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IntroductionT cell receptor (TCR) repertoires provide valuable insights into complex human diseases, including cancers. Recent advancements in immune sequencing technology have significantly improved our understanding of TCR repertoire. Some computational methods have been devised to identify cancer-associated TCRs and enable cancer detection using TCR sequencing data. However, the existing methods are often limited by their inadequate consideration of the correlations among TCRs within a repertoire, hindering the identification of crucial TCRs. Additionally, the sparsity of cancer-associated TCR distribution presents a challenge in accurate prediction.MethodsTo address these issues, we presented DeepLION2, an innovative deep multi-instance contrastive learning framework specifically designed to enhance cancer-associated TCR prediction. DeepLION2 leveraged content-based sparse self-attention, focusing on the top k related TCRs for each TCR, to effectively model inter-TCR correlations. Furthermore, it adopted a contrastive learning strategy for bootstrapping parameter updates of the attention matrix, preventing the model from fixating on non-cancer-associated TCRs.ResultsExtensive experimentation on diverse patient cohorts, encompassing over ten cancer types, demonstrated that DeepLION2 significantly outperformed current state-of-the-art methods in terms of accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the curve (AUC). Notably, DeepLION2 achieved impressive AUC values of 0.933, 0.880, and 0.763 on thyroid, lung, and gastrointestinal cancer cohorts, respectively. Furthermore, it effectively identified cancer-associated TCRs along with their key motifs, highlighting the amino acids that play a crucial role in TCR-peptide binding.ConclusionThese compelling results underscore DeepLION2's potential for enhancing cancer detection and facilitating personalized cancer immunotherapy. DeepLION2 is publicly available on GitHub, at https://github.com/Bioinformatics7181/DeepLION2, for academic use only.

IntroductionPrevious studies have indicated different immunological recovery trajectories based on CD4 count or CD4/CD8 ratio. However, these immune indicators are interconnected, and relying solely on one indicator may lead to inaccurate estimates. Therefore, it is essential to develop a comprehensive trajectory model that integrates CD4 count, CD8 count and CD4/CD8 ratio.MethodsWe utilized a group-based multi-trajectory model to characterize the latent cluster of recovery based on measurements of CD4 count, CD8 count and CD4/CD8 ratio over a period of up to 96 months following ART initiation. Subsequently, we investigated the characteristics associated with trajectory groups, especially sex and age. Cox model and Kaplan-Meier survival curve were employed to assess differences in all-cause, AIDS-related and non-AIDS related mortality between trajectory groups.ResultsA total of 14,718 eligible individuals were followed for a median of 55 months. Longitudinal model identified four subgroups: group 1 (32.5%, low CD4 and CD4/CD8 inversion), group 2 (25.9%, high CD8 and CD4/CD8 inversion), group 3 (27.2%, slow recovery of CD4 and CD4/CD8 inversion) and group 4 (14.4%, rapid increase of CD4 and normal CD4/CD8). Immune recovery was slower in male than in female, and in elders than in youngers. Compared to group 2, group 1 (adjusted hazard ratio [aHR]=3.28; 95% CI 2.33-4.60) and group 3 (aHR=1.56; 95% CI 1.09-2.24) had increased risk of all-cause mortality after adjusting for other factors. Besides, group 1 (aHR=2.17) and group 3 (aHR=1.58) had higher risk of non-AIDS related mortality, and group 1 (aHR=5.92) had significantly increased risk of AIDS related mortality.ConclusionLongitudinal trajectory analysis of multiple immune indicators can be employed to guide targeted interventions among vulnerable populations in clinical practice.

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

Tumor mutation burden (TMB) is a widely recognized stratification biomarker for predicting the efficacy of immunotherapy; however, the number and universal definition of the categorizing thresholds remain debatable due to the multifaceted nature of efficacy and the imprecision of TMB measurements. We proposed a minimal joint p-value criterion from the perspective of differentiating the comprehensive therapeutic advantages, termed TMBcat, optimized TMB categorization across distinct cancer cohorts and surpassed known benchmarks. The statistical framework applies to multidimensional endpoints and is fault-tolerant to TMB measurement errors. To explore the association between TMB and various immunotherapy outcomes, we performed a retrospective analysis on 78 patients with non-small cell lung cancer and 64 patients with nasopharyngeal carcinomas who underwent anti-PD-(L)1 therapy. The stratification results of TMBcat confirmed that the relationship between TMB and immunotherapy is non-linear, i.e., treatment gains do not inherently increase with higher TMB, and the pattern varies across carcinomas. Thus, multiple TMB classification thresholds could distinguish patient prognosis flexibly. These findings were further validated in an assembled cohort of 943 patients obtained from 11 published studies. In conclusion, our work presents a general criterion and an accessible software package; together, they enable optimal TMB subgrouping. Our study has the potential to yield innovative insights into therapeutic selection and treatment strategies for patients.

Sepsis-associated encephalopathy (SAE) is a cognitive impairment associated with sepsis that occurs in the absence of direct infection in the central nervous system or structural brain damage. Microglia are thought to be macrophages of the central nervous system, devouring bits of neuronal cells and dead cells in the brain. They are activated in various ways, and microglia-mediated neuroinflammation is characteristic of central nervous system diseases, including SAE. Here, we systematically described the pathogenesis of SAE and demonstrated that microglia are closely related to the occurrence and development of SAE. Furthermore, we comprehensively discussed the function and phenotype of microglia and summarized their activation mechanism and role in SAE pathogenesis. Finally, this review summarizes recent studies on treating cognitive impairment in SAE by blocking microglial activation and toxic factors produced after activation. We suggest that targeting microglial activation may be a putative treatment for SAE.

Large artery atherosclerotic (LAA) stroke is closely associated with atherosclerosis, characterized by the accumulation of immune cells. Early recognition of LAA stroke is crucial. Circulating exosomal circRNAs profiling represents a promising, noninvasive approach for the detection of LAA stroke. Exosomal circRNA sequencing was used to identify differentially expressed circRNAs between LAA stroke and normal controls. From a further validation stage, the results were validated using RT-qPCR. We then built logistic regression models of exosomal circRNAs based on a large replication stage, and receiver operating characteristic (ROC) curves were constructed to assess the diagnostic efficacy. Using exosomal circRNA sequencing, large sample validation, and diagnostic model construction revealed that exosomal circ_0043837 and circ_ 0001801were independent predictive factors for LAA stroke, and had better diagnostic efficacy than plasma circRNAs. In the atherosclerotic group (AS), we developed a nomogram for clinical use that integrated the two-circRNA-based risk factors to predict which patients might have the risk of plaque rupture. Circulating exosomal circRNAs profiling identifies novel predictive biomarkers for the LAA stroke and plaque rupture, with superior diagnostic value than plasma circRNAs. It might facilitate the prevention and better management of this disease.