Direct Comparative Studies Revealing the Contribution of TADF Activity of Organic Emitters Towards Efficient Electrochemiluminescence.

Electrochemiluminescence (ECL) featuring thermally activated delayed fluorescence (TADF) properties has attracted considerable interest, showcasing their potential for 100% exciton harvesting, which marks a significant advancement in the realm of organic ECL. However, the challenge of elucidating the precise contribution of TADF to the enhanced ECL efficiency arises due to the lack of comparative studies of organic compounds with or without efficient TADF properties. In this study, we present four carbazole-benzonitrile (Cz-BN) molecules possessing similar chemical structures and comparable exchange energy (ΔEST). Despite their comparable properties, these compounds exhibited varying TADF efficiencies, warranting a closer examination of their underlying structural and electronic characteristics governing the optical properties. Consequently, intense ECL emission was only observed from 4CzBN with a remarkable TADF efficiency, underscoring the substantial difference in the ECL signal among molecules with comparable ΔEST and similar spectral properties but varying TADF activity.

Thoracic spine infection caused by Pseudomonas fluorescens: A case report and review of literature.

BACKGROUND: The clinical incidence of spinal infection is gradually increasing, and its onset is insidious, easily leading to missed diagnosis and misdiagnosis, which may lead to serious complications such as nervous system dysfunction, spinal instability and/or deformity, and cause a huge burden on society and families. Early identification of the causative agent and precision medicine will greatly reduce the suffering of patients. At present, the main pathogenic bacteria that cause spinal infection are Staphylococcus aureus, Streptococcus, Pneumococcus, Escherichia coli, and Klebsiella. There are no reports of spinal infection caused by Pseudomonas fluorescens. CASE SUMMARY: We report a 32-year-old female patient with spinal infection. She presented with flank pain, initially thought to be bone metastases or bone tuberculosis, and had a family background of tumors. Her clinical features and changes in imaging and laboratory tests led to the suspicion of thoracic spine infection. Histopathology of the lesion showed inflammation, tissue culture of the lesion was negative several times, and the possible pathogen - Pseudomonas fluorescens was found after gene sequencing of the lesion. The patient recovered completely after a full course of antibiotic treatment. CONCLUSION: This report increases the range of pathogens involved in spinal infections, highlights the unique advantages of gene sequencing technology in difficult-to-diagnose diseases, and validates conservative treatment with a full course of antibiotics for spinal infections without complications.

A Review of Chloride Penetration of Recycled Concrete with Enhancement Treatment and Service Life Prediction.

The application of recycled coarse aggregate (RA) in structural concrete can save non-renewable resources and reduce land occupation. Developing comprehensive knowledge of chloride penetration and service life modeling of recycled coarse aggregate concrete (RAC) is a prerequisite for practice. However, compared with the natural aggregate concrete (NAC), the inferior durability performance, especially chloride penetration resistance, of RAC hinders its application in structural concrete. Therefore, many RAC performance enhancement methods have been proposed. This paper presents a holistic review focused on the chloride penetration of RAC with/without enhancement methods and service life prediction. The current RAC performance enhancement methods are introduced. The improvement effect of the corresponding enhancement methods on the chloride penetration resistance of RAC are discussed and analyzed in turn. Based on the reviewed data on the chloride diffusion coefficient, the modification efficiencies of assorted enhancement methods are summarized. With the hope of promoting RAC application in structural concrete, the current literature on chloride-ingress-based service life prediction for RAC is also overviewed. In addition, the typical influencing factors on chloride transport properties are also discussed, i.e., RA quality. It can be concluded that enhancement techniques can effectively improve the chloride penetration resistance of RAC. The old mortar enhancement or removal methods can improve the chloride penetration resistance by 15-30%, depending on the specific treatment measures. The modification efficiency of the modifier material depends on the specific type and content of the incorporated substance, which ranges from approximately 5% to 95%. The estimated service life of RAC structures decreases with the increasing RA replacement ratio. Finally, concluding remarks are provided concerning future research on the chloride transport behavior of RAC.

