Neurulation of the cynomolgus monkey embryo achieved from 3D blastocyst culture.

Neural tube (NT) defects arise from abnormal neurulation and result in the most common birth defects worldwide. Yet, mechanisms of primate neurulation remain largely unknown due to prohibitions on human embryo research and limitations of available model systems. Here, we establish a three-dimensional (3D) prolonged in vitro culture (pIVC) system supporting cynomolgus monkey embryo development from 7 to 25 days post-fertilization. Through single-cell multi-omics analyses, we demonstrate that pIVC embryos form three germ layers, including primordial germ cells, and establish proper DNA methylation and chromatin accessibility through advanced gastrulation stages. In addition, pIVC embryo immunofluorescence confirms neural crest formation, NT closure, and neural progenitor regionalization. Finally, we demonstrate that the transcriptional profiles and morphogenetics of pIVC embryos resemble key features of similarly staged in vivo cynomolgus and human embryos. This work therefore describes a system to study non-human primate embryogenesis through advanced gastrulation and early neurulation.

Filamentous GLS1 promotes ROS-induced apoptosis upon glutamine deprivation via insufficient asparagine synthesis.

GLS1 orchestrates glutaminolysis and promotes cell proliferation when glutamine is abundant by regenerating TCA cycle intermediates and supporting redox homeostasis. CB-839, an inhibitor of GLS1, is currently under clinical investigation for a variety of cancer types. Here, we show that GLS1 facilitates apoptosis when glutamine is deprived. Mechanistically, the absence of exogenous glutamine sufficiently reduces glutamate levels to convert dimeric GLS1 to a self-assembled, extremely low-Km filamentous polymer. GLS1 filaments possess an enhanced catalytic activity, which further depletes intracellular glutamine. Functionally, filamentous GLS1-dependent glutamine scarcity leads to inadequate synthesis of asparagine and mitogenome-encoded proteins, resulting in ROS-induced apoptosis that can be rescued by asparagine supplementation. Physiologically, we observed GLS1 filaments in solid tumors and validated the tumor-suppressive role of constitutively active, filamentous GLS1 mutants K320A and S482C in xenograft models. Our results change our understanding of GLS1 in cancer metabolism and suggest the therapeutic potential of promoting GLS1 filament formation.

Primate gastrulation and early organogenesis at single-cell resolution.

Our understanding of human early development is severely hampered by limited access to embryonic tissues. Due to their close evolutionary relationship with humans, nonhuman primates are often used as surrogates to understand human development but currently suffer from a lack of in vivo datasets, especially from gastrulation to early organogenesis during which the major embryonic cell types are dynamically specified. To fill this gap, we collected six Carnegie stage 8-11 cynomolgus monkey (Macaca fascicularis) embryos and performed in-depth transcriptomic analyses of 56,636 single cells. Our analyses show transcriptomic features of major perigastrulation cell types, which help shed light on morphogenetic events including primitive streak development, somitogenesis, gut tube formation, neural tube patterning and neural crest differentiation in primates. In addition, comparative analyses with mouse embryos and human embryoids uncovered conserved and divergent features of perigastrulation development across species-for example, species-specific dependency on Hippo signalling during presomitic mesoderm differentiation-and provide an initial assessment of relevant stem cell models of human early organogenesis. This comprehensive single-cell transcriptome atlas not only fills the knowledge gap in the nonhuman primate research field but also serves as an invaluable resource for understanding human embryogenesis and developmental disorders.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support.

ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Plasma levels of hydrogen sulfide and homocysteine correlate with the efficacy of antidepressant agents and serve as potential diagnostic and therapeutic markers.

