medRxiv : serveur de preprint pour les sciences médicales

Effects of accidental radiation exposure on the human germline remain a topic of medical interest. Sequencing technology now allows the accurate analysis of the entire human genome in potentially exposed cohorts to search for possible signatures of ionizing radiation exposure. Clustered de novo mutations (cDNMs), that are multiple DNA lesions within 20 bp of each other, have been hypothesized to be a signature for paternal exposure to ionizing radiation. We analyzed whole genome trio data of 240 children and their likely exposed parents as well as 1,275 offspring from unexposed families. The cohort of exposed children consists of 130 offspring of Chernobyl cleanup workers (CRU, exposure range = 0-4,080 mSv) and 110 offspring of former soldiers from both German armies which have likely been irradiated during their service (Radar cohort, exposure range = 0-353 mSv). For the Radar cohort, a retrospective dosage estimation was conducted based on the service records from each soldier, but it remains a challenging task to retrospectively estimate received dose accurately. In agreement with previous epidemiological data, no transgenerational effects could be detected for the class of isolated de novo mutations, which cause the majority of genetic disorders. We found that the number of isolated de novo mutations increases by 2% per year of age of the father at conception, which is also in line with previous works. On average, we observed 2.65 cDNMs per offspring in the CRU cohort, 1.48 in the Radar cohort and 0.88 in the control cohort, with a median of two clustered mutations per genome in offspring of irradiated fathers. This is a significant increase (p < 0.005) in the number of cDNMs compared to a set of age-matched controls. Furthermore, we show that the cDNM rates scale with paternal exposure to ionizing radiation (p < 0.001). Our findings corroborate that clustered de novo mutations represent a transgenerational biomarker for paternal exposure to ionizing radiation.

Focal segmental glomerulosclerosis (FSGS) is a common cause of nephrotic syndrome with an annual incidence in the United States in African-Americans compared to European-Americans of 24 cases and 5 cases per million, respectively. Among glomerular diseases in Europe and Latin-America, FSGS was the second most frequent diagnosis, and in Asia the fifth. We expand previous efforts in understanding genetics of FSGS by performing a case-control study involving ethnically-diverse groups FSGS cases (726) and a pool of controls (13,994), using panel sequencing of approximately 2,500 podocyte-expressed genes. Through rare variant association tests, we replicated known risk genes - KANK1, COL4A4, and APOL1. A novel significant association was observed for the gene encoding complement receptor 1 (CR1). High-risk rare variants in CR1 in the European-American cohort were commonly observed in Latin- and African-Americans. Therefore, a combined rare and common variant analysis was used to replicate the CR1 association in non-European populations. The CR1 risk variant, rs17047661, gives rise to the Sl1/Sl2 (R1601G) allele that was previously associated with protection against cerebral malaria. Pleiotropic effects of rs17047661 may explain the difference in allele frequencies across continental ancestries and suggest a possible role for genetically-driven alterations of adaptive immunity in the pathogenesis of FSGS.

Methods to estimate polygenic scores (PGS) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method 15 comparisons are often limited. Here, we evaluate polygenic scores derived using seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta- analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling and target biobank on PGS performance. We found that no single method consistently 20 outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well-tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target 25 phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes ({beta}-coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best performing single methods when tuned with cross-validation). Our interactively browsable online-results 30 (https://methodscomparison.intervenegeneticscores.org/) and open-source workflow prspipe (https://github.com/intervene-EU-H2020/prspipe) provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.

