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Gasque P.,French Institute of Health and Medical Research | Jaffar-Bandjee M.C.,French Institute of Health and Medical Research | Jaffar-Bandjee M.C.,Biology Laboratory
Journal of Infection | Year: 2015

Melanin is a canonical and major defense molecule in invertebrates but its role in mammalian immunity remains unexplored. In contrast, several recent studies have highlighted the emerging innate immune activities of human melanin-producing cells which can sense and respond to bacterial and viral infections. Indeed, the skin is a major portal of entry for pathogens such as arboviruses (C. hikungunya, Dengue) and bacteria (mycobacterium leprae, Leptospira spirochetes). Melanocytes of the epidermis could contribute to the phagocytosis of these invading pathogens and to present antigens to competent immune cells. Melanocytes are known to produce key cytokines such as IL-1β, IL6 and TNF-α as well as chemokines. These molecules will subsequently alert macrophages, neutrophils, fibroblasts and keratinocytes through unique crosstalk mechanisms. The infection and the inflammatory responses will control melanocyte's immune and metabolic functions and could contribute to skin manifestations (rash, hyper or de-pigmentation, epidermolysis and psoriasis-like lesions). This review will address the potential role of melanocytes in immunity, inflammation and infection of the skin in health and diseases. © 2015 The British Infection Association. Source


Cabie A.,CHU de Fort de France | Cabie A.,French Institute of Health and Medical Research | Bissuel F.,Transmissible Diseases Unit | Huc P.,Biology Laboratory | And 2 more authors.
International Journal of STD and AIDS | Year: 2011

Summary: To strengthen HIV screening in the French West Indies (FWI), we evaluated the feasibility of rapid tests in sexually transmitted infection (STI) testing centres. Rapid testing was offered to each user ahead of the standard screening tests. Between October 2007 and December 2008, 847 users had HIV rapid testing, and 1724 users did not have rapid testing. The results of rapid testing were returned to 99.1% of testers. However, clients who underwent rapid testing were significantly more likely than others to have not returned to get the results of their standard screening tests (for HIV and other STIs): 27.4% versus 14.0% with a relative risk of 1.96 (95% confidence interval [CI] 1.67-2.30, P < 0.0001). Rapid HIV testing has the capacity to reduce the return rates for confirmatory results of HIV testing and other STIs. Source


Halasz A.,Biology Laboratory | Szamos J.,Central Food Research Institute | Viranyi F.,Szent Istvan University
Acta Alimentaria | Year: 2013

The aim of this study was to develop an accurate, fast and safe routine diagnostic method based on protein studies for differentiating between T. caries and T. controversa at species level. Since import of wheat contaminated with T. controversa is restricted by several countries, differentiation of T. controversa from the more prevalent T. caries is of economic interest. The newly developed method is based on distilled water washing followed by the rupturing of the teliospore walls in PBS extraction medium, and an SDS electrophoresis (10% resolving gel). The electrophoretic pattern showed consistent species-related differences in a 106 kDa polypeptide that appeared in each extract of T. controversa, but was not present in the protein extracts of T. caries. The newly developed method could be of value for the authorities performing routine monitoring of T. controversa as an up-to-date diagnostic assay in wheat shipments. Source


