Did acute COVID-19 public health measures lead to enhanced susceptibility to later infections in paediatric populations?
Bibliographic record
Abstract
During the COVID-19 pandemic, a variety of infection control measures—including masking, increased sanitation and social distancing—were used in efforts to reduce the transmission of SARS-CoV-2. These were effective at that time—but did they inadvertently enhance our susceptibility to infections down the line? In late 2022, there was significant surge globally in paediatric patients presenting to accident and emergency departments with severe respiratory illnesses particularly associated with influenza and respiratory syncytial virus infections. Was this due to our nationwide infection control mechanisms resulting in our immune systems being significantly less exposed to infectious challenges and thus, lacked an appropriate pathogen-specific response to future infections. And in particular relevance to a paediatric population, the potential that a reduction in maternal exposure to common respiratory viruses may have also led to the reduced transference of transplacental antibodies in infants, thus leaving young infants more vulnerable to viral infections. The QJM has previously published definitive articles on the epidemiology and prognosis of acute COVID-19 infection in paediatric populations.1 As we learn to live with the COVID-19 virus, we welcome the Commentary piece by Dr. Deng and colleagues from the University of Toronto who provide a of potential causes as well as management and preventive strategies against the recent increase in paediatric viral respiratory illnesses. Epigenetic changes modify the activation of certain genes, but not the DNA code. Epigenetic mechanisms consist of four components: epigenetically modified writers, erasers, readers and chromatin remodellers. Mutations in genes encoding these four components lead to many common Neurodevelopmental Disorders (NDDs). For example, mutations in KMT2A, which encode a DNA-binding protein that methylates histone H3lys4 (H3K4), cause the Wiedemann–Steiner syndrome (OMIM 605130). Recently, a group of NDDs resulting from mutations in genes encoding components of the epigenetic machinery have been defined as Mendelian disorders of the epigenetic machinery (MDEMs). Recent studies have shown that MDEM-associated mutations may disrupt the balance of chromatin states and trigger dysplasia. To date, 70 epigenetic machinery genes have been reported to cause 82 human genetic diseases, involving in particular the KMT, KDM and CHD genetic families. In an analysis of eight unrelated Chinese families with NDD, Dr. Li and colleagues from Central South University, Hunan, China have significantly expanded our knowledge of NDD disease causing genetic variants by identifying eight variants of six epigenetic machinery genes were identified including KMT2A, KMT2D and KMT5B which are epigenetic writers that encode histone methyltransferases, while also identifying variants in the KDM5C and KDM6A genes which are epigenetic erasers that encode histone demethylases. This study extends the mutation spectrum and functional studies of MDEMs and significantly contributes to genetic testing and prenatal diagnosis in families with NDDs.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".