The course of COVID-19 is characterized by wide variability, with genetics playing a contributing role. Through large-scale genetic association studies, a significant link between genetic variants and disease severity was established. However, individual genetic variants identified thus far have shown modest effects, indicating a polygenic nature of this trait. To address this, a polygenic risk score (PRS) can be employed to aggregate the effects of multiple single nucleotide polymorphisms (SNPs) into a single number, allowing practical application to individuals within a population. In this work, we investigated the performance of a PRS model in the context of COVID-19 severity in 1085 Russian participants using low-coverage NGS sequencing. By developing a genome-wide PRS model based on summary statistics from the COVID-19 Host Genetics Initiative consortium, we demonstrated that the PRS, which incorporates information from over a million common genetic variants, can effectively identify individuals at significantly higher risk for severe COVID-19. The findings revealed that individuals in the top 10% of the PRS distribution had a markedly elevated risk of severe COVID-19, with an odds ratio (OR) of 2.2 (95% confidence interval (CI): 1.3-3.3, p-value=0.0001). Furthermore, incorporating the PRS into the prediction model significantly improved its accuracy compared to a model that solely relied on demographic information (p-value < 0.0001). This study highlights the potential of PRS as a valuable tool for identifying individuals at increased risk of severe COVID-19 based on their genetic profile.

Prolonged immobilization of joints after distal radius fracture (DRF) leads to cerebral disuse-dependent plasticity (DDP) and deterioration of upper extremity function. Action observation therapy (AOT) can improve DDP. This nonrandomized controlled trial (UMIN 000039973) tested the hypothesis that AOT improves hand-use difficulties during activities of daily living in patients with DRF. Right-handed women with volar locking plate fixation for DRF were divided into AOT and Non-AOT groups for a 12-week intervention. The primary outcome was the difficulty in using the fractured hand, as examined by the Japanese version of the Patient-Related Wrist Evaluation (PRWE). Secondary outcomes were (1) range of motion (ROM) of the injured side and (2) difference between the measured and patient-estimated ROM. The survey was conducted immediately postoperatively and at 4, 8, and 12 weeks postoperatively. The AOT groups used a head-mounted display and three-dimensional video during ROM exercises, whereas the Non-AOT group used active ROM exercises alone. A generalized linear model (GLM) was used to confirm interactions and main effects by group and time period, and multiple comparisons were performed. In total, 35 patients were assigned to the AOT (n=18, median age 74 years) and Non-AOT (n=17, 70 years) groups. In the GLM, PRWE Total, PRWE Specific, and PRWE Usual scores showed interactions between groups and periods. A post-hoc test showed that the PRWE Specific (z=3.43, p=0.02) and PRWE Usual (z=7.53, p<0.01) scores were significantly lower in the AOT group than in the Non-AOT group at 4 weeks postoperatively, whereas PRWE Total scores (z=3.29, p=0.04) were lower at 8 weeks postoperatively. These results suggest that AOT can improve hand-use difficulties in right-handed women after DRF surgery. AOT positively affects the motor imagery of patients with DRF and can reverse the patients perceived difficulty of using the fractured hand during rehabilitation.

Background Chronic cerebral ischemia (CCI) is considered as a prelude to neurodegeneration. Endothelial progenitor cells (EPCs) have been implicated in revascularization and vascular repair in cerebral ischemic diseases. Due to the safety concern and the low survival rate of the transplanted cells, interest has shifted toward the paracrine effect of EPCs. Here, we investigate the effects of EPC-derived conditioned medium (EPC-CM) on the vascular and functional impairments in a rodent model of CCI and the mechanism via which the EPC-CM involves. Methods Bilateral internal carotid artery ligation (BICAL) was performed in rats to induce cerebral ischemia. EPC-CM was intracisternally injected 1 week after BICAL. The changes of the microvasculature and behavior were examined 3 weeks after BICAL. The EPC-CM was analyzed by cytokine array for the factors that involved in angiogenesis. The therapeutic effects and mechanism of the candidate factor was validated with oxygen-glucose deprivation-injured endothelial cells and EPCs in vitro. Results EPC-CM significantly improved the vascular, motor and cognitive impairments of the BICAL rats. Macrophage migration inhibitory factor (MIF) was identified as a key factor in EPC-CM involved in angiogenesis and anti-senescence. Furthermore, recombinant MIF protein mirrored the effects of EPC-CM on EPCs and ECs. These therapeutic effects were decreased by the co-treatment with EPC-CM and MIF-specific antibody both in vivo and in vitro. MIF operates through multiple pathways, including the AKT pathway, which plays a crucial role in cellular homeostasis. Inhibiting the AKT pathway diminished the protective effect of MIF in the CCI model. Conclusions We demonstrated that EPC-CM protected the chronic ischemic rat brain from ischemic injury and promoted functional recovery in rats through MIF-mediated AKT pathway, which indicated that EPC-CM may serve as an alternative potential therapy in chronic cerebral ischemia.

