Comparison of prostate cancer detection rates in patients undergoing MRI/TRUS fusion prostate biopsy with two different software-based systems.

BACKGROUND: Magnetic resonance imaging (MRI)-targeted prostate biopsy is a routinely used diagnostic tool for prostate cancer (PCa) detection. However, a clear superiority of the optimal approach for software-based MRI processing during biopsy procedures is still unanswered. To investigate the impact of robotic approach and software-based image processing (rigid vs. elastic) during MRI/transrectal ultrasound (TRUS) fusion prostate biopsy (FBx) on overall and clinically significant (cs) PCa detection. METHODS: The study relied on the instructional retrospective biopsy data collected data between September 2013 and August 2017. Overall, 241 men with at least one suspicious lesion (PI-RADS ≥ 3) on multiparametric MRI underwent FBx. The study protocol contains a systematic 12-core sextant biopsy plus 2 cores per targeted lesion. One experienced urologist performed 1048 targeted biopsy cores; 467 (45%) cores were obtained using rigid processing, while the remaining 581 (55%) cores relied on elastic image processing. CsPCa was defined as International Society of Urological Pathology (ISUP) grade ≥ 2. The effect of rigid versus elastic FBx on overall and csPCa detection rates was determined. Propensity score weighting and multivariable regression models were used to account for potential biases inherent to the retrospective study design. RESULTS: In multivariable regression analyses, age, prostate-specific antigen (PSA), and PIRADS ≥ 3 lesion were related to higher odds of finding csPCa. Elastic software-based image processing was independently associated with a higher overall PCa (odds ratio [OR] = 3.6 [2.2-6.1], p < 0.001) and csPCa (OR = 4.8 [2.6-8.8], p < 0.001) detection, respectively. CONCLUSIONS: Contrary to existing literature, our results suggest that the robotic-driven software registration with elastic fusion might have a substantial effect on PCa detection.

U.S. Food and Drug Administration Precertification Pilot Program for Digital Health Software: Weighing the Benefits and Risks.

In 2017, the U.S. Food and Drug Administration (FDA) announced a new program for software classified as a medical device. The Digital Health Software Precertification (Pre-Cert) Program is designed to expedite regulatory review for companies that demonstrate quality and organizational excellence in software development. Although Pre-Cert is intended to promote the worthy goals of access and innovation in digital health, many questions have been raised. In particular, Pre-Cert may reduce incentives for developers to study the safety and effectiveness of their software products before patients start to rely on them. Although postmarket surveillance can mitigate risks of these products, the FDA does not have as much authority after a product's widespread use to enforce data collection deadlines. Pre-Cert may also create confusion for patients and physicians, who may believe that marketed products were subject to rigorous study.

Familial DNA searching- an emerging forensic investigative tool.

In recent years, jurisdictions across the United States have expressed a growing interest in aiding criminal investigations through the use of familial DNA searching (FDS)- a forensic technique to identify family members through DNA databases. The National Survey of CODIS Laboratories surveyed U.S. CODIS laboratories about their perceptions, policies, and practices related to FDS. In total, 103 crime labs completed the survey (77% response rate). Labs in 11 states reported using FDS, while labs in 24 states reported using a similar-but distinct- practice of partial matching. Although the majority of labs had positive perceptions about the ability of FDS to assist investigations, labs also reported a number of concerns and challenges with implementing FDS. Respondents reported using either practice a limited amount with modest numbers of convictions resulting from both FDS and partial matching. The article reports on varying practices related to official policies, training, eligibility, the software search, lineage testing, requirements for releasing information, and subsequent investigative work. Finally, the article discusses what can be learned from this survey, accompanying limitations, and implications for decision-makers considering using FDS.

Medical Devices; Hematology and Pathology Devices; Classification of Blood Establishment Computer Software and Accessories. Final rule.

The Food and Drug Administration (FDA, Agency, or we) is issuing a final rule to classify blood establishment computer software (BECS) and BECS accessories (regulated under product code MMH) into class II (special controls). FDA has identified special controls for BECS and BECS accessories that are necessary to provide a reasonable assurance of safety and effectiveness. FDA is also giving notice that the Agency does not intend to exempt BECS and BECS accessories from premarket notification requirements of the Federal Food, Drug, and Cosmetic Act (FD&C Act).

