Automated analysis of internal quantum efficiency using chain order regression

Spectral analysis of internal quantum efficiency (IQE) measurements of solar cells is a powerful method to identify performance-limiting mechanisms in photovoltaic devices. This analysis is usually performed using complex curve-fitting methods to extract various electrical and optical performance parameters. As these traditional fitting methods are not easy to use and are often sensitive to measurement noise, many users do not utilize the full potential of the IQE measurements to provide the key properties of their solar cells. In this study, we propose a simplified approach to analyze IQE curves of silicon solar cells using machine learning models that are trained to extract valuable information regarding the cell's performance and decoupling the parasitic absorption of the anti-reflection coating. The proposed approach is demonstrated to be a powerful characterization tool for solar cells as machine learning unlocks the full potential of IQE measurements

Biomarker qualification at the European Medicines Agency: a review of biomarker qualification procedures from 2008 to 2020

Regulatory qualification of biomarkers facilitates their harmonised use across drug developers, enabling more personalised medicine. This study reviews various aspects of the European Medicines Agency's (EMA) biomarker qualification procedure, including frequency and outcome, common challenges, and biomarker characteristics. Our findings provide insights into EMA's biomarker qualification process and will thereby support future applications. All biomarker-related "Qualification of Novel Methodologies for Medicine Development" procedures that started from 2008 to 2020 were included. Procedural data were extracted from relevant documents and analysed descriptively. In total, 86 biomarker qualification procedures were identified, of which 13 resulted in qualified biomarkers. Whereas initially many biomarker qualification procedures were linked to a single company and specific drug development program, a shift was observed to qualification efforts by consortia. Most biomarkers were proposed (n=45) and qualified (n=9) for use in patient selection, stratification, and enrichment, followed by efficacy biomarkers (37 proposed, 4 qualified). Overall, many issues were raised during qualification procedures, mostly related to biomarker properties and assay validation (in 79% and 77% of all procedures, respectively). Issues related to the proposed context of use and rationale were least common, yet, were still raised in 54% of all procedures. While few qualified biomarkers are currently available, procedures focus increasingly on biomarkers for general use instead of those linked to specific drug compounds. The issues raised during qualification procedures illustrate the thorough discussions taking place between applicants and regulators - highlighting aspects that need careful consideration and underlining the importance of an appropriate validation strategy

Perspectives on a Way Forward to Implementation of Precision Medicine in Patients With Diabetic Kidney Disease; Results of a Stakeholder Consensus-Building Meeting

Aim: This study aimed to identify from different stakeholders the benefits and obstacles of implementing precision medicine in diabetic kidney disease (DKD) and to build consensus about a way forward in order to treat, prevent, or even reverse this disease. Methods: As part of an ongoing effort of moving implementation of precision medicine in DKD forward, a two-day consensus-building meeting was organized with different stakeholders involved in drug development and patient care in DKD, including patients, patient representatives, pharmaceutical industry, regulatory agencies representatives, health technology assessors, healthcare professionals, basic scientists, and clinical academic researchers. The meeting consisted of plenary presentations and discussions, and small group break-out sessions. Discussion topics were based on a symposium, focus groups and literature search. Benefits, obstacles and potential solutions toward implementing precision medicine were discussed. Results from the break-out sessions were presented in plenary and formed the basis of a broad consensus discussion to reach final conclusions. Throughout the meeting, participants answered several statement and open-ended questions on their mobile device, using a real-time online survey tool. Answers to the statement questions were analyzed descriptively. Results of the open-ended survey questions, the break-out sessions and the consensus discussion were analyzed qualitatively. Results and conclusion: Seventy-one participants from 26 countries attended the consensus-building meeting in Amsterdam, April 2019. During the opening plenary on the first day, the participants agreed with the statement that precision medicine is the way forward in DKD (n = 57, median 90, IQR [75–100]). Lack of efficient tools for implementation in practice and generating robust data were identified as significant obstacles. The identified benefits, e.g., improvement of the benefit-risk ratio of treatment, offer substantive incentives to find solutions for the identified obstacles. Earlier and increased multi-stakeholder collaboration and specific training may provide solutions to alter clinical and regulatory guidelines that lie at the basis of both obstacles and solutions. At the end of the second day, the opinion of the participants toward precision medicine in DKD was somewhat more nuanced (n = 45, median 83, IQR [70–92]) and they concluded that precision medicine is an important way forward in improving the treatment of patients with DKD

Corticotropin-stimulated steroid profiles to predict shock development and mortality in sepsis: From the HYPRESS study

Rationale Steroid profiles in combination with a corticotropin stimulation test provide information about steroidogenesis and its functional reserves in critically ill patients. Objectives We investigated whether steroid profiles before and after corticotropin stimulation can predict the risk of in-hospital death in sepsis. Methods An exploratory data analysis of a double blind, randomized trial in sepsis (HYPRESS [HYdrocortisone for PRevention of Septic Shock]) was performed. The trial included adult patients with sepsis who were not in shock and were randomly assigned to placebo or hydrocortisone treatment. Corticotropin tests were performed in patients prior to randomization and in healthy subjects. Cortisol and precursors of glucocorticoids (17-OH-progesterone, 11-desoxycortisol) and mineralocorticoids (11-desoxycorticosterone, corticosterone) were analyzed using the multi-analyte stable isotope dilution method (LC–MS/MS). Measurement results from healthy subjects were used to determine reference ranges, and those from placebo patients to predict in-hospital mortality. Measurements and main results Corticotropin tests from 180 patients and 20 volunteers were included. Compared to healthy subjects, patients with sepsis had elevated levels of 11-desoxycorticosterone and 11-desoxycortisol, consistent with activation of both glucocorticoid and mineralocorticoid pathways. After stimulation with corticotropin, the cortisol response was subnormal in 12% and the corticosterone response in 50% of sepsis patients. In placebo patients (n = 90), a corticotropin-stimulated cortisol-to-corticosterone ratio > 32.2 predicted in-hospital mortality (AUC 0.8 CI 0.70–0.88; sensitivity 83%; and specificity 78%). This ratio also predicted risk of shock development and 90-day mortality. Conclusions In this exploratory analysis, we found that in sepsis mineralocorticoid steroidogenesis was more frequently impaired than glucocorticoid steroidogenesis. The corticotropin-stimulated cortisol-to-corticosterone ratio predicts the risk of in-hospital death. Trial registration Clinical trial registered with www.clinicaltrials.gov Identifier: NCT00670254. Registered 1 May 2008, https://clinicaltrials.gov/ct2/show/NCT00670254

Automated analysis of internal quantum efficiency measurements of GaAs solar cells using machine learning

Investigating the internal quantum efficiency (IQE) of solar cells is essential for identifying performance limitations and improving their efficiency. However, fitting IQE measurements of gallium arsenide solar cells using numerical simulation programs can be a laborious and tedious process, often limiting the depth of the analysis to only qualitative levels. In this study, we propose the use of machine learning to automate the fitting process and enable the extraction of key electrical quantities that represent the performance-limiting mechanisms of the cells. This novel method can help unlock the full potential of IQE measurements as a powerful characterization tool for further research and development of gallium arsenide solar cells

Change in Albuminuria and Estimated GFR as End Points for Clinical Trials in Early Stages of CKD: A Perspective From European Regulators.

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