Background: Sleep, physical activity, and nutrition (SPAN) are crucial modifiable factors for health, yet most research has examined them independently rather than exploring their combined and incremental impact on disease risk and mortality. Objective: To determine the collective associations of SPAN exposures and establish clinically relevant targets for reducing all-cause mortality risk. Methods: This study included 59,078 UK Biobank participants with valid wearable tracker and nutrition data (Median age [IQR]: 64.0 [7.8] years; 45.4% male). Sleep duration (hours/day) and moderate to vigorous physical activity duration (MVPA; mins/day) were calculated using a machine learning based wearable data schema. A 10-item diet quality score (DQS) assessed the consumption of vegetables, fruits, fish, dairy, whole grains, and vegetable oils, as well as lower intakes of refined grains, processed meats, unprocessed red meats, and sugar-sweetened beverages using a food frequency questionnaire. The DQS assigned values from 0-10 for each component, totalling 100 points, with higher values indicating higher diet quality. Associations with all-cause mortality were explored using Cox proportional hazard models with combinations of SPAN exposure tertiles. Results: During the median 8.1-year follow-up period, 2,458 deaths occurred. MVPA exhibited the strongest overall effect on mortality risk, followed by sleep (with a U-shaped relationship), and diet quality. Compared to the referent group of combined SPAN exposure (lowest tertiles for all three behaviours), the optimal SPAN combination involving moderate sleep duration (7.2-8.0 hours/day), high MVPA (42-103 mins/day), and high DQS (57.5-72.5) was associated with a hazard ratio (HR) of 0.45 (95% CI: 0.37, 0.53). Relative to the 5th percentile of sleep (5.5 hours/day), physical activity (7.3 mins/day), and nutrition (36.9 DQS), a minimum increase of 15 mins/day of sleep, 1.6 min/day MVPA, and 5 DQS points was associated with a 10% reduction in all-cause mortality risk (HR: 0.90; 95% CI: 0.88, 0.93). Additionally, compared to the referent group, an additional 75 mins/day of sleep, 12.5 min/day MVPA, and 25 DQS points was associated with a 50% reduction in all-cause mortality risk (HR: 0.50; 95%CI: 0.44, 0.58). Conclusion: These findings underscore the importance of combined incremental lifestyle modifications in reducing the risk of all-cause mortality.

Alterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between changes in sleep macrostructure, quantitative sleep EEG and health. We use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N=3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors and the use of 48 medications. Of sleep macrostructure variables, increased REM latency and reduced REM duration was the most common finding across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG PSD alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85-95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases are associated with cardiovascular problems and associated medications, in line with immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects.

In vivo exposure is a highly effective but rarely implemented treatment for agoraphobia. Most of the patients receive medication or cognitive therapy without exposure because of a high expenditure of money and time for in vivo exposure. Exposure in virtual reality (VR) is easier to implement but the effectiveness of stimulating fear compared to in vivo exposure is still questionable. Therefore, in this study, the effects of in vivo and VR exposure on subjective symptom burden and heart rate variability (HRV) were assessed. 30 healthy individuals with fears in narrow rooms went through in vivo and VR exposure in a randomized order while HRV parameters (RMSSD, HF) and subjective symptom burden was assessed. Linear mixed models were calculated. The effect of condition (VR vs. in vivo), scenario (several narrow rooms) and slot (first 30 seconds, peak, last 30 seconds) on RMSSD and HF was assessed. A random effect for participants (random-intercept term) to allow the intercept to vary across participants was included. Regarding RMSSD and HF, participants showed significantly higher levels during in vivo exposure compared to exposure in VR (RMSSD: p = .005; HF: p < .001), reflecting a stronger activation of the parasympathetic nervous system during in vivo exposure or presumably higher stress levels during VR exposure. This study highlights the necessity of assessing subjective and objective parameters allowing the evaluation of the effectiveness of fear stimulation by exposure approaches. The effectiveness of VR exposure for agoraphobic patients' needs to be assessed in future studies.