Nomograms to predict long-term survival for patients with gallbladder carcinoma after resection.

BACKGROUND: Surgical resection remains the primary treatment option for gallbladder carcinoma (GBC). However, there is a pressing demand for prognostic tools that can refine patients' treatment choices and tailor personalized therapies accordingly. AIMS: The nomograms were constructed using the data of a training cohort (n = 378) of GBC patients at Eastern Hepatobiliary Surgery Hospital (EHBH) between 2008 and 2018. The model's performance was validated in GBC patients (n = 108) at Guangzhou Centre from 2007 to 2018. METHODS AND RESULTS: The 5-year overall survival (OS) rate in the training cohort was 24.4%. Multivariate analyses were performed using preoperative and postoperative data to identify independent predictors of OS. These predictors were then incorporated into preoperative and postoperative nomograms, respectively. The C-index of the preoperative nomogram was 0.661 (95% CI, 0.627 to 0.694) for OS prediction and correctly delineated four subgroups (5-year OS rates: 48.1%, 19.0%, 15.6%, and 8.1%, p < 0.001). The C-index of the postoperative nomogram was 0.778 (95%CI, 0.756 -0.800). Furthermore, this nomogram was superior to the 8th TNM system in both C-index and the net benefit on decision curve analysis. The results were externally validated. CONCLUSION: The two nomograms showed an optimally prognostic prediction in GBC patients after curative-intent resection.

Cyanuric Acid-Functionalized Perovskite Nanocrystals toward Low Interface Impedance, High Environmental Stability, and Superior Electrochemiluminescence.

Perovskite nanocrystals (PNs) have received much attention as luminescence materials in the field of electrochemiluminescence (ECL). However, as one key factor for determining the optoelectronic properties of the surface state of PNs, the surface passivation layer of PNs has enormous difficulty in simultaneously meeting the requirements of high ECL efficiency, conductivity, and stability. Herein, an effective surface modification strategy with cyanuric acid (CA) is used to solve such issue. As confirmed, the CA molecules are chemically anchored onto the surface of PNs via the Lewis interaction between π electrons of the triazine ring and the empty orbit of Pb2+. Benefiting from the above interaction, the electrochemical impedance of PNs is decreased greatly without the loss of light-emitting efficiency. Moreover, the stability of PNs under O2 exposure is improved by almost sixfold. These improvements are confirmed to be beneficial for enhancing the ECL behaviors of PNs under electrochemical operation. Upon cathode ECL driving conditions in aqueous media, the ECL intensity and efficiency of PNs are increased to 200 and 170%, respectively. This work provides a new modification strategy to holistically improve the ECL performance of PNs, which is instructive to exploring robust perovskite nanomaterials for electrochemical applications.

Phosphorus fertiliser application mitigates the negative effects of microplastic on soil microbes and rice growth.

Soil microplastics (MPs) have attracted widespread attention recently. Most studies have explored how soil MPs affect the soil's physicochemical parameters, matter circulation, and soil microbial community assembly. Similarly, a key concern in agricultural development has been the use of phosphorus (P) fertiliser, which is essential for plant health and development. However, the relationship between MPs and phosphate fertilisers and their effects on the soil environment and plant growth remains elusive. This study assessed the influence of adding low-density polyethylene MPs (1%) with different phosphate fertiliser application rates on microbial communities and rice biomass. Our results showed that MPs changed the structure of soil bacterial and phoD-harbouring microbial communities in the treatment with P fertiliser at the same level and suppressed the interactions of phoD-harbouring microorganisms. In addition, we found that MPs contamination inhibited rice growth; however, the inclusion of P fertiliser in MP-contaminated soils reduced the inhibitory action of MPs on rice growth, probably because the presence with P fertiliser promoted the uptake of NO3--N by rice in MP-contaminated soils. Our results provide further insights into guiding agricultural production, improving agricultural management, and rationally applying phosphate fertilisers in the context of widespread MPs pollution and global P resource constraints.

HFD-exacerbated Metabolic Side Effects of Olanzapine Are Suppressed by ER Stress Inhibitor.

OBJECTIVE: Numerous schizophrenic patients are suffering from obesity primarily attributed to antipsychotic medication and poor dietary habits. This study investigated the progressive deterioration of olanzapine-induced metabolic disorders in the presence of a high-fat diet (HFD) and explored the involvement of endoplasmic reticulum (ER) stress. METHODS: Female Sprague-Dawley rats fed on a standard chow diet or HFD were treated with olanzapine (3 mg/kg/day) and the ER stress inhibitor 4-phenylbutyric acid (4-PBA, 1 and 0.5 g/kg/day) for 8 days. Changes in body weight, food intake, and plasma lipids were assessed. Hepatic fat accumulation was evaluated using oil red O staining. Western blotting and immunofluorescence assays were employed to examine the expression of ER stress markers, NOD-like receptor pyrin domain-containing protein 3 (NLRP3), and proopiomelanocortin (POMC) in the hypothalamus or liver. RESULTS: Compared to olanzapine alone, olanzapine+HFD induced greater weight gain, increased hyperlipidemia, and enhanced hepatic fat accumulation (P<0.05). Co-treatment with 4-PBA exhibited a dose-dependent inhibition of these effects (P<0.05). Further mechanistic investigations revealed that olanzapine alone activated ER stress, upregulated NLRP3 expression in the hypothalamus and liver, and downregulated hypothalamic POMC expression. The HFD exacerbated these effects by 50%-100%. Moreover, co-administration of 4-PBA dose-dependently attenuated the olanzapine+HFD-induced alterations in ER stress, NLRP3, and POMC expression in the hypothalamus and liver (P<0.05). CONCLUSION: HFD worsened olanzapine-induced weight gain and lipid metabolic disorders, possibly through ER stress-POMC and ER stress-NLRP3 signaling. ER stress inhibitors could be effective in preventing olanzapine+HFD-induced metabolic disorders.

Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging.

BACKGROUND: Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. RESULTS: Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. CONCLUSIONS: This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.

Effect of different types of biochar on soil properties and functional microbial communities in rhizosphere and bulk soils and their relationship with CH4 and N2O emissions.

Biochar as an agricultural soil amendment plays vital roles in mediating methane (CH4) and nitrous oxide (N2O) emissions in soils. The link between different types of biochar, bulk soil, and rhizosphere microbial communities in relation to CH4 and N2O emissions is being investigated in this study. The rice pot experiment was conducted using biochar at two temperatures (300°C and 500°C) in combination with three biochar levels (0, 2, 10% w/w). Soil properties and the abundance of genes associated with CH4 and N2O emissions from both rhizosphere and bulk soils were investigated. The study also aimed to examine the structure of microbial communities (pmoA, nosZ) in rhizosphere and bulk soils whereas CH4 and N2O emissions were monitored while growing rice. Results showed that biochar at 300°C and 10% incorporation significantly increased the CH4 emissions by up to 59% rise compared to the control group. Random Forest analysis revealed that the ratio of mcrA/pmoA along with the abundance of mcrA from both rhizosphere and bulk soils, the abundance of AOA, TN, DOC, and the community composition of pmoA-harboring microorganisms from both bulk and rhizosphere soils were important predictors of CH4 emissions. Therefore, the ratio of mcrA/pmoA in rhizosphere soil and the abundance of AOA in bulk soil were the main factors influencing CH4 emissions. Variation Partitioning Analysis (VPA) results indicated that the effects of these factors on bulk soil were 9% of CH4 emissions variations in different treatments, which contributed more than rhizosphere soils' factors. Moreover, random forest analysis results indicated that the abundance of AOB in bulk soil was the most important predictor influencing N2O emissions. The VPA result revealed that the factors in rhizosphere soil could explain more than 28% of the variations in N2O emissions. Our study highlights that rhizosphere soil has a more significant effect than bulk soil on N2O production. Our findings further the understanding of the link between bulk and rhizosphere attributes, and their impact on CH4 and N2O emissions in paddy soils. In summary, we recommend the application of biochar at 500°C and 2% incorporation rate for agricultural production in the area.

Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows.

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigation. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy. To address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. First, the fruit tree trunk is identified based on the improved YOLOv4 model; second, the camera coordinates of the tree trunk are calculated using the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined, the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall rate of the improved YOLOv4 model for fruit tree trunk detection are 5.92% and 7.91% higher, respectively, than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows and provide a reference for the autonomous visual navigation of orchard robots.

N6-methyladenosine modification promotes hepatocarcinogenesis through circ-CDYL-enriched and EpCAM-positive liver tumor-initiating exosomes.

CircRNAs play multiple roles in a variety of cellular processes. We found that Circ-CDYL is highly enriched in early HCC plasma exosomes. Moreover, EpCAM+ HCC cells and exosomes had significant Circ-CDYL levels. We postulated that Circ-CDYL-enriched and EpCAM-positive exosomes would function as liver tumor-initiating exosomes (LTi-Exos). As predicted, intercellular transfer of LTi-Exos activates the HDGF-PI3K-AKT-mTOR and HIF1AN-NOTCH2 axes in recipient cells, promoting malignancy. Upstream, we found that the N6-methyladenosine (m6A) modification of Circ-CDYL exerted its action in HCC cells through a dual mechanism. First, it stimulated back-splicing processes via YTHDC1 to promote Circ-CDYL biogenesis. Second, it facilitates the active sorting of Circ-CDYL into exosomes via hnRNPA2/B1. Clinically, the combination of LTi-Exos and plasma alpha-fetoprotein (AFP) provides a promising early diagnostic biomarker for HCC with an AUC of 0.896. This study highlights the effect and mechanism by which m6A modification promotes hepatocarcinogenesis via modulation of the tumor microenvironment by LTi-Exos.

Intelligent Responsive Nanoparticles with Multilevel Triggered Drug Penetration for Tumor Photochemotherapy.

Nanomedicines have contradictory size requirements to overcome systemic barriers and penetrate the tumor extracellular matrix (ECM). Larger-sized nanoparticles (50-200 nm) exhibit prolonged blood circulation half-life and improved tumor enrichment, while small-sized nanoparticles (4-20 nm) easily penetrate deep tumor tissues. Therefore, the development of intelligent responsive nanomedicine systems can not only increase nanodrug tumor accumulation but also improve their penetration into the ECM. Herein, we propose an intelligent responsive nanoparticle triggered by near-infrared light (NIR). The nanoparticle was constructed by a temperature-sensitive liposome (TSL) encapsulating ultrasmall melanin nanoparticles (MNPs) loaded with doxorubicin (MNP/doxorubicin (DOX)@TSL). When exposed to NIR irradiation, the tailor-made nanoparticles not only effectively ablated the tumor cells around blood vessels but also destroyed the structural integrity and released loaded ultrasmall MNP/DOX (<10 nm) to promote deep tumor penetration and enhance interior tumor cell killing. This NIR-triggered intelligent nanoparticle successfully integrated photothermal therapy (PTT) for perivascular tumor cells and chemotherapy for deep tumor cell inhibition. The in vivo results showed remarkable tumor regression in 4T1 breast tumor-bearing mice by 74.2%. This controllable size switchable nanosystem with efficient tumor accumulation and penetration has shown great potential in improving synergistic antitumor effects of photochemotherapy.

Association of inactivated COVID-19 vaccination with maternal coagulation function in early pregnancy.

Reports of rare but severe thrombotic events after receiving some COVID-19 vaccines brought concerns for the possibility of vaccine-induced coagulation abnormality. However, no study has reported the impacts of COVID-19 vaccination on coagulation function in pregnant women. We aimed to explore whether vaccination with inactivated COVID-19 vaccines before pregnancy was associated with coagulation changes in pregnant women. We conducted a retrospective cohort study in a tertiary-care hospital in Shanghai, China. A total of 5166 pregnant women were included, of whom 2721 (52.7%) completed vaccination before conception. Compared with unvaccinated women, the mean serum levels of prothrombin time (PT) and fibrinogen (FIB) were lower in vaccinated women by 0.09 (ß = -0.09, 95% confidence interval [CI], -0.13, -0.05) mg/L and 0.11 (ß = -0.11, 95% CI, -0.15, -0.07) mg/L, and the mean D-Dimer (D-D) levels were higher by 0.12 (ß = 0.12, 95% CI, 0.09, 0.15) mg/L. However, no significant association was observed between COVID-19 vaccination and serum levels of activated partial thromboplastin time (APTT), fibrinogen degradation product (FDP) or thrombin time (TT). Our findings suggested that inactivated COVID-19 vaccination before conception resulted in a small change in maternal coagulation function, but this might not have clinical significance.

Effect of particle sizes of biochar on CO2 emissions in a poplar plantation of ancient Yellow River channel, China.

Forest soil is a vital pool of organic carbon, which is sensitive to management. Biochar addition could change the CO2 emissions from soil, but its effects are still ambiguous. Moreover, the impacts of particle sizes of biochar on CO2 emissions are still unknown. In this study, a series of field experiments were conducted to investigate the effects of biochar addition on CO2 emissions in a poplar plantation (Populus nigra), China. Biochar with two application rates of (10 and 50 t/ha) and three particle sizes (3-1 mm, 1-0.1 mm, and <0.1 mm) was applied into the surface soil (0-10 cm), and the soil without biochar was set as control. The results showed that a high level of fine biochar addition (1-0.1 mm and <0.1 mm) had similar and positive effects on CO2 emissions by increasing the contents of soil ammonium, available phosphorus, easily oxidizable carbon, soil moisture, soil capillary pore, and the activity of ß-glucosidase. However, biochar addition (1-0.1 mm and <0.1 mm) reduced the bioavailability of dissolved organic carbon (DOC), producing a negative relationship between DOC content and CO2 emissions. This investigation highlights the importance of biochar with different particle sizes in adjusting CO2 emissions from temperate soils.

Market framing bias and cross-sectional stock returns.

This paper introduces the market framing bias (MFB): a framing effect that affects the return-risk tradeoff under different frameworks of aggregate market losses and profits, which is measured by the absolute difference between betas in the rising and falling markets. The paper finds that the MFB can predict lower future stock return on the cross-section. Specifically, after controlling for various firm-specific characteristics, this predictive power of the FMB declines over time. Furthermore, the predictive power of the FMB is stable in the short term even after controlling for various pricing factors and firm-specific characteristics.

WIF1 promoter hypermethylation induce endometrial carcinogenesis through the Wnt/ß-catenin signaling pathway.

