Clusters of leprosy in Castanhal.

<p>(A) The spatial distribution of individual leprosy cases overlying the respective Kernel density estimation layer, representing areas with a high and low density of cases per km². (B) LISA test (local Moran's I) characterizing areas with a statistically significant (p<0.05) positive spatial association according to the raw detection rate. The areas marked as high-high indicate a high rate in an area surrounded by high values of the weighted average rate of the neighboring areas, and low-low represents areas with a lower rate surrounded by lower values. (C) The most likely cluster of leprosy detected by the Kulldorff's spatial scan statistics (<i>p</i><0.01).</p

Characteristics of the specific regions in the urban area of Castanhal.

*<p>Annual detection rate per 100,000 people.</p><p>SEB = Spatially empirical Bayes.</p><p>LISA = Local indicator of spatial association (Local Moran's I).</p

Space-time links among cases and proximity to students.

<p>An expanded view of a specific region identified as a cluster of leprosy (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002665#pntd-0002665-g002" target="_blank">Figure 2C</a>, Kulldorff's spatial scan statistics), showing the space-time links among cases and the spatial relationship with a surveyed school and seropositive students.</p

Population density and spatial distribution of leprosy in Castanhal.

<p>(A) Population density per km² in the urban census tracts. (B) Raw number of leprosy cases per census tract. (C) Number of cases normalized by the population of each census tract per year (annual raw case detection rate per 100,000 people), classifying areas according to their level of endemicity, from low to hyperendemic, according to official parameters. (D) Spatially empirical Bayes smoothed detection rate (based on a queen spatial weight matrix) to smooth the differences between contiguous areas.</p

Spatial distribution of surveyed household contacts and school children.

<p>The spatial distribution of surveyed household contacts and school children according to their level of antibodies compared to the level of endemicity of the different census tracts.</p

Evidence of hidden leprosy in a supposedly low endemic area of Brazil

<div><p> OBJECTIVES Show that hidden endemic leprosy exists in a municipality of inner São Paulo state (Brazil) with active surveillance actions based on clinical and immunological evaluations. METHODS The study sample was composed by people randomly selected by a dermatologist during medical care in the public emergency department and by active surveillance carried out during two days at a mobile clinic. All subjects received a dermato-neurological examination and blood sampling to determine anti-PGL-I antibody titers by enzyme-linked immunosorbent assay (ELISA). RESULTS From July to December 2015, 24 new cases of leprosy were diagnosed; all were classified as multibacillary (MB) leprosy, one with severe Lucio's phenomenon. Seventeen (75%) were found with grade-1 or 2 disability at the moment of diagnosis. Anti-PGL-I titer was positive in 31/133 (23.3%) individuals, only 6/24 (25%) were positive in newly diagnosed leprosy cases. CONCLUSIONS During the last ten years before this study, the average new case detection rate (NCDR) in this town was 2.62/100,000 population. After our work, the NCDR was raised to 42.8/100,000. These results indicate a very high number of hidden leprosy cases in this supposedly low endemic area of Brazil.</p></div

data_sheet_1.xlsx

<p>Leprosy remains as a public health problem and its physiopathology is still not fully understood. MicroRNAs (miRNA) are small RNA non-coding that can interfere with mRNA to regulate gene expression. A few studies using DNA chip microarrays have explored the expression of miRNA in leprosy patients using a predetermined set of genes as targets, providing interesting findings regarding the regulation of immune genes. However, using a predetermined set of genes restricted the possibility of finding new miRNAs that might be involved in different mechanisms of disease. Thus, we examined the miRNome of tuberculoid (TT) and lepromatous (LL) patients using both blood and lesional biopsies from classical leprosy patients (LP) who visited the Dr. Marcello Candia Reference Unit in Sanitary Dermatology in the State of Pará and compared them with healthy subjects. Using a set of tools to correlate significantly differentially expressed miRNAs with their gene targets, we identified possible interactions and networks of miRNAs that might be involved in leprosy immunophysiopathology. Using this approach, we showed that the leprosy miRNA profile in blood is distinct from that in lesional skin as well as that four main groups of genes are the targets of leprosy miRNA: (1) recognition and phagocytosis, with activation of immune effector cells, where the immunosuppressant profile of LL and immunoresponsive profile of TT are clearly affected by miRNA expression; (2) apoptosis, with supportive data for an antiapoptotic leprosy profile based on BCL2, MCL1, and CASP8 expression; (3) Schwann cells (SCs), demyelination and epithelial–mesenchymal transition (EMT), supporting a role for different developmental or differentiation gene families, such as Sox, Zeb, and Hox; and (4) loss of sensation and neuropathic pain, revealing that RHOA, ROCK1, SIGMAR1, and aquaporin-1 (AQP1) may be involved in the loss of sensation or leprosy pain, indicating possible new therapeutic targets. Additionally, AQP1 may also be involved in skin dryness and loss of elasticity, which are well known signs of leprosy but with unrecognized physiopathology. In sum, miRNA expression reveals new aspects of leprosy immunophysiopathology, especially on the regulation of the immune system, apoptosis, SC demyelination, EMT, and neuropathic pain.</p