Abstract:
Malaria is a common parasitic disease spread by mosquitoes that is caused by protozoan known
as Plasmodium. Among different species, Plasmodium falciparum and Plasmodium vivax are
being identified as the most lethal and responsible for the majority of deaths, particularly in sub Saharan Africa. Malaria can be diagnosed by parasitological test, rapid diagnostic tests in low
resource setting. In central and research laboratories, molecular methods such as polymerase
chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) can be used. In areas
where cost reduction is crucial, LAMP addresses PCR's drawbacks by being simpler, cheaper,
and faster while still maintaining accuracy, making it a promising tool for diagnosing malaria in
field. While the LAMP technique offers a sensitive near point-of-care solution, interpreting
results through direct observation is prone to errors and lacks the ability to quantify parasite
load, which reduces its effectiveness and can lead to misdiagnosis and improper treatment. The
objective of this study is to develop a detection system to test and relative quantification of
malaria parasite in LAMP machine in low resource settings. The general frame work mainly
consists of sample preparation, isothermal amplification and detection system. To construct the
prototype hardware and software components like microcontroller, LCD display, Light source,
photodiode, pushbutton, resistor, breadboard, Arduino IDE and Thinkercad, were utilized. The
developed system was tested on both malaria positive and negative samples and result also
compared with the gold standard. Accordingly, the newly developed system is capable of
distinguishing malaria positive and negative samples with 100% accuracy as well as significant
correlation was obtained between parasite load and ADC(Analog to digital converted) value
(Spearman r = 1.000 with 95% CI, P = 0.0167) using GraphPad prism. Furthermore, student t test has been assessed between ADC Value of positive and negative samples (t = 7.300 and p value of less than 0.001) as well as between positive control and positive samples (t = 1.607 and
p-value of 0.159, which is greater than 0.05 the threshold) using SPSS. As a result, significant
difference was observed between positive and negative samples and in significant difference was
obtained between positive samples and control. Our LAMP technique offers a rapid, cost effective, and user-friendly solution for malaria detection in clinical samples. It aids early
treatment, reduces mortality, and combats misdiagnosis and drug resistance.
