Experiences of Mothers of Preterm Infants in the Neonatal Intensive Care Unit During the COVID-19 Pandemic.

Background: The neonatal intensive care unit (NICU) stay following the birth of a preterm infant can be stressful and traumatic for families. During the COVID-19 pandemic, the NICU environment changed precipitously as infection control and visitor restriction measures were implemented.

Purpose: Our study aimed to examine the impact of the pandemic policies on the experiences of mothers of preterm infants during their stay in the NICU.

COVID-19 Outbreak: Understanding Moral-Distress Experiences Faced by Healthcare Workers in British Columbia, Canada.

Pandemic-management plans shift the care model from patient-centred to public-centred and increase the risk of healthcare workers (HCWs) experiencing moral distress (MD). This study aimed to understand HCWs' MD experiences during the COVID-19 pandemic and to identify HCWs' preferred coping strategies. Based on a qualitative research methodology, three surveys were distributed at different stages of the pandemic response in British Columbia (BC), Canada.

Factors Associated with Umbilical Venous Catheter Malposition in Newborns: A Tertiary Center Experience.

Objective: Umbilical venous catheters (UVC) are widely used in neonatal intensive care (NICU). Noncentral catheter position is known to be associated with multiple adverse complications; however, risk factors for catheter malposition are unclear. This work aimed to identify clinical risk factors and complications associated with UVC malposition in neonates admitted in an NICU.

Whole blood genome-wide transcriptome profiling and metagenomics next-generation sequencing in young infants with suspected sepsis in a low-and middle-income country: A study protocol.

Conducting collaborative and comprehensive epidemiological research on neonatal sepsis in low- and middle-income countries (LMICs) is challenging due to a lack of diagnostic tests. This prospective study protocol aims to obtain epidemiological data on bacterial sepsis in newborns and young infants at Kamuzu Central Hospital in Lilongwe, Malawi. The main goal is to determine if the use of whole blood transcriptome host immune response signatures can help in the identification of infants who have sepsis of bacterial causes.

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

Importance: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression.

Objective: To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression.