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AnteayerPLOS ONE Medicine&Health

Puberty, brain network connectivity and neuropsychiatric outcomes following pediatric traumatic brain injury in females: A research protocol

by Abigail Livny, Tamar Silberg

Background

Examining the role of sex on recovery from pediatric TBI (pTBI) is a complex task, specifically when referring to injuries occurring during critical developmental and maturation periods. The effect of sex hormones on neurological and neuropsychiatric outcomes has been studied among adult TBI females, but not in children. During development, puberty is considered a key milestone accompanied by changes in physical growth, neuronal maturation, sex hormones, and psychological symptoms. Following pTBI, such changes might have a significant effect on brain re-organization and on long-term neuropsychiatric outcomes. While hormonal dysfunction is a common consequence following pTBI, only few studies have systematically evaluated hormonal changes following pTBI.

Aims

To describe a multimodal protocol aimed to examine the effect of puberty on brain connectivity and long-term neuropsychiatric outcomes following TBI in female girls and adolescents.

Methods

A case-control longitudinal prospective design will be used. 120 female participants aged 9 to 16 years (N = 60 per group) will be recruited. In the acute phase (T0-1 month), participants will undergo an MRI protocol for brain connectivity, as well as a clinical evaluation for puberty stage and hormonal levels. In the chronic phase (T1-18-24 months), participants will complete a neuropsychiatric assessment in addition to the MRI and puberty evaluations. Hormonal levels will be monitored at T0 and T1. A moderation-mediation model will be used to examine the moderating effects of puberty on the association between pTBI and neuropsychiatric symptoms in female girls and adolescents, through the mediating effect of brain network connectivity.

Significance

This study will highlight sex-specific factors related to outcomes among females following pTBI and enhance our understanding of the unique challenges they face. Such information has a substantial potential to guide future directions for research, policy and practice.

Heterogeneity of COVID-19 symptoms and associated factors: Longitudinal analysis of laboratory-confirmed COVID-19 cases in San Antonio

by Byeong Yeob Choi, Abigail R. Grace, Jack Tsai

Few studies have examined heterogeneous associations of risk factors with Coronavirus Disease-2019 (COVID-19) symptoms by type. The objectives of this study were to estimate the prevalence of and risk factors associated with COVID-19 symptoms and to investigate whether the associations differ by the type of symptoms. This study obtained longitudinal data over 6 months from laboratory-confirmed COVID-19 cases in a citywide sample in San Antonio. Sixteen symptoms of COVID-19 infection, measured at baseline and three follow-up times (1, 3, and 6 months), were analyzed using generalized estimating equations (GEE) to investigate potential risk factors while accounting for the repeated measurements. The risk factors included time in months, sociodemographic characteristics, and past or current medical and psychiatric conditions. To obtain interpretable results, we categorized these sixteen symptoms into five categories (cardiopulmonary, neuro-psychological, naso-oropharyngeal, musculoskeletal, and miscellaneous). We fitted GEE models with a logit link using each category as the outcome variable. Our study demonstrated that the associations were heterogeneous by the categories of symptoms. The time effects were the strongest for naso-oropharyngeal symptoms but the weakest for neuro-psychological symptoms. Female gender was associated with increased odds of most of the symptoms. Hispanic ethnicity was also associated with higher odds of neuro-psychological, musculoskeletal, and miscellaneous symptoms. Depression was the most robust psychiatric condition contributing to most of the symptoms. Different medical conditions seemed to contribute to different symptom expressions of COVID-19 infection.

Using microbiological data to improve the use of antibiotics for respiratory tract infections: A protocol for an individual patient data meta-analysis

by Irene Boateng, Beth Stuart, Taeko Becque, Bruce Barrett, Jennifer Bostock, Robin Bruyndonckx, Lucy Carr-Knox, Emily J. Ciccone, Samuel Coenen, Mark Ebell, David Gillespie, Gail Hayward, Katarina Hedin, Kerenza Hood, Tin Man Mandy Lau, Paul Little, Dan Merenstein, Edgar Mulogo, Jose Ordóñez-Mena, Peter Muir, Kirsty Samuel, Nader Shaikh, Sharon Tonner, Alike W. van der Velden, Theo Verheij, Kay Wang, Alastair D. Hay, Nick Francis

Background

Resistance to antibiotics is rising and threatens future antibiotic effectiveness. ‘Antibiotic targeting’ ensures patients who may benefit from antibiotics receive them, while being safely withheld from those who may not. Point-of-care tests may assist with antibiotic targeting by allowing primary care clinicians to establish if symptomatic patients have a viral, bacterial, combined, or no infection. However, because organisms can be harmlessly carried, it is important to know if the presence of the virus/bacteria is related to the illness for which the patient is being assessed. One way to do this is to look for associations with more severe/prolonged symptoms and test results. Previous research to answer this question for acute respiratory tract infections has given conflicting results with studies has not having enough participants to provide statistical confidence.

Aim

To undertake a synthesis of IPD from both randomised controlled trials (RCTs) and observational cohort studies of respiratory tract infections (RTI) in order to investigate the prognostic value of microbiological data in addition to, or instead of, clinical symptoms and signs.

Methods

A systematic search of Cochrane Central Register of Controlled Trials, Ovid Medline and Ovid Embase will be carried out for studies of acute respiratory infection in primary care settings. The outcomes of interest are duration of disease, severity of disease, repeated consultation with new/worsening illness and complications requiring hospitalisation. Authors of eligible studies will be contacted to provide anonymised individual participant data. The data will be harmonised and aggregated. Multilevel regression analysis will be conducted to determine key outcome measures for different potential pathogens and whether these offer any additional information on prognosis beyond clinical symptoms and signs.

Trial registration

PROSPERO Registration number: CRD42023376769.

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