This study explores the mechanism underlying WIF1 promoter methylation and its relationship with the pathogenesis of endometrial carcinoma. WIF1 promoter methylation was detected using methylation-specific polymerase chain reaction (MSP). WIF1 expression was examined through qRT-PCR and western blotting. Furthermore, 5-aza-2'-deoxycytidine (5-Aza) was used to demethylate the WIF1 promoter. The roles of WIF1 were investigated using in vitro loss- and gain-of-function assays. Xenograft models were used to analyze WIF1 expression and downstream genes, and results were confirmed using immunofluorescence and western blotting. WIF1 promoter methylation in endometrial cancer cells was significantly higher than that in normal cells, but the WIF1 mRNA and protein levels were reduced. The expression of WIF1 increased significantly after 5-Aza treatment (p < .05). Thus, 5-Aza treatment can inhibit the proliferation of endometrial cancer cells and induce apoptosis, while knockdown of WIF1 significantly inhibits the effects of 5-Aza. 5-Aza treatment can also inhibit Wnt pathway genes, including phosphorylation of ß-catenin protein, c-Myc, and CyclinD1, inhibit downstream functional genes, and activate the tumor suppressor gene APC, which can be blocked by WIF1 knockdown in endometrial carcinoma cells. Finally, 5-Aza inhibited the proliferation of subcutaneous tumor-bearing nude mice with endometrial cancer cells, but the effect was weaker than that of WIF1 overexpression. Our research shows that WIF1 promoter hypermethylation may promote the progression of endometrial cancer by downregulating WIF1 expression, activating the Wnt/ß-catenin pathway, and promoting proliferation and inhibiting apoptosis. WIF1 may be a potential biological target for gene therapy and drug development for the treatment of endometrial cancer.

Enhancing Unsupervised Anomaly Detection With Score-Guided Network.

Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems. Owing to the limited number of anomaly labels in these complex systems, unsupervised anomaly detection methods have attracted great attention in recent years. Two major challenges faced by the existing unsupervised methods are as follows: 1) distinguishing between normal and abnormal data when they are highly mixed together and 2) defining an effective metric to maximize the gap between normal and abnormal data in a hypothesis space, which is built by a representation learner. To that end, this work proposes a novel scoring network with a score-guided regularization to learn and enlarge the anomaly score disparities between normal and abnormal data, enhancing the capability of anomaly detection. With such score-guided strategy, the representation learner can gradually learn more informative representation during the model training stage, especially for the samples in the transition field. Moreover, the scoring network can be incorporated into most of the deep unsupervised representation learning (URL)-based anomaly detection models and enhances them as a plug-in component. We next integrate the scoring network into an autoencoder (AE) and four state-of-the-art models to demonstrate the effectiveness and transferability of the design. These score-guided models are collectively called SG-Models. Extensive experiments on both synthetic and real-world datasets confirm the state-of-the-art performance of SG-Models.

Deep learning-based classification model for GPR151 activator activity prediction.

BACKGROUND: GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of metabolic disorders has been demonstrated in several studies, highlighting the demand to explore its activators further. Activity prediction serves as a vital preliminary step in drug discovery, which is both costly and time-consuming. Thus, the development of reliable activity classification model has become an essential way in the process of drug discovery, aiming to enhance the efficiency of virtual screening. RESULTS: We propose a learning-based method based on feature extractor and deep neural network to predict the activity of GPR151 activators. We first introduce a new molecular feature extraction algorithm which utilizes the idea of bag-of-words model in natural language to densify the sparse fingerprint vector. Mol2vec method is also used to extract diverse features. Then, we construct three classical feature selection algorithms and three types of deep learning model to enhance the representational capacity of molecules and predict activity label by five different classifiers. We conduct experiments using our own dataset of GPR151 activators. The results demonstrate high classification accuracy and stability, with the optimal model Mol2vec-CNN significantly improving performance across multiple classifiers. The svm classifier achieves the best accuracy of 0.92 and F1 score of 0.76 which indicates promising applications for our method in the field of activity prediction. CONCLUSION: The results suggest that the experimental design of this study is appropriate and well-conceived. The deep learning-based feature extraction algorithm established in this study outperforms traditional feature selection algorithm for activity prediction. The model developed can be effectively utilized in the pre-screening stage of drug virtual screening.

Liver resection versus liver transplantation for hepatocellular carcinoma within the Milan criteria based on estimated microvascular invasion risks.

