Think Before You Ink: Modeling Laser Tattoo Removal

Prior to laser treatment tattoos were removed by destroying the skin containing the ink. The skin would be burned, frozen, or excised surgically. The use of Q-Switched lasers has effectively diminished the abrasive nature of tattoo removal with successful results and is now a commonly used method for tattoo removal. Scientific studies have been conducted that examine the laser intensities and mechanism of removal. These studies have found that the laser selectively heats the thin ink layer beneath the skin, leading to an explosion of the microscopic ink particles. The remnants of these particles, and the cells in which they reside, are subsequently removed by the lymphatic system. The primary aim of this project is to model this laser tattoo removal process. This model uses the heat transfer equation with a laser heat generation term to find the temperature profiles of the ink and surrounding skin layers. Also included in the model are the heat energy effects of evaporation within the tissue as it is heated. A mass transfer equation accounts for the moisture content of the tissue as it is lost to vaporization during heating. Sensitivity analyses performed during the modeling process produced optimal values for the absorptivity of the ink for the Q-Switched Ruby laser, 165m-1. They also determined the optimal value for the absorptivity of the skin, 20 m-1. The developed model was validated with clinical experimental results which claimed that within one 40 nanosecond laser pulse time, the ink particles reached 900 degrees Celsius while the surrounding skin temperature was between 45 and 55 degrees Celsius. Further applications of this model include optimizing laser intensities and pulsation times to reduce the tissue damage and the pain of the procedure

Timing of host feeding drives rhythms in parasite replication

Circadian rhythms enable organisms to synchronise the processes underpinning survival and reproduction to anticipate daily changes in the external environment. Recent work shows that daily (circadian) rhythms also enable parasites to maximise fitness in the context of ecological interactions with their hosts. Because parasite rhythms matter for their fitness, understanding how they are regulated could lead to innovative ways to reduce the severity and spread of diseases. Here, we examine how host circadian rhythms influence rhythms in the asexual replication of malaria parasites. Asexual replication is responsible for the severity of malaria and fuels transmission of the disease, yet, how parasite rhythms are driven remains a mystery. We perturbed feeding rhythms of hosts by 12 hours (i.e. diurnal feeding in nocturnal mice) to desynchronise the hosts' peripheral oscillators from the central, light-entrained oscillator in the brain and their rhythmic outputs. We demonstrate that the rhythms of rodent malaria parasites in day-fed hosts become inverted relative to the rhythms of parasites in night-fed hosts. Our results reveal that the hosts' peripheral rhythms (associated with the timing of feeding and metabolism), but not rhythms driven by the central, light-entrained circadian oscillator in the brain, determine the timing (phase) of parasite rhythms. Further investigation reveals that parasite rhythms correlate closely with blood glucose rhythms. In addition, we show that parasite rhythms resynchronise to the altered host feeding rhythms when food availability is shifted, which is not mediated through rhythms in the host immune system. Our observations suggest that parasites actively control their developmental rhythms. Finally, counter to expectation, the severity of disease symptoms expressed by hosts was not affected by desynchronisation of their central and peripheral rhythms. Our study at the intersection of disease ecology and chronobiology opens up a new arena for studying host-parasite-vector coevolution and has broad implications for applied bioscience

Tracking Resilience to Infections by Mapping Disease Space

<div><p>Infected hosts differ in their responses to pathogens; some hosts are resilient and recover their original health, whereas others follow a divergent path and die. To quantitate these differences, we propose mapping the routes infected individuals take through “disease space.” We find that when plotting physiological parameters against each other, many pairs have hysteretic relationships that identify the current location of the host and predict the future route of the infection. These maps can readily be constructed from experimental longitudinal data, and we provide two methods to generate the maps from the cross-sectional data that is commonly gathered in field trials. We hypothesize that resilient hosts tend to take small loops through disease space, whereas nonresilient individuals take large loops. We support this hypothesis with experimental data in mice infected with <i>Plasmodium chabaudi</i>, finding that dying mice trace a large arc in red blood cells (RBCs) by reticulocyte space as compared to surviving mice. We find that human malaria patients who are heterozygous for sickle cell hemoglobin occupy a small area of RBCs by reticulocyte space, suggesting this approach can be used to distinguish resilience in human populations. This technique should be broadly useful in describing the in-host dynamics of infections in both model hosts and patients at both population and individual levels.</p></div

Disease maps of mice with warped disease spaces.

