Specific Database Research Articles

Development of Korean CARcinogen EXposure: occupation-based exposure matrix.

Information systems focusing on occupational carcinogen exposure, whether categorized by industry or occupation, play a pivotal role in the prevention of occupational cancers. Recently, the Korean CARcinogen EXposure (K-CAREX) system was developed to assess carcinogen exposure by industry. However, corresponding information categorized by occupation has yet to be developed. This study aimed to develop an occupation-based exposure matrix for occupational carcinogens as an extension of the K-CAREX framework. The Work Environment Measurement Database (WEMD) and the Special Health Examination Database (SHED) were combined to estimate exposure intensity by occupation. The WEMD supplied exposure levels, whereas the SHED provided occupation information. Additionally, the SHED served as the primary data source for constructing an indicator of exposure prevalence by occupation, utilizing standard occupational classification. A total of 22 carcinogens were selected for evaluating exposure intensity, and 20 carcinogens were selected for assessing exposure prevalence. Exposure intensity and prevalence were assigned to 156 occupational groups based on these carcinogens. For instance, in terms of welding fume exposure, welders were assigned an exposure intensity rating of 3 and exhibited exposure prevalence of 26%. Our findings complement the previously developed K-CAREX, which offered an industry-based exposure matrix. The resultant comprehensive K-CAREX, incorporating both industry- and occupation-based matrices, can be utilized for occupational cancer prevention and epidemiological studies.

Exome sequencing in Nigerian children with early-onset epilepsy syndromes.

Nigeria, along with other Sub-Saharan African countries, bears the highest burden of epilepsy worldwide. This high prevalence is attributed to a combination of factors, including a significant incidence of infectious diseases, perinatal complications, and genetic etiologies. Genetic testing is rarely available and is not typically included in the routine diagnostic work-up for individuals with infantile and childhood epilepsy syndromes in these regions. Exome sequencing (ES) offers a diagnostic yield of 24%-62%, but these figures primarily reflect data from high-income countries (HICs) and may not be applicable to low- and middle-income countries (LMICs). In this study, we employed ES to investigate the genetic basis of early-onset epilepsy in 22 affected children from Nigeria. The study involved sampling of patients diagnosed with early-onset epilepsy syndromes at the Lagos State University Teaching Hospital (LASUTH) Neurology clinic. Venous blood samples were collected, and genomic DNA was isolated and purified. Molecular analysis included DNA fragmentation, ligation, target enrichment, library preparation, and whole-exome sequencing. Computational analysis involved variant calling, curation, and classification using specialized tools and databases. Pathogenic variants were identified in 6 out of 22 individuals, equaling a diagnostic yield of 27.3% and comprising variants in BPTF, NAA15, SCN1A, TUBA1A and twice in CACNA1A. In this study, we present the first exome study on early-onset epilepsy syndromes from West Africa, facilitated by a Nigerian-German research collaboration. Our findings reveal a genetic diagnostic yield comparable to that of HICs. The integration of genomic medicine into epilepsy management in Nigeria holds promising prospects for improving patient care and reducing mortality rates. This study represents the first published exome findings in Nigerian children with early-onset epilepsy, revealing a genetic diagnosis in 27% of cases. Pathogenic variants were identified in five genes amongst 6 of 22 patients, underscoring the potential of genetic testing to enhance epilepsy management in developing nations like Nigeria.

The Computational Tools to Identify DNA Repeats and Motifs: A Systematic Review.

DNA repeats and motifs are specific nucleotide patterns/DNA sequences frequently present in the genomes of prokaryotes and eukaryotes. Computational identification of these discrete patterns is of considerable importance since they are associated with gene regulation, genomic instability, and genetic diversity and result in a variety of diseases/disorders. In this article, the myriad of computational tools/algorithms and databases (~200 distinct resources) implicated in the detection of DNA repeats and motifs have been enlisted. This article will not only provide guidance to the users regarding the accuracy, reliability, and popularity (reflected by the citation index) of currently available tools but also enable them to select the best tool(s) to carry out a desired task. The structured literature review, with its dependable and reproducible research process, allowed us to acquire 200 peer-reviewed publications from indexing databases, such as Scopus, ScienceDirect, Web of Science (WoS), PubMed, and EMBASE, by utilizing PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations. Numerous keyword combinations regarding DNA repeats and motifs were used to create the query syntax. Initially, 3,233 research publications were retrieved, and 200 of them that satisfied the eligibility criteria for the detection and identification of DNA repeats and motifs by computational tools were chosen. A total of 200 research publications were recovered, of which 99 dealt with repeat prediction tools, 12 with repetitive sequence databases, 19 with specialized regulatory element databases, and 69 with motif prediction tools. This article lists numerous databases and computational tools/algorithms (~ 200 different resources) that are involved in the identification of DNA repeats and motifs. It will help users choose the appropriate tool(s) for carrying out a particular task in addition to offering guidance on the reliability, dependability, and popularity (as indicated by the citation index) of currently available tools.

