Neoplastic transformation often results in cells with unusual glycosylation patterns specific to the type and stage of different cancers, providing numerous venues for therapeutic and diagnostic reagents (1, 2). Lectins are non-catalytic proteins with strict specificity for individual mono-or oligosaccharides, which can make them powerful tools for detecting changes in the carbohydrate structure in glycoproteins and glycolipids.

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The structure of a antigen-binding fragment (Fab) from the bactericidal monoclonal antibody LPT3-1 specific to lipooligosaccharide (LOS) inner cores from Neisseria meningitidis has been solved in complex with an eight-sugar inner core fragment NmL3 galE lpt3 KOH to 2.69 Å resolution. The epitope is centered about an inner core N-acetylglucosamine residue unique to N. meningitidis and does not include the lipid A moiety, which is disordered in the structure, but is positioned to allow the binding of free and membrane-anchored full-length LOS. All the amino acid residues that contact antigen are of germline origin but, remarkably, two consecutive somatic mutations of serine to glycine in the heavy chain at residues 52 and 52a are positioned to deprive the antibody of advantageous interactions and so weaken binding. However, these mutations are key to allowing selective cross-reactivity with the HepII-3-PEtn inner core variant expressed by 70% of strains. Neisseria meningitidis is a leading cause of disease in the developed world and is especially dangerous to children, who lack the necessary protective antibodies. The structure of Fab LPT3-1 in complex with LOS provides insight into the antibody's selective ability to recognize multiple clinically relevant variations of the LOS inner core from N. meningitidis.

Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor and several candidate machine learning (ML) models. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments.

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