Influence of graphene oxide on metal-insulator- semiconductor tunneling diodes

Chu-Hsuan Lin
Wei-Ting Yeh
Chun-Hui Chan
Chun-Chieh Lin
Influence of graphene oxide on metal-insulator- semiconductor tunneling diodes
N A N O E X P R E S Sgraphene oxideMIStunneling diodephotodetector
In recent years, graphene studies have increased rapidly. Graphene oxide, which is an intermediate product to form graphene, is insulating, and it should be thermally reduced to be electrically conductive. We herein describe an attempt to make use of the insulating properties of graphene oxide. The graphene oxide layers are deposited onto Si substrates, and a metal-insulator-semiconductor tunneling structure is formed and its optoelectronic properties are studied. The accumulation dark current and inversion photocurrent of the graphene oxide device are superior to the control device. The introduction of graphene oxide improves the rectifying characteristic of the diode and enhances its responsivity as a photodetector. At 2 V, the photo-to-dark current ratio of the graphene oxide device is 24, larger than the value of 15 measured in the control device.Authors' contributions CHL conceived the experimental methodology, modified the process flow, and prepared the manuscript. WTY and CHC performed the device fabrication and measurement. CCL assisted with the device fabrication and participated in the modification of the methodology. All authors read and approved the final manuscript.

Background

Metal-oxide-semiconductor field-effect transistors (MOS-FETs) are frequently used in daily life. Strictly speaking, it is more precise to name such structures as metal-insulatorsemiconductor field-effect transistors (MISFETs) since oxides represent only one class of the various insulators used today. To maximize the packing density and increase the operation speed of integrated circuits, transistor size must be reduced. The insulator thickness decreases as the dimension of a transistor becomes smaller, resulting in a significant gate tunneling current [1]. Such a tunneling current in the vertical direction (from metal to semiconductor or from semiconductor to metal) of a metal-insulator-semiconductor (MIS) structure has been employed in a number of applications, such as microwave devices [2], flash memory devices [3], solar cells [4], and photodetectors [5]. For photodetectors, the thickness of the insulator layer in the MIS tunneling diode is critical. For example, if the insulator layer is too thick, only limited tunneling can occur, leading to a small responsivity. On the contrary, if the insulator layer is too thin, Fermi level pinning may degrade the device currentvoltage (IV) characteristics from rectifying behavior to ohmic behavior (or vice versa) [6,7]. In addition to the thickness dependence, insulator composition also affects the IV characteristics of MIS devices. In this letter, we describe the effect with graphene oxide introduced in the insulator layer in MIS tunneling diodes. Graphene studies increased rapidly following the report of Novoselov et al. in 2004 describing graphene obtained from repeated peeling of graphite [8]. Many methods to prepare graphene have since been reported [9][10][11]. One method involves chemical oxidation of graphite followed by exfoliation of graphene oxide (GO). These GO layers are insulating and could be thermally reduced to be electrically conductive [12]. Nevertheless, the insulating property of GO may be utilized to certain applications. Each GO layer consists of the pure two-dimensional honeycomb lattice bearing functional groups, and the two-dimensional structure is not present in commonly employed insulators such as amorphous SiO 2 . Therefore, the influence of the GO layer in the MIS structure will be very interesting. In this study, we demonstrate MIS tunneling diodes with GO in the insulator layer. We deposited the GO layers onto the Si substrates and evaluated these structures based on the IV characteristics. We highlight the simple fabrication process employed to achieve high-performance GO-based MIS devices.

Methods

The schematic process used to prepare graphene oxide on Si substrates is shown in Figure 1, and the details are given below.

Graphite oxidation (modified Hummers method)

Graphite oxide was prepared using the modified Hummers method [13,14]. First, H 2 SO 4 (120 mL, 98%) was placed in an ice bath at < 5°C. Then, graphite powder (5 g) and KMnO 4 (15 g) was slowly added and stirred in H 2 SO 4 for 2 h. The temperature was controlled so as not to exceed 35°C. Deionized (DI) water (250 mL) was then slowly added to dilute the solution, which was then stirred for 2 h. DI water (700 mL) and H 2 O 2 (20 mL) were then added and stirred for 12 h. The solution was filtered and the slurry on the filter paper was washed with HCl (3%) to remove inorganic impurities. The slurry was then washed with DI water (1 L) to remove the residual acid. Finally, the slurry was dried to yield graphite oxide.

GO suspension

Graphite oxide powder (30 mg) was added to DI water (20 mL). After stirring for 10 min, ultrasonication was performed for 2 h to exfoliate the GO sheets from the multilayer flakes. The solution was then centrifuged at 5,000 rpm for 15 min, and the supernatant was collected. Due to the polar oxygen-containing functional groups, GO sheets were well suspended in the supernatant [15], which was subsequently used for dip-coating.

Treatment on Si and GO dip-coating

In this study, 1-to 10-Ω cm p-type Si with native oxide was used as the substrates. The substrate was first cleaned and made hydrophilic by treatment with SC1 solution (NH 4 OH/H 2 O 2 /H 2 O = 1:2:8) for 15 min. The SC1 treatment rendered the surface hydrophilic by attaching polar hydroxyl groups as described in [16]. The GO sheets were then deposited on substrates by dip-coating. The GO deposition was driven by the van der Waals interaction between the hydrophilic substrate and the polar oxygen-containing groups of the GO. Following dip-coating for 40 min, the dried substrate was covered with multilayers of GO sheets. Each single GO sheet might be approximately 1 nm thick as described in [12]. However, the method of chemical exfoliation resulted in multiple-sheet deposition. From the profile of the atomic force microscopy (AFM) image, the thickness of the GO film in the GO device was estimated to be approximately 10 nm.

