Reinforcement Learning for SBM Graphon Games with Re-Sampling

The Mean-Field approximation is a tractable approach for studying large population dynamics. However, its assumption on homogeneity and universal connections among all agents limits its applicability in many real-world scenarios. Multi-Population Mean-Field Game (MP-MFG) models have been introduced in the literature to address these limitations. When the underlying Stochastic Block Model is known, we show that a Policy Mirror Ascent algorithm finds the MP-MFG Nash Equilibrium. In more realistic scenarios where the block model is unknown, we propose a re-sampling scheme from a graphon integrated with the finite N-player MP-MFG model. We develop a novel learning framework based on a Graphon Game with Re-Sampling (GGR-S) model, which captures the complex network structures of agents' connections. We analyze GGR-S dynamics and establish the convergence to dynamics of MP-MFG. Leveraging this result, we propose an efficient sample-based N-player Reinforcement Learning algorithm for GGR-S without population manipulation, and provide a rigorous convergence analysis with finite sample guarantee

Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n=300; GSE15459, n=191; and GSE26901, n=109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n=600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n=432; GSE84437, n=431; and TCGA, n=336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC>0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p<0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p<0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p=0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p=0.00085). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC

Comprehensive analysis of metabolic pathway activity subtypes derived prognostic signature in hepatocellular carcinoma

Abstract Objective Metabolic reprogramming is one of the hallmarks of cancer, but metabolic pathway activity‐related subtypes of hepatocellular carcinoma (HCC) have not been identified. Methods Based on the quantification results of 41 metabolic pathway activities by gene set variation analysis, the training cohort (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) with the nonnegative matrix factorization method. Totally 1371 differentially expressed genes among C1, C2, and C3 were identified, and an 8‐gene risk score was established by univariable Cox regression analysis, least absolute shrinkage and selection operator method, and multivariable Cox regression analysis. Results C1 had the strongest metabolic activity, good prognosis, the highest CTNNB1 mutation rate, with massive infiltration of eosinophils and natural killer cells. C2 had the weakest metabolic activity, poor prognosis, was younger, was inclined to vascular invasion and advanced stage, had the highest TP53 mutation rate, exhibited a higher expression level of immune checkpoints, accompanied by massive infiltration of regulatory T cells. C3 had moderate metabolic activity and prognosis, the highest LRP1B mutation rate, and a higher infiltration level of neutrophils and macrophages. Internal cohorts (TCGA, n = 370; GSE14520, n = 239), external cohorts (ICGC, n = 231; GSE116174, n = 64), and clinical subgroup validation showed that the risk score was applicable for patients with diverse clinical features and was effective in predicting the prognosis and malignant progression of patients with HCC. Compared with the low‐risk group, the high‐risk group had a poor prognosis, enhanced cancer stem cell characteristics, activated DNA damage repair, weakened metabolic activity, cytolytic activity, and interferon response. Conclusion We identified HCC subtypes from the perspective of metabolism‐related pathway activity and proposed a robust prognostic signature for HCC

Activated CD4 T cells/Tregs derived immune-metabolism signature provide precise prognosis assessment for gastric cancer and beneficial for treatment option

Background: The prognostic significance of the ratio of activated CD4 T cells to Tregs infiltrating tumor tissues in gastric cancer (GC) remains unknown. Materials and methods: For the quantification of infiltration of immune cells, the ssGSEA algorithm, which is a single sample gene set enrichment analysis, was utilized. Group A was defined as having activated CD4 T cells/Tregs >1, while group B was defined as having activated CD4 T cells/Tregs <1. To compare the overall survival (OS) of the two groups, the Kaplan-Meier survival analysis was employed. The R package 'limma' was used to identify the immune and metabolism related genes that were expressed differentially between the two groups, with a false discovery rate (FDR) less than 0.05. The risk score (RS) was constructed by combining univariate Cox regression analysis, LASSO penalized Cox regression analysis, and multivariate Cox regression analysis. The median RS was used to classify high-risk (HR) and low-risk (LR) groups. Results: A predicted unfavorable outcome of GC was observed when the ratio of activated CD4 T cells to Tregs was less than 1. Our proposed RS was utilized for prognostic risk categorization in ten distinct independent cohorts (TCGA-STAD, n = 371; GSE84437, n = 433; GSE26253, n = 432; GSE13861, n = 65; GSE15459, n = 192; GSE26899, n = 93; GSE26901, n = 109; GSE28541, n = 40; GSE34942, n = 56; GSE62254, n = 300) and exhibited exceptional precision. In terms of tumor microenvironment (TME) and treatment strategies, compared to the LR group, the HR group was characterized by a higher infiltration levels of stromal cells, Tregs, macrophages, Tfh, mast cells, and NK cells, inclined to activated CD4 T cells/Tregs <1, and exhibited insensitivity to immunotherapy and multiple chemotherapy drugs. In relation to the potential molecular mechanism, the excessive activation of oncogenic pathways such as MAPK, hedgehog, WNT, calcium, and TGF-β signaling pathways may accelerate the malignant progression of GC by stimulating angiogenesis, promoting EMT, and altering ECM. Conversely, the overactivation of the P53 pathway is likely to inhibit tumor proliferation by regulating the cell cycle. Conclusion: The immune-metabolism signature associated with the ratio of activated CD4 T cells and Tregs could be used to assess prognosis, TME, and treatment strategies in GC patients