[Qualification and classification of medical apps : What should be noted and what is BfArM's contribution?] / Abgrenzung und Klassifizierung von Medical Apps : Was ist zu beachten und welche Unterstützung bietet das BfArM?

Smartphones and tablets with their nearly unlimited number of different applications have become an integral part of everyday life. Thus, mobile devices and applications have also found their way into the healthcare sector.For developers, manufacturers, or users as well, it is often difficult to decide whether a mobile health application is a medical device.In this context, it is extremely important for manufacturers to decide at an early stage of the development whether the product is to be introduced into the market as a medical device and is therefore subject to the legislation on medical devices.This article first presents the regulatory framework and subsequently introduces the reader to the Federal Institute for Drugs and Medical Devices' (BfArM) view of the criteria for differentiating between apps as non-medical products and apps as medical apps as well as the classification thereof. Various examples are presented to demonstrate how these criteria are applied practically and options that support developers and manufacturers in their decision making are shown. The article concludes with a reference to current developments and offers a perspective on the new European medical device regulations MDR/IVDR (Medical Device Regulation/In-Vitro Diagnostic Regulation) as well as on future challenges regarding medical apps.

Medical Devices; Immunology and Microbiology Devices; Classification of the Automated Indirect Immunofluorescence Microscope and Software-Assisted System. Final order.

The Food and Drug Administration (FDA or we) is classifying the automated indirect immunofluorescence microscope and software-assisted system into class II (special controls). The special controls that apply to the device type are identified in this order and will be part of the codified language for the automated indirect immunofluorescence microscope and software-assisted system's classification. We are taking this action because we have determined that classifying the device into class II (special controls) will provide a reasonable assurance of safety and effectiveness of the device. We believe this action will also enhance patients' access to beneficial innovative devices, in part by reducing regulatory burdens.

Medical Devices; Neurological Devices; Classification of the Computerized Behavioral Therapy Device for Psychiatric Disorders. Final order.

The Food and Drug Administration (FDA or we) is classifying the computerized behavioral therapy device for psychiatric disorders into class II (special controls). The special controls that apply to the device type are identified in this order and will be part of the codified language for the computerized behavioral therapy device for psychiatric disorders' classification. We are taking this action because we have determined that classifying the device into class II (special controls) will provide a reasonable assurance of safety and effectiveness of the device. We believe this action will also enhance patients' access to beneficial innovative devices, in part by reducing regulatory burdens.

Medical Devices; Neurological Devices; Classification of the Computerized Cognitive Assessment Aid for Concussion. Final order.

The Food and Drug Administration (FDA) is classifying the Computerized Cognitive Assessment Aid for Concussion into class II (special controls). The special controls that will apply to the device are identified in this order and will be part of the codified language for the computerized cognitive assessment aid for concussion's classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Certain aspects on medical devices software law regulation.

INTRODUCTION: some kind of easiness of entry in creating software products on various computing platforms has led to such products being made available perhaps without due consideration of potential risks to users and patients and the most valuable reason for this have been lack of regulatory clarity. Some key points on legal regulation of abovementioned sphere is a base of this study. MATERIAL AND METHODS: Ukrainian legislation, European Union`s Guidelines on the qualification and classification of standalone software; Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices that works in United States of America. Article is based on dialectical, comparative, analytic, synthetic and comprehensive research methods. CONCLUSION: in accordance with Ukrainian legislation, software that has a medical purpose could be a medical device. Ukrainian legislation which is established on European Union Medical Devices Directives divide all medical devices on classes. But there aren't any special recommendations or advices on classifications for software medical devices in Ukraine. It is necessary to develop and adopt guidelines on the qualification and classification of medical device software in Ukraine especially considering the harmonization of Ukrainian legislation with the EU legislation, develop special rules for the application of the national mark of conformity for medical device software and defined the « responsible organization ¼ for the medical device software approval process.

