Spontaneous pneumothorax and pneumomediastinum as a rare complication of COVID-19 pneumonia: Report of 6 cases

Spontaneous pneumothorax (SPT) and pneumomediastinum (SPM) have been reported as uncommon complications of coronavirus disease (COVID-19) pneumonia. The exact incidence and risk factors are still unrecognized. We report 6 nonventilated, COVID-19 pneumonia cases with SPT and SPM and their outcomes. The major risk factors for development of SPT and SPM in our patients were male gender, advance age, and pre-existing lung disease. These complications may occur in the absence of mechanical ventilation and associated with increasing morbidity (chest tube insertion, sepsis, hospital admission) and mortality. SPT and SPM should be considered as a potential predictive factor for adverse outcome and probable cause of unexplained deterioration of clinical condition in COVID-19 pneumonia. © 2021 The Author

The Most Common Comorbidities in Dandy-Walker Syndrome Patients: A Systematic Review of Case Reports.

OBJECTIVE: Dandy-Walker syndrome (DWS) is a rare neurologic multi-entity malformation. This review aimed at reporting its main nonneurologic comorbidities. METHODS: Following PRISMA guidelines, search in Medline was conducted (2000-2014, keyword: dandy-walker). Age, sex, country, DWS type, consanguinity or siblings with DWS, and recorded coexistent conditions (by ICD10 category) were extracted for 187 patients (46.5% male, 43% from Asia) from 168 case reports. RESULTS: Diagnosis was most often set in 12 years old (27.8%). One-third of cases had a chromosomal abnormality or syndrome (n = 8 PHACE), 27% had a cardiovascular condition (n = 7 Patent Ductus Arteriosus), 24% had a disease of eye and ear (n = 9 cataract); most common malignancy was nephroblastoma (n = 8, all Asian). Almost one-fifth had a mental illness diagnosis; only 6.4% had mild or severe intellectual disability. CONCLUSION: The spread of comorbidities calls for early diagnosis and multidisciplinary research and practice, especially as many cases remain clinically asymptomatic for years

Exact stochastic constraint optimisation with applications in network analysis

We present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These problems are prevalent in science, governance and industry. The first method we study is generic and decomposes stochastic constraints into a multitude of smaller local constraints that are solved using a constraint programming (CP) or mixed-integer programming (MIP) solver. However, many SCPs are formulated on probability distributions with a monotonic property, meaning that adding a positive decision to a partial solution to the problem cannot cause a decrease in solution quality. The second method is specifically designed for solving global stochastic constraints on monotonic probability distributions (SCMDs) in CP. Both methods use knowledge compilation to obtain a decision diagram encoding of the relevant probability distributions, where we focus on ordered binary decision diagrams (OBDDs). We discuss theoretical advantages and disadvantages of these methods and evaluate them experimentally. We observed that global approaches to solving SCMDs outperform decomposition approaches from CP, and perform complementarily to MIP-based decomposition approaches, while scaling much more favourably with instance size. Both methods have many alternative design choices, as both knowledge compilation and constraint solvers are used in a single pipeline. To identify which configurations work best, we apply programming by optimisation. Specifically, we show how an automated algorithm configurator can be used to find optimised configurations of our pipeline. After configuration, our global SCMD solving pipeline outperforms its closest competitor (a MIP-based decomposition pipeline) on all test sets we considered by up to two orders of magnitude in terms of PAR10 scores

Changes in Physiological Levels of Cortisol and Adrenocorticotropic Hormone upon Hospitalization Can Predict SARS-CoV-2 Mortality: A Cohort Study

There is some indication that coronavirus disease 2019 (COVID-19) causes hypothalamic-pituitary-adrenal axis insufficiency. However, being on glucocorticoids makes it difficult to fully investigate this axis, especially in patients with severe COVID-19. We aimed to discover if there was a connection between blood total cortisol and adrenocorticotropic hormone (ACTH) levels and mortality in patients with COVID-19. In Iran, 154 hospitalized patients with COVID-19 were studied in a prospective cohort study. ACTH and cortisol levels in the blood were measured on the first or second day of hospitalization. Most patients (52.6 vs. 47.4) were men over 50 years old (55.8), and 44.4 had an underlying illness. Serum cortisol and plasma ACTH medians were 15.6 (mu g/dl) and 11.4 (pg/ml), respectively. 9.09 of the patients died. Cortisol levels were substantially lower in those who died (11.3 mu g/dl) than in patients who were discharged (16.7 mu g/dl, P < 0.01), while ACTH levels were unaffected. The most important factors determining mortality, according to the logistic model, were blood cortisol levels, the existence of an underlying disease, and the use of a mechanical ventilator. Cortisol levels that rose by one-unit correlated with a 26 lower risk of mortality. Comorbidities and mechanical ventilation increased the risk of death by 260 and 92 times, respectively. It can be concluded that in patients with COVID-19, a low cortisol level is linked to a high risk of mortality. Patients may sometimes have relative primary adrenal insufficiency. To judge and decide on therapeutic interventions, more reliable and long-term follow-up studies are required

Partition-based clustering using constraint optimization

Partition-based clustering is the task of partitioning a dataset in a number of groups of examples, such that examples in each group are similar to each other. Many criteria for what constitutes a good clustering have been identified in the literature; furthermore, the use of additional constraints to find more useful clusterings has been proposed. In this chapter, it will be shown that most of these clustering tasks can be formalized using optimization criteria and constraints. We demonstrate how a range of clustering tasks can be modelled in generic constraint programming languages with these constraints and optimization criteria. Using the constraint-based modeling approach we also relate the DBSCAN method for density-based clustering to the label propagation technique for community discovery

Clustering Formulation Using Constraint Optimization

The problem of clustering a set of data is a textbook machine learning problem, but at the same time, at heart, a typical optimization problem. Given an objective function, such as minimizing the intracluster distances or maximizing the inter-cluster distances, the task is to find an assignment of data points to clusters that achieves this objective. In this paper, we present a constraint programming model for a centroid based clustering and one for a density based clustering. In particular, as a key contribution, we show how the expressivity introduced by the formulation of the problem by constraint programming makes the standard problem easy to be extended with other constraints that permit to generate interesting variants of the problem. We show this important aspect in two different ways: first, we show how the formulation of the density-based clustering by constraint programming makes it very similar to the label propagation problem and then, we propose a variant of the standard label propagation approach