Probabilistic sensitivity analysis of optimized preventive maintenance strategies for deteriorating infrastructure assets

Efficient life-cycle management of civil infrastructure systems under continuous deterioration can be improved by studying the sensitivity of optimised preventive maintenance decisions with respect to changes in model parameters. Sensitivity analysis in maintenance optimisation problems is important because if the calculation of the cost of preventive maintenance strategies is not sufficiently robust, the use of the maintenance model can generate optimised maintenances strategies that are not cost-effective. Probabilistic sensitivity analysis methods (particularly variance based ones), only partially respond to this issue and their use is limited to evaluating the extent to which uncertainty in each input contributes to the overall output's variance. These methods do not take account of the decision-making problem in a straightforward manner. To address this issue, we use the concept of the Expected Value of Perfect Information (EVPI) to perform decision-informed sensitivity analysis: to identify the key parameters of the problem and quantify the value of learning about certain aspects of the life-cycle management of civil infrastructure system. This approach allows us to quantify the benefits of the maintenance strategies in terms of expected costs and in the light of accumulated information about the model parameters and aspects of the system, such as the ageing process. We use a Gamma process model to represent the uncertainty associated with asset deterioration, illustrating the use of EVPI to perform sensitivity analysis on the optimisation problem for age-based and condition-based preventive maintenance strategies. The evaluation of EVPI indices is computationally demanding and Markov Chain Monte Carlo techniques would not be helpful. To overcome this computational difficulty, we approximate the EVPI indices using Gaussian process emulators. The implications of the worked numerical examples discussed in the context of analytical efficiency and organisational learning

Association of Serum Leptin with Prognostic Factors in Breast Cancer

Background: Nowadays, cytokines such as Leptin and adiponectin are introduced as prognostic factors which, according to some studies, are also associated with body mass index. This study aimed to determine serum leptin level and its relationship with prognostic factors in breast cancer patients.Methods: This case–control study was conducted in the oncology department of Tohid Hospital, Sanandaj, Iran, between 2019 and 2020. Hundred new cases of breast cancer patients with histological evidence were enrolled in this study. Additionally, 100 age-and BMI-matched healthy individuals were recruited as the control group. The serum leptin level was measured using the ELISA method.Results: Serum leptin levels were significantly higher in breast cancer patients compared to the control group (21.68 ± 9.16 vs 11.89 ± 4.45; p < 0.001). There was no significant relationship between plasma leptin levels with ER, PR, and HER2 expressions (p > 0.05). Also, no significant associations were noted between leptin levels and grading and disease staging (p > 0.05).Conclusion: The study found that leptin is higher in breast cancer patients than in healthy individuals, however, it did not prove that leptin is a predictive or prognostic factor.Keywords: leptin, breast cancer, staging, gradin

Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients.

BACKGROUND: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools