Improving the Quality of Hospital Antibiotic Use: Impact on Multidrug-Resistant Bacterial Infections in Children

Antimicrobial resistance (AMR) is considered a rapidly growing global public health emergency. Neonates and children are among patients for whom antibiotics are largely prescribed and for whom the risk of AMR development is high. The phenomenon of increasing AMR has led to the need to develop measures aimed at the rational and effective use of the available drugs also in children and antimicrobial stewardship (AS), which is one of the measures that in adults has showed the highest efficacy in reducing antibiotic abuse and misuse, appears as an attractive approach. The aim of this manuscript is to analyze the basic principles and strategies of pediatric AS. To this end, we searched in PubMed articles published in years 2000 to 2019 containing “antimicrobial resistance,” “antibiotic use,” “antimicrobial stewardship,” and “children” or “pediatric” as keywords. Our review showed that the balance between multi-resistant organisms and new antimicrobials is extremely precarious. The AS tools are the most important weapon at our disposal to stem the phenomenon. Careful monitoring of prescriptions, continuous training of prescribing physicians and collaboration with highly qualified multidisciplinary staff, creation of local and national guidelines, use of rapid diagnostic tests, technological means of support, and research activities by testing new broad-spectrum antibiotics are mandatory. However, all of these measures must be supported by adequate investment by national and international health organizations. Only by making AS daily practice, through the use of financial resources and dedicated staff, we can fight AMR to ensure safe and effective care for our young patients

Should we be concerned when COVID-19-positive patients take opioids to control their pain? Insights from a pharmacological point of view

Objective: The purpose of this narrative review is to discuss the available information regarding the currently utilized COVID-19 therapies (and the evidence level supporting them) and opioids for chronic pain with a focus on warnings of potential interactions between these two therapeutic approaches. Materials and methods: Papers were retrieved from a PubMed search, using different combinations of keywords [e.g., pain treatment AND COVID-19 AND drug-drug interaction (DDI)], without limitations in terms of publication date and language. Results: Remdesivir is an inhibitor of CYP3A4 and may increase the plasma concentration of CYP3A4 substrates (e.g., fentanyl). Dexamethasone is an inducer of CYP3A4 and glycoprotein P, thus coadministration with drugs metabolized by this isoform will lead to their increased clearance. Dexamethasone may cause hypokalemia, thus potentiating the risk of ventricular arrhythmias if it is given with opioids able to prolong the QT interval, such as oxycodone and methadone. Finally, the existing differences among opioids with regard to their impact on immune responses should also be taken into account with only tapentadol and hydromorphone appearing neutral on both cytokine production and immune parameters. Conclusions: Clinicians should keep in mind the frequent DDIs with drugs extensively metabolized by the CYP450 system and prefer opioids undergoing a limited hepatic metabolism. Identification and management of DDIs and dissemination of the related knowledge should be a major goal in the delivery of chronic care to ensure optimized patient outcomes and facilitate updating recommendations for COVID-19 therapy in frail populations, namely comorbid, poly-medicated patients or individuals suffering from substance use disorder

Role of artificial intelligence in fighting antimicrobial resistance in pediatrics

Artificial intelligence (AI) is a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior. AI is extremely useful in many human activities including medicine. The aim of our narrative review is to show the potential role of AI in fighting antimicrobial resistance in pediatric patients. We searched for PubMed articles published from April 2010 to April 2020 containing the keywords “artificial intelligence”, “machine learning”, “antimicrobial resistance”, “antimicrobial stewardship”, “pediatric”, and “children”, and we described the different strategies for the application of AI in these fields. Literature analysis showed that the applications of AI in health care are potentially endless, contributing to a reduction in the development time of new antimicrobial agents, greater diagnostic and therapeutic appropriateness, and, simultaneously, a reduction in costs. Most of the proposed AI solutions for medicine are not intended to replace the doctor’s opinion or expertise, but to provide a useful tool for easing their work. Considering pediatric infectious diseases, AI could play a primary role in fighting antibiotic resistance. In the pediatric field, a greater willingness to invest in this field could help antimicrobial stewardship reach levels of effectiveness that were unthinkable a few years ago

The Tachyon Inflationary Models with Exact Mode Functions

We show two analytical solutions of the tachyon inflation for which the spectrum of curvature (density) perturbations can be calculated exactly to linear order, ignoring both gravity and the self-interactions of the tachyon field . The main feature of these solutions is that the spectral indices are independent with scale.Comment: 5 pages, no figure, to appear in Phys. Rev.

Semi-supervised protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.</p> <p>Results</p> <p>In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions.</p> <p>Conclusion</p> <p>Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.</p

Deployment of AI-based RBF network for photovoltaics fault detection procedure

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions

Cooling tower waste reduction

At Lawrence Livermore National Laboratory (LLNL), the two main cooling tower systems (central and northwest) were upgraded during the summer of 1997 to reduce the generation of hazardous waste. In 1996, these two tower systems generated approximately 135,400 lbs (61,400 kg) of hazardous sludge, which is more than 90 percent of the hazardous waste for the site annually. At both, wet decks (cascade reservoirs) were covered to block sunlight. Covering the cascade reservoirs reduced the amount of chemical conditioners (e.g. algaecide and biocide), required and in turn the amount of waste generated was reduced. Additionally, at the northwest cooling tower system, a sand filtration system was installed to allow cyclical filtering and backflushing, and new pumps, piping, and spray nozzles were installed to increase agitation. the appurtenance upgrade increased the efficiency of the cooling towers. The sand filtration system at the northwest cooling tower system enables operators to continuously maintain the cooling tower water quality without taking the towers out of service. Operational costs (including waste handling and disposal) and maintenance activities are compared for the cooling towers before and after upgrades. Additionally, the effectiveness of the sand filter system in conjunction with the wet deck covers (northwest cooling tower system), versus the cascade reservoir covers alone (south cooling tower south) is discussed. the overall expected return on investment is calculated to be in excess of 250 percent. this upgrade has been incorporated into the 1998 DOE complex-wide water conservation project being led by Sandia National Laboratory/Albuquerque

67-kilodalton laminin receptor expression correlates with worse prognostic indicators in non-small cell lung carcinomas

Tumor samples obtained from 72 patients resected for non-small cell lung cancer were stained immunohistochemically using an immunoperoxidase method and the MLuC5 monoclonal antibody specific for the 67-kDa laminin receptor, Sixty-one of 72 patients (84.7%) displayed a MLuC5-positive reaction, which was usually localized in both the inner surface of the plasmatic membranes and the cytoplasm of neoplastic cells. When we compared the laminin receptor expression with clinicopathological and biological parameters such as histotype, grading, T status, N status, ploidy, proliferative activity, vessel invasion, and p53 protein accumulation, the following results were observed: (a) the mean expression of the receptor was higher in the group of patients with metastatic nodal involvement than in those with uninvolved lymph nodes (P=0.02); (b) a high Ki-67 score (>13% of positive cells) was observed in tumors with a higher mean value of laminin receptor (P=0.004); (c) the tumors harboring neoplastic emboli in their vessels showed a higher laminin receptor immunoreactivity (P=0.02); and (d) a borderline association was found between the high mean value of laminin receptor immunopositivity and p53 accumulation in neoplastic cell nuclei (P=0.05). Our observations indicate that detection of high tissue levels of 67-kDa laminin receptor is associated with an invasive phenotype in non-small cell lung cancer and may provide further information in the biological characterization of this type of cancer