Modelling success after perinatal post-haemorrhagic hydrocephalus: a single-centre study.

INTRODUCTION: Post-haemorrhagic hydrocephalus is common amongst premature infants and one of the leading indications for paediatric cerebrospinal fluid (CSF) diversion. Permanent CSF diversion is often delayed until the infant is older but there is no clear consensus on the timing for this. The outcomes for permanent shunting in this patient group are poor, with higher rates of failure and infection compared to other aetiologies of hydrocephalus. METHODS: We conduct a single-centre retrospective review of infants with post-haemorrhagic hydrocephalus requiring a permanent shunt insertion over a 5-year period. Demographic and clinical data from time of shunt insertion were collected and used to generate generalised linear models (GLMs) to predict shunt success at 12 months after insertion. RESULTS: Twenty-six infants underwent permanent shunting in this period for post-haemorrhagic hydrocephalus, with 10 suffering shunt failure within the first 12 months. The best-performing GLM was able to predict shunt success with a sensitivity of 1 and specificity of 0.90, with head circumference, weight, and corrected age at the time of shunt insertion being the most significantly associated variables for shunt success in this model. CONCLUSION: Our proof-of-principle study suggests that highly accurate prediction of shunt success for infants with post-haemorrhagic hydrocephalus is possible using routinely available clinical variables. Further work is required to test this model in larger cohorts and validate whether pre-operative use can improve outcomes for this patient group.

Immediate and delayed effects of climatic factors on hospital admissions for schizophrenia in Queensland Australia: A time series analysis.

BACKGROUND: Evidence of immediate and delayed effects of climatic drivers on hospital admissions for schizophrenia is limited and inconsistent. We aimed to assess the association between climatic factors and daily hospital admissions for schizophrenia in Queensland, Australia. METHODS: Daily hospital admissions for schizophrenia from January 1, 1996 to December 31, 2015 in all private and public hospitals of Queensland were obtained from Queensland Health. The association between climatic factors and hospital admissions for schizophrenia were analysed using Generalised Linear Models with Poisson distribution (GLM) and Distributed Lag non-linear Models (DLNM) across different climatic zones. RESULTS: In South East Queensland, only daily mean temperature showed an immediate negative effect on schizophrenia admissions (RR 0.93, 95%CI 0.90-0.98, p value < 0.001). For other regions, the adverse effect of temperature on hospital admissions was not significant, however, relative humidity (North: RR 1.01, 95%CI 1.00-1.02, p = 0.05) and air pressure (North: RR 1.03, 95%CI 1.00-1.05, p = 0.04; South West: RR 1.01, 95%CI 1.00-1.02, p = 0.05) had an immediate and positive effect on hospital admissions. Moreover, climatic factors had some delayed effects on schizophrenia admissions in different regions of Queensland, i.e. temperature over 0-4 lag days (South East: RR 0.97, 95%CI 0.94-0.98, p = 0.05; South West: RR 0.96, 95%CI 0.94-0.98, p = 0.01), relative humidity over 0-7 lag days (North: RR 0.95, 95%CI 0.92-0.98, p = 0.01; Central: RR 1.02, 95%CI 1.00-1.03, p = 0.05) and rainfall over 0-21 lag days (North: RR 1.03, 95%CI 1.01-1.04, p = 0.01). Meta-analysis showed significant pooled delayed effects of temperature (0-15 days lag: RR 0.95, 95% CI 0.93-0.98, p value < 0.001), relative humidity (0-7 days: RR 0.96, 95%CI 0.92-0.99, p < 0.001); rainfall (0-21 lag days: RR 1.03, 95%CI 1.01-1.04, p < 0.001) and air pressure (0-7 days lag: RR 1.02, 95%CI 1.00-1.04, p < 0.001) on schizophrenia admissions in Queensland. DISCUSSION: As this is the largest study from Australia and also internationally to extensively examine both short term and delayed association between climatic factors and daily admissions for schizophrenia, the results of the study indicate that climate plays an important role in the sudden exacerbation of acute episodes of schizophrenia. Thus, preventive measures could be taken to reduce the severity of symptoms as well as hospital admissions due to schizophrenia during vulnerable periods.

Assessing evidence for seasonality of hospital admissions for schizophrenia in Queensland, Australia: a time series observational study.

