Acquired chylothorax is an established complication of CHD surgery, affecting 2–9% of patients. CHD places a child at risk for failure to thrive, with subsequent chylothorax imposing additional risk.

We conducted a retrospective chart review to ascertain quantitative markers of nutrition and growth in children affected by chylothorax following CHD surgery between 2018 and 2022 compared to controls.

We utilised electronic medical record system, EPIC, at Children’s Hospital, New Orleans, targeting subjects < 18 years old who underwent CHD surgery between 2018 and 2022 and developed a subsequent chylothorax. Study subjects were identified using the 10th revision of the International Classification of Diseases codes (ICD-10 codes: J94.0, I89.8, and J90.0). Each chylothorax case (n = 20) was matched by procedure type and age to a control with no chylothorax (n = 20). Data were recorded in REDCap and analysed using SPSS.

After removal of outliers, we analysed 19 total matched pairs. There was no statistical difference in growth velocity (p = 0.12), weight change (operation to discharge) (p = 0.95), weight change (admission to discharge) (p = 0.35), Z-score change (operation to discharge) (p = 0.90), Z-score change (admission to discharge) (p = 0.21), serum protein (p = 0.88), or serum albumin (p = 0.82). Among cases, linear regression demonstrated no significant association between maximum chylous output and growth velocity (p = 0.91), weight change (operation to discharge) (p = 0.15), or weight change (admission to discharge) (p = 0.98).

We did not observe statistically significant markers of growth or nutrition in children with chylothorax post-CHD surgery compared to those without chylothorax. Multisite data collection and analysis is required to better ascertain clinical impact and guide clinical practice.

The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

In response to the COVID-19 pandemic, we rapidly implemented a plasma coordination center, within two months, to support transfusion for two outpatient randomized controlled trials. The center design was based on an investigational drug services model and a Food and Drug Administration-compliant database to manage blood product inventory and trial safety.

A core investigational team adapted a cloud-based platform to randomize patient assignments and track inventory distribution of control plasma and high-titer COVID-19 convalescent plasma of different blood groups from 29 donor collection centers directly to blood banks serving 26 transfusion sites.

We performed 1,351 transfusions in 16 months. The transparency of the digital inventory at each site was critical to facilitate qualification, randomization, and overnight shipments of blood group-compatible plasma for transfusions into trial participants. While inventory challenges were heightened with COVID-19 convalescent plasma, the cloud-based system, and the flexible approach of the plasma coordination center staff across the blood bank network enabled decentralized procurement and distribution of investigational products to maintain inventory thresholds and overcome local supply chain restraints at the sites.

The rapid creation of a plasma coordination center for outpatient transfusions is infrequent in the academic setting. Distributing more than 3,100 plasma units to blood banks charged with managing investigational inventory across the U.S. in a decentralized manner posed operational and regulatory challenges while providing opportunities for the plasma coordination center to contribute to research of global importance. This program can serve as a template in subsequent public health emergencies.

To assess the potential contribution of large-scale food fortification (LSFF) towards meeting dietary micronutrient requirements in Tanzania.

We used household food consumption data from the National Panel Survey 2014–15 to estimate fortifiable food vehicle coverage and consumption (standardised using the adult female equivalent approach) and the prevalence at risk of inadequate apparent intake of five micronutrients included in Tanzania’s fortification legislation. We modelled four LSFF scenarios: no fortification, status quo (i.e. compliance with current fortification contents) and full fortification with and without maize flour fortification.

Tanzania.

A nationally representative sample of 3290 Tanzanian households.

The coverage of edible oils and maize and wheat flours (including products of wheat flour and oil such as bread and cakes) was high, with 91 percent, 88 percent and 53 percent of households consuming these commodities, respectively. We estimated that vitamin A-fortified oil could reduce the prevalence of inadequate apparent intake of vitamin A (retinol activity equivalent) from 92 percent without LSFF to 80 percent with LSFF at current fortification levels. Low industry LSFF compliance of flour fortification limits the contribution of other micronutrients, but a hypothetical full fortification scenario shows that LSFF of cereal flours could substantially reduce the prevalence at risk of inadequate intakes of iron, zinc, folate and vitamin B12.

The current Tanzania LSFF programme likely contributes to reducing vitamin A inadequacy. Policies that support increased compliance could improve the supply of multiple nutrients, but the prominence of small-scale maize mills restricts this theoretical benefit.

