External quality monitoring facilitates improvement in already well-performing stroke units: insights from RES-Q Poland

INTRODUCTION

The Registry of Stroke Care Quality (RES-Q) is used in Poland for quality monitoring by numerous hospitals participating in the Angels Initiative. Our aim was to assess the degree of improvement in highly stroke-oriented centres that report cases to the RES-Q each year.

MATERIAL AND METHODS

This retrospective analysis included Polish stroke units that from January 2017 to December 2020 contributed to the RES-Q at least 25 patients annually.

RESULTS

Seventeen out of 180 Polish stroke units reported patients each year (2017, n = 1,691; 2018, n = 2,986; 2019, n = 3,750; 2020, n = 3,975). The percentage of ischaemic stroke patients treated with alteplase remained stable (26%, 29%, 30% and 28%, respectively). The door-to-needle time progressively decreased, from a median 49 minutes to 32 minutes. The percentage of patients treated ≤ 60 minutes and ≤ 45 minutes significantly increased (from 68% to 86% and from 43% to 70%, respectively), with no change observed between 2019 and 2020. Despite a general improvement in dysphagia screening (81%, 91%, 98% and 99%), screening performed within the first 24h from admission became less frequent (78%, 76%, 69% and 65%). In-hospital mortality significantly increased (11%, 11%, 13% and 15%), while the proportion of patients discharged home remained stable.

CONCLUSIONS

Quality-oriented projects facilitate the improvement of stroke care, even in centres demonstrating good baseline performance. Polish stroke units that consistently reported cases to the RES-Q demonstrated improvement in terms of door-to- -needle time and dysphagia screening. However, there is still a need to shorten the time to dysphagia screening, and carefully monitor stroke unit mortality following the COVID-19 pandemic.

Proximate Content Monitoring of Black Soldier Fly Larval (Hermetia illucens) Dry Matter for Feed Material using Short-Wave Infrared Hyperspectral Imaging

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R2 values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Perspectives on Novel Technologies of Processing and Monitoring the Safety and Quality of Prepared Food Products

With the changes of lifestyles and rapid growth of prepared food industry, prepared fried rice that meets the consumption patterns of contemporary young people has become popular in China. Although prepared fried rice is convenient and nutritious, it has the following concerns in the supply chain: (1) susceptible to contamination by microorganisms; (2) rich in starch and prone to stall; and (3) vegetables in the ingredients have the issues of water loss and discoloration, and meat substances are vulnerable to oxidation and deterioration. As different ingredients are used in prepared fried rice, their food processing and quality monitoring techniques are also different. This paper reviews the key factors that cause changes in the quality of prepared fried rice, and the advantages and limitations of technologies in the processing and monitoring processes. The processing technologies for prepared fried rice include irradiation, high-voltage electric field, microwave, radio frequency, and ohmic heating, while the quality monitoring technologies include Raman spectral imaging, near-infrared spectral imaging, and low-field nuclear magnetic resonance technology. These technologies will serve as the foundation for enhancing the quality and safety of prepared fried rice and are essential to the further development of prepared fried rice in the emerging market.

Dynamic monitoring of maize grain quality based on remote sensing data

Remote sensing data have been widely used to monitor crop development, grain yield, and quality, while precise monitoring of quality traits, especially grain starch and oil contents considering meteorological elements, still needs to be improved. In this study, the field experiment with different sowing time, i.e., 8 June, 18 June, 28 June, and 8 July, was conducted in 2018-2020. The scalable annual and inter-annual quality prediction model for summer maize in different growth periods was established using hierarchical linear modeling (HLM), which combined hyperspectral and meteorological data. Compared with the multiple linear regression (MLR) using vegetation indices (VIs), the prediction accuracy of HLM was obviously improved with the highest R ², root mean square error (RMSE), and mean absolute error (MAE) values of 0.90, 0.10, and 0.08, respectively (grain starch content (GSC)); 0.87, 0.10, and 0.08, respectively (grain protein content (GPC)); and 0.74, 0.13, and 0.10, respectively (grain oil content (GOC)). In addition, the combination of the tasseling, grain-filling, and maturity stages further improved the predictive power for GSC (R ² = 0.96). The combination of the grain-filling and maturity stages further improved the predictive power for GPC (R ² = 0.90). The prediction accuracy developed in the combination of the jointing and tasseling stages for GOC (R ² = 0.85). The results also showed that meteorological factors, especially precipitation, had a great influence on grain quality monitoring. Our study provided a new idea for crop quality monitoring by remote sensing.

Intelligent Colorimetric Indicators for Quality Monitoring and Multilevel Anticounterfeiting with Kinetics-Tunable Fluorescence

The spoilage and forgery of perishable products such as food, drugs, and vaccines cause serious health hazards and economic loss every year. Developing highly efficient and convenient time-temperature indicators (TTIs) to realize quality monitoring and anticounterfeiting simultaneously is urgent but remains a challenge. To this end, a kind of colorimetric fluorescent TTI, based on CsPbBr₃@SiO₂ nanoparticles with tunable quenching kinetics, is developed. The kinetics rate of the CsPbBr₃-based TTIs is easily regulated by adjusting temperature, concentration of the nanoparticles, and addition of salts, stemming from the cation exchange effect, common-ion effect, and structural damage by water. Typically, when combined with europium complexes, the developed TTIs show an irreversible dynamic change in fluorescent colors from green to red upon increasing temperature and time. Furthermore, a locking encryption system with multiple logics is also realized by combining TTIs with different kinetics. The correct information only appears at specific ranges of time and temperature under UV light and is irreversibly self-erased afterward. The simple and low-cost composition and the ingenious design of kinetics-tunable fluorescence in this work stimulate more insights and inspiration toward intelligent TTIs, especially for high-security anticounterfeiting and quality monitoring, which is really conducive to ensuring food and medicine safety.

A comprehensive review of intelligent packaging for fruits and vegetables: Target responders, classification, applications, and future challenges

Post-harvest fruits and vegetables are extremely susceptible to dramatic and accelerated quality deterioration deriving from their metabolism and adverse environmental influences. Given their vigorous physiological metabolism, monitoring means are lacking due to the extent that unnecessary waste and damage are caused. Numerous intelligent packaging studies have been hitherto carried out to investigate their potential for fruit and vegetable quality monitoring. This state-of-the-art overview begins with recent advances in target metabolites for intelligent packaging of fruits and vegetables. Subsequently, the mechanisms of action between metabolites and packaging materials are presented. In particular, the exact categorization and function of intelligent packaging of fruits and vegetables, are all extensively and comprehensively described. In addition, for the sake of further research in this field, the obstacles that impede the scaling up and commercialization of intelligent packaging for fruits and vegetables are also explored, to present valuable references.

Reflecting the Quality Degradation of Engine Oil by the Thermal Diffusivity: Radiative and Nonradiative Analyses

Ageing of engine oil is an important issue determining the engine life and performance. The present work attempts to delineate the ageing-induced changes in engine oil through the mode-mismatched dual-beam thermal lens (MMDBTL) technique and other conventional spectroscopic techniques. For the analyses, engine oil samples were collected after every 200 km of runtime. As the thermal diffusivity is related to the nonradiative deexcitation upon optical absorption, comprehensive radiative and nonradiative analyses were carried out. The Ultraviolet-Visible, Fourier transform infrared, and Nuclear magnetic resonance spectroscopic analyses point to the structural modification as a result of the breaking of the long-chain hydrocarbons into ketones, aldehydes, esters, and other compounds. This modifies the absorption pattern, which can also be understood from the nonlinear refractive index study using the Z-scan technique. The compositional variations associated with the degradation upon ageing, the length of the hydrocarbon chain, and the formation of newer molecules account for the enhancement of the thermal diffusivity revealed through the MMBDTL techniques. The complementary nature of the radiative and nonradiative emission is understood from the fluorescence study. Thus, the study reveals the possibility of thermal diffusivity measurement as an effective tool for the quality monitoring of engine oil.

Algorithms for Weld Depth Measurement in Laser Welding of Copper with Scanning Optical Coherence Tomography

In-process monitoring of weld penetration depth is possible with optical coherence tomography (OCT). The weld depth can be identified with OCT by statistical signal processing of the raw OCT signal and keyhole mapping. This approach is only applicable to stable welding processes and requires a time-consuming keyhole mapping to identify the optimal placement of a singular OCT measuring beam. In this work, we use an OCT measurement line for the identification of the weld depth. This approach shows the advantage that the calibration effort can be reduced as the measurement line requires only calibration in one dimension. As current literature focuses on weld depth measurement with a singular measurement point in the keyhole, no optimal algorithm exists for weld depth measurement with an OCT measurement line. We developed seven different weld depth processing pipelines and tested these algorithms under different weld conditions, such as stable deep penetration welding, unstable deep penetration welding, and heat conduction welding. We analyzed the accuracy of the weld depth processing algorithms by comparing the measured weld depth with metallographic weld depths. The intensity accumulation approach is identified as the most accurate algorithm for successful weld depth measurement with a scanning OCT measurement line.

In-Process Analysis of Melt Pool Fluctuations with Scanning Optical Coherence Tomography for Laser Welding of Copper for Quality Monitoring

Optical coherence tomography (OCT) is an inline process monitoring technology for laser welding with various applications in the pre-, in-, and post-process. In-process monitoring with OCT focuses on the measurement of weld depth by the placement of a singular measurement beam into the keyhole. A laterally scanned measurement beam gives the opportunity to measure the keyhole and melt pool width. The processing region can be identified by separating higher signal intensities on the workpiece surface from lower signal intensities from the keyhole and the melt pool. In this work, we apply a scanned measurement beam for the identification of keyhole fluctuations. Different laser processing parameters are varied for laser welding of copper to evoke welds in the heat conduction regime, stable deep penetration welding, and unstable deep penetration welding. As keyhole instabilities can be related to the generation of spatter and other defects, we identified a feature for the classification of different weld statuses. In consequence, feedback can be given about possible defects which are originated in keyhole fluctuations (e.g., spatter).

Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach

A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural good, obtaining global information of its interior. While deep learning (DL) methods have outperformed state-of-the-art benchmarks in various applications, the reason for lacking adaptation of DL algorithms such as convolutional neural networks (CNNs) can be traced back to its high data inefficiency and the absence of annotated data. Active learning is a framework that has been heavily used in machine learning when the labelled instances are scarce or cumbersome to obtain. This is specifically of interest when the DL algorithm is highly uncertain about the label of an instance. By allowing the human-in-the-loop for guidance, a continuous improvement of the DL algorithm based on a sample efficient manner can be obtained. This paper seeks to study the applicability of active learning when grading 'Galia' muskmelons based on its shelf life. We propose k-Determinantal Point Processes (k-DPP), which is a purely diversity-based method that allows to take influence on the exploration within the feature space based on the chosen subset k. While getting coequal results to uncertainty-based approaches when k is large, we simultaneously obtain a better exploration of the data distribution. While the implementation based on eigendecomposition takes up a runtime of O(n3), this can further be reduced to O(n·poly(k)) based on rejection sampling. We suggest the use of diversity-based acquisition when only a few labelled samples are available, allowing for better exploration while counteracting the disadvantage of missing the training objective in uncertainty-based methods following a greedy fashion.

Simultaneous Burr and Cut Interruption Detection during Laser Cutting with Neural Networks

In this contribution, we compare basic neural networks with convolutional neural networks for cut failure classification during fiber laser cutting. The experiments are performed by cutting thin electrical sheets with a 500 W single-mode fiber laser while taking coaxial camera images for the classification. The quality is grouped in the categories good cut, cuts with burr formation and cut interruptions. Indeed, our results reveal that both cut failures can be detected with one system. Independent of the neural network design and size, a minimum classification accuracy of 92.8% is achieved, which could be increased with more complex networks to 95.8%. Thus, convolutional neural networks reveal a slight performance advantage over basic neural networks, which yet is accompanied by a higher calculation time, which nevertheless is still below 2 ms. In a separated examination, cut interruptions can be detected with much higher accuracy as compared to burr formation. Overall, the results reveal the possibility to detect burr formations and cut interruptions during laser cutting simultaneously with high accuracy, as being desirable for industrial applications.

Accuracy and other quality indicators of solid pancreatic mass endoscopic ultrasound-guided fine needle aspiration and biopsy in two academic endoscopy centers

BACKGROUND AND AIMS

Endoscopic ultrasound fine-needle aspiration/biopsy (EUS-FNA/FNB) is highly accurate, but discrepancies between cytological and surgical diagnoses are still observed. We aimed to determine its accuracy and monitor quality indicators in our facilities.

PATIENTS AND METHODS

We performed a retrospective review of all cases of pancreatic solid lesions evaluated by EUS-FNA/FNB, between July 2015 and June 2018, in two centers. Cytological and surgical findings were categorized into five groups: benign, malignant, suspect of malignancy, undetermined and insufficient for diagnosis. Final diagnosis was based on surgical diagnosis and, in patients who did not undergo surgery, on clinical outcome after 6 months follow-up.

RESULTS

Altogether, 142 patients were included. FNA was the preferred tissue acquisition method (88%), with a predilection for the FNA 22G needle (57%). Cytology was insufficient for diagnosis in 2 cases, therefore a full diagnostic sample was available in 98.6% of the patients (>90%, ESGE target). Fifty-five (38.7%) patients underwent surgery. In term of cancer diagnosis, comparison with final surgical pathology (n=55) revealed 89% true positives, 5.5% true negatives, 3.6% false positives and 1.8% false negatives. When combining surgical diagnosis and clinical outcomes together, EUS-guided sampling sensitivity was 97.4% (92.5-99.5), specificity was 92.3% (74.9-99.1), positive predictive value was 98.2% (93.6- 99.5), negative predictive value was 88.9% (72.3-96.1) and accuracy was 96.4% (91.9-98.8). Post-procedural acute pancreatitis was reported in 2 patients (1.4%).

CONCLUSIONS

These results reveal a performance for diagnostic tissue sampling well above the ESGE proposed target standard. Also, the uncommon high specificity illustrates the determining role of the pathologist's final interpretation and diagnosis.

Monitoring the quality of cardiac surgery based on three or more surgical outcomes using a new variable life-adjusted display

Quality problem or issue

The traditional variable life-adjusted display (VLAD) is a graphical display of the difference between expected and actual cumulative deaths. The VLAD assumes binary outcomes: death within 30 days of an operation or survival beyond 30 days. Full recovery and bedridden for life, for example, are considered the same outcome. This binary classification results in a great loss of information.

Initial assessment

Although there are many grades of survival, the binary outcomes are commonly used to classify surgical outcomes. Consequently, quality monitoring procedures are developed based on binary outcomes. With a more refined set of outcomes, the sensitivities of these procedures can be expected to improve.

Choice of solution

A likelihood ratio method is used to define a penalty-reward scoring system based on three or more surgical outcomes for the new VLAD. The likelihood ratio statistic W is based on testing the odds ratio of cumulative probabilities of recovery R. Two methods of implementing the new VLAD are proposed.

Implementation

We accumulate the statistic W-W¯R to estimate the performance of a surgeon where W¯R is the average of the W's of a historical data set. The accumulated sum will be zero based on the historical data set. This ensures that if a new VLAD is plotted for a future surgeon of performance similar to this average performance, the plot will exhibit a horizontal trend.