Publicly available continuously updated topic specific databases of randomised clinical trials: A scoping review.

Conducting systematic reviews of clinical trials is arduous and resource consuming. One potential solution is to design databases that are continuously and automatically populated with clinical trial data from harmonised and structured datasets. We aimed to map publicly available, continuously updated, topic-specific databases of randomised clinical trials (RCTs). We systematically searched PubMed, Embase, the preprint servers medRxiv, ArXiv, and Open Science Framework, and Google. We described seven features (access model, database architecture, data input sources, retrieval methods, data extraction methods, trial presentation, and export options) and narratively summarised the results. We did not register a protocol for this review. We identified 14 continuously updated clinical trial databases, seven related to COVID-19 (first active in 2020) and seven non-COVID databases (first active in 2009). All databases, except one, were publicly funded and accessible without restrictions. They mainly employed methods similar to those from static article-based systematic reviews and retrieved data from journal publications and trial registries. The COVID-19 databases and some non-COVID databases implemented semi-automated features of data import, which combined automated and manual data curation, whereas the non-COVID databases mainly relied on manual workflows. Most reported information was metadata, such as author names, years of publication, and link to publication or trial registry. Two databases included trial appraisal information (risk of bias assessments). Six databases reported aggregate group level results, but only one database provided individual participant data on request. We identified few continuously updated trial databases, and existing initiatives mainly employ methods known from static article -based reviews. The main limitation to create truly live evidence synthesis is the access and import of machine-readable and harmonised clinical trial data.

Acetate-producing bacterium Paenibacillus odorifer hampers lung cancer growth in lower respiratory tract: an in vitro study.

Lung cancer accounts for the large majority of cancer incidence and mortality worldwide for decades. The dysbiotic microbiome and its metabolite secretions in the gut have been regarded as the dominant biological factors in oncogenesis, development, and progression, adding probiotic components of which have come to be potential therapeutic regimes. However, there still exists little knowledge about whether probiotic microorganisms in lower airways inhibit lung cancer by lung microenvironment remodulation. In this study, we performed bioinformatics analysis from previous sequencing data and specific microbiome databases to identify the potent protective microbes in lower airways, followed by bacterial cultivation and morphological verifications in vitro. We found that Paenibacillus odorifer was correlated closely with the anti-tumorous by-product acetic acid in lower respiratory tract. Additionally, the enrichment of this microorganism in the health, rather than in lung neoplasms from public data sets, further confirmed its protective activity in preserving pulmonary homeostasis. Colony cultivation of this strain and targeted metabolite analysis indicated that Paenibacillus odorifer proliferation was weakened at 37°C but lasted longer than it did at the optimal temperature. And performing as a candidate origin of acetic acid, this strain was liable to inhibit the growth of lung cancer cells in time- and dose-dependent approaches which was validated by colony formation assays. These results suggested that Paenibacillus odorifer functions as a candidate probiotic in lower airways to restrict lung cancer cell growth by releasing protective molecules, indicating a potential preventive microbial strategy.IMPORTANCEVarious types of microorganisms in lower respiratory tracts protect local homeostasis against oncogenesis. Although extensive efforts engaged in gut microbiome-mediated pulmonary carcinogenesis, emerging evidence suggested the crucial role of microbial metabolites from respiratory tracts in modulating carcinogenesis-related host inflammation and DNA damage in lung cancer, which was still not fully understood in lower respiratory tract microbes and its metabolite-mediated microecological environment homeostasis in preventing or alleviating lung cancer. In this study, we analyzed the lower respiratory tract microbiome and SCFAs expression among different lung segments from the same participants, further identifying that Paenibacillus odorifer was correlated closely with anti-tumorous by-product, acetate acid in lower respiratory tract by multi-omics analysis. And previous experiments showed this strain could inhibit the growth of lung cancer cells in vitro. These findings indicated that Paenibacillus odorifer in lower respiratory tracts might perform as a candidate probiotic against lung carcinogenesis by releasing protective factor acetate, which further presented a promising diagnostic and interventional approach in clinical settings of lung cancer.

Analysis of the impact of periodontal disease management before the onset of type 2 diabetes mellitus on medical costs using administrative claims database

ObjectiveAlthough periodontal disease management has been shown to reduce the medical costs of diabetes mellitus, no studies have investigated management before the onset of type 2 diabetes mellitus. Therefore, this study examined the association between periodontal disease management before the onset of type 2 diabetes mellitus and medical costs. MethodsWe extracted information for periodontal disease management status from Japanese medical claims and specific health check-up databases among 4010 patients with periodontal disease and type 2 diabetes mellitus aged ≥ 30 years who had continued periodontal disease management for 2 years after diabetes onset. We divided patients into two groups: those who had received periodontal disease management for 2 years before and 2 years after onset and those who had received periodontal disease management for only 2 years after the onset of type 2 diabetes mellitus. ResultsThe HbA1c level in patients without periodontal disease management during the year of diabetes onset improved by 0.13 % in male and 0.24% in female 2 years later, while that in the group with periodontal disease management improved by 0.49 % and 0.74%, respectively. The medical costs were significantly lower in the group with periodontal disease management 2 years later than in the group with no periodontal disease management. ConclusionsRegular periodontal disease prevention and management under professional care before type 2 diabetes mellitus onset contribute to improved health and reduced medical costs.

Analysis of the case law establishing circumstances of illegal distribution of generative content created using artificial intelligence

The authors consider various cases from judicial and investigative practice related to the illegal distribution and use of generative content created using artificial intelligence technologies. Special attention is paid to the issues of proving the falsification of digital products created by neural networks, including voice and graphic deepfakes. The legal and technological aspects of the use of such evidence in court proceedings are analyzed. The authors also emphasize that there is no legal definition of a deepfake in the current legislation, and without this it is not possible to talk about the modernization of domestic legislation. Taking into account the pace of development of artificial intelligence technologies, it is necessary to define legally the deepfake. In the context of the rapid development of artificial intelligence technologies, it is necessary to regulate the deepfakes, taking into account the legal gaps that accompany the current level of artificial intelligence development and threats that are already real, which is confirmed by the considered judicial practice. The necessity of creating a data set for conducting experimental phonoscopic studies of phonograms with recordings cloned using neural networks of voices is substantiated. The methodological basis of the research is the universal dialectical method, general scientific (description, comparison, generalization, modeling, etc.) and private scientific methods. The novelty of the research lies in the identification and systematization of key problems related to the conduct of forensic examinations and the legal regulation of generative content. The paper presents recommendations for improving legislative norms and expert methods (using the example of forensic phonoscopic examination), including the need to create specialized databases and scientific and methodological approaches for the study of generative content. The conclusions of the article emphasize the importance of developing standards for diagnosing the use of generative artificial intelligence in the creation of digital products, as well as the need to improve the skills of experts conducting forensic examinations in relation to such objects. The obtained results can be used to form more effective mechanisms of legal response to the challenges associated with artificial intelligence technologies.

TRANSPLANTE DE CÓRNEA, UMA REVISÃO INTEGRATIVA

Currently, cornea transplantation is the transplant performed in the largest number and with the best success rates. This article sought to bring together the main information about corneal transplantation, including indications, procedures and complications, in order to facilitate access to knowledge about this surgery. To this end, an integrative literature review was carried out with six well-defined steps, including defining the guiding question, searching for articles in specific databases and clear inclusion/exclusion criteria. The descriptors “cornea transplant”, “corneal transplant”, “indications”, “procedures” and “complications” were crossed in the following databases: Virtual Health Library (VHL); National Library of Medicine (PubMed MEDLINE), Scientific Electronic Library Online (SCIELO) and EbscoHost. The final selection included 20 articles. The data collected made it possible to identify some characteristics and statistical data on cornea transplantation. The results include main procedures and techniques, indications, risks, complications and epidemiology. The review concluded that the most used procedure is still penetrating keratoplasty, despite the various evolutions observed; The main indications vary from region to region, but overall the most prevalent was bullous keratopathy and the most prevalent complication is graft failure due to rejection.

