COVID-19: Looking Into the Overlooked
Bibliographic record
Abstract
"Viral infections have plagued humanity since the beginning of time causing numerous deaths through specific pathogenic events directly related to the virus and indirectly, through secondary infections accompanying or following the viral episode. Highly pathogenic bacteria, such as Neisseria meningitidis, Streptococcus pneumoniae, and Haemophilus influenzae can overgrow during viral infections, and cause severe bacterial infections. During the 1918 Spanish influenza pandemic, for instance, the majority of influenza fatalities were likely caused by secondary pneumococcal pneumonia (Morens et al., 2008), but we were not aware of it until 2008, when tissue biopsy investigations revealed this important association. Secondary bacterial infections also accounted for 25–50% of deaths during the 2009 H1N1 influenza pandemic (Macintyre et al., 2018). In this instance, the association with secondary infections, particularly with S. pneumoniae, was acknowledged only almost 10 years after the pandemic. In the current COVID-19 pandemic, the role of co-infectiveness remains unclear. At the peak of the pandemic, broad-spectrum antibiotics have been administered to the majority of patients admitted with COVID-19 to hospital to prevent secondary infections but also, some antibiotics, like teicoplanin, have been used due to their alleged antiviral properties. In an early report, secondary infections were detected in 1 out of 7 patients. Among those that died, 50% had secondary infections (Zhou F. et al., 2020). Other reports indicate that bacterial infections were documented in <10% of COVID-19 patients (Zhou P. et al., 2020). In light of this uncertainty, antibiotic use has been reported to be as high as 74% among patients with COVID-19 who were admitted to ICUs (Cox et al., 2020). Some infections may have been caused by changes in colonization resistance, others due to increase use of corticosteroids, parenteral nutrition or just intravenous catheter infections. The administration of broad-spectrum antibiotics in the absence of high suspicion or documented infection challenges the current dogma of antimicrobial stewardship. This problem is further compounded by the fact that acute respiratory syndromes due to infection or non-infection are notoriously difficult to discriminate. However, the risk and potential severity of secondary infections associated with COVID-19, particularly in overcrowded clinical settings and in immune-compromised patients, has in many cases reduced the compliance with the local prescription practice guidelines. A quick look at host factors may now be more relevant than ever. As the first wave of the pandemic is tailing off in Europe, we have gathered substantial and actionable evidence that suggests that both severity and mortality associated with COVID-19 is due to host factors, ranging from endothelial and coagulation disturbances leading to the formation of micro thrombi to a major dysregulation of the host immunity and inflammatory response. Here, a recent study has shown significant survival benefits of corticosteroids administration to patients with severe COVID-19 infection. How is the host contributing to the severity of COVID-19? It was a matter of time that a genome wide analysis study (GWAS) showed some association with disease severity. Indeed, we have recently learnt that particular blood groups (group A) may be linked to respiratory failure in COVID-19 patients (Ellinghaus et al., 2020). However, with a moderate increase severity risk (odds ratio: 1.45 with respect to other blood groups), it is unlikely that this association explain all the variability of disease severity. Here we should not forget that one of the major contributors that regulate individual immune/inflammatory responses is the human microbiome. In particular, the gut microbiome may influence directly and indirectly the immune response generated. Accordingly, the lymphocyte populations may adopt a more pro-inflammatory phenotype when microbiome diversity is reduced, as typically observed in old age patients (Figure 1) and recently reviewed by Dhar and Mohanty (2020)."@eng
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".