Device fabrication

A 100-nm-thick Al gate with a circular area of 5 × 10 −4 cm 2 defined by a shadow mask was sputtered onto the GO-deposited side of the Si substrate. Al was also largescale sputtered onto the back side of the Si substrate to form an ohmic contact. In order to identify the influence of the GO layer on the device characteristics, we also prepared and evaluated a control device without a GO film. White-light LEDs operating at a power density of 0.5 mW/cm 2 were the illumination source used for photocurrent measurements.

Results and discussion

We performed AFM to investigate the morphology of the GO sheets on the Si substrate ( Figure 2). The root mean square roughness was 3.1 nm. It seemed that only the  The basic operation principle of an MIS photodetector can be found in [5]. Details regarding the conduction mechanisms in MIS tunneling diodes under an accumulation bias have been discussed in [17], and mechanisms under an inversion bias have been discussed in [18,19]. The tunnel diode is broadly used in low-power microwave applications. However, the MIS tunnel structure is also promising for use as a photodetector. As reviewed in [5], MIS tunneling diodes can be used to detect ultraviolet, visible, and infrared light. Photodetectors based on the MIS structure could own a smaller dark current than metal-semiconductor-metal photodetectors. The fabrication process is also simpler than that of pn photodetectors. Figure 4a,b shows the band diagrams under the accumulation bias and the inversion bias, respectively. Negative and positive biases applied to the top Al gate correspond to the accumulation and inversion operation biases, respectively, for the MIS tunneling diodes on a p-type semiconductor. At accumulation (negative) biases, holes are accumulated at the insulator/semiconductor interface and tunnel to the top gate. In addition, the Fermi level of the top gate continuously rises above the conduction band edge of the semiconductor. Hence, electrons can also tunnel from the top gate to the semiconductor (Figure 4a). The magnitude of the accumulation current is therefore related to the magnitude of the applied bias voltage (Figure 3). The magnitude of the inversion current is less dependent on the magnitude of the bias voltage. It is because that the inversion dark current is mainly contributed by thermally generated electronhole pairs at the insulator/semiconductor interface and bulk traps in the depletion region. When the devices are irradiated, extra photo-generated electron-hole pairs result in a photocurrent (Figure 4b).

From the IV measurements, it is evident that the dark current of the GO device at the accumulation bias (for example, at −2 V) is larger than that of the control device. Since the magnitudes of the inversion (for example, at 2 V) dark currents of both devices are similar, the GO device has improved rectifying characteristics. At the inversion bias, the photocurrent of the GO device is also larger than that of the control device, i.e., at 2 V, the photocurrent of the GO MIS tunneling diode is 1.34 × 10 −6 A, while that of the control diode is 1.94 × 10 −7 A. This shows that the use of GO can increase the responsivity of MIS photodetectors. To show the characteristic variation within different devices, the cumulative probability plot of the collected data is shown in Figure 5. There are nine samples used both for the GO and control samples. In the cumulative probability plot, the formula of i/n is used for the cumulative probability calculation of the y-axis (in percentage), where i is the rank number and n is the number of data points. The presence of GO layers can indeed increase the accumulation (−2 V) dark currents and inversion (2 V) photocurrents measured in MIS devices.

At 2 V, the photo-to-dark current ratio of the GO device is 24 compared to the value of 15 obtained for the control device. The photocurrent of the GO device is much larger than that of the control device due to the introduction of GO. Meanwhile, the dark current of the GO device is only slightly increased. Overall, the GO device can achieve a better photo-to-dark current ratio.  In [18], the photocurrent density of an MIS tunneling diode is approximately 0.03 mA/cm 2 under 0.71-mW/cm 2 illumination. The photocurrent density of our GO MIS tunneling diode is approximately 3 mA/cm 2 under 0.5-mW/cm 2 illumination; therefore, a much higher responsivity could be achieved via GO incorporation. Although the smaller dark current of the diode reported in [18] would result in a better photo-to-dark current ratio, the photo-to-dark current ratio of our GO device could increase if the GO is deposited on a high-quality thermal oxide as opposed to the native oxide.

The beneficial influence of GO may be attributed to its negative interface charge (or oxide charge). Al 2 O 3 has been previously used to passivate the surface of p-Si due to the negative charge at its interface, and the capacitance-voltage curves shift toward positive voltage when more negative charge is present [20]. We have measured the capacitancevoltage relation of the GO and control samples. Although the negative-bias data could not be obtained due to the large accumulation currents of our devices, the positivebias data show a positive shift similar to the Al 2 O 3 case ( Figure 6). If there is negative charge at the interface, holes prefer to accumulate at the surface of p-Si. Minority carriers, electrons, could be pushed away from the interface of p-Si, and it leads to a low surface recombination [20]. Low surface recombination could contribute to the high inversion photocurrent of the GO sample. On the other hand, the inherent hole accumulation present at the surface of p-Si of the GO sample also results in the larger accumulation currents observed at the negative bias.

Although thick GO flakes may prevent the tunneling of carriers, carriers can still flow through the native oxide that it is not covered with thick GO flakes for the GO sample. In other words, the GO sample is only partially covered with GO, and carriers can tunnel via the uncovered regions.

