Targeting MS4A4A: A novel pathway to improve immunotherapy responses in glioblastoma.

INTRODUCTION: Glioblastoma (GBM) remains a challenging brain tumor to treat, with limited response to PD-1 immunotherapy due to tumor-associated macrophages (TAMs), specifically the M2 phenotype. This study explores the potential of MS4A4A (membrane spanning four domains, subfamily A, member 4A) inhibition in driving M2 macrophage polarization toward the M1 phenotype via the ferroptosis pathway to enhance the effectiveness of immunotherapy in GBM. METHODS: Single-cell RNA sequencing and spatial transcriptomic analyses were employed to characterize M2 macrophages and MS4A4A expression in GBM. In vitro studies utilizing TAM cultures, flow cytometry, and western blot validations were conducted to assess the impact of MS4A4A on the tumor immune microenvironment and M2 macrophage polarization. In vivo models, including subcutaneous and orthotopic transplantation in mice, were utilized to evaluate the effects of MS4A4A knockout and combined immune checkpoint blockade (ICB) therapy on tumor growth and response to PD-1 immunotherapy. RESULTS: Distinct subsets of GBM-associated macrophages were identified, with spatial distribution in tumor tissue elucidated. In vivo experiments demonstrated that inhibiting MS4A4A and combining ICB therapy effectively inhibited tumor growth, reshaped the tumor immune microenvironment by reducing M2 TAM infiltration and enhancing CD8+ T-cell infiltration, ultimately leading to complete tumor eradication. CONCLUSION: MS4A4A inhibition shows promise in converting M2 macrophages to M1 phenotype via ferroptosis, decreasing M2-TAM infiltration, and enhancing GBM response to PD-1 immunotherapy. These findings offer a novel approach to developing more effective immunotherapeutic strategies for GBM.

Single Effective Complex Loading into Zero-Mode Waveguides Optimized with Fluorescence Evaluation at Quenching and Accumulation Checkpoints.

Single-molecule detection with high accuracy and specialty plays an important role in biomedical diagnosis and screening. Zero-mode waveguides (ZMWs) enable the possibility of single biological molecule detection in real time. Nevertheless, the absence of a reliable assessment for single effective complex loading has constrained further applications of ZMWs in complex interaction. Both the quantity and activity of the complex loaded into ZMWs have a critical effect on the efficiency of detection. Herein, a fluorescence evaluation at quenching and accumulation checkpoints was established to assess and optimize single effective complex loading into ZMWs. A primer-template-enzyme ternary complex was designed, and then an evaluation for quantity statistics at the quenching checkpoint and functional activity at the accumulation checkpoint was used to validate the effectiveness of complexes loaded into ZMWs. By optimizing the parameters such as loading time, procedures, and enzyme amount, the single-molecule effective occupancy was increased to 25.48%, achieving 68.86% of the theoretical maximum value (37%) according to Poisson statistics. It is of great significance to provide effective complex-loading validation for improving the sample-loading efficiency of single-molecule assays or sequencing in the future.

Self-correction of cycle threshold values by a normal distribution-based process to improve accuracy of quantification in real-time digital PCR.

The digital polymerase chain reaction (dPCR) is a new and developing nucleic acid detection technology with high sensitivity that can realize the absolute quantitative analysis of samples. In order to improve the accuracy of quantitative results, real-time digital PCR emphasizes the kinetic information during amplification to identify prominent abnormal data. However, it is challenging to use a unified standard to accurately classify the amplification curve of each well as negative and positive, due to the interference caused by various factors in the experiment. In this work, a normal distribution-based cycle threshold value self-correcting model (NCSM) was established, which focused on the feature of the cycle threshold values in amplification curves and conducted continuous detection and correction on the whole. The cycle threshold value distribution was closer to the ideal normal distribution to avoid the influence of interference. Thus, the model achieves a more accurate classification between positive and negative results. The corrective process was applied to plasmid samples and resulted in an accuracy improvement from 92 to 99%. The coefficient of variation was below 5% when considering the quantitation of a range between 100 and 10,000 copies. At the same time, by utilizing this model, the distribution of cycle threshold values at the endpoint can be predicted with fewer thermal cycles, which can reduce the cycling time by around 25% while maintaining a consistency of more than 98%. Therefore, using the NCSM can effectively enhance the quantitative accuracy and increase the detection efficiency based on the real-time dPCR platform.

Automatic Seizure Detection Based on Stockwell Transform and Transformer.

Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.