Background: Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) may optimize individualized treatment decision-making. This study aimed to investigate the prognostic differences between HCC patients undergoing liver resection (LR) and liver transplantation (LT) based on predicted MVI risks. Methods: We analysed 905 patients who underwent LR, including 524 who underwent anatomical resection (AR) and 117 who underwent LT for HCC within the Milan criteria using propensity score matching. A nomogram model was used to predict preoperative MVI risk. Results: The concordance indices of the nomogram for predicting MVI were 0.809 and 0.838 in patients undergoing LR and LT, respectively. Based on an optimal cut-off value of 200 points, the nomogram defined patients as high- or low-risk MVI groups. LT resulted in a lower 5-year recurrence rate and higher 5-year overall survival (OS) rate than LR among the high-risk patients (23.6% vs 73.2%, P < 0.001; 87.8% vs 48.1%, P < 0.001) and low-risk patients (19.0% vs 45.7%, P < 0.001; 86.5% vs 70.0%, P = 0.002). The hazard ratios (HRs) of LT vs LR for recurrence and OS were 0.18 (95% confidence interval [CI], 0.09-0.37) and 0.12 (95% CI, 0.04-0.37) among the high-risk patients and 0.37 (95% CI, 0.21-0.66) and 0.36 (95% CI, 0.17-0.78) among the low-risk patients. LT also provided a lower 5-year recurrence rate and higher 5-year OS rate than AR among the high-risk patients (24.8% vs 63.5%, P = 0.001; 86.7% vs 65.7%, P = 0.004), with HRs of LT vs AR for recurrence and OS being 0.24 (95% CI, 0.11-0.53) and 0.17 (95% CI, 0.06-0.52), respectively. The 5-year recurrence and OS rates between patients undergoing LT and AR were not significantly different in the low-risk patients (19.4% vs 28.3%, P = 0.129; 85.7% vs 77.8%, P = 0.161). Conclusions: LT was superior to LR for patients with HCC within the Milan criteria with a predicted high or low risk of MVI. No significant differences in prognosis were found between LT and AR in patients with a low risk of MVI.

Development and validation of prognostic dynamic nomograms for hepatitis B Virus-related hepatocellular carcinoma with microvascular invasion after curative resection.

Background and Aim: The prediction models of postoperative survival for hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) have not been well established. The study objective was the development of nomograms to predict disease recurrence and overall survival (OS) in these patients. Methods: Data were obtained from 1046 HBV-related MVI-positive HCC patients who had undergone curative resection from January 2014 to December 2017. The study was approved by the Eastern Hepatobiliary Surgery Hospital and Jinling Hospital ethics committee, and patients provided informed consent for the use of their data. Nomograms for recurrence and OS were created by Cox regression model in the training cohort (n=530). The modes were verified in an internal validation cohort (n= 265) and an external validation cohort (n= 251). Results: The nomograms of recurrence and OS based on preoperative serological indicators (HBV-DNA, neutrophil-lymphocyte ratio, a-fetoprotein), tumor clinicopathologic features (diameter, number), surgical margin and postoperative adjuvant TACE achieved high C-indexes of 0.722 (95% confidence interval [CI], 0.711-0.732) and 0.759 (95% CI, 0.747-0.771) in the training cohort, respectively, which were significantly higher than conventional HCC staging systems (BCLC, CNLC, HKLC).The nomograms were validated in the internal validation cohort (0.747 for recurrence, 0.758 for OS) and external validation cohort(0.719 for recurrence, 0.714 for OS) had well-fitted calibration curves. Our nomograms accurately stratified patients with HBV-HCC with MVI into low-, intermediate- and high-risk groups of postsurgical recurrence and mortality. Prediction models for recurrence-free survival (https://baishileiehbh.shinyapps.io/HBV-MVI-HCC-RFS/) and OS (https://baishileiehbh.shinyapps.io/HBV-MVI-HCC-OS/) were constructed. Conclusions: The two nomograms showed good predictive performance and accurately distinguished different recurrence and OS by the nomograms scores for HBV-HCC patients with MVI after resection.