Did acute COVID-19 public health measures lead to enhanced susceptibility to later infections in paediatric populations?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,002 | 0,006 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,022 | 0,030 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».