The journey of decellularized vessel: from laboratory to operating room.

Over the past few decades, there has been a remarkable advancement in the field of transplantation. But the shortage of donors is still an urgent problem that requires immediate attention. As with xenotransplantation, bioengineered organs are promising solutions to the current shortage situation. And decellularization is a unique technology in organ-bioengineering. However, at present, there is no unified decellularization method for different tissues, and there is no gold-standard for evaluating decellularization efficiency. Meanwhile, recellularization, re-endothelialization and modification are needed to form transplantable organs. With this mind, we can start with decellularization and re-endothelialization or modification of small blood vessels, which would serve to address the shortage of small-diameter vessels while simultaneously gathering the requisite data and inspiration for further recellularization of the whole organ-scale vascular network. In this review, we collect the related experiments of decellularization and post-decellularization approaches of small vessels in recent years. Subsequently, we summarize the experience in relation to the decellularization and post-decellularization combinations, and put forward obstacle we face and possible solutions.

Radiomics features based on dual-area CT predict the expression levels of fatty acid binding protein 4 and outcome in hepatocellular carcinoma.

RATIONALE AND OBJECTIVES: To evaluate the predictive value of tumor and peritumor radiomics in the fatty acid binding protein 4 (FABP4) expression levels and overall survival in patients with hepatocellular carcinoma. MATERIALS AND METHODS: The genomic data of HCC patients were obtained from The Cancer Genome Atlas. The Dual-area CT images of corresponding patients were downloaded from The Cancer Imaging Archive, for radiomics feature extraction, model construction and prognosis analysis. Simultaneously, using patients from Sichuan Provincial People's Hospital, the prognostic value of the radiomics model in HCC patients was validated. RESULTS: In the TCIA database, the area under the curve (AUC) values of the volumes of interest (VOI)whole model in the training set and internal validation set were 0.812 and 0.754, respectively, and the AUC value of VOIwhole+periphery in the training set and internal validation set were 0.866 and 0.779, respectively. In the VOIwhole and the VOIwhole+periphery model of the independent cohort, there were significant differences in OS between the high and low rad-score groups (P = 0.009, P = 0.021, respectively). Significant positive correlations can be observed between FABP4 expression and correlations with rad-score of VOIwhole model (r = 0.691) and VOIwhole+periphery model (r = 0.732) in the independent cohort. CONCLUSION: Radiomics models of tumor and peritumor Dual-area CT images could predict stably the expression levels of FABP4 and may be helping in personalized treatment strategies.

Study protocol for factors influencing the adoption of ChatGPT technology by startups: Perceptions and attitudes of entrepreneurs.

BACKGROUND: Generative Artificial Intelligence (AI) technology, for instance Chat Generative Pre-trained Transformer (ChatGPT), is continuously evolving, and its userbase is growing. These technologies are now being experimented by the businesses to leverage their potential and minimise their risks in business operations. The continuous adoption of the emerging Generative AI technologies will help startups gain more and more experience with adoptions, helping them to leverage continuously evolving technological innovation landscape. However, there is a dearth of prior research on ChatGPT adoption in the startup context, especially from Entrepreneur perspective, highlights the urgent need for a thorough investigation to identify the variables influencing this technological adoption. The primary objective of this study is to ascertain the factors that impact the uptake of ChatGPT technology by startups, anticipate their influence on the triumph of companies, and offer pragmatic suggestions for various stakeholders, including entrepreneurs, and policymakers. METHOD AND ANALYSIS: This study attempts to explore the variables impacting startups' adoption of ChatGPT technology, with an emphasis on comprehending entrepreneurs' attitudes and perspectives. To identify and then empirically validate the Generative AI technology adoption framework, the study uses a two-stage methodology that includes experience-based research, and survey research. The research method design is descriptive and Correlational design. Stage one of the research study is descriptive and involves adding practical insights, and real-world context to the model by drawing from the professional consulting experiences of the researchers with the SMEs. The outcome of this stage is the adoption model (also called as research framework), building Upon Technology Adoption Model (TAM), that highlight the technology adoption factors (also called as latent variables) connected with subset of each other and finally to the technology adoption factor (or otherwise). Further, the latent variables and their relationships with other latent variables as graphically highlighted by the adoption model will be translated into the structured questionnaire. Stage two involves survey based research. In this stage, structured questionnaire is tested with small group of entrepreneurs (who has provided informed consent) and finally to be distributed among startup founders to further validate the relationships between these factors and the level of influence individual factors have on overall technology adoption. Partial Least Squares Structural Equation Modeling (PLS-SEM) will be used to analyze the gathered data. This multifaceted approach allows for a comprehensive analysis of the adoption process, with an emphasis on understanding, describing, and correlating the key elements at play. DISCUSSION: This is the first study to investigate the factors that impact the adoption of Generative AI, for instance ChatGPT technology by startups from the Entrepreneurs perspectives. The study's findings will give Entrepreneurs, Policymakers, technology providers, researchers, and Institutions offering support for entrepreneurs like Academia, Incubators and Accelerators, University libraries, public libraries, chambers of commerce, and foreign embassies important new information that will help them better understand the factors that encourage and hinder ChatGPT adoption. This will allow them to make well-informed strategic decisions about how to apply and use this technology in startup settings thereby improving their services for businesses.