Most evidence on seasonal admission patterns for schizophrenia derives from the Northern Hemisphere with results from the Southern Hemisphere less documented. This study examines seasonal patterns in hospital admissions due to schizophrenia in Queensland, Australia, a large area that has a range of different climatic features. Daily hospital admissions data for people with the primary diagnosis of schizophrenia were collected from Queensland Health Department for the period from January 1996 to December 2015. A generalised linear regression model with Quasi-Poisson distribution was used to assess seasonal admission patterns across different climatic regions. The evidence for seasonality was also explored in subgroups that had different socio-demographic characteristics or history of prior hospitalisation for psychiatric disorders. Overall, a significant winter pattern (RR 1.05, 95%CI 1.01-1.13) was found with a peak in August (RR 1.08, 95%CI 1.03-1.17) in temperate Southeast Queensland. However, the hot humid North and Far North Queensland showed a peak in October (RR 1.10, 95%CI 1.02-1.22). Males (RR 1.11, 95%CI 1.07-1.14), people aged 40-59 years old (RR 1.10, 95%CI 1.05-1.15) and those who had never married (RR 1.09, 95%CI 1.06-1.12), were Australian by birth (RR 1.07, 95%CI 1.04-1.10) or were unemployed (RR 1.13, 95%CI 1.09-1.18) had significantly higher risk for hospital admissions, particularly during the winter months. The seasonal admission pattern for schizophrenia did not change significantly according to admission status and history of outpatient or community psychiatric treatment. The study found some evidence for seasonality of hospital admissions for schizophrenia that differed from northern tropical to southern temperate regions of Queensland.

Length conversions and mass-length relationships of five forage-fish species in the California current ecosystem.

Length-measurement conversions and seasonal mass-length relationships (MLR) for Pacific herring Clupea pallasii, northern anchovy Engraulis mordax, Pacific sardine Sardinops sagax, Pacific mackerel Scomber japonicus and jack mackerel Trachurus symmetricus in the California Current are presented. The conversions between total (LT ), fork (LF ,) and standard lengths (LS ) should facilitate comparisons of data across disciplines and institutions. These equations resulted from an analysis of measurements spanning 14 years and the western seaboard of North America, from the north end of Vancouver Island to the USA-Mexico border. Major-axis regressions were used to calculate reciprocal length-measurement conversions (e.g., LT to LS and LS to LT ) and generalised linear models and ordinary least-squares models were used to create MLRs that account for seasonal variations. The MLR models indicated seasonal differences for all species except C. pallasii, for which there was no multi-season data. Discrepancies between these and published models were examined, along with the suitability and benefit of the various types of models used for length-measurement conversion and MLRs.

Life tables for global surveillance of cancer survival (the CONCORD programme): data sources and methods.

BACKGROUND: We set out to estimate net survival trends for 10 common cancers in 279 cancer registry populations in 67 countries around the world, as part of the CONCORD-2 study. Net survival can be interpreted as the proportion of cancer patients who survive up to a given time, after eliminating the impact of mortality from other causes (background mortality). Background mortality varies widely between populations and over time. It was therefore necessary to construct robust life tables that accurately reflected the background mortality in each of the registry populations. METHODS: Life tables of all-cause mortality rates by single year of age and sex were constructed by calendar year for each population and, when possible, by racial or ethnic sub-groups. We used three different approaches, based on the type of mortality data available from each registry. With death and population counts, we adopted a flexible multivariable modelling approach. With unsmoothed mortality rates, we used the Ewbank relational method. Where no data were available from the registry or a national statistical office, we used the abridged UN Population Division life tables and interpolated these using the Elandt-Johnson method. We also investigated the impact of using state- and race-specific life tables versus national race-specific life tables on estimates of net survival from four adult cancers in the United States (US). RESULTS: We constructed 6,514 life tables covering 327 populations. Wide variations in life expectancy at birth and mortality by age were observed, even within countries. During 1995-99, life expectancy was lowest in Nigeria and highest in Japan, ranging from 47 to 84 years among females and 46 to 78 years among males. During 2005-09, life expectancy was lowest in Lesotho and again highest in Japan, ranging from 45 to 86 years among females and 45 to 80 years among males. For the US, estimates of net survival differed by up to 4% if background mortality was fully controlled with state- and race-specific life tables, rather than with national race-specific life tables. CONCLUSIONS: Background mortality varies worldwide. This emphasises the importance of using population-specific life tables for geographic and international comparisons of net survival.

