[Platelet Count Doubling after One Course of Demethylated Drug Therapeutic Predicts the Treatment Response and Efficacy of Patients with Myelodysplastic Syndrome].

OBJECTIVE: To investigate the predictive value of platelet doubling (platelet count doubling) after one course of hypomethylating agents (HMA) on the treatment response and efficacy of myelodysplastic syndrome (MDS). METHODS: Clinical and pathological data of 75 patients who received HMA in our hospital from January 2017 to March 2022 were collected and analyzed. All patients were divided into two groups according to whether their platelet count doubled after one course of treatment, including platelet doubling group and non-doubling group, and statistical analysis was performed to compare the treatment response and efficacy between the two groups. In addition, platelet count changes were compared between azacitidine and decitabine therapy. RESULTS: Compared with the non-doubling platelet count group, the ORR of the doubling platelet group was significantly better after 3 courses of treatment (P =0.002), and there was a statistically significant difference in the number of HI between the two groups (P =0.005). In addition, the median survival time (MST) was 26 months in the platelet doubling group and 11 months in the non-doubling group (P =0.001). The overall survival (OS) and 1- and 2-year survival rates of the platelet doubling group were also significantly better than those of the non-doubing group. Multivariate COX analysis showed that platelet count doubling was an independent predictor of OS in MDS patients after 1 course of treatment (P =0.013). There was no significant difference in the response rate of platelet count doubling between MDS patients treated with azacitidine and decitabine (33.3% vs 23.8%, P >0.05). CONCLUSION: Platelet count doubling after one course of treatment can be used as a predictor of response rate and survival of demethylated drug therapy in MDS patients. In addition, there was no significant difference in the response rate of platelets in MDS patients treated with azacitidine or dicetabine.

An Autologous Macrophage-Based Phenotypic Transformation-Collagen Degradation System Treating Advanced Liver Fibrosis.

In advanced liver fibrosis (LF), macrophages maintain the inflammatory environment in the liver and accelerate LF deterioration by secreting proinflammatory cytokines. However, there is still no effective strategy to regulate macrophages because of the difficulty and complexity of macrophage inflammatory phenotypic modulation and the insufficient therapeutic efficacy caused by the extracellular matrix (ECM) barrier. Here, AC73 and siUSP1 dual drug-loaded lipid nanoparticle is designed to carry milk fat globule epidermal growth factor 8 (MFG-E8) (named MUA/Y) to effectively inhibit macrophage proinflammatory signals and degrade the ECM barrier. MFG-E8 is released in response to the high reactive oxygen species (ROS) environment in LF, transforming macrophages from a proinflammatory (M1) to an anti-inflammatory (M2) phenotype and inducing macrophages to phagocytose collagen. Collagen ablation increases AC73 and siUSP1 accumulation in hepatic stellate cells (HSCs) and inhibits HSCs overactivation. Interestingly, complete resolution of liver inflammation, significant collagen degradation, and HSCs deactivation are observed in methionine choline deficiency (MCD) and CCl4 models after tail vein injection of MUA/Y. Overall, this work reveals a macrophage-focused regulatory treatment strategy to eliminate LF progression at the source, providing a new perspective for the clinical treatment of advanced LF.

Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study.

Background: Identifying patients with hepatocellular carcinoma (HCC) at high risk of recurrence after hepatectomy can help to implement timely interventional treatment. This study aimed to develop a machine learning (ML) model to predict the recurrence risk of HCC patients after hepatectomy. Methods: We retrospectively collected 315 HCC patients who underwent radical hepatectomy at the Third Affiliated Hospital of Sun Yat-sen University from April 2013 to October 2017, and randomly divided them into the training and validation sets at a ratio of 7:3. According to the postoperative recurrence of HCC patients, the patients were divided into recurrence group and non-recurrence group, and univariate and multivariate logistic regression were performed for the two groups. We applied six machine learning algorithms to construct the prediction models and performed internal validation by 10-fold cross-validation. Shapley additive explanations (SHAP) method was applied to interpret the machine learning model. We also built a web calculator based on the best machine learning model to personalize the assessment of the recurrence risk of HCC patients after hepatectomy. Results: A total of 13 variables were included in the machine learning models. The multilayer perceptron (MLP) machine learning model was proved to achieve optimal predictive value in test set (AUC = 0.680). The SHAP method displayed that γ-glutamyl transpeptidase (GGT), fibrinogen, neutrophil, aspartate aminotransferase (AST) and total bilirubin (TB) were the top 5 important factors for recurrence risk of HCC patients after hepatectomy. In addition, we further demonstrated the reliability of the model by analyzing two patients. Finally, we successfully constructed an online web prediction calculator based on the MLP machine learning model. Conclusion: MLP was an optimal machine learning model for predicting the recurrence risk of HCC patients after hepatectomy. This predictive model can help identify HCC patients at high recurrence risk after hepatectomy to provide early and personalized treatment.

Enhanced nuclear gene delivery via integrating and streamlining intracellular pathway.

The essential challenge of gene therapy is to develop safe and efficient vectors that escort genes to target sites. However, due to the cumbersome workflow of gene transfection into cells, successive gene loss occurs. This leads to considerable reductions in nuclear gene uptake, eventually causing low gene expression. Herein, we designed a gene vector named CA3S2 (C: N,N'-cystamine-bis-acrylamide [CBA], A: agmatine dihydrochloride [Agm], S: 4-(2-aminoethyl) benzenesulfonamide [ABS]) with excellent gene transfection ability. This vector can promote gene delivery to the nucleus via enhanced endoplasmic reticulum (ER) targeting through integrating and streamlining of the complex intracellular pathway. Briefly, ABS endowed CA3S2/DNA nanoparticles with not only a natural ER-targeting tendency attributed to the caveolae-mediated pathway but also direct receptor-binding capacity on the ER surface. Agm enabled CA3S2 to enhance lysosomal escape and nuclear uptake ability. The gene delivery efficiency of CA3S2 was significantly better than that of polyethyleneimine 25K (PEI 25K). Therefore, CA3S2 is a promising gene carrier, and the ER-targeting strategy involving intracellular pathway integration and streamlining has potential for gene therapy.