Abstract: Described herein are antigen binding proteins that bind to pathogenic mycobacteria-derived Mannose-Capped Lipoarabinomannan (ManLAM) and methods and kits for using and making the antigen binding proteins. Also described herein are antigen binding proteins that bind to the alpha 1-2 linkage mannose caps of ManLAM, antigen binding proteins that bind to a mannose cap with up to three alpha 1-2 linked mannose residues, and antigen binding proteins that bind to LAM with a mannose sugar capping motif.

Abstract: We describe a method of generating a classification model capable of distinguishing between two biological states, the method comprising the steps of: (a) providing a training dataset, X, comprising concentrations of a plurality of lipids in a biological sample in a first state and a biological sample in a second state; (b) subjecting the training dataset X to Principal Components Analysis (PCA), in which the PCA analysis generates a transformation matrix, C, and a transformed dataset, Yl; (c) subjecting the transformed dataset Yl to Support Vector Machines (SVM) analysis, in which the SVM analysis generates a SVM model, S; (d) forming a classification model comprising (i) the transformation matrix C, and (ii) the corresponding SVM model S.

Abstract: Described herein are antigen binding proteins that bind to pathogenic mycobacteria-derived Mannose-Capped Lipoarabinomannan (ManLAM) and methods and kits for using and making the antigen binding proteins. Also described herein are antigen binding proteins that bind to the alpha 1-2 linkage mannose caps of ManLAM, antigen binding proteins that bind to a mannose cap with up to three alpha 1-2 linked mannose residues, and antigen binding proteins that bind to LAM with a mannose sugar capping motif.

Abstract: We describe a method of generating a classification model capable of distinguishing between two biological states, the method comprising the steps of: (a) providing a training dataset, X, comprising concentrations of a plurality of lipids in a biological sample in a first state and a biological sample in a second state; (b) subjecting the training dataset X to Principal Components Analysis (PCA), in which the PCA analysis generates a transformation matrix, C, and a transformed dataset, Yl; (c) subjecting the transformed dataset Yl to Support Vector Machines (SVM) analysis, in which the SVM analysis generates a SVM model, S; (d) forming a classification model comprising (i) the transformation matrix C, and (ii) the corresponding SVM model S.

Abstract: There is provided at least one agent delivery vehicle comprising at least one membrane of cubic structure or derivable from a membrane of cubic structure, methods for preparing them, and their use for transport of at least one foreign agent. In particular, the agent delivery vehicle may be in the form of at least one organelle, agent delivery particle and/or liposome. The invention further provides kits and methods of treatment using the agent delivery vehicle(s).