scholarly journals Perspectives in solubility measurement and interpretation

ADMET & DMPK ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Christel A.S. Bergström ◽  
Alex Avdeef
Keyword(s):  
Solution Chemistry ◽  
Solubility Prediction ◽  
Solubility Measurement ◽  
Micellar Aggregates ◽  
Data Quality Assessment ◽  
Key Topics ◽  
Assay Design ◽  
Kinetic Solubility ◽  
Ph Curve

Several key topics in solubility measurement and interpretation are briefly summarized and illustrated with case studies drawing on published solubility determinations as a function of pH. Featured are examples of ionizable molecules that exhibit solubility-pH curve distortion from that predicted by the traditionally used Henderson-Hasselbalch equation and possible interpretations for these distortions are provided. The scope is not exhaustive; rather it is focused on detailed descriptions of a few cases. Topics discussed are limitations of kinetic solubility, ‘brick-dust and grease-balls,’ applications of simulated and human intestinal fluids, supersaturation and the relevance of pre-nucleation clusters and sub-micellar aggregates in the formation of solids, drug-buffer/excipient complexation, hydrotropic solubilization, acid-base ‘supersolubilization,’ cocrystal route to supersaturation, as well as data quality assessment and solubility prediction. The goal is to highlight principles of solution equilibria – graphically more than mathematically – that could invite better assay design, to result in improved quality of measurements, and to impart a deeper understanding of the underlying solution chemistry in suspensions of drug solids. The value of solid state characterizations is stressed but not covered explicitly in this mini-review.

scholarly journals Equilibrium solubility measurement of ionizable drugs – consensus recommendations for improving data quality

ADMET & DMPK ◽  
2016 ◽  
Vol 4 (2) ◽  
pp. 117 ◽  
Author(s):  
Alex Avdeef ◽  
Elisabet Fuguet ◽  
Antonio Llinàs ◽  
Clara Ràfols ◽  
Elisabeth Bosch ◽  
...  
Keyword(s):  
Solid State ◽  
Data Quality ◽  
Solution Chemistry ◽  
Equilibration Time ◽  
Equilibrium Solubility ◽  
Factors Affecting ◽  
Solubility Measurement ◽  
Type Water ◽  
Reported Data

<p class="ADMETabstracttext">This commentary addresses data quality in equilibrium solubility measurement in aqueous solution. Broadly discussed is the “gold standard” shake-flask (SF) method used to measure equilibrium solubility of ionizable drug-like molecules as a function of pH. Many factors affecting the quality of the measurement are recognized. Case studies illustrating the analysis of both solution and solid state aspects of solubility measurement are presented. Coverage includes drug aggregation in solution (sub-micellar, micellar, complexation), use of mass spectrometry to assess aggregation in saturated solutions, solid state characterization (salts, polymorphs, cocrystals, polymorph creation by potentiometric method), solubility type (water, buffer, intrinsic), temperature, ionic strength, pH measurement, buffer issues, critical knowledge of the pK<sub>a</sub>, equilibration time (stirring and sedimentation), separating solid from saturated solution, solution handling and adsorption to untreated surfaces, solubility units, and tabulation/graphic presentation of reported data. The goal is to present cohesive recommendations that could lead to better assay design, to result in improved quality of measurements, and to impart a deeper understanding of the underlying solution chemistry in suspensions of drug solids.</p>

scholarly journals Body image, self-esteem, and sense of masculinity in patients with prostate cancer: a qualitative meta-synthesis

2021 ◽  
Author(s):  
Jessica Bowie ◽  
Oliver Brunckhorst ◽  
Robert Stewart ◽  
Prokar Dasgupta ◽  
Kamran Ahmed
Keyword(s):  
Prostate Cancer ◽  
Body Image ◽  
Cancer Survivors ◽  
Self Esteem ◽  
Qualitative Studies ◽  
Prostate Cancer Survivors ◽  
Key Factor ◽  
Key Topics

Abstract Purpose Body image, self-esteem, and masculinity are three interconnected constructs in men with prostate cancer, with profound effects on quality of life. This meta-synthesis aimed to evaluate all known qualitative studies published studying the effect of prostate cancer on these constructs. Methods A systematic review utilising PubMed, Embase, MEDLINE, and PsycINFO databases up to May 2020 was conducted in line with PRISMA and ENTREQ guidelines. All qualitative studies of men’s experiences with body image, self-esteem, and masculinity whilst living with prostate cancer were included. A thematic meta-synthesis was conducted to identify emergent descriptive and analytical themes under the main study constructs. Results Of 2188 articles identified, 68 were included. Eight descriptive themes were identified under two analytical themes: ‘Becoming a Prostate Cancer Patient’ and ‘Becoming a Prostate Cancer Survivor’. These described the distress caused by changes to body image, sexual functioning, sense of masculinity, and self-esteem, and the subsequent discourses men engaged with to cope with and manage their disease. A key element was increased flexibility in masculinity definitions, and finding other ways to re-affirm masculinity. Conclusions Prostate cancer has an important effect on men’s health post-diagnosis, and we identified strong relationships between each construct evaluated. The role of hegemonic masculinity is important when considering men’s coping mechanisms and is also a key factor when addressing these constructs in counselling post-treatment. Implications for Cancer Survivors This meta-synthesis provides key topics that uniquely affect prostate cancer survivors, enabling these patients to be effectively counselled, and have their concerns recognised by clinicians.

scholarly journals Data Quality: A Negotiator between Paper-based and Digital Records in the Pakistan’s TB Control Program

2018 ◽  
Author(s):  
Syed Mustafa Ali ◽  
Farah Naureen ◽  
Arif Noor ◽  
Maged Kamel N. Boulos ◽  
Javariya Aamir ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Control Program ◽  
Digital Data ◽  
Patient Treatment ◽  
Assessment Framework ◽  
Healthcare Organizations ◽  
Data Quality Assessment ◽  
Quality Issues

Background Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records by using data quality assessment framework. Methodology We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled TB clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n=59; 59 out of 117 TB01 cards) were digitized. There were 1,239 comparable data fields, out of which 65% (n=803) were correctly matched between paper based and digital records. However, 35% of the data fields (n=436) had anomalies, either in paper-based records or in digital records. 1.9 data quality issues were calculated per digital patient record, whereas it was 2.1 issues per record for paper-based record. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n=123) than in digital records (n=110). Conclusion There were fewer data quality issues in digital records as compared to the corresponding paper-based records. Greater use of mobile data capture and continued use of the data quality assessment framework can deliver more meaningful information for decision making.

Performance of Carbon Emission Management, Carbon Information Disclosure and Firm Value Creation: A Theoretical Research Framework

2012 ◽  
Vol 524-527 ◽  
pp. 2602-2606
Author(s):  
Qiao Liang Zhang ◽  
Qin Qiong Yu
Keyword(s):  
Firm Value ◽  
Information Disclosure ◽  
Carbon Emission ◽  
Theoretical Research ◽  
Disclosure Quality ◽  
Global Capital ◽  
Key Topics ◽  
The Relationship ◽  
Functioning Mechanism

The companies’ performance of carbon emission management will have effects on the firms value via the carbon information disclosure. It is better to based on the global capital market when addressing the relationship among the performance of carbon emission reduction，the quality of carbon information disclosure and the firm value. The key topics of research should be focused on the motivation and efficiency of carbon information disclosure, the quality index of carbon information disclosure, the functioning mechanism of carbon emission management performance and carbon information disclosure quality on firm value, and the preparation of carbon disclosure report.

Use of Third-Party Indicators in Fund Reports

Policy Papers ◽  
2017 ◽  
Vol 2017 (61) ◽  
Author(s):  
Keyword(s):  
International Organizations ◽  
Common Good ◽  
Financial Stability ◽  
Best Practice ◽  
Current Practice ◽  
Business Environment ◽  
Third Party ◽  
Data Quality Assessment ◽  
Quality Of Governance

Fund staff use indicators developed by other organizations as input into analysis in surveillance and, to a lesser extent, in program work. While the Fund has been able to rely on data and statistics provided by member countries and compiled internally, continued efforts to foster global economic and financial stability require staff to work with indicators drawn from numerous third-party compilers. These indicators of varied qualities are used to measure concepts such as business environment, competitiveness, and quality of governance. It is anticipated that staff will continue to draw on other institutions’ expertise and estimates. This practice is consistent with the Executive Board’s guidance in areas where internal expertise is lacking or limited. It also puts a premium on staff’s understanding of the third-party indicators (TPIs) used to add analytical value, avoid flawed conclusions and presentation, and support traction with the membership. This paper outlines a framework to promote best practice with respect to use of TPIs in Fund reports. The framework will apply to all documents that are subject to the Fund’s Transparency Policy. Staff are encouraged to follow similar guidelines for other Fund documents. It draws on lessons from the current practice in the Fund and other selected international organizations (IOs), and insights from the application of an adapted data quality assessment framework (DQAF) to a subset of TPIs commonly used by Fund staff. Common good practices across IOs include the emphasis on staff judgment, review, and consultation with stakeholders.

scholarly journals Pushing the limits of solubility prediction via quality-oriented data selection

2020 ◽  
Author(s):  
Murat Sorkun ◽  
J. M. Koelman ◽  
Süleyman Er
Keyword(s):  
Prediction Models ◽  
Aqueous Solubility ◽  
Data Selection ◽  
Data Driven ◽  
Solubility Prediction ◽  
Quality Of Data ◽  
Statistical Validation ◽  
Solubility Predictions ◽  
Machine Learning Approach

Abstract Accurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of datasets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

scholarly journals A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS

2020 ◽  
Vol V-4-2020 ◽  
pp. 39-47
Author(s):  
I. Maidaneh Abdi ◽  
A. Le Guilcher ◽  
A-M. Olteanu-Raimond
Keyword(s):  
Regression Model ◽  
Spatial Data ◽  
Reference Data ◽  
Spatial Accuracy ◽  
Machine Learning Methods ◽  
Data Matching ◽  
Data Quality Assessment ◽  
Intrinsic Quality ◽  
Abstract Data

Abstract. Data quality assessment of OpenStreetMap (OSM) data can be carried out by comparing them with a reference spatial data (e.g authoritative data). However, in case of a lack of reference data, the spatial accuracy is unknown. The aim of this work is therefore to propose a framework to infer relative spatial accuracy of OSM data by using machine learning methods. Our approach is based on the hypothesis that there is a relationship between extrinsic and intrinsic quality measures. Thus, starting from a multi-criteria data matching, the process seeks to establish a statistical relationship between measures of extrinsic quality of OSM (i.e. obtained by comparison with reference spatial data) and the measures of intrinsic quality of OSM (i.e. OSM features themselves) in order to estimate extrinsic quality on an unevaluated OSM dataset. The approach was applied on OSM buildings. On our dataset, the resulting regression model predicts the values on the extrinsic quality indicators with 30% less variance than an uninformed predictor.

scholarly journals Citation needed? Wikipedia bibliometrics during the first wave of the COVID-19 pandemic

GigaScience ◽  
2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Omer Benjakob ◽  
Rona Aviram ◽  
Jonathan Aryeh Sobel
Keyword(s):  
English Language ◽  
Shared Knowledge ◽  
Quality Of Information ◽  
New Information ◽  
Unique Model ◽  
Key Topics ◽  
Digital Knowledge ◽  
Scientific Infrastructure ◽  
Insight Into

Abstract Background With the COVID-19 pandemic’s outbreak, millions flocked to Wikipedia for updated information. Amid growing concerns regarding an “infodemic,” ensuring the quality of information is a crucial vector of public health. Investigating whether and how Wikipedia remained up to date and in line with science is key to formulating strategies to counter misinformation. Using citation analyses, we asked which sources informed Wikipedia’s COVID-19–related articles before and during the pandemic’s first wave (January–May 2020). Results We found that coronavirus-related articles referenced trusted media outlets and high-quality academic sources. Regarding academic sources, Wikipedia was found to be highly selective in terms of what science was cited. Moreover, despite a surge in COVID-19 preprints, Wikipedia had a clear preference for open-access studies published in respected journals and made little use of preprints. Building a timeline of English-language COVID-19 articles from 2001–2020 revealed a nuanced trade-off between quality and timeliness. It further showed how pre-existing articles on key topics related to the virus created a framework for integrating new knowledge. Supported by a rigid sourcing policy, this “scientific infrastructure” facilitated contextualization and regulated the influx of new information. Last, we constructed a network of DOI-Wikipedia articles, which showed the landscape of pandemic-related knowledge on Wikipedia and how academic citations create a web of shared knowledge supporting topics like COVID-19 drug development. Conclusions Understanding how scientific research interacts with the digital knowledge-sphere during the pandemic provides insight into how Wikipedia can facilitate access to science. It also reveals how, aided by what we term its “citizen encyclopedists,” it successfully fended off COVID-19 disinformation and how this unique model may be deployed in other contexts.

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

2019 ◽  
pp. 469-487
Author(s):  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Padraig Corcoran ◽  
Amerah Alghanim
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Quality Assessment ◽  
Spatial Data ◽  
Current Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Data Quality Assessment ◽  
Expensive Process

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

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