OBJECTIVE: Hydrogen sulfide (H2S) is associated with depressive-like behavior in rodents. We undertook cross-sectional and longitudinal analyses of plasma levels of H2S and its substrate homocysteine (Hcy) in depression and assessed the association of both parameters with psychopathology and cognitive function. METHODS: Forty-one patients suffering from depression (PSDs) and 48 healthy volunteers were recruited. PSDs were treated for 8 weeks. Analyzable data were collected from all participants for assessment of their psychopathology and cognitive function. Plasma was collected for determination of levels of H2S and Hcy, and data were correlated to determine their potential as plasma biomarkers. RESULTS: Cross-sectional analyses revealed PSDs to have a low plasma H2S level and high Hcy level. Longitudinal analyses revealed that 8 weeks of treatment reversed the changes in plasma levels of H2S and Hcy in PSDs. Plasma levels of H2S and Hcy were associated with psychopathology and cognitive function in depression. The area under the receiver operating characteristic curve (AUC) for a combination of plasma levels of H2S and Hcy and expression of the TNF gene (i.e., H2S-Hcy-TNF) was 0.848 for diagnosing depression and 0.977 for predicting the efficacy of antidepressant agents. CONCLUSION: Plasma levels of H2S and Hcy reflect changes in psychopathology and cognitive function in depression and H2S-Hcy-TNF has the potential to diagnose depression and predict the efficacy of antidepressant medications.

The impact of apparent temperature on the emergency visits for traumatic fractures in Hangzhou, China.

BACKGROUND: Traumatic fractures occur frequently worldwide. However, research remains limited on the association between short-term exposure to temperature and traumatic fractures. This study aims to explore the impact of apparent temperature (AT) on emergency visits (EVs) due to traumatic fractures. METHODS: Based on EVs data for traumatic fractures and the contemporary meteorological data, a generalized Poisson regression model along with a distributed lag nonlinear model (DLNM) were undertaken to determine the impact of AT on traumatic fracture EVs. Subgroup analysis by gender and age and sensitivity analysis were also performed. RESULTS: A total of 25,094 EVs for traumatic fractures were included in the study. We observed a wide "J"-shaped relationship between AT and risk of traumatic fractures, with AT above 9.5 °C positively associated with EVs due to traumatic fractures. The heat effects became significant at cumulative lag 0-11 days, and the relative risk (RR) for moderate heat (95th percentile, 35.7 °C) and extreme heat (99.5th percentile, 38.8 °C) effect was 1.311 (95% CI: 1.132-1.518) and 1.418 (95% CI: 1.191-1.688) at cumulative lag 0-14 days, respectively. The cold effects were consistently non-significant on single or cumulative lag days across 0-14 days. The heat effects were higher among male and those aged 18-65 years old. The sensitivity analysis results remained robust. CONCLUSION: Higher AT is associated with cumulative and delayed higher traumatic fracture EVs. The male and those aged 18-65 years are more susceptible to higher AT.

Customizing biomimetic surface attributes of dendritic lipopeptide nanoplatforms for extended circulation.

The pressing demand for innovative approaches to create delivery systems with heightened drug loading and prolonged circulation has spurred numerous efforts, yielding some successes but accompanied by constraints. Our study proposes employing dendritic lipopeptide with precisely balanced opposing charges to extend blood residency for biomimetic nanoplatforms. Neutrally mixed-charged zwitterionic nanoparticles (NNPs) achieved a notable 19 % simvastatin loading content and kept stable even after one-month storage at 4 °C. These nanoplatforms demonstrated low cytotoxicity in NIH-3T3 and L02 cells and negligible hemolysis (<5 %). NNPs inhibited protein adhesion (>95 %) from positively and negatively charged sources through surface hydration. In comparison to positively charged CNPs, NNPs demonstrated an 86 % decrease in phagocytic rate by BMDMs, highlighting their efficacy. Importantly, NNPs showed prolonged circulation compared to CNPs and free simvastatin. These findings highlight the potential of this biomimetic nanoplatform for future therapeutic applications with enhanced drug loading and circulation traits.

CCLOOW: Chinese children's lexicon of oral words.

In this article, we introduce the Chinese Children's Lexicon of Oral Words (CCLOOW), the first lexical database based on animated movies and TV series for 3-to-9-year-old Chinese children. The database computes from 2.7 million character tokens and 1.8 million word tokens. It contains 3920 unique character and 22,229 word types. CCLOOW reports frequency and contextual diversity metrics of the characters and words, as well as length and syntactic categories of the words. CCLOOW frequency and contextual diversity measures correlated well with other Chinese lexical databases, particularly well with that computed from children's books. The predictive validity of CCLOOW measures were confirmed with Grade 2 children's naming and lexical decision experiments. Further, we found that CCLOOW frequencies could explain a considerable proportion in adults' written word recognition, indicating that early language experience might have lasting impacts on the mature lexicon. CCLOOW provides validated frequency and contextual diversity estimates that complements current children's lexical database based on written language samples. It is freely accessible online at https://www.learn2read.cn/ccloow .

