Nanovaccine-Mediated Cell Selective Delivery of Neoantigens Potentiating Adoptive Dendritic Cell Transfer for Personalized Immunization

Neoantigen vaccines and adoptive dendritic cell (DC) transfer are major clinical approaches to initiate personalized immunity in cancer patients. However, the immunization efficacy is largely limited by the in vivo trajectory including neoantigens’ access to resident DCs and DCs’ access to lymph nodes (LNs). Herein, an innovative strategy is proposed to improve personalized immunization through neoantigen-loaded nanovaccines synergized with adoptive DC transfer. It is found that it enables selective delivery of neoantigens to resident DCs and macrophages by coating cancer cell membranes onto neoantigen-loaded nanoparticles. In addition, the nanovaccines promote the secretion of chemokine C-C motif ligand 2 (CCL2), CCL3, and C-X-C motif ligand 10 from macrophages, thus potentiating the access of transferred DCs to LNs. This immunization strategy enables coordinated delivery of identified neoantigens and autologous tumor lysate-derived undefined antigens, leading to initiation of antitumor T cell immunity in a personalized manner. It significantly inhibits tumor growth in prophylactic and established mouse tumor models. The findings provide a new vision for potentiating adoptive cell transfer by nanovaccines, which may open the door to a transformative possibility for improving personalized immunization.     

Model of reactive precipitation process of preparation nano-ZnO in rotating packed bed: Contribution ratio

With excellent micromixing characteristic of rotating packed bed (RPB), many nanoparticles with small average size, narrower distribution and good morphology had been successfully and continuously prepared. To reveal complex crystal process, an empirical model were developed to simulate nano-ZnO by considering mass changed, population balance equation, growth rate G, nucleation rate B, drop sizes D_i, and resident time t. The predicted particle sizes were shown good agreement with experimental data with error of ±10%. Therefore, it was further adopted to predict the effects of rotating speed, liquid flow rate and reactant concentration on the mean particle size. To look more deeply insight in this process, their contribution ratios were further analyzed. The proposed empirical models were of great helpful to obtain suitable operation conditions for preparing much better properties of nanoparticles with fewer experiments. It was also beneficial to produce other nanoparticles in RPB.     

Treatment of Brain Metastases of Non-Small Cell Lung Carcinoma

Lung cancer is one of the most common malignant neoplasms. As a result of the disease’s progression, patients may develop metastases to the central nervous system. The prognosis in this location is unfavorable; untreated metastatic lesions may lead to death within one to two months. Existing therapies—neurosurgery and radiation therapy—do not improve the prognosis for every patient. The discovery of Epidermal Growth Factor Receptor (EGFR)—activating mutations and Anaplastic Lymphoma Kinase (ALK) rearrangements in patients with non-small cell lung adenocarcinoma has allowed for the introduction of small-molecule tyrosine kinase inhibitors to the treatment of advanced-stage patients. The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein with tyrosine kinase-dependent activity. EGFR is present in membranes of all epithelial cells. In physiological conditions, it plays an important role in the process of cell growth and proliferation. Binding the ligand to the EGFR causes its dimerization and the activation of the intracellular signaling cascade. Signal transduction involves the activation of MAPK, AKT, and JNK, resulting in DNA synthesis and cell proliferation. In cancer cells, binding the ligand to the EGFR also leads to its dimerization and transduction of the signal to the cell interior. It has been demonstrated that activating mutations in the gene for EGFR-exon19 (deletion), L858R point mutation in exon 21, and mutation in exon 20 results in cancer cell proliferation. Continuous stimulation of the receptor inhibits apoptosis, stimulates invasion, intensifies angiogenesis, and facilitates the formation of distant metastases. As a consequence, the cancer progresses. These activating gene mutations for the EGFR are present in 10–20% of lung adenocarcinomas. Approximately 3–7% of patients with lung adenocarcinoma have the echinoderm microtubule-associated protein-like 4 (EML4)/ALK fusion gene. The fusion of the two genes EML4 and ALK results in a fusion gene that activates the intracellular signaling pathway, stimulates the proliferation of tumor cells, and inhibits apoptosis. A new group of drugs—small-molecule tyrosine kinase inhibitors—has been developed; the first generation includes gefitinib and erlotinib and the ALK inhibitor crizotinib. These drugs reversibly block the EGFR by stopping the signal transmission to the cell. The second-generation tyrosine kinase inhibitor (TKI) afatinib or ALK inhibitor alectinib block the receptor irreversibly. Clinical trials with TKI in patients with non-small cell lung adenocarcinoma with central nervous system (CNS) metastases have shown prolonged, progression-free survival, a high percentage of objective responses, and improved quality of life. Resistance to treatment with this group of drugs emerging during TKI therapy is the basis for the detection of resistance mutations. The T790M mutation, present in exon 20 of the EGFR gene, is detected in patients treated with first- and second-generation TKI and is overcome by Osimertinib, a third-generation TKI. The I117N resistance mutation in patients with the ALK mutation treated with alectinib is overcome by ceritinib. In this way, sequential therapy ensures the continuity of treatment. In patients with CNS metastases, attempts are made to simultaneously administer radiation therapy and tyrosine kinase inhibitors. Patients with lung adenocarcinoma with CNS metastases, without activating EGFR mutation and without ALK rearrangement, benefit from immunotherapy. This therapeutic option blocks the PD-1 receptor on the surface of T or B lymphocytes or PD-L1 located on cancer cells with an applicable antibody. Based on clinical trials, pembrolizumab and all antibodies are included in the treatment of non-small cell lung carcinoma with CNS metastases.     