Actinic Keratosis (AK) is a frequent dermatological diagnosis which contributes to a large proportion of routine dermatopathology. A current development in histopathology is in the digitization of specimens by creating whole slide images (WSI) with slide scanners. Deep Learning Models (DLM) have been introduced to radiology or pathology for image recognition but dermatopathology lacks available solutions. Building on previous work about skin pathologies, this paper proposes a DLM following the U-Net architecture to detect AK in histopathological samples. In total, 297 histopathological slides (269 with AK and 28 without AK) have been retrospectively selected. They were randomly assigned to training, validation and testing groups. Performance was evaluated by conducting a Case Control Accuracy Study on three levels of granularity. The DLM model achieved an overall accuracy of 99.13% on the WSI level, 99.02% on the patch level and an intersection over union (IoU) of 83.88%. The proposed DLM reliably recognizes AK in histopathological images, supporting the implementation of DLMs in dermatopathology practice. Given existing technical capabilities and advancements, DLMs could have a significant influence on dermatopathology routine in the future.

Severity of periodontal disease may be determined by measurement of alveolar crestal height (ACH) on dental bitewing radiographs; however, the prevailing method of assessment is through visualization which is time consuming and not a direct measure. The primary objective of this manuscript is to create and validate a deep learning technique for precise evaluation of alveolar bone loss in bitewing radiographs. Additionally, surveys were conducted with dental professionals to determine accuracy of visualized measures of ACH for severe periodontal disease versus the deep learning program and to determine the acceptability of utility of the program among diverse dental professionals. Lastly, the deep learning program was utilized in research to evaluate the role of COVID on periodontal disease through longitudinal measures of bitewing radiograph ACH from patients during the: "pre-pandemic" (Feb 2017 - Feb 2020) and "post-pandemic" (Feb 2020 - Feb 2023) periods. The pre-pandemic group had a mean percentage loss of ACH of -1.74 + 16.5%, representing a gain in alveolar bone. In contrast, the post-pandemic group had a gain in ACH of 2.46 + 14.6%, representing a loss in alveolar bone. There remained a trend for greater annualized percent change in ACH in the post-pandemic vs pre-pandemic group (1.33 + 11.9% vs -0.94 + 12.5%, p=0.07), after accounting for differences in duration between xrays. Overall, this study demonstrates the successful training and validation of a deep learning program for ACH.

Game theory-inspired deep learning using a generative adversarial network provides an environment to competitively interact and accomplish a goal. In the context of medical imaging, most work has focused on achieving single tasks such as improving image resolution, segmenting images, and correcting motion artifacts. We present a dual-objective adversarial learning framework that simultaneously (1) reconstructs higher quality brain magnetic resonance images (MRIs) that (2) retain disease-specific imaging features critical for predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). We obtained 3-Tesla, T1-weighted brain MRIs of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=342) and the National Alzheimer's Coordinating Center (NACC, N=190) datasets. We simulated MRIs with missing data by removing 50% of sagittal slices from the original scans (i.e., diced scans). The generator was trained to reconstruct brain MRIs using the diced scans as input. We introduced a classifier into the GAN architecture to discriminate between stable (i.e., sMCI) and progressive MCI (i.e., pMCI) based on the generated images to facilitate encoding of AD-related information during reconstruction. The framework was trained using ADNI data and externally validated on NACC data. In the NACC cohort, generated images had better image quality than the diced scans (SSIM: snm{$0.553 pm 0.116$} versus snm{$0.348 pm 0.108$}). Furthermore, a classifier utilizing the generated images distinguished pMCI from sMCI more accurately than with the diced scans (F1-score: snm{$0.634 pm 0.019$} versus snm{$0.573 pm 0.028$}). Competitive deep learning has potential to facilitate disease-oriented image reconstruction in those at risk of developing Alzheimer's disease.

