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Cardiac repair remains a thorny issue for survivors of acute myocardial infarction (AMI), due to the regenerative inertia of myocardial cells. Cell-free therapies, such as exosome transplantation, have become a potential strategy for myocardial injury. The aim of this study was to investigate the role of engineered exosomes in overexpressing Growth Differentiation Factor-15 (GDF-15) (GDF15-EVs) after myocardial injury, and their molecular mechanisms in cardiac repair. H9C2 cells were transfected with GDF-15 lentivirus or negative control. The exosomes secreted from H9C2 cells were collected and identified. The cellular apoptosis and autophagy of H O -injured H9C2 cells were assessed by Western blotting, TUNEL assay, electron microscopy, CCK-8 and caspase 3/7 assay. A rat model of AMI was constructed by ligating the left anterior descending artery. The anti-apoptotic, pro-angiogenic effects of GDF15-EVs treatment, as well as ensuing functional and histological recovery were evaluated. Then, mRNA sequencing was performed to identify the differentially expressed mRNAs after GDF15-EVs treatment. GDF15-EVs inhibited apoptosis and promoted autophagy in H O injured H9C2 cells. GDF15-EVs effectively decreased the infarct area and enhanced the cardiac function in rats with AMI. Moreover, GDF15-EVs hindered inflammatory cell infiltration, inhibited cell apoptosis, and promoted cardiac angiogenesis in rats with AMI. RNA sequence showed that telomerase reverse transcriptase (TERT) mRNA was upregulated in GDF15-EVs-treated H9C2 cells. AMPK signaling was activated after GDF15-EVs. Silencing TERT impaired the protective effects of GDF15-EVs on H O -injured H9C2 cells. GDF15-EVs could fulfil their protective effects against myocardial injury by upregulating the expression of TERT and activating the AMPK signaling pathway. GDF15-EVs might be exploited to design new therapies for AMI.

Background Quiescent self-renewal of leukemia stem cells (LSCs) and resistance to conventional chemotherapy are the main factors leading to relapse of acute myeloid leukemia (AML). Alpha-enolase ( ENO1 ), a key glycolytic enzyme, has been shown to regulate embryonic stem cell differentiation and promote self-renewal and malignant phenotypes in various cancer stem cells. Here, we sought to test whether and how ENO1 influences LSCs renewal and chemoresistance within the context of AML. Methods We analyzed single-cell RNA sequencing data from bone marrow samples of 8 relapsed/refractory AML patients and 4 healthy controls using bioinformatics and machine learning algorithms. In addition, we compared ENO1 expression levels in the AML cohort with those in 37 control subjects and conducted survival analyses to correlate ENO1 expression with clinical outcomes. Furthermore, we performed functional studies involving ENO1 knockdown and inhibition in AML cell line. Results We used machine learning to model and infer malignant cells in AML, finding more primitive malignant cells in the non-response (NR) group. The differentiation capacity of LSCs and progenitor malignant cells exhibited an inverse correlation with glycolysis levels. Trajectory analysis indicated delayed myeloid cell differentiation in NR group, with high ENO1 -expressing LSCs at the initial stages of differentiation being preserved post-treatment. Simultaneously, ENO1 and stemness-related genes were upregulated and co-expressed in malignant cells during early differentiation. ENO1 level in our AML cohort was significantly higher than the controls, with higher levels in NR compared to those in complete remission. Knockdown of ENO1 in AML cell line resulted in the activation of LSCs, promoting cell differentiation and apoptosis, and inhibited proliferation. ENO1 inhibitor can impede the proliferation of AML cells. Furthermore, survival analyses associated higher ENO1 expression with poorer outcome in AML patients. Conclusions Our findings underscore the critical role of ENO1 as a plausible driver of LSC self-renewal, a potential target for AML target therapy and a biomarker for AML prognosis.

Apatinib was the first anti-angiogenic agent approved for treatment of metastatic gastric cancer (GC). However, the emergence of resistance was inevitable. Thus investigating new and valuable off-target effect of apatinib directly against cancer cells is of great significance. Here, we identified extra spindle pole bodies-like 1 (ESPL1) was responsible for apatinib resistance in GC cells through CRISPR genome-wide gain-of-function screening. Loss of function studies further showed that ESPL1 inhibition suppressed cell proliferation, migration and promoted apoptosis in vitro, and accordingly ESPL1 knockdown sensitized GC cells to apatinib. In addition, we found ESPL1 interacted with mouse double minute 2 (MDM2), a E3 ubiquitin protein ligase, and the combination of MDM2 siRNA with apatinib synergistically ameliorated the resistance induced by ESPL1 overexpression. In summary, our study indicated that ESPL1 played a critical role in apatinib resistance in GC cells. Inhibition of MDM2 could rescue the sensitivity of GC cells to apatinib and reverse ESPL1-mediated resistance.

BackgroundPredictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients.MethodsPatients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model.ResultsA total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively.ConclusionMachine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients.

