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Background Effective gene-delivery systems for primary human T cell engineering are useful tools for both basic research and clinical immunotherapy applications. Pseudovirus-based systems and electro-transfection are the most popular strategies for genetic material transduction. Compared with viral-particle-mediated approaches, electro-transfection is theoretically safer, because it does not promote transgene integration into the host genome. Additionally, the simplicity and speed of the procedure increases the attractiveness of electroporation. Here, we developed and optimized an electro-transfection method for the production of engineered chimeric antigen receptor (CAR)-T cells. Results Stimulation of T cells had the greatest effect on their transfection, with stimulation of cells for up to 3 days substantially improving transfection efficiency. Additionally, the strength of the external electric field, input cell number, and the initial amount of DNA significantly affected transfection performance. The voltage applied during electroporation affected plasmid permeation and was negatively correlated with the number of viable cells after electroporation. Moreover, higher plasmid concentration increased the proportion of positively transfected cells, but decreased cell viability, and for single-activated cells, higher cell density enhanced their viability. We evaluated the effects of two clinically relevant factors, serum supplementation in the culture medium and cryopreservation immediately after the isolation of peripheral blood lymphocytes. Our findings showed that our protocol performed well using xeno-free cultured, fresh T cells, with application resulting in a lower but acceptable transfection efficiency of cells cultured with fetal bovine serum or thawed cells. Furthermore, we described an optimized procedure to generate CAR-T cells within 6 days and that exhibited cytotoxicity toward targeted cells. Conclusions Our investigation of DNA electro-transfection for the use in human primary T cell engineering established and validated an optimized method for the construction of functional CAR-T cells.

High-Voltage Direct Current (HVDC) transmission systems require fast and reliable fault diagnosis to ensure secure and stable operation. However, existing methods, including conventional Convolutional Neural Networks (CNNs), often suffer from limited accuracy and degraded training performance as network depth increases. To address these limitations, this study proposes an improved one-dimensional Residual Neural Network (1D-ResNet) that integrates an attention mechanism within the residual blocks to enhance feature extraction, stabilize gradient propagation, and accelerate model convergence. A comprehensive simulated HVDC platform is established to generate multiple fault scenarios, and the proposed network is trained to identify one normal condition and six typical fault types. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 99.15%, outperforming traditional CNN-based approaches by 12.89%. Moreover, the loss value is significantly lower than that of the conventional CNN model, indicating substantial improvements in both robustness and learning efficiency. These findings confirm the effectiveness of the proposed attention-enhanced residual framework for high-precision HVDC fault diagnosis.

This study examined how linguistic complexity features contribute to second language (L2) processing effort by analyzing the Dutch English-L2 learners’ eye movements from GECO and MECO, two eye-tracking corpora. Processing effort was operationalized as reading rate, mean fixation duration, regression rate, skipping rate, and mean saccade amplitude. In Study 1, the lexical, syntactic, and discoursal indices in 272 snippets of a novel in GECO were regressed against these eye-movement measures. The results showed that the one-component partial least square regression (PLS-R) models could explain 11%–37% of the variance in these eye-movement measures and outperformed eight readability formulas (six traditional and two recent cognitively inspired formulas based on the readers’ perception on text difficulty) in predicting L2 processing effort. In Study 2, the eye-tracking data from MECO were used to evaluate whether the findings from Study 1 could be applied more broadly. The results revealed that although the predictability of these PLS-R components decreased, they still performed better than the readability formulas. These findings suggest that the linguistic indices identified can be used to predict L2 text processing effort and provide useful implications for developing systems to assess text difficulty for L2 learners.

Immune cell therapy as a revolutionary treatment modality, significantly transformed cancer care. It is a specialized form of immunotherapy that utilizes living immune cells as therapeutic reagents for the treatment of cancer. Unlike traditional drugs, cell therapies are considered “living drugs,” and these products are currently customized and require advanced manufacturing techniques. Although chimeric antigen receptor (CAR)-T cell therapies have received tremendous attention in the industry regarding the treatment of hematologic malignancies, their effectiveness in treating solid tumors is often restricted, leading to the emergence of alternative immune cell therapies. Tumor-infiltrating lymphocytes (TIL) cell therapy, cytokine-induced killer (CIK) cell therapy, dendritic cell (DC) vaccines, and DC/CIK cell therapy are designed to use the body’s natural defense mechanisms to target and eliminate cancer cells, and usually have fewer side effects or risks. On the other hand, cell therapies, such as chimeric antigen receptor-T (CAR-T) cell, T cell receptor (TCR)-T, chimeric antigen receptor-natural killer (CAR-NK), or CAR-macrophages (CAR-M) typically utilize either autologous stem cells, allogeneic or xenogeneic cells, or genetically modified cells, which require higher levels of manipulation and are considered high risk. These high-risk cell therapies typically hold special characteristics in tumor targeting and signal transduction, triggering new anti-tumor immune responses. Recently, significant advances have been achieved in both basic and clinical researches on anti-tumor mechanisms, cell therapy product designs, and technological innovations. With swift technological integration and a high innovation landscape, key future development directions have emerged. To meet the demands of cell therapy technological advancements in treating cancer, we comprehensively and systematically investigate the technological innovation and clinical progress of immune cell therapies in this study. Based on the therapeutic mechanisms and methodological features of immune cell therapies, we analyzed the main technical advantages and clinical transformation risks associated with these therapies. We also analyzed and forecasted the application prospects, providing references for relevant enterprises with the necessary information to make informed decisions regarding their R&D direction selection.