Volume Measurement of Various Tissues Using the Image J Software.

Various methods have been introduced to assess the tissue volume because volumetric evaluation is recognized as one of the most important steps in reconstructive surgery. Advanced volume measurement methods proposed recently use three-dimensional images. They are convenient but have drawbacks such as requiring expensive equipment and volume-analysis software. The authors devised a volume measurement method using the Image J software, which is in the public domain and does not require specific devices or software packages. The orbital and breast volumes were measured by our method using Image J data from facial computed tomography (CT) and breast magnetic resonance imaging (MRI). The authors obtained the final volume results, which were similar to the known volume values. The authors propose here a cost-effective, simple, and easily accessible volume measurement method using the Image J software.

Medical Devices; Cardiovascular Devices; Classification of the Coronary Vascular Physiologic Simulation Software Device. Final order.

The Food and Drug Administration (FDA) is classifying the coronary vascular physiologic simulation software device into class II (special controls). The special controls that will apply to the device are identified in this order and will be part of the codified language for the coronary vascular physiologic simulation software device's classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Medical Devices; Neurological Devices; Classification of the Computerized Cognitive Assessment Aid. Final order.

The Food and Drug Administration (FDA) is classifying the computerized cognitive assessment aid into class II (special controls). The special controls that will apply to the device are identified in this order, and will be part of the codified language for the computerized cognitive assessment aid's classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Medical devices; neurological devices; classification of the neuropsychiatric interpretive electroencephalograph assessment aid. Final order.

The Food and Drug Administration (FDA) is classifying the neuropsychiatric interpretive electroencephalograph (EEG) assessment aid into class II (special controls). The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

GOParGenPy: a high throughput method to generate gene ontology data matrices.

BACKGROUND: Gene Ontology (GO) is a popular standard in the annotation of gene products and provides information related to genes across all species. The structure of GO is dynamic and is updated on a daily basis. However, the popular existing methods use outdated versions of GO. Moreover, these tools are slow to process large datasets consisting of more than 20,000 genes. RESULTS: We have developed GOParGenPy, a platform independent software tool to generate the binary data matrix showing the GO class membership, including parental classes, of a set of GO annotated genes. GOParGenPy is at least an order of magnitude faster than popular tools for Gene Ontology analysis and it can handle larger datasets than the existing tools. It can use any available version of the GO structure and allows the user to select the source of GO annotation. GO structure selection is critical for analysis, as we show that GO classes have rapid turnover between different GO structure releases. CONCLUSIONS: GOParGenPy is an easy to use software tool which can generate sparse or full binary matrices from GO annotated gene sets. The obtained binary matrix can then be used with any analysis environment and with any analysis methods.

To twist, roll, stroke or poke? A study of input devices for menu navigation in the cockpit.

Modern interfaces within the aircraft cockpit integrate many flight management system (FMS) functions into a single system. The success of a user's interaction with an interface depends upon the optimisation between the input device, tasks and environment within which the system is used. In this study, four input devices were evaluated using a range of Human Factors methods, in order to assess aspects of usability including task interaction times, error rates, workload, subjective usability and physical discomfort. The performance of the four input devices was compared using a holistic approach and the findings showed that no single input device produced consistently high performance scores across all of the variables evaluated. The touch screen produced the highest number of 'best' scores; however, discomfort ratings for this device were high, suggesting that it is not an ideal solution as both physical and cognitive aspects of performance must be accounted for in design. PRACTITIONER SUMMARY: This study evaluated four input devices for control of a screen-based flight management system. A holistic approach was used to evaluate both cognitive and physical performance. Performance varied across the dependent variables and between the devices; however, the touch screen produced the largest number of 'best' scores.

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.

Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

Medication reminder applications to improve adherence in coronary heart disease: a randomised clinical trial.