Quantifying parasite presence in relation to biological parameters of harbour porpoises Phocoena phocoena stranded on the Dutch coast.

Harbour porpoises are often found to be infected by endoparasites in several organs including the lungs and stomach as well as the heart, liver and ears. Nevertheless there is still little knowledge about the impact, ecology, transmission, and virulence of these parasitic infections. Here, we profile the presence of parasites in 4 frequently infected organs (lungs, stomach, liver and ears) in relation to biological parameters of harbour porpoises stranded along the Dutch coastline between December 2008 and December 2013. We found that parasites were common, with prevalence of 68% in lungs, 74.4% in ears, 26% in stomach and 23.5% in liver. We used generalised linear models to further quantify parasite presence in relation to biological data gathered during necropsy (sex, body length and nutritive condition). Body length (used as a proxy for age) was significant in explaining parasite presence for all organs with increasing probability of having the parasite with increasing body length. For the parasitic infections in the ears and stomach the nutritive condition was an additional significant factor, with a higher probability of parasite presence in porpoises in a poorer nutritive condition. The results of this study can be used as a baseline for assessing parasite presence in harbour porpoises and are a first step towards linking parasite infections to basic biological data gathered during necropsy.

Multivariable flexible modelling for estimating complete, smoothed life tables for sub-national populations.

BACKGROUND: The methods currently available to estimate age- and sex-specific mortality rates for sub-populations are subject to a number of important limitations. We propose two alternative multivariable approaches: a relational model and a Poisson model both using restricted cubic splines. METHODS: We evaluated a flexible Poisson and flexible relational model against the Elandt-Johnson approach in a simulation study using 100 random samples of population and death counts, with different sampling proportions and data arrangements. Estimated rates were compared to the original mortality rates using goodness-of-fit measures and life expectancy. We further investigated an approach for determining optimal knot locations in the Poisson model. RESULTS: The flexible Poisson model outperformed the flexible relational and Elandt-Johnson methods with the smallest sample of data (1%). With the largest sample of data (20%), the flexible Poisson and flexible relational models performed comparably, though the flexible Poisson model displayed a slight advantage. Both approaches tended to underestimate infant mortality and thereby overestimate life expectancy at birth. The flexible Poisson model performed much better at young ages when knots were fixed a priori. For ages 30 and above, results were similar to the model with no fixed knots. CONCLUSIONS: The flexible Poisson model is recommended because it derives robust and unbiased estimates for sub-populations without making strong assumptions about age-specific mortality profiles. Fixing knots a priori in the final model greatly improves fit at the young ages.

Santa Ana winds and predictors of wildfire progression in southern California.

Santa Ana winds have been implicated as a major driver of large wildfires in southern California. While numerous anecdotal reports exist, there is little quantitative analysis in peer-reviewed literature on how this weather phenomenon influences fire progression rates. We analysed fire progression within 158 fire events in southern California as a function of meteorologically defined Santa Ana conditions between 2001 and 2009. Our results show quantitatively that burned area per day is 3.5-4.5 times larger on Santa Ana days than on non-Santa Ana days. Santa Ana definition parameters (relative humidity, wind speed) along with other predictor variables (air temperature, fuel temperature, 10-h fuel moisture, population density, slope, fuel loading, previous-day burn perimeter) were tested individually and in combination for correlation with subsets of daily burned area. Relative humidity had the most consistently strong correlation with burned area per day. Gust and peak wind speed had a strong positive correlation with burned area per day particularly within subsets of burned area representing only the first day of a fire, >500 ha burned areas, and on Santa Ana days. The suite of variables comprising the best-fit generalised linear model for predicting burned area (R 2 = 0.41) included relative humidity, peak wind speed, previous-day burn perimeter and two binary indicators for first and last day of a fire event.

Variable selection for individualised treatment rules with discrete outcomes.

An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement. Thus, selecting variables to improve the treatment rule is crucial. We propose a doubly robust variable selection method for ITRs, and show that it compares favourably with competing approaches. We illustrate the proposed method on data from an adaptive, web-based stress management tool.

Trends and projections of cause-specific premature mortality in Australia to 2044: a statistical modelling study.