The character and composition of leukemia-related T cells are closely related to the treatment response and prognosis for patients. Though B cell-acute lymphoblastic leukemia (B-ALL) patients have benefited from immune-based approaches, such as chimeric antigen receptor T cells therapy, some of them still end with poor prognosis, especially for adult patients. Therefore, deep understanding of the developmental relationship between T cell subtypes in relation to B-ALL patient prognosis is urgently needed. We analyzed the peripheral blood T cell single-cell RNA sequencing data of three B-ALL patients, using data from 11 healthy individuals as controls. In total, 16,143 and 53,701 T cells from B-ALL patients and healthy adults, respectively, were objectively analyzed for detailed delineation of 13 distinct T cell clusters. Cluster-specific genes were used as marker genes to annotate each T cell subtype. Unbiased analysis enabled the discovery of circulating CD103+ T cell (CD3+CD103+MKI67+), also defined as tissue-resident memory-like T (Trm-like) cell, populations were elevated in B-ALL patients, which expressed high level of cell proliferation and exhaustion related genes. In addition, cell fate trajectory analysis showed these Trm-like cells, which shared T-cell receptor (TCR) clonotypes with exhausted T (Tex) cells and effector T (Teff) cells, were supposed to transition into Teff cells; however, mainly transformed into Tex cells in leukemia environment. More importantly, Trm-like cells transformation into Teff cells and Tex cells potentially led to favorable or poor prognosis for B-ALL patients, respectively. In sum, a circulating Trm-like cell subset with high level expression of cell proliferation and exhaustion related genes was elevated in B-ALL patients. The bidirectional developmental potential of these T cells into Teff or Tex is closely associated with favorable or poor prognosis, respectively. Together, our study provided a unique insight of alteration of leukemia related T cells, also showed a potential immunotherapy direction and prognosis assessment model for B-ALL patients.

Background Exhaustion is a key factor that influences the efficacy of chimeric antigen receptor T (CAR-T) cells. Our previous study demonstrated that a bromodomain protein 4 (BRD4) inhibitor can revise the phenotype and function of exhausted T cells from leukemia patients. This study aims to elucidate the mechanism by which a BRD4 inhibitor reduces CAR-T cell exhaustion using single-cell RNA sequencing (scRNA-Seq). Methods Exhausted CD123-specific CAR-T cells were prepared by co-culture with CD123 antigen-positive MV411 cells. After elimination of MV411 cells and upregulation of inhibitory receptors on the surface, exhausted CAR-T cells were treated with a BRD4 inhibitor (JQ1) for 72 h. The CAR-T cells were subsequently isolated, and scRNA-Seq was conducted to characterize phenotypic and functional changes in JQ1-treated cells. Results Both the proportion of exhausted CD8 + CAR-T cells and the exhausted score of CAR-T cells decreased in JQ1-treated compared with control-treated cells. Moreover, JQ1 treatment led to a higher proportion of naïve, memory, and progenitor exhausted CD8 + CAR-T cells as opposed to terminal exhausted CD8 + CAR-T cells accompanied by enhanced proliferation, differentiation, and activation capacities. Additionally, with JQ1 treatment, BATF activity and expression in naïve, memory, and progenitor exhausted CD8 + CAR-T cells decreased, whereas EGR1 activity and expression increased. Interestingly, AML patients with higher EGR1 and EGR1 target gene ssGSEA scores, coupled with lower BATF and BATF target gene ssGSEA scores, had the best prognosis. Conclusions Our study reveals that a BRD4 inhibitor can reduce CAR-T cell exhaustion and block exhausted T cell terminal differentiation by downregulating BATF activity and expression together with upregulating EGR1 activity and expression, presenting an approach for improving the effectiveness of CAR-T cell therapy.

We identified SARS-CoV-2 specific antigen epitopes by HLA-A2 binding affinity analysis and characterized their ability to activate T cells. As the pandemic continues, variations in SARS-CoV-2 virus strains have been found in many countries. In this study, we directly assess the immune response to SARS-CoV-2 epitope variants. We first predicted potential HLA-A*02:01-restricted CD8 + T-cell epitopes of SARS-CoV-2. Using the T2 cell model, HLA-A*02:01-restricted T-cell epitopes were screened for their binding affinity and ability to activate T cells. Subsequently, we examined the identified epitope variations and analyzed their impact on immune response. Here, we identified specific HLA-A2-restricted T-cell epitopes in the spike protein of SARS-CoV-2. Seven epitope peptides were confirmed to bind with HLA-A*02:01 and potentially be presented by antigen-presenting cells to induce host immune responses. Tetramers containing these peptides could interact with specific CD8 + T cells from convalescent COVID-19 patients, and one dominant epitope (n-Sp1) was defined. These epitopes could activate and generate epitope-specific T cells in vitro , and those activated T cells showed cytolytic activity toward target cells. Meanwhile, n-Sp1 epitope variant 5L>F significantly decreased the proportion of specific T-cell activation; n-Sp1 epitope 8L>V variant showed significantly reduced binding to HLA-A*02:01 and decreased proportion of n-Sp1-specific CD8 + T cell, which potentially contributes to the immune escape of SARS-CoV-2. Our data indicate that the variation of a dominant epitope will cause the deficiency of HLA-A*02:01 binding and T-cell activation, which subsequently requires the formation of a new CD8 + T-cell immune response in COVID-19 patients.