Lung cancer is the leading cause of cancer deaths in China, and its incidence is closely related to increasing levels of air pollution. The purpose of this study was to investigate the relationship between atmospheric particulate pollutants (including O 3 , CO, NO 2 , SO 2 , PM 2.5 and PM 10 ) of different ages, genders and seasons and pulmonary nodule occurrence in Shijiazhuang city. Using the time series analysis method, focusing on the monthly lag time, the relationship between the concentration of atmospheric particulate pollutants and the incidence of pulmonary nodules was analyzed to understand the effects of pollutants on different demographic and seasonal characteristics. Modest but statistically significant associations were observed between pulmonary nodule detection and atmospheric particulate pollutants. For every IQR increase in monthly concentrations, the relative risks were 1.059 (95% CI 1.006–1.116) for PM 2.5 and 1.117 (95% CI 1.037–1.204) for SO 2 , with a 4 month lag. Converting to standardized 10 µg/m 2 increases, these correspond to 4.8 and 11.6% increases in nodule detection risk, respectively. Slightly stronger associations were observed in women compared to men, and in adults aged 60 and over, though effect sizes were modest with wide confidence intervals. These results suggest potential associations between air pollution exposure and pulmonary nodule detection, though the modest effect sizes and study limitations require cautious interpretation. The observed time-lag patterns and demographic differences may inform future research, but additional prospective studies are needed to establish causal relationships and clinical significance.

This paper investigates the optimal distribution of damping material in vibrating structures subject to harmonic excitations by using topology optimization method. Therein, the design objective is to minimize the structural vibration level at specified positions by distributing a given amount of damping material. An artificial damping material model that has a similar form as in the SIMP approach is suggested and the relative densities of the damping material are taken as design variables. The vibration equation of the structure has a non-proportional damping matrix. A system reduction procedure is first performed by using the eigenmodes of the undamped system. The complex mode superposition method in the state space, which can deal with the non-proportional damping, is then employed to calculate the steady-state response of the vibrating structure. In this context, an adjoint variable scheme for the response sensitivity analysis is developed. Numerical examples are presented for illustrating validity and efficiency of this approach. Impacts of the excitation frequency as well as the damping coefficients on topology optimization results are also discussed.

Current diagnostic biomarkers for acute myocardial infarction (AMI), such as troponins, often lack specificity, leading to false positives under non-cardiac conditions. Recent studies have implicated circadian rhythm and immune infiltration in the pathogenesis of AMI. This study hypothesizes that analyzing the interplay between circadian rhythm-related gene expression and immune infiltration identify highly specific diagnostic biomarkers for AMI. Our results demonstrated differential expression of 15 circadian rhythm-related genes (CRGs) between AMI patients and healthy individuals, with five key genes—JUN, NAMPT, S100A8, SERPINA1, and VCAN identified as key contributors to this process. Functional enrichment analyses suggest these genes significantly influence cytokine and chemokine production in immune responses. Immune infiltration assessments using ssGSEA indicated elevated levels of neutrophils, macrophages, and eosinophils in AMI patients. Additionally, we identified potential therapeutic implications with 13 pivotal miRNAs and 10 candidate drugs targeting these genes. The Benjamini–Hochberg method was employed to adjust for multiple testing, and the results retained statistical significance. RT-qPCR analysis further confirmed the upregulation of these five genes under hypoxic conditions, compared to controls. Collectively, our findings highlight the critical role of CRGs in AMI, providing a foundation for improved diagnostic approaches and novel therapeutic targets.