Stability of specific personality network features corresponding to openness trait across different adult age periods: A machine learning analysis.

The functional connectivity patterns of the brain during resting state are closely related to an individual's cognition, emotion, behavior, and social interactions, making it an important research method to measure personality traits in an unbiased way, replacing traditional paper-and-pencil tests. However, due to the dynamic nature of the brain, whether the changes in functional connectivity caused by age can stably map onto personality traits has not been previously investigated. This study focuses on whether network features that are significantly related to personality traits can effectively distinguish subjects with different personality traits, and whether these network features vary across different periods of adulthood. The study included 343 healthy adult participants, divided into early adulthood and middle adulthood groups according to the age threshold of 35. Resting-state functional magnetic resonance imaging (fMRI) and the Big Five personality questionnaire were collected. we investigated the relationship between personality traits and intrinsic whole-brain functional connectome. We then used support vector machine (SVM) to evaluate the performance of personality network features in distinguishing subjects with high and low scores in the early-adulthood sample, and cross-validated in the mid-adulthood sample. Additionally, edge-based analysis (NBS) was used to explore the stability of personality networks across the two age samples. Our results show that the network features corresponding to openness personality trait are stable and can effectively differentiate subjects with different scores in both age samples. Furthermore, this study found that these network features vary to some extent across different periods of adulthood. These findings provide new evidence and insights into the application of resting-state functional connectivity patterns in measuring personality traits and help us better understand the dynamic characteristics of the human brain.

MICER: a pre-trained encoder-decoder architecture for molecular image captioning.

MOTIVATION: Automatic recognition of chemical structures from molecular images provides an important avenue for the rediscovery of chemicals. Traditional rule-based approaches that rely on expert knowledge and fail to consider all the stylistic variations of molecular images usually suffer from cumbersome recognition processes and low generalization ability. Deep learning-based methods that integrate different image styles and automatically learn valuable features are flexible, but currently under-researched and have limitations, and are therefore not fully exploited. RESULTS: MICER, an encoder-decoder-based, reconstructed architecture for molecular image captioning, combines transfer learning, attention mechanisms and several strategies to strengthen effectiveness and plasticity in different datasets. The effects of stereochemical information, molecular complexity, data volume and pre-trained encoders on MICER performance were evaluated. Experimental results show that the intrinsic features of the molecular images and the sub-model match have a significant impact on the performance of this task. These findings inspire us to design the training dataset and the encoder for the final validation model, and the experimental results suggest that the MICER model consistently outperforms the state-of-the-art methods on four datasets. MICER was more reliable and scalable due to its interpretability and transfer capacity and provides a practical framework for developing comprehensive and accurate automated molecular structure identification tools to explore unknown chemical space. AVAILABILITY AND IMPLEMENTATION: https://github.com/Jiacai-Yi/MICER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Experimental demonstration of deterministic quantum search for multiple marked states without adjusting the oracle.

Grover's search algorithm is a well-known quantum algorithm that has been extensively studied and improved to increase its success rate and enhance its flexibility. However, most improved search algorithms require an adjustment of the oracle, which may not be feasible in practical problem-solving scenarios. In this work, we report an experimental demonstration of a deterministic quantum search for multiple marked states without adjusting the oracle. A linear optical setup is designed to search for two marked states, one in a 16-state database with an initial equal-superposition state and the other in an 8-state database with different initial nonequal-superposition states. The evolution of the probability of finding each state in the database is also measured and displayed. Our experimental results agree well with the theoretical predictions, thereby proving the feasibility of the search protocol and the implementation scheme. This work is a pioneering experimental demonstration of deterministic quantum search for multiple marked states without adjusting the oracle.

Estimating the Mass of Pharmaceuticals Harbored in Municipal Solid Waste Landfills by Analyzing Refuse Samples at Various Ages and Depths.

