The changes in the coexpression of PNNs, PV and PTPσ provide valuable insights into the connection between CSPGs and PV neurons.

Background. There is a poor prognosis for diffuse large B-cell lymphoma (DLBCL), one of the most common types of non-Hodgkin lymphoma (NHL). Through gene expression profiles, this study intends to reveal potential subtypes among patients with DLBCL by evaluating their prognostic impact on immune cells. Methods. Immune subtypes were developed based on CD8+ T cells and natural killer cells calculated from gene expression profiles. The comparison of prognoses and enriched pathways was made between immune subtypes. Following this validation step, samples from the independent data set were analyzed to determine the correlation between immune subtype and prognosis and immune checkpoint blockade (ICB) response. To provide a model to predict the DLBCL immune subtypes, machine learning methods were used. The virtual screening and molecular docking were adopted to identify small molecules to target the immune subtype biomarkers. Results. A training data set containing 432 DLBCL samples from five data sets and a testing dataset containing 420 DLBCL samples from GSE10846 were used to develop and validate immune subtypes. There were two novel immune subtypes identified in this study: an inflamed subtype (IS) and a noninflamed subtype (NIS). When compared with NIS, IS was associated with higher levels of immune cells and a better prognosis for immunotherapy. Based on the random forest algorithm, a robust machine learning model has been established by 12 hub genes, and the area under the curve (AUC) value is 0.948. Three small molecules were selected to target NIS biomarkers, including VGF, RAD54L, and FKBP8. Conclusion. This study assessed immune cells as prognostic factors in DLBCL, constructed an immune subtype that could be used to identify patients who would benefit from ICB, and constructed a model to predict the immune subtype.

A big data-based heterogeneous Internet of Vehicles engineering cloud system resource allocation optimization algorithm is proposed for the sake of meeting the needs of Internet of Vehicles applications and improving the rationality and efficiency of cloud system resource allocation. Based on taking the minimum cloud system delay as the resource allocation target, a multislot cloud system delay optimization model and its indicative function are constructed, the probability distribution function is derived according to the obtained multidimensional probability distribution function set, and the available channels of the vehicle in different time periods are determined. In this way, the matching degree between the vehicle and the channel is solved, the delay optimization model is turned into a convex optimization problem with independent variables, and the resource allocation algorithm for different task offload destinations is optimized. Meanwhile, by building a heterogeneous vehicle network simulation system, the performance of the algorithm is evaluated from the perspectives of resource rental cost, weighted resource utilization, and bit loss rate. As can be learned from the simulation results, the proposed algorithm can effectively reduce the cost of resource rental, and at the same time, the advantages of resource utilization and bit loss rate are relatively significant, so it has certain effectiveness and practicability.

Extramedullary blast crisis of chronic myeloid leukemia (CML) is defined as extramedullary disease composed of blasts regardless of the proliferation of blasts in the bone marrow. The commonly affected sites are the lymph node, central nervous system, bone, skin, and soft tissue. However, skin infiltration of CML patients as the initial presentation while their bone marrow is still in the chronic phase is extremely rare. In this article, we present a case of a 51-year-old woman who was admitted to our hospital complaining about a skin nodule in her right calf and easy fatigability for 1 week. The peripheral blood and bone marrow analysis both supported the diagnosis of CML in the chronic phase, whereas the excisional biopsy specimen obtained from her right calf showed immature cells infiltration, and fluorescence in situ hybridization test was positive for p210 BCR/ABL1 gene rearrangement. Based on the presence of extramedullary myeloid sarcoma, the patient was diagnosed with extramedullary myeloid blast crisis of CML despite the chronic phase in the bone marrow.

Background. Oxidative stress (OS) can either lead to leukemogenesis or induce tumor cell death by inflammation and immune response accompanying the process of OS through chemotherapy. However, previous studies mainly focus on the level of OS state and the salient factors leading to tumorigenesis and progression of acute myeloid leukemia (AML), and nothing has been done to distinguish the OS-related genes with different functions. Method. First, we downloaded single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) data from public databases and evaluated the oxidative stress functions between leukemia cells and normal cells by the ssGSEA algorithm. Then, we used machine learning methods to screen out OS gene set A related to the occurrence and prognosis of AML and OS gene set B related to treatment in leukemia stem cells (LSCs) like population (HSC-like). Furthermore, we screened out the hub genes in the above two gene sets and used them to identify molecular subclasses and construct a model for predicting therapy response. Results. Leukemia cells have different OS functions compared to normal cells and significant OS functional changes before and after chemotherapy. Two different clusters in gene set A were identified, which showed different biological properties and clinical relevance. The sensitive model for predicting therapy response based on gene set B demonstrated predictive accuracy by ROC and internal validation. Conclusion. We combined scRNAseq and bulk RNAseq data to construct two different transcriptomic profiles to reveal the different roles of OS-related genes involved in AML oncogenesis and chemotherapy resistance, which might provide important insights into the mechanism of OS-related genes in the pathogenesis and drug resistance of AML.

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Cyanobacteria in Chaohu Lake multiply rapidly and diffuse in large quantities every summer, which has a serious impact on the normal life of the surrounding residents and the local economic development. Therefore, it is urgent to control the cyanobacterial pollutants in Chaohu Lake. In this context, in order to improve the scientificalness and feasibility of control measures, it is an important prerequisite and condition to grasp the change of cyanobacterial pollutant diffusion in Chaohu Lake. For this reason, a computational model for cyanobacterial pollutant diffusion in Chaohu Lake, China, was designed based on the relevant large data. The design of the model is divided into three parts: the first part builds an area calculation model to analyze the change of cyanobacterial pollutant diffusion area; the second part builds a concentration calculation model to analyze the change of cyanobacterial pollutant concentration; and the third part combines the previous two to build a diffusion change calculation model to analyze the rule of cyanobacterial pollutant diffusion change in Chaohu Lake. In order to verify the feasibility and validity of the model, simulation experiments were carried out. The results show that, under the large data related to cyanobacteria pollution in Chaohu Lake, China, from May to August 2017, the calculation model is used to calculate the cyanobacteria pollutant diffusion change. The data obtained are basically consistent with the actual situation, which proves the feasibility and validity of the model. This provides data support for the cyanobacteria pollution control in Chaohu Lake and improves the efficiency and effect of the control.