Standard Database Research Articles

A Visual–Audio-Based Emotion Recognition System Integrating Dimensional Analysis

Dimensional emotion recognition research is an important branch of affective computing, which uses continuous values to represent complex human emotions. In this study, we propose a visual–audio emotion recognition system that integrated emotional dimensions. For the visual part, the corresponding relationship between emotion category and emotion dimension interval is established based on rules, and the respective classifiers are trained and fused using the machine learning methods. For the audio part, some emotion-related features are extracted, and a 128-D global feature is extracted through a deep convolutional neural network (DCNN). We use a combination of Bayesian and machine learning to integrate the information of visual–audio modalities. We have tested the proposed system and its single modalities on the standard databases CK <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> and eNTERFACE ’05, and the experimental results and comparison showed the efficiency of the proposed system. Furthermore, our proposed system uses emotion category label and dimension values simultaneously to represent emotion, providing strong interpretability and expansibility for emotion recognition, which goes beyond the methods only come with either classification or dimension.

Impact of Body Mass Index on Cardiac Arrest Outcomes: A Systematic Review and Meta-Analysis.

The influence of an individual's body mass index (BMI) on cardiac arrest outcomes remains uncertain. The aim of this study is to evaluate the impact of BMI categories (underweight, normal BMI, overweight, and obese) on mortality and neurological outcomes in patients experiencing cardiac arrest. We comprehensively searched standard electronic databases (PubMed, EMBASE, and Scopus) for relevant observational studies published in peer-reviewed journals written in English. We calculated pooled effect estimates using random-effects models and reported them as odds ratios (ORs) with 95% confidence intervals (CIs). We included 20 studies in our meta-analysis. Individuals with normal BMIs and those who were underweight had similar risks of in-hospital mortality (OR, 1.20; 95% CI, 0.90-1.60), mortality within 6 months of discharge (OR, 0.92; 95% CI, 0.59-1.42), mortality after the 1-year follow-up (OR, 2.42; 95% CI, 0.96-6.08), and odds of favorable neurological outcomes at hospital discharge (OR, 0.86; 95% CI, 0.53-1.39) and at the 6-month follow-up (OR, 0.73; 95% CI, 0.47-1.13). The risks of in-hospital mortality and mortality within 6 months of discharge in overweight and obese individuals were similar to those in individuals with normal BMIs. However, overweight (OR, 0.57; 95% CI, 0.35-0.92) and obese individuals (OR, 0.67; 95% CI, 0.51-0.89) had lower risks of mortality after their 1-year follow-ups. For overweight and obese subjects, the reduced risk of mortality after the 1 year of follow-up was noted only for those with in-hospital cardiac arrest and not for those with out-of-hospital cardiac arrest. The odds of favorable neurological outcomes in both overweight and obese individuals were similar to those with normal BMIs. BMI does not significantly impact short-term mortality or neurological outcomes. Overweight and obese individuals appear to have a lower risk of long-term mortality, but this differed by the place of arrest and needs to be confirmed by others.

Design and Management of a Spatial Database for Monitoring Building Comfort and Safety

As the impacts of climate change on urban environments and buildings become more and more prominent, building comfort and structural safety monitoring becomes crucial. However, efficiently storing and managing the multi-source monitoring data generated during the long-term monitoring process has been an urgent challenge. In order to solve the above problems, this paper designs and develops a spatial database management system for building comfort and structural safety monitoring based on standard database development tools. A conceptual model of a spatial database for building comfort and structural safety monitoring is proposed, and the entities, attributes, and connections in the model are discussed to transform the E-R conceptual model into a logical model supported by an object-relational spatial database management system. Based on this conceptual and logical model, a mainstream backend framework was adopted and combined with common database and programming language tools and BIM (Building Information Modeling) technology for development to establish a spatial database management system with data storage, management, analysis, and visualization functions. We designed building monitoring experiments and proved through the experiments that the database management system can stably store, analyze, and manage the monitoring data and visualize the display, with the advantages of a fast response speed and low error rate. The spatial database system improves the storage and management efficiency of building comfort and structural safety monitoring data, eliminates redundant data, and realizes comprehensive analysis and management of building comfort and structural safety monitoring data. It provides data support for building comfort and structural safety assessment, helps users analyze the formation mechanism and evolution law of the urban heat island effect, assesses the interrelationship between climate change and urban building morphology, and constructs an urban thermal environment that is more adaptable to climate change.

