Impact of Bariatric Surgery on metabolic health in a Uruguayan cohort and the emerging predictive role of FSTL1.

Obesity poses significant challenges, necessitating comprehensive strategies for effective intervention. Bariatric Surgery (BS) has emerged as a crucial therapeutic approach, demonstrating success in weight loss and comorbidity improvement. This study aimed to evaluate the outcomes of BS in a cohort of 48 Uruguayan patients and investigate the interplay between BS and clinical and metabolic features, with a specific focus on FSTL1, an emerging biomarker associated with obesity and inflammation. We quantitatively analyzed BS outcomes and constructed linear models to identify variables impacting BS success. The study revealed the effectiveness of BS in improving metabolic and clinical parameters. Importantly, variables correlating with BS success were identified, with higher pre-surgical FSTL1 levels associated with an increased effect of BS on BMI reduction. FSTL1 levels were measured from patient plasma using an ELISA kit pre-surgery and six months after. This research, despite limitations of a small sample size and limited follow-up time, contributes valuable insights into understanding and predicting the success of BS, highlighting the potential role of FSTL1 as a useful biomarker in obesity.

Medical costs of Swedish nursing home residents at the end of life: a retrospective observational registry study.

BACKGROUND: There are many studies of medical costs in late life in general, but nursing home residents' needs and the costs of external medical services and interventions outside of nursing home services are less well described. METHODS: We examined the direct medical costs of nursing home residents in their last year of life, as well as limited to the period of stay in the nursing home, adjusted for age, sex, Hospital Frailty Risk Score (HFRS), and diagnosis of dementia or advanced cancer. This was an observational retrospective study of registry data from all diseased nursing home residents during the years 2015-2021 using healthcare consumption data from the Stockholm Regional Council, Sweden. T tests, Wilcoxon rank sum tests and chi-square tests were used for comparisons of groups, and generalized linear models (GLMs) were constructed for univariable and multivariable linear regressions of health cost expenditures to calculate risk ratios (RRs) with 95% confidence intervals (95% CIs). RESULTS: According to the adjusted (multivariable) models for the 38,805 studied nursing home decedents, when studying the actual period of stay in nursing homes, we found significantly greater medical costs associated with male sex (RR 1.29 (1.25-1.33), p < 0.0001) and younger age (65-79 years vs. ≥90 years: RR 1.92 (1.85-2.01), p < 0.0001). Costs were also greater for those at risk of frailty according to the Hospital Frailty Risk Score (HFRS) (intermediate risk: RR 3.63 (3.52-3.75), p < 0.0001; high risk: RR 7.84 (7.53-8.16), p < 0.0001); or with advanced cancer (RR 2.41 (2.26-2.57), p < 0.0001), while dementia was associated with lower medical costs (RR 0.54 (0.52-0.55), p < 0.0001). The figures were similar when calculating the costs for the entire last year of life (regardless of whether they were nursing home residents throughout the year). CONCLUSIONS: Despite any obvious explanatory factors, male and younger residents had higher medical costs at the end of life than women. Having a risk of frailty or a diagnosis of advanced cancer was strongly associated with higher costs, whereas a dementia diagnosis was associated with lower external, medical costs. These findings could lead us to consider reimbursement models that could be differentiated based on the observed differences.

Generalized nonlinearity in animal ecology: Research, review, and recommendations.

Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point-count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out-performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species-specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision-making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs.

A Bayesian non-stationary heteroskedastic time series model for multivariate critical care data.

We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non-stationary time series data. Model comparison can then be easily performed using the WAIC.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.

Spatial distribution and risk assessment of microplastics in surface waters of the St. Lawrence Estuary.

Development of effective prevention and mitigation strategies for marine plastic pollution requires a better understanding of the pathways and transport mechanisms of plastic waste. Yet the role of estuaries as a key interface between riverine inputs of plastic pollution and delivery to receiving marine environments remains poorly understood. This study quantified the concentration and distribution of microplastics (MPs) (50-3200 µm) in surface waters of the St. Lawrence Estuary (SLE) in eastern Canada. Microplastics were identified and enumerated based on particle morphology, colour, and size class. Fourier Transform Infrared (FTIR) spectroscopy was used on a subset of particles to identify polymers. Generalized linear models (Gamma distribution with log-link) examined the relationship between MP concentrations and oceanographic variables and anthropogenic sources. Finally, a risk assessment model, using MP concentrations and chemical hazards based on polymer types, estimated the MP pollution risk to ecosystem health. Mean surface MP concentration in the SLE was 120 ± 42 SD particles m-3; MP concentrations were highest in the fluvial section and lowest in the Northwest Gulf of St. Lawrence. However, MP concentrations exhibited high heterogeneity along the length and width of the SLE. Microplastics were elevated at stations located closer to wastewater treatment plant outflows and downstream sites with more agricultural land. Black, blue, and transparent fibers and fragments ≤250 µm were most commonly encountered. Predominant polymer types included polyethylene terephthalate, regenerated cellulose, polyethylene, and alkyds. While the overall risk to ecosystem health in the entire estuary was considered low, several stations, particularly near urban centres were at high or very high risk. This study provides new insights into the quantification and distribution of MPs and first estimates of the risk of MP pollution to ecosystem health in one of the world's largest estuaries.

Procedure Volume and Outcomes With WATCHMAN Left Atrial Appendage Occlusion.

BACKGROUND: Procedure volumes are associated with outcomes for many cardiovascular procedures, leading to guidelines on minimum volume thresholds for certain procedures; however, the volume-outcome relationship with left atrial appendage occlusion is poorly understood. As such, we sought to determine the relationship between hospital and physician volume and WATCHMAN left atrial appendage occlusion procedural success overall and with the new generation WATCHMAN FLX device. METHODS: We performed an analysis of WATCHMAN procedures (January 2019 to October 2021) from the National Cardiovascular Data Registry LAAO Registry. Three-level hierarchical generalized linear models were used to assess the adjusted relationship between procedure volume and procedural success (device released with peridevice leak <5 mm, no in-hospital major adverse events). RESULTS: Among 87 480 patients (76.2±8.0 years; 58.8% men; mean CHA2DS2-VASc score, 4.8±1.5) from 693 hospitals, the procedural success rate was 94.2%. With hospital volume Q4 (greatest volume) as the reference, the likelihood of procedural success was significantly less among Q1 (odds ratio [OR], 0.66 [CI, 0.57-0.77]) and Q2 (OR, 0.78 [CI, 0.69-0.90]) but not Q3 (OR, 0.95 [CI, 0.84-1.07]). With physician volume Q4 (greatest volume) as the reference, the likelihood of procedural success was significantly less among Q1 (OR, 0.72 [CI, 0.63-0.82]), Q2 (OR, 0.79 [CI, 0.71-0.89]), and Q3 (OR, 0.88 [CI, 0.79-0.97]). Among WATCHMAN FLX procedures, there was attenuation of the volume-outcome relationships, with statistically significant but modest absolute differences of only ≈1% across volume quartiles. CONCLUSIONS: In this contemporary national analysis, greater hospital and physician WATCHMAN volumes were associated with increased procedure success. The WATCHMAN FLX transition was associated with increased procedural success and less heterogeneity in outcomes across volume quartiles. These findings indicate the importance of understanding the volume-outcome relationship for individual left atrial appendage occlusion devices.

Exchange-correlation effects in interatomic energies for pure density functionals and their application to the molecular energy prediction.

In this proof-of-concept paper, we show how exchange-correlation effects can be simply recovered for interatomic energies within the interacting quantum atoms decomposition when local, gradient generalized, or meta-gradient generalized approximations are used in density functional theory (DFT) calculations. We also demonstrate how inhomogeneity and non-local effects can be introduced even from a pure local scheme, without resorting to any orbital information. Finally, we provide numerical evidence on a database of selected energetic molecules that this decomposition scheme can be efficiently used to build accurate models for the prediction of molecular energies from an initial "cheap" DFT calculation.

Factors influencing speech improvement following maxillary complete-arch implant-supported restorations: A retrospective study.

OBJECTIVE: This study explored factors affecting speech improvement in patients with an edentulous maxilla after the delivery of a complete-arch implant-supported fixed dental prosthesis (IFDP). MATERIALS AND METHODS: Patients who had received IFDP for edentulous maxilla were enrolled, and various potential speech improvement-related factors were considered, including patient demographics, anterior residual bone volume, preoperative facial features, preoperative acoustic parameters, and adaptation time. Acoustic analysis and perceptual ratings were used to assess three fricatives [s], [f], and [ɕ]. Correlation and regression analyses were conducted to assess the association between changes in fricatives and potential factors (α = .05). RESULTS: The study included 50 patients (18 females and 32 males, aged 50.62 ± 15.71 years, range 19-76). Significant correlations were found among the change in the center of gravity (ΔCoG) of [s] and anterior residual bone volume, zygomatic implants number and proportion (p < .05). These correlations were largely mirrored in the perceptual score (ΔPS) changes. After controlling for age, sex, preoperative acoustic parameters, and adaptation time, the ΔCoG and ΔPS of fricatives were mainly correlated with the anterior residual bone volume, preoperative acoustic parameters, and adaptation time. CONCLUSION: Speech improvements post-IFDP delivery are mainly related to preoperative speech characteristics, anterior residual bone volume, and adaptation time. The residual bone volume's impact on consonants varies with specific articulatory gestures. This study provides insights into forecasting speech outcomes following IFDP restoration and provides recommendations and methods for data collection in developing future prediction models.

Predictive accuracy of time series models applied to economic data: the European countries retail trade.

Modeling and accurately forecasting trend and seasonal patterns of a time series is a crucial activity in economics. The main propose of this study is to evaluate and compare the performance of three traditional forecasting methods, namely the ARIMA models and their extensions, the classical decomposition time series associated with multiple linear regression models with correlated errors, and the Holt-Winters method. These methodologies are applied to retail time series from seven different European countries that present strong trend and seasonal fluctuations. In general, the results indicate that all the forecasting models somehow follow the seasonal pattern exhibited in the data. Based on mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute scaled error (MASE) and U-Theil statistic, the results demonstrate the superiority of the ARIMA model over the other two forecasting approaches. Holt-Winters method also produces accurate forecasts, so it is considered a viable alternative to ARIMA. The performance of the forecasting methods in terms of coverage rates matches the results for accuracy measures.

Determining critical periods for thermal acclimatisation using a distributed lag non-linear modelling approach.

Rapid changes in thermal environments are threatening many species worldwide. Thermal acclimatisation may partially buffer species from the impacts of these changes, but currently, the knowledge about the temporal dynamics of acclimatisation remains limited. Moreover, acclimatisation phenotypes are typically determined in laboratory conditions that lack the variability and stochasticity that characterise the natural environment. Through a distributed lag non-linear model (DLNM), we use field data to assess how the timing and magnitude of past thermal exposures influence thermal tolerance. We apply the model to two Scottish freshwater Ephemeroptera species living in natural thermal conditions. Model results provide evidence that rapid heat hardening effects are dramatic and reflect high rates of change in temperatures experienced over recent hours to days. In contrast, temperature change magnitude impacted acclimatisation over the course of weeks but had no impact on short-term responses. Our results also indicate that individuals may de-acclimatise their heat tolerance in response to cooler environments. Based on the novel insights provided by this powerful modelling approach, we recommend its wider uptake among thermal physiologists to facilitate more nuanced insights in natural contexts, with the additional benefit of providing evidence needed to improve the design of laboratory experiments.

Linear mixed-effect models for correlated response to process electroencephalogram recordings.

A data set of clinical studies of electroencephalogram recordings (EEG) following data acquisition protocols in control individuals (Eyes Closed Wakefulness - Eyes Open Wakefulness, Hyperventilation, and Optostimulation) are quantified with information theory metrics, namely permutation Shanon entropy and permutation Lempel Ziv complexity, to identify functional changes. This work implement Linear mixed-effects models (LMEMs) for confirmatory hypothesis testing. The results show that EEGs have high variability for both metrics and there is a positive correlation between them. The mean of permutation Lempel-Ziv complexity and permutation Shanon entropy used simultaneously for each of the four states are distinguishable from each other. However, used separately, the differences between permutation Lempel-Ziv complexity or permutation Shanon entropy of some states were not statistically significant. This shows that the joint use of both metrics provides more information than the separate use of each of them. Despite their wide use in medicine, LMEMs have not been commonly applied to simultaneously model metrics that quantify EEG signals. Modeling EEGs using a model that characterizes more than one response variable and their possible correlations represents a new way of analyzing EEG data in neuroscience.