News Article
Site: http://phys.org/biology-news/

Led by Sara Howden, a postdoctoral fellow at MCRI and formerly with the Morgridge Institute, the study demonstrates how genetically repaired stem cells can be derived from patient skin cells in as little as two weeks, compared to conventional multi-step approaches that take more than three months. The key to the advance, published in today's issue of the journal Stem Cell Reports, is to combine two essential steps in preparing cells for potential therapy. First, adult cells must be reprogrammed to an embryonic cell-like state in order to be differentiated into the cells of interest. Second, the cells need to undergo a sophisticated gene editing process to correct the disease-causing mutation. Howden and colleagues successfully combined these two steps in skin cells derived from an adult patient with retinal degeneration, and an infant patient with severe immunodeficiency. "The method developed in our study could potentially advance transplant medicine by making gene-corrected cells available to patients in a much more timely manner, and at a lower cost," says Howden. "It will have implications immediately for researchers working in regenerative medicine." Howden completed much of the research in the Morgridge Regenerative Biology Laboratory, led by stem cell pioneer James Thomson. "If you want to conduct therapies using patient-specific iPS cells, the timeline makes it hard to accomplish," Thomson says. "If you add correcting a genetic defect, it really looks like a non-starter. You have to make the cell line, characterize it, correct it, then differentiate it to the cells of interest." Adds Thomson: "In this new approach, Dr. Howden succeeded in combining the reprogramming and the gene correction steps together using the new Cas9/CRISPR technology, greatly reducing the time required." Howden says the faster process also means the cell culture period is greatly reduced, potentially minimizing the risks associated with culturing cells outside of the human body, such as genome instability or other epigenetic changes. Induced pluripotent cells (iPS cells) hold great promise for medical research because they can essentially be derived from any individual and are capable of becoming any of the 220 types of cells in the human body. And the ability to efficiently and precisely modify the DNA in these cells offers enormous potential for the development of personalized stem cell therapies that benefit people with many different types of genetic disorders. Howden says one potential next step is to adapt the protocol to work with blood samples. Not only is a blood draw less invasive than a skin biopsy, it also could further reduce the time to obtain genetically repaired iPS cells. Skin cells need to be expanded for several weeks before initiating reprogramming. This "fast-tracked process" could be most influential in cases where urgent medical intervention is needed, adds Howden. One example is severe combined immunodeficiency, where children typically die within the first few years of life. However, Howden says scientists still need to derive a long-term source of blood cells from pluripotent stem cells before such treatments are viable. Explore further: Study shows patient's own cells may hold therapeutic promise after reprogramming, gene correction