Evaluation

For illustration of the new VLAD, we consider 3-outcome surgical results: death within 30 days, partial and full recoveries. In our first illustration, we show the effect of partial recoveries on surgical results of a surgeon. In our second and third illustrations, the surgical results of two surgeons are compared using both the traditional VLAD based on binary-outcome data and the new VLAD based on 3-outcome data. A reversal in relative performance of surgeons is observed when the new VLAD is used. In our final illustration, we display the surgical results of four surgeons using the new VLAD based completely on 3-outcome data.

Lessons learned

Full recovery and bedridden for life are two completely different outcomes. There is a great loss of information when different grades of 'successful' operations are naively classified as survival. When surgical outcomes are classified more accurately into more than two categories, the resulting new VLAD will reveal more accurately and fairly the surgical results.

[Head trauma patients and their life course]

A traumatic brain injury constitutes a complex treatment situation. Nurses are on the frontline having to assume a difficult position between idealism and fatalism with regard to the care pathway and quality of life. Taking a long-term approach to the care and systemised feedback from the patients form simple and economical levers for improvement.

Quality of secondary prevention of coronary heart disease in Swiss primary care: Lessons learned from a 6-year observational study

INTRODUCTION

Across Europe, great variations have been identified in the quality of preventive healthcare services delivered in primary care (PC). We aimed to assess the quality of secondary prevention in Swiss PC patients with coronary heart disease (CHD) and its evolution over six years.

METHODS

In the database of the Swiss «Family Medicine ICPC Research using Electronic Medical Records» (FIRE) project, we identified electronical record data of 2,807 patients with CHD treated for at least 15 months between 2009 and 2014. Primary outcome was the proportion of patients per year meeting four quality indicators of the British Quality and Outcome Framework (QOF): 1) blood pressure (BP) ≤ 150mmHg, 2) total serum cholesterol ≤ 5mmol/L, 3) prescription of anti-platelet therapy, 4) recommended drug prescriptions for patients with previous myocardial infarction (MI). Secondary outcome was the proportion of patients who were ineligible for indicator calculation because of incomplete record data.

RESULTS

From 2009 to 2014, 85.9, 83.1, 82.0, 81.9, 81.5, and 81.0 % of the patients met BP targets and 73.6, 77.0, 69.2, 73.6, 69.4, and 69.1% met cholesterol targets. Anti-platelet therapy was prescribed to 74.8, 76.1, 73.9, 70.2, 72.2, and 72.5 % of the patients. Finally, 83.3, 84.4, 87.5, 75.6, 89.8, and 89.2 % of the patients with previous MI received the recommended drug therapy. Changes over time were not significant. Missing BP records concerned 12.4-15.9 % of the patients, and missing cholesterol records 69.0-75.6 %. Females and patients with less cardiovascular comorbidities were more likely to show missing records.

CONCLUSIONS

Quality of secondary prevention did not improve when measured against QOF indicators in the period under investigation. Missing data in electronic medical records inhibited full quality indicator assessment. Especially in female patients and those with less cardiovascular comorbidity, closer medical documentation should be encouraged in order to facilitate quality of care measurements.

How Do Primary Care Physicians Perceive the Role of Nurses in Quality Measurement and Improvement? The Israeli Story

Background

Israel has boasted a highly effective national quality monitoring program for community-based health services since 2004. The program involves ongoing monitoring of the quality of selected services provided by Israeli health plans and includes approximately 70 indicators.

Objective

To analyze Israeli primary care physicians’ (PCPs) perceptions of nurses’ roles in the national quality monitoring program and their contribution to improving health-care quality.

Design

A cross sectional survey using self-reported questionnaire.

Setting

Four Israeli health plans, covering 100% of the Israeli population.

Participants

A representative sample of 1,000 Israeli PCPs. Response rate of 69% (605 out of the 884 physicians who met the study criteria).