OBJECTIVE: The aim of the MEDication reminder APPs to improve medication adherence in Coronary Heart Disease Study was to evaluate the effectiveness and feasibility of using publicly available high-quality medication reminder applications (apps) to improve medication adherence compared with usual care in patients with coronary heart disease (CHD). An additional aim was to examine whether an app with additional features improved adherence further. METHODS: Patients with CHD (n=163) were randomised to one of three groups: (1) usual care, (2) a basic app or (3) an advanced app with interactive/customisable features. The primary analysis compared usual care versus app use on the primary outcome of the 8-item Morisky Medication Adherence Scale (MMAS-8) at 3 months. Secondary outcomes included blood pressure and cholesterol levels. RESULTS: The mean age was 57.9 years and 87.7% were male. At 3 months, patients using an app had higher adherence (mean MMAS-8 score 7.11) compared with the usual care group (mean MMAS-8 score 6.63) with a mean difference between groups of 0.47 (95% CI 0.12 to 0.82, p=0.008). There was no significant difference in patients using the basic app versus the advanced app (mean difference -0.16, 95% CI -0.56 to 0.24, p=0.428). There were no significant differences in secondary clinical outcome measures. CONCLUSION: Patients with CHD who used medication reminder apps had better medication adherence compared with usual care, and using apps with additional features did not improve this outcome further. These data suggest medication apps are likely to help patients with chronic health conditions adhere to medicines, but further examination of whether such benefits are sustained is warranted. CLINICAL TRIAL REGISTRATION NUMBER: ACTRN12616000661471; Results.

Using contextual and lexical features to restructure and validate the classification of biomedical concepts.

BACKGROUND: Biomedical ontologies are critical for integration of data from diverse sources and for use by knowledge-based biomedical applications, especially natural language processing as well as associated mining and reasoning systems. The effectiveness of these systems is heavily dependent on the quality of the ontological terms and their classifications. To assist in developing and maintaining the ontologies objectively, we propose automatic approaches to classify and/or validate their semantic categories. In previous work, we developed an approach using contextual syntactic features obtained from a large domain corpus to reclassify and validate concepts of the Unified Medical Language System (UMLS), a comprehensive resource of biomedical terminology. In this paper, we introduce another classification approach based on words of the concept strings and compare it to the contextual syntactic approach. RESULTS: The string-based approach achieved an error rate of 0.143, with a mean reciprocal rank of 0.907. The context-based and string-based approaches were found to be complementary, and the error rate was reduced further by applying a linear combination of the two classifiers. The advantage of combining the two approaches was especially manifested on test data with sufficient contextual features, achieving the lowest error rate of 0.055 and a mean reciprocal rank of 0.969. CONCLUSION: The lexical features provide another semantic dimension in addition to syntactic contextual features that support the classification of ontological concepts. The classification errors of each dimension can be further reduced through appropriate combination of the complementary classifiers.

Research on Molecular Mechanism of Fructus Ligustri Lucidi against Osteoporosis based on Network Pharmacology

Abstract TCMSP platform of systematic pharmacology of traditional Chinese medicine This study aimed to investigate the molecular mechanism of Fructus Ligustri Lucidi (NZZ, Chinese abbreviation) against osteoporosis (OP) by means of network pharmacology.ChemDraw Professional 15.1 software and Molinspiration Smiles database were used to draw the chemical formulas of the components. The active ingredients and related target proteins of NZZ were searched in platform of systematic pharmacology of traditional Chinese medicine database, Drugbank, Therapeutic Target Database, SymMap and other databases. Gene Ontology(GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out on the selected target through Enrichr and KEGG Automatic Annotation databases, and their mechanism was studied. A total of 29 compounds and 140 corresponding targets, including 14 key targets and 14 protein factors in protein-protein interaction core network were obtained. The key targets were tumor necrosis factor(TNF), interleukin(IL)-6R and sestrogen receptor alpha. The number of GO items was 466 (P<0.05), including 399 items of biological process (BP), 54 items of cell composition (MF) and 13 items of molecular function (CC). KEGG pathway enrichment screened 85 signaling pathways (P<0.05), including the IL-17 signaling pathway, TNF signaling pathway, advanced glycation end products and their receptors signaling pathway and cAMP signaling pathway. The active ingredients of NZZ. exert their anti-OP effects through multi-components, multi-targets and multi-pathways, which can provide new evidence for further study of their anti-OP mechanism.