1,721,064 research outputs found
Impact of comorbidity on the risk and cost of hospitalization in HIV-infected patients: real-world data from Abruzzo Region [Corrigendum]
No full textCammarota S, Citarella A, Manzoli L, Flacco ME, Parruti G. Clinicoecon Outcomes Res. 2018;10:389–398. Page 393, Table 2, CCI scorec (vs 0), ≥2 row, the values for the Unadjusted IRR and Adjusted IRR columns are incorrect, the data “2.32 (1.79–3.01)” should read “2.87 (2.22–3.72)” and “2.04 (1.56–2.66)” should read “2.43 (1.86–3.17)”. Read the original article 
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Hydroxamic Acid Derivatives: From Synthetic Strategies to Medicinal Chemistry Applications
Full text linkSince the approval of three hydroxamic acid-based HDAC inhibitors as anticancer drugs, such functional groups acquired even more notoriety in synthetic medicinal chemistry. The ability of hydroxamic acids (HAs) to chelate metal ions makes this moiety an attractive metal binding group - in particular, Fe(III) and Zn(II) - so that HA derivatives find wide applications as metalloenzymes inhibitors. In this minireview, we will discuss the most relevant features concerning hydroxamic acid derivatives. In a first instance, the physicochemical characteristics of HAs will be summarized; then, an exhaustive description of the most relevant methods for the introduction of such moiety into organic substrates and an overview of their uses in medicinal chemistry will be presented
Cost impact of oral capecitabine compared to 5-fluorouracil for treatment of patients with metastatic colorectal cancer
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L’utilizzo dei database amministrativi per la ricerca di base e la programmazione sanitaria: l’esperienza dell’ASL Salerno 2
No full textA Cloud Approach for Melanoma Detection Based on Deep Learning Networks
No full textIn the era of digitized images, the goal is to extract information from them and create new knowledge thanks to Computer Vision techniques, Machine Learning and Deep Learning. This enables the use of images for early diagnosis and subsequent treatment of a wide range of diseases. In the dermatological field, deep neural networks are used to distinguish between melanoma and non-melanoma images. In this paper, we have underlined two essential points of melanoma detection research. The first aspect considered is how even a simple modification of the parameters in the dataset determines a change of the accuracy of classifiers. In this case, we investigated the Transfer Learning issues. Following the results of this first analysis, we suggest that continuous training-test iterations are needed to provide robust prediction models. The second point is the need to have a more flexible system architecture that can handle changes in the training datasets. In this context, we proposed the development and implementation of a hybrid architecture based on Cloud, Fog and Edge Computing to provide a Melanoma Detection service based on clinical and dermoscopic images. At the same time, this architecture must deal with the amount of data to be analyzed by reducing the running time of the continuous retrain. This fact has been highlighted with experiments carried out on a single machine and different distribution systems, showing how a distributed approach guarantees output achievement in a much more sufficient time
SNARER: new molecular descriptors for SNARE proteins classification
No full textBackground SNARE proteins play an important role in different biological functions. This study aims to investigate the contribution of a new class of molecular descriptors (called SNARER) related to the chemical-physical properties of proteins in order to evaluate the performance of binary classifiers for SNARE proteins. Results We constructed a SNARE proteins balanced dataset, D128, and an unbalanced one, DUNI, on which we tested and compared the performance of the new descriptors presented here in combination with the feature sets (GAAC, CTDT, CKSAAP and 188D) already present in the literature. The machine learning algorithms used were Random Forest, k-Nearest Neighbors and AdaBoost and oversampling and subsampling techniques were applied to the unbalanced dataset. The addition of the SNARER descriptors increases the precision for all considered ML algorithms. In particular, on the unbalanced DUNI dataset the accuracy increases in parallel with the increase in sensitivity while on the balanced dataset D128 the accuracy increases compared to the counterpart without the addition of SNARER descriptors, with a strong improvement in specificity. Our best result is the combination of our descriptors SNARER with CKSAAP feature on the dataset D128 with 92.3% of accuracy, 90.1% for sensitivity and 95% for specificity with the RF algorithm. Conclusions The performed analysis has shown how the introduction of molecular descriptors linked to the chemical-physical and structural characteristics of the proteins can improve the classification performance. Additionally, it was pointed out that performance can change based on using a balanced or unbalanced dataset. The balanced nature of training can significantly improve forecast accuracy
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