The global prevalence of persons living with dementia will soon exceed 50 million. Most of these individuals reside in low- and middle-income countries (LMICs). In South Africa, one such LMIC, the physician-to-patient ratio of 9:10 000 severely limits the capacity of clinicians to screen, assess, diagnose, and treat dementias. One way to address this limitation is by using mobile health (mHealth) platforms to scale-up neurocognitive testing. In this paper, we describe one such platform, a brief tablet-based cognitive assessment tool (NeuroScreen) that can be administered by lay health-providers. It may help identify patients with cognitive impairment (related, for instance, to dementia) and thereby improve clinical care and outcomes. However, there is a lack of data regarding (a) the acceptability of this novel technology for delivery of neurocognitive assessments in LMIC-resident older adults, and (b) the influence of technology-use experience on NeuroScreen performance of LMIC-resident older adults. This study aimed to fill that knowledge gap, using a sample of cognitively impaired South African older adults.

Participants were 60 older adults (63.33% female; 91.67% right-handed; age M = 68.90 years, SD = 9.42, range = 50-83), all recruited from geriatric and memory clinics in Cape Town, South Africa. In a single 1-hour session, they completed the entire NeuroScreen battery (Trail Making, Number Speed, Finger Tapping, Visual Discrimination, Number Span Forward, Number Span Backward, List Learning, List Recall) as well as a study-specific questionnaire assessing acceptability of NeuroScreen use and overall experience and comfort with computer-based technology. We summed across 11 questionnaire items to derive a single variable capturing technology-use experience, with higher scores indicating more experience.

Almost all participants (93.33%) indicated that NeuroScreen was easy to use. A similar number (90.00%) indicated they would be comfortable completing NeuroScreen at routine doctor's visits. Only 6.67% reported feeling uncomfortable using a tablet, despite about three-quarters (76.67%) reporting never having used a tablet with a touchscreen before. Almost one in five participants (18.33%) reported owning a computer, 10.00% a tablet, and 70.00% a smartphone. Correlations between test performance and technology-use experience were statistically significant (or strongly tended toward significance) for most NeuroScreen subtests that assessed higherorder cognitive functioning and that required the participant to manipulate the tablet themselves: Trail Making 2 (a measure of cognitive switching ability), r = .24, p = .05; Visual Discrimination A (complex processing speed [number-symbol matching]), r = .38, p = .002; Visual Discrimination B (pattern recognition), r = .37, p = .004; Number Speed (simple information processing speed), r = .36, p = .004. For the most part, there were no such significant associations when the NeuroScreen subtest required only verbal input from the participant (i.e., on the list learning and number span tasks).

NeuroScreen, a tablet-based neurocognitive screening tool, appears feasible for use among older South Africans, even if they are cognitively impaired and have limited technological familiarity. However, test performance might be influenced by amount of technology-use experience; clinicians using the battery must consider this in their interpretations.

Aging is associated with changes in cortical excitability which may affect motor learning and cognitive function via selective modulation of gamma aminobutyric acid (GABA). Previous studies using magnetic resonance spectroscopy (MRS) to measure GABA in older adults found that increased baseline GABA levels in the sensorimotor cortex (M1S1) were associated with better motor performance. GABA levels in M1S1 have tended to decrease during the execution of a repeated motor sequence. The dynamic change in GABA density in M1S1 in older adults is currently unknown and represents a critical gap in our understanding of how it could impact motor learning and cognitive performance. As such, the purpose of the current study is to quantify changes in cortical GABA during motor learning in the aging brain and examine those changes in relation to motor and cognitive performance. We hypothesize that older adults with greater dynamic range in M1S1 GABA levels will display more efficient motor learning and increased cognitive scores.

We report on a total of 18 healthy older adults aged 64 to 80 years (M = 70.44, SD = 4.99, 12 females). Using MRS at 3T, we measured changes in GABA concentration in M1S1 at rest, during an eight or 12 finger-movement motor entrainment task, and during a recall task. Gannett was used for GABA quantification relative to water. Change in GABA was calculated by subtracting Rest1 GABA from Recall1 GABA. In a separate session, participants completed a battery of cognitive assessments. We computed linear regressions to examine the relationship between dynamic GABA change, recall accuracy of the motor task and cognitive performance.

In relation to motor performance, we found that both greater baseline (Rest1) GABA levels and greater dynamic change in GABA significantly predicted better recall accuracy on the motor task. For cognitive performance, we found that greater dynamic change in GABA significantly predicted better performance on Word Reading in the Stroop Color and Word Test and Delayed Recall in the Hopkins Verbal Learning Test (HVLT). No additional significant relationships were found for the remaining cognitive assessments.

Older adults who were able to accurately perform the task had a greater dynamic change in GABA and increased baseline GABA levels. These adults with greater dynamic change also had better cognitive performance on HVLT Delay and Stroop Word Reading. This modulation of GABA associated with better performance could be related to changes in neuroplasticity. Although these results are in the preliminary stages, they point to a greater understanding of aging related changes in motor and cognitive performance. We’ll continue to explore the relationship between sensory motor performance and changes in GABA concentration as a potential predictor for cognitive performance and future rehabilitation.