<p>(A) A topological network map for malaria-infected mice following the mice for a maximum of 26 d post infection. The surviving mice are marked in blue (<i>n</i> = 3), while those who died are marked in red (<i>n</i> = 4); other colors show overlap in the map. (B–C) show the same disease map as in (A), but colored according to (B) time or (C) reticulocytes (Ferrochelatase). Phase plots for parameters parasite density by RBC (D) and Fech by RBC (E) that deviate in looping systems in dying mice. Note that the axes have been arranged (D–E) so that all graphs start at the top left and the sick mice follow a clockwise path through phase space. The graph shows “comfortable” (days 0–6, green), “sick” (days 7–10, blue), and “recovering” (days 11–15, yellow) regions. Areas not encompassed by the paths followed by surviving mice are colored red and reveal the dangerous spaces traversed by dying mice. The path of dying mice is outlined in thick lines compared to the thin lines used for survivors. Ranges for (A–C) and parameters for deriving the graphs are listed in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002436#pbio.1002436.s015" target="_blank">S8 Table</a>.</p

A Macrophage Colony-Stimulating-Factor-Producing gamma delta T Cell Subset Prevents Malarial Parasitemic Recurrence

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Prediction of mice fated to die using polar transformed data.

<p>(A) Disease map of live (<i>n</i> = 4) and dead (<i>n</i> = 11) mice in Fech by RBC space. Data collected through qRT-PCR. The grey area shows the range of angles analyzed just before the time of death. (B) Radius and angle for live (circle, orange) and dead mice (x, blue). (C) Box plot measuring the radius of live and dead mice. (D) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line). Observed values are plotted as histograms. (E) Disease map of live (<i>n</i> = 4) and dead (<i>n</i> = 11) mice in Fech by RBC space. The grey area shows the range of angles analyzed around time zero. (F) Radius and angle for live (circle, orange) and dead (x, blue) mice at early time points. (G) Box plot measuring the radius of live and dead mice at early time points; <i>p</i> = 0.0477. (H) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line) for early time points. Data provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002436#pbio.1002436.s003" target="_blank">S3 Data</a>.</p

Disease space analysis of malaria-infected children.

<p>(A) The mean radius for individuals with the sickle cell trait (AS, red) is below the average random distribution for a group size of 47 patients. (B) Marking individuals with Hemoglobin A (AA, blue, triangle), Hemoglobin C (AC, green, square), and Hemoglobin S (AS, red, circle) in RBC by reticulocyte (Fech) space. Individuals with Hemoglobin S form a smaller cluster than the other two hemoglobins, suggesting a smaller route through disease space. See also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002436#pbio.1002436.s006" target="_blank">S2 Fig</a>. Data provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002436#pbio.1002436.s004" target="_blank">S4 Data</a>.</p

Disease space maps of malaria-infected mice.

<p>(A) Average values for eight parameters for three mice measured (and averaged) daily for 20 d are plotted in a timeline marked in blue. The paths for the three mice plotted individually are shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002436#pbio.1002436.s006" target="_blank">S2 Fig</a>. The transcript markers used to define B cells, NK cells, granulocytes, and reticulocytes are, respectively, Cd79b, Nkg7, Camp, and Trim 10, which are reported as log₂ values. Time is indicated by the increasing thickness of the curve. (B,C) Phase plots for representative looping pairs of parameters. Note that the axes have been flipped so that all graphs start at the top, and the sick mice follow a clockwise path through phase space. The graph shows “comfortable” (days 0–6, green), “sick” (days 7–10, blue) and “recovering” (days 11–15, yellow) regions. See also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002436#pbio.1002436.s005" target="_blank">S1 Fig</a>.</p

Prediction of mice fated to die based on anemia.

<p>(A) Time series data of RBCs for survivors (<i>n</i> = 4, orange) and non-survivors (<i>n</i> = 11, blue). (B–C) Box plots of RBC counts on day 8 (B) and all days (C) of the infection. Significant difference between conditions on day 8 <i>p</i>-value = 0.0015. The <i>p</i>-value when considering all of the time points was <i>p</i> = 0.842. Data provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002436#pbio.1002436.s001" target="_blank">S1 Data</a>.</p

The relationship between time and angle in a disease map.

<p>(A) Disease map of live mice (<i>n</i> = 3) through Nkg7 by RBC space. (B) Linear correlation between angle and days post infection from day 11 to day 20 (r² = 0.942). Only points colored red were included in the regression analysis. Data provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002436#pbio.1002436.s002" target="_blank">S2 Data</a>.</p