Bibliographic record
Abstract
Advances in immunology are inherently linked to progress in implementing novel methods as best illustrated by the development of the cre-lox technique that allows to analyse the effect of single genes on lymphocyte development and function by the generation of "conditional" knock-out mice (1). Development of novel as well as the optimization of existing technologies and methods furthers constant progress in biomedical research. The most recent game changer being the development of the bacterial immune system CRISPR-Cas9 into a universal tool for gene and genome editing (2).Here in this research topic on "Methods in Molecular Innate Immunity: 2022" we provide a brief collection of state-of the art methods and protocols to enable in-depth studies of innate immune responses in in vitro cell culture systems as well as in in vivo models.The identification of innate lymphoid cells (ILCs) and the rapid progress made in this field showed that ILCs exert essential roles in immune responses and tissue homeostasis (3). Four detailed protocols deal with the characterization of ILCs, their genetic manipulation, as well as the analysis of their metabolic states, respectively. Macrophages and neutrophils are the first line of the innate immune defence. While macrophages emerged as key instruments to study innate immune responses due to their easy differentiation in vitro and their robustness in cell culture (5), neutrophils are extremely short-lived and isolation strategies for in vitro assays were only recently developed (6). In addition to its central role in host defence upon microbial challenge, the immune system is increasingly recognized as an integral part of fundamental physiological processes such as development, reproduction and wound healing, which involves a very close crosstalk with other body systems such as metabolism, the central nervous system and the cardiovascular system is evident (7). One prominent example being the discovery that TNFa is secreted from adipose tissue in obese mice and drives insulin resistance, highlighting that metabolic disorders are intimately linked to dysregulated immune responses (8). In an original research article, Iovino et al.present novel insights into the link of macrophage activation by saturated fatty acids and IRE1 RNase in metabolic reprogramming. Their work highlights a key role of IRE1α in HIF-1α-mediated glycolysis in macrophages independent of XBP1s.Immune cell activation is tightly linked to changes in the metabolic wiring and mitochondrial activity. The development of devices to measure extracellular flux by redox potential changes in small volumes generated the basis to study cellular metabolic changes upon immune cell activation in great detail (9). Grudzinska et al. provide a protocol that exemplifies how extracellular flux (XF) analysis can be used to measure metabolism and oxidate burst in activated neutrophils.The core function of innate immunity is the quick and often cell intrinsic reaction towards pathogen challenge (10). Zhi et al. detail investigations of the cGAS-STING signaling pathway and its modulation by traditional Chinese medicines. Furthermore, detailed studies of host-pathogen interactions at a time-resolved and molecular level ------------------------------Editorials frame the aims and objectives of the research within your Topic, as well as placing its findings in a broader context. Your Editorial should present the contributing articles of the Research Topic but should not be just a table of contents. Editorials should not include unpublished or original data. Editorials have a maximum word limit of 1000 for Topics with 5-10 articles, and may include 1 figure. The word limit can be increased by 100 words for each additional article in the Topic, up to a maximum of 5,000 words for 50 articles or more.Articles published within a Research Topic should not be listed in the reference list but rather the in-text citation should be hyperlinked directly to the article.Editorials should have the title format: "Editorial: [Title of Research Topic]". Topic Editors are not required to pay a fee to publish an Editorial.Each Topic Editor is encouraged to provide feedback on the Editorial and be listed as an author.Only one Editorial can be published per Research Topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.031 | 0.030 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".