As the COVID-19 epidemic progresses with the emergence of different SARS-CoV-2 variants, it is important to know the effectiveness of inactivated SARS-CoV-2 vaccines against the variants. To maximize efficiency, a third boost injection of the high-dose SARS-CoV-2 inactivated vaccine KCONVAC was selected for investigation. In addition to the ancestral strain, KCONVAC boost vaccination induced neutralizing antibodies and antigen-specific CD8 T cells to recognize several variants, including B.1.617.2 (Delta), B.1.1.529 (Omicron), B.1.1.7 (Alpha), B.1.351 (Beta), P.3, B.1.526.1 (Lota), B.1.526.2, B.1.618, and B.1.617.3. Both humoral and cellular immunity against variants were lower than those of ancestral variants but continued to increase from day 0 to day 7 to day 50 after boost vaccination. Fifty days post-boost, the KCONVAC-vaccinated CD8 T-cell level reached 1.23-, 2.59-, 2.53-, and 1.01-fold that of convalescents against ancestral, Delta, Omicron and other SARS-CoV-2 variants, respectively. Our data demonstrate the importance of KCONVAC boosters to broaden both humoral and cellular immune responses against SARS-CoV-2 variants.

In this study, a risk score for ventricular arrhythmias (VA) were evaluated for predicting the risk of ventricular arrhythmia (VA) of acute myocardial infarction (AMI) patients. Patients with AMI were divided into two sets according to whether VA occurred during hospitalization. Another cohort was enrolled for external validation. The area under the curve (AUC) of receiver operating characteristic (ROC) was calculated to evaluate the accuracy of the model. A total of 1493 eligible patients with AMI were enrolled as the training set, of whom 70 (4.7%) developed VA during hospitalization. In-hospital mortality was significantly higher in the VA set than in the non-VA set (31.4% vs 2.7%, P=0.001). The independent predictors of VA in patients with AMI including Killip grade ≥3, STEMI patients, LVEF <50%, frequent premature ventricular beats, serum potassium <3.5 mmol/L, type 2 diabetes, and creatinine level. The AUC of the model for predicting VT/VF in the training set was 0.815 (95% CI: 0.763-0.866). A total of 1149 cases were enrolled from Xuzhou Center Hospital as the external validation set. The AUC of the model in the external validation set for predicting VT/VF was 0.755 (95% CI: 0.687-0.823). Calibration curves indicated a good consistency between the predicted and the observed probabilities of VA in both sets. We have established a clinical prediction risk score for predicting the occurrence of VA in AMI patients. The prediction score is easy to use, performs well and can be used to guide clinical practice.

High consumption of colorful fruits and vegetables correlates with low dementia risk, but the exact molecules and the underlying biological mechanisms governing their bioactive profiles are largely unknown. Using a 10‐year observational cohort study coupled with an AI‐driven systems pharmacology platform, we identified a natural triterpenoid compound found in colorful fruits and vegetables, α‐Amyrin (αA), as a therapeutic candidate for Alzheimer's disease (AD). The efficacy of αA in treating the symptoms of AD, such as Tau tangles, damaged mitochondria, and memory loss, was examined using cross‐species models; αA retained memory in AD‐like animal models while also strongly inhibiting Tau pathology, especially p‐Tau217, in a cellular ‘Tau seeding’ system and in Tau[P301S] mice, followed by validation using a human 3D microfluidic system. At molecular level, αA is a robust mitochondrial regulator, enhancing mitochondrial stress resilience and activation of mitophagy. Mechanistically, αA inhibits dual leucine zipper kinase (DLK), leading to the inhibition of DLK‐Sterile Alpha and TIR Motif Containing 1 (SARM1)‐dependent neurodegeneration; this inhibition frees unc‐51 Like Autophagy Activating Kinase 1 (ULK1) from the ULK1‐SARM1 complex, allowing it to participate in autophagy/mitophagy. αA also shows strong translational potential with a 10.1 h half‐life and the ability to cross the blood‐brain barrier. Our results indicate that αA may act as a mitochondrial guardian against AD via modulating the DLK‐SARM1‐ULK1‐autophagy/mitophagy axis while further preclinical and clinical studies are warranted. Dietary habits play a key role in chronic diseases, and higher annual consumption of fruit and vegetable may lower risk of dementia. Artificial intelligence predicts the lipid‐like compound α‐Amyrin (αA) from plants with edible peels as a drug candidate against Alzheimer's disease. The authors suggest that αA promotes cellular mitophagy (mitochondrial self‐clearance) and secures energy production, which then slows down memory loss.