The Consistency of Probabilistic Databases with Independent Cells

A probabilistic database with attribute-level uncertainty consists of relations where cells of some attributes may hold probability distributions rather than deterministic content. Such databases arise, implicitly or explicitly, in the context of noisy operations such as missing data imputation, where we automatically fill in missing values, column prediction, where we predict unknown attributes, and database cleaning (and repairing), where we replace the original values due to detected errors or violation of integrity constraints. We study the computational complexity of problems that regard the selection of cell values in the presence of integrity constraints. More precisely, we focus on functional dependencies and study three problems: (1) deciding whether the constraints can be satisfied by any choice of values, (2) finding a most probable such choice, and (3) calculating the probability of satisfying the constraints. The data complexity of these problems is determined by the combination of the set of functional dependencies and the collection of uncertain attributes. We give full classifications into tractable and intractable complexities for several classes of constraints, including a single dependency, matching constraints, and unary functional dependencies

A pilot study for a non-invasive system for detection of malignancy in canine subcutaneous and cutaneous masses using machine learning

IntroductionEarly diagnosis of cancer enhances treatment planning and improves prognosis. Many masses presenting to veterinary clinics are difficult to diagnose without using invasive, time-consuming, and costly tests. Our objective was to perform a preliminary proof-of-concept for the HT Vista device, a novel artificial intelligence-based thermal imaging system, developed and designed to differentiate benign from malignant, cutaneous and subcutaneous masses in dogs.MethodsForty-five dogs with a total of 69 masses were recruited. Each mass was clipped and heated by the HT Vista device. The heat emitted by the mass and its adjacent healthy tissue was automatically recorded using a built-in thermal camera. The thermal data from both areas were subsequently analyzed using an Artificial Intelligence algorithm. Cytology and/or biopsy results were later compared to the results obtained from the HT Vista system and used to train the algorithm. Validation was done using a “Leave One Out” cross-validation to determine the algorithm's performance.ResultsThe accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the system were 90%, 93%, 88%, 83%, and 95%, respectively for all masses.ConclusionWe propose that this novel system, with further development, could be used to provide a decision-support tool enabling clinicians to differentiate between benign lesions and those requiring additional diagnostics. Our study also provides a proof-of-concept for ongoing prospective trials for cancer diagnosis using advanced thermodynamics and machine learning procedures in companion dogs

Abnormal glucose challenge test in absence of oral glucose tolerance test – are there consequences?

This is a retrospective analysis of mothers with abnormal 1-hour, 50-grams glucose challenge test (GCT) who did not take a 3-hour, 100-gram oral glucose tolerance test (OGTT). This study group of women was compared to three control groups, based on an OGTT diagnostic test- normal OGTT, single pathological value and gestational diabetes mellitus. Overall- 4,185 women were included and sub-divided accordingly into four groups: Group A-340 (8.12%)- no OGTT; Group B-2,585 (61.77%)- Norm OGTT (All values normal); Group C- 564 (13.48%)- SinOGTT (single pathological value) and Group D- 696 (16.63%)- Gestational Diabetes Mellitus (GDM, ≥ 2 pathological values). Groups A, C and D had higher rates of intrapartum Caesarean Delivery (10.29%, 11.52% and 10.19% vs. 8.43%, p < .0001). Group A had highest rates of neonatal adverse outcomes, as neonatal intensive care unit (NICU) admission (12.4% vs. 8.4%, 11.0% and 10.0%, p = .039), small for gestational age (SGA) neonates (7.0% vs. 5.3%, 3.7% and 6.0%, p = .0092) and neonatal hypoglycaemia (3.5% vs. 1.3%, 3.2% and 2.9%, p = .007). A multivariable regression revealed that having an abnormal GCT without an OGTT was an independent risk factor for neonatal intensive care unit admission, neonatal hypoglycaemia and intrapartum caesarean delivery. We concluded that women with pathological GCT who did not complete OGTT have higher rates of obstetric adverse outcomes. They should be closely monitored during delivery and should not be overlooked.IMPACT STATEMENT What is already known on this subject? Adverse outcomes of gestational diabetes mellitus are well established. But, the group of women who fail to complete a confirmatory OGTT following a pathological GCT is not well described. What the results of this study add? Our results point out that women who fail to complete an OGTT, suffer from higher rates of obstetric complications, presumably attributed to disrupted glucose values, but also to poor prenatal care. What the implications are of these findings for clinical practice and/or further research? These women should not be overlooked. They should be closely monitored during labour and delivery

Risk of caesarean delivery after induction of labour stratified by foetal sex

This study describes a retrospective analysis of all women admitted for induction of labour (IoL), carrying a viable singleton foetus, after 34 + 0 weeks of gestation. We aimed to evaluate if foetal sex has an impact on the rate of caesarean delivery following labour induction. Our results demonstrate that among the 1062 women who met the inclusion criteria, 49% (521/1062) were carrying a male foetus. Other than a lower rate of Oxytocin use for the female sex pregnancies, there were no significant differences in pre-labour and labour characteristics between male or female sex pregnancies. There was no difference in caesarean delivery rate between groups (14.4% vs. 14.2%, male vs. female, respectively, p = .505). We concluded conclude that foetal sex does not impact the caesarean delivery rate among women undergoing IoL, regardless of the indication for induction and the indication for the caesarean delivery.Impact statement Male sex foetuses are at increased risk for adverse perinatal outcomes including, among others, an increased risk for caesarean delivery. The possible contribution of male sex to caesarean delivery after labour induction has not been specifically explored. Following induction of labour, there is no difference in failed induction or caesarean delivery rate between male and female sex pregnancies. Induction of labour may be safely employed for both male and female foetuses