Background: Long-term projections of premature mortality (defined as deaths age <75 years) help to inform decisions about public health priorities. This study aimed to project premature mortality rates in Australia to 2044, and to estimate numbers of deaths and potential years of life lost (PYLL) due to premature mortality overall and for 59 causes. Methods: We examined the past trends in premature mortality rates using Australian mortality data by sex, 5-year age group and 5-year calendar period up to 2019. Cigarette smoking exposure data (1945-2019) were included to project lung cancer mortality. Age-period-cohort or generalised linear models were developed and validated for each cause to project premature mortality rates to 2044. Findings: Over the 25-year period from 1990-1994 to 2015-2019, there was a 44.4% decrease in the overall age-standardised premature mortality rate. This decline is expected to continue, from 162.4 deaths/100,000 population in 2015-2019 to 141.7/100,000 in 2040-2044 (12.7% decrease). Despite declining rates, total numbers of premature deaths are projected to increase by 22.8%, rising from 272,815 deaths in 2015-2019 to 334,894 deaths in 2040-2044. This is expected to result in 1.58 million premature deaths over the 25-year period 2020-2044, accounting for 24.5 million PYLL. Of the high-level cause categories, cancer is projected to remain the most common cause of premature death in Australia by 2044, followed by cardiovascular disease, external causes (including injury, poisoning, and suicide), and respiratory diseases. Interpretation: Despite continuously declining overall premature mortality rates, the total number of premature deaths in Australia is projected to remain substantial, and cancer will continue to be the leading cause. These projections can inform the targeting of public health efforts and can serve as benchmarks against which to measure the impact of future interventions. They emphasise the ongoing importance of accelerating the prevention, early detection, and treatment of key health conditions. Funding: No funding was provided for this study.

Evolving a Model for Cochlear Implant Outcome.

BACKGROUND: Cochlear implantation is an efficient treatment for postlingually deafened adults who do not benefit sufficiently from acoustic amplification. Implantation is indicated when it can be foreseen that speech recognition with a cochlear implant (CI) is superior to that with a hearing aid. Especially for subjects with residual speech recognition, it is desirable to predict CI outcome on the basis of preoperative audiological tests. PURPOSE: The purpose of the study was to extend and refine a previously developed model for CI outcome prediction for subjects with preoperative word recognition to include subjects with no residual hearing by incorporating additional results of routine examinations. RESULTS: By introducing the duration of unaided hearing loss (DuHL), the median absolute error (MAE) of the prediction was reduced. While for subjects with preoperative speech recognition, the model modification did not change the MAE, for subjects with no residual speech recognition before surgery, the MAE decreased from 23.7% with the previous model to 17.2% with the extended model. CONCLUSIONS: Prediction of word recognition with CI is possible within clinically relevant limits. Outcome prediction is particularly important for preoperative counseling and in CI aftercare to support systematic monitoring of CI fitting.

Farmers' perception of the ecosystem services provided by diurnal raptors in arid Rajasthan.

Farmers are the most important stakeholders in wildlife conservation in the agricultural landscape. Understanding the farmer's perceptions, attitude, behaviour, and knowledge toward conservation is critical in developing an effective conservation programme in human-dominated landscapes. We conducted semi-structured face-to-face interviews with 373 farmers to understand the farmer's perception of ecosystem services provided by diurnal raptors in the arid region of Rajasthan from July 2020 to February 2021 and from August 2021 to January 2022. We grouped ecosystem services and disservices into larger categories and estimated the correlation between them, finding that disservices are negatively correlated with benefits. Raptors were perceived as beneficial for their role in controlling rodents and pests, but negatively for poultry predation. In addition, we built a binomial generalised linear model with a logit function to better understand the factors that influence farmers' perceptions of raptors (positive or negative). We observed that males and females have different attitudes toward the ecosystem services provided by raptors. It is critical to understand social perceptions in order to conserve species that are rare on a global scale but may face negative perceptions on a local scale. Our study connects ecological information with socio-demographic factors, which can be useful in developing policy measures for raptor conservation.

MetaboVariation: Exploring Individual Variation in Metabolite Levels.