Analysis of safety climate and individual factors affecting bus drivers’ crash involvement using a two-level logit model

Although traffic crashes involving buses are less frequent than those involving other vehicle types, the consequences of bus crashes are high due to the potential for multiple injuries and casualties. As driver error is a primary factor affecting bus crashes, driver safety education is one of the main countermeasures used to mitigate crash risk. In China, however, safety education is not as focused as it should be, largely due to the limited research identifying the specific driver behaviors, and potential influences on those behaviors, that are correlated with crashes. The aim of this study is, therefore, to explore the fleet- and driver-level risk factors underlying bus drivers’ self-reported crash involvement, including analyzing the effect of psychological distress on the most influential driver-level factors. A survey was conducted of 725 drivers from a large Shanghai bus company, and a random-effects two-level logit model was developed to integrate fleet and individual variables. Results showed that: 1) the fleet-level safety climate explained about 8.5% of the model’s variance, indicating it was a valid predictor of self-reported crash involvement; 2) the driver-level factors of drivers’ age, seniority, marital status, positive behavior, and driving anger influenced drivers’ self-reported crash involvement, but ordinary violations, lapses, aggressive violations, and insomnia were the most influential variables; 3) psychological distress appeared to associate with the high frequency of risky driving behavior and the high severity of driving anger. This study’s findings will help bus companies to give more attention to their safety climate and implement more targeted improvements to their driver safety education programs

Real-world systemic sequential therapy with regorafenib for recurrent hepatocellular carcinoma: analysis of 93 cases from a single center

Abstract Background Regorafenib is an oral multikinase inhibitor and became the first second-line systemic treatment for hepatocellular carcinoma (HCC) following the phase III RESORCE trial. This single-center study retrospectively analyzed the clinical data and follow-up results of patients with recurrent HCC treated with regorafenib and discussed the prognostic factors to provide guidance for clinical treatment. Methods Ninety-three recurrent HCC patients were enrolled in the research and follow up from December 2017 to December 2020. Clinical and pathological data were collected. SPSS software v26.0 was used (Chicago, IL, USA) for statistical analysis. A two-sided P < 0.05 was considered statistically significant. Results The patients included 81 males and 12 females with a median age of 57 years. Eighty-seven patients had hepatitis B virus (HBV) infection. The objective response rate (ORR) was 14.0%, and the disease control rate (DCR) was 62.4%. The median overall survival (mOS) and median time to progression (mTTP) were 15.9 and 5.0 months. Multivariate analysis showed that Child–Pugh classification, the Eastern Cooperative Oncology Group performance status (ECOG PS), the neutrophil-to-lymphocyte ratio (NLR), combined treatment, and the time from first diagnosis of HCC to second-line treatment were independent factors affecting the prognosis of recurrent HCC patients. Conclusions This real-world study demonstrated similar findings to those of the RESORCE trial. Regorafenib could effectively improve the prognosis of patients after first-line treatment failure. Combination therapy under multidisciplinary treatment (MDT) team guidance could be effective in impeding tumor progression and improving the prognosis of recurrent HCC patients

Differentiation of Bone Marrow Stem Cells into Schwann Cells for the Promotion of Neurite Outgrowth on Electrospun Fibers

Seeding nerve guidance conduits with Schwann cells can improve the outcome of peripheral nerve injury repair. Bone marrow stem cells (BMSCs) represent a good choice of cell source as they can differentiate into Schwann cells under appropriate conditions. In this work, we systematically investigated the differentiation of BMSCs into Schwann cells on scaffolds comprising electrospun fibers. We changed the alignment, diameter, and surface properties of the fibers to optimize the differentiation efficiency. The uniaxial alignment of fibers not only promoted the differentiation of BMSCs into Schwann cells but also dictated the morphology and alignment of the derived cells. Coating the surface of aligned fibers with laminin further enhanced the differentiation and thus increased the secretion of neurotrophins. When co-cultured with PC12 cells or chick dorsal root ganglion, the as-derived Schwann cells were able to promote the outgrowth of neurites from cell bodies and direct their extension along the fibers, demonstrating the positive impacts of both the neurotrophic effect and the morphological contact guidance. This work offers a promising strategy for integrating fiber guidance with stem cell therapy to augment peripheral nerve injury repair

A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation

Abstract Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases