Method of measuring efficacy of treatment for multiple sclerosis
A method of measuring efficacy of treatment of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. This dataset is compared to healthy animals or a standard of care to determine efficacy. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.
1. A method of measuring efficacy of a first treatment of multiple sclerosis (MS) in an animal in a vivarium comprising the steps of:
(a) placing a study animal in a cage in the vivarium and enrolling the study animal in a study;
(b) electronically observing one or more animal activities in real-time of the study animal using a combination of electronic cameras, infrared (IR) lighting of the study animals, and electronic hardware including computation and communication hardware;
(c) selecting a single “LASSO metric” with an associated scalar “activity-drop value”;
(d) selecting an “MS health detection function,” wherein an input to the MS health detection function comprises an animal health dataset; and wherein an output of the MS health detection function comprises an animal health severity scalar for the animal;
(e) collecting a set of nightly activity data for the animal comprising a plurality of activity scalars for the animal each night, wherein the nightly activity scalars are collected repeatedly and continually throughout a night;
(f) computing a LASSO best-fit of a first piece-wise linear function to the nightly activity data, generating a LASSO L0, L1 and L2;
(g) applying the LASSO metric to the generated LASSO L0, L1 and L2, generating a nightly activity-drop value;
(h) adding the nightly activity-drop value into the “animal health dataset,” wherein the animal health dataset comprises the nightly activity-drop values;
(i) applying the MS health detection function to the animal health dataset;
(j) iterating steps (f) through (h) for sequential nights until a terminating condition is reached;
wherein a first measured efficacy of the first treatment comprises
comparing the animal health severity scalars from step (i) to animal health severity scalars from a reference treatment;
(k) removing the study animal from the study when the terminating condition is reached.
2. The method of claim 1 wherein:
the first piece-wise linear function comprises two linear pieces; and
wherein the nightly activity-drop value comprises a linear combination of LASSO L0, L1 and L2.
3. The method of claim 1 wherein:
the first piece-wise linear function comprises three linear pieces; and
wherein the nightly activity-drop value comprises a linear combination of LASSO L0, L1 and L2.