A Systematic Review of Blended Learning in Higher Education: Second Language Acquisition through the Community of Inquiry Framework

This study examined the application of the Community of Inquiry (CoI) framework in blended learning environments for second language acquisition in higher education. A systematic literature review was conducted using the PRISMA protocol, analyzing 25 peer-reviewed articles published between 2014 and 2023 from Scopus, Web of Science, and ERIC databases. The review investigated CoI implementation characteristics, the interplay of CoI elements, research methodologies, challenges, and best practices. Findings revealed a predominance of studies on undergraduate English learners, with mixed-methods approaches being most common. The integration of social, cognitive, and teaching presence significantly enhanced language acquisition and student engagement. Key challenges included technological integration, maintaining online engagement, effective instructional design, and addressing varying language proficiency levels. Best practices emphasized the strategic use of mobile applications, fostering online communities, enhancing teaching presence, and implementing personalized learning approaches. Gaps identified in the research included a predominant focus on English language studies, limited exploration at the graduate level, and a scarcity of longitudinal investigations examining long-term outcomes of CoI-informed blended learning. This review provided valuable insights for researchers and practitioners in blended language learning, offering a foundation for future research and implementation strategies. However, the study was limited by its focus on peer-reviewed articles from specific databases.

Strategy to develop and validate digital droplet PCR methods for global antimicrobial resistance wastewater surveillance.

According to World Health Organization (WHO), antimicrobial resistance (AMR) is currently one of the world's top 10 health threats, causing infections to become difficult or impossible to treat, increasing the risk of disease spread, severe illness, disability, and death. Accurate surveillance is a key component in the fight against AMR. Wastewater is progressively becoming a new player in AMR surveillance, with the promise of a cost-effective real-time tracking of global AMR profiles in specific regions. One of the most useful analytical methods for wastewater surveillance is currently based on real-time PCR (qPCR) and digital droplet PCR (ddPCR) technologies. As stated in the EU Wastewater Treatment Directive proposal, methodological standardization, including a workflow for method development and validation, will play a crucial role in global monitoring of AMR in wastewater. However, according to our knowledge, there are currently no qPCR and ddPCR methods for AMR surveillance available that have been validated according to international standard performance criteria. Therefore, this study proposes a workflow for the development and validation of PCR-based methods for a harmonized and global AMR surveillance, including the construction of specific sequence databases and microbial collections for an efficient method development and method specificity evaluation. Following this strategy, we have developed and validated four duplex ddPCR methods responding to international standard performance criteria, focusing on seven AMR genes (ARG's), including extended spectrum beta-lactam (blaCTX-M), carbapenem (blaKPC-2/3), tetracycline (tet(M)), erythromycin (erm(B)), vancomycin (vanA), sulfonamide (sul2), and aminoglycoside (aac(3)-IV), as well as one indicator of antibiotic (multi-) resistance and horizontal gene transfer, named the class I integron (intl1). The performance of these ddPCR methods was successfully assessed for their specificity, as no false-positive and false-negative results were observed. These ddPCR methods were also considered to be highly sensitive as showing a limit of detection below 25 copies of the targets. In addition, their applicability was confirmed using 14 wastewater samples collected from two Belgian water resource recovery facilities. The proposed study represents therefore a step forward to reinforce method harmonization in the context of the global AMR surveillance in wastewater. PRACTITIONER POINTS: In the context of wastewater surveillance, no PCR-based methods for global AMR monitoring are currently validated according to international standards. Consequently, we propose a workflow to develop and validate PCR-based methods for a harmonized and global AMR surveillance. This workflow resulted here in four duplex ddPCR methods targeting seven ARGs and one general indicator for mobilizable resistance genes. The applicability of these validated ddPCR methods was confirmed on 14 wastewater samples from two Belgian water resource recovery facilities.

Endoscopic ultrasound-guided biliary rendezvous after failed cannulation, and comparison between benign vs malignant biliopancreatic disorders: outcomes at a single tertiary-care centre

Background Endoscopic ultrasound (EUS)-guided biliary rendezvous (RV) is an EUS-assisted technique described as a rescue method in cases of failed biliary cannulation via endoscopic retrograde cholangiography (ERC). Current literature remains unclear regarding its current role. The study aim was to evaluate the effectiveness for biliary EUS-RV, and comparison between benign vs malignant biliopancreatic disorders. Methods Retrospective observational study with prospective consecutive inclusion in a specific database from a tertiary-center. All patients with biliopancreatic diseases that underwent a EUS-assisted ERC between October-2010 and November-2022 for failed ERC were included. Main outcomes were technical/overall success. Secondary outcomes were safety, potential factors related to failure/success or safety; and a comparative analysis between EUS-RV and EUS-guided transmural drainage (TMD) in malignant cases. Results A total of 69 patients who underwent EUS-RV procedures, with benign and malignant pathologies (n = 40 vs n = 29), were included. Technical / overall success and related-adverse events (AEs) were 79.7% (95%CI, 68.3-88.4) / 74% (95%CI, 61-83.7) and 24% (95%CI, 15.1-36.5), respectively. Failed cases were mainly related with guidewire manipulation. Seven failed RV were successfully rescued by EUS-TMD. On multivariable analysis, EUS-RV and malignant pathology was associated with a greater failure rate (technical success: OR,0.21; 95%CI,0.05-0.72; p = 0.017), and higher AEs rate (OR,3.46; 95%CI,1.13-11.5; p = 0.034). Also, the EUS-TMD group had greater technical success (OR,16.96; 95%CI,4.69-81.62; p < 0.001) and overall success (OR, 3.09; 95%CI,1.18-8-16; p < 0.026) with a lower AEs rate (OR,0.30; 95%CI,0.11-0.78; p = 0.014) than EUS-RV in malignant disorders. Conclusions EUS-RV is a demanding technique with better outcomes in benign than in malignant biliopancreatic diseases. Comparison of the EUS-TMD group on malignant disorders showed worse outcomes with EUS-RV. Given these findings, maybe EUS-RV is not the best option for malignant biliopancreatic disorders.

Conceptualizing an interpretative framework for energy transition among Italian innovative small and medium enterprises

Energy transition (ET) is becoming essential for most countries worldwide. Companies can only adapt their behaviours to the objectives set by countries’ institutions. A new research stream has thus emerged, which investigates the factors facilitating ET among companies. To date, less attention has been paid to small and medium-sized enterprises (SMEs). One of the greatest difficulties in examining SMEs is their extreme heterogeneity, making results difficult to interpret.This study analyses the factors supporting ET among a specific population of companies in a defined territorial context: the so-called innovative SMEs belonging to a special Italian database. The high propensity towards eco-environmental innovations characterizing these SMEs and their inclination towards sustainable development suggests that their experience represents a prospective path that policymakers could adopt for other types of SMEs.Through a partial least squares analysis of data collected from these SMEs, outcomes show that the only factor supporting the ET is the sensitivity of these economic organizations towards energy issues developed over time; that is, the corporate culture with regard to sustainability.This result is only seemingly surprising under the specific features of these SMEs. Conversely, it presupposes new implications regarding policy measures, questioning the traditional approach based on providing incentives and/or regulations.

Proteomic Analysis of Fibroblasts Exposed to Resin Composite Release.

To investigate the potential effects of products released by a resin composite on the proteome of human gingival fibroblasts. Fifteen resin composite cylinders of a Bis-GMA-based resin composite (Tetric EvoCeram, Ivoclar) were made and placed in a culture medium for 24h. Then, 30mL of this medium was placed for 72h in contact with human gingival fibroblasts and a second control group consisted of cells placed in culture medium only. Afterward, cells were collected, washed, and their proteins extracted. Three two-dimensional electrophoresis were performed per condition. Image analysis of the gels was carried out to highlight the differential protein spots. These spots were then analyzed by an ESI/qTOF mass spectrometer. Finally, specific databases provided protein identification, their interactions, and the pathways where they are implicated. Delta2D software allowed the detection of 21 spots of different proteins. The MASCOT identified 28 proteins. Five proteins from four spots were upregulated, 23 proteins from 17 spots were downregulated. The UniProt database showed that all these proteins were involved in cellular architecture, structural modifications and quality control of proteins, cellular homeostasis, and metabolic pathways. The STRING database revealed the interactions between the regulated proteins. The GO enrichment analysis showed that 19 pathways were affected. The products released from the resin composite tested led to changes in the fibroblast proteome. Under the conditions of this study, resin composite released products can cause early adverse effects on cells, but without complete inhibition of their cellular functions.

Venous Thromboembolic Events Following Revisional Gastric Bypass: An Analysis of the MBSAQIP Database from 2015 to 2019 Using Propensity Matching.

Primary bariatric surgery is associated with moderate-to-high risk of venous thromboembolic events (VTE); however, the risk for revisional surgery lacks granularity. Our primary objective was to define the risk of VTE following revisional Roux-en-Y gastric bypass (RYGB) compared to primary RYGB. Adults who underwent primary or revision/conversion RYGB between January 1, 2015, and December 31, 2019, with a BMI ≥ 35kg/m2 were identified in a bariatric specific database. VTE was defined as pulmonary embolus and/or deep venous thrombosis. Thirty-day VTE and transfusion rates were compared between the two groups using propensity score matching of 3:1. Primary RYGB was performed in 197,186 (92.4%) patients compared to 16,144 (7.6%) in the revisional group. Patients in the revisional group had fewer comorbidities than those undergoing primary RYGB. In the matched cohort of 64,258 procedures, there were 48,116 (74.9%) primary RYGB cases compared to 16,142 (25.1%) RYGB revisions. The rate of VTE was similar in the revisional surgery group compared to the propensity matched primary RYGB group (0.4% vs. 0.3%, p > 0.580); however, transfusion was more common in the revisional group (1.4% vs. 1.0%, p = 0.005). Revisional group had higher rates of readmission, reoperation, increased length of stay, and operation length ≥ 180min compared to matched primary RYGB group (p < 0.001). VTE rates for both primary and revisional RYGB are similar. Revisional RYGB cases impose increased risk of bleeding among other outcomes. Thus, identifying those at higher risk of complications is critical.

Impact of Artificial Intelligence Chatbots on Student Well-being and Mental Health: A Systematic Review

Students in schools and colleges face some challenges, including rigorous academic schedules, academic work load, standardized tests, and assignments deadline, which can contribute to significant stress and mental health risks. Thus, numerous studies have been conducted to control stress between students, such as self-guided stress management programs. This paper reviews several studies published between 2019 and 2023 exploring the impacts of deploying state-of-the-art artificial intelligence chatbots which are used to boost and manage psychological disorders and mental health symptoms such as anxiety, depression, fear, and worry between prospective and current undergraduate students. This study outlines the key phases of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It begins with screening (115) articles and subsequently includes (13) articles for intensive review, which all focus on psychological disorders and mental health conditions that can be influenced by the conversational agents and chatbots. However, the results revealed that anxiety and depression are the main psychological disorders that can be influenced by conversational agents and chatbots. Moreover, this review revealed multiple cases of universities and schools, emphasizing the implementation of chatbots for both learning and advising functions. The Systematic Literature Review (SLR) in this study is constrained to the specific databases, and the search keywords were limited to the article title fields only; which excluded the abstracts. This could have caused some missing relevant studies. In future work, other essential databases will be included, such as Scopus and WoS. In addition, we will include more information about the technique and the complexities of using the chatbot as it may have a significant impact on student behaviors and emotions.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BackgroundDerivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.ResultsThe results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism’s specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.ConclusionsWhile we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Pediatric Intensive Care Unit Length of Stay Prediction by Machine Learning.

Purpose: To develop and validate machine learning models for predicting the length of stay (LOS) in the Pediatric Intensive Care Unit (PICU) using data from the Virtual Pediatric Systems (VPS) database. Methods: A retrospective study was conducted utilizing machine learning (ML) algorithms to analyze and predict PICU LOS based on historical patient data from the VPS database. The study included data from over 100 North American PICUs spanning the years 2015-2020. After excluding entries with missing variables and those indicating recovery from cardiac surgery, the dataset comprised 123,354 patient encounters. Various ML models, including Support Vector Machine, Stochastic Gradient Descent Classifier, K-Nearest Neighbors, Decision Tree, Gradient Boosting, CatBoost, and Recurrent Neural Networks (RNNs), were evaluated for their accuracy in predicting PICU LOS at thresholds of 24 h, 36 h, 48 h, 72 h, 5 days, and 7 days. Results: Gradient Boosting, CatBoost, and RNN models demonstrated the highest accuracy, particularly at the 36 h and 48 h thresholds, with accuracy rates between 70 and 73%. These results far outperform traditional statistical and existing prediction methods that report accuracy of only around 50%, which is effectively unusable in the practical setting. These models also exhibited balanced performance between sensitivity (up to 74%) and specificity (up to 82%) at these thresholds. Conclusions: ML models, particularly Gradient Boosting, CatBoost, and RNNs, show moderate effectiveness in predicting PICU LOS with accuracy slightly over 70%, outperforming previously reported human predictions. This suggests potential utility in enhancing resource and staffing management in PICUs. However, further improvements through training on specialized databases can potentially achieve better accuracy and clinical applicability.

Procedimentos para modelagem BIM de lajes nervuradas de concreto armado em edificações

It is impossible not to admit that commercial computational tools for structural analysis and detailing have become indispensable in the various applications of Structural Engineering nowadays. The importance of these tools equipped with graphical interfaces based on the BIM (Building Information Modeling) environment is mainly due to the flexibility and ease in making common corrections and changes during the elaboration stage, as well as the proposition of different structural conceptions, not always innovative, but almost always bold, as in the case of reinforced concrete ribbed slabs. The use of ribbed slabs in reinforced concrete is an old practice in Structural Engineering, which has encouraged the adaptation of commercial computational tools, both in the development of structurally consistent mathematical formulations, as well as in the development of specific databases, equipped with information from mold manufacturers and materials intended for the execution of this type of slabs. It is highly efficient in building structures in which there are numerous problems of interference of structural elements with elements of installations, such as large-diameter pipes for wastewater, sewage, gas, fire, and even air conditioning ducts, exhaust and architectural elements. This efficiency is mainly due to the fact that large beams are not considered in their supports, making the lower face of the slab practically without obstacles to the building pipes, and is also due to the low average thickness of concrete used in the pavement compared to conventional rectangular supported slabs and flat mushroom slabs. However, it is not always possible to obtain an optimal final result, even with all the adaptations previously described, mainly due to executive and computational modeling problems. This work intends to present a possible structural modeling methodology, which provides results within the limits of the feasibility of the slab, both in the construction aspect of the computational structural model, as well as the possibility of using materials from manufacturers and labor, using a commercial computational tool for structural analysis and detailing.

Biometric access system with automatic registration and loyalty check in the system of recognition and database entry

ABSTRACT Against the backdrop of ongoing terrorist threats and uncontrolled migration flows, there is increased interest in biometric human recognition technology and barcoding technology combined with it. As a possible solution, a biometric access system is proposed, designed to implement three main urgent tasks – automatic registration of people crossing the border; recording registration information in a special biometric database; checking registration information and facial image on the database of wanted people. Effective biometric systems combine verification and identification tasks to increase the reliability of person identification and comparison with reference data sets. In the course of the study, various methods of information processing with its subsequent encoding were considered and analyzed, and the experience of previous systems of this type was investigated. As a result of the study, the effectiveness of using the latest biometric technologies in the migration sphere and in law enforcement agencies for the effective search of offenders has been substantiated. This paper will be useful as a material for further study of facial recognition technology and the integration of QR into the field of biometrics.

Targeted and non-targeted identification of dye and chemical contaminants in Loji River, Indonesia using FT-ICR-MS

This study utilized liquid chromatography (LC) alongside Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to explore the dyes and chemical contaminants in Loji River, Indonesia. We tentatively identified a total of 655 contaminants at various confidence level, subsequently classifying them into 22 distinct categories. Of the 54 dyes we detected, 12 corresponded with entries in our specialized in-house database. These 12 dyes were further confirmed by reference standards, matching both retention time (RT) and MS/MS spectra. LC-FT-ICR MS data showed that dyes from printing batik and textile industries are key contributors to river pollution. Particularly noteworthy were two sample locations that displayed substantial contamination, predominantly from azoic and reactive dyes. Additionally, pharmaceuticals were identified as one of the most frequently occurring contaminants, underscoring the inadequacies in the area's sewage management. To corroborate these findings, we conducted physicochemical, phytotoxicity, and acute toxicity tests, all of which verified the harmful effects of the Loji River's water on both the local flora and human populations. Notably, water samples that tested positive for dye contamination exhibited elevated toxicity levels. To the best of our knowledge, this study is pioneering in its molecular-level investigation of dye contamination in Southeast Asian rivers. Our results accentuate the pressing need for both targeted and non-targeted screening methods to identify contaminants in the surface waters of developing nations.