Multi-Functional 2D Covalent Organic Frameworks with Diketopyrrolopyrrole as Electron Acceptor.

2D covalent organic framework (COF) materials with extended conjugated structure and periodic columnar π-arrays exhibit promising applications in organic optoelectronics. However, there is a scarcity of reports on optoelectronic COFs, mainly due to the lack of suitable π-skeletons. Here, two multi-functional optoelectronic 2D COFs DPP-TPP-COF and DPP-TBB-COF are constructed with diketopyrrolopyrrole as electron acceptor (A), and 1,3,6,8-tetraphenylpyrene and 1,3,5-triphenylbenzene as electron donor (D) through imine bonds. Both 2D COFs showed good crystallinities and AA stacking with a rhombic framework for DPP-TPP-COF and hexagonal one for DPP-TBB-COF, respectively. The electron D-A and ordered intermolecular packing structures endow the COFs with broad UV-vis absorptions and narrow bandgaps along with suitable HOMO/LUMO energy levels, resulting in multi-functional optoelectronic properties, including photothermal conversion, supercapacitor property, and ambipolar semiconducting behavior. Among them, DPP-TPP-COF exhibits a high photothermal conversion efficiency of 47% under 660 nm laser irradiation, while DPP-TBB-COF exhibits superior specific capacitance of 384 F g-1. Moreover, P-type doping and N-type doping are achieved by iodine and tetrakis(dimethylamino)ethylene on a single host COF, resulting in ambipolar semiconducting behavior. These results provide a paradigm for the application of multi-functional optoelectronic COF materials.

The Combination of Buchloe dactyloides Engelm and Biochar Promotes the Remediation of Soil Contaminated with Polycyclic Aromatic Hydrocarbons.

Polycyclic aromatic hydrocarbons (PAHs) cause serious stress to biological health and the soil environment as persistent pollutants. Despite the wide use of biochar in promoting soil improvement, the mechanism of biochar removing soil PAHs through rhizosphere effect in the process of phytoremediation remain uncertain. In this study, the regulation of soil niche and microbial degradation strategies under plants and biochar were explored by analyzing the effects of plants and biochar on microbial community composition, soil metabolism and enzyme activity in the process of PAH degradation. The combination of plants and biochar significantly increased the removal of phenanthrene (6.10%), pyrene (11.50%), benzo[a]pyrene (106.02%) and PAHs (27.10%) when compared with natural attenuation, and significantly increased the removal of benzo[a]pyrene (34.51%) and PAHs (5.96%) when compared with phytoremediation. Compared with phytoremediation, the combination of plants and biochar significantly increased soil nutrient availability, enhanced soil enzyme activity (urease and catalase), improved soil microbial carbon metabolism and amino acid metabolism, thereby benefiting microbial resistance to PAH stress. In addition, the activity of soil enzymes (dehydrogenase, polyphenol oxidase and laccase) and the expression of genes involved in the degradation and microorganisms (streptomyces, curvularia, mortierella and acremonium) were up-regulated through the combined action of plants and biochar. In view of the aforementioned results, the combined application of plants and biochar can enhance the degradation of PAHs and alleviate the stress of PAH on soil microorganisms.

Integrated physiological, transcriptomic and metabolomic analyses provide insights into phosphorus-mediated cadmium detoxification in Salix caprea roots.