Pharmaceuticals have been detected at high concentrations in municipal solid waste (MSW) landfill leachates, which are recognized as an underestimated source of pharmaceutical residues in the environment. However, limited efforts have been made to characterize pharmaceuticals in MSW landfill refuse, which is also of significant concern given the potential long-term environmental impact. Herein, we excavated landfill refuse from six cells with landfill ages of 7-27 years in the largest MSW landfill in Shanghai (in each cell, landfill refuse was collected from different depths of 2-8 m) and analyzed samples for the presence of 55 pharmaceuticals, including antibiotics and non-antibiotics. The results reveal the presence of 42 pharmaceuticals in landfill refuse, with median concentrations ranging from 0.30 to 116 µg/kg. Antibiotic and non-antibiotic pharmaceuticals exhibited diverse concentration trends with age, related to changes in policy intervention and consumption over time. Different concentration variations of individual pharmaceuticals were observed in refuse samples excavated at different depths and positively correlated to their sorption ability. The mass of pharmaceuticals in the investigated landfill was estimated from the obtained concentrations to be 80-220 tons with 95% probability, based on Monte Carlo analysis. To the best of our knowledge, this study provides the first estimate of pharmaceutical mass in an MSW landfill. The results will be helpful for understanding the potential long-term environmental impact of pharmaceuticals in landfills.

Plasma exosomes lncRNA-miRNA-mRNA network construction and its diagnostic efficacy identification in first-episode schizophrenia.

BACKGROUND: The exosomal lncRNA-miRNA-mRNA networks in first episode schizophrenia (FOS) have not reported yet. This study examined the lncRNA, miRNA and mRNA expression level in exosome derived from first episode schizophrenia (FOS) patients, and explored the the potential of exosomes as biomarkers for schizophrenia. METHODS: We recruited 10 FOS patients and healthy controls (HCs) respectively, examined the lncRNA, miRNA and mRNA expression level of plasma exosome by high throughput sequencing, constructed lncRNA-miRNA-mRNA network, and performed correlation analysis, GO and KEGG pathway analysis, PPI network construction and ROC analysis. RESULTS: There were 746 differently expressed lncRNA, 22 differently expressed miRNA, and 2637 differently expressed mRNA in plasma exosome in FOS compared with HCs. Then we constructed ceRNA network consisting of 8 down-regulated lncRNA, 7 up-regulated miRNA and 65 down-regulated mRNA, and 1 up-regulated lncRNA, 1 down-regulated miRNA and 4 up-regulated mRNA. The expression level of 1 lncRNA and 7 mRNA in exosomal network were correlated with PANSS score. GO and KEGG pathway analysis showed that 4 up-regulated mRNAs were enriched in neuropsychiatric system function. Down-regulated mRNA EZH2 and SIRT1 were identified as hub gene. Finally, we detected the ROC curve of ENSG00000251562, miR-26a-5p, EZH2, miR-22-3p, SIRT1, ENSG00000251562-miR-26a-5p-EZH2, ENSG00000251562-miR-22-3p-SIRT1, and found that the AUC of ceRNA network was higher than lncRNA, miRNA and mRNA alone. CONCLUSION: We constructed the lncRNA-miRNA-mRNA network in exosome derived from FOS plasma, and found that lncRNA-miRNA-mRNA network has potential as biomarkers for FOS.

Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm.

Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper proposed an improved CR-YOLOv5s to recognize flower buds and blooms for chrysanthemums by emphasizing feature representation through an attention mechanism. The coordinate attention mechanism module has been introduced to the backbone of the YOLOv5s so that the network can pay more attention to chrysanthemum flowers, thereby improving detection accuracy and robustness. Specifically, we replaced the convolution blocks in the backbone network of YOLOv5s with the convolution blocks from the RepVGG block structure to improve the feature representation ability of YOLOv5s through a multi-branch structure, further improving the accuracy and robustness of detection. The results showed that the average accuracy of the improved CR-YOLOv5s was as high as 93.9%, which is 4.5% better than that of normal YOLOv5s. This research provides the basis for the automatic picking and grading of flowers, as well as a decision-making basis for estimating flower yield.

Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and Improved ConvNext.

This article introduces a novel framework for diagnosing faults in rolling bearings. The framework combines digital twin data, transfer learning theory, and an enhanced ConvNext deep learning network model. Its purpose is to address the challenges posed by the limited actual fault data density and inadequate result accuracy in existing research on the detection of rolling bearing faults in rotating mechanical equipment. To begin with, the operational rolling bearing is represented in the digital realm through the utilization of a digital twin model. The simulation data produced by this twin model replace traditional experimental data, effectively creating a substantial volume of well-balanced simulated datasets. Next, improvements are made to the ConvNext network by incorporating an unparameterized attention module called the Similarity Attention Module (SimAM) and an efficient channel attention feature referred to as the Efficient Channel Attention Network (ECA). These enhancements serve to augment the network's capability for extracting features. Subsequently, the enhanced network model is trained using the source domain dataset. Simultaneously, the trained model is transferred to the target domain bearing using transfer learning techniques. This transfer learning process enables the accurate fault diagnosis of the main bearing to be achieved. Finally, the proposed method's feasibility is validated, and a comparative analysis is conducted in comparison with similar approaches. The comparative study demonstrates that the proposed method effectively addresses the issue of low mechanical equipment fault data density, leading to improved accuracy in fault detection and classification, along with a certain level of robustness.

Ligand-Controlled Nickel-Catalyzed Regiodivergent Cross-Electrophile Alkyl-Alkyl Couplings of Alkyl Halides.

Functionalizing specific positions on a saturated alkyl molecule is a key challenge in synthetic chemistry. Herein, a ligand-controlled regiodivergent alkylations of alkyl bromides at different positions by Ni-catalyzed alkyl-alkyl cross-electrophile coupling with the second alkyl bromides has been developed. The reaction undergoes site-selective isomerization on one alkyl bromides in a controlled manner, providing switchable access to diverse alkylated structures at different sites of alkyl bromides. The reaction occurs at three similar positions with excellent chemo- and regioselectivity, representing a remarkable ligand tuned reactivity between alkyl-alkyl cross-coupling and nickel migration along the hydrocarbon side chain. This reaction offers a catalytic platform to diverse saturated architectures by alkyl-alkyl bond-formation from identical starting materials.

Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage.

Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early-onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early-onset (EOS) and adult-onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co-varying patterns by jointly analyzing three MRI features: fractional amplitude of low-frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug-naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub-cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug-naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug-naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.

Liver transplantation for Hepatocellular Carcinoma: A prognostic model incorporating pretransplant inflammatory cytokines.

PURPOSE: Liver transplantation (LT) remains an optimal treatment for selected hepatocellular carcinoma (HCC). Cytokines can be obtained by minimally invasive techniques and are associated with the development of HCC. The purpose of our investigation was to explore the prognostic value of pretransplant serum inflammatory cytokine profiles in HCC patients for LT. METHODS: We detected forty inflammatory cytokines in pre-LT serum from 42 HCC patients by using an inflammation-related antibody array. A pretransplant serum inflammatory cytokine-associated risk assessment model (pre-SCRAM) was developed and was validated in an external cohort of 213 HCC patients who underwent LT and were then followed prospectively. RESULTS: The pre-LT factors independently associated with recurrence-free survival (RFS) were as follows: B-lymphocyte chemoattractant (BLC), interleukin (IL)-12p40 and maximum tumor diameter. High IL-12P40 level was associated with a significantly smaller maximum tumor diameter (p = 0.021), decreased proportion of nodules ≥ 3 (p = 0.001), lower platelet counts (p = 0.011) and lower portal vein tumor thrombus (p = 0.031). Conversely, recipients with poor BLC level had higher alpha-fetoprotein (AFP) levels (p = 0.016). Kaplan-Meier analyses revealed that high pre-LT BLC or IL-12p40 level was associated with superior RFS. The pre-SCRAM stratified recipients into three risk groups: high risk, intermediate risk and low risk. In the validation cohort, for patients in the high, intermediate, and low risk groups, the 3-year RFS rates were 29.3%, 58.7%, and 82.2%, respectively, the 3-year HCC-specific survival rates were 54.5%, 73.8%, and 86%, respectively, and the 3-year overall survival rates were 44.4%, 60.9%, and 79.9%, respectively. The pre-SCRAM model performed well and remained significant in optimizing the risk stratification of recurrence in patients beyond the Milan criteria or the AFP model. CONCLUSION: Pretransplant cytokine profiles can provide powerful prognostic information in the setting of LT for HCC. A pre-LT risk model incorporating cytokines showed excellent efficiency in recurrence prediction for HCC patients, which could ultimately stratify the prognosis in patients beyond the Milan criteria or the AFP model.