Cherry-picking the Wrong Patients?
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ABSTRACT:
SUBMITTER: Fiehler J
PROVIDER: S-EPMC8217008 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Quality assurance is one of the most important aspects of an epidemiological study, as its validity is largely determined by data quality. The mounting success of quality management in the industrial sector caused a rapid spread throughout manufacturing industries and beyond. Yet, little has been published so far on quality assurance in epidemiology. In this article we review three models for quality assurance (Juran, Donabedian and ISO 9000) and showcase how these can be brought together in one intuitive, systematic and flexible approach to quality assurance in epidemiology. The resulting Open Quality approach refers back to the three processes identified by Juran (planning, control and verification). During the planning stage, we propose a subdivision of the study process in a set of steps and a definition of quality attributes corresponding to activities in that step as suggested by the ISO approach. We refer to the Donabedian model to determine the level at which the control/monitoring should take place-structure, processes or outcomes. Along with an overview of the Open Quality approach we propose an Open Quality tool to support the definition of quality attributes, failure modes, preventive strategies, verification activities, and corrective actions, which form the backbone of the Open Quality approach.
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Project description:Skin picking (excoriation) disorder is a mental health condition characterized by repetitive picking of one's skin leading to tissue damage as well as functional impairment and/or distress. A convenience sampling of 10,169 adults, aged 18-69 years, representative of the general US population, completed a survey to establish occurrence of skin picking disorder. 213 participants (2.1%) (55.4% female) identified as having current skin picking disorder and 318 (3.1%) (54.1% female) reported lifetime skin picking disorder (i.e. current or past). Those with current skin picking disorder were significantly more likely to be female compared to those who never had skin picking (Likelihood Ratio, LR chi-square = 31.705, p < 0.001). Mental health comorbidities were common with generalized anxiety disorder (63.4%), depression (53.1%), and panic disorder (27.7%) being the most frequently endorsed. This study suggests that skin picking disorder is relatively common in the general population and typically characterized by high rates of comorbidity.
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Project description:New knowledge on two fabaviruses infecting Prunus species gained through high throughput sequencing
| PRJEB25523 | ENA
Project description:Wrong-route or -patient medication errors due to human mistakes have been considered difficult to resolve in clinical settings. In this study, we suggest a safety injection system that can help to prevent an injection when a mismatch exists between the drug and route or patient. For this, we prepared two distinct adapters with key and keyhole patterns specifically assigned to a pair of drug and route or patient. When connected to a syringe tip and its counterpart, a catheter injection-port, respectively, the adapters allowed for a seamless connection only with their matching patterns. In this study, each of the adapters possessed a specific key and keyhole pattern at one end and the other end was shaped to be a universal fit for syringe tips or catheter injection-ports in clinical use. With the scheme proposed herein, we could generate 27,000 patterns, depending on the location and shape of the key tooth in the adapters. With a rapid prototyping technique, multiple distinct pairs of adapters could be prepared in a relatively short period of time and thus, we envision that a specific adapter pair can be produced on-site after patient hospitalization, much like patient identification barcodes.
| S-EPMC5680491 | biostudies-literature
Project description: Not available
| S-EPMC7047446 | biostudies-literature
Project description:MotivationChromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task.ResultsA machine-learning based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot's architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box.In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion.PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.AvailabilityThe package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Objective: Excessive calorie intake constitutes a global public health concern, due to its associated range of untoward outcomes. Impulsivity and compulsivity have been linked to dietary intake. However, nothing is known about dietary intake and body-focused repetitive behaviors, despite their classification as obsessive-compulsive related conditions, and high co-morbidity with impulsive and compulsive conditions. Methods: One hundred and ninety six adults with trichotillomania or skin picking disorder were recruited. Dietary intake over the preceding year was quantified using the Dietary Fat and Free Sugar Short questionnaire. Relationships between dietary fat/sugar intake and behaviors were evaluated using regression modeling. Results: Sugar intake was significantly related to higher trans-diagnostic compulsivity (p = 0.011) and higher non-planning impulsivity (p = 0.013) In terms of saturated fat intake, there was no significant relationship to the explanatory variables. A combination high fat/high sugar diet was significantly associated with higher motor impulsivity (p = 0.005). Conclusions: Past-year nutrition appears to be significantly associated with trans-diagnostic impulsivity and compulsivity. The role of poor nutrition in these disorders and related conditions, and its link with impulsivity and compulsivity, requires longitudinal research attention; and clinical work should address not only psychiatric symptoms but also impact of lifestyle of overall health.
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Project description:The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub.com/doi/10.1177/2374289517715040.
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Project description:The Rosetta de novo structure prediction and loop modeling protocols begin with coarse grained Monte Carlo searches in which the moves are based on short fragments extracted from a database of known structures. Here we describe a new object oriented program for picking fragments that greatly extends the functionality of the previous program (nnmake) and opens the door for new approaches to structure modeling. We provide a detailed description of the code design and architecture, highlighting its modularity, and new features such as extensibility, total control over the fragment picking workflow and scoring system customization. We demonstrate that the program provides at least as good building blocks for ab-initio structure prediction as the previous program, and provide examples of the wide range of applications that are now accessible.
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