Assessing the Impact of Climate Change on Argyrotaenia sphaleropa (Meyrick, 1909) Voltinism: Implications for Fruit Production in Southern Brazil.

The leafroller Argyrotaenia sphaleropa (Meyrick) is an important pest of temperate fruits. Its biology and population dynamics are strongly influenced by temperature. In this context, this study aims to select a mathematical model that accurately describes the temperature-dependent development rate of A. sphaleropa and applies this model to predict the impact of climate change on the number of annual generations (voltinism) of the pest in southern Brazil. Nine mathematical models were employed to fit the species' developmental rate at different constant temperatures. Voltinism was projected using climate data from the current period (1994-2013) and projections for 2050 and 2070. The Brière-1 model (D(T) = aT(T-TL)(TH-T)1/2) provided the best fit for the temperature-dependent developmental rate of A. sphaleropa. According to this model, the regions with the highest voltinism under current climatic conditions are the northern and central areas of Paraná, the western and northeastern regions of Santa Catarina, and northwestern Rio Grande do Sul. The model also predicts a rise in A. sphaleropa voltinism as a consequence of climate change, especially in the mountainous regions of Santa Catarina and Rio Grande do Sul, with projected increases of up to 25.1%. These regions encompass most areas where temperate fruits used as hosts by the leafroller are cultivated. This study represents a significant advancement in understanding the implications of global warming on A. sphaleropa voltinism and suggests that forthcoming climatic conditions will likely favor the species across much of southern Brazil.

Short-term effects of temperature-related indices on emergency ambulance dispatches due to mental and behavioral disorders in Shenzhen, China.

Introduction: The precise associations between temperature-related indices and mental and behavioral disorders (MBDs) have yet to be fully elucidated. Our study aims to ascertain the most effective temperature-related index and assess its immediate impact on emergency ambulance dispatches (EADs) due to MBDs in Shenzhen, China. Methods: EADs data and meteorological data from January 1, 2013, to December 31, 2020, in Shenzhen were collected. Distributed lag non-linear models (DLNMs) were utilized to examine the non-linear and lagged effects of temperature-related indices on EADs due to MBDs. The Quasi Akaike Information criterion (QAIC) was used to determine the optimal index after standardizing temperature-related indices. After adjusting for confounding factors in the model, we estimated the immediate and cumulative effects of temperature on EADs due to MBDs. Results: The analysis of short-term temperature effects on EADs due to MBDs revealed Humidex as the most suitable index. Referring to the optimal Humidex (3.2th percentile, 12.00°C), we observed a significant effect of Humidex over the threshold (34.6th percentile, 26.80°C) on EADs due to MBDs at lag 0-5. The cumulative relative risks for high temperature (90th percentile, 41.90°C) and extreme high temperature (99th percentile, 44.20°C) at lag 0-5 were 1.318 (95% CI: 1.159-1.499) and 1.338 (95% CI: 1.153-1.553), respectively. No significant cold effect was observed on EADs due to MBDs. Conclusion: High Humidex was associated with more EADs due to MBDs in subtropical regions. Health authorities should implement effective measures to raise public awareness of risks related to high temperature and protect vulnerable populations.

Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels.

In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity's consequences in multilevel models is clarified in novel ways.

Long-term declines in chlorophyll a and variable phenology revealed by a 60-year estuarine plankton time series.

Long-term ecological time series provide a unique perspective on the emergent properties of ecosystems. In aquatic systems, phytoplankton form the base of the food web and their biomass, measured as the concentration of the photosynthetic pigment chlorophyll a (chl a), is an indicator of ecosystem quality. We analyzed temporal trends in chl a from the Long-Term Plankton Time Series in Narragansett Bay, Rhode Island, USA, a temperate estuary experiencing long-term warming and changing anthropogenic nutrient inputs. Dynamic linear models were used to impute and model environmental variables (1959 to 2019) and chl a concentrations (1968 to 2019). A long-term chl a decrease was observed with an average decline in the cumulative annual chl a concentration of 49% and a marked decline of 57% in winter-spring bloom magnitude. The long-term decline in chl a concentration was directly and indirectly associated with multiple environmental factors that are impacted by climate change (e.g., warming temperatures, water column stratification, reduced nutrient concentrations) indicating the importance of accounting for regional climate change effects in ecosystem-based management. Analysis of seasonal phenology revealed that the winter-spring bloom occurred earlier, at a rate of 4.9 ± 2.8 d decade-1. Finally, the high degree of temporal variation in phytoplankton biomass observed in Narragansett Bay appears common among estuaries, coasts, and open oceans. The commonality among these marine ecosystems highlights the need to maintain a robust set of phytoplankton time series in the coming decades to improve signal-to-noise ratios and identify trends in these highly variable environments.

