Principal Component Analysis of clinical and immunological data.

<p>Data analysis from Cameroonian samples where clinical parameters and immunological data were analyzed using Principal Component Analysis. The principal component analysis generated a model that explains 23% of the data segregation (R2X = 0.234545). Clinical parameters: temperature (Temp), breath rate (breath), pulse rate (pulse), glucose level (glucose), haemoglobin (Hb), parasitemia, age, rosetting rates (RR, after collection from patients). Immunological parameters: plasma sample recognition of NTS-DBL1α It4var60 domain (ELISA), ability to disrupt FCR3S1.2 rosettes (RD) and ability to recognize FCR3S1.2 iRBC surface by flow cytometry (Surface reactivity).