To date, most metabolomics biomarker research has focused on identifying disease biomarkers. However, there is a need for biomarkers of early metabolic dysfunction to identify individuals who would benefit from lifestyle interventions. Concomitantly, there is a need to develop strategies to analyse metabolomics data at an individual level. We propose "MetaboVariation", a method that models repeated measurements on individuals to explore fluctuations in metabolite levels at an individual level. MetaboVariation employs a Bayesian generalised linear model to flag individuals with intra-individual variations in their metabolite levels across multiple measurements. MetaboVariation models repeated metabolite levels as a function of explanatory variables while accounting for intra-individual variation. The posterior predictive distribution of metabolite levels at the individual level is available, and is used to flag individuals with observed metabolite levels outside the 95% highest posterior density prediction interval at a given time point. MetaboVariation was applied to a dataset containing metabolite levels for 20 metabolites, measured once every four months, in 164 individuals. A total of 28% of individuals with intra-individual variations in three or more metabolites were flagged. An R package for MetaboVariation was developed with an embedded R Shiny web application. To summarize, MetaboVariation has made considerable progress in developing strategies for analysing metabolomics data at the individual level, thus paving the way toward personalised healthcare.

Emerging patterns of fluoroquinolone resistance in Campylobacter jejuni in the UK [1998-2018].

Campylobacter jejuni (C.jejuni) is the most common causative agent of bacterial food poisoning worldwide and is known to be genetically highly diverse. C. jejuni is increasingly resistant to fluoroquinolone antibiotics, but very few studies have investigated variant-specific patterns of resistance across time. Here we use statistical modelling and clustering techniques to investigate patterns of fluoroquinolone resistance amongst 10,359 UK isolates from human disease sampled over 20 years. We observed six distinct patterns of fluoroquinolone sensitivity/resistance in C. jejuni across time, grouping by clonal complex (CC). Some CCs were fully resistant, some shifted from susceptible to resistant following a sigmoidal shape, and some remained susceptible over time. Our findings indicate that the fluoroquinolone resistance patterns of C. jejuni are complicated and cannot be analysed as a single species but divided into variant dynamics so that the factors driving resistance can be thoroughly investigated.

Analytical results for directional and quadratic selection gradients for log-linear models of fitness functions.

Log-linear models are widely used for assessing determinants of fitness in empirical studies, for example, in determining how reproductive output depends on trait values or environmental conditions. Similarly, theoretical works of fitness and natural selection employ log-linear models, often with a negative quadratic term, generating Gaussian fitness functions. However, in the specific application of regression-based analysis of natural selection, such models are rarely employed. Rather, OLS regression is the predominant means of assessing the form of natural selection. OLS regressions allow specific evolutionary quantitative parameters, selection gradients, to be estimated, and benefit from the fact that the associated statistical models are easily applied. We examine whether selection gradients can be directly expressed in terms of the coefficients of models using exponential fitness functions with linear or quadratic arguments. Such models can be easily fitted with generalized linear models (GLMs). The expressions we obtain coincide with those for Gaussian functions, but relax the major constraint that the (log) fitness function is concave (downwardly curved). Additionally these results lead to univariate and multivariate analyses of both linear and quadratic selection that potentially incorporate pragmatic and interpretable models of fitness functions, where the parameters can be related analytically to selection gradients, and that can be operationalized using widely available statistical tools.

Modelling habitat suitability of the invasive tick Rhipicephalus microplus in West Africa.

Ticks have medical and economic importance due to their ability to transmit pathogens to humans and animals. In tropical and sub-tropical countries, tick-borne diseases (TBD) are among the most important diseases affecting livestock and humans. The fast spread of ticks and TBD requires a quick development and application of efficient prevention and/or control programs. Therefore, prior investigations on TBD and related vectors epidemiology, for instance, through accurate epidemiological models, are mandatory. This study aims to develop models to forecast suitable habitat for Rhipicephalus microplus distribution in West Africa. Tick occurrences were assembled from 10 different studies carried out in six West African countries in the past decade. Six statistical models (maximum entropy in a single model and generalised linear model, generalised additive model, random forest, boosted regression tree and support vector machine model in an ensemble model) were applied and compared to predict the habitat suitability of R. microplus distribution in West Africa. Each model was evaluated with the area under the receiver operating characteristic curve (AUC), the true skill statistic (TSS) and the Boyce index (BI). The selected models had good performance according to their AUC (above .8), TSS (above .7) and BI (above .8). Temperature played a key role in MaxEnt model, whereas normalised difference vegetation index (NDVI) was the most important variable in the ensemble model. The model predictions showed coastal countries of West Africa as more suitable for R. microplus. However, some Sahelian areas seems also favourable. We stress the importance of vector surveillance and control in countries that have not yet detected R. microplus but are in the areas predicted to host suitable habitat. Indeed, awareness-raising and training of different stakeholders must be reinforced for better prevention and control of this tick in these different countries according to their status.