We used X-ray photoelectron spectroscopy (XPS) and reflective Fourier transform infrared spectroscopy (FTIR) to identify the chemical composition of the GO films. Figure 7a shows the carbon (C) 1s XPS spectra of GO and control samples. The peak at approximately 285 eV corresponds to C 1s peak of an sp 2 C or sp 3 C [21], and the small peak at approximately 289 eV corresponds to a carbonyl carbon (C = O) [22]. Since the control sample has been cleaned in acetone and methanol, a small C 1s peak at approximately 285 eV can be observed. Figure 7b shows the reflective FTIR spectra of GO and control samples. The GO spectrum is obtained with the Si substrate used in background measurement; hence, the final spectrum only reveals the reflection characteristics of the graphene-oxide film. In addition to the absorption due to C = C bonds, the absorption due to C-O bonds can also be observed. Similar results have been observed in [23]. The 'carbon-oxygen' functional groups are responsible for the insulating properties of GO. We attempted to perform Hall measurement to obtain the resistivity and mobility of the GO films; however, since the top layers were insulators instead of semiconductors, no reliable electrical data could be obtained.

Conclusions

Graphene oxide was deposited on Si to form an Al/graphene oxide/native oxide/Si MIS tunneling diode. With GO insertion, the accumulation dark current and inversion photocurrent are greater than those measured in the control device. The photocurrent of the GO MIS tunneling diode is 1.34 × 10 −6 A, while the photocurrent of the control diode is 1.94 × 10 −7 A; thus, the GO-based device is a

Figure 1
1Process flow to deposit graphene oxide on the Si substrate. After ultrasonication and centrifugation, small GO sheets can be suspended in DI water. The hydrophilic treatment of the Si surface promotes GO sheet deposition on Si

Figure 2
2AFM image of GO on the Si substrate. The sample seems to be only partially covered with GO flakes partial area was covered with GO flakes. The dark and photo-currents versus voltage characteristics of GO and control devices are shown inFigure 3, and the structures of GO and control devices are shown in the inset ofFigure 3.

Figure 3
3Dark and photo-currents versus voltage characteristics of GO and control devices. The inset shows the structures of GO and control devices. The accumulation (negative bias) dark current and inversion (positive bias) photocurrent of the GO device are superior to the control device

Figure 4
4Band diagrams of MIS tunneling diodes under (a) accumulation bias and (b) inversion bias. Negative and positive biases applied to the top Al gate correspond to the accumulation and inversion operation biases for the MIS tunneling diodes on a ptype semiconductor

Figure 5 Figure 6 Figure 7
567Current cumulative probability plot of the GO and control devices. All GO diodes exhibit larger currents than the control diodes whether for the accumulation dark currents or inversion photocurrents Capacitance-voltage relation of the GO and control devices. The negative-bias data could not be obtained due to the large accumulation currents of our devices. The shift of the GO curve as compared to the control curve may be due to the negative interface charge (or oxide charge) of GO promising candidate for detector applications. Future work can involve optimization of GO coverage in MIS tunneling diodes. XPS spectra (a) and reflective FTIR spectra (b) of GO and control samples. The carbon-oxygen functional groups, such as C = O and C-O, are observed
http://www.nanoscalereslett.com/content/7/1/343
AcknowledgementsThe authors are grateful to the National Nano Device Laboratories of the Republic of China for the facility access and the National Center for High-Performance Computing of the Republic of China for the computer time and facility access. This work was supported by the National Science Council of the Republic of China under contract no. 98-2221-E-259-002-MY3.Competing interestsThe authors declare that they have no competing interests.
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Influence of graphene oxide on metalinsulator-semiconductor tunneling diodes. Lin, Nanoscale Research Letters. Cite this article as. Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.comCite this article as: Lin et al.: Influence of graphene oxide on metal- insulator-semiconductor tunneling diodes. Nanoscale Research Letters 2012 7:343. Submit your manuscript to a journal and benefi t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.com


Automated interpretation of ANCA patterns - a new approach in the serology of ANCA-associated vasculitis Automated interpretation of ANCA patterns - a new approach in the serology of ANCA- associated vasculitis

Ilka Knütter
Rico Hiemann
Therese Brumma
Thomas Büttner
Kai Großmann
Marco Cusini
Francesca Pregnolato
Maria Orietta Borghi
Ursula Anderer
Karsten Conrad
Dirk Reinhold
Dirk Roggenbuck
Elena Csernok
Automated interpretation of ANCA patterns - a new approach in the serology of ANCA-associated vasculitis Automated interpretation of ANCA patterns - a new approach in the serology of ANCA- associated vasculitis
Knütter et al. Knütter et al. Arthritis Research & Therapy 2012, 14:R271 http://arthritis-research.com/content/14/6/R271 (14 December 2012) R E S E A R C H A R T I C L E Open Access
Introduction: Indirect immunofluorescence (IIF) employing ethanol-fixed neutrophils (ethN) is still the method of choice for assessing antineutrophil cytoplasmic antibodies (ANCA) in ANCA-associated vasculitides (AAV). However, conventional fluorescence microscopy is subjective and prone to high variability. The objective of this study was to evaluate novel pattern recognition algorithms for the standardized automated interpretation of ANCA patterns. Methods: Seventy ANCA-positive samples (20 antimyeloperoxidase ANCA, 50 antiproteinase3 ANCA) and 100 controls from healthy individuals analyzed on ethN and formalin-fixed neutrophils (formN) by IIF were used as a 'training set' for the development of pattern recognition algorithms. Sera from 342 patients ('test set') with AAV and other systemic rheumatic and infectious diseases were tested for ANCA patterns using the novel pattern recognition algorithms and conventional fluorescence microscopy. Results: Interpretation software employing pattern recognition algorithms was developed enabling positive/ negative discrimination and classification of cytoplasmic ANCA (C-ANCA) and perinuclear ANCA (P-ANCA). Comparison of visual reading of the 'test set' samples with automated interpretation revealed Cohen's kappa () values of 0.955 on ethN and 0.929 on formN for positive/negative discrimination. Analysis of the 'test set' with regard to the discrimination between C-ANCA and P-ANCA patterns showed a high agreement for ethN ( = 0.746) and formN ( = 0.847). There was no significant difference between visual and automated interpretation regarding positive/negative discrimination on ethN and formN, as well as ANCA pattern recognition (P > 0.05, respectively). Conclusions: Pattern recognition algorithms can assist in the automated interpretation of ANCA IIF. Automated reading of ethN and formN IIF patterns demonstrated high consistency with visual ANCA assessment.

