Bacterial coinfections in dengue virus disease: what we know and what is still obscure about an emerging concern

Dengue virus is the most frequent arthropod-borne viral infection worldwide. Simultaneously to the growth of its incidence, cases of bacterial coinfection in dengue have been increasingly reported. The clinical course of dual infections may worsen for reciprocal interactions and delays in the diagnosis, so that clinicians should be aware of this eventuality. Therefore, we reviewed literature to provide an overview of the epidemiological, clinical, and physiopathological issues related to bacterial coinfections and bacteremia in dengue.Clinical studies and case reports regarding bacteremia and bacterial coinfections in dengue and the interactions between the pathogens published on PubMed were reviewed.We found 26 case reports, only 3 studies on concurrent bacteremia and 12 studies reporting data on bacterial coinfections in dengue. According to the three available studies, the 0.18-7 % of dengue infections are accompanied by concurrent bacteremia, while the 14.3-44.4 % of dengue-related deaths seem associated to bacterial coinfections. Comorbidities, advanced age, and more severe dengue manifestations could be risk factors for dual infections. A longer duration of fever and alterations in laboratory parameters such as procalcitonin, hyponatremia, leukocyte count, and renal function tests can raise the suspicion.Despite the real burden and consequences of this emerging concern is still not computable accurately due to the lack of a significant number of studies on large cohorts, clinicians need a greater awareness about it to early recognize warning signs, to properly use available diagnostic tools and to readily start antibiotic treatment able to prevent worsening in mortality and morbidity

A DECISION SUPPORT TOOL ON DERELICT BUILDINGS FOR URBAN REGENERATION

Abstract. We present a decision suppport tool for the comparison and selection of projects of integrated renovation of derelict buildings and areas for the purpose of urban regeneration. Each project is defined as a subset of derelict properties to renovate together with their respective designated use, and is scored by the decision support tool on two criteria: expected effort and estimated effectiveness in terms of improved urban capabilities in the urban area of interest. The expected effort is estimated as a global transformation cost, factoring in legal and management overhead costs as well as possible economies of scale. The effectiveness in evaluated in terms of extension of urban capabilities centred on walkable distances. We have implemented a bi-objective evolutionary search algorithm to address the computational complexity of the problem of search for efficient (non-dominated) projects over the two criteria. For the purpose of illustration, we present an example case-study application on the historical core of the city of Sassari, Italy.</p

Regression Models to Study the Total LOS Related to Valvuloplasty

Background: Valvular heart diseases are diseases that affect the valves by altering the normal circulation of blood within the heart. In recent years, the use of valvuloplasty has become recurrent due to the increase in calcific valve disease, which usually occurs in the elderly, and mitral valve regurgitation. For this reason, it is critical to be able to best manage the patient undergoing this surgery. To accomplish this, the length of stay (LOS) is used as a quality indicator. Methods: A multiple linear regression model and four other regression algorithms were used to study the total LOS function of a set of independent variables related to the clinical and demographic characteristics of patients. The study was conducted at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) in the years 2010–2020. Results: Overall, the MLR model proved to be the best, with an R2 value of 0.720. Among the independent variables, age, pre-operative LOS, congestive heart failure, and peripheral vascular disease were those that mainly influenced the output value. Conclusions: LOS proves, once again, to be a strategic indicator for hospital resource management, and simple linear regression models have shown excellent results to analyze it

Block size estimation for data partitioning in HPC applications using machine learning techniques

The extensive use of HPC infrastructures and frameworks for running data-intensive applications has led to a growing interest in data partitioning techniques and strategies. In fact, finding an effective partitioning, i.e. a suitable size for data blocks, is a key strategy to speed-up parallel data-intensive applications and increase scalability. This paper describes a methodology for data block size estimation in HPC applications, which relies on supervised machine learning techniques. The implementation of the proposed methodology was evaluated using as a testbed dislib, a distributed computing library highly focused on machine learning algorithms built on top of the PyCOMPSs framework. We assessed the effectiveness of our solution through an extensive experimental evaluation considering different algorithms, datasets, and infrastructures, including the MareNostrum 4 supercomputer. The results we obtained show that the methodology is able to efficiently determine a suitable way to split a given dataset, thus enabling the efficient execution of data-parallel applications in high performance environments