Methods

A questionnaire combined with closed questions on the attitudes and behaviors of the physicians regarding nurses’ involvement in quality monitoring and open questions about the changes that had made in their practice as a result of the quality monitoring program.

Results

Most respondents (74%) agreed that nurses contribute to practice quality and share responsibility for improving quality measures. Physicians who felt that quality monitoring improved the quality of care and those who supported the program were more likely to consider that nurses shared responsibility for the quality of care. However, in open-ended questions about the changes they made in their practices as a result of the program, they made minimal reference to the importance of nurses and their contribution to improved quality indicators.

Conclusion

There was a disparity between the closed-ended and open-ended questions regarding the way physicians depicted the role of nurses in quality monitoring and improvement. This disparity may be due to the fact that physicians do not yet fully appreciate the growing involvement of nurses in these areas.

Gastrointestinal nurse navigation: implementation of a novel role

Gastrointestinal (GI) cancer is the second most frequent cancer diagnosis in the United States, and the care for patients with GI cancer is multifaceted, with each clinical encounter impacting patients' overall experience. Patients and families often navigate this complicated journey on their own with limited resources and knowledge; therefore, innovative, patient-centered, and quality-focused programs must be developed. The purpose of this article is to discuss the development of GI nurse navigators (NNs) and the important role they have in providing coordinated evidence-based cancer care and in the benchmarking of quality metrics to allow more transparency and improve GI cancer care. This article provides a foundation for developing a GI NN role within the context of a newly developed multidisciplinary GI cancer program, and identifies the importance of tracking specific quality metrics. This innovative model is useful for healthcare organizations and nursing practice because it identifies the importance of a nurse in the navigator role, as well as highlights the numerous functions the NN can provide to the GI multidisciplinary team and patients.

Monitoring childbirth morbidity using hospital discharge data: further development and application of a composite measure

OBJECTIVE

The purpose of this study was to evaluate the use of a childbirth composite morbidity indicator for monitoring childbirth morbidity at hospital and regional levels in California.

STUDY DESIGN

Study data were obtained from the 2005 linked maternal and neonatal discharge dataset for California hospitals. The study population was limited to laboring women with singleton, term (≥37 weeks' gestation), inborn, and live births. Women with and without pregnancy complications were stratified into high- and low-risk groups. The composite outcome was defined as any significant morbidity of the mother or newborn infant during the childbirth admission. Submeasures for maternal and neonatal composite morbidity and for severe maternal morbidity were examined with both aggregate and hospital-level analyses.

RESULTS

Of 377,869 eligible deliveries, 120,218 (31.8%) were categorized as high risk and 257,651 (68.2%) were categorized as low risk. High-risk women had higher morbidity rates for all comparisons. The mean childbirth composite morbidity rate was 21% overall: 28% for high-risk women and 18% for low-risk women. For high- and low-risk strata, the rates of maternal complications were 18% and 13%, and the rates of severe maternal morbidity were 1.4% and 0.5%, respectively. There was substantial variation across hospitals for all measures.

CONCLUSION

The childbirth composite morbidity rate is designed to report childbirth complication rates that combine maternal and neonatal morbidity. This measure and its submeasures met the criteria for quality indicator evaluation as specified by the Agency for Healthcare Research and Quality and can be used for benchmarking or for monitoring childbirth outcomes at regional levels.

Quality Visualization of Microarray Datasets Using Circos

Quality control and normalization is considered the most important step in the analysis of microarray data. At present there are various methods available for quality assessments of microarray datasets. However there seems to be no standard visualization routine, which also depicts individual microarray quality. Here we present a convenient method for visualizing the results of standard quality control tests using Circos plots. In these plots various quality measurements are drawn in a circular fashion, thus allowing for visualization of the quality and all outliers of each distinct array within a microarray dataset. The proposed method is intended for use with the Affymetrix Human Genome platform (i.e., GPL 96, GPL570 and GPL571). Circos quality measurement plots are a convenient way for the initial quality estimate of Affymetrix datasets that are stored in publicly available databases.