Allograft function predicts mortality in kidney transplant recipients with severe COVID-19: a paradoxical risk factor.

Introduction: Kidney transplant recipients (KTRs) are at a higher risk of severe coronavirus disease (COVID-19) because of their immunocompromised status. However, the effect of allograft function on the prognosis of severe COVID-19 in KTRs is unclear. In this study, we aimed to analyze the correlation between pre-infection allograft function and the prognosis of severe COVID-19 in KTRs. Methods: This retrospective cohort study included 82 patients who underwent kidney transplantation at the Sichuan Provincial Peoples Hospital between October 1, 2014 and December 1, 2022 and were diagnosed with severe COVID-19. The patients were divided into decreased eGFR and normal eGFR groups based on the allograft function before COVID-19 diagnosis (n=32 [decreased eGFR group], mean age: 43.00 years; n=50 [normal eGFR group, mean age: 41.88 years). We performed logistic regression analysis to identify risk factors for death in patients with severe COVID-19. The nomogram was used to visualize the logistic regression model results. Results: The mortality rate of KTRs with pre-infection allograft function insufficiency in the decreased eGFR group was significantly higher than that of KTRs in the normal eGFR group (31.25% [10/32] vs. 8.00% [4/50], P=0.006). Pre-infection allograft function insufficiency (OR=6.96, 95% CI: 1.4633.18, P=0.015) and maintenance of a mycophenolic acid dose >1500 mg/day before infection (OR=7.59, 95% CI: 1.0853.20, P=0.041) were independent risk factors, and the use of nirmatrelvir/ritonavir before severe COVID-19 (OR=0.15, 95% CI: 0.030.72, P=0.018) was a protective factor against death in severe COVID-19. Conclusions: Pre-infection allograft function is a good predictor of death in patients with severe COVID-19. Allograft function was improved after treatment for severe COVID-19, which was not observed in patients with non-severe COVID-19.

Global estimates of gap-free and fine-scale CO2 concentrations during 2014-2020 from satellite and reanalysis data.

Carbon dioxide (CO2) is a crucial greenhouse gas with substantial effects on climate change. Satellite-based remote sensing is a commonly used approach to detect CO2 with high precision but often suffers from extensive spatial gaps. Thus, the limited availability of data makes global carbon stocktaking challenging. In this paper, a global gap-free column-averaged dry-air mole fraction of CO2 (XCO2) dataset with a high spatial resolution of 0.1° from 2014 to 2020 is generated by the deep learning-based multisource data fusion, including satellite and reanalyzed XCO2 products, satellite vegetation index data, and meteorological data. Results indicate a high accuracy for 10-fold cross-validation (R2 = 0.959 and RMSE = 1.068 ppm) and ground-based validation (R2 = 0.964 and RMSE = 1.010 ppm). Our dataset has the advantages of high accuracy and fine spatial resolution compared with the XCO2 reanalysis data as well as that generated from other studies. Based on the dataset, our analysis reveals interesting findings regarding the spatiotemporal pattern of CO2 over the globe and the national-level growth rates of CO2. This gap-free and fine-scale dataset has the potential to provide support for understanding the global carbon cycle and making carbon reduction policy, and it can be freely accessed at https://doi.org/10.5281/zenodo.7721945.

The gut microbiome and metabolome in kidney transplant recipients with normal and moderately decreased kidney function.

BACKGROUND: The kidney transplant recipients (KTRs) were diagnosed with Chronic Kidney Disease after transplantation (CKD-T). CKD-T can be affected by the microbial composition and metabolites. The present study integrates the analysis of gut microbiome and metabolites to further identify the characteristics of CKD-T. METHODS: We collected 100 fecal samples of KTRs and divided them into two groups according to the stage progression of CKD-T. Among them, 55 samples were analyzed by Hiseq sequencing, and 100 samples were used for non-targeted metabolomics analysis. The gut microbiome and metabolomics of KTRs were comprehensively characterized. RESULTS: As well as significant differences in gut microbiome diversity between the CKD G1-2T group and CKD G3T group. Eight flora including Akkermansia were found to be enriched in CKD G3T group. As compared with CKD G1-2T group, the relative abundance of some amino acid metabolism, glycerophospholipid metabolism, amino acid biosynthesis, carbohydrate metabolism and purine metabolism in CKD G3T group were differential expressed significantly. In addition, fecal metabolome analysis indicated that CKD G3T group had a unique metabolite distribution characteristic. Two differentially expressed metabolites, N-acetylornithine and 5-deoxy-5'-(Methylthio) Adenosine, were highly correlated with serum creatinine, eGFR and cystatin C. The enrichment of gut microbial function in CKD-T is correlated with the expression of gut metabolites. CONCLUSION: Gut microbiome and metabolites in the progression of CKD-T display some unique distribution and expression characteristics. The composition of the gut microbiome and their metabolites appears to be different between patients with CKD G3T and those with CKD G1-2T.

Microbial and metabolic features in renal transplant recipients with post-transplantation diabetes mellitus.

OBJECTIVE: Post-transplantation diabetes mellitus (PTDM) is a common complication in renal transplant recipients (RTRs). Gut microbiome plays important roles in a variety of chronic metabolic diseases, but its association with the occurrence and development of PTDM is still unknown. The present study integrates the analysis of gut microbiome and metabolites to further identify the characteristics of PTDM. METHODS: A total of 100 RTRs fecal samples were collected in our study. Among them, 55 samples were submitted to Hiseq sequencing, and 100 samples were used for non-targeted metabolomics analysis. The gut microbiome and metabolomics of RTRs were comprehensively characterized. RESULTS: The species Dialister invisus was significantly associated with fasting plasma glucose (FPG). The functions of tryptophan and phenylalanine biosynthesis were enhanced in RTRs with PTDM, while the functions of fructose and butyric acid metabolism were reduced. Fecal metabolome analysis indicated that RTRs with PTDM had unique metabolite distribution characteristics, and two differentially expressed specific metabolites were significantly correlated with FPG. The correlation analysis of gut microbiome and metabolites showed that gut microbiome had an obvious effect on the metabolic characteristics of RTRs with PTDM. Moreover, the relative abundance of microbial function is associated with the expression of several specific gut microbiome and metabolites. CONCLUSIONS: Our study identified the characteristics of gut microbiome and fecal metabolites in RTRs with PTDM, and we also found two important metabolites and a bacterium were significantly associated with PTDM, which might be used as novel targets in the research field of PTDM.

Diagnostic performance of GcfDNA in kidney allograft rejection: a meta-analysis.

In this comprehensive meta-analysis, our objective was to evaluate the diagnostic utility of graft-derived cell-free DNA (GcfDNA) in kidney allograft rejection and explore associated factors. We conducted a thorough search of PubMed, Embase, and the Cochrane Library databases, spanning from their inception to September 2022. Statistical analysis was executed utilizing Stata 15, Meta-DiSc 1.4, and Review Manager 5.4 software. The combined pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristics (SROC) curve from the synthesis of findings across ten studies were as follows: 0.75 (0.67-0.81), 0.78 (0.72-0.83), 3.36 (2.89-4.35), 0.32 (0.24-0.44), 8.77 (4.34-17.74), and 0.83 (0.80-0.86), respectively. Among the ten studies primarily focused on GcfDNA's diagnostic potential for antibody-mediated rejection (ABMR), the optimal cut-off threshold demonstrated substantial diagnostic efficacy, with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, DOR, and area under the summary receiver operating characteristics curve values of 0.83 (0.74-0.89), 0.75 (0.70-0.80), 3.37 (2.64-4.30), 0.23 (0.15-0.36), 14.65 (7.94-27.03), and 0.85 (0.82-0.88), respectively. These results underscore the high diagnostic accuracy of GcfDNA in detecting rejection. Furthermore, the optimal cut-off threshold proves effective in diagnosing ABMR, while a 1% threshold remains a robust diagnostic criterion for rejection. Notably, for ABMR diagnosis, droplet digital PCR digital droplet polymerase chain reaction emerges as a superior method in terms of accuracy when compared to other techniques. Nonetheless, further research is warranted to substantiate these findings.

Unlocking Novel Ultralow-Frequency Band Gap: Assembled Cellular Metabarrier for Broadband Wave Isolation.

Admittedly, the design requirements of compactness, low frequency, and broadband seem to constitute an impossible trinity, hindering the further development of elastic metamaterials (EMMs) in wave shielding engineering. To break through these constraints, we propose theoretical combinations of effective parameters for wave isolation based on the propagation properties of Lamb waves in the EMM layer. Accordingly, we design compact EMMs with a novel ultralow-frequency bandgap, and the role of auxeticity in the dissociation between the dipole mode and the toroidal dipole mode is clearly revealed. Finally, under the guidance of the improved gradient design, we integrate multiple bandgaps to assemble metamaterial barriers (MMBs) for broadband wave isolation. In particular, the original configuration is further optimized and its ultralow-frequency and broadband performance are proven by transmission tests. It is foreseeable that our work will provide a meaningful reference for the application of the new EMMs in disaster prevention and protection engineering.

Quantification of Postoperative Graft-Derived Cell-Free DNA to Evaluate the Risks of Impaired Allograft Function at Early Stage of Kidney Transplantation.

BACKGROUND: Graft-derived cell-free DNA (GcfDNA) is a promising biomarker for comprehensive monitoring of allograft injury because it overcomes the limitations of traditional approaches. The aim of this study is to investigate the association between the outliers of GcfDNA at initial time post transplantation and short-term renal graft function. METHODS: A total of 230 recipients who underwent primary kidney transplantation were recruited in the study. For each recipient, 10 mL of peripheral blood were collected at day 1 post transplantation. Both of the GcfDNA fraction (%) and GcfDNA concentration (cp/mL) were determined using droplet digital PCR. The study was conducted in accordance with the 1964 Helsinki Declaration and its later amendments. RESULTS: There were no values that fall outside of the lower extreme in both of the GcfDNA fraction and GcfDNA concentration, and the upper fence of GcfDNA fraction and GcfDNA concentration were 13.5% and 680 cp/mL, respectively. Recipients with GcfDNA concentration ≥680 cp/mL had a statistically significant higher serum creatinine at day 7 post-transplantation, when compared with the other group (P = .008). The receiver operating characteristic analysis obtained an area under the curve value of 0.869 when using GcfDNA concentration to predict the risk of serum creatinine ≥400 µmol/L, an optimal cut-off value was indicated at 975 cp/mL with high sensitivity (87.5%) and specificity (85%). CONCLUSION: Our results suggest that the quantification of GcfDNA at initial time after transplantation might be used as a novel strategy for predicting short-term risk of impaired kidney allograft function or delayed graft function.

Detection of BK polyomavirus-associated nephropathy using plasma graft-derived cell-free DNA: Development of a novel algorithm from programmed monitoring.

Graft-derived cell-free DNA (GcfDNA) is a promising non-invasive biomarker for detecting allograft injury. In this study, we aimed to evaluate the efficacy of programmed monitoring of GcfDNA for identifying BK polyomavirus-associated nephropathy (BKPyVAN) in kidney transplant recipients. We recruited 158 kidney transplant recipients between November 2020 and December 2021. Plasma GcfDNA was collected on the tenth day, first month, third month, and sixth month for programmed monitoring and one day before biopsy. ΔGcfDNA (cp/mL) was obtained by subtracting the baseline GcfDNA (cp/mL) from GcfDNA (cp/mL) of the latest programmed monitoring before biopsy. The receiver operating characteristic curve showed the diagnostic performance of GcfDNA (cp/mL) at biopsy time and an optimal area under the curve (AUC) of 0.68 in distinguishing pathologically proven BKPyVAN from pathologically unconfirmed BKPyVAN. In contrast, ΔGcfDNA (cp/mL) had a sensitivity and specificity of 80% and 84.6%, respectively, and an AUC of 0.83. When distinguishing clinically diagnosed BKPyVAN from clinical excluded BKPyVAN, the AUC of GcfDNA (cp/mL) was 0.59 at biopsy time, and ΔGcfDNA (cp/mL) had a sensitivity and specificity of 81.0% and 76.5%, respectively, and an AUC of 0.81. Plasma ΔGcfDNA (cp/mL) was not significantly different between TCMR [0.15 (0.08, 0.24) cp/mL] and pathologically proven BKPyVAN[0.34 (0.20, 0.49) cp/mL]. In conclusion, we recommend programmed monitoring of plasma GcfDNA levels after a kidney transplant. Based on our findings from the programmed monitoring, we have developed a novel algorithm that shows promising results in identifying and predicting BKPyVAN.

Pseudogene AK4P1 promotes pancreatic ductal adenocarcinoma progression through relieving miR-375-mediated YAP1 degradation.

Pseudogenes have been reported to play oncogenic or tumor-suppressive roles in cancer progression. However, the molecular mechanism of most pseudogenes in pancreatic ductal adenocarcinoma (PDAC) remains unknown. Herein, we characterized a novel pseudogene-miRNA-mRNA network associated with PDAC progression using bioinformatics analysis. After screening by dreamBase and GEPIA, 12 up-regulated and 7 down-regulated differentially expressed pseudogenes (DEPs) were identified. According to survival analysis, only elevated AK4P1 indicated a poor prognosis for PDAC patients. Moreover, we found that AK4 acts as a cognate gene of AK4P1 and also predicts worse survival for PDAC patients. Furthermore, 32 miRNAs were predicted to bind to AK4P1 by starBase, among which miR-375 was identified as the most potential binding miRNA of AK4P1. A total of 477 potential target genes of miR-375 were obtained by miRNet, in which 49 hub genes with node degree ≥ 20 were identified by STRING. Subsequent analysis for hub genes demonstrated that YAP1 may be a functional downstream target of AK4P1. To confirmed the above findings, microarray, and qRT-PCR assay revealed that YAP1 was dramatically upregulated in both PDAC cells and tissues. Functional experiments showed that knockdown of YAP1 significantly suppressed PDAC cells growth, increased apoptosis, and decreased the ability of invasion. In conclusion, amplification of AK4P1 may fuel the onset and development of PDAC by targeting YAP1 through competitively binding to miR-375, and serve as a promising biomarker and therapeutic target for PDAC.

RNF26 Promotes Pancreatic Cancer Proliferation by Enhancing RBM38 Degradation.

OBJECTIVES: RING finger protein 26 (RNF26) plays an essential role in determining malignant tumor growth, whereas the role of which in pancreatic cancer (PC) has not been reported. This study aimed to investigate the role of RNF26 in PC cells. METHODS: The Gene Expression Profiling Interactive Analysis was applied to study the role of RNF26 in malignant tumors. The in vitro or in vivo cell proliferation assays were used to investigate the role of RNF26 on the PC. The protein-protein interaction network analysis was used to search the binding partner of RNF26. The Western blot was used to reveal whether RNF26 promoted RNA binding motif protein-38 (RBM38) degradation in PC cells. RESULTS: The Gene Expression Profiling Interactive Analysis tool showed that RNF26 was overexpressed in PC. Repressing RNF26 expression decreased PC cells growth, but overexpression of RNF26 increased PC proliferation. Furthermore, we demonstrated RNF26 degraded RBM38 to promote PC cell proliferation. CONCLUSIONS: RNF26 was abnormally increased in PC, and upregulated RNF26 was correlated with a poor prognosis. RNF26 enhanced PC proliferation by inducing RBM38 degradation. We identified a novel RNF26-RBM28 axis involved in the progression of PC.

LncRNA SNHG6 promotes glycolysis reprogramming in hepatocellular carcinoma by stabilizing the BOP1 protein.

Metabolic reprogramming is an important feature in tumor progression. Long noncoding RNA's (lncRNA) small nucleolar RNA host gene 6 (SNHG6) acts as a proto-oncogene in hepatocellular carcinoma (HCC) but its role in glycolysis is mostly unknown. The role of SNHG6 and Block of proliferation 1 (BOP1) on glycolysis is assessed by glucose uptake, lactate production, oxygen consumptive rate (OCR) and extracellular acidification rate (ECAR) and glycolytic enzyme levels. The regulatory effect of SNHG6 on BOP1 protein was confirmed by Western blotting, MS2 pull-down, RNA pull-down, and RIP assay. SNHG6 and BOP1 levels were increased in HCC tissues and cells. SNHG6 and BOP1 were prognostic factors in HCC patients and significantly correlated to TP53 mutant and tumor grade. SNHG6 promoted proliferation, inhibited apoptosis, enhanced glucose uptake and lactate production, decreased OCR, and increased ECAR in HCC cell lines. SNHG6 could bind the BOP1 protein and enhance its stability. BOP1 overexpression rescued the change of proliferation, apoptosis, and glycolysis in HCCLM3 and SMMC-7721 cells. Our data indicate that SNHG6 accelerates proliferation and glycolysis and inhibits the apoptosis of HCC cell lines by binding the BOP1 protein and enhancing its stability. Both SNHG6 and BOP1 are promising prognostic and therapeutic markers in HCC.

A Recurrent Neural Network-Based Method for Dynamic Load Identification of Beam Structures.

The determination of structural dynamic characteristics can be challenging, especially for complex cases. This can be a major impediment for dynamic load identification in many engineering applications. Hence, avoiding the need to find numerous solutions for structural dynamic characteristics can significantly simplify dynamic load identification. To achieve this, we rely on machine learning. The recent developments in machine learning have fundamentally changed the way we approach problems in numerous fields. Machine learning models can be more easily established to solve inverse problems compared to standard approaches. Here, we propose a novel method for dynamic load identification, exploiting deep learning. The proposed algorithm is a time-domain solution for beam structures based on the recurrent neural network theory and the long short-term memory. A deep learning model, which contains one bidirectional long short-term memory layer, one long short-term memory layer and two full connection layers, is constructed to identify the typical dynamic loads of a simply supported beam. The dynamic inverse model based on the proposed algorithm is then used to identify a sinusoidal, an impulsive and a random excitation. The accuracy, the robustness and the adaptability of the model are analyzed. Moreover, the effects of different architectures and hyperparameters on the identification results are evaluated. We show that the model can identify multi-points excitations well. Ultimately, the impact of the number and the position of the measuring points is discussed, and it is confirmed that the identification errors are not sensitive to the layout of the measuring points. All the presented results indicate the advantages of the proposed method, which can be beneficial for many applications.

DNA Damage Repair Status Predicts Opposite Clinical Prognosis Immunotherapy and Non-Immunotherapy in Hepatocellular Carcinoma.