News Article
Site: http://www.nature.com/nature/current_issue/

Three experiments were performed (two on USOs, and one on meat). Experiment number 1 used 14 subjects (7 male, 7 female; aged 29 ± 8 years) to quantify the amount of masticatory muscular effort required to consume USOs. Experiment number 2 used 10 subjects (5 male, 5 female; aged 32 ± 10 years) to quantify comminution (intra-oral food breakdown) of one USO, beetroots. The dark colour of beetroots (but not yams and carrots) provided the colour contrast necessary to image and measure small food particles. Experiment number 3 used 10 male subjects (aged 36 ± 17 years) to quantify both the muscular effort required to consume meat, and how well the subjects were able to comminute the food. All participants had a complete set of permanent teeth with the exception of the third molars (which were variably present), possessed no major cavities, and reported no pain or difficulty during chewing. All experiments were approved by the Harvard Institutional Review Board (IRB), and all subjects provided informed consent before participation. No statistical methods were used to predetermine sample size. USOs. Organic USOs, jewel yams (Ipomoea batatas), carrots (Daucus carota), and red beetroots (Beta vulgaris), were purchased from a local grocery store. The average fracture toughness of these USOs is approximately 1,060 J m−2 (ref. 20), similar to published values from Africa of wild tubers (1,304 J m−2), greater than wild bulbs (325 J m−2) and corms (265 J m−2), but less than wild rhizomes (5,448 J m−2) (ref. 22). Each USO was cut into two portions; one was used for the unprocessed samples and the other for the processed samples. Unprocessed, sliced and pounded samples were prepared in a similar manner. First, small bite-sized cubes (13 mm × 13 mm × 13 mm) were cut from the inner medullary region of each USO. Sample dimensions were measured using digital callipers (accuracy ± 0.01 mm). Because of their small size, some of the carrot samples included a small portion of the outer cortex. Sample weight did not differ among the USOs (digital scale, accuracy ± 0.1 g; yam 2.2 ± 0.06 g; carrot 2.3 ± 0.05 g; beetroot 2.2 ± 0.06 g). After the sample cubes were cut, they were either left unprocessed, or were processed by slicing them into eight smaller 6.5 mm × 6.5 mm × 6.5 mm cubes (sliced samples), or by pounding them six times with a hand-sized rock replica of a Lower Palaeolithic hammerstone (pounded samples). Tenderizing in this manner tended to break the USOs into many relatively large, intact pieces. Roasted samples were created by cutting the USOs into 17-mm-thick slices and then cooking them on a pre-heated tabletop propane grill (Perfect Flame) with the lid open and the gas flow valve set to ‘high’. USO slices were roasted for 15 min, and to ensure uniform heating, they were flipped after 7.5 min and rotated every 2.5 min to different positions on the grill surface. Cooking in this manner heated yams to 89.0 ± 2.7 °C, carrots to 78.5 ± 1.1 °C, and beetroots to 78.6 ± 2.2 °C (based on the internal temperatures of 5 slices of each USO; Thermoworks thermometer, accuracy ± 0.1 °C). After cooking, 13 mm × 13 mm × 13 mm cubes were cut from the medullary region of the slices, avoiding the charred surfaces that were in contact with the grill. Cooked cubes were approximately 14% heavier than the unprocessed cubes (cooked yam 2.6 ± 0.05 g; cooked carrot 2.6 ± 0.05 g; cooked beetroot 2.6 ± 0.05 g). All samples were stored in sealed plastic containers at 4 °C and were used within 12 h of processing. Meat. Fresh adult goat carcasses (Capra aegagrus; female) were purchased from a local farm (Blood Farms, Groton, Massachusetts) and transported on ice to the Skeletal Biology Laboratory, Harvard University. Neck and epaxial muscles (with little associated fat) were removed using aseptic procedures, sealed in vacuum bags and stored at −20 °C. Although freezing has a slight tenderizing effect31, 32, this step was required by the IRB to perform pathogen tests on the meat before using it in the experiment. The meat was defrosted slowly at 4 °C for approximately 12–24 h before sample preparation. Samples were randomized to include meat from both neck and epaxial muscles. Three-gram samples of meat were cut from defrosted muscles (digital scale, accuracy = 0.1 g). These samples were either left unprocessed, or were cut into eight, approximately equal sized pieces (sliced samples). Pounded samples were created by cutting the muscle into a 50.0 g steak and hitting it 50 times with a replica Lower Palaeolithic hammerstone. Processing in this manner disorganized the muscle fibres, resulting in a ‘mashed’ appearance, but did not fracture the steak into separate pieces. After tenderizing, 3.0 g samples were cut from the pounded steaks. Roasted samples were created by cooking steaks on the same grill used to cook USOs (see earlier for details). Internal temperature was monitored using a digital thermometer inserted into the steak centre (Thermoworks, accuracy ± 0.1 °C). Steaks were flipped regularly to ensure even heating and were roasted to a final internal temperature equal to medium-well done (slightly pink centre, ~70 °C). On average, cook time was 25.0 ± 5.3 min and water (weight) loss was 26.8 ± 5.6% when roasting in this manner (based on the average of three steaks). After roasting, 3.0 g samples were cut from the steaks, avoiding the charred outer surfaces. All samples were stored in sealed plastic containers at 4 °C and were used within 12 h of processing. In each of the experiments described later, subjects were presented with triplicate samples of the unprocessed and processed foods. While USO samples were presented in random order, owing to IRB restrictions the cooked meat samples were presented before the unprocessed, sliced and pounded meat samples (the latter three sample types were presented in random order). Additionally, although