Microwave radar, utilizing the differences in Doppler frequencies from moving target echoes, offers remote sensing capabilities and continuous all‐weather monitoring for geological disasters. However, intelligent identification of debris flow signals using such radar remains unexplored. Therefore, we implemented 12 deep learning models coupled with a voting strategy to develop classification models for identifying the debris flow, using 24,000 samples across eight categories of targets obtained from field experiments. Each model demonstrated significant proficiency in classification, achieving a remarkable highest accuracy of 95.46% for the multi‐object classification. Among the individual models, the vgg16 model with a simple and deep architecture excelled in debris flow identification, exhibiting a high precision and a low false alarm rate. The voting strategy further improved the reliability of individual deep learning model. We propose that employing radar‐based deep learning techniques combined with extensive field data represents a crucial advancement in the monitoring and early warning of debris flow. Plain Language Summary Microwave radar detects geological disasters by sending out signals and analyzing how they bounce back from moving targets. It has many advantages, such as being able to see through obstructions and working in any weather conditions, both day and night. However, using radar signals and artificial intelligence models to identify debris flow has not been thoroughly studied. In our research, through field experiments and indoor compilation, we developed a large data set of 24,000 samples with eight different categories including the debris flow and falling rocks. Then we tested 12 deep learning algorithms with a voting approach to create a series of models that can recognize the debris flow in the complex environment. Each model performed well, with the best one achieving an impressive accuracy of 95.46% in classifying multiple objectives. The vgg16 model with a simple and deep architecture stood out for its effectiveness in identifying the debris flow. Our findings suggest that combining radar technology with deep learning models, especially with extensive real‐world data, will significantly improve how we prevent the debris flow, making it a major step forward in monitoring and early warning of similar natural disasters. Key Points Radar‐based deep learning models were established for debris flow identification Eight labeled targets, including debris flow and falling rocks, can be effectively classified Energy spectrums from radar signals were skillfully used for the multi‐object detection

Central precocious puberty (CPP) may lead to premature pubertal onset. While the GnRH stimulation test remains the gold standard, its invasive nature and prolonged procedure limit clinical utility, particularly in pediatric populations. Emerging biomarkers, including luteinizing hormone, estradiol, kisspeptin, and vitamin D, have shown promise in distinguishing CPP from normal puberty. This study systematically evaluated the diagnostic utility of these biomarkers both individually and in novel combinatorial models, with the aim of establishing a non-invasive and more accessible diagnostic alternative for CPP. This retrospective study included 129 girls with CPP and 116 age-matched controls. Clinical characteristics, including height, weight, body mass index, and bone age, were recorded. Serum levels of luteinizing hormone, estradiol, kisspeptin, vitamin D, progesterone, and prolactin were measured. The diagnostic performance of individual biomarkers and three combined biomarker models was assessed using receiver operating characteristic curve analysis. Model 1 included luteinizing hormone and kisspeptin, Model 2 incorporated vitamin D, and Model 3 added estradiol. CPP patients exhibited significantly higher levels of luteinizing hormone (2.51 vs. 0.23 mIU/mL), kisspeptin (1.59 vs. 0.96 μg/L), and estradiol (25.86 vs. 13.41 pg/mL) and lower vitamin D levels (20.13 vs. 25.90 ng/mL) compared to controls (all p < 0.001). Model 3 demonstrated the highest diagnostic accuracy with an AUC of 0.939 (95 % CI: 0.910–0.968), sensitivity of 89.06 %, and specificity of 87.93 %, outperforming individual biomarkers and other models. This study highlights the potential of combining luteinizing hormone, kisspeptin, vitamin D, and estradiol into a single diagnostic model for CPP.

This paper investigates topology optimization of the electrode coverage over piezoelectric patches attached to a thin-shell structure to reduce the energy consumption of active vibration control under harmonic excitations. The constant gain velocity feedback control method is employed, and the structural frequency response under control is analyzed with the finite element method. In the mathematical formulation of the proposed topology optimization model, the total energy consumption of the control system is taken as the objective function, and a constraint of the maximum allowable dynamic compliance is considered. The pseudo-densities indicating the distribution of surface electrode coverage over the piezoelectric layers are chosen as the design variables, and a penalized model is employed to relate the active damping effect and these design variables. The sensitivity analysis scheme of the control energy consumption with respect to the design variables is derived with the adjoint-variable method. Numerical examples demonstrate that the proposed optimization model is able to generate optimal topologies of electrode coverage over the piezoelectric layers, which can effectively reduce the energy consumption of the control system. Also, numerical comparisons with a minimum-volume optimization model show the advantage of the proposed method with respect to energy consumption. The proposed method may provide useful guidance to the layout optimization of piezoelectric smart structures where the energy supply is limited, such as miniature vibration control systems.