Diseases of the ear

Nesterova, Anastasia P.; Klimov, Eugene; Zharkova, Maria; Sozin, Sergey; Соболев, В. В.; Ivanikova, Natalia V.; Shkrob, Maria; Yuryev, Anton

doi:10.1016/b978-0-12-817086-1.00007-5

Cited by 5 publications

(6 citation statements)

References 61 publications

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“…Shown in Supplementary Table 8, Figure 2e and Supplementary Figure 4, pathway enrichment using the 22 genes mapped from the 22 SNPs was performed with Enrichr (Kuleshov et al 2016) and as expected, found neural and microtubule structural related pathway hits from Reactome (Fabregat et al 2018) and Elsevier pathway (Nesterova et al 2019) database resources. The top pathways were Synaptic Exocytosis in Dementia (MAPT and NSF, p-value 0.00026), Genes with Mutations Associated with Frontotemporal Dementia (MAPT, p-value 0.006) and Hypoxia related pathways (BNIP3, p-value 0.024).…”

Section: Gwas Summary Statisticssupporting

confidence: 53%

Exploring autism and autism co-occurring condition associations to elucidate multivariate genetic mechanisms and insights

Salenius,

Väljä,

Thusberg

et al. 2024

Preprint

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BackgroundGenome-wide association (GWAS) and large scaled exome based studies have successfully uncovered hundreds of associations and genes related to autism. Autism is a partially heritable neurodevelopmental condition. Autistic people may also have other co-occurring conditions such as ADHD, anxiety disorders, depression, mental health issues, learning difficulty, physical health conditions and communication challenges. The concomitant development of autism and other neurological conditions is assumed to result from a complex interplay between genetics and the environment. However, only a limited number of studies have performed multivariate genetic autism association studies.MethodsWe conducted the largest multivariate GWAS with autism and 11 autism co-occurring traits (ADHD, ADHD child, anxiety, IBS, depression, digestion disorders, education attainment, epilepsy, panic, social phobia) using summary statistics from leading reputable consortiums: iPSYCH, PGC, 23andMe and UK Biobank. Multivariate associations and central traits were further identified. Subsequently, colocalization and Mendelian randomization (MR) analysis were performed on the associations identified with the central traits containing autism. To further validate our findings we utilized independent datasets consisting of 35,584 exome sequence iPSYCH data and 53 (13 probands) Whole Genome Sequence from the GEMMA project.ResultsMultivariate GWAS resulted in 380 significant associations (p-value < 5e-8), among which 203 are reported for the first time for any trait. 22 SNPs were identified to contain autism and one or more comorbidities in their central trait set, including variants mapped to known SFARI autism genes such as MAPT and NEGR1. NSF, a serotonin receptor binding gene, was found to be associated with autism with education attainment. Mendelian randomization and colocalization analyses found that education attainment, ADHD and ADHD childhood are liable traits toward autism. Additionally, allele proportions and distribution differences between MAPT (17q21.31) region aberrations and MAPT H1/H2 haplotypes, known to associate with neurodevelopment, and the reported autism-central SNPs were found within GEMMA autism probands and controls. Pathway and gene card analysis also implicated microbiome, enteric inflammation, and central nervous system enrichments.ConclusionsOur study combined multivariate genome-wide association testing with systematic decomposition to identify novel genetic associations related to autism and autism co-occurring driver traits. Statistical tests were applied to discern evidence for shared and interpretable liability between autism and co-occurring traits. We additionally detected differences in the SNP association frequency patterns between autism probands and familial controls in an independent validation cohort. These findings contribute to our understanding of the complex genetic underpinnings of autism and provide valuable insights into its potential driver traits and genetic markers.

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Section: Gwas Summary Statisticssupporting

confidence: 53%

Exploring autism and autism co-occurring condition associations to elucidate multivariate genetic mechanisms and insights

Salenius,

Väljä,

Thusberg

et al. 2024

Preprint

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“…Ear diseases are varied and can influence the sense of balance and hearing. Ear disorders can be categorised according to the part of the ear affected, i.e., the inner, middle, or external ear [1,2]. The innermost part of the ear is the inner ear, which includes the organs responsible for the sense of hearing and balance.…”

Section: Introductionmentioning

confidence: 99%

“…The innermost part of the ear is the inner ear, which includes the organs responsible for the sense of hearing and balance. The inner ear is located in the temporal bone and comprises three primary parts: the semicircular canals, the vestibule, and the cochlea [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Hospital Admission Profile Related to Inner Ear Diseases in England and Wales

Taybeh

Naser

2023

Healthcare

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Background: Due to an expansion in the usage of medications (such as anticancer therapies), increased exposure to noise, and an increase in life expectancy, the prevalence of inner ear disease-related hearing loss is rising. Diseases of the inner ear are frequently accompanied by other conditions, such as chronic heart failure, systemic inflammation, arterial hypertension, and cerebrovascular disease. The aim of this study was to investigate the profile of hospital admissions linked to inner ear diseases in England and Wales. Method: This was an ecological descriptive study using public medical databases in England and Wales. Diagnostic codes for diseases of the inner ear (H80–H83) were used to identify all hospital admissions. Between 1999 and 2020, the chi-squared test was used to assess the difference between the admission rates. Results: From 5704 in 1999 to 19,097 in 2020, the total annual number of hospital admissions increased by 234.8%, which corresponds to a 192.3% increase in the admission rate [from 10.94 (95% CI 10.66–11.22) in 1999 to 31.98 (95% CI 31.52–32.43) in 2020 per 100,000 people, p < 0.01]. “Disorders of vestibular function” and “other inner ear diseases” were the most frequent causes of hospital admissions due to inner ear diseases, accounting for 47.6% and 43.6%, respectively. The age range of 15 to 59 years accounted for 42.3% of all diseases of the inner ear hospital admissions. Around 59.6% of all admissions were made by females. The female admission rate increased by 210.1% (from 12.43 (95% CI 12.01–12.85) in 1999 to 38.54 (95% CI 37.84–39.24) in 2020 per 100,000 people). The male admission rate for diseases of the inner ear increased by 169.6% [from 9.37 (95% CI 9.00–9.75) in 1999 to 25.26 (95% CI 24.69–25.84) per 100,000 people] in 2020. Conclusion: Inner ear disease admissions increased markedly in England and Wales during the past two decades. Females and the middle-aged population were at higher risk of being admitted for inner ear diseases. Further cohort studies are warranted to identify other risk factors and develop effective prevention strategies.

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“…The ear consists of three basic parts which are the outer ear, middle ear, and inner ear. The outer ear forms a pinna and ear canal (Nesterova et al, 2019). The tympanic membrane (TM) separating the outer ear and middle ear is located in the deep of the ear canal.…”

Section: Introductionmentioning

confidence: 99%

Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

Alhudhaif

Cömert

Polat

2021

PeerJ Computer Science

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Background Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone. Methods In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes. Results The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.

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Diseases of the ear

Cited by 5 publications

References 61 publications

Exploring autism and autism co-occurring condition associations to elucidate multivariate genetic mechanisms and insights

Exploring autism and autism co-occurring condition associations to elucidate multivariate genetic mechanisms and insights

Hospital Admission Profile Related to Inner Ear Diseases in England and Wales

Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

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