Immune checkpoint inhibitors(ICIs) that activate tumor-specific immune responses bring new hope for the treatment of hepatocellular carcinoma(HCC). However, there are still some problems, such as uncertain curative effects and low objective response rates, which limit the curative effect of immunotherapy. Therefore, it is an urgent problem to guide the use of ICIs in HCC based on molecular typing. We downloaded the The Cancer Genome Atlas-Liver hepatocellular carcinoma(TCGA-LIHC) and Mongolian-LIHC cohort. Unsupervised clustering was applied to the highly variable data regarding expression of DNA damage repair(DDR). The CIBERSORT was used to evaluate the proportions of immune cells. The connectivity map(CMap) and pRRophetic algorithms were used to predict the drug sensitivity. There were significant differences in DDR molecular subclasses in HCC(DDR1 and DDR2), and DDR1 patients had low expression of DDR-related genes, while DDR2 patients had high expression of DDR-related genes. Of the patients who received traditional treatment, DDR2 patients had significantly worse overall survival(OS) than DDR1 patients. In contrast, of the patients who received ICIs, DDR2 patients had significantly prolonged OS compared with DDR1 patients. Of the patients who received traditional treatment, patients with high DDR scores had worse OS than those with low DDR scores. However, the survival of patients with high DDR scores after receiving ICIs was significantly higher than that of patients with low DDR scores. The DDR scores of patients in the DDR2 group were significantly higher than those of patients in the DDR1 group. The tumor microenvironment(TME) of DDR2 patients was highly infiltrated by activated immune cells, immune checkpoint molecules and proinflammatory molecules and antigen presentation-related molecules. In this study, HCC patients were divided into the DDR1 and DDR2 group. Moreover, DDR status may serve as a potential biomarker to predict opposite clinical prognosis immunotherapy and non-immunotherapy in HCC.

An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation.

Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.

Use of graft-derived cell-free DNA as a novel biomarker to predict allograft function after kidney transplantation.

OBJECTIVE: To investigate the association between graft-derived cell-free DNA and pretransplantation clinical variables, and to determine whether the former could be used as a novel biomarker to predict renal function. METHODS: A total of 87 recipients who underwent primary kidney transplantation were recruited to the study. For each recipient, 10 mL peripheral blood was collected on days 1, 7, 14-20, and 30-45 after transplantation. The fractional abundance of graft-derived cell-free DNA was determined using droplet digital polymerase chain reaction. RESULTS: For most recipients, graft-derived cell-free DNA fraction values were significantly elevated on the first day after transplantation, followed by a rapid decline, and reaching baseline values of graft-derived cell-free DNA fraction in the range of <1% at 7 days. Statistical analysis showed that longer cold ischemia time was significantly associated with higher graft-derived cell-free DNA fraction values (P = 0.02). Moreover, we also found that graft-derived cell-free DNA fraction values among recipients with delayed graft function were significantly higher than those of recipients without delayed graft function on the first day after transplantation. Kaplan-Meier analysis showed that recipients who had a graft-derived cell-free DNA fraction value of <1% at 7 days had a significantly lower probability of an estimated glomerular filtration rate ≤60 mL/min/1.73 m2 at 90 days. Using a random forest regression model, the predicted values of estimated glomerular filtration rate at 90 days were almost the same as the actual values. CONCLUSIONS: Our findings suggest that graft-derived cell-free DNA might be used as a novel biomarker to predict delayed graft function and renal function.

Properties of regulatory B cells regulating B cell targets.

Regulatory B cells (Bregs) have shown promise as anti-rejection therapy applied to organ transplantation. However, less is known about their effect on other B cell populations that are involved in chronic graft rejection. We recently uncovered that naïve B cells, stimulated by TLR ligand agonists, converted into B cells with regulatory properties (Bregs-TLR) that prevented allograft rejection. Here, we examine the granular phenotype and regulatory properties of Breg-TLR cells suppressing B cells. Cocultures of Bregs-TLR with LPS-activated B cells showed a dose-dependent suppression of targeted B cell proliferation. Adoptive transfers of Bregs-TLR induced a decline in antibody responses to antigenically disparate skin grafts. The role of Breg BCR specificity in regulation was assessed using B cell-deficient mice replenished with transgenic BCR (OB1) and TCR (OT-II) lymphocytes of matching antigenic specificity. Results indicated that proliferation of OB1 B cells, mediated through help from CD4+ OT-II cells, was suppressed by OB1 Bregs of similar specificity. Transcriptomic analyses indicated that Bregs-TLR suppression is associated with a block in targeted B cell differentiation controlled by PRDM1 (Blimp1). This work uncovered the regulatory properties of a new brand of Breg cells and provided mechanistic insights into potential applications of Breg therapy in transplantation.