Multivariate modelling of milk fatty acid profile to discriminate the forages in dairy cows’ ration

Although there are many studies on the importance of fatty acids (FA) in our diet and on the influence of dairy diets on FA metabolism, only a few investigate their predictive capacity to discriminate the type, amount and conservation method of farm forages. This research quantifies differences in milk FA concentrations and, using a supervised factorial discriminant analysis, assesses potential biomarkers when replacing maize with other silages, grass/lucerne hays or fresh grass. The statistical modelling identified three main clusters of milk FA profiles associated with silages, hays and fresh grass as dominant roughages. The main implication of a dairy cow feeding system based on poliphytic forages from permanent meadows is enhancing milk’s nutritional quality due to an increase in beneficial omega-3 polyunsaturated FA, conjugated linoleic acids and odd chain FA, compared to feeding maize silage. The study also identified a small but powerful and reliable pool of milk FA that can act as biomarkers to authenticate feeding systems: C16:1 c-9, C17:0, C18:0, C18:3 c-9, c-12, c-15, C18:1 c-9, C18:1 t-11 and C20:0.

Association between the chronology of gestation and the morphometrical skin characteristics at childbirth: a development of predictive model

ObjectiveThe structural maturation of the skin is considered a potential marker of pregnancy dating. This study investigated the correlation between the morphometrical skin characteristics with the pregnancy chronology to propose models for predicting gestational age.MethodsA cross-sectional analysis selected 35 corpses of newborns. The biopsy was performed up to 48 hours after death in the periumbilical abdomen, palm and sole regions. Pregnancy chronology was based on the obstetric ultrasound before 14 weeks. The dimensions of the skin layers, area of glands and connective fibrous tissue were measured with imaging software support. Univariate and multivariate regression models on morphometric values were used to predict gestational age.ResultsGestational age at birth ranged from 20.3 to 41.2 weeks. Seventy-one skin specimens resulted in the analysis of 1183 digital histological images. The correlation between skin thickness and gestational age was positive and strong in both regions of the body. The highest univariate correlation between gestational age and skin thickness was using the epidermal layer dimensions, in palm (r=0.867, p<0.001). The multivariate modelling with the thickness of the abdominal epidermis, the dermis and the area of the sebaceous glands adjusted had the highest correlation with gestational age (r=0.99, p<0.001).ConclusionThe thickness of the protective epidermal barrier is, in itself, a potential marker of pregnancy dating. However, sets of values obtained from skin morphometry enhanced the estimation of the gestational age. Such findings may support non-invasive image approaches to estimate pregnancy dating with various clinical applications.

A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis

A massive amount of multimodal data are continuously collected in the intensive care unit (ICU) along each patient stay, offering a great opportunity for the development of smart monitoring devices based on artificial intelligence (AI). The two main sources of relevant information collected in the ICU are the electronic health records (EHRs) and vital sign waveforms continuously recorded at the bedside. While EHRs are already widely processed by AI algorithms for prompt diagnosis and prognosis, AI-based assessments of the patients’ pathophysiological state using waveforms are less developed, and their use is still limited to real-time monitoring for basic visual vital sign feedback at the bedside. This study uses data from the MIMIC-III database (PhysioNet) to propose a novel AI approach in ICU patient monitoring that incorporates features estimated by a closed-loop cardiovascular model, with the specific goal of identifying sepsis within the first hour of admission. Our top benchmark results (AUROC = 0.92, AUPRC = 0.90) suggest that features derived by cardiovascular control models may play a key role in identifying sepsis, by continuous monitoring performed through advanced multivariate modelling of vital sign waveforms. This work lays foundations for a deeper data integration paradigm which will help clinicians in their decision-making processes. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology

Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.

General regression methods for respondent-driven sampling data

Respondent-driven sampling is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals’ social relationships. As such, a respondent-driven sample has a graphical component which represents a partially observed network of unknown structure. Moreover, it is common to observe homophily, or the tendency to form connections with individuals who share similar traits. Currently, there is a lack of principled guidance on multivariate modelling strategies for respondent-driven sampling to address peer effects driven by homophily and the dependence between observations within the network. In this work, we propose a methodology for general regression techniques using respondent-driven sampling data. This is used to study the socio-demographic predictors of HIV treatment optimism (about the value of antiretroviral therapy) among gay, bisexual and other men who have sex with men, recruited into a respondent-driven sampling study in Montreal, Canada.

