Editorial: Intracellular bacterial pathogens: Infection, immunity and interventions
Bibliographic record
Abstract
particularly the organism's survival within the macrophages by referring to various studies (4-7). 27 More specifically, they have drawn the attention of the readers to absence of a uniform, well accepted nomenclature for describing sRNAs and the difficulty it poses to compare their regulatory 29 functions across different Brucella species. The authors have proposed to designate new sRNAs as 30 they are being identified with acronym "Bsr" for Brucella sRNA regardless of the species in which the 31 sRNA is identified. The need to elucidate the sRNAs-mediated pathways in the brucellae was also 32The next two papers addresses important gaps in our current knowledge with respect to the disease 35 pathogenesis and vaccine development against Mycobacterium avium subspecies paratuberculosis 36 (MAP). The MAP is responsible for causing chronic debilitating enteritis known as Johne's Disease in 37 ruminants that accrue substantial losses to the livestock industry globally (8,9). 38The first paper Purdie et al. reported important gene transcripts as correlates of vaccine protection 39 following vaccination of sheep with the commercial Johne's disease vaccine Gudair® using a 40 transcriptomic approach; whereas the second manuscript by Blake et al. described development of 41 3D bovine intestinal organoid to understand host-pathogen interaction and pathogenesis of MAP. 42Having a thorough understanding of the correlates of protection help designing better vaccines (10). 43 For majority of the currently approved vaccines, antibodies in the serum or mucosa correlates with 44 disease protection and is quantified using ELISA, neutralization, and phagocytic assays. The first 45 paper presented a transcriptomics approach to identify vaccine-induced correlates of protection. 46 Since the genes were differentially expressed between sheep with protective vs. unprotected responses, these genetic correlates have the potential as tools for identification and culling of poor 48 vaccine responders to curtail losses. 49The other paper details out the development and characterisation of physiologically relevant in vitro 51 3D bovine intestinal organoid models for their application to investigate MAP pathogenesis and 3 understand host-pathogen interaction in the small intestine without the need of a target host to 53 carry out experimental studies. They have achieved this by challenging these enteroid-derived 54 models of bovine intestine with a laboratory reference strain and a field MAP isolate and using a 55 combination of techniques such as confocal microscopy and qRT-PCR. 56The last manuscript Urbani et al. described the clinical presentation, pathogenicity and therapeutic 58 response in tick-borne Ehrlichia canis and Hepatozoon canis in concomitant infection of canine 59 parvovirus in dogs. It also emphasized the need to screen and evaluate dogs for infections such as 60 ehrlichiosis etc. when they come from geographic areas endemic for vector-borne pathogens. 61 62 In summary, the results of the above mentioned studies and review represent new findings on the 63 host pathogen interaction, disease pathogenesis, and vaccine-induced correlates of protection 64 against some intracellular pathogens of veterinary importance that have a zoonotic potential. The 65 papers published in this research topic highlight application of cutting edge methods such as 66 transcriptomics and organoids to study disease pathogenesis and evaluation of vaccine efficacy, 67 which introduces reader to the new avenues to explore host pathogen interaction and the 68 development of novel therapeutics and prophylactics against intracellular pathogens. 69
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".