Care home residents are vulnerable to severe outcomes from infections such as COVID-19 and influenza. However, measures to control outbreaks, such as care home closures to visitors and new admissions, have a detrimental impact on their quality of life. Many infections and outbreaks could be prevented but the first step is to measure them reliably. This is challenging in care homes due to the lack of data and research infrastructure. During the pandemic, the VIVALDI study measured COVID-19 infections in residents and staff by partnering with care providers and using routinely collected data. This study aims to establish sentinel surveillance and a research database to enable observational and future interventional studies in care homes. The project has been co-produced with care providers, staff, residents, relatives, and researchers. The study (October 2023 to March 2025) will explore the feasibility of establishing a network of 500-1500 care homes for older adults in England that is underpinned by a linked data platform. No data will be collected from staff. The cohort will be created by regularly extracting resident identifiers from Digital Social Care Records (DSCR), followed by pseudonymisation and linkage to routinely collected datasets. Following extensive consultation, we decided not to seek informed consent from residents for data collection, but they can opt out of the study. Our goal is to be inclusive, and it is challenging to give every resident the opportunity to opt in due to cognitive impairment and the requirement for consultees. The project, and all requests to use the data will be overseen by relatives, residents, staff, and care providers. The study has been provisionally approved by the Health Research Authority Confidentiality Advisory Group and the South-West Frenchay Research Ethics Committee. It is funded by the UK Health Security Agency.

This document provides the full statistical analysis plan (SAP) for the INTIMET study (Insulin Resistance in Type 1 Diabetes Managed with Metformin), a randomised double-blinded placebo-controlled trial, designed to evaluate the effect of metformin on insulin resistance and cardiometabolic health in type 1 diabetes. This trial was prospectively registered within the Australian and New Zealand Clinical Trials Registry (ACTRN12619001440112). The study protocol has previously been published (Snaith JR et al, Diabetic Medicine 2021).

Analyses of a bipartite Genotype and Phenotype Network (GPN), linking the genetic variants and phenotypes based on statistical associations, provide an integrative approach to elucidate the complexities of genetic relationships across diseases and identify pleiotropic loci. In this study, we first assess contributions to constructing a well-defined GPN with a clear representation of genetic associations by comparing the network properties with a random network, including connectivity, centrality, and community structure. Next, we construct network topology annotations of genetic variants that quantify the possibility of pleiotropy and apply stratified linkage disequilibrium (LD) score regression to 12 highly genetically correlated phenotypes to identify enriched annotations. The constructed network topology annotations are informative for disease heritability after conditioning on a broad set of functional annotations from the baseline-LD model. Finally, we extend our discussion to include an application of bipartite GPN in phenome-wide association studies (PheWAS). The community detection method can be used to obtain a priori grouping of phenotypes detected from GPN based on the shared genetic architecture, then jointly test the association between multiple phenotypes in each network module and one genetic variant to discover the cross-phenotype associations and pleiotropy. Significance thresholds for PheWAS are adjusted for multiple testing by applying the false discovery rate (FDR) control approach. Extensive simulation studies and analyses of 633 electronic health record (EHR)-derived phenotypes in the UK Biobank GWAS summary dataset reveal that most multiple phenotype association tests based on GPN can well-control FDR and identify more significant genetic variants compared with the tests based on UK Biobank categories.

Background and Purpose: Dehydration is common in hospitalized patients and associated with poor outcome in ischemic stroke patients. Intracerebral hemorrhage patients use hyperosmolar agents frequently after admission,which may lead to dehydration.Since the blood urea nitrogen to creatinine ratio (BUN/Cr) is an indicator of dehydration,it is unknown whether there is a relationship between BUN/Cr ratio during hospitalization and clinical outcome of intracerebral hemorrhage patients. Mehtods: A total of 211 patients with supratentorial cerebral hemorrhage were included. Clinical data was collected retrospectively.BUN/Cr ratio on day 7 after onset (7dBUN/Cr ) was calculated.Poor outcome was defined as 90-day mRS>2. Univariate and multivariate logistic regression analyses were used to determine the relationship between 7dBUN/Cr ratio and 90-day poor outcome. Receiver operating curve was used to determine the best cutoff of 7dBUN/Cr ratio for predicting poor outcome.Results: NIHSS score,hematoma volume and 7dBUN/Cr ratio were independently correlated with 90-day poor outcome. Under receiver operating curve, 7dBUN/Cr ratio exhibited similar prognostic capability, as compared to hematoma volume.The best cutoff for 7dBUN/Cr ratio to predict poor outcome was 22 in the hyperosmolar agents subgroup.Conclusions: Elevated BUN/Cr ratio at day 7 is associated with 90-day poor outcome in ICH patients. Further prospective study will be required to confirm this result and explore the value of BUN/Cr ratio in the application of hyperosmolar agents and hydration therapy.

Background: Medical datasets containing musculoskeletal disorders may have data imbalances due to the incidence of the disease, which may limit the predictive ability, such as the sensitivity, of musculoskeletal diagnostic prediction models built from these data. This study aimed to increase the sensitivity performance of osteoarthritis (OA) prediction when building a model by adjusting an OA imbalanced dataset using a sensitivity-based multi-sampling (SMS) technique. Methods: OA Data were obtained from the Korea National Health and Nutrition Examination Survey (KNHANES). SMS technique combining oversampling and undersampling was applied to the imbalanced OA data, and the RandomForest algorithm was used for machine learning modeling. Model performance was evaluated based on accuracy, sensitivity, and specificity and compared with other hybrid sampling techniques. Result: In the SMS technique, ADASYN, Borderline-SMOTE, SMOTE oversampling and ENN undersampling techniques were combined and applied. The OA prediction model using the SMS technique showed the highest sensitivity (82.20) but the lowest specificity (82.26) and accuracy (82.26) compared to other hybrid models. Conclusion: SMS technology offers a potential solution for improving sensitivity performance for prediction models built on medical data imbalances due to low-incidence diseases. Nonetheless, caution is warranted due to the concern that while improving sensitivity, it may decrease specificity with a trade-off.

Cerebrovascular Reactivity Imaging (CVR) is a diagnostic method for assessment of alterations in cerebral blood flow in response to a controlled vascular stimulus. The principal utility is the capacity to evaluate the cerebrovascular reserve, thereby elucidating autoregulatory functioning. Over the past decade, CVR has accumulated large interest, emerging as an expanding research field and application in a diverse spectrum of patient populations. In CVR, CO2 gas challenge is the most prevalent method, which elicits a vascular response by alterations in inspired CO2 concentrations. While several systems have been proposed in the literature, only a limited number have been devised to operate in tandem with mechanical ventilation, thus constraining the majority CVR investigations to spontaneous breathing individuals. We have developed a new method, denoted Additional CO2, designed to enable CO2 challenge in ventilators. The central idea is the introduction of an additional flow of highly concentrated CO2 into the respiratory circuit, as opposed to administration of the entire gas mixture from a reservoir. By monitoring the main respiratory gas flow emanating from the ventilator, the CO2 concentration in the inspired gas can be manipulated by adjusting the proportion of additional CO2. We evaluated the efficacy of this approach in controlled settings: 1) in a ventilator coupled with a test-lung and 2) in spontaneous breathing healthy volunteers. Additionally, we made a comparative analysis using a conventional method employing a gas reservoir containing a blend of O2, N2, and CO2 in varying concentrations. The methods were evaluated by assessment of the precision in attaining target inspired CO2 levels and examination of their performance within a Magnetic Resonance Imaging (MRI) environment. Our investigations revealed that the Additional CO2 method consistently achieved a high degree of accuracy in reaching target inspired CO2 levels in both mechanical ventilation and spontaneous breathing. We anticipate that these findings will lay the groundwork for a broader implementation of CVR assessments in mechanically ventilated patients.