Introduction

Antineutrophil cytoplasmic antibodies (ANCA)-associated systemic small vessel vasculitis (AAV) comprising granulomatosis with polyangiitis (GPA, previously known as Wegener's granulomatosis, microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA), previously known as Churg-Strauss syndrome, is a group of related autoimmune disorders characterized by microvascular inflammation, tissue necrosis, and circulating ANCA [1][2][3][4][5][6]. According to the recommendations for ANCA diagnostics, positive findings of standard screening tests by indirect immunofluorescence (IIF) on ethanol-fixed neutrophils (ethN) need to be confirmed with antigen-specific enzymelinked immunosorbent assays (ELISAs) [4]. Dependent on ethN IIF pattern, ANCA can be subclassified into cytoplasmic ANCA (C-ANCA) and perinuclear ANCA (P-ANCA) patterns. Non-C/P-ANCA patterns are usually reported as atypical ANCA, which have been found in particular in patients with inflammatory bowel disease [7][8][9]. The majority of C-ANCA recognizes proteinase 3 (PR3) and a positive C-ANCA pattern confirmed by an anti-PR3-ANCA ELISA is pathognomonic for GPA [1,3]. In contrast, the main autoantigenic target of P-ANCA is myeloperoxidase (MPO) and such ANCA have been demonstrated in patients with MPA, EGPA and less frequently in Goodpasture's syndrome patients. Furthermore, the titer of both anti-PR3-ANCA and anti-MPO-ANCA is strongly associated with the active and inactive state of GPA and MPA, respectively. Due to the observations that anti-MPO-ANCA and antinuclear antibodies (ANAs) may demonstrate similar IIF patterns on ethN, IIF on formalin-fixed neutrophils (formN) is employed for their discrimination [10].

Pattern interpretation of ANCA is characterized by human bias and high variability due to methodological issues such as differing fixation protocols for neutrophils and fluorescence microscopy components (for example, lamps, filters, objectives) [11]. Remarkably, computerbased image analysis of IIF patterns by pattern recognition algorithms has recently been successfully applied for automated analysis of ANA by HEp-2 cell-based assays [12][13][14], of dsDNA antibodies by Crithidia cellbased assays and of ANCA by neutrophil cell-based assays [15,16]. However, the study of Melegari et al. [16] published as a review covered a small number of samples and only positive/negative discrimination between manual and automated ANCA pattern interpretation. Interestingly, Boomsma et al. reported earlier an IIF method for the quantitative image analysis of anti-PR3 antibody positive GPA patients [17]. The study did not reveal major differences between quantitative image analysis and the other techniques including ELISA and titration by manual IIF in their capacity to predict relapses of disease activity. However, no comprehensive approach using pattern recognition algorithms for automated ANCA pattern interpretation like in the present study has been reported so far. Furthermore, we provide for the first time variability data of an automated ANCA IIF pattern interpretation in the present study. In particular, a novel pattern recognition algorithm software module for ANCA pattern analysis has been established on the automated reading system AKLIDES™ and was compared to conventional routine interpretation of ANCA by IIF on ethN and formN.

Materials and methods

Patients

Seventy ANCA positive samples with distinct ANCA specificities (20 anti-MPO-ANCA, 7 males, 13 females, median age 68 years, range 57 to 74 years and 50 anti-PR3-ANCA positives, 32 males, 18 females, median age 63 years, range 17 to 83 years) and sera from 100 ageand sex-matched healthy volunteers were used as a 'training set' for the development of a ANCA pattern recognition algorithm module for the automated AKLIDES™ system. Sera were tested for MPO or PR3 ANCA by ELISA and line immunodot assay (LIA) (GA Generic Assays GmbH, Dahlewitz/Berlin, Germany).

As the 'test set', 342 serum samples from patients with AAV, other systemic rheumatic and infectious diseases as controls and from healthy individuals were used (Table 1). Patients were diagnosed based on typical disease history, characteristic clinical findings, and confirmed clinical histology according to the criteria of the 1992 Chapel Hill Consensus Conference, the consensus statement of 1999 and the 1990 American College of Rheumatology [2,4,18]. Serum samples were obtained from patients with a confirmed clinical diagnosis of GPA, EGPA or MPA irrespective of serology (that is, presence of ANCA was not used as a diagnostic criterion). All serum samples were taken at the time of diagnosis. Serum samples from patients with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) were also used as disease controls. Disease extent was assessed using the Disease Extent Index (DEI) and disease activity using the Birmingham Vasculitis Activity Score (BVAS) at the time point when the sera were collected from the GPA, EGPA and MPA patients [19,20]. In total, 51 sera from patients with infectious disease (Treponema pallidum (n = 1), cytomegalovirus (n = 25), rubella virus (n = 5), Toxoplasma gondii (n = 16), hepatitis C (n = 1), Eppstein-Barr virus (n = 3) were included as disease controls in the test set. Furthermore, sera from 44 age-and sex-matched healthy volunteers were tested as control samples in the test set.

The study received approval from the ethical committee of the Technical University of Dresden (EK226112006) and fulfilled the ethical guidelines of the

AKLIDES™ -technical system and pattern recognition algorithm software

The concept of the fully automated interpretation system AKLIDES™ for evaluation of ANCA IIF patterns is based on novel mathematical software algorithms for pattern recognition [12,21]. Neutrophils were assessed automatically using a motorized inverse microscope (IX81, Olympus Corporation, Tokyo, Japan) with a motorized scanning stage (IM120, Märzhäuser, Wetzlar, Germany); 400 nm and 490 nm light-emitting diodes (LED) (Preci-sExcite, CoolLED, Andover, UK), and a charge-coupled device grey-scale camera (DX4, Kappa, Gleichen, Germany). The interpretation system is controlled by the AKLIDES™ software consisting of modules for device and autofocus control, image analysis, and pattern recognition algorithms. The novel autofocus based on Haralick's image characterization of objects through grey-scale transition using DAPI as fluorescent dye for focusing, quality evaluation, and object recognition. Two-dimensional images were acquired using an objective with 40-fold magnification (Olympus semiapochromat LUCPLFLN 40X, 0.60 NA, W.D. 2.7-4.0 mm). Fluorescence detection was performed using LED excitation with appropriate multiband filter for the DAPI and FITC dyes (DA/FI-A, Semrock, Rochester, USA). Single DAPI and FITC image were serially captured and stored in lossless compressed Tagged Image File (TIF) format.

To eliminate artifacts, an additional qualitative image analysis was performed by dividing the image content into tiles of equal size with subsequent calculation of tile sharpness and homogeneity. Object segmentation was conducted by histogram-based threshold algorithm followed by watershed transformation [21]. Cell aggregates were excluded by analyzing convexity of objects. Segmented granulocytes were characterized by regional, topological, and texture/surface descriptors by employing DAPI and FITC image data (Figure 1a). A minimum of 20 granulocytes were counted at each slide.

Statistical analysis

Fisher's exact test was used to check the differences between the two classification systems. To test for the strength of agreement, inter-rater agreement statistics was conducted. McNemar test was performed to check the difference for paired proportions. P values of less than 0.05 were considered as or to be significant. Calculations were performed by using MedCalc™ statistical software (MedCalc, Mariakerke, Belgium).

Results

Development of ANCA pattern recognition algorithms on AKLIDES™

For the automated interpretation of ANCA IIF patterns and evaluation of its diagnostic performance, a novel ANCA pattern recognition algorithm software module was established for the multicolor-fluorescence reading system AKLIDES™ employing topographical, texture, boundary, and regional descriptors for image analysis. Both ANCA pattern detection and description follow a two-step approach. At first, DAPI staining was used for image focusing, checking of scene quality, and neutrophil identification. Afterwards, signal intensity and pattern classification for appropriate neutrophils were calculated for FITC fluorescence (Figure 1a, b).

First, a 'training set' of 70 ANCA positive samples with distinct ANCA specificities (anti-MPO-ANCA, n = 20 and anti-PR3-ANCA, n = 50) and 100 healthy controls was used for the development and refinement of pattern recognition algorithms for the new ANCA pattern recognition algorithm module. The analysis of IIF patterns by image-processing algorithms was separated into two profiles for the recognition of ANCA IIF patterns on ethN and formN. Furthermore, a differentiation between positive and negative samples (quantitative threshold values are given in Table 2) and additionally between the two different basic ANCA IIF patterns cytoplasmic and nuclear/perinuclear (Figure 1a, b) was implemented. Five images per sample were taken automatically. The images were focused employing the DAPI fluorescence and the pattern recognition was performed by adding FITC fluorescence signals of at least 20 neutrophils. Running the 'training set' samples, a 100% consistency was achieved regarding positive/negative discrimination and determination of ANCA IIF patterns by using the novel pattern recognition algorithms (data not shown) in comparison to manual interpretation.

Signal variability of automated ANCA pattern interpretation

In contrast to visual reading, measurement of quantitative fluorescence signals for ANCA IIF pattern interpretation provides for the first time the basis for objective assay performance assessment by determination of assay variability. Inter-assay coefficients of variation (CV) were determined by running six ANCA positive samples with differing ANCA titers in three different runs. The functional assay sensitivity representing the lowest ANCA concentration with a CV of lower than 20% was determined at a fluorescence intensity level of 40 AU for ethN and 30 AU for formN (data not shown)

Positive/negative discrimination of ANCA by automated interpretation

The established ANCA IIF pattern recognition algorithm module was evaluated with the 'test set' comprising 342 patient and control sera (Table 1)  samples. To compare manual and automated positive/ negative discrimination, ANCA IIF patterns were interpreted automatically with AKLIDES™ followed by visual interpretation with a routine fluorescence microscope (Figure 1a). Appropriate ranges of quantitative fluorescence intensity data obtained with the 'training set' samples with AKLIDES™ were used to classify automated findings into negative (-), borderline (+/-), weak positive (+), positive (++), strong positive (+++), and very strong positive (++++) ( Table 2). The threshold values were used for the positive/negative classification of the samples in the test set ( Figure 2, Table 2). Counting borderline samples as negative, the differences in positive and negative findings of 2.34% (95% confidence interval (CI): -0.22 to 4.0%) on ethN and 0.58% (95% CI: -1.57 to 2.44%) on formN between both interpretation methods were not significant according to McNemar's test (P = 0.0768, 0.7744, respectively). Applying inter-rater agreement statistics for testing the strength of agreement between two classifications, Cohen's kappa () values above 0.8 on ethN and formN were obtained ( = 0.955, 95% CI: 0.944 to 0.965; = 0.929, 95% CI: 0.895 to 0.964; respectively). Thus, visual and automated interpretation revealed a very good strength of agreement for the 'test set' sera.