The Choice of Anticoagulant at First Recurrence Does Not Alter the Risk for the Second Recurrence

Introduction : Venous thromboembolism (VTE), encompassing deep venous thrombosis (DVT) and pulmonary embolism (PE), is a major public health hazard, with up to 30-50% of patients with unprovoked VTE experiencing a recurrent VTE event. These recurrent VTE events are associated with significant morbidity and mortality. However, transition strategies to second-line anticoagulation regimens following the first recurrence on anticoagulation have not been well studied. We sought to examine whether rates of the second VTE recurrence were different in those patients who changed their anticoagulation regimen from those who did not . Methods: With an IRB approval for the study, we created a database of all patients 18 years and older diagnosed with the first recurrent VTE at a large tertiary academic hospital between January 1, 2010, and December 31, 2019. The collected data was de-identified and compiled in a secure, standardized institutional database. We assessed the anticoagulation status at the time of and immediately following the first VTE recurrence, and identified patients who were switched to a different anticoagulant regimen. We used Fisher's exact test to evaluate proportions. Statistical significance was set at p&amp;lt;0.05. Results:We identified 138 patients with the first VTE recurrence. Of these patients, 114 (83%) developed DVT, 7 (5%) PE, and 17 (12%) both DVT and PE. Mean age was 63 years at the time of first VTE; 70 (51%) were female. Racial/ethnic representation was 84 (61%) White, 46 (33%) Black, and 133 (96%) non-Hispanic. The majority of patients had cardiovascular risk factors, including hypertension 80 (58%), hyperlipidemia 44 (32%), ordiabetes 40 (29%). Among 138 patients, 32 (23%) had active cancer, May-Thurner syndrome or inferior vena cava (IVC) atresia were reported in 13 (9%) of cases, and 21 (17%) underwent IVC filter placement at the time of the first VTE recurrence. The median follow-up time after diagnosis of a second VTE was 5 years. Following the first VTE recurrence, 19 (14%) of patients switched to a different anticoagulant agent, 7 (5%) had the dose of the anticoagulant adjusted, 1 (0.7%) was given a different INR target, and one had a change in the frequency of administration. The second VTE occurred in 49 (46%) patients who remained on the same anticoagulation regimen and in 3 (16%) who had the agent changed, in 5 who had dose of anticoagulant changed, in 1 (100%) each who had frequency or INR target changed. The association between unprovoked first VTE and the development of the second VTE was not significant (p= 0.85, Fisher's exact test). 49 (36%) of patients had at least one bleeding episode requiring the interruption of anticoagulation or hospitalization. Of those, 43 (88%) occurred while on an anticoagulant. Conclusion: The choice of an anticoagulant regimen after the first VTE recurrence does not impact the rate of the second recurrence.

Construction of a Searchable Database for Gene Expression Changes in Spinal Cord Injury Experiments.

Spinal cord injury (SCI) is a debilitating condition with an estimated 18,000 new cases annually in the United States. The field has accepted and adopted standardized databases such as the Open Data Commons for Spinal Cord Injury (ODC-SCI) to aid in broader analyses, but these currently lack high-throughput data despite the availability of nearly 6000 samples from over 90 studies available in the Sequence Read Archive. This limits the potential for large datasets to enhance our understanding of SCI-related mechanisms at the molecular and cellular level. Therefore, we have developed a protocol for processing RNA-Seq samples from high-throughput sequencing experiments related to SCI resulting in both raw and normalized data that can be efficiently mined for comparisons across studies, as well as homologous discovery across species. We have processed 1196 publicly available RNA-Seq samples from 50 bulk RNA-Seq studies across nine different species, resulting in an SQLite database that can be used by the SCI research community for further discovery. We provide both the database as well as a web-based front-end that can be used to query the database for genes of interest, differential gene expression, genes with high variance, and gene set enrichments.

Easy and green synthesis of TiO2 (Anatase and Rutile): Estimation of crystallite size using Scherrer equation, Williamson-Hall plot, Monshi-Scherrer Model, size-strain plot, Halder- Wagner Model

TiO2 is one of the most important metal oxides which are widely applied in various fields, and the modifications of synthesis process and in-depth characterization are growing up day by day. Nano Anatase and Rutile phases of TiO2 were synthesized from titanium iso-butoxide following an easy approach where no toxic chemicals were utilized. Nano-TiO2 (Rutile phase) was synthesized from the thermal conversion of the nano-Anatase phase (which was obtained at room temperature) at 700 °C temperature for 2 h. Anatase and Rutile phases were confirmed by the X-ray diffraction (XRD) data and matched with the standard database. Different crystallographic parameters such as lattice parameters, and preference growth were calculated from the XRD data. Different methods were utilized to estimate the crystallite size of the Anatase and Rutile phase of TiO2 by using the Scherrer equation, Williamson-Hall plot, Monshi-Scherrer Model, Size-strain plot method, Halder- Wagner Model, and Sahadat-Scherrer Model. All the models presented crystallite size in the accepted region, and linear regression coefficients were also taken into consideration. In addition to the crystallite size, various intrinsic parameters such as stress, strain and energy density were also computed from the mentioned models based on XRD.

Implementation Of The Swin Transformer and Its Application In Image Classification

There are big differences between the field of view of the calculator and the field of natural languages, for example, in the field of vision, the difference is in the size of the object as well as in the accuracy of the pixels in the image, and this contradicts the words in the text, and this makes the adaptation of the transformers to see somewhat difficult.Very recently a vision transformer named Swin Transformer was introduced by the Microsoft research team in Asia to achieve state-of-the-art results for machine translation. The computational complexity is linear and proportional to the size of the input image, because the processing of subjective attention is within each local window separately, and thus results in processor maps that are hierarchical and in deeper layers, and thus serve as the backbone of the calculator's vision in image classification and dense recognition applications. This work focuses on applying the Swin transformer to a demonstrated mathematical example with step-by-step analysis. Additionally, extensive experimental results were carried out on several standardized databases from CIFAR-10, CIFAR-100, and MNIST. Their results showed that the Swin Transformer can achieve flexible memory savings. Test accuracy for CIFAR-10 gave a 71.54% score, while for the CIFAR-100 dataset the accuracy was 46.1%. Similarly, when the Swin transformer was applied to the MNIST dataset, the accuracy increased in comparison with other vision transformer results.

Laser-induced breakdown spectroscopy for rapid and accurate qualitative analysis of elemental composition in fertilizers

Through elemental analysis, laser-induced breakdown spectroscopy (LIBS), a quick and effective analytical method, has enormous potential to advance agriculture. In this context, our study intends to better the fertilizer sector by utilizing LIBS’s capabilities. The main objective is to make it possible to rapidly and accurately analyze the elements in frequently used fertilizers such as nitrophos (NP), diammonium phosphate (DAP), and single super phosphate (SSP). We identify all elements contained in the fertilizers using qualitative and semi-quantitative analysis utilizing a single LIBS shot by noting their matching spectral lines and peak positions. This identification is validated more thoroughly by matching peak positions to the National Institute of Standards and Technology (NIST) database. Phosphorus (P), potassium (K), iron (Fe), calcium (Ca), nickel (Ni), and aluminum (Al) are effectively recognized due to their distinct spectral fingerprints. Our findings highlight the vital need to precisely characterize fertilizer content in order to maximize agricultural yield. Furthermore, our approach presents an efficient means for rapid and precise analysis of fertilizers, offering the advantage of minimal sample requirements.

Расширение стандартного сбалансированного лингвистического корпуса, построенного по правилам spaCy, коннотативными характеристиками

The aim of the research is to develop the technology for automatically determining the sentiment of a text based on the existing author’s software package. The scientific novelty lies in the fact that the work proposes a structural and functional model of a fully automated process for assessing the sentiment of a text in conjunction with an analysis of its morphological characteristics; the technical terms “connotative amplitude” and “connotative density” are also introduced for the first time. The study built a database model that accommodates connotative numeric parameters; further, the program code for the “add-on” for the database generator has been written, which allows one to supplement the standard database with these parameters; finally, the technology was tested on the material of three novels by F. Kafka (“Castle”, “The Trial” and “America”) and two novels by E. M. Remarque (“All Quiet on the Western Front” and “Flotsam”) in the German language. As a result, it is proven that the “add-on” is a high-quality software product that does not cause technical failures and is capable of providing researchers with a whole set of connotative data for subsequent comprehensive interpretation of the text, on condition that the input tone dictionary is of high quality.

OpenProt 2.0 builds a path to the functional characterization of alternative proteins.

The OpenProt proteogenomic resource (https://www.openprot.org/) provides users with a complete and freely accessible set of non-canonical or alternative open reading frames (AltORFs) within the transcriptome of various species, as well as functional annotations of the corresponding protein sequences not found in standard databases. Enhancements in this update are largely the result of user feedback and include the prediction of structure, subcellular localization, and intrinsic disorder, using cutting-edge algorithms based on machine learning techniques. The mass spectrometry pipeline now integrates a machine learning-based peptide rescoring method to improve peptide identification. We continue to help users explore this cryptic proteome by providing OpenCustomDB, a tool that enables users to build their own customized protein databases, and OpenVar, a genomic annotator including genetic variants within AltORFs and protein sequences. A new interface improves the visualization of all functional annotations, including a spectral viewer and the prediction of multicoding genes. All data on OpenProt are freely available and downloadable. Overall, OpenProt continues to establish itself as an important resource for the exploration and study of new proteins.

Learning Representations from Heart Sound: A Comparative Study on Shallow and Deep Models.

Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade. Nevertheless, lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset. However, inconsistent standards on data collection, annotation, and partition are still restraining a fair and efficient comparison between different works. To this line, we introduced and benchmarked a first version of the Heart Sounds Shenzhen (HSS) corpus. Motivated and inspired by the previous works based on HSS, we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study. First, we segmented the heart sound recording into shorter recordings (10 s), which makes it more similar to the human auscultation case. Second, we redefined the classification tasks. Besides using the 3 class categories (normal, moderate, and mild/severe) adopted in HSS, we added a binary classification task in this study, i.e., normal and abnormal. In this work, we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies, which are reproducible by using open-source toolkits. Last but not least, we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable.

Scale and pattern adaptive local binary pattern for texture classification

Local binary pattern (LBP) with a fixed sampling template is sensitive to scale changes. Furthermore, under rotation changes or noise corruptions, one uniform LBP pattern can be corrupted to fall into a non-uniform pattern which loses its discrimination power to describe the corresponding texture feature. To overcome these two main drawbacks, we propose a scale and pattern adaptive local binary pattern (SPALBP). Firstly, in the gradient-based sampling radius adaptive scheme, eight directional adaptive sampling radius of each center pixel can be obtained by using its eight Kirsch gradient values. Secondly, in the noise and rotation robust neighborhood sampling scheme, three neighborhood sampling templates are used to extract three kinds of averaging neighborhood pixels. Thirdly, for each center pixel, three kinds of LBPriu2 patterns can be extracted by sampling these three kinds of averaging neighborhood pixels along eight directional adaptive sampling radius. Finally, an optimal SPALBP uniform pattern can be adaptively selected from these three LBPriu2 patterns. Hence, all SPALBP patterns show more robustness against scale changes, rotation changes and noise corruptions. Extensive experiments are conducted on four standard texture databases: Outex, UIUC, CUReT and XU_HR. Comparing with state-of-the-art LBP-based variants, the proposed SPALBP method consistently shows superior performance both in dramatic environment changes and high-levels of noise conditions, meanwhile it maintains a lower texture feature dimension.

Direct oral anticoagulants (DOAC) versus vitamin K antagonist in left ventricular thrombus: An updated meta-analysis.

Current clinical guidelines for treating left ventricular thrombus (LVT) are limited by inadequate evidence to inform the comparative efficacy of oral anticoagulants. In this meta-analysis, we aimed to compare the efficacy and safety of direct oral anticoagulants (DOAC) to vitamin K antagonists (VKA) in patients with LVT. Four standard databases were searched for relevant literature comparing the efficacy and safety between DOAC and VKA for LVT treatment, published before August 19, 2023. Both the randomized controlled trials and observational studies were included in the analysis. The outcomes of interest were the resolution of LVT, all-cause mortality, stroke, systemic embolism, and bleeding. Data from the selected studies were extracted and analyzed using RevMan 5.4 using odds ratio. Among 3959 studies from the database search and bibliography review, 33 were included in the analysis. LVT resolution was observed in 72.59% in the DOAC group versus 67.49% in the VKA group (odds ratio [OR]: 1.28, confidence interval [CI]: 1.07-1.53). Mortality was lower in the DOAC group (11.71% vs. 18.56%) (OR: 0.60, CI: 0.36-1.00; borderline statistical significance). Likewise, bleeding events (9.60% vs. 13.19%) (OR: 0.65, CI: 0.52-0.81) and stroke (7.54% vs. 11.04%) (OR: 0.71, CI: 0.53-0.96) were also significantly lower in the DOAC group. DOAC use for LVT showed better thrombus resolution and reduced risk of bleeding and stroke compared to VKA. Likewise, DOAC use was associated with lower mortality with borderline statistical significance.

Blockchain-Powered Education: A Sustainable Approach for Secured and Connected University Systems

The collection and examination of student data, encompassing academic achievements, awards, and certifications, assume an essential function within the field of education as a means of showing students’ capabilities. Nevertheless, it is crucial to note that regular paper-based records are vulnerable to both physical destruction and the act of fabrication, while standard databases can have security holes. Moreover, the process of manually gathering physical papers from centralized organizations is both laborious and complicated. To address the concerns above and foster sustainability in the field of education, this study first suggests using Scorechain. This innovative solution integrates blockchain technology into a comprehensive data-management system for managing all student-related data. Secondly, by utilizing the inherent security features of blockchain technology, Scorechain develops a stable multi-role hierarchy, increasing the integrity and reliability of data. This also facilitates the efficient transfer of information among various stakeholders, including parents, recruiters, and educational institutions, thus fostering transparency and accountability. Lastly, the Scorechain system facilitates collaboration and data exchange among universities inside a shared network. Scorechain was constructed using the Rust programming language and is based on the Substrate blockchain architecture. It underwent careful development, testing, and analysis to ensure operational efficiency. The feasibility and long-term viability of Scorechain in genuine educational contexts are highlighted as blockchain technology facilitates seamless integration into the education sector.

Noise-assisted transport mechanism analysis and synaptic characteristics in ZrOX/HfAlOX-based memristor for neuromorphic systems

In this work, we compare the resistive switching characteristics between Ti/ZrOX/TiN and Ti/ZrOX/HfAlOX/TiN. The bilayer structure of the ZrOX-based device enables power consumption reduction owing to a lower forming voltage and compliance current. Moreover, the on/off ratio of the Ti/ZrOX/HfAlOX/TiN device (&amp;gt;102) is higher than that of the Ti/ZrOX/TiN device (&amp;gt;10). We use the 1/f noise measurement technique to clarify the transport mechanism of the Ti/ZrOX/HfAlOX/TiN device; consequently, ohmic conduction and Schottky emission are confirmed in the low- and high-resistance states, respectively. In addition, the multilevel cell, potentiation, and depression characteristics of the Ti/ZrOX/HfOX/TiN device are considered to assess its suitability as a neuromorphic device. Accordingly, a modified National Institute of Standards and Technology database simulation is conducted using Python to test the pattern recognition accuracy.

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.

Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associations still suffer from imbalanced category distribution and a lack of labeled data in small categories. To address the problem of labeled-data scarcity, we propose a self-supervised learning strategy for gene-phenotype association prediction, called SSLpheno. Our approach utilizes an attributed network that integrates protein-protein interactions and gene ontology data. We apply a Laplacian-based filter to ensure feature smoothness and use self-supervised training to optimize node feature representation. Specifically, we calculate the cosine similarity of feature vectors and select positive and negative sample nodes for reconstruction training labels. We employ a deep neural network for multi-label classification of phenotypes in the downstream task. Our experimental results demonstrate that SSLpheno outperforms state-of-the-art methods, especially in categories with fewer annotations. Moreover, our case studies illustrate the potential of SSLpheno as an effective prescreening tool for gene-phenotype association identification. https://github.com/bixuehua/SSLpheno.

AFRbase: a database of protein mutations responsible for antifungal resistance.

Fungal pathogens are known to cause life threatening invasive infections with rising global mortality rates. Besides, the indiscriminate use of antifungals in both clinics and agriculture has promoted antifungal drug resistance in the last decade. Fungi can show drug resistance by a variety of mechanisms. But primary driver in all these hitherto documented mechanisms is stable and heritable point mutations in the key proteins. Therefore, cataloguing mutations that can confer resistance is the first step toward understanding the mechanisms leading to the emergence of antifungal resistance. In the present, work we have described a database of all the mutations responsible for antifungal resistance. Named as antifungal resistance database (AFRbase), it is better than the existing databases of antifungal resistance namely, FunResDB and MARDy which have a limited scope and inadequate information. Data of AFRbase was collected using both text mining and manual curation. AFRbase provides a separate window for visualization of mutations in the 2D and 3D formats making it easy for researchers to analyze the mutation data and ensures interoperability with other standard molecular biology databases like NCBI and UniProtKB. We hope AFRbase can be useful to both clinicians and basic biomedical scientists as we envision it as an important resource for genotypic susceptibility testing of fungi and to study/predict the course of evolution of antifungal resistance. The current version of AFRbase contains manually curated 3691 unique mutations present in 29 proteins of 32 fungal species along with the information of drugs against which resistance is caused. AFRbase is an open access database available at http://proteininformatics.org/mkumar/afrbase/.

Elemental Composition of Betel Leaves Using a Novel Optical Spectroscopic Technique.

Objective: Assess trace elements in betel leaves and slaked lime from different regions of Karnataka, India using Laser Induced Breakdown Spectroscopy (LIBS). Materials and Method: Betel leaves from six different regions of Karnataka were obtained and named (for the purpose of the study) BL1, BL2, BL3, BL4, BL5, and BL6 and they were sun-dried. A single tube of slaked lime was obtained from the local ‘paan’ shop. Each dried leaf and a single blob of slaked lime was subjected to elemental analysis using Laser-induced breakdown spectroscopy. Results: A ten-trial experiment was carried out in all six leaves and a blob of the slaked lime. The National Institute of Standards and Technology (NIST) database was used to assess the emission lines. The elements that were predominantly present in all six betel leaves from different regions of Karnataka are calcium, copper, and iron. Slaked lime showed only the presence of calcium. Conclusion: It is widely accepted that the consumption of betel quid causes various changes in the oral mucosa including oral potentially malignant disorders (OPMDs) and oral cancer. It is important to analyze each component of betel quid to understand the disease progression. Copper is found to be relatively higher in betel leaves, and it is known that copper-induced fibrogenesis via the lysyl oxidase pathway in oral submucous fibrosis.

AN OVERVIEW OF EXISTING LITERATURE ON DOCUMENT SKEW DETECTION

This article presents somewhat detail discussions on document image skew detection methods available in the existing literature. We first categorise the document images into several types and then we discuss different methods available to detect the skew angle of document images belonging to the individual categories. Various methods of document image skew detection include Hough Transform, Principal Components Analysis, Projection Profile based methods, Nearest Neighbor clustering, Connected Component Analysis, Cross Correlation, Radon Transform etc. Discussions on different standard databases of document image samples that have been used by the authors for experimentation purpose are included in the survey. These databases include NIST Federal Register Document Image Database, UW-I, UW-II, UW-III databases, ICDAR DISEC 2013 database, IFN/ENIT database etc. Here, we consider about eight such standard databases. Moreover, we compare different methods with respect to their accuracy and the respective range of skew angles detected by the concerned methods.

Observational study of drug-drug interactions in oncological inpatients

ObjectiveTo determine the prevalence of potential clinically relevant drug-drug interactions in adult oncological inpatients, as well as to describe the most frequent interactions. A standard database was used. MethodAn observational, transversal, and descriptive study including patients admitted to the Oncology Service of a reference hospital. All prescriptions were collected twice a week during a month. They were analysed using Lexicomp® database, recording all interactions classified with a level of risk: C, D or X. ResultsA total of 1 850 drug-drug interactions were detected in 218 treatments. The prevalence of treatments with at least one clinically relevant interaction was 95%, being 94.5% for those at level C and 26.1% for levels D and X. The drugs most commonly involved in the interactions detected were opioid analgesics, antipsychotics (butyrophenones), benzodiazepines, pyrazolones, glucocorticoids and heparins, whereas interactions with antineoplastics were minimal, highlighting those related to paclitaxel and between metamizole and various antineoplastics. ConclusionsThe prevalence of clinically relevant drug-drug interactions rate was very high, highlighting the high risk percentage of them related to level of risk X. Due to the frequency of onset and potential severity, highlighted the concomitant use of central nervous system depressants drugs with risk of respiratory depression, the risk of onset of anticholinergic symptoms when combining morphine or haloperidol with butylscopolamine, ipratropium bromide or dexchlorpheniramine and the multiple interactions involving metamizole.