Phosphorus (P) plays a crucial role in facilitating plant adaptation to cadmium (Cd) stress. However, the molecular mechanisms underlying P-mediated responses to Cd stress in roots remain elusive. This study investigates the effects of P on the growth, physiology, transcriptome, and metabolome of Salix caprea under Cd stress. The results indicate that Cd significantly inhibits plant growth, while sufficient P alleviates this inhibition. Under Cd exposure, P sufficiency resulted in increased Cd accumulation in roots, along with reduced oxidative stress levels (superoxide anion and hydrogen peroxide contents were reduced by 16.8% and 30.1%, respectively). This phenomenon can be attributed to the enhanced activities of antioxidant enzymes such as superoxide dismutase (SOD) and catalase (CAT), as well as increased levels of antioxidants including ascorbic acid (AsA) and flavonoids under sufficient P conditions. A total of 4208 differentially expressed genes (DEGs) and 552 differentially accumulated metabolites (DAMs) were identified in the transcriptomic and metabolomic analyses, with 2596 DEGs and 113 DAMs identified among treatments with different P levels under Cd stress, respectively. Further combined analyses reveal the potential roles of several pathways in P-mediated Cd detoxification, including flavonoid biosynthesis, ascorbate biosynthesis, and plant hormone signal transduction pathways. Notably, sufficient P upregulates the expression of genes including HMA, ZIP, NRAMP and CAX, all predicted to localize to the cell membrane. This may elucidate the heightened Cd accumulation under sufficient P conditions. These findings provide insights into the roles of P in enhancing plant resistance to Cd stress and improving of phytoremediation.

From gut to brain: understanding the role of microbiota in inflammatory bowel disease.

With the proposal of the "biological-psychological-social" model, clinical decision-makers and researchers have paid more attention to the bidirectional interactive effects between psychological factors and diseases. The brain-gut-microbiota axis, as an important pathway for communication between the brain and the gut, plays an important role in the occurrence and development of inflammatory bowel disease. This article reviews the mechanism by which psychological disorders mediate inflammatory bowel disease by affecting the brain-gut-microbiota axis. Research progress on inflammatory bowel disease causing "comorbidities of mind and body" through the microbiota-gut-brain axis is also described. In addition, to meet the needs of individualized treatment, this article describes some nontraditional and easily overlooked treatment strategies that have led to new ideas for "psychosomatic treatment".

BmSPP is a virus resistance gene in Bombyx mori.

Introduction: Signal peptide peptidase (SPP) is an intramembrane protease involved in a variety of biological processes, it participates in the processing of signal peptides after the release of the nascent protein to regulate the endoplasmic reticulum associated degradation (ERAD) pathway, binds misfolded membrane proteins, and aids in their clearance process. Additionally, it regulates normal immune surveillance and assists in the processing of viral proteins. Although SPP is essential for many viral infections, its role in silkworms remains unclear. Studying its role in the silkworm, Bombyx mori , may be helpful in breeding virus-resistant silkworms. Methods: First, we performed RT-qPCR to analyze the expression pattern of BmSPP. Subsequently, we inhibited BmSPP using the SPP inhibitor 1,3-di-(N-carboxybenzoyl-L-leucyl-L-leucylaminopropanone ((Z-LL)2-ketone) and downregulated the expression of BmSPP using CRISPR/Cas9 gene editing. Furthermore, we assessed the impact of these interventions on the proliferation of Bombyx mori nucleopolyhedrovirus (BmNPV). Results: We observed a decreased in the expression of BmSPP during viral proliferation. It was found that higher concentration of the inhibitor resulted in greater inhibition of BmNPV proliferation. The down-regulation of BmSPP in both in vivo and in vitro was found to affect the proliferation of BmNPV. In comparison to wild type silkworm, BmSPPKO silkworms exhibited a 12.4% reduction in mortality rate. Discussion: Collectively, this work demonstrates that BmSPP plays a negative regulatory role in silkworm resistance to BmNPV infection and is involved in virus proliferation and replication processes. This finding suggests that BmSPP servers as a target gene for BmNPV virus resistance in silkworms and can be utilized in resistance breeding programs.

Phosphorus deficiency-induced cell wall pectin demethylesterification enhances cadmium accumulation in roots of Salix caprea.

Regions affected by heavy metal contamination frequently encounter phosphorus (P) deficiency. Numerous studies highlight crucial role of P in facilitating cadmium (Cd) accumulation in woody plants. However, the regulatory mechanism by which P affects Cd accumulation in roots remains ambiguous. This study aims to investigate the effects of phosphorus (P) deficiency on Cd accumulation, Cd subcellular distribution, and cell wall components in the roots of Salix caprea under Cd stress. The results revealed that under P deficiency conditions, there was a 35.4% elevation in Cd content in roots, coupled with a 60.1% reduction in Cd content in shoots, compared to the P sufficiency conditions. Under deficient P conditions, the predominant response of roots to Cd exposure was the increased sequestration of Cd in root cell walls. The sequestration of Cd in root cell walls increased from 37.1% under sufficient P conditions to 66.7% under P deficiency, with pectin identified as the primary Cd binding site under both P conditions. Among cell wall components, P deficiency led to a significant 31.7% increase in Cd content within pectin compared to P sufficiency conditions, but did not change the pectin content. Notably, P deficiency significantly increased pectin methylesterase (PME) activity by regulating the expression of PME and PMEI genes, leading to a 10.4% reduction in the degree of pectin methylesterification. This may elucidate the absence of significant changes in pectin content under P deficiency conditions and the concurrent increase in Cd accumulation in pectin. Fourier transform infrared spectroscopy (FTIR) results indicated an increase in carboxyl groups in the root cell walls under P deficiency compared to sufficient P treatment. The results provide deep insights into the mechanisms of higher Cd accumulation in root mediated by P deficiency.

Determination of trace morphine and its metabolites in mouse urine using a TpBD functionalized bivalve magnetic nano-adsorbent.

In this study, a new type of covalent organic framework (TpBD) functionalized bivalved magnetic microsphere (TpBD-DS MNS) adsorbent was applied for the enrichment and detection of trace morphine and its metabolites in mouse urine. The main factors affecting the efficiency of magnetic solid phase extraction were optimized, and the optimal MSPE conditions were obtained. Combined with the UPLC-MS/MS technique, a new method for determining trace morphine and its metabolites in urine was established. The detection (LOD) and quantification (LOQ) limits for morphine and its metabolites ranged from 0.16 pg mL-1 to 0.53 pg mL-1 and 0.26 pg mL-1 to 1.25 pg mL-1, respectively. The recovery of the methods ranged from 87.4-97.3%, and the RSD was less than 5%. By employing this methodology, we successfully obtained the temporal change curve of morphine and its metabolites in mouse urine through collection and measurement post intravenous administration of morphine. This approach not only presents a novel means for investigating pharmacokinetics and drug monitoring but also demonstrates significant potential in the fields of forensic toxicology and drug abuse surveillance.

TPA: Two-stage progressive attention segmentation framework for hepatocellular carcinoma on multi-modality MRI.

BACKGROUND: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the diagnosis and measurement of hepatocellular carcinoma (HCC). The multi-modality information contained in the multi-phase images of DCE-MRI is important for improving segmentation. However, this remains a challenging task due to the heterogeneity of HCC, which may cause one HCC lesion to have varied imaging appearance in each phase of DCE-MRI. In particular, some phases exhibit inconsistent sizes and boundaries will result in a lack of correlation between modalities, and it may pose inaccurate segmentation results. PURPOSE: We aim to design a multi-modality segmentation model that can learn meaningful inter-phase correlation for achieving HCC segmentation. METHODS: In this study, we propose a two-stage progressive attention segmentation framework (TPA) for HCC based on the transformer and the decision-making process of radiologists. Specifically, the first stage aims to fuse features from multi-phase images to identify HCC and provide localization region. In the second stage, a multi-modality attention transformer module (MAT) is designed to focus on the features that can represent the actual size. RESULTS: We conduct training, validation, and test in a single-center dataset (386 cases), followed by external test on a batch of multi-center datasets (83 cases). Furthermore, we analyze a subgroup of data with weak inter-phase correlation in the test set. The proposed model achieves Dice coefficient of 0.822 and 0.772 in the internal and external test sets, respectively, and 0.829, 0.791 in the subgroup. The experimental results demonstrate that our model outperforms state-of-the-art models, particularly within subgroup. CONCLUSIONS: The proposed TPA provides best segmentation results, and utilizing clinical prior knowledge for network design is practical and feasible.