Progress and major BARRIERS of nanocatalyst development in direct methanol fuel cell: A review

Direct methanol fuel cells (DMFC), among the most suited and prospective alternatives for portable electronics, have lately been treated with nanotechnology. DMFCs may be able to remedy the energy security issue by having low operating temperatures, high conversion efficiencies, and minimal emission levels. Though, slow reaction kinetics are a significant restriction of DMFC, lowering efficiency and energy output. Nowadays, research is more focused on fundamental studies that are studying the factors that can improve the capacity and activity of catalysts. In DMFC, among the most widely explored catalysts are platinum and ruthenium which are enhanced in nature by the presence of supporting materials such as nanocarbons and metal oxides. As a result, this research sheds light on nanocatalyst development for DMFCs based on Platinum noble metal. To summarize, this research focuses on the structure of nanocatalysts, as well as support materials for nanocatalysts that can be 3D, 2D, 1D, or 0D. The support material described is made up of CNT, CNF, and CNW, which are the most extensively used because they improve the performance of catalysts in DMFCs. In addition, cost estimations for fuel cell technology are emphasized to show the technology's status and requirements. Finally, challenges to nanocatalyst development have been recognized, as well as future prospects, as recommendations for more innovative future research.     

Trade-off study between risk and benefit in safety devices for hydrogen refueling stations using a dynamic physical model

As hydrogen refueling stations become increasingly common, it is clear that a high level of economic efficiency and safety is crucial to promoting their use. One way to reduce costs is to use a simple orifice instead of an excess flow valve, which Japanese safety regulations have identified as a safety device. However, there is concern about its effect on refueling time and on risk due to hydrogen leakage. To clarify the effect, we did a study of model-based refueling time evaluation and quantitative risk assessment for a typical refueling station. This study showed that an orifice is an effective alternative safety device. The increase in refueling time was less than 10%, based on simulations using a dynamic physical model of the station. Neither was there a significant difference in the risk between a configuration with excess flow valves and one with an orifice.     

Topology optimization of flexure hinges with a prescribed compliance matrix based on the adaptive spring model and stress constraint

This paper focuses on the configuration design of flexure hinges with a prescribed compliance matrix and preset rotational center position. A new method for the topology optimization of flexure hinges is proposed based on the adaptive spring model and stress constraint. The hinge optimization model is formulated by maximizing the bending displacement with a spring while optimizing the compliance matrix to a prescribed value. To avoid numerical instability, an artificial spring is used as an auxiliary calculation, and a new strategy is developed for adaptively adjusting the spring stiffness according to the prescribed compliance matrix. The maximum stress of flexure hinge is limited by using a normalized P-norm of the effective von Mises stress, and a position constraint of rotational center is proposed to predetermine the position of the rotational center. In addition, to reduce the error of the stress measurement, a simple but effective filtering method is presented to obtain a complete black-and-white design. Numerical examples are used to verify the proposed method. Topology results show that the obtained flexure hinges have the prescribed compliance matrix and preset rotational center position while also meeting the stress requirements.     

5A06-O aluminium–magnesium alloy sheet warm hydroforming and optimization of process parameters

The uniaxial tensile test of the 5A06-O aluminium–magnesium (Al–Mg) alloy sheet was performed in the temperature range of 20–300 °C to obtain the true stress–true strain curves at different temperatures and strain rates. The constitutive model of 5A06-O Al–Mg alloy sheet with the temperature range from 150 to 300°C was established. Based on the test results, a unique finite element simulation platform for warm hydroforming of 5A06-O Al–Mg alloy was set up using the general finite element software MSC.Marc to simulate warm hydroforming of classic specimen, and a coupled thermo-mechanical finite element model for warm hydroforming of cylindrical cup was built up. Combined with the experiment, the influence of the temperature field distribution and loading conditions on the sheet formability was studied. The results show that the non-isothermal temperature distribution conditions can significantly improve the forming performance of the material. As the temperature increases, the impact of the punching speed on the forming becomes particularly obvious; the optimal values of the fluid pressure and blank holder force required for forming are reduced.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.