Idiopathic generalized epilepsies in the epilepsy monitoring unit: Systematic quantification of focal EEG and semiological signs.

OBJECTIVE: Focal seizure symptoms (FSS) and focal interictal epileptiform discharges (IEDs) are common in patients with idiopathic generalized epilepsies (IGEs), but dedicated studies systematically quantifying them both are lacking. We used automatic IED detection and localization algorithms and correlated these EEG findings with clinical FSS for the first time in IGE patients. METHODS: 32 patients with IGEs undergoing long-term video EEG monitoring were systematically analyzed regarding focal vs. generalized IEDs using automatic IED detection and localization algorithms. Quantitative EEG findings were correlated with FSS. RESULTS: We observed FSS in 75% of patients, without significant differences between IGE subgroups. Mostly varying/shifting lateralizations of FSS across successive recorded seizures were seen. We detected a total of 81,949 IEDs, whereof 19,513 IEDs were focal (23.8%). Focal IEDs occurred in all patients (median 13% focal IEDs per patient, range 1.1 - 51.1%). Focal IED lateralization and localization predominance had no significant effect on FSS. CONCLUSIONS: All included patients with IGE showed focal IEDs and three-quarter had focal seizure symptoms irrespective of the specific IGE subgroup. Focal IED localization had no significant effect on lateralization and localization of FSS. SIGNIFICANCE: Our findings may facilitate diagnostic and treatment decisions in patients with suspected IGE and focal signs.

Relational local electroencephalography representations for sleep scoring.

Computational sleep scoring from multimodal neurophysiological time-series (polysomnography PSG) has achieved impressive clinical success. Models that use only a single electroencephalographic (EEG) channel from PSG have not yet received the same clinical recognition, since they lack Rapid Eye Movement (REM) scoring quality. The question whether this lack can be remedied at all remains an important one. We conjecture that predominant Long Short-Term Memory (LSTM) models do not adequately represent distant REM EEG segments (termed epochs), since LSTMs compress these to a fixed-size vector from separate past and future sequences. To this end, we introduce the EEG representation model ENGELBERT (electroEncephaloGraphic Epoch Local Bidirectional Encoder Representations from Transformer). It jointly attends to multiple EEG epochs from both past and future. Compared to typical token sequences in language, for which attention models have originally been conceived, overnight EEG sequences easily span more than 1000 30 s epochs. Local attention on overlapping windows reduces the critical quadratic computational complexity to linear, enabling versatile sub-one-hour to all-day scoring. ENGELBERT is at least one order of magnitude smaller than established LSTM models and is easy to train from scratch in a single phase. It surpassed state-of-the-art macro F1-scores in 3 single-EEG sleep scoring experiments. REM F1-scores were pushed to at least 86%. ENGELBERT virtually closed the gap to PSG-based methods from 4-5 percentage points (pp) to less than 1 pp F1-score.

Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction.

OBJECTIVE: Ultra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm. METHODS: A multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts. RESULTS: Data reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69-100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0-13.0). CONCLUSIONS: Our findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity. SIGNIFICANCE: Ultra long-term sqEEG bears the potential of improving objective seizure quantification.

Seizure detection with deep neural networks for review of two-channel electroencephalogram.

Ultra-long-term electroencephalographic (EEG) registration using minimally invasive low-channel devices is an emerging technology to assess sporadic seizure events. Highly sensitive automatic seizure detection algorithms are needed for semiautomatic evaluation of these prolonged recordings. We describe the design and validation of a deep neural network for two-channel seizure detection. The model is trained using EEG recordings from 590 patients in a publicly available seizure database. These recordings are based on the full 10-20 electrode system and include seizure annotations created by reviews of the full set of EEG channels. Validation was performed using 48 scalp EEG recordings from an independent epilepsy center and consensus seizure annotations from three neurologists. For each patient, a three-electrode subgroup (two channels with a common reference) of the full montage was selected for validation of the two-channel model. Mean sensitivity across patients of 88.8% and false positive rate across patients of 12.9/day were achieved. The proposed training approach is of great practical relevance, because true recordings from low-channel devices are currently available only in small numbers, and the generation of gold standard seizure annotations in two EEG channels is often difficult. The study demonstrates that automatic seizure detection based on two-channel EEG data is feasible and review of ultra-long-term recordings can be made efficient and effective.

Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis.

OBJECTIVE: To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns. METHODS: We analyzed long-term EEGs from 76 patients with at least one recorded epileptic seizure during monitoring. IEDs were detected with an AI-based algorithm and validated by visual inspection. We then used unsupervised clustering to characterize patient sub-cohorts with similar IED activation patterns regarding circadian rhythms, deep sleep activation, and seizure occurrence. RESULTS: Five sub-cohorts with similar IED activation patterns were found: "Sporadic" (14%, n = 10) without or few IEDs, "Continuous" (32%, n = 23) with weak circadian/deep sleep or seizure modulation, "Nighttime & seizure activation" (23%, n = 17) with high IED rates during normal sleep times and after seizures but without deep sleep modulation, "Deep sleep" (19%, n = 14) with strong IED modulation during deep sleep, and "Seizure deactivation" (12%, n = 9) with deactivation of IEDs after seizures. Patients showing "Deep sleep" IED pattern were diagnosed with temporal lobe epilepsy in 86%, while 80% of the "Sporadic" cluster were extratemporal. CONCLUSIONS: Patients with epilepsy can be characterized by using temporal relationships between rates of IEDs, circadian rhythms, deep sleep and seizures. SIGNIFICANCE: This work presents the first approach to data-driven classification of epilepsy patients based on their fully validated temporal pattern of IEDs.

Deep learning for estimation of functional brain maturation from EEG of premature neonates.

EEG monitoring of early brain function and development in neonatal intensive care units may help to identify infants with high risk of serious neurological impairment and to assess brain maturation for evaluation of neurodevelopmental progress. Automated analysis of EEG data makes continuous evaluation of brain activity fast and accessible. A convolutional neural network (CNN) for regression of EEG maturational age of premature neonates from marginally preprocessed serial EEG recordings is proposed. The CNN was trained and validated using 141 EEG recordings from 43 preterm neonates born below 28 weeks of gestation with normal neurodevelop-mental outcome at 12 months of corrected age. The estimated functional brain maturation between the first and last EEG recording increased in each patient. On average over 96% of repeated measures within an infant had an increasing EEG maturational age according to the post menstrual age at EEG recording time. Our algorithm has potential to be deployed to support neonatologists for accurate estimation of functional brain maturity in premature neonates.

An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard.

OBJECTIVE: To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy. METHODS: We developed an algorithm for automatic detection of EDs, based on a novel deep learning method that requires a low amount of labeled EEG data for training. Detected EDs are automatically grouped into clusters, consisting of the same type of EDs, for rapid visual inspection. We validated the algorithm on an independent dataset of 100 patients with sharp transients in their EEG recordings (54 with epilepsy and 46 with non-epileptic paroxysmal events). The diagnostic gold standard was derived from the video-EEG recordings of the patients' habitual events. RESULTS: The algorithm had a sensitivity of 89% for identifying EEGs with EDs recorded from patients with epilepsy, a specificity of 70%, and an overall accuracy of 80%. CONCLUSIONS: Automated detection of EDs using an artificial intelligence-based computer algorithm had a high sensitivity. Human (expert) supervision is still necessary for confirming the clusters of detected EDs and for describing clinical correlations. Further studies on different patient populations will be needed to confirm our results. SIGNIFICANCE: The automated algorithm we describe here is a useful tool, assisting neurophysiologist in rapid assessment of EEG recordings.

Automated Long-Term EEG Review: Fast and Precise Analysis in Critical Care Patients.

Background: Ongoing or recurrent seizure activity without prominent motor features is a common burden in neurological critical care patients and people with epilepsy during ICU stays. Continuous EEG (CEEG) is the gold standard for detecting ongoing ictal EEG patterns and monitoring functional brain activity. However CEEG review is very demanding and time consuming. The purpose of the present multirater, EEG expert reviewer study, is to test and assess the clinical feasibility of an automatic EEG pattern detection method (Neurotrend). Methods: Four board certified EEG reviewers used Neurotrend to annotate 76 CEEG datasets à 6 h (in total 456 h of EEG) for rhythmic and periodic EEG patterns (RPP), unequivocal ictal EEG patterns and burst suppression. All reviewers had a predefined time limit of 5 min (± 2 min) per CEEG dataset and were compared to a predefined gold standard (conventional EEG review with unlimited time). Subanalysis of specific features of RPP was conducted as well. We used Gwet's AC1 and AC2 coefficients to calculate interrater agreement (IRA) and multirater agreement (MRA). Also, we determined individual performance measures for unequivocal ictal EEG patterns and burst suppression. Bonferroni-Holmes correction for multiple testing was applied to all statistical tests. Results: Mean review time was 3.3 min (± 1.9 min) per CEEG dataset. We found substantial IRA for unequivocal ictal EEG patterns (0.61-0.79; mean sensitivity 86.8%; mean specificity 82.2%, p < 0.001) and burst suppression (0.68-0.71; mean sensitivity 96.7%; mean specificity 76.9% p < 0.001). Two reviewers showed substantial IRA for RPP (0.68-0.72), whereas the other two showed moderate agreement (0.45-0.54), compared to the gold standard (p < 0.001). MRA showed almost perfect agreement for burst suppression (0.86) and moderate agreement for RPP (0.54) and unequivocal ictal EEG patterns (0.57). Conclusions: We demonstrated the clinical feasibility of an automatic critical care EEG pattern detection method on two levels: (1) reasonable high agreement compared to the gold standard, (2) reasonable short review times compared to previously reported EEG review times with conventional EEG analysis.

Early Epileptiform Discharges and Clinical Signs Predict Nonconvulsive Status Epilepticus on Continuous EEG.

BACKGROUND: Critical care continuous electroencephalography (CCEEG) represents the gold standard for detection of nonconvulsive status epilepticus (NCSE) in neurological critical care patients. It is unclear which findings on short-term routine EEG and which clinical parameters predict NCSE during subsequent CCEEG reliably. The aim of the present study was to assess the prognostic significance of changes within the first 30 min of EEG as well as of clinical parameters for the occurrence of NCSE during subsequent CCEEG. METHODS: Systematic analysis of the first 30 min and the remaining segments of prospective CCEEG recordings according to the ACNS Standardized Critical Care EEG Terminology and according to recently proposed NCSE criteria as well as review of clinical parameters of 85 consecutive neurological critical care patients. Logistic regression and binary classification tests were used to determine the most useful parameters within the first 30 min of EEG predicting subsequent NCSE. RESULTS: The presence of early sporadic epileptiform discharges (SED) and early rhythmic or periodic EEG patterns of "ictal-interictal uncertainty" (RPPIIIU) (OR 15.51, 95% CI 2.83-84.84, p = 0.002) and clinical signs of NCS (OR 18.43, 95% CI 2.06-164.62, p = 0.009) predicted NCSE on subsequent CCEEG. Various combinations of early SED, early RPPIIIU, and clinical signs of NCS showed sensitivities of 79-100%, specificities of 49-89%, and negative predictive values of 95-100% regarding the incidence of subsequent NCSE (p < 0.001). CONCLUSIONS: Early SED and early RPPIIIU within the first 30 min of EEG as well as clinical signs of NCS predict the occurrence of NCSE during subsequent CCEEG with high sensitivity and high negative predictive value and may be useful to select patients who should undergo CCEEG.

Monitoring burst suppression in critically ill patients: Multi-centric evaluation of a novel method.