ANCA IIF pattern recognition by automated interpretation

Employing the novel pattern recognition algorithms, 'test set' patient samples (n = 342) were interpreted automatically with AKLIDES™ followed by visual interpretation to compare the ANCA IIF pattern findings. Four different patterns such as cytoplasmic, nuclear/ perinuclear, atypical, and negative were used to classify automated and visual findings on ethN for inter-rater agreement analysis (Table 3). Automated and visual interpretation of ANCA pattern demonstrated a good agreement with a value of 0.746 on ethN (95% CI: 0.667 to 0.825). The main differences between visual and automated interpretation for IIF patterns on ethN were found for visual findings regarding cytoplasmic and atypical patterns. Out of 88 cytoplasmic pattern findings by visual interpretation, the automated reading defined 22 (25.0%) as negative results. All these 22 IIF images demonstrated a borderline cytoplasmic immunofluorescence on AKLIDES™ that did not reach the threshold for positivity used by the pattern recognition algorithms. Nevertheless, there was no significant difference in the number of cytoplasmic patterns like for all other patterns determined on ethN regarding automated and visual interpretation (87/342 by visual vs. 62/342 by automated interpretation, P = 1.00). Moreover, out of 55 atypical patterns determined visually, 10 (18.2%) were defined as nuclear pattern using the automated pattern recognition algorithms.

Due to the impaired mobility of MPO during formalin fixation of neutrophils, only three kinds of patterns were classified on formN (cytoplasmic, atypical, and negative). Table 2 Comparison of manual and automated positive/negative discrimination assessing fluorescence intensity of 342 samples on ethN and formN patterns. by visual interpretation were classified as cytoplasmic pattern by automated interpretation, which was the only significant difference between both methods for pattern recognition on formN (P = 0.0075).

Comparison of automated and visual interpretation on

ANCA IIF pattern recognition comparison in different clinical entities

Automated and visual interpretation of ANCA IIF patterns on ethN and formN included different patient and control group samples ( Figure 3). Investigating sera from patients with GPA (generalized and localized with active and inactive disease state), EGPA (active and inactive), MPA, SLE, RA, rheumatoid vasculitis, cryoglobulinaemic vasculitis, systemic sclerosis, infectious disease and healthy controls, no significant difference between both interpretation methods was established for the respective clinical entities and controls (P > 0.05, respectively).

Discussion

Testing for ANCA plays a pivotal role in the serological diagnosis of AAV [22]. In accordance with international guidelines, IIF and independent detection techniques like ELISA should be used for the assessment of ANCA [4,23]. Interestingly, IIF has been reported to be used as a screening method due to its high sensitivity followed by anti-PR3-ANCA and anti-MPO-ANCA ELISAs as confirmatory tests under routine laboratory conditions due to its high sensitivity and negative predictive value [24]. In contrast to the ELISA technique, IIF is prone to human bias and ANCA IIF image interpretation has been not automated until recently [16]. Thus, the recommended combination of IIF and confirmatory testing requires a high level of expert knowledge and lacks standardization [25]. Several studies tried to demonstrate that ELISA technology is better adaptable for standardization of ANCA assessment and has a lower lab-to-lab variability compared to IIF [26][27][28]. However, most commercially available ELISAs have been reported to be inferior to IIF in terms of sensitivity [29]. Therefore, automated interpretation of ANCA IIF patterns as demonstrated for ANA detection by novel pattern recognition algorithms could provide a cost-efficient and standardized alternative approach for ANCA screening [13][14][15][16]. In particular, regarding routine laboratories with large numbers of ANCA determinations, automated interpretation would overcome shortcomings of IIF such as high level of manual work and exceeding data management [13,16]. Several studies have confirmed the usefulness of automated IIF systems for objective ANA pattern interpretations recently, providing the basis for the employment of IIF as gold standard for ANA testing as required by the American College of Rheumatology even under differing routine conditions [13,14,16,30].

In contrast to ANA detection on HEp-2 cells, polymorphonuclear granulocytes are characterized by varying shapes of the nucleus, which is usually lobed into three segments. Therefore, algorithms for identification of granulocyte staining patterns proved to be more complex compared to those for HEp-2 cells. Furthermore, ethN and formN show different boundary characteristics with DAPI staining. Consequently, the type of fixation needs to be taken into account assessing signal intensity and pattern classification to adapt the algorithms to the changed morphology.

Due to polymorphic nucleus and cytoplasmic ANCA staining patterns, signal assessment and pattern classification cannot be performed simply inside or outside of the DAPI positive area as described for ANA pattern detection [12,21]. Cytoplasmic and perinuclear ANCA patterns need to be detected and identified in border regions of the nucleus, rendering detection of ANCA more complex and challenging for automation.

As a fact, our study did not establish a significant difference between automated and visual ANCA IIF detection investigating patients with AAV and disease controls. Furthermore, the positive/negative discrimination and ANCA pattern assessment demonstrated a very good agreement for ethN and formN, confirming the usefulness of pattern recognition algorithms for the automated interpretation of IIF patterns. In contrast to the work of Melegari et al. reporting a very good consistency for positive/negative discrimination by automated   and visual interpretation in a review, in this study ANCA patterns were also analyzed on neutrophils with two different fixation methods. Moreover, this is the first study on ANCA IIF pattern reading, reporting quantitative data for interassay variability. As shown for anti-dsDNA and ANA detection by IIF recently, this approach allows determining a functional assay sensitivity giving the lowest fluorescence intensity with an interassay variability of equal or less than 20% [15,31,32]. This creates the opportunity for a standardized cutoff determination improving the test accuracy for borderline samples and might provide a better reporting of ANCA IIF results to clinicians in order to support the progress in the treatment of AAV [33]. Relying on quantitative data for ANCA IIF reading may even give the opportunity to report findings for different test-result intervals as proposed recently for ANCA testing by ELISA [34].

