Use of a Network-Based Method to Identify Latent Genes Associated with Hearing Loss in Children

Hearing loss is a total or partial inability to hear. Approximately 5% of people worldwide experience this condition. Hearing capacity is closely related to language, social, and basic emotional development; hearing loss is particularly serious in children. The pathogenesis of childhood hearing loss remains poorly understood. Here, we sought to identify new genes potentially associated with two types of hearing loss in children: congenital deafness and otitis media. We used a network-based method incorporating a random walk with restart algorithm, as well as a protein-protein interaction framework, to identify genes potentially associated with either pathogenesis. A following screening procedure was performed and 18 and 87 genes were identified, which potentially involved in the development of congenital deafness or otitis media, respectively. These findings provide novel biomarkers for clinical screening of childhood deafness; they contribute to a genetic understanding of the pathogenetic mechanisms involved.

Polysomnography and Other Sleep Testing

Multiple methods are available for evaluating sleep concerns: clinical screening instruments, sleep diaries, polysomnography with multiple sleep latency test and maintenance of wakefulness test, and actigraphy. This chapter reviews the tools used to assess patients with sleep concerns. Screening tools such as questionnaires are used to stratify patients on the basis of their clinical characteristics and risk factors for sleep difficulties. Patients at high risk may need urgent polysomnography or further treatment.

Endemic HBV among hospital in-patients in Bangladesh, including evidence of occult infection

Bangladesh is one of the top-ten most heavily burdened countries for viral hepatitis, with hepatitis B (HBV) infections responsible for the majority of cases. Recombinant and occult HBV infections (OBI) have been reported previously in the region. We investigated an adult fever cohort (n=201) recruited in Dhaka, to determine the prevalence of HBV and OBI. A target-enrichment deep sequencing pipeline was applied to samples with HBV DNA >3.0 log10 IU ml−1. HBV infection was present in 16/201 (8 %), among whom 3/16 (19 %) were defined as OBI (HBsAg-negative but detectable HBV DNA). Whole genome deep sequences (WGS) were obtained for four cases, identifying genotypes A, C and D. One OBI case had sufficient DNA for sequencing, revealing multiple polymorphisms in the surface gene that may contribute to the occult phenotype. We identified mutations associated with nucleos(t)ide analogue resistance in 3/4 samples sequenced, although the clinical significance in this cohort is unknown. The high prevalence of HBV in this setting illustrates the importance of opportunistic clinical screening and DNA testing of transfusion products to minimise OBI transmission. WGS can inform understanding of diverse disease phenotypes, supporting progress towards international targets for HBV elimination.

Predicting youth diabetes risk using NHANES data and machine learning

Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06–0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10−5).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.