the subjects were allowed to swallow the USO samples, the risk of foodborne illness precluded swallowing of the non-cooked meat samples. We assessed the potential for bias that non-swallowing might cause by having the subjects chew six samples of cooked meat. Half of the samples were consumed as normal (chewed and swallowed), while the other half were chewed until the subjects felt they would typically swallow, and then spit out. There was no difference in the number of chews used (linear mixed models, P = 0.65) or muscle recruitment (linear mixed models, per chew P = 0.20, per sample P = 0.99). All of the data presented here are based on the cooked meat samples that were not swallowed. For each subject, surface electromyography (EMG) electrodes (Cleartrace, Conmed Corporation) were placed onto the skin overlying the major force-producing muscles of mastication, the right and left temporalis and masseter muscles, and a ground electrode was placed on the back of the non-dominant hand. EMG electrodes were connected to amplifiers (a pre-amplifier and amplifier; MA300 EMG system, Motion Lab Systems) and a PowerLab 16sp A/D board (ADinstruments). All data were collected at 1,000 Hz in LabChart v.7 (ADinstruments). (Temporalis muscle activity was not collected for 3 subjects in the USO experiment, and masseter muscle activity was not collected for 1 subject in the meat experiment.) After electrode placement, we calibrated each subject’s EMGs with force. First, a small, dime-sized Kistler SlimLine force transducer (output voltage calibrated to known forces, r2 = 0.99, for transducer details see later) was placed between the subjects’ left first molars. The subjects were then instructed to bite down with sub-maximal force and then release while EMG activity and resulting bite forces were recorded. This procedure was repeated approximately 30 times over a range of bite forces (which were monitored in real time by K.D.Z.). To ensure a comfortable and sterile biting surface, the top and bottom of the transducer was fitted with a thin (2.4 mm) layer of rubber and was loosely covered with a layer of waterproof tape and a sterile plastic sleeve. After wrapping, the transducer was 8.8 mm tall with a diameter of 14.1 mm. After the calibration trial, subjects were presented with unprocessed and processed foods in randomized order and instructed to chew the samples as normally as possible on only the left side, so that the balancing- and working-side muscles would be readily identifiable. During chewing, the EMG activity of each muscle was recorded. Two sets of analyses were performed: one that assessed the effects of food processing on chewing muscle recruitment, and one that estimated the applied forces necessary to fracture each food. The investigators were not blinded to allocation during experiments and outcome assessment. The EMG signals were processed using custom Matlab codes. Specifically, the data were filtered (Butterworth bandpass; 4th order zero-lag; 60 and 300 Hz frequency cutoffs), rectified, binned with a 5 ms integral reset, and background EMG activity was removed using Thexton’s randomization method33. Mid-trial swallows, which sometimes occurred during the consumption of the USO samples, were identified by non-uniform patterns of the muscle EMG signals and were omitted from analysis. For each muscle, the time-integral of the EMG signal was calculated both per chew and per sample. The time-integral EMG data were then normalized within each subject by calculating the relative change in muscular recruitment caused when consuming processed versus unprocessed foods (percentage change = 100 × ((EMG voltage − EMG voltage )/(EMG voltage )). Sample triplicates were averaged for each subject. Because the data were not normally distributed, we used 95% confidence intervals generated from studentized bootstraps34 with 10,000 repeats to test whether food processing significantly increased (a positive value) or decreased (a negative value) muscle recruitment. (studentized bootstraps generate confidence intervals based on the resampled distribution of Student’s t-tests.) EMG data were analysed for each muscle separately, and also with all of the muscles averaged. Similarly, USO data were analysed both for each specific USO (beetroot, carrot and yams), and with all of the USOs pooled together. All calculations were performed in Excel (Microsoft 2007) and R35. To compare directly the masticatory effort used to chew USOs and meat, we transformed the time-integral EMG data of the balancing-side masseter into estimates of applied chewing force. Although we were not able to estimate the work done by chewing, the time-integral of estimated force is indicative of the total metabolic work done by the muscle, since the percentage of muscle work that generates force is relatively constant (about 25%). Standardization of the EMG signals was necessary because USO and meat samples were different sizes, and EMG signals from different experiments can only be compared when they are normalized. The balancing-side masseter was used because Proeschel and Morneburg36 found a different EMG–force relationship between isometric bites, such as those used in our calibration experiments, and chewing bites for all major masticatory muscles with the exception of the balancing-side masseter. To estimate applied chewing forces, subject-specific calibration equations were calculated using the data collected during the calibration trials (see earlier) to transform each subject’s muscle recruitment data into chew forces. Specifically, using methods described earlier, we filtered and rectified the balancing-side masseter EMG signal, and calculated the time-integral of the signal for each bite taken in the calibration trial. We then used LabChart v.7 to calculate the time-integral of the force signal used per bite (N.s). Each subject’s force data were then regressed against their time-integral EMG data for each