Cucurbituril-based supramolecular host-guest complexes: single-crystal structures and dual-state fluorescence enhancement.

Two supramolecular complexes were prepared using cucurbiturils [CBs] as mediators and a four-armed p-xylene derivative (M1) as a guest molecule. The single crystals of these two complexes were obtained and successfully analyzed by single-crystal X-ray diffraction (SCXRD). An unexpected and intriguing 1 : 2 self-assembly arrangement between M1 and CB[8] was notably uncovered, marking its first observation. These host-guest complexes exhibit distinctive photophysical properties, especially emission behaviors. Invaluable insights can be derived from these single-crystal structures. The precious single-crystal structures provide both precise structural information regarding the supramolecular complexes and a deeper understanding of the intricate mechanisms governing their photophysical properties.

Diagnostic performance of MRI for residual or recurrent hepatocellular carcinoma after locoregional treatment according to contrast agent type: a systematic review and meta­analysis.

PURPOSE: The ideal contrast agent for imaging patients with hepatocellular carcinoma (HCC) following locoregional therapies (LRT) remains uncertain. We conducted a meta-analysis to assess the diagnostic performance of magnetic resonance imaging with extracellular contrast agent (ECA-MRI) and hepatobiliary agent (EOB-MRI) in detecting residual or recurrence HCC following LRT. METHODS: Original studies comparing the diagnostic performance of ECA-MRI and EOB-MRI were systematically identified through comprehensive searches in PubMed, EMBASE, Cochrane Library and Web of Science databases. The pooled sensitivity and specificity of ECA-MRI and EOB-MRI were calculated using a bivariate-random-effects model. Subgroup-analyses were conducted to compare the diagnostic performance of ECA-MRI and EOB-MRI according to different variables. Meta-regression analysis was employed to explore potential sources of study heterogeneity. RESULTS: A total of 15 eligible studies encompassing 803 patients and 1018 lesions were included. Comparative analysis revealed no significant difference between ECA-MRI and EOB-MRI in the overall pooled sensitivity (87% vs. 79%) and specificity (92% vs. 96%) for the detection of residual or recurrent HCC after LRT (P = 0.41), with comparable areas under the HSROC of 0.95 and 0.92. Subgroup analyses indicated no significant diagnostic performance differences between ECA-MRI and EOB-MRI according to study design, type of LRT, most common etiology of liver disease, baseline lesion size, time of post-treated examination and MRI field strength (All P > 0.05). CONCLUSION: ECA-MRI exhibited overall comparable diagnostic performance to EOB-MRI in assessing residual or recurrent HCC after LRT.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study.

BACKGROUND: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival. MATERIALS AND METHODS: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test. RESULTS: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05). CONCLUSION: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Effects of Low-Osmolar Contrast Media on Long-Term Renal Impairment After Coronary Angiography: Iohexol Versus Iopromide.

To investigate the long-term effects of 2 commonly used low-osmolar contrast media, iohexol and iopromide, on renal function and survival in patients who underwent coronary angiography. A total of 14,141 cardiology patients from 2006 to 2013 were recruited, of whom 1,793 patients (679 patients on iohexol and 1,114 on iopromide) were evaluated for long-term renal impairment and 5,410 patients (1,679 patients on iohexol and 3,731 on iopromide) were admitted for survival analyses spanning as long as 15 years. Univariate and multivariate logistic regression were used to explore the risk factors for long-term renal impairment. Cox proportional hazard regression was used to investigate the risk factors affecting survival. Propensity score matching and inverse probability of treatment weighting were applied to balance the baseline clinical characteristics. Patients receiving iohexol demonstrated a greater occurrence of renal impairment compared with those who received iopromide. Such difference remained consistent both before and after propensity score matching or inverse probability of treatment weighting, with a statistical significance of p <0.05. Among clinical variables, receiving contrast-enhanced contrast tomography/magnetic resonance imaging during follow-up, antihypertensive medication usage, presence of proteinuria, and anemia were identified as risk factors for long-term renal impairment (p = 0.041, 0.049, 0.006, and 0.029, respectively). During survival analyses, the difference was insignificant after propensity score matching and inverse probability of treatment weighting. In conclusion, administration of iohexol was more likely to induce long-term renal impairment than iopromide, particularly among patients diagnosed with anemia and proteinuria and those taking antihypertensive medication and with additional contrast exposure. The all-cause mortality, however, showed no significant difference between iohexol and iopromide administration.

Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk.

Background A better understanding of the association between liver MRI proton density fat fraction (PDFF) and liver diseases might support the clinical implementation of MRI PDFF. Purpose To quantify the genetically predicted causal effect of liver MRI PDFF on liver disease risk. Materials and Methods This population-based prospective observational study used summary-level data mainly from the UK Biobank and FinnGen. Mendelian randomization analysis was conducted using the inverse variance-weighted method to explore the causal association between genetically predicted liver MRI PDFF and liver disease risk with Bonferroni correction. The individual-level data were downloaded between August and December 2020 from the UK Biobank. Logistic regression analysis was performed to validate the association between liver MRI PDFF polygenic risk score and liver disease risk. Mediation analyses were performed using multivariable mendelian randomization. Results Summary-level and individual-level data were obtained from 32 858 participants and 378 436 participants (mean age, 57 years ± 8 [SD]; 203 108 female participants), respectively. Genetically predicted high liver MRI PDFF was associated with increased risks of malignant liver neoplasm (odds ratio [OR], 4.5; P < .001), alcoholic liver disease (OR, 1.9; P < .001), fibrosis and cirrhosis of the liver (OR, 3.0; P < .004), fibrosis of the liver (OR, 3.6; P = .002), cirrhosis of the liver (OR, 3.8; P < .001), nonalcoholic steatohepatitis (OR, 7.7; P < .001), and nonalcoholic fatty liver disease (NAFLD) (OR, 4.4; P < .001). Individual-level evidence supported these associations after grouping participants based on liver MRI PDFF polygenic risk score (all P < .004). The mediation analysis indicated that genetically predicted high-density lipoprotein cholesterol, type 2 diabetes mellitus, and waist-to-hip ratio (mediation effects, 25.1%-46.3%) were related to the occurrence of fibrosis and cirrhosis of the liver, cirrhosis of the liver, and NAFLD at liver MRI PDFF (all P < .05). Conclusion This study provided evidence of the association between genetically predicted liver MRI PDFF and liver health. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Reeder and Starekova and Monsell in this issue.

Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma.

Background & Aims: Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC. Methods: Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated. Results: The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p <0.05). By contrast, anatomic resection had no impact on RFS in patients at model-predicted low or high risk (both p >0.05). Conclusions: The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection. Impact and implications: MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

A Novel Approach for Screening Sericin-Derived Therapeutic Peptides Using Transcriptomics and Immunoprecipitation.

With the demand for more efficient and safer therapeutic drugs, targeted therapeutic peptides are well received due to their advantages of high targeting (specificity), low immunogenicity, and minimal side effects. However, the conventional methods of screening targeted therapeutic peptides in natural proteins are tedious, time-consuming, less efficient, and require too many validation experiments, which seriously restricts the innovation and clinical development of peptide drugs. In this study, we established a novel method of screening targeted therapeutic peptides in natural proteins. We also provide details for library construction, transcription assays, receptor selection, therapeutic peptide screening, and biological activity analysis of our proposed method. This method allows us to screen the therapeutic peptides TS263 and TS1000, which have the ability to specifically promote the synthesis of the extracellular matrix. We believe that this method provides a reference for screening other drugs in natural resources, including proteins, peptides, fats, nucleic acids, and small molecules.