in goats after birth. Weighted gene co-expression network analysis (WGCNA) identified two metabolite modules (Turquoise and Yellow) and key metabolites within these modules that were significantly associated with phenotypic features. L-carnitine may be a metabolite related to muscle development in goats. The findings of this study demonstrate notable variations in serum metabolites across distinct developmental phases in goats. Lipids and organic acids play important roles in different developmental stages of goats.

Transcriptome analysis revealed the characteristics and functions of long non-coding RNAs in the hypothalamus during sexual maturation in goats.

The hypothalamus is an essential neuroendocrine area in animals that regulates sexual development. Long non-coding RNAs (lncRNAs) are hypothesized to regulate physiological processes related to animal reproduction. However, the regulatory mechanism by which lncRNAs participate in sexual maturity in goats is poorly known, particularly from birth to sexual maturation. In this study, RNAseq analysis was conducted on the hypothalamus of four developmental stages (1day (D1, n = 5), 2 months (M2, n = 5), 4 months (M4, n = 5), and 6 months (M6, n = 5)) of Jining grey goats. The results showed that a total of 237 differentially expressed lncRNAs (DELs) were identified in the hypothalamus. Among these, 221 DELs exhibited cis-regulatory effects on 693 target genes, while 24 DELs demonstrated trans-regulatory effects on 63 target genes. The target genes of these DELs are mainly involved in biological processes related to energy metabolism, signal transduction and hormone secretion, such as sphingolipid signaling pathway, adipocytokine signaling pathway, neurotrophic signaling pathway, glutamatergic synapse, P53 signaling pathway and GnRH signaling pathway. In addition, XR_001918477.1, TCONS_00077463, XR_001918760.1, and TCONS_00029048 and their potential target genes may play a crucial role in the process of goat sexual maturation. This study advances our understanding of lncRNA in hypothalamic tissue during sexual maturation in goats and will give a theoretical foundation for improving goat reproductive features.

Transcriptomic and metabolomic data of goat ovarian and uterine tissues during sexual maturation.

The ovaries and uterus are crucial reproductive organs in mammals, and their coordinated development ensures the normal development of sexual maturity and reproductive capacity. This study aimed to comprehensively capture the different physiological stages of the goat's sexual maturation by selecting four specific time points. We collected samples of ovarian and uterine tissues from five female Jining Gray goats at each time point: after birth (D1), 2-month-old (M2), 4-month-old (M4), and 6-month-old (M6). By combining transcriptomic sequencing of 40 samples (including rRNA-depleted RNA-seq libraries with 3607.8 million reads and miRNA-seq libraries with 444.0 million reads) and metabolomics analysis, we investigated the transcriptomic mechanisms involved in reproductive regulation in the ovary and uterus during sexual maturation, as well as the changes in metabolites and their functional potential. Additionally, we analyzed blood hormone indices and uterine tissue sections to examine temporal changes. These datasets will provide a valuable reference for the reproductive regulation of the ovary and uterus, as well as the regulation of metabolites during sexual maturation in goats.

Value of the virtual monoenergetic image from dual-layer dual-energy computed tomography enterography in the preoperative assessment of the internal penetrating complication of Crohn's disease.

BACKGROUND: To determine the utility of virtual-monoenergetic imaging (VMI) at low energy levels from contrast-enhanced dual-layer dual-energy (DLDE) computed tomography enterography (CTE) in the preoperative assessment of internal penetrating lesions of Crohn's disease (CD). MATERIALS AND METHODS: Thirty-eight patients with penetrating lesions of CD by surgery undergoing contrast-enhanced DLDE CTE were retrospectively included. Polyenergetic imaging (PEI) and VMIs at low energy levels [40-70 kiloelectron volts (keV)] with 10 keV intervals were reconstructed. The objective parameters of image quality [noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR)] and the subjective parameter of image quality [diagnostic performance of lesions (DPL), overall image quality(OIQ)] of PEI and all VMIs at the low energy level were compared to determine the VMI on the optimal energy level. The lesion detection capability between PEI and the optimal VMI was compared. RESULTS: VMI40 was determined to be the optimal VMI among all VMIs at the low energy level for owning the best image quality. No significant difference was found in the detecting capability in penetrating lesions between VMI40 and PEI (p = 1.0), whereas a significant difference was found in the detecting capability in the bowel origin of the penetrating lesions (p = 0.004), the involved organ or structure by the fistula (p = 0.016) and the orifice of the fistula connected to the involved organ or structure ( p = 0.031) between them. CONCLUSIONS: Compared to conventional PEI, VMI40 improves the detection capability in anatomical details of penetrating lesions of CD, helping colorectal surgeons rationalizing preoperative plans of internal penetrating lesions of CD.

Prediction of extracapsular extension in prostate cancer using the Likert scale combined with clinical and pathological parameters.

Abstract: This study aimed to investigate the independent clinical, pathological, and radiological factors associated with extracapsular extension in radical prostatectomy specimens and to improve the accuracy of predicting extracapsular extension of prostate cancer before surgery. Methods: From August 2018 to June 2023, the clinical and pathological data of 229 patients with confirmed prostate cancer underwent radical prostatectomy from The Second Hospital of Yinzhou. The patients' multiparametric magnetic resonance imaging data were graded using the Likert scale. The chi-square or independent-sample T-test was used to analyze the related factors for an extracapsular extension. Multivariate analysis was used to identify independent factors associated with extracapsular extension in prostate cancer. Additionally, receiver operating characteristic curve analysis was used to calculate the area under the curve and assess the diagnostic performance of our model. The clinical decision curve was used to analyze the clinical net income of Likert scale, biopsy positive rate, biopsy GG, and combined mode. Results: Of the 229 patients, 52 had an extracapsular extension, and 177 did not. Multivariate analysis showed that the Likert scale score, biopsy grade group and biopsy positive rate were independent risk factors for extracapsular extension in prostate cancer. The area under the curves for the Likert scale score, biopsy grade group, and biopsy positive rate were 0.802, 0.762, and 0.796, respectively. Furthermore, there was no significant difference in the diagnostic efficiency for extracapsular extension (P>0.05). However, when these three factors were combined, the diagnostic efficiency was significantly improved, and the area under the curve increased to 0.905 (P<0.05). In the analysis of the decision curve, The clinical net income of the combined model is obviously higher than that of Likert scale, biopsy positive rate, and biopsy GG. Conclusion: The Likert scale, biopsy grade group and biopsy positive rate are independent risk factors for extracapsular extension in prostate cancer, and their combination can significantly improve the diagnostic efficiency for an extracapsular extension.