Open-Source Artificial Intelligence System Supports Diagnosis of Mendelian Diseases in Acutely Ill Infants.

Mendelian disorders are prevalent in neonatal and pediatric intensive care units and are a leading cause of morbidity and mortality in these settings. Current diagnostic pipelines that integrate phenotypic and genotypic data are expert-dependent and time-intensive. Artificial intelligence (AI) tools may help address these challenges. Dx29 is an open-source AI tool designed for use by clinicians. It analyzes the patient's phenotype and genotype to generate a ranked differential diagnosis. We used Dx29 to retrospectively analyze 25 acutely ill infants who had been diagnosed with a Mendelian disorder, using a targeted panel of ~5000 genes. For each case, a trio (proband and both parents) file containing gene variant information was analyzed, alongside patient phenotype, which was provided to Dx29 by three approaches: (1) AI extraction from medical records, (2) AI extraction with manual review/editing, and (3) manual entry. We then identified the rank of the correct diagnosis in Dx29's differential diagnosis. With these three approaches, Dx29 ranked the correct diagnosis in the top 10 in 92-96% of cases. These results suggest that non-expert use of Dx29's automated phenotyping and subsequent data analysis may compare favorably to standard workflows utilized by bioinformatics experts to analyze genomic data and diagnose Mendelian diseases.

A Validation Study of PROMIS Sleep Disturbance (PROMIS-SD) and Sleep Related Impairment (PROMIS-SRI) Item Banks in Individuals with Idiopathic Parkinson's Disease and Matched Controls.

BACKGROUND: Parkinson's disease (PD) is associated with sleep disturbance (SD) and sleep-related impairment (SRI). Validation of self-report measures of these problems is needed in PD. The Patient-Reported Outcomes Measurement Information System (PROMIS) includes tools that assess these problems (PROMIS-SD and PROMIS-SRI, respectively). OBJECTIVE: This study aimed to further validate these measures in individuals with PD and matched controls. METHODS: Individuals with early-stage PD (n=50) and matched controls (n=48) completed measures of SD including the PROMIS-SD, Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI). They also completed measures of daytime impairment including the PROMIS-SRI, Epworth Sleepiness Scale, State-Trait Anxiety Inventory, Beck Depression Inventory 2nd edition, and Parkinson's Disease Questionnaire-39. Internal consistency for the PROMIS measures were assessed using Cronbach's α coefficient and item-total correlations in the total sample. Convergent and divergent validity of the PROMIS item banks were assessed using Spearman correlations. RESULTS: The PROMIS item banks had excellent internal consistency (α>0.94). Supporting convergent validity, the PROMIS-SD had strong correlations with other measures of SD (ρ>0.68, for PSQI and ISI) and the PROMIS-SRI had moderate to strong correlations with all measures of daytime impairment (ρ=0.41-0.72). Supporting divergent validity within the PD group, the PROMIS-SD correlated more strongly with SRI than with the Parkinson's Disease Questionnaire total score, a metric of PD related impairment. CONCLUSION: In middle-aged and older adults, with and without early-stage PD, the PROMIS-SD and PROMIS-SRI are reliable and valid measures of SD and SRI, respectively.

Susceptibility of Multidrug-Resistant Bacteria, Isolated from Water and Plants in Nigeria, to Ceragenins.

The continuous emergence of multidrug resistant pathogens is a major global health concern. Although antimicrobial peptides (AMPs) have shown promise as a possible means of combatting multidrug resistant strains without readily engendering resistance, costs of production and targeting by proteases limit their utility. Ceragenins are non-peptide AMP mimics that overcome these shortcomings while retaining broad-spectrum antimicrobial activity. To further characterize the antibacterial activities of ceragenins, their activities against a collection of environmental isolates of bacteria were determined. These isolates were isolated in Nigeria from plants and water. Minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of selected ceragenins and currently available antimicrobials against these isolates were measured to determine resistance patterns. Using scanning electron microscopy (SEM), we examined the morphological changes in bacterial membranes following treatment with ceragenins. Finally, we investigated the effectiveness of ceragenins in inhibiting biofilm formation and destroying established biofilms. We found that, despite high resistance to many currently available antimicrobials, including colistin, environmental isolates in planktonic and biofilm forms remain susceptible to ceragenins. Additionally, SEM and confocal images of ceragenin-treated cells confirmed the effective antibacterial and antibiofilm activity of ceragenins.