ReviewInternational journal of molecular sciences2026
Single-Cell Immune Atlases to Map Small Extracellular Vesicle Cargo in Tuberculosis-Diabetes Comorbidity: A Narrative Review and Conceptual Roadmap.
Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Does Tuberculosis Leave a Thromboinflammatory Memory After Cure? A Narrative Review with a Conceptual Framework on Hypercoagulability, Cellular Reservoirs, and Extracellular Vesicle Signaling.International journal of molecular sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis-diabetes mellitus (TB-DM) is increasingly recognized as a syndemic in which chronic metabolic dysregulation amplifies tuberculosis severity, delays treatment response, and increases relapse and mortality. However, conventional systemic correlates soluble cytokines and bulk whole-blood transcriptomic signatures often appear broadly similar between TB and TB-DM. This highlights a key gap: clinically meaningful immune dysfunction in TB-DM likely resides in specific lung and blood cell states that are poorly resolved by bulk assays. Small extracellular vesicles (EVs) in plasma and bronchoalveolar lavage (BAL) provide a tractable "liquid biopsy" layer because their RNA and protein cargo can integrate information from infected macrophages, neutrophils, and epithelial/endothelial compartments, and may also include pathogen-derived components. Yet most EV studies remain bulk and cell-agnostic, and interpretation is constrained by heterogeneous vesicle mixtures, selective cargo packaging, and co-isolated non-vesicular contaminants, issues that are especially problematic for nucleic-acid claims without rigorous controls. In this targeted narrative review (2010-2026), we argue that single-cell and multimodal immune reference atlases, including scRNA-seq/CITE-seq, provide a needed scaffold to link EV cargo patterns to specific immune cell states, pathways, and anatomic compartments in TB-DM, enabling prioritized candidates and testable hypotheses. We outline three complementary frameworks: reference-atlas anchoring to project EV cargo modules onto atlas-defined immune states; orthogonal triangulation combining computational inference with immunoaffinity enrichment, targeted validation, and functional assays; and cautious use of "droplet-era" extracellular signals as hypothesis-generating priors for EV-producing states. Implemented in longitudinal, clinically annotated cohorts with standardized EV workflows, atlas-guided EV profiling could yield cell-of-origin-resolved biomarkers of TB-DM immunopathology and treatment response, while prioritizing mechanistically plausible targets for host-directed intervention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.