Bioactive peptides pose a great threat to sports integrity. The detection of these peptides is essential for enforcing their prohibition in sports. Identifying the catabolites of these peptides that are formed ex vivo in plasma may improve their detection. In the present study, the stability of 27 bioactive peptides with protection at both termini in equine plasma was examined under different incubation conditions, using HILIC coupled to HRMS. Of the 27 peptides, 13 were stable after incubation at 37°C for 72 hr, but the remaining 14 were less stable. Ex vivo catabolites of these 14 peptides were detected using their theoretical masses generated in silico, their appearance was monitored over the time course of incubation, and their identity was verified by their product ion spectra. Catabolites identified for chemotactic peptide, DALDA, dmtDALDA, deltorphins I and II, Hyp6‐dermorphin, Lys7‐dermorphin, and dermorphin analog are novel. A d‐amino acid residue at position 2 or 1 of a peptide or next to its C‐terminus protected the relevant terminal from degradation by exopeptidases, but such a residue at position 3 did not. A pGlu residue or N‐methylation at the N‐terminus of a peptide did not protect its N‐terminal. Ethylamide at the C‐terminus of a peptide provided the C‐terminal protection from attacks by carboxypeptidases. The C‐terminal Lys amide in DALDA, dmtDALDA, and Lys7‐dermorphin was susceptible to cleavage by plasma enzymes, which is the first report, to the authors’ knowledge. The results from the present study provide insights into the stability of peptides in plasma.
To address the limitations of current targeted analytical methods that can only detect known doping agents, a novel methodology that permits untargeted drug detection (UDD) has been developed to help in the fight against doping in sports. Fiftyseven drugs were spiked into blank equine plasma and were treated as unknowns since their exact masses and chromatographic retention times were not utilized for detection. The spiked drugs were extracted from the plasma samples and were analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC−HRMS). The acquired LC−HRMS raw data files were processed using metabolomic software for compound detection and identification. For UDD with the resultant data, a mathematical model was created, and two algorithms were generated to calculate the ratio of the mean (ROM) and outlier index (OLI). Using ROM and OLI, the majority of the 57 drugs were accurately detected by name (52 of 57) or chemical formula (1 of 57). The limit of detection for the drugs was from tens of picograms to nanograms per milliliter. Xenobiotics and endogenous substances relevant to doping control were also identified using this untargeted approach following their extraction from real-world race samples, thus validating the UDD methodology. To the authors' knowledge, this is the first completely UDD methodological approach and represents significant advance toward using artificial intelligence for the detection of both known and emerging doping agents in sports.
Rapid and accurate identification of unknown compounds within suspicious samples confiscated for sports doping control and law enforcement drug testing is critical, but such analyses are often conducted manually and can be time-consuming. Here, we report a methodology for automated identification of unknown substances in confiscation samples by rapid automatic flow-injection analysis on a liquid chromatography coupled to high-resolution mass spectrometry system and identifying unknown compounds with Compound Discoverer software. The developed methodology was validated by comparing the automated identification results with those obtained from manual syringe-infusion experiments and manual tandem mass spectral library searches. The automated methodology resulted in far higher throughput and remarkably shorter turnaround time for analysis when compared with manual procedures and, in most cases, yielded more compounds. As this is the first such report to the authors' knowledge, this methodology may potentially transform analysis of confiscated samples in sports doping control and law enforcement drug testing.
A computer model of associative learning in the invertebrate, Hermissenda crassicornig, has previously been shown to demonstrate many characteristics of vertebrate conditioning. The model is tested in experimental paradigms which mimic those applied in behavioral studies. Temporal learning characteristics of the model are shown to be quantitatively similar to that of the animal. These results demonstrate the value of this computational learning model as a tool for examining associative learning in biological systems and for uncovering insights concerning associative learning, memory, and recall which have been applied to the development of artificial neural networks. IN'IRODUCI'IONIn [l], we describe a computer model of associative learning as demonstrated in a marine snail, Hermissenda grassicornis [2]. The model aggregates and simplifies the dynamics of the component mechanisms and captures only the essential cellular and network features of associative learning. It reproduces associative learning that is quantitatively similar to that of Hermissenda and exhibits many characteristics of Pavlovian conditioning when the model is trained with paired stimuli. In this paper, we explore sensitivities of the model's learning to the temporal relationship of the conditioning stimulus (CS) and the unconditioned stimulus (US). In particular, we examine the model's sensitivities to interstimulus interval (ISI) and to relative timing of the stimulus offsets. DESCRLFTION OF THE MODELThe studies of associative learning in Hermissenda focus on information flow in a four-neuron circuit that is capable of learning to associate light (CS) and rotation (US), even when isolated from the animal ( Figure 1). The computer model of associative learning incorporates intercellular and intracellular information flow, and physiological and biochemical events essential to the learning process ( Figure 2):1. The resistance-capacitance (RC) circuit concept used to model the currents through (including the generator current dynamics, GCD, in Figure 2) and the voltage across each neuron membrane (membrane dynamics, MD, in Figure 2); 2. Post-inhibitory rebound currents in the E-cell ( a ganglion cell) and vestibular hair cells (rebound current dynamics, RCD, in Figure 2); 3. Voltage-dependent calcium current in the B-cell (calcium current dynamics, CCD, in Figure 2); 4. Calcium-dependent and synapse-specific increase of the B photoreceptor's input resistance and ability to maintain an increased membrane resistance (resistance dynamics, RD, in Figure 2). This synapse-specific mechanism receives an important contribution from GABA-mediated stimulation of the photoreceptor by the caudal hair cell during paired stimulus presentations [3].5. Time-varying shunting of B-cell synaptic inputs during light exposure (RD in Figure 2). The input resistance of the photoreceptor, initially small, gradually increases during a light stimulus [4]. Figure 1 : htenensory integration by the Hermissenda newous system. (A) convergence of synapb;c inhibition trom type ...
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