Consensus expert recommendations for identification and management of asparaginase hypersensitivity and silent inactivation

L-asparaginase is an integral component of therapy for acute lymphoblastic leukemia. However, asparaginase-related complications, including the development of hypersensitivity reactions, can limit its use in individual patients. Of considerable concern in the setting of clinical allergy is the development of neutralizing antibodies and associated asparaginase inactivity. Also problematic in the use of asparaginase is the potential for the development of silent inactivation, with the formation of neutralizing antibodies and reduced asparaginase activity in the absence of a clinically evident allergic reaction. Here we present guidelines for the identification and management of clinical hypersensitivity and silent inactivation with Escherichia coli- and Erwinia chrysanthemi- derived asparaginase preparations. These guidelines were developed by a consensus panel of experts following a review of the available published data. We provide a consensus of expert opinions on the role of serum asparaginase level assessment, indications for switching asparaginase preparation, and monitoring after change in asparaginase preparation

Automated quality control of small animal MR neuroimaging data

MRI is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large data sets for potential poor-quality outliers can be a challenge. We present AIDAqc, a machine learning-assisted automated Python-based command-line tool for small animal MRI quality assessment. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection for a given dataset combines the interquartile range and the machine learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. To evaluate the reliability of individual quality control metrics, a simulation of noise (Gaussian, salt and pepper, speckle) and motion was performed. In outlier detection, single scans with induced artifacts were successfully identified by AIDAqc. AIDAqc was challenged in a large heterogeneous dataset collected from 19 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater agreement (mean Fleiss Kappa score 0.17) is low when identifying poor-quality data. A direct comparison of AIDAqc results, therefore, showed only low to moderate concordance. In a manual post-hoc validation of AIDAqc output, precision was high (>70%). The outlier data can have a significant impact on further post-processing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility

A consensus protocol for functional connectivity analysis in the rat brain

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience