SUMMARY: The genus Entamoeba contains many species, six of which ( Entamoeba histolytica, Entamoeba dispar, Entamoeba moshkovskii, Entamoeba polecki, Entamoeba coli , and Entamoeba hartmanni ) reside in the human intestinal lumen. Entamoeba histolytica is the causative agent of amebiasis and is considered a leading parasitic cause of death worldwide in humans. Although recent studies highlight the recovery of E. dispar and E. moshkovskii from patients with gastrointestinal symptoms, there is still no convincing evidence of a causal link between the presence of these two species and the symptoms of the host. New approaches to the identification of E. histolytica are based on detection of E. histolytica -specific antigen and DNA in stool and other clinical samples. Several molecular diagnostic tests, including conventional and real-time PCR, have been developed for the detection and differentiation of E. histolytica , E. dispar , and E. moshkovskii in clinical samples. The purpose of this review is to discuss different methods that exist for the identification of E. histolytica , E. dispar , and E. moshkovskii which are available to the clinical diagnostic laboratory. To address the need for a specific diagnostic test for amebiasis, a substantial amount of work has been carried out over the last decade in different parts of the world. The molecular diagnostic tests are increasingly being used for both clinical and research purposes. In order to minimize undue treatment of individuals infected with other species of Entamoeba such as E. dispar and E. moshkovskii , efforts have been made for specific diagnosis of E. histolytica infection and not to treat based simply on the microscopic examination of Entamoeba species in the stool. The incorporation of many new technologies into the diagnostic laboratory will lead to a better understanding of the public health problem and measures to control the disease.

SUMMARY: Several enteric protozoa cause severe morbidity and mortality in both humans and animals worldwide. In developed settings, enteric protozoa are often ignored as a cause of diarrheal illness due to better hygiene conditions, and as such, very little effort is used toward laboratory diagnosis. Although these protozoa contribute to the high burden of infectious diseases, estimates of their true prevalence are sometimes affected by the lack of sensitive diagnostic techniques to detect them in clinical and environmental specimens. Despite recent advances in the epidemiology, molecular biology, and treatment of protozoan illnesses, gaps in knowledge still exist, requiring further research. There is evidence that climate-related changes will contribute to their burden due to displacement of ecosystems and human and animal populations, increases in atmospheric temperature, flooding and other environmental conditions suitable for transmission, and the need for the reuse of alternative water sources to meet growing population needs. This review discusses the common enteric protozoa from a public health perspective, highlighting their epidemiology, modes of transmission, prevention, and control. It also discusses the potential impact of climate changes on their epidemiology and the issues surrounding waterborne transmission and suggests a multidisciplinary approach to their prevention and control.

The reconstruction of phylogenetic history is predicated on being able to accurately establish hypotheses of character homology, which involves sequence alignment for studies based on molecular sequence data. In an empirical study investigating nucleotide sequence alignment, we inferred phylogenetic trees for 43 species of the Apicomplexa and 3 of Dinozoa based on complete small-subunit rDNA sequences, using six different multiple-alignment procedures: manual alignment based on the secondary structure of the 18S rRNA molecule, and automated similarity-based alignment algorithms using the PileUp, ClustalW, TreeAlign, MALIGN, and SAM computer programs. Trees were constructed using neighboring-joining, weighted-parsimony, and maximum-likelihood methods. All of the multiple sequence alignment procedures yielded the same basic structure for the estimate of the phylogenetic relationship among the taxa, which presumably represents the underlying phylogenetic signal. However, the placement of many of the taxa was sensitive to the alignment procedure used; and the different alignments produced trees that were on average more dissimilar from each other than did the different tree-building methods used. The multiple alignments from the different procedures varied greatly in length, but aligned sequence length was not a good predictor of the similarity of the resulting phylogenetic trees. We also systematically varied the gap weights (the relative cost of inserting a new gap into a sequence or extending an already-existing gap) for the ClustalW program, and this produced alignments that were at least as different from each other as those produced by the different alignment algorithms. Furthermore, there was no combination of gap weights that produced the same tree as that from the structure alignment, in spite of the fact that many of the alignments were similar in length to the structure alignment. We also investigated the phylogenetic information content of the helical and nonhelical regions of the rDNA, and conclude that the helical regions are the most informative. We therefore conclude that many of the literature disagreements concerning the phylogeny of the Apicomplexa are probably based on differences in sequence alignment strategies rather than differences in data or tree-building methods.

SUMMARY: Angiostrongylus cantonensisis a metastrongyloid nematode found widely in the Asia-Pacific region, and the aetiological agent of angiostrongyliasis; a disease characterized by eosinophilic meningitis.Rattusrats are definitive hosts ofA. cantonensis, while intermediate hosts include terrestrial and aquatic molluscs. Humans are dead-end hosts that usually become infected upon ingestion of infected molluscs. A presumptive diagnosis is often made based on clinical features, a history of mollusc consumption, eosinophilic pleocytosis in cerebral spinal fluid, and advanced imaging such as computed tomography. Serological tests are available for angiostrongyliasis, though many tests are still under development. While there is no treatment consensus, therapy often includes a combination of anthelmintics and corticosteroids. Angiostrongyliasis is relatively rare, but is often associated with morbidity and sometimes mortality. Recent reports suggest the parasites’ range is increasing, leading to fatalities in regions previously consideredAngiostrongylus-free, and sometimes, delayed diagnosis in newly invaded regions. Increased awareness of angiostrongyliasis would facilitate rapid diagnosis and improved clinical outcomes. This paper summarizes knowledge on the parasites’ life cycle, clinical aspects and epidemiology. The molecular biology ofAngiostrongylusspp. is also discussed. Attention is paid to the significance of angiostrongyliasis in Australia, given the recent severe cases reported from the Sydney region.

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.