Conclusions

Novel ANCA pattern recognition algorithms are a very useful tool for the automated reading of ANCA patterns on ethN and formN and can further improve the standardization efforts for the detection of ANCA. The automated evaluation is prone to render the comparison of diagnostic data possible. This is most important for clinical studies but also for diagnostic purposes and in clinical research. Automated pattern interpretation demonstrated high diagnostic performance for the assessment of ANCA and revealed no difference to visual reading. Further studies are warranted to evaluate this novel fully automated approach for automated software-based screening of ANCA in patients with suspected AAV.

Figure 1
1to assess the ability to differentiate between positive and negative ANCA patterns and recognition thereof by mathematical algorithms. (a) Images (detail) of serum samples demonstrating C-ANCA, P-ANCA, and atypical ANCA patterns on ethN and formN taken automatically by AKLIDES™. Chromatin is stained by DAPI (blue) and specific ANCA interactions are revealed by FITC (green)-labeled secondary anti-human IgG. (b) Pattern recognition image of perinuclear (A) and cytoplasmic (B) specific staining of neutrophils used by the novel algorithms for pattern differentiation: DAPI (blue) and FITC fluorescence intensity signals (green) of respective images were combined and illustrated in three dimensions (x -object length, y -object width, light intensity of fluorescence signal). C-ANCA, cytoplasmic antineutrophil cytoplasmic antibody; ethN, ethanol-fixed neutrophils; formN, formalin-fixed neutrophils; P-ANCA, perinuclear antineutrophil cytoplasmic antibody.

Figure 2
2patterns were first interpreted automatically with AKLIDES™ followed by manual reading with a routine fluorescence microscope. Both interpretation methods demonstrated very good agreement on ethN and formN ( = 0.955, 95% confidence interval (CI): 0.944 to 0.965; = 0.929, 95% CI: 0.895 to 0.964; respectively). Quantitative fluorescence intensity threshold values for ethN: < 15 negative (-); ≥ 15 borderline (+/-) < 40; ≥ 40 weak positive (+) < 150; ≥ 150 positive (++) < 400; ≥ 400 strong positive (+++) < 700; ≥ 700 very strong positive (++++). Quantitative fluorescence intensity threshold values for formN: < 20 negative (-); ≥ 20 borderline (+/-) < 30; ≥ 30 weak positive (+) < 100; ≥ 100 positive (++) < 300; ≥ 300 strong positive (+++) < 600; ≥ 600 very strong positive (+ +++). ANCA, antineutrophil cytoplasmic antibody; ethN, ethanol-fixed neutrophils; formN, formalin-fixed neutrophils; IIF, indirect immunofluorescence.formN revealed a very good agreement ( = 0.847, 95% CI: 0.790 to 0,904). Out of 100 cytoplasmic pattern findings by visual interpretation, the automated reading defined nine (9.0%) as negative results. These nine IIF images demonstrated also a borderline cytoplasmic immunofluorescence that did not reach the threshold for positivity used by the pattern recognition algorithms. In contrast, out of 234 negative samples by visual interpretation, nine (3.8%) IIF pattern images were interpreted as cytoplasmic pattern. All eight atypical patterns Fluorescence intensity (AU) obtained by automated reading of AKLIDES™ compared to visual interpretation of ANCA IIF pattern images on ethN (A) and formN (B) investigating serum samples of the 'test set'. ANCA, antineutrophil cytoplasmic antibody; ethN, ethanol-fixed neutrophils; formN, formalin-fixed neutrophils; IIF, indirect immunofluorescence.

Figure 3
3Comparison of automated and visual interpretation of ANCA IIF patterns in patient and control samples of the 'test set' on ethN (A) and formN (B). Patterns of ethN and formN were first detected automatically by AKLIDES™ and then interpreted manually by a routine fluorescence microscope. GPA, granulomatosis with polyangiitis (Wegener's) (n = 59); EGPA, eosinophilic granulomatosis with polyangiitis (Churg-Strauss Syndrome) (n = 40); MPA, microscopic polyangiitis (n = 20); SLE, systemic lupus erythematosus (n = 40); RA, rheumatoid arthritis (n = 30); RA RV, rheumatoid arthritis with rheumatoid vasculitis (n = 10); Cr V, cryoglobulinaemic vasculitis (n = 10); HC, healthy control (n = 44); S Scl, systemic sclerosis (n = 38), Inf dis, infectious disease (n = 51).

Abbreviations ANA: antinuclear antibody; ANCA: antineutrophil cytoplasmic antibody; AAV: ANCA-associated vasculitides; C-ANCA: cytoplasmic antineutrophil cytoplasmic antibody; CI: confidence interval; EGPA: eosinophilic granulomatosis with polyangiitis (Churg-Strauss Syndrome); ELISA: enzymelinked immunosorbent assay; ethN: ethanol-fixed neutrophils; formN: formalin-fixed neutrophils; GPA: granulomatosis with polyangiitis (Wegener's); IIF: indirect immunofluorescence; κ: Cohen's kappa; MPA: microscopic polyangiitis; MPO: myeloperoxidase; P-ANCA: perinuclear antineutrophil cytoplasmic antibody; PR3: proteinase 3; RA: rheumatoid arthritis; SLE: systemic lupus erythematosus.

Table 1
1Patient characteristics of the 'test set' comprising 342 serum samples.Diagnosis
n (%)
age
gender f/m

GPA
59 (17.3)
21 -81
36/23

EGPA
40 (11.7)
21 -70
20/20
MPA
20 (5.8)
17 -80
13/7

SLE
40 (11.7)
20 -72
31/9

RA
30 (8.8)
40 -81
23/7

RA and rheumatoid vasculitis
10 (2.9)
44 -67
5/5

Cryoglobulinaemic vasculitis
10 (2.9)
43 -78
7/3

Systemic sclerosis
38 (11.1)
35 -84
27/11

Infectious diseases
51 (14.9)
5 -86
46/5

Healthy controls
44 (12.9)
23 -71
28/16

Patients with GPA consist of cases with generalized and localized GPA
demonstrating inactive as well as active disease. Patients with EGPA cover
cases with active and inactive disease. EGPA, eosinophilic granulomatosis with
polyangiitis (Churg-Strauss Syndrome); GPA, granulomatosis with polyangiitis
(Wegener's); f, female; m, male; MPA, microscopic polyangiitis; RA, rheumatoid
arthritis; SLE, systemic lupus erythematosus.

Table 3
3Comparison of visual and automated interpretation of IIF ANCA patterns of the 'test set' patients (n = 342) on ethN and formN. ANCA IIF patterns were first read automatically with AKLIDES™ followed by manual interpretation with a routine fluorescence microscope. Both interpretation methods demonstrated good agreement on ethN and formN ( = 0.746, 95% CI: 0.667 to 0.825; = 0.847, 95% CI: 0.79 to 0,904; respectively). ANCA, antineutrophil cytoplasmic antibody; ethN, ethanol-fixed neutrophils; formN, formalin-fixed neutrophils; IIF, indirect immunofluorescence.ethN
Automated interpretation by AKLIDES™

n (%)
cytoplasmic nuclear atypical negative

cytoplasmic 59 (17.2)
0 (0)
7 (2.0)
22 (6.4)

nuclear
0 (0)
61
(17.8)

4 (1.2)
0 (0)

atypical
3 (0.9)
10 (2.9) 40 (11.7) 2 (0.6)

negative
0 (0)
0 (0)
1 (0.3)
133
(38.9)

Visual
interpretation

formN

n (%)
cytoplasmic atypical negative

cytoplasmic 91 (26.6)
0 (0)
9 (2.6)
atypical
8 (2.3)
0 (0)
0 (0)

negative
9 (2.6)
0 (0)
225
(65.8)

Knütter et al. Arthritis Research & Therapy 2012, 14:R271 http://arthritis-research.com/content/14/6/R271
AcknowledgementsThis work was supported by the Federal Ministry of Economics and Technology Central Innovation Programme SME ZIM-KOOP, grants KF2379003AJ0 and KF2379004AJ1. We thank Samantha Goddard for assistance in editing the manuscript.Authors' contributions IK, TBr, TB, KG, and EC carried out indirect immunofluorescence assays. RH developed the pattern recognition algorithms. MC, FP, and MOB provided the serum samples from patients with infectious diseases and carried out the respective laboratory investigations. KC, DRe, UA, EC, and DRo conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.Competing interestsDirk Roggenbuck has a management role and is a shareholder of GA Generic Assays GmbH and Medipan GmbH. Both companies are diagnostic manufacturers. All other authors declare that they have no competing financial interests.
. Development Research, Department, GA Generic Assays GmbH, Ludwig-Erhard-Ring. 315827Research and Development Department, GA Generic Assays GmbH, Ludwig-Erhard-Ring 3, 15827

Faculty of Science. / Dahlewitz, Germany Berlin, Lausitz University of Applied Sciences, Großenhainer Str. 5721968Dahlewitz/Berlin, Germany. 2 Faculty of Science, Lausitz University of Applied Sciences, Großenhainer Str. 57, 01968

Department of Dermatology, Fondazione Ca Granda Ospedale Maggiore Policlinico, via S. Barnaba. Germany Senftenberg, 8Milan, ItalyIRCCS Istituto Auxologico Italiano, Immune research laboratory and Department of Clinical Science and Community, University of Milanvia SpagnolettoSenftenberg, Germany. 3 Department of Dermatology, Fondazione Ca Granda Ospedale Maggiore Policlinico, via S. Barnaba 8, 20122 Milan, Italy. 4 IRCCS Istituto Auxologico Italiano, Immune research laboratory and Department of Clinical Science and Community, University of Milan, via Spagnoletto 3, 20149

Italy Milan, of Molecular and Clinical Immunology. Dresden, Germany. 6 Institute4239120Institute of Immunology, Technical University Dresden, Fiedler Str ; Otto-von-Guericke-University, Leipziger StrMilan, Italy. 5 Institute of Immunology, Technical University Dresden, Fiedler Str. 42, 01307 Dresden, Germany. 6 Institute of Molecular and Clinical Immunology, Otto-von-Guericke-University, Leipziger Str. 44, 39120

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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Knütter, Arthritis Research & Therapy. 14271Automated interpretation of ANCA patterns -a new approach in the serology of ANCA-associated vasculitis. Submit your manuscript at www.biomedcentral.com/submitCite this article as: Knütter et al.: Automated interpretation of ANCA patterns -a new approach in the serology of ANCA-associated vasculitis. Arthritis Research & Therapy 2012 14:R271. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit