Organophosphorus (OP) compounds are neurotoxins that are among the most widely used pesticides in agriculture in the United States. In this application, a new integrated point-of-care smartphone/resistive nanosensor device is developed for onsite rapid and sensitive detection of exposure to OP pesticides from a drop of finger-stick blood among a sample of farmworkers. The nanosensor leverages the transport properties of a multiwalled carbon nanotube/polyaniline nanofiber (MWCNT/PAnNF) nanocomposite film on a gold interdigitated electrode and acetylcholinesterase/butyrylcholinesterase (AChE/BChE) hydrolysis of their respective substrates generating protons doping PAnNFs, thereby increasing the conductance of the film. As such, a conductance change can be used to quantify cholinesterase activity, enabling assessment of acute/chronic OP poisoning. Additionally, a mobile app was developed for the nanosensor to process, display, track, and share results. Under optimal conditions, the nanosensor demonstrated exceptional sensitivity with the detection limits of 0.11 U/mL for AChE and 0.093 U/mL for BChE, physiologically relevant dynamic ranges of 2.0-18.0 U/mL for AChE and 0.5-5.0 U/mL for BChE in whole blood, and high reproducibility with the relative standard variation of <4%. The nanosensor was further validated with widely used radiometric and Ellman's methods, utilizing both in vitro pesticide-spiked blood samples and blood samples from 22 farmworkers. The results between this nanosensor and those two methods demonstrated a strong agreement. This platform provides a new avenue for the simple, rapid, and sensitive biomonitoring of OP pesticide exposure.

Untargeted MS experiments produce complex, multi-dimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Based on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is open-source software for the processing of raw spectral data generated by different MS platforms: liquid chromatography–MS (LC–MS), gas chromatography–MS (GC–MS), and MS–imaging. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: LC–(IMS–)MS, GC–MS, and (IMS–)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e. list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 hours for new MZmine users and non-experts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

The number of metabolomics studies and spectral libraries for compound annotation (i.e., assigning possible compound identities to a fragmentation spectrum) has been growing steadily in recent years. Accompanying this growth is the number of mass spectra available for searching through those libraries. As the size of spectral libraries grows, accurate and fast compound annotation becomes more challenging. We herein report a prescreening algorithm that was developed to address the speed of spectral search under the constraint of low memory requirements. This prescreening has been incorporated into the Automated Data Analysis Pipeline Spectral Knowledgebase (ADAP-KDB) and can be applied to compound annotation by searching other spectral libraries as well. Performance of the prescreening algorithm was evaluated for different sets of parameters and compared to the original ADAP-KDB spectral search and the MSSearch software. The comparison has demonstrated that the new algorithm is about four-times faster than the original when searching for low-resolution mass spectra, and about as fast as the original when searching for high-resolution mass spectra. However, the new algorithm is still slower than MSSearch due to the relational database design of the former. The new search workflow can be tried out at the ADAP-KDB web portal.

Obtaining protein samples with high yield, high purity, and high activity is the foundation for most modern biological studies. Besides, highly purified protein preparations, such as monoclonal antibodies, have been widely used in the treatment of cancer, genetic and orphan diseases.