OBJECTIVE: To develop a computational method to detect and quantify burst suppression patterns (BSP) in the EEGs of critical care patients. A multi-center validation study was performed to assess the detection performance of the method. METHODS: The fully automatic method scans the EEG for discontinuous patterns and shows detected BSP and quantitative information on a trending display in real-time. The method is designed to work without setting any patient specific parameters and to be insensitive to EEG artifacts and periodic patterns. For validation a total of 3982 h of EEG from 88 patients were analyzed from three centers. Each EEG was annotated by two reviewers to assess the detection performance and the inter-rater agreement. RESULTS: Average inter-rater agreement between pairs of reviewers was κ=0.69. On average 22% of the review segments included BSP. An average sensitivity of 90% and a specificity of 84% were measured on the consensus annotations of two reviewers. More than 95% of the periodic patterns in the EEGs were correctly suppressed. CONCLUSION: A fully automatic method to detect burst suppression patterns was assessed in a multi-center study. The method showed high sensitivity and specificity. SIGNIFICANCE: Clinically applicable burst suppression detection method validated in a large multi-center study.

Rhythmic and periodic EEG patterns of 'ictal-interictal uncertainty' in critically ill neurological patients.

OBJECTIVES: To study periodic and rhythmic EEG patterns classified according to Standardized Critical Care EEG Terminology (SCCET) of the American Clinical Neurophysiology Society and their relationship to electrographic seizures. METHODS: We classified 655 routine EEGs in 371 consecutive critically ill neurological patients into (1) normal EEGs or EEGs with non-specific abnormalities or interictal epileptiform discharges, (2) EEGs containing unequivocal ictal EEG patterns, and (3) EEGs showing rhythmic and periodic EEG patterns of 'ictal-interictal uncertainty' (RPPIIIU) according to SCCET. RESULTS: 313 patients (84.4%) showed normal EEGs, non-specific or interictal abnormalities, 14 patients (3.8%) had EEGs with at least one electrographic seizure, and 44 patients (11.8%) at least one EEG containing RPPIIIU, but no EEG with electrographic seizures. Electrographic seizures occurred in 11 of 55 patients (20%) with RPPIIIU, but only in 3 of 316 patients (0.9%) without RPPIIIU (p⩽0.001). Conversely, we observed RPPIIIU in 11 of 14 patients (78.6%) with electrographic seizures, but only in 44 of 357 patients (12.3%) without electrographic seizures (p⩽0.001). CONCLUSIONS: On routine-EEG in critically ill neurological patients RPPIIIU occur 3 times more frequently than electrographic seizures and are highly predictive for electrographic seizures. SIGNIFICANCE: RPPIIIU can serve as an indication for continuous EEG recordings.

Spatial correlation based artifact detection for automatic seizure detection in EEG.

Automatic EEG-processing systems such as seizure detection systems are more and more in use to cope with the large amount of data that arises from long-term EEG-monitorings. Since artifacts occur very often during the recordings and disturb the EEG-processing, it is crucial for these systems to have a good automatic artifact detection. We present a novel, computationally inexpensive automatic artifact detection system that uses the spatial distribution of the EEG-signal and the location of the electrodes to detect artifacts on electrodes. The algorithm was evaluated by including it into the automatic seizure detection system EpiScan and applying it to a very large amount of data including a large variety of EEGs and artifacts.

Pharmacokinetics of teduglutide in subjects with renal impairment.

PURPOSE: Teduglutide is a recombinant analogue of human glucagon-like peptide-2 that has recently been approved for the treatment of short bowel syndrome in adults. This study was designed to study the influence of renal function and age on teduglutide pharmacokinetics. METHODS: This was an open-label study with six parallel groups (6 subjects each). Three groups with renal impairment (moderate, severe and end-stage renal disease) were compared to healthy subjects with normal renal function, which were matched to the renal-impaired subjects with respect to demographics. At least two elderly subjects (≥65 years) were enrolled per group. A single dose of 10 mg teduglutide was subcutaneously administered to each subject. Teduglutide plasma concentrations were measured using a validated liquid chromatography method with tandem mass spectrometric detection, and the primary pharmacokinetic variables (AUCinf and Cmax) were calculated. RESULTS: Area under the concentration versus time curve extrapolated to infinity (AUCinf) and maximum plasma concentration (Cmax) of teduglutide in subjects with end-stage renal disease were approximately 2.59- and 2.08-fold higher, respectively, than those of healthy subjects. The AUCinf and Cmax were also slightly higher in subjects with moderate and severe renal impairment. Comparison of healthy subjects aged <65 years with healthy elderly subjects revealed very similar pharmacokinetics in both subgroups. CONCLUSIONS: In our study population, the primary pharmacokinetic parameters of teduglutide increased with increased severity of renal impairment. These results suggest that the daily dose of teduglutide should be reduced by 50 % in patients with moderate and severe renal impairment and end-stage disease. We found no effect of age on the pharmacokinetics of teduglutide in healthy subjects. The treatment was well tolerated, and there were no safety concerns.

A Thorough QT Study of Teduglutide in Healthy Subjects.

Teduglutide, an analog of the endogenous hormone glucagon-like peptide-2, is currently being developed for the treatment of short bowel syndrome. This study investigated the potential effects of teduglutide on cardiac conduction and repolarization. Seventy-two healthy volunteers underwent 4 treatment periods in randomized order with a single subcutaneous injection of placebo, 5 and 20 mg teduglutide, and a single oral 400-mg dose of moxifloxacin. The primary variable to investigate the effect on cardiac repolarization was the difference between QTcF after administration versus predose. The observed upper bounds of the 95% one-sided confidence intervals were 3.0 ms (5 mg) and 4.5 ms (20 mg). Arithmetic mean curves of QTcF intervals over time of both doses of teduglutide and of placebo were almost superposable. Assay sensitivity for the positive control moxifloxacin was shown. In conclusion, teduglutide at intended therapeutic and supratherapeutic doses had no effect on cardiac repolarization. No safety concerns were identified. Treatment with teduglutide was well tolerated.

EpiScan: online seizure detection for epilepsy monitoring units.

An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients the algorithm detected 100% of the seizures. A mean sensitivity of 83% was achieved. An average of 7.2 false alarms within 24 hours for unselected EEG makes the algorithm attractive for epilepsy monitoring units.

Regression-based analysis of synchronization in multichannel EEG in epilepsy.

In this paper we assess a dependency measure for multivariate time series termed Extrinsic-to-Intrinsic-Power-Ratio (EIPR) using two different signal models. In a comparison with Partial Directed Coherence (PDC) we show that both measures correctly identify imposed couplings, but that limitations of the PDC do not affect EIPR. Moreover, EIPR is successfully used for the localization of the seizure onset zone in ECoG recordings from two epilepsy patients, given the exact seizure onset time. The electrodes identified by the proposed method are in excellent accordance with the clinical findings.

Intragastric pH during continuous infusion with pantoprazole in patients with bleeding peptic ulcer.

OBJECTIVES: In managing patients with bleeding peptic ulcers, prevention of rebleeding is a particular challenge to hemostasis and fibrinolysis, both of which involve reactions that are impaired in acidic gastric environment. Therefore, such patients are expected to benefit from profound acid suppression. The present investigation aimed to establish a safe and, with regard to pH elevation, effective treatment that, based on in vitro evidence, should provide clinical benefit in this patient population. METHODS: Patients with acute bleeding peptic ulcers (Forrest Ia, Ib, IIa) after successful endoscopic hemostasis were enrolled in two pilot studies (N = 20 each). They were given an intravenous bolus injection of 80 mg of pantoprazole immediately followed by continuous infusion of either 6 mg/h or 8 mg/h pantoprazole for 72 h. Intragastric pH was measured continuously over 24 h and, if possible, for up to 48 h. RESULTS: Intragastric pH increased rapidly to values of about 6 with both treatments. For the 0-24 h period, the median pH values were 6.1 (68% range 4.5-7.4) and 6.1 (68% range 5.2-6.7) in patients receiving 6 mg/h and 8 mg/h continuous infusion, respectively; the values for the 0-48 h period were 5.9 (4.9-6.7) and 6.3 (5.5-7.0), respectively. The median percentage time that pH was > or =6 during the 0-48 h interval was 47% (68% range 28-89) for the 6 mg/h treatment group and 64% (68% range 41-84) for the 8 mg/h treatment group. Both treatment regimens with pantoprazole were well tolerated based on electrocardiographic measurements, vital signs, clinical laboratory values, and adverse events. CONCLUSIONS: Compared with the infusion with 6 mg/h pantoprazole, the continuous infusion of 8 mg/h pantoprazole showed a lower interindividual variability of the intragastric pH and a greater percentage of time that pH was >/ or =6. Thus, with regard to safety and efficacy, an initial 80-mg bolus injection, followed by 8 mg/h continuous infusion, seems to be the adequate treatment in patients with a high risk of rebleeding.

Fusion proteins with anticoagulant and fibrinolytic properties: functional studies and structural considerations.

In an effort to combine the benefits of fibrinolytics, such as staphylokinase, with those of thrombin inhibitors for the prevention of vessel reocclusion after vascular injury, we have produced several chimeric proteins with plasminogen-activating and thrombin-inhibiting properties. Fusion proteins were constructed consisting of the modules staphylokinase (Sak), the factor Xa cleavage site, and various dipetalin (Dip) domains (H(6)-Sak-Dip-I+II, H(6)-Sak-Dip-I, and H(6)-Sak-Dip-II). Sak stimulates fibrinolysis via activation of plasminogen, whereas dipetalin is a two-domain, Kazal-type inhibitor of thrombin. NMR spectroscopy of the fusion proteins revealed that the molecular structures of the modules are retained in the fusion protein and that no significant interactions occur between the modules in terms of their functionally relevant regions. In enzymatic thrombin inhibition tests and blood coagulation assays (thrombin, prothrombin, and activated partial thromboplastin times), no significant differences in anticoagulant capacity were observed between the fusion protein H(6)-Sak-Dip-I+II and isolated Dip-I+II, even at nanomolar concentrations. Similar results (i.e., the inhibition of thrombin-induced platelet aggregation and the inhibition of thrombin-induced vascular relaxation) were obtained when the cellular thrombin effects were studied. The fusion protein containing Dip-I has less but still significant thrombin inhibitory effects compared with those of H(6)-Sak-Dip-I+II. In contrast, the H(6)-Sak-Dip-II protein failed to inhibit thrombin in each of the assays used. The plasminogen-activating and fibrinolytic activities of the fusion proteins are similar to those of wild-type Sak. The individual dipetalin domains do not activate plasminogen. In conclusion, the fusion protein H(6)-Sak-Dip-I+II is a bifunctional molecule able to activate fibrinolysis via plasminogen activation and inhibit blood coagulation via direct inhibition of thrombin.

Interaction of Kazal-type inhibitor domains with serine proteinases: biochemical and structural studies.

The interaction of domains of the Kazal-type inhibitor protein dipetalin with the serine proteinases thrombin and trypsin is studied. The functional studies of the recombinantly expressed domains (Dip-I+II, Dip-I and Dip-II) allow the dissection of the thrombin inhibitory properties and the identification of Dip-I as a key contributor to thrombin/dipetalin complex stability and its inhibitory potency. Furthermore, Dip-I, but not Dip-II, forms a complex with trypsin resulting in an inhibition of the trypsin activity directed towards protein substrates. The high resolution NMR structure of the Dip-I domain is determined using multi-dimensional heteronuclear NMR spectroscopy. Dip-I exhibits the canonical Kazal-type fold with a central alpha-helix and a short two-stranded antiparallel beta-sheet. Molecular regions essential for inhibitor complex formation with thrombin and trypsin are identified. A comparison with molecular complexes of other Kazal-type thrombin and trypsin inhibitors by molecular modeling shows that the N-terminal segment of Dip-I fulfills the structural prerequisites for inhibitory interactions with either proteinase and explains the capacity of this single Kazal-type domain to interact with different proteinases.