Background Schools provide universal access to children over five years of age, representing a key opportunity for nutrition interventions to prevent the development of chronic disease. However, an important impediment to the large-scale adoption of evidence-based school nutrition interventions is the lack of evidence on effective strategies to implement them. This paper describes the protocol for a Collaborative Network Trial to support the simultaneous testing of different strategies undertaken by New South Wales Local Health Districts to facilitate the adoption of an effective school-based healthy lunchbox program (SWAP IT). The primary objective of this study is to assess the effectiveness of different implementation strategies to increase school adoption of the SWAP across New South Wales Local Health Districts. Methods Within a Master Protocol framework, a collaborative network trial will be undertaken. Independent randomised controlled trials to test implementation strategies to increase school adoption of SWAP IT within primary schools in 10 different New South Wales Local Health Districts will occur. Schools within each Local Health District will be randomly allocated to either the intervention or control condition. Schools allocated to the intervention group will receive a combination of implementation strategies developed by each of the Local Health Districts independently, based on their existing capacities and local contexts. Across the 10 participating Local Health Districts, six broad strategies were developed and combinations of these strategies will be executed over a 6 month period. In six districts an active comparison group (containing one or more implementation strategies) was selected. The primary outcome of the trial will be adoption of SWAP IT, assessed via electronic registration records captured automatically following online school registration to the program. The primary trial outcome, between-group differences at 6 month follow-up, will be assessed using logistic regression analyses for each trial. Individual participant data component network meta-analysis, under a Bayesian framework, will be used to explore strategy-covariate interactions; to model additive main effects (separate effects for each component of an implementation strategy); two way interactions (synergistic/antagonistic effects of components), and full interactions. Discussion The study will provide rigorous evidence of the effects of a variety of implementation strategies, employed in different contexts, on the adoption of a school-based healthy lunchbox program at scale. Importantly, it will also provide evidence as to whether health service-centred, collaborative research models can rapidly generate new knowledge and yield health service improvements.

Abstract Small for gestational age (SGA) and large for gestational age (LGA) births are topical issues due to their devastating effects on the life course which are also accountable for neonatal mortalities and long term morbidities. Objectives: We tested the hypothesis that abnormal haemoglobin levels in each trimester of pregnancy will increase the risk of SGA and LGA deliveries in Northern Ghana. Design: Retrospective cohort study was conducted from April to July 2020. Settings and Participants: 422 postpartum mothers who had delivered within six weeks prior to the study were recruited through systematic random sampling from five primary and public health facilities in Northern Ghana. Primary outcome measure: Using INTERGROWTH standards, SGA and LGA births were computed. Haemoglobin levels from antenatal records were analyzed to determine their effect on SGA and LGA births by employing multinomial logistic regression after adjusting for sociodemographic and obstetric factors at a significance level of =0.05. Results: Prevalence of anaemia in the first, second, and third trimesters of pregnancy was 63.5%, 71.3%, and 45.3% respectively and that of polycythaemia in the corresponding trimesters of pregnancy was 5.9%, 3.6%, and 1.7%. About 8.8% and 9.2% of the women delivered SGA and LGA babies respectively. After adjusting for confounders, anaemic mothers in the third trimester of pregnancy had increased risk of SGA births (aOR:5.56; 95%CI:0.64 - 48; p<0.001). Mothers with polycythaemia in the first, second, and third trimesters of pregnancy were 93% (aOR:0.07; 95%CI:0.01 - 0.46; p<0.040), 85% (aOR:0.15; 95%CI:0.08 - 1.65; p<0.001), and 88% (aOR:0.12; 95%CI:0.07 - 2.15; p=0.001) protected from SGA births respectively. Additionally, anaemia and polycythaemia across all trimesters of pregnancy were not statistically significant with LGA births. Conclusion: The prevalence of anaemia in each trimester of pregnancy increased and that of polycythaemia decreased as the pregnancy progressed from first to third trimester. Delivery of LGA babies was more predominant compared to SGA babies. While anaemia in the third trimester of pregnancy increased the risk of SGA births, polycythaemia across the trimesters gave protection against SGA births. Healthcare providers and stakeholders should target pressing interventions for anaemia throughout pregnancy, especially during the third trimester.