Background: Update diagnostic methods play essential roles in dealing with the current global malaria situation and decreasing malaria incidence.Aim: Global malaria control programs require the availability of adequate laboratory tests in the quick and convenient field.Results: There are several methods to find out the existence of parasites within the blood. The oldest one is by microscopy, which is still a gold standard, although rapid diagnostic tests (RDTs) have rapidly become a primary diagnostic test in many endemic areas. Because of microscopy and RDTs limitation, novel serological and molecular methods have been developed. Many kinds of polymerase chain reaction (PCR) provide rapid results and higher specificity and sensitivity. The loop‐mediated isothermal amplification (LAMP) and biosensing‐based molecular techniques as point of care tests (POCT) will become a cost‐effective approach to advance diagnostic testing.Conclusion: Despite conventional techniques are still being used in the field, the exploration and field implementation of advanced techniques for the diagnosis of malaria are still being developed rapidly.

Intestinal protozoan infection is a persisting public health problem affecting the populations of developing countries in the tropical and subtropical regions. The diagnosis of intestinal protozoa remains a challenge especially in developing countries due to a shortage of laboratory facilities, limited health funding, and the remoteness of communities. Despite still being widely used, conventional diagnoses using microscopy and staining methods pose important limitations, particularly due to their low sensitivities and specificities. The selection of diagnostic methods needs to be carefully considered based on the objective of examination, availability of resources, and the expected parasite to be found. In this review, we describe various immunodiagnosis and molecular diagnostic methods for intestinal protozoa infection, including their advantages, disadvantages, and suitability for different settings, with a focus on Entamoeba histolytica, Giardia duodenalis, and Cryptosporidium spp.

Purpose: During Plasmodium berghei (P. berghei) infection, infected erythrocytes are sequestered in gut tissues through microvascular circulation, leading to dysbiosis. This study aimed to investigate the effect of Lactobacillus casei (L. casei) and Bifidobacterium longum (B. longum) administration on the parasitemia level, gut microbiota composition, expression of cluster of differentiation 103 (CD103) in intestinal dendritic and T regulatory cells (T reg), plasma interferon gamma (IFN-γ) and tumor necrosis factor (TNF-α) levels in P. berghei infected mice. Methods: P. berghei was inoculated intraperitoneally. Infected mice were randomly divided into 5 groups and treated with either L. casei, B. longum, or the combination of both for 5 days before up to 6 days post-infection (p.i). The control group was treated with phosphate-buffered saline (PBS), while uninfected mice were used as negative control. Levels of CD103 and forkhead box P3 (FoxP3) expression were measured by direct immunofluorescense, while plasma IFN-γ and TNF-α level were determined using enzyme-linked immunosorbent assay (ELISA). Results: All treated groups showed an increase in parasitemia from day 2 to day 6 p.i, which was significant at day 2 p.i (p = 0.001), with the group receiving B. longum displaying the lowest degree of parasitemia. Significant reduction in plasma IFN-γ and TNF-α levels was observed in the group receiving B. longum (p = 0.022 and p = 0.026, respectively). The CD103 and FoxP3 expression was highest in the group receiving B. longum (p = 0.01 and p = 0.02, respectively). Conclusion: B. longum showed the best protective effect against Plasmodium infection by reducing the degree of parasitemia and modulating the gut immunity. This provides a basis for further research involving probiotic supplementation in immunity modulation of infectious diseases.

Malaria remains to be a national and global challenge and priority, as stated in the strategic plan of the Indonesian Ministry of Health and Sustainable Development Goals. In Indonesia, it is targeted that malaria elimination can be achieved by 2030. Unfortunately, the development and spread of antimalarial resistance inflicts a significant risk to the national malaria control programs which can lead to increased malaria morbidity and mortality. In Indonesia, resistance to widely used antimalarial drugs has been reported in two human species, Plasmodium falciparum and Plasmodium vivax. With the exception of artemisinin, resistance has surfaced towards all classes of antimalarial drugs. Initially, chloroquine, sulfadoxine-pyrimethamine, and primaquine were the most widely used antimalarial drugs. Regrettably, improper use has supported the robust spread of their resistance. Chloroquine resistance was first reported in 1974, while sulfadoxine-pyrimethamine emerged in 1979. Twenty years later, most provinces had declared treatment failures of both drugs. Molecular epidemiology suggested that variations in pfmdr1 and pfcrt genes were associated with chloroquine resistance, while dhfr and dhps genes were correlated with sulfadoxine-pyrimethamine resistance. Additionally, G453W, V454C and E455K of pfk13 genes appeared to be early warning sign to artemisinin resistance. Here, we reported mechanisms of antimalarial drugs and their development of resistance. This insight could provide awareness toward designing future treatment guidelines and control programs in Indonesia. Graphical Abstract:

Background: Malaria remains a major public health concern around the world. Microscopic blood smear examination continues to be the gold standard for diagnosis; however, it requires high technical skills and expertise, limiting diagnostic accuracy in resource-poor settings. Artificial intelligence (AI) has emerged as a promising tool to support malaria detection. This systematic review provides an overview of the diagnostic performance of AI-based systems for malaria diagnosis in a clinical setting. Methods: This study followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and involved articles within the last 10 years that were collected from PubMed, ScienceDirect, Cochrane, EBSCO, and Wiley Online Library. Original articles that reported AI diagnostic accuracy with external validation were involved. The quality of each study was evaluated using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Results: Ten studies with 6754 patients were analyzed. Pooled results of sensitivity [87.7% (95% CI: 78.2–93.4)] and specificity [91.4% (95% CI: 77.3–97.1)] revealed how much the AI agrees with each method when that method is used as a gold standard. Additionally, AI achieved a sensitivity of 87.7% and a specificity of 91.4% compared to microscopy examination and a sensitivity of 90.7% and a specificity of 88.3% compared to polymerase chain reaction (PCR). Conclusions: AI-based systems improve malaria diagnosis by providing high accuracy, automation, and lower costs. Showing performance comparable to reference methods such as microscopy and PCR, AI is a promising complementary tool for malaria control.