Notice bibliographique
Résumé
Sensorineural hearing loss (SNHL) is a widely and increasingly prevalent sensory deficit that has been recently suggested to contribute to the onset of dementia in older adults.1 Age-related hearing loss (ARHL), the most common type of SNHL, affects over 90 percent of individuals aged 70 years and older.2 The slow development of ARHL and the presence of inter-current disease in most sufferers make determining the cause difficult.Shutterstock, hearing loss, DNA, RNATable 1: Differentially Expressed miRNAs in SSNHL Patients Compared with Normal-Hearing Controls7. Hearing loss, DNA, RNA.Table 2: Validated Target Genes Enriched Pathways of Differentially Expressed miRNAs7. Hearing loss, DNA, RNA.DNA that conveys a person's genetic code is in the central nucleus of the cell, yet it controls much of what takes places in the surrounding cell body and tissue. The DNA chain is made up of coding segments or genes—the blueprint for building the cell's proteins—separated from one another by non-coding segments. The DNA message is transmitted out of the nucleus by messenger ribonucleic acid (mRNA). The non-coding DNA segments were thought to have no function during translation beyond acting as breaks in the DNA code to prevent important gene messages from getting mixed up when the mRNA is built from the DNA strand's code. It is now clear that these non-coding segments of DNA are the home of microRNA (miRNA) “genes.” miRNAs are short chains of non-coding ribonucleic acids that regulate gene expression by binding to complementary sites on mRNAs. miRNA regulation of inner ear sensory cell aging and death may have an etiological role in ARHL, but there is little evidence of a similar role in sudden sensorineural hearing loss (SSNHL).3 SSNHL, an acute-onset, acquired hearing loss of undetermined etiology,4 provides an opportunity to better investigate the role of miRNA's in SNHL. The annual incidence of SSNHL in recent studies varies between 27 and 160 new cases per 100,000 individuals.5,6 The average age of patients presenting to the author's institution is 55 years old (Lee, et al., unpublished data), and the majority of those affected are otherwise in good health and free of the confounding inter-current diseases commonly present in the ARHL population. The sudden onset and significant nature of the hearing loss in SSNHL increase the likelihood of finding biological disease markers. Hence, our research group investigated serum miRNAs in SSNHL patients and age-matched normal-hearing control subjects in a recently published study.7 STUDY HIGHLIGHTS In our study, adult patients who presented within 28 days of onset with an average of 30 dB or greater SSNHL across three contiguous pure-tone audiometric (PTA) frequencies to a tertiary urban academic hospital in 2017 and 2018 were prospectively recruited with the approval of the University of British Columbia's Clinical Research Ethics Board. A contemporaneous age-matched control sample of subjects with average PTA hearing thresholds of 25 dB or better was recruited from the hospital staff and their contacts. Patients whose SSNHL cause was identified and controls with a personal or family history of hearing loss or features of current or past ear disease were excluded. About 10 milliliters of blood were collected from study participants for RNA isolation. TaqMan Low Density Array (TLDA) real-time PCR array cards were used for miRNA profiling of 754 human miRNAs. Relative miRNA content was calculated using the comparative cycle threshold method (Ct).7 The mean miRNA Ct values in the control and SSNHL groups were analyzed using students’ t-test to identify differentially expressed miRNAs that demonstrated a statistically significant ≥2.0 or ≤0.5 inter-group fold difference. The corresponding target genes of the significantly differentially expressed miRNAs were predicted using miRWalk 2.08at a cutoff p-value of <0.01. The DAVID bioinformatics database version 6.89 at a significant level of p <0.05 and miRTarBase10 were utilized for functional annotation and gene pathway enrichment analysis. A total of 36 SSNHL patients (mean age of 53.0 years) and 12 control subjects (50.9 years) were studied. Eight miRNAs were significantly differentially expressed in SSNHL patients’ sera compared to controls. Furthermore, 21 miRNAs were significantly differentially expressed between untreated SSNHL (n=04, 57.7 years) and treated SSNHL patients (n=32, 52.6 years; see Table 1). Most of these miRNAs are abundantly identified in the nervous system and the putative target mRNAs were primarily enriched in signaling pathways phosphatidyl inositol 3 kinase/protein Kinase B (PI3K/Akt), Ras, and mitogen-activated protein kinase (MAPK) (Table 2). Li, et al., in a smaller study11 of nine SSNHL patients and three controls, identified 24 differentially expressed miRNAs (DEMs) that were different from the ones we identified, but their KEGG pathway enrichment analysis also found the MAPK signaling pathway to be one enriched by the target genes of the identified DEMs. Brain-derived neurotrophic factor (BDNF) binds to tyrosine kinase B TrKB receptors expressed by inner ear spiral ganglion neurons (SGNs). This binding triggers the recruitment of multiple effector and adaptor proteins, including PI3K, which in turn activates protein kinase B Akt and MAPK. The kinases act on cytoplasmic proteins and nuclear transcription to stimulate SGN survival and neurite outgrowth.12 BDNF is a strong target of one of the DEMs our group identified miR-132-3p (Table 1) in keeping with previous evidence that miRNAs regulate BDNF activity in adults.13 SGN survival is strongly dependent upon the neutrophins BDNF, neurotrophin-3 (NT-3), and glial cell line-derived neurotrophic factor (GDNF), but their effects on SGNs differ.12 Increasing evidence shows that SSNHL patients illustrate differential expression of miRNAs compared with normal hearing controls. mRNAs of genes in the PI3K/Akt, Ras, and MAPK signalling pathways are targets of these DEMs, and miRNAs may be involved in the pathogenesis of SSNHL. In summary, SSNHL patients have different amounts of some miRNAs compared with normal-hearing individuals. These miRNAs control some of the most important aspects of cells such as their growth and death.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».