Prevalence Rate and Associated Risk Factors of Anaemia among under Five Years Children in Ethiopia.

Background: Anaemia is a condition characterised by a decrease in the concentration of haemoglobin (Hb) in the blood. Anaemia suffers under five years children about 47.4% and 67.6% worldwide and developing countries including Ethiopia, respectively. The aim of this study was to assess the prevalence rate and the associated socio-economic, geographic and demographic factors of anaemia status of under five years children in Ethiopia. Methods: The data for this study were obtained from the 2011 Ethiopia National Malaria Indicator Survey (EMIS 2011). A sample of 4356 under five years age children was obtained from three regional states of Ethiopia. Based on haemoglobin level, child anaemia status was ordered and takes an ordinal value as no anaemia, mild anaemia, moderate anaemia and severe anaemia, respectively. Ordinal logistic regression model, specifically the proportional odds model was used by considering with and without survey design features. Results: Of the 4356 complete cases, 2190 (50.28%) were male and 1966 (49.72%) were female children under five years old. The children overall mean (SD) age was 2.68 (1.21) years. It was observed that both the mean ages and their variabilities in the regions are approximately equal to the overall mean and variability. It was also observed that in Amhara, Oromiya and SNNP regions 72.28%, 67.99% and 73.63% of the children, respectively had no anaemia; 15.93%, 13.47% and 13.56% of the children, respectively had mild anaemia; 10.99%, 15.61% and 11.33% of the children, respectively had moderate anaemia; and only 0.81%, 2.93% and 1.49% had severe anaemia, respectively. The prevalence of severe child anaemia status was higher in Oromiya region compared to Amhara and SNNP regions, respectively. Our result indicates that age, use of mosquito net, malaria RDT outcome, type of toilet facility, household wealth index, region and median altitude were significantly related to child anaemia status. However, it was observed that some covariates were model dependent, for example household wealth index and type of toilet facility were not significant when considering survey features. Conclusions: Anaemia burden remains high particularly in developing countries. Controlling the burden of anaemia necessitates the formulation of integrated interventions which prioritise the highest risk groups including children under five years. The statistical model used in this paper identified individual, household and cluster level risk factors of child anaemia. The identified risk factors for example not having improved toilet facility in the dwelling where a child lived as well as poorest household wealth index suggest the policymakers should target to focus more on children from poor community. Further, the strong association between malaria infection and anaemia suggests that malaria preventative methods such as vector control methods namely, long-lasting insecticidal nets (LLINs) and indoor residual spraying of households with insecticides and including case diagnostic testing and treatment may be the most effective ways to reduce infections associated with anaemia. Such collective assessment approach may lead to more effective public health strategies and could have important policy implications for health promotion and for the reduction of health disparities.

Clinical and genetic determinants of the fatty liver-coagulation balance interplay in individuals with metabolic dysfunction.

Background & Aims: The aim of this study was to examine the determinants of the interplay between liver damage and the coagulation balance in individuals at risk of non-alcoholic fatty liver disease (NAFLD). Methods: We considered 581 healthy participants with ≥3 metabolic alterations undergoing clinical and genomic evaluation, measurement of liver stiffness (LSM) and controlled attenuation parameter (CAP) by Fibroscan, Pro-C3, coagulation balance (von Willebrand factor [vWF], factor VIII/protein C ratio [F8/PC] as the main outcome, D-dimer as marker of coagulation/fibrinolysis activation). Results: Liver fibrosis indices (both Fibrosis-4 [FIB-4] and liver stiffness measurement [LSM]), but not liver fat (CAP), were independently associated with higher F8/PC ratio (p <0.01), triggering D-dimer formation (p = 2E-21). In keeping with a causal role of liver damage in determining a procoagulant status, the main fatty liver inherited risk variant PNPLA3 p.I148M was independently associated with the F8/PC ratio (p = 0.048). Vice versa, the main determinant of the coagulation balance was ABO locus variation (p = 1E-16), through the impact on vWF (p = 8E-26). Both rs687289 ABO and factor V Leiden were independently associated with higher Pro-C3 (p <0.025), with the effect of ABO being mediated by the impact on vWF (p = 5E-10 for association with Pro-C3). Mendelian randomisation analysis was consistent with a causal association of procoagulant imbalance with heightened fibrogenesis (p = 0.001 at robust MR-Egger for Pro-C3), but not with fibrosis (for LSM; p = not significant). Conclusions: In individuals with metabolic dysfunction, liver damage severity and possibly the PNPLA3 p.I148M variant were associated with procoagulant status. Vice versa, evaluation of inherited variants in ABO and other genes influencing coagulation was consistent with a causal role of procoagulant imbalance in activation of early stages of fibrogenesis. Lay summary: In individuals with metabolic alterations at risk of metabolic fatty liver disease, there is a tendency toward heightened blood coagulation (clotting), but the cause and the impact on the progression of liver disease remain unclear. Here we show that liver damage severity and metabolic alterations, but not hepatic fat, are mainly responsible for heightened coagulation in patients with metabolic fatty liver disease. By using genetic approaches, we showed that hepatic inflammation due to lipotoxicity may favour heightened coagulation, which in turn can trigger liver fibrosis, igniting a vicious cycle that leads to progressive liver disease.

