TNFR2 and Regulatory T Cells: Potential Immune Checkpoint Target in Cancer Immunotherapy

TNF has both proinflammatory and antiinflammatory effects. It binds to two structurally related but functionally distinct receptors TNFR1 and TNFR2. Unlike TNFR1 that is ubiquitously expressed, TNFR2 expression is more limited to myeloid and lymphoid cell lineages including a fraction of regulatory T cells (Treg). In general, TNFR1 is responsible for TNF-mediated cell apoptosis and death, and mostly induces proinflammatory reactions. However, TNFR2 mainly leads to functions related to cell survival and immune suppression. Treg play an indispensable role in maintaining immunological self-tolerance and restraining excessive immune reactions deleterious to the host. Impaired Treg-mediated immune regulation has been observed in various autoimmune diseases as well as in cancers. Therefore, Treg might provide an ideal therapeutic target for diseases where the immune balance is impaired and could benefit from the regulation of Treg properties. TNFR2 is highly expressed on Treg in mice and in humans, and TNFR2+ Treg reveal the most potent suppressive capacity. TNF-TNFR2 ligation benefits Treg proliferation, although the effect on Treg suppressive function remains controversial. Here, we will describe in detail the TNF-mediated regulation of Treg and the potential clinical applications in cancer immunotherapy as well as in autoimmune diseases, with the focus on human Treg subsets

Pricing decision research for TPL considering different logistics service level influencing the market demand

Purpose: With the rapid development of economy and the support of government policy, the development of the logistics industry has become a new economic growth engine. As we all know, the reasonable price of logistics service is the most critical factor for logistics enterprises to win market share and make profit. At the same time, the service level is one of the most important factors which will influence the size of the market share. Therefore, this paper constructs a pricing model considering a situation that the logistics service level affects the market demand. This model helps the enterprises to make scientific decisions. Methodology: To achieve this objective, this paper constructs the TPL service and the pricing decision models based on the game theory. Findings: The conclusion shows that under the situation of independent decision-making, the enterprise which has strong ability of logistics service does not necessarily have a competitive advantage, while pricing equilibrium under the situation of joint decision-making, not only make both sides get more income, but also be conducive to improve the level of service. Research limitations: In this research, there are some assumptions that might affect the accuracy the model such as there are only two TPL enterprises to participate in, and considerations are taken under the condition of complete information environment. These assumptions can be relaxed in the future work. Originality: In this research, logistics service level is taken account into the areas of logistics service pricing, which makes the models more practical and more perfect. And this paper constructs game models based on game theory to make up the limitations of traditional pricing theories in logistics service pricing.Peer Reviewe

A framework for characterising energy consumption of machining manufacturing systems

Energy consumption in machining manufacturing systems is increasingly of interest due to concern for global climate change and manufacturing sustainability. To utilise energy more effectively, it is paramount to understand and characterise the energy consumption of machining manufacturing systems. To this end, a framework to analyse energy consumption characteristics in machining manufacturing systems from a holistic point of view is proposed in this paper. Taking into account the complexity of energy consumption in machining manufacturing systems, energy flow is described in terms of three layers of machining manufacturing systems including machine tool layer, task layer and auxiliary production layer. Furthermore, the energy consumption of machining manufacturing systems is modelled in the spatial and temporal dimensions, respectively, in order to quantitatively characterise the energy flow. The application of the proposed modelling framework is demonstrated by employing a comprehensive analysis of energy consumption for a real-world machining workshop. The characteristics of energy consumption for machine tool layer, task layer and auxiliary production layer are, respectively, obtained using quantitative models in the spatial and temporal dimensions, which provides a valuable insight into energy consumption to support the exploration of energy-saving potentials for the machining manufacturing systems

Modeling and Modulation of NNPC Four-Level Inverter for Solar Photovoltaic Power Plant

Photovoltaic (PV) power plant is an attractive way of utilizing the solar energy. For high-power PV power plant, the multilevel inverter is of potential interest. In contrast to the neutral-point clamped (NPC) or flying capacitor (FC) multilevel inverter, the nested neutral point clamped (NNPC) four-level inverter has better features for solar photovoltaic power plant. In practical applications, the common mode voltage reduction of the NNPC four-level is one of the important issues. In order to solve the problem, a new modulation strategy is proposed to minimize the common mode voltage. Compared with the conventional solution, our proposal can reduce the common mode voltage to 1/18 of the DC bus voltage. Moreover, it has the capability to balance the capacitor voltages. Finally, we carried out time-domain simulations to test the performance of the NNPC four-level inverter

Arterial stiffness in subclinical atherosclerosis quantified with ultrafast pulse wave velocity measurements: a comparison with a healthy population using propensity score matching

Purpose This study aimed to evaluate changes in ultrafast pulse wave velocity (ufPWV) in individuals with arterial stiffness and subclinical atherosclerosis (subAS), and to provide cutoff values. Methods This retrospective study recruited 231 participants, including 67 patients with subAS. The pulse wave velocity was measured at the beginning and end of systole (PWV-BS and PWVES, respectively) using ultrafast ultrasonography to assess arterial stiffness. The right and left common carotid arteries were measured separately, and laboratory metabolic parameters were also collected. Participants were balanced between groups using propensity score matching (PSM) at a 1:1 ratio, adjusting for age, sex, and waist-to-hip ratio as potential confounders. Cutoff values of ufPWV for monitoring subAS were determined via receiver operating characteristic (ROC) curve analysis. Results PWV-ES, unlike PWV-BS, was higher in the subAS subgroup than in the subAS-free group after PSM (all P0.05). Conclusion PWV-ES measured using ultrafast ultrasonography was significantly higher in individuals with subAS than in those without. Specific PWV-ES cutoff values showed potential for predicting an increased risk of subAS

Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions of differentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RA-CD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso

Dbh+ catecholaminergic cardiomyocytes contribute to the structure and function of the cardiac conduction system in murine heart

The heterogeneity of functional cardiomyocytes arises during heart development, which is essential to the complex and highly coordinated cardiac physiological function. Yet the biological and physiological identities and the origin of the specialized cardiomyocyte populations have not been fully comprehended. Here we report a previously unrecognised population of cardiomyocytes expressing Dbhgene encoding dopamine beta-hydroxylase in murine heart. We determined how these myocytes are distributed across the heart by utilising advanced single-cell and spatial transcriptomic analyses, genetic fate mapping and molecular imaging with computational reconstruction. We demonstrated that they form the key functional components of the cardiac conduction system by using optogenetic electrophysiology and conditional cardiomyocyte Dbh gene deletion models. We revealed their close relationship with sympathetic innervation during cardiac conduction system formation. Our study thus provides new insights into the development and heterogeneity of the mammalian cardiac conduction system by revealing a new cardiomyocyte population with potential catecholaminergic endocrine function

Blood immune cell profiling in adults with longstanding type 1 diabetes is associated with macrovascular complications

Aims/hypothesisThere is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity.MethodsAdult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects.ResultsHierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications.ConclusionsMachine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications