Antimicrobial Activity of Citrate-Coated Cerium Oxide Nanoparticles.

The purpose of this study was to investigate the antimicrobial activity of citrate-stabilized sols of cerium oxide nanoparticles at different concentrations via different microbiological methods and to compare the effect with the peroxidase activity of nanoceria for the subsequent development of a regeneration-stimulating medical and/or veterinary wound-healing product providing new types of antimicrobial action. The object of this study was cerium oxide nanoparticles synthesized from aqueous solutions of cerium (III) nitrate hexahydrate and citric acid (the size of the nanoparticles was 3-5 nm, and their aggregates were 60-130 nm). Nanoceria oxide sols with a wide range of concentrations (10-1-10-6 M) as well as powder (the dry substance) were used. Both bacterial and fungal strains (Bacillus subtilis, Bacillus cereus, Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Proteus vulgaris, Candida albicans, Aspergillus brasielensis) were used for the microbiological studies. The antimicrobial activity of nanoceria was investigated across a wide range of concentrations using three methods sequentially; the antimicrobial activity was studied by examining diffusion into agar, the serial dilution method was used to detect the minimum inhibitory and bactericidal concentrations, and, finally, gas chromatography with mass-selective detection was performed to study the inhibition of E. coli's growth. To study the redox activity of different concentrations of nanocerium, we studied the intensity of chemiluminescence in the oxidation reaction of luminol in the presence of hydrogen peroxide. As a result of this study's use of the agar diffusion and serial dilution methods followed by sowing, no significant evidence of antimicrobial activity was found. At the same time, in the current study of antimicrobial activity against E. coli strains using gas chromatography with mass spectrometry, the ability of nanoceria to significantly inhibit the growth and reproduction of microorganisms after 24 h and, in particular, after 48 h of incubation at a wide range of concentrations, 10-2-10-5 M (48-95% reduction in the number of microbes with a significant dose-dependent effect) was determined as the optimum concentration. A reliable redox activity of nanoceria coated with citrate was established, increasing in proportion to the concentration, confirming the oxidative mechanism of the action of nanoceria. Thus, nanoceria have a dose-dependent bacteriostatic effect, which is most pronounced at concentrations of 10-2-10-3 M. Unlike the effects of classical antiseptics, the effect was manifested from 2 days and increased during the observation. To study the antimicrobial activity of nanomaterials, it is advisable not to use classical qualitative and semi-quantitative methods; rather, the employment of more accurate quantitative methods is advised, in particular, gas chromatography-mass spectrometry, during several days of incubation.

Medulloblastomas in Pediatric and Adults.

Medulloblastoma is the primary malignant embryonic tumor of the cerebellum and the most common malignant tumor of childhood, accounting up to 25% of all CNS tumors in children, but is extremely rare in adults. Despite the fact that medulloblastomas are one of the most malignant human tumors, it is worthy to note that a great breakthrough has been achieved in our understanding of oncogenesis and the development of real methods of treatment. The main objective of surgical treatment is a maximum resection of tumor with minimal impairment of neurological functions, in order to reduce the volume, remove tumor tissue, get the biopsy, and restore the cerebrospinal fluid flow. The progress of surgical techniques (using a microscope, ultrasound suction), anesthesiology, and intensive care has significantly decreased surgical mortality and increased radicality of tumor removal. Postoperative mortality is less than one percent in most studies, while neurological complications have been reported between 5-10%. Radiotherapy is the main method of treatment in patients older than 3 years, which dramatically improved the recurrence-free survival. Nevertheless, the radiation therapy without systemic chemotherapy leads to a high risk of systemic metastases. After the role of chemotherapy was statistically proven, investigations of the optimal combination of different chemotherapy regimens continued around the world. Currently, 80% of patients can already be cured, however, the quality of life of patients in the long-term period remains quite low, which depends on many factors including endocrinological, cognitive, neurological, and otoneurologic aspects. Thus, the main strategic goal of the development of neuro-oncology is to reduce the doses of radiation therapy to the CNS and the main task of international research is to optimize existing protocols and develop fundamentally new ones based on molecular genetic research in order to improve the quality of life.

Craniopharyngiomas: Surgery and Radiotherapy.

Taking into account the benign nature of craniopharyngiomas, the main method of treatment is the resection of the tumor. However, the tendency of these tumors to invade critical structures (such as optic pathways, the hypothalamic-pituitary system, the Willis circle vessels) often limits the possibility of a radical surgery.Craniopharyngiomas of the third ventricle represent the greatest challenge for surgery. After radical surgery, hypothalamic disorders often occur, including not only obesity but also cognitive, emotional, mental, and metabolic disturbances. Metabolic disorders associated with damage to the hypothalamus progress after surgery and lead to impaired functions of the internal organs. This process is irreversible and, in many cases, becomes the direct cause of mortality. The life expectancy of patients with the surgically affected hypothalamus is significantly shorter than in patients with preserved diencephalic function. The incidence of hypothalamic disorders after surgery can reach 40%.Even with macroscopically total resection, craniopharyngiomas can recur in 10-30% of cases, and in the presence of tumor remnants and with no further radiation treatment, the risk of recurrence significantly increases to up to 50-85% according to various studies. For this reason, the observation of patients with residual tumors after surgery is an incorrect strategy.Radiation therapy significantly improves progression-free survival (PFS), and the use of stereotactic irradiation techniques ensures conformity of irradiation of tumor remnants with a complicated shape and location (Iwata H et al., J Neurooncol 106(3):571-577, 2012; Aggarwal et al., Pituitary 16(1):26-33, 2013; Savateev et al., Zh Vopr Neirokhir Im N N Burdenko 81(3):94-106; 2017), which potentially reduces the risk of undesirable postradiation effects. Therefore, the quality of life in patients with craniopharyngiomas infiltrating the hypothalamus is significantly higher after non-radical operations with subsequent stereotactic radiation than after a total or subtotal removal.

ACTH(6-9)-Pro-Gly-Pro ameliorates anxiety-like and depressive-like behaviour and gut mucosal microbiota composition in rats under conditions of chronic restraint stress.

The effects of the peptide ACTH(6-9)-Pro-Gly-Pro at doses of 5; 50; 500 µg/kg on the Wistar rats' behaviour and gut mucosal microbiota composition under conditions of chronic immobilization stress (CRS) were studied. CRS increased anxiety-like and depressive-like behaviour, disturbances in locomotor activity and gut dysbiosis. Administration of ACTH(6-9)-Pro-Gly-Pro showed many phenotypic results. Peptide demonstrated anti-depressant activity at doses of 5 and 500 µg/kg by a decrease in the total immobile time in the FST. ACTH(6-9)-Pro-Gly-Pro administered at a dose of 50 µg/kg resulted in an anxiolytic effect which is shown by an increase in the time in the open arms of EPM (p < 0.05) and a decrease in the time in the closed arms (p < 0.05). Moreover, the peptide led to a decrease in alpha- and beta-diversity of the gut microbiota (p < 0.01). Correlation and linear regression analysis demonstrated central mechanisms of ACTH(6-9)-Pro-Gly-Pro anxiolytic activity and both central and peripheral ones in an anti-depressant effect. In this way, peptide ACTH(6-9)-Pro-Gly-Pro could prevent the development of behavioural disturbances and gut dysbiosis caused by chronic restraint stress.

Encapsulated Methionine γ-Lyase: Application in Enzyme Prodrug Therapy of Pseudomonas aeruginosa Infection.

Lung disease caused by Pseudomonas aeruginosa is the leading reason for death in cystic fibrosis patients. Therapeutic efficacy of the pharmacological pairs, the naked/encapsulated mutant form of Citrobacter freundii methionine γ-lyase and the substrates, sulfoxides of S-substituted l-cysteine, generating thiosulfinates, was evaluated on the murine model of experimental sepsis caused by the multidrug-resistant P. aeruginosa 203-2 strain. The pairs containing the naked enzyme and substrates did not have antibacterial activity. The treatment of mice with the pair encapsulated enzyme and S-methyl-l-cysteine sulfoxide, generating dimethyl thiosulfinate, led to a complete recovery of the animals of the model, with the infecting dose equal to LD50. The pair generating diallyl thiosulfinate (allicin) proved to be less effective. So, the substituents, attached to the thiosulfinate moiety, affect the antibacterial activity of thiosulfinates against P. aeruginosa.

DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115-8. ©2017 AACR.

Automated annotation and classification of BI-RADS assessment from radiology reports.

The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach. We divided the BI-RADS category extraction task into two specific tasks: (1) annotation of all BI-RADS category values within a report, (2) classification of the laterality of each BI-RADS category value. We used one algorithm for task 1 and evaluated three algorithms for task 2. Across all evaluations and model training, we used a total of 2159 radiology reports from 18 hospitals, from 2003 to 2015. Performance with the existing rule-based algorithm was not satisfactory. Conditional random fields showed a high performance for task 1 with an F-1 measure of 0.95. Rules from partial decision trees (PART) algorithm showed the best performance across classes for task 2 with a weighted F-1 measure of 0.91 for BIRADS 0-6, and 0.93 for BIRADS 3-5. Classification performance by class showed that performance improved for all classes from Naïve Bayes to Support Vector Machine (SVM), and also from SVM to PART. Our system is able to annotate and classify all BI-RADS mentions present in a single radiology report and can serve as the foundation for future studies that will leverage automated BI-RADS annotation, to provide feedback to radiologists as part of a learning health system loop.

TCGA Expedition: A Data Acquisition and Management System for TCGA Data.

BACKGROUND: The Cancer Genome Atlas Project (TCGA) is a National Cancer Institute effort to profile at least 500 cases of 20 different tumor types using genomic platforms and to make these data, both raw and processed, available to all researchers. TCGA data are currently over 1.2 Petabyte in size and include whole genome sequence (WGS), whole exome sequence, methylation, RNA expression, proteomic, and clinical datasets. Publicly accessible TCGA data are released through public portals, but many challenges exist in navigating and using data obtained from these sites. We developed TCGA Expedition to support the research community focused on computational methods for cancer research. Data obtained, versioned, and archived using TCGA Expedition supports command line access at high-performance computing facilities as well as some functionality with third party tools. For a subset of TCGA data collected at University of Pittsburgh, we also re-associate TCGA data with de-identified data from the electronic health records. Here we describe the software as well as the architecture of our repository, methods for loading of TCGA data to multiple platforms, and security and regulatory controls that conform to federal best practices. RESULTS: TCGA Expedition software consists of a set of scripts written in Bash, Python and Java that download, extract, harmonize, version and store all TCGA data and metadata. The software generates a versioned, participant- and sample-centered, local TCGA data directory with metadata structures that directly reference the local data files as well as the original data files. The software supports flexible searches of the data via a web portal, user-centric data tracking tools, and data provenance tools. Using this software, we created a collaborative repository, the Pittsburgh Genome Resource Repository (PGRR) that enabled investigators at our institution to work with all TCGA data formats, and to interrogate these data with analysis pipelines, and associated tools. WGS data are especially challenging for individual investigators to use, due to issues with downloading, storage, and processing; having locally accessible WGS BAM files has proven invaluable. CONCLUSION: Our open-source, freely available TCGA Expedition software can be used to create a local collaborative infrastructure for acquiring, managing, and analyzing TCGA data and other large public datasets.

Automated detection of heuristics and biases among pathologists in a computer-based system.

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

Perceptual analysis of the reading of dermatopathology virtual slides by pathology residents.

CONTEXT: The process by which pathologists arrive at a given diagnosis-a combination of their slide exploration strategy, perceptual information gathering, and cognitive decision making-has not been thoroughly explored, and many questions remain unanswered. OBJECTIVE: To determine how pathology residents learn to diagnose inflammatory skin dermatoses, we contrasted the slide exploration strategy, perceptual capture of relevant histopathologic findings, and cognitive integration of identified features between 2 groups of residents, those who had and those who had not undergone their dermatopathology rotation. DESIGN: Residents read a case set of 20 virtual slides (10 depicting nodular and diffuse dermatitis and 10 depicting subepidermal vesicular dermatitis), using an in-house-developed interface. We recorded residents' reports of diagnostic findings, conjectured diagnostic hypotheses, and final (or differential) diagnosis for each case, and time stamped each interaction with the interface. We created search maps of residents' slide exploration strategy. RESULTS: No statistically significant differences were observed between the resident groups in the number of correctly or incorrectly reported diagnostic findings, but residents with dermatopathology training generated significantly more correct hypotheses (mean improvement of 88.5%) and correct diagnoses (70% of all correct diagnoses). CONCLUSIONS: Two types of slide exploration strategy were identified for both groups: (1) a focused and efficient search, observed when the final diagnosis was correct; and (2) a more dispersed, time-consuming strategy, observed when the final diagnosis was incorrect. This difference was statistically significant, and it suggests that initial interpretation of a slide may bias further slide exploration.

Factors affecting feeling-of-knowing in a medical intelligent tutoring system: the role of immediate feedback as a metacognitive scaffold.

Previous studies in our laboratory have shown the benefits of immediate feedback on cognitive performance for pathology residents using an intelligent tutoring system (ITS) in pathology. In this study, we examined the effect of immediate feedback on metacognitive performance, and investigated whether other metacognitive scaffolds will support metacognitive gains when immediate feedback is faded. Twenty-three participants were randomized into intervention and control groups. For both groups, periods working with the ITS under varying conditions were alternated with independent computer-based assessments. On day 1, a within-subjects design was used to evaluate the effect of immediate feedback on cognitive and metacognitive performance. On day 2, a between-subjects design was used to compare the use of other metacognitive scaffolds (intervention group) against no metacognitive scaffolds (control group) on cognitive and metacognitive performance, as immediate feedback was faded. Measurements included learning gains (a measure of cognitive performance), as well as several measures of metacognitive performance, including Goodman-Kruskal gamma correlation (G), bias, and discrimination. For the intervention group, we also computed metacognitive measures during tutoring sessions. Results showed that immediate feedback in an intelligent tutoring system had a statistically significant positive effect on learning gains, G and discrimination. Removal of immediate feedback was associated with decreasing metacognitive performance, and this decline was not prevented when students used a version of the tutoring system that provided other metacognitive scaffolds. Results obtained directly from the ITS suggest that other metacognitive scaffolds do have a positive effect on G and discrimination, as immediate feedback is faded. We conclude that immediate feedback had a positive effect on both metacognitive and cognitive gains in a medical tutoring system. Other metacognitive scaffolds were not sufficient to replace immediate feedback in this study. However, results obtained directly from the tutoring system are not consistent with results obtained from assessments. In order to facilitate transfer to real-world tasks, further research will be needed to determine the optimum methods for supporting metacognition as immediate feedback is faded.

Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths.

OBJECTIVES: Determine effects of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Determine if limited enforcement in a medical tutoring system inhibits students from learning the optimal and most efficient solution path. Describe the type of deviations from the optimal solution path that occur during tutoring, and how these deviations change over time. Determine if the size of the problem-space (domain scope), has an effect on learning gains when using a tutor with limited enforcement. METHODS: Analyzed data mined from 44 pathology residents using SlideTutor-a Medical Intelligent Tutoring System in Dermatopathology that teaches histopathologic diagnosis and reporting skills based on commonly used diagnostic algorithms. Two subdomains were included in the study representing sub-algorithms of different sizes and complexities. Effects of the tutoring system on student errors, goal states and solution paths were determined. RESULTS: Students gradually increase the frequency of steps that match the tutoring system's expectation of expert performance. Frequency of errors gradually declines in all categories of error significance. Student performance frequently differs from the tutor-defined optimal path. However, as students continue to be tutored, they approach the optimal solution path. Performance in both subdomains was similar for both errors and goal differences. However, the rate at which students progress toward the optimal solution path differs between the two domains. Tutoring in superficial perivascular dermatitis, the larger and more complex domain was associated with a slower rate of approximation towards the optimal solution path. CONCLUSIONS: Students benefit from a limited-enforcement tutoring system that leverages diagnostic algorithms but does not prevent alternative strategies. Even with limited enforcement, students converge toward the optimal solution path.

Concept recognition for extracting protein interaction relations from biomedical text.

BACKGROUND: Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing. RESULTS: Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist. CONCLUSION: Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet http://bionlp.sourceforge.net.

Evaluation of an intelligent tutoring system in pathology: effects of external representation on performance gains, metacognition, and acceptance.

OBJECTIVE: Determine effects of computer-based tutoring on diagnostic performance gains, meta-cognition, and acceptance using two different problem representations. Describe impact of tutoring on spectrum of diagnostic skills required for task performance. Identify key features of student-tutor interaction contributing to learning gains. DESIGN: Prospective, between-subjects study, controlled for participant level of training. Resident physicians in two academic pathology programs spent four hours using one of two interfaces which differed mainly in external problem representation. The case-focused representation provided an open-learning environment in which students were free to explore evidence-hypothesis relationships within a case, but could not visualize the entire diagnostic space. The knowledge-focused representation provided an interactive representation of the entire diagnostic space, which more tightly constrained student actions. MEASUREMENTS: Metrics included results of pretest, post-test and retention-test for multiple choice and case diagnosis tests, ratios of performance to student reported certainty, results of participant survey, learning curves, and interaction behaviors during tutoring. RESULTS: Students had highly significant learning gains after one tutoring session. Learning was retained at one week. There were no differences between the two interfaces in learning gains on post-test or retention test. Only students in the knowledge-focused interface exhibited significant metacognitive gains from pretest to post-test and pretest to retention test. Students rated the knowledge-focused interface significantly higher than the case-focused interface. CONCLUSIONS: Cognitive tutoring is associated with improved diagnostic performance in a complex medical domain. The effect is retained at one-week post-training. Knowledge-focused external problem representation shows an advantage over case-focused representation for metacognitive effects and user acceptance.

An intelligent tutoring system for visual classification problem solving.

OBJECTIVE: This manuscript describes the development of a general intelligent tutoring system for teaching visual classification problem solving. MATERIALS AND METHODS: The approach is informed by cognitive theory, previous empirical work on expertise in diagnostic problem-solving, and our own prior work describing the development of expertise in pathology. The architecture incorporates aspects of cognitive tutoring system and knowledge-based system design within the framework of the unified problem-solving method description language component model. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. RESULTS: In this paper, we outline the empirically derived requirements and design principles, describe the knowledge representation and dynamic solution graph, detail the functioning of the instructional layer, and demonstrate two implemented interfaces to the system. CONCLUSION: Using the general visual classification tutor, we have created SlideTutor, a tutoring system for microscopic diagnosis of inflammatory diseases of skin.

A method for automated detection of usability problems from client user interface events.

Think-aloud usability analysis provides extremely useful data but is very time-consuming and expensive to perform because of the extensive manual video analysis that is required. We describe a simple method for automated detection of usability problems from client user interface events for a developing medical intelligent tutoring system. The method incorporates (1) an agent-based method for communication that funnels all interface events and system responses to a centralized database, (2) a simple schema for representing interface events and higher order subgoals, and (3) an algorithm that reproduces the criteria used for manual coding of usability problems. A correction factor was empirically determining to account for the slower task performance of users when thinking aloud. We tested the validity of the method by simultaneously identifying usability problems using TAU and manually computing them from stored interface event data using the proposed algorithm. All usability problems that did not rely on verbal utterances were detectable with the proposed method.

A general architecture for intelligent tutoring of diagnostic classification problem solving.

We report on a general architecture for creating knowledge-based medical training systems to teach diagnostic classification problem solving. The approach is informed by our previous work describing the development of expertise in classification problem solving in Pathology. The architecture envelops the traditional Intelligent Tutoring System design within the Unified Problem-solving Method description Language (UPML) architecture, supporting component modularity and reuse. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. We outline the advantages and limitations of this general approach, and describe it's implementation in SlideTutor - a developing Intelligent Tutoring System in Dermatopathology.