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Background: 8-oxoguanine DNA glycosylase can maintain genomic stability and integrity. However, it can interfere with the regular DNA damage repair process, possibly leading to the development of cancer and various other human diseases when its activity becomes abnormal. Current methods for detecting 8-oxoguanine DNA glycosylase activity often suffer from low sensitivity, time-consuming procedures, labor-intensive requirements, and the need for specialized equipment and trained professionals for execution. Consequently, there is an urgent need for a portable, user-friendly 8-oxoguanine DNA glycosylase assay that offers swift results and supports real-time testing. Results: We've developed a PERFUME method that combines primer exchange reaction and functionalized G-quadruplex/hemin DNAzyme for sensitive detection of Fpg, a typical 8-oxoguanine DNA glycosylase. Utilizing a single probe and T4 Polynucleotide Kinase (PNK) simplifies the experiment to a one-step reaction at 37 °C in 3 h, reducing sample consumption and improving sensitivity. We chose functionalized hemin cofactors, significantly improving catalytic efficiency and enhancing detection capability. This biosensor detects Fpg activity with a sensitivity as low as 0.11 U mL−1, displaying exceptional sensitivity, selectivity, and interference resistance in human serum and bacterial cell extracts under isothermal conditions. The biosensor demonstrates remarkable selectivity and ability for Fpg inhibitors screening. In addition, this biosensor enables reading the sample's RGB values using a smartphone, facilitating accurate quantification of Fpg activity without the necessity for specialized equipment. Significance: PERFUME simplifies Fpg detection by using a single hairpin and PNK in a one-step process. We utilize FUME to enhance catalytic efficiency, it surpassing the performance of traditional G-quadruplex/hemin DNAzyme methods. This approach excels in analyzing Fpg in human serum and bacterial extracts. It allows quantitative Fpg detection using UV–Vis and smartphones under isothermal conditions, making it valuable for clinical diagnosis.

Abstract Non‐viral vector chimeric antigen receptor (CAR)‐T cells have garnered increasing attention due to their ability to efficiently eradicate cancer cells while mitigating undesirable side effects. However, the current methods for engineering chimeric antigen receptor T (CAR‐T) cells employ viral vectors that result in permanent CAR expression and potentially severe negative impacts. As a solution to these challenges, triggering transitory expression of CARs in T cells via messenger RNA (mRNA) has emerged as a promising strategy. Currently, electroporation is a common method used to introduce the mRNA encoding the CAR into the T cells. Moreover, there has been increasing attention on the exploration of innovative mRNA delivery systems, including lipid, polymer‐based nanoparticle, exosomes and peptide transduction domains. Additionally, we also explored the functions of different types of mRNA in mRNA‐based CAR‐T cell therapy. The auxiliary mRNA, exemplified by systems such as megaTAL and nuclease transposon systems, demonstrates its capacity to extend CAR‐T cell viability and survival. This perspective offers the current state of mRNA‐based CAR‐T cell therapy and provides valuable insights into future research avenues.

In this paper, we construct neural networks with ReLU, sine, and 2x as activation functions. For a general continuous f defined on [0, 1]d with continuity modulus ωf (·), we construct ReLU-sine-2x networks that enjoy an approximation rate... where M,N ∊ N+ are the hyperparameters related to widths of the networks. As a consequence, we can construct ReLU-sine-2x network with the depth 6 and width max... that approximates... within a given tolerance ∊ > 0 measured in the Lp norm with... denotes the Hölder continuous function class defined on [0, 1]d with order α ∊ (0, 1] and constant μ > 0. Therefore, the ReLU-sine-2x networks overcome the curse of dimensionality in an approximation on Hαμ ([0, 1]d). In addition to its super expressive power, functions implemented by ReLU-sine-2x networks are (generalized) differentiable, enabling us to apply stochastic gradient descent to train. [ABSTRACT FROM AUTHOR]

Variable selection and parameter estimation are fundamental and important problems in high dimensional data analysis. In this paper, we employ the hard thresholding regularization method [1] to handle these issues under the framework of high-dimensional and sparse linear regression model. Theoretically, we establish a sharp non-asymptotic estimation error for the global solution and further show that the support of the global solution coincides with the target support with high probability. Motivated by the KKT condition, we propose a primal dual active set algorithm (PDAS) to solve the minimization problem, and show that the proposed PDAS algorithm is essentially a generalized Newton method, which guarantees that the proposed PDAS algorithm will converge fast if a good initial value is provided. Furthermore, we propose a sequential version of the PDAS algorithm (SPDAS) with a warm-start strategy to choose the initial value adaptively. The most significant advantage of the proposed procedure is its fast calculation speed. Extensive numerical studies demonstrate that the proposed method performs well on variable selection and estimation accuracy. It has favorable exhibition over the existing methods in terms of computational speed. As an illustration, we apply the proposed method to a breast cancer gene expression data set. [ABSTRACT FROM AUTHOR]