Evaluation of pathogen concentration in anaerobic digestate using a predictive modelling approach (ADRISK).

Farmyard manure and slurry (FYM&S) is a valuable feedstock for anaerobic digestion (AD) plants. However, FYM&S may contain high concentrations of pathogens, and complete inactivation through the AD process is unlikely. Thus, following land application of digestate, pathogens may contaminate a range of environmental media posing a potential threat to public health. The present study aimed to combine primary laboratory data with literature-based secondary data to develop an Excel-based exposure assessment model (ADRISK) using a gamma generalised linear model to predict the final microorganism count in the digestate. This research examines the behaviour of a suite of pathogens (Cryptosporidium parvum, norovirus, Mycobacterium spp., Salmonella spp., Listeria monocytogenes, Clostridium spp., and pathogenic Escherichia coli) and indicators (total coliforms, E. coli, and enterococci) during mesophilic anaerobic digestion (M-AD) at 37 °C, pre/post-AD pasteurisation, and after a period of storage (with/without lime) for different feedstock proportions (slurry:food waste: 0:1, 1:3, 2:1, and 3:1). ADRISK tool simulations of faecal indicator bacteria levels across all scenarios show that the digestate can meet the EU standard without pasteurisation if the AD runs at 37 °C or a higher temperature with a higher C:N ratio (recipe 3) and a hydraulic retention time ≥ 7 days. The storage of digestate also reduced levels of microorganisms in the digestate. The Irish pasteurisation process (60 °C for 4 days), although more energy-intensive, is more effective than the EU pasteurisation (70 °C for 1 h) specification. Pre-AD pasteurisation was more effective for C. parvum, norovirus, Mycobacterium thermoresistibile. However, post-AD literature-based pasteurisation is most likely to assure the safety of the digestate. The information generated from this model can inform policy-makers regarding the optimal M-AD process parameters necessary to maximise the inactivation of microorganisms, ensuring adverse environmental impact is minimised, and public health is protected.

Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning.

Defining genes that are essential for life has major implications for understanding critical biological processes and mechanisms. Although essential genes have been identified and characterised experimentally using functional genomic tools, it is challenging to predict with confidence such genes from molecular and phenomic data sets using computational methods. Using extensive data sets available for the model organism Caenorhabditis elegans, we constructed here a machine-learning (ML)-based workflow for the prediction of essential genes on a genome-wide scale. We identified strong predictors for such genes and showed that trained ML models consistently achieve highly-accurate classifications. Complementary analyses revealed an association between essential genes and chromosomal location. Our findings reveal that essential genes in C. elegans tend to be located in or near the centre of autosomal chromosomes; are positively correlated with low single nucleotide polymorphim (SNP) densities and epigenetic markers in promoter regions; are involved in protein and nucleotide processing; are transcribed in most cells; are enriched in reproductive tissues or are targets for small RNAs bound to the argonaut CSR-1. Based on these results, we hypothesise an interplay between epigenetic markers and small RNA pathways in the germline, with transcription-based memory; this hypothesis warrants testing. From a technical perspective, further work is needed to evaluate whether the present ML-based approach will be applicable to other metazoans (including Drosophila melanogaster) for which comprehensive data sets (i.e. genomic, transcriptomic, proteomic, variomic, epigenetic and phenomic) are available.