Alterations in Inflammatory Markers and Cognitive Ability after Treatment of Pediatric Obstructive Sleep Apnea

Eldin, Mohamed Shams; Alahmer, Mohamed; Alkashlan, Ebrahim; Zahran, M. A.; Eltonsy, Mohamed; Zewail, Amr; Kasem, Abdelfattah; Abdelaal, Khaled; Seddeek, Mahrous; Ahmed, Zakaria

doi:10.3390/medicina59020204

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…LSI tends better to understand concrete words and direct relationships between documents. However, in the analysis of texts for job matching, it is often necessary to understand abstract terms [13] [14] [15], Cognitive abilities [11] [16], and aspects of the prospective worker's personality [17].…”

Section: Introductionmentioning

confidence: 99%

“…In a jobmatching model, understanding context, relationships between words, and deeper meanings are required [19][20] [21]. The SI often has to limit the number of dimensions (semantic concepts) used to represent documents (difficult to interpret) [22][23] [24][25], Sensitivity to changes in documents [14] [26], and cognitive abilities [27] [16], or aspects of the job candidate's personality.…”

Section: Introductionmentioning

confidence: 99%

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Word2vec-based Latent Semantic Indexing (Word2Vec-LSI) for Contextual Analysis in Job-Matching Application

Sukri,

Samsudin,

Fadzrin

et al. 2024

IJACSA

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Job-matching applications have become a technology that provides solutions for making decisions about accepting and looking for work. The contextual analysis of documents or data from job matching is needed to make decisions. Some existing studies on the analysis of job-matching applications can use the Latent Semantic Indexing (LSI) method, which is based on word-to-word comparisons in the text. LSI has the advantage of contextual analysis. It can analyze amounts of data above 10,000 words. However, the conventional LSI method has limitations in contextual analysis because it uses the exact words but different meanings. Therefore, this paper proposes a new technique called word2vec-based latent semantic indexing (Word2vec-LSI) for contextual analysis, which is based on gensim as a multi-language word library. Then, modeling in text and wordnet and stopword as basic text modeling. We then used word2vec-LSI to perform contextual analysis based on the Irish (IE), Swedish (SE), and United Kingdom (UK) languages in the dataset (Jobs on CareerBuilder UK). The results of applying conventional LSI have an accuracy level of 79%, recall has a value of 79%, precision has a value of 62%, and Fi-Scor has a value of 70% with a similarity level of up to 50%. After implementing word2vec-LSI, it can increase accuracy, recall, and precision, and Fi-Scor both have 84% in contextual analysis, and the similarity level reaches up to 95%. Experiments confirm the usefulness of word2vec-LSI in increasing accuracy for contextual analysis applicable in natural language text mining.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Word2vec-based Latent Semantic Indexing (Word2Vec-LSI) for Contextual Analysis in Job-Matching Application

Sukri,

Samsudin,

Fadzrin

et al. 2024

IJACSA

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“…At variance, the blood levels of some of these proinflammatory somnogenic cytokines, IL-1β, and TNF-α showed large intergroup variations. Their levels decrease following effective treatment (weight loss, CPAP in adults, and AT in children) and thus could be useful for follow-up [12,32].…”

Section: Introductionmentioning

confidence: 99%

Cytokines and Obstructive Sleep Apnea in Childhood: Study of a Group of Children

Nosetti,

Tirelli,

Marino

et al. 2024

Biologics

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Introduction: Obstructive Sleep Apnea (OSA) in children is characterized by repeated episodes of partial or complete obstruction of the upper airways that impair normal ventilation and cause hypoxia and sleep disruption. These episodes activate innate and adaptive immunity resulting in the production of proinflammatory cytokines: IL-1β, IL-6, TNF-α, and reactive oxygen species. The hypothalamic–pituitary–adrenal (HPT) axis is also activated with alteration of the circadian rhythm of cortisol synthesis. OSA in children, and even more in adults, induces a systemic inflammatory condition that contributes to the genesis of clinical complications: poor growth, learning disabilities, cardiovascular changes, insulin resistance, and metabolic syndrome. Methods: A total of 42 non-obese children (age 1–15 years) were enrolled among those sent to our sleep center to perform full polysomnography (PSG). After PSG, 6 children did not show OSA (controls), 20 had mild OSA (m OSA), and 16 had medium-severe OSA (MS OSA). In vitro IL-1β, TNF-α, and serum cortisol levels were measured at 2 and 8 am in the analyzed groups. Results: Cortisol levels did not differ between controls and OSA children. At 2 am, there were no differences between controls and OSA in TNF-α production, whereas at 8 am, TNF-α was reduced in MS-OSA. IL-1β production showed no differences between OSA and controls. Conclusions: In our population, only TNF-α production is suppressed in MS-OSA: this might indicate a role of OSA severity in inducing inflammation. In adults, the phenomenon is more pronounced due to the habitual greater severity/duration of OSA, presence of comorbidities (cardiovascular and metabolic), and different immune system function.

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Is the Pediatric Sleep Questionnaire sensitive for sleep-disordered breathing in children with complex chronic disease?

Kalyoncu,

Namlı,

Yegit

et al. 2023

Sleep Breath

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Alterations in Inflammatory Markers and Cognitive Ability after Treatment of Pediatric Obstructive Sleep Apnea

Cited by 4 publications

References 28 publications

Word2vec-based Latent Semantic Indexing (Word2Vec-LSI) for Contextual Analysis in Job-Matching Application

Word2vec-based Latent Semantic Indexing (Word2Vec-LSI) for Contextual Analysis in Job-Matching Application

Cytokines and Obstructive Sleep Apnea in Childhood: Study of a Group of Children

Is the Pediatric Sleep Questionnaire sensitive for sleep-disordered breathing in children with complex chronic disease?

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