Using Oral Microbiota Data to Design a Short Sucrose Intake Index

Excessive sucrose consumption is associated with numerous health problems, including dental caries, and is considered to play a critical role in shaping the human microbiota. Here, we aimed to confirm the association between sucrose exposure and oral microbiota profile, develop a short food-based index capturing variation among sucrose consumers and validate it against oral microbiota and dental caries in a derivation cohort with 16- to 79-year-old participants (n = 427). Intake and food preferences were recorded by questionnaires and saliva microbiota by 16S rDNA sequencing. Taxonomic similarities clustered participants into five clusters, where one stood out with highest sucrose intake and predicted sugar related metabolic pathways but lowest species diversity in the microbiota. Multivariate modelling of food intake and preferences revealed foods suitable for a sucrose index. This, similarly to sucrose intake, was related to bacterial pattern and caries status. The validity of the sucrose index was replicated in the population-based Gene-Lifestyle Interactions in Dental Endpoints (GLIDE, n = 105,520 Swedish adults) cohort. This suggested that the index captured clinically relevant variation in sucrose intake and that FFQ derived information may be suitable for screening of sucrose intake in the clinic and epidemiological studies, although adjustments to local consumption habits are needed.

Prospective BMI changes in preschool children are not effected by changes in EBRBs but by parental characteristics and body weight perceptions: The ToyBox-study

Objective: To examine the effect of the intervention implemented in the ToyBox study on changes observed in age and sex specific BMI percentile and investigate the role of perinatal factors, parental perceptions and characteristics on this change. Design: A multicomponent, kindergarten-based, family-involved intervention with a cluster-randomized design. A standardized protocol was used to measure children’s body weight and height. Information was also collected from parents/caregivers via the use of validated questionnaires. Linear mixed effect models with random intercept for country, socio-economic status and school were used. Setting: Selected preschools within the provinces of Oost-Flanders and West-Flanders (Belgium), Varna (Bulgaria), Bavaria (Germany), Attica (Greece), Mazowieckie (Poland) and Zaragoza (Spain). Participants: A sample of 6,268 pre-schoolers aged 3.5-5.5 (51.9% boys). Results: There was no intervention effect on the change in children’s BMI percentile. However, parents’ underestimation of their children’s actual weight status, parental overweight and mothers’ pre-pregnancy overweight/obesity were found to be significantly and independently associated with increases in children’s BMI percentile in multivariate modelling. Conclusion: Before or as part of the implementation of any childhood obesity intervention initiative, it is important to assist parents/caregivers to correctly perceive their own and their children’s weight status. Recognition of excessive weight by parents/caregivers can increase their readiness to change and as such facilitate higher adherence to favourable behavioural changes within the family.

Multivariate Modelling of Milk Fatty Acid Profile to Discriminate the Forages in Dairy Cows' Ration

Although there are many studies on the importance of fatty acids (FA) in our diet and on the influence of cows’ diet on FA metabolism, only a few investigate their predictive capacity to discriminate the type, amount and conservation method of the dietary forages. This research quantitatifies differences in FA concentrations and, using a supervised factorial discriminant analysis (FDA), assesses which could be biomarkers when replacing maize silage with other silages, grass/lucerne hays or fresh grass. The statistical modelling identified three main clusters in milk FA profiles associated with silages, hays and fresh grass as dominant roughages. The main implication of a dairy cow feeding system based on poliphytic forages from permanent meadows is to enhance milk’s nutritional value thanks to an increase in beneficial FA belonging to omenga-3 polyunsaturated FA, conjugated linoleic acids and odd chain FA, compared to the use of maize silage from arable land. The study also identified a small but powerful and reliable pool of FA that can act as biomarkers to authenticate feeding systems: C16:1, C17:0, C18:0, C18:3n-3, C18:1 c-9, C18:1 t-11, C20:0.

Age above 70 years and Charlson Comorbidity Index higher than 3 are associated with reduced survival probabilities after radical cystectomy for bladder cancer. Data from a contemporary series of 334 consecutive patients

Objective: To assess the joint effect of age and comorbidities on clinical outcomes of radical cystectomy (RC).Methods: 334 consecutive patients undergoing open RC for bladder cancer (BC) during the years 2005-2015 were analyzed. Pre-, peri- and post-operative parameters, including age at RC (ARC) and Charlson Comorbidity Index (CCI), were evaluated. Overall and cancer-specific survivals (OS, CSS) were assessed by univariate and multivariate modelling. Furthermore, a three-knot restricted cubic spline (RCS) was fitted to survival data to detect dependency between death-rate ratio (HR) and ARC. Results: Median follow-up time was 3.8 years (IQR = 1.3-7.5) while median OS was 5.9 years (95%CL = 3.8-9.1). Globally, 180 patients died in our cohort (53.8%), 112 of which (62.2%) from BC and 68 patients (37.8%) for unrelated causes. After adjusting for preoperative, pathological and perioperative parameters, patients with CCI > 3 showed significantly higher death rates (HR = 1.61; p = 0.022). The highest death rate was recorded in ARC = 71-76 years (HR = 2.25; p = 0.034). After fitting an RCS to both OS and CSS rates, two overlapping nonlinear trends, with common highest risk values included in ARC = 70-75 years, were observed. Conclusions: Age over 70 years and CCI > 3 were significant factors limiting the survival of RC and should both be considered when comparing current RC outcomes.