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Record W4378674261 · doi:10.1093/qjmed/hcad080

Did acute COVID-19 public health measures lead to enhanced susceptibility to later infections in paediatric populations?

2023· article· en· W4378674261 on OpenAlexaboutno aff
Seamas C. Donnelly

Bibliographic record

VenueQJM · 2023
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Lead (geology)2019-20 coronavirus outbreakPublic healthSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineEnvironmental healthIntensive care medicineVirologyBiologyInternal medicineOutbreakDiseaseInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0020.006
Open science0.0040.002
Research integrity0.0220.030
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.156
GPT teacher head0.404
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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