bite. Overall, the relationship between the time-integral of the balancing-side masseter EMG and the time-integral of measured bite force was strong and significant: the average R2 (± 1 s.d.) for all subject-specific calibration regressions was 0.73 ± 0.14; P ≤ 0.001). The subject-specific calibration equations generated by the regressions were then used to transform each subject’s balancing-side masseter activity per chew into an estimate of applied masticatory force per chew. Total applied masticatory force per sample was then calculated by multiplying the average applied force per chew by the number of chews that a subject used to consume the food. Finally, the average masticatory force and number of chews used per kcal of each food sample was calculated by dividing by the weight of each sample and the number of calories available per gram of food (see Table 1). All meat samples weighed 3.0 g and USO samples weighed an average of 2.2 g when unprocessed and sliced, 2.1 g when pounded, and 2.6 g when roasted. Food caloric density was obtained from the USDA National Nutrient Database for Standard Reference ( http://www.ars.usda.gov/ba/bhnrc/ndl): unprocessed jewel yam = 0.86 kcal g−1; unprocessed red beetroot = 0.43 kcal g−1; unprocessed carrot = 0.41 kcal g−1; unprocessed goat meat = 1.09 kcal g−1; baked jewel yam = 0.90 kcal g−1; boiled red beetroot = 0.44 kcal g−1; boiled carrot = 0.35 kcal g−1; roasted goat = 1.43 kcal g−1. Caloric data were unavailable for roasted USOs, and baked or boiled USO values were substituted in the calculations. Sliced and pounded foods were assumed to have the same number of calories per gram as their unprocessed counterparts. Yam, carrot and beetroot data were pooled and the average masticatory force per kcal of USO was calculated. A two-tailed Mann–Whitney U-test was used to assess whether the number of chews and masticatory force used to eat a kilocalorie of food differed between USOs and meat. All calculations were performed in Excel (Microsoft 2007) and StatView (SAS Institute). Significance was set to P ≤ 0.05. It should be noted that the caloric values used in these calculations are based on the Atwater system, which calculates food energy as the total available energy minus the indigestible components. This system assumes a standard digestibility, however, and also fails to take into account other key variables, such as the cost of digestion, which is lower in processed foods23, 24. Therefore, these caloric data probably under-report the net energy gained from processed foods. Subjects were instructed to chew the meat and USO (beetroot) samples on the left side of their mouth until they felt that they would typically swallow. At this point they stopped chewing and the food bolus was collected in 50 ml tubes and stored in ~50% ethanol for no more than 8 days before image analysis. Comminuted boli were dispersed onto a transparent plastic tray fitted onto an Epson perfection v500 flatbed scanner. Food particles comprising each bolus were easily separated using water, and were arranged so that the particles did not touch one another and to maximize surface area contact with the tray. Particles were then scanned to create a 400 dpi grey-scale image against a white background. Images were viewed and measured in iVision v.4 (BioVision Technologies). Comminution effectiveness was quantified as the two-dimensional surface area of the largest particle of food within the chewed bolus. We use this variable rather than average particle size because the chewed boli of unprocessed meat were predominantly composed of a single large particle, making average size uninformative (see Fig. 1). In most instances, the largest particle in a chewed meat bolus was readily identifiable in the scanned images. Using the drawing tool, the pixels comprising the largest particle were manually transformed into the measurement colour (green), and the total two-dimensional surface area (mm2) of the particle was then quantified based on the number of coloured pixels. In some samples, multiple particles had to be measured to locate the largest particle. In contrast to meat, the comminuted USO samples contained a large number of similarly sized particles, and it was not possible to discern the largest particle simply by viewing the scanned images. Therefore, all of the particles that made up the sample were measured. To do this, the scanned image was thresholded so that every coloured pixel with a value ranging from 0 to 230 (pure black to very light grey, respectively) was transformed into the measurement colour (green). (Preliminary tests indicated that thresholding to 230 was the boundary between very small, light particles and shadows resulting from the scanner’s moving light source.) After thresholding, the image was reviewed and digitally cleaned by hand if needed. The surface area of every individual food particle was measured by quantifying the number of green pixels comprising the particle (a single particle was defined as the sum of all green pixels in contact). For consistency, we report only data on the size of the largest particle in the chewed USO boli, which correlated strongly with average particle size (r = 0.73; P < 0.0001) (see Extended Data Table 1). Triplicates of each sample type were averaged, and the size of the largest particle in raw and processed comminuted samples was compared using linear mixed models, a type of model that estimates separate intercepts for each subject37. All calculations were performed in Excel (Microsoft 2007) and R35. Significance was set to P ≤ 0.05. Measurement precision was quantified by measuring the bolus of one randomly chosen sample (unprocessed meat) five times. The standard deviation of the resulting measurements (1.4 mm2) was 0.2% that of the average particle area (542.6 mm2). The maximum difference between any two repeats was 0.5% of the average.

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