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Objectives: To estimate the prevalence of cytomegalovirus (CMV) infections among newborn-mother pairs, neonates with sepsis, and infants with hydrocephalus in Uganda. Design and Methods: Three populations—newborn-mother pairs, neonates with sepsis, and infants (≤3 months) with nonpostinfectious (NPIH) or postinfectious (PIH) hydrocephalus—were evaluated for CMV infection at 3 medical centers in Uganda. Quantitative PCR (qPCR) was used to characterize the prevalence of CMV. Results: The overall CMV prevalence in 2498 samples across all groups was 9%. In newborn-mother pairs, there was a 3% prevalence of cord blood CMV positivity and 33% prevalence of maternal vaginal shedding. In neonates with clinical sepsis, there was a 2% CMV prevalence. Maternal HIV seropositivity (adjusted odds ratio [aOR] 25.20; 95% confidence interval [CI] 4.43–134.26; p = 0.0001), residence in eastern Uganda (aOR 11.06; 95% CI 2.30–76.18; p = 0.003), maternal age

Summary: Inflammation during neonatal brain infections leads to significant secondary sequelae such as hydrocephalus, which often follows neonatal sepsis in the developing world. In 100 African hydrocephalic infants we identified the biological pathways that account for this response. The dominant bacterial pathogen was a Paenibacillus species, with frequent cytomegalovirus co-infection. A proteogenomic strategy was employed to confirm host immune response to Paenibacillus and to define the interplay within the host immune response network. Immune activation emphasized neuroinflammation, oxidative stress reaction, and extracellular matrix organization. The innate immune system response included neutrophil activity, signaling via IL-4, IL-12, IL-13, interferon, and Jak/STAT pathways. Platelet-activating factors and factors involved with microbe recognition such as Class I MHC antigen-presenting complex were also increased. Evidence suggests that dysregulated neuroinflammation propagates inflammatory hydrocephalus, and these pathways are potential targets for adjunctive treatments to reduce the hazards of neuroinflammation and risk of hydrocephalus following neonatal sepsis.

As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to properly diagnose and treat a given disease and evaluate the role of machine learning algorithms, such as deep learning, in the enhancement of lower quality images. In this post hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. CT images of post-infectious infant hydrocephalus were degraded in terms of spatial resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in low- to middle-income countries (LMIC) for assessment of clinical utility in treatment planning for hydrocephalus. In addition, enhanced images were presented alongside their ground-truth CT counterparts in order to assess whether reconstruction errors caused by the deep learning enhancement routine were acceptable to the evaluators. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of an image being characterized as useful for hydrocephalus treatment planning. Deep learning enhancement substantially increases contrast-to-noise ratio improving the apparent likelihood of the image being useful; however, deep learning enhancement introduces structural errors which create a substantial risk of misleading clinical interpretation. We find that images with lower quality than is customarily acceptable can be useful for hydrocephalus treatment planning. Moreover, low quality images may be preferable to images enhanced with deep learning, since they do not introduce the risk of misleading information which could misguide treatment decisions. These findings advocate for new standards in assessing acceptable image quality for clinical use.