Insights into the genetic architecture of Phytophthora capsici root rot resistance in chile pepper (Capsicum spp.) from multi-locus genome-wide association study.

BACKGROUND: Phytophthora root rot, a major constraint in chile pepper production worldwide, is caused by the soil-borne oomycete, Phytophthora capsici. This study aimed to detect significant regions in the Capsicum genome linked to Phytophthora root rot resistance using a panel consisting of 157 Capsicum spp. genotypes. Multi-locus genome wide association study (GWAS) was conducted using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing (GBS). Individual plants were separately inoculated with P. capsici isolates, 'PWB-185', 'PWB-186', and '6347', at the 4-8 leaf stage and were scored for disease symptoms up to 14-days post-inoculation. Disease scores were used to calculate disease parameters including disease severity index percentage, percent of resistant plants, area under disease progress curve, and estimated marginal means for each genotype. RESULTS: Most of the genotypes displayed root rot symptoms, whereas five accessions were completely resistant to all the isolates and displayed no symptoms of infection. A total of 55,117 SNP markers derived from GBS were used to perform multi-locus GWAS which identified 330 significant SNP markers associated with disease resistance. Of these, 56 SNP markers distributed across all the 12 chromosomes were common across the isolates, indicating association with more durable resistance. Candidate genes including nucleotide-binding site leucine-rich repeat (NBS-LRR), systemic acquired resistance (SAR8.2), and receptor-like kinase (RLKs), were identified within 0.5 Mb of the associated markers. CONCLUSIONS: Results will be used to improve resistance to Phytophthora root rot in chile pepper by the development of Kompetitive allele-specific markers (KASP®) for marker validation, genomewide selection, and marker-assisted breeding.

Statistical methods for gene-environment interaction analysis.

Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene-environment interactions (G × E). In this review, we present state-of-the-art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single-variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology.

The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

Relationship between maternal arterial and foetal cord carbon dioxide tension and neonatal outcome in critically ill pregnant women at delivery.

Background and Aims: No studies have evaluated the relationship between maternal arterial partial pressure of carbon dioxide (mPaCO2) and umbilical cord venous partial pressure of carbon dioxide (PCO2) in critically ill pregnant women at delivery. Based on the studies in healthy pregnant women, an mPaCO2 target of ≤50 mmHg is a suggested threshold during mechanical ventilation in critically ill parturients. We evaluated the relationship between mPaCO2 and neonatal cord gases in critically ill parturients at delivery as the primary objective. The relationship between mPaCO2 and APGAR scores at delivery was also analysed as a secondary objective. Methods: Maternal and neonatal cord gas data at delivery and APGAR scores were obtained by a retrospective chart review of 25 consecutive parturients with severe respiratory compromise who were delivered during mechanical ventilation. Linear regression was used to assess the relationship between mPaCO2 and umbilical artery and vein PCO2 and between mPaCO2 and APGAR scores at 1 and 5 min. Results: There was a positive correlation between mPaCO2 and neonatal cord venous PCO2 (P = 0.013). Foetal venous PCO2 exceeded predelivery mPaCO2 by 17.5 (7.5) mmHg. There was an inverse relationship between mPaCO2 and neonatal APGAR scores at 1 and 5 min (P = 0.006 and P = 0.007, respectively). Conclusion: Foetal cord venous PCO2 can be predicted if mPaCO2 values are known. Unlike in healthy pregnant women, there was an inverse relationship between rising mPaCO2 levels and neonatal APGAR scores in critically ill pregnant women who had several associated compounding factors.