Flaviviridae family, is a mosquito-borne virus known to cause microcephaly through vertical transmission. Infection presents with mild, self-limiting symptoms. Currently, a Zika virus outbreak has spread across most of South and Central America. Travel-related and sexually transmitted cases have been reported across the United States. However, the vector-borne transmission has been limited to Florida and Texas. We present seven cases of Zika virus infection that presented at a single institution in South Florida.Zika virus, an arbovirus of the Patients were included that had real-time polymerase-chain reaction (RT-PCR) for Zika virus RNA in urine or serum or enzyme-linked immunosorbent assay (ELISA) for Immunoglobulin M (IgM) antibody against Zika virus in serum.All seven patients reported recent travel or employment in areas of active Zika virus transmission and at least two of the four most commonly reported symptoms  with a rash present in all patients. All patients had positive RT-PCR for Zika virus RNA in urine. RT-PCR for Zika virus RNA in serum was negative in four of five patients that were tested, indicating that these patients likely presented one to two weeks after symptom onset.The future of Zika virus outbreaks in other cities in the United States is still uncertain. However, it is clear that prevention and control policies are urgently needed. We have presented seven confirmed cases of Zika virus infection in South Florida. In addition to conducting research concerning both the diagnostic and therapeutic aspects of the virus, there is a need for public awareness of its presentation, methods of transmission, and subsequent clinical outcomes. Zika Virus (ZIKV) is an arbovirus of the Flaviviridae family that is most commonly transmitted via Aedes aegypti and Aedes albopictus mosquito vectors . Other mIn 1947, ZIKV was first discovered in rhesus monkeys in the Zika Forest of Uganda. The first human cases were reported in Nigeria in 1954. ZIKV spread to Malaysia by 1969 and Indonesia by 1977. The first major ZIKV outbreak spread by mosquitos was recognized in 2007 in the Yap Islands of Micronesia, followed by a second larger outbreak in French Polynesia between 2013-2014 ) and dengue (IgM) virus serology were negative. Body fluid samples were analyzed at an offsite laboratory; therefore, the results were not available until after discharge. A pregnancy test was negative. The patient was treated with one dose of each of the following medications: morphine 2 mg IV, ondansetron 4 mg IV, and methylprednisolone 80 mg IV. She was also given 1 L of normal saline IV. On discharge, the patient was instructed to follow up with her primary care physician.A 33-year old female presented to the emergency department complaining of a gradually worsening rash localized to her chest, neck, and right side of her face for two days. Associated symptoms were fatigue, sore throat, nasal congestion, “itchy, red, watery eyes,” and myalgia. She denied fever, chills, and arthralgia. She had returned to the United States from the Dominican Republic one week prior, but she did not recall being bitten by a mosquito. She reported that her son had a positive test for ZIKV. On physical exam, the patient was afebrile and hemodynamically stable. She had an erythematous posterior pharynx and a trace fine papular rash on the chest, neck, and right face. Blood chemistry tests were all within normal limits. Hematologic tests showed mild anemia , mildly increased lymphocyte percentage , and mildly increased monocyte percentage . RT-PCR for ZIKV RNA was positive in urine and negative in serum. The patient was not tested for chikungunya or dengue virus infections. The ZIKV blood and urine samples were analyzed at an offsite laboratory; therefore, the results were not available at the time of discharge. A pregnancy test was negative. The patient was treated with one dose of each of the following medications: diphenhydramine 25 mg IV, acetaminophen 975 mg IV, and ondansetron 4 mg IV. She was also given 1 L of normal saline IV. On discharge, the patient was instructed to follow up with her primary care physician.3/mL, normal range: 4.5–13.5 × 103/mL) and a mildly increased hemoglobin  and hematocrit . Lymphocyte percentage was decreased . RT-PCR for ZIKV RNA was positive in urine and negative in serum. Serology was positive for dengue virus  and negative for chikungunya virus (IgM and IgG negative). Body fluid samples were analyzed at an offsite laboratory; therefore, the results were not available at the time of discharge. The patient received no treatment during this visit. On discharge, the patient was instructed to follow up with his primary care physician and to avoid sexual contact.A 17-year-old male presented to the emergency department due to his father’s concern that the patient was infected with ZIKV. He had no complaints or symptoms at the time of the visit. The patient reported experiencing joint pain, pruritic generalized rash, headache, and fever on the previous day. He denied any ocular complaints. He had returned from Puerto Rico on the previous day. While in Puerto Rico, he was in contact with a friend who experienced the same symptoms. Per the patient, the friend’s physician told him he had a viral condition. On physical exam, he was afebrile and hemodynamically stable. There were no positive relevant findings on physical exam. Blood chemistry tests were all within normal limits. Hematologic tests showed a mildly decreased white blood cell count  from a 12-day trip to Saint Martin and Saint Barthélemy: territories with known vector-borne ZIKV transmission . The patA 49-year-old female presented to her primary care physician with fever and pruritic rash for four days. She denied symptoms of conjunctivitis and joint pain. Three days before the onset of symptoms, she returned with her husband (see Case 6 above) from a 12-day trip to Saint Martin and Saint Barthélemy: territories with known vector-borne ZIKV transmission . The patThese seven cases illustrate the common clinical picture of a ZIKV infection. All patients had risk factors for acquiring the infection – whether by working in an active vector-borne area, person-to-person transmission, or traveling to endemic areas. The patients presented with classic signs and symptoms of ZIKV infection, including fever, chills, fatigue, maculopapular rash, arthralgia, headache, and conjunctivitis. Five patients (71%) had fever, arthralgia, and rash. All seven had rash, and at least one more of the common ZIKV infection symptoms. Only two patients (29%) had less than three of the common symptoms. One of these two patients presented with other less common symptoms . Six of the seven patients were symptomatic at the time of presentation, and all of their symptoms were self-limiting.RT-PCR of ZIKV RNA was positive in urine for all seven cases. Four patients (57%) were negative for ZIKV RNA in serum Table . These fThe patients in cases 6 and 7 were most likely infected while traveling, but local vector-borne infection cannot be ruled out. With the lower end of the range of the incubation period being two days after exposure , it is pSevere disease requiring hospitalization is uncommon, and case-fatality rates are low . HoweverAlthough local transmission of ZIKV has not been reported in recent months, it remains a concern. As summer months arrive, Aedes aegypti populations have historically grown up to 10 times larger than in winter. Therefore, there is a need for public awareness of its presentation, methods of transmission, and subsequent clinical outcomes. Efforts in these areas now could serve to help prevent or limit local transmissions from returning to South Florida in the warmer months.


Facial Action Coding System. Looking at the total sample, the hospital clown intervention elicited more frequent facial expressions of genuine enjoyment (Duchenne smiles) than other smiles (Non-Duchenne smiles), and more Duchenne smiles went along with more perceived funniness, a higher level of global positive feelings and transcendence. This supports the notion that overall, hospital clown interventions are beneficial for patients. However, when considering individual differences in the receptiveness to humor, results confirmed that high trait cheerful patients showed more Duchenne smiles than low trait cheerful patients (with no difference in Non-Duchenne smiles), and reported a higher level of positive emotions than low trait cheerful individuals. In summary, although hospital clown interventions on average successfully raise the patients’ level of positive emotions, not all patients in hospitals are equally susceptible to respond to humor with amusement, and thus do not equally benefit from a hospital clown intervention. Implications for research and practitioners are discussed.Trait cheerfulness predicts individual differences in experiences and behavioral responses in various humor experiments and settings. The present study is the first to investigate whether trait cheerfulness also influences the impact of a hospital clown intervention on the emotional state of patients. Forty-two adults received a clown visit in a rehabilitation center and rated their emotional state and trait cheerfulness afterward. Facial expressions of patients during the clown visit were coded with the  Hospital clown interventions have been described to “represent a particular way of using humor in order to promote people’s well-being” , no study has investigated whether these humorous interventions are beneficial for all recipients, or whether some groups of individuals benefit more than others , which in classifications of emotions is defined as a facet of joy  is a valid indicator of genuine enjoyment (Non-Duchenne smiles). These types of smiles are present, for example, when individuals mask a negative emotional state (masking smile) or smile when nothing much is felt (phony smile) but individuals attempt to appear as if positive emotions are felt  than individuals low in trait cheerfulness . For example, studies found a reduction of preoperative anxiety and worries in children undergoing medical procedures when interacting with a clown pair compared to a control group without a clown visit e.g., . Regardi29 Clown Emotion List (CLEM-29), which is a collection of single adjectives and short phrases, but can be reduced to four factors: amusement, transcendence, unease and arousal. The factor of amusement merges a variety of positive humor-related states including a calmer cheerfulness and a more aroused hilarity. Transcendence was defined according to its non-religious connotation as the feeling of being uplifted and surpassing the ordinary. It includes positive feelings induced by clowns such as feeling privileged, appreciated, connected to the clown and elevated. The negative factor of unease consisted of negative feelings induced by clowns . Ratings of the factor of arousal relate to different states of arousal, in which positive loadings (touched and speechless) refer to a calm state, that is, low arousal, whereas negative loadings (overexcited and schadenfreude) refer to a more heightened arousal. Studies that used the CLEM-29 showed that individuals watching videos of , and was a convenient sample. Patients suffered from paraplegia, amputations, or other multiple injuries. The age of patients ranged from 19 to 75 years . Inclusion criteria were age 18 or older, voluntary participation, not bedridden, and being cognitively and physically able to participate in the study. Patients were filmed during the study, and videos of a subsample of 26 patients could be used for coding facial actions.The sample consisted of State-Trait-Cheerfulness Inventory . The answer format is a four-point Likert-scale ranging from 1 (strongly disagree) to 4 (strongly agree) and Cronbachs alpha was 0.87.The standard trait version of the 29 Clown Emotion List . It consisted of a semi-standardized performance of a hospital clown pair , aiming at the induction of a positive emotional state in the patients. The same clowns performed in all trials, and used the same roles, clothes and make-up. They worked according to a script and did the same performance (same punchlines) in every trial. They were instructed to limit the length of the interaction to about 5–8 min. Both wore a red nose. The male clown carried a ukulele, wore a Doctor-like jacket. The female clown wore a yellow dirndl dress with yellow socks, a pink blouse. She had an abnormally large handbag in one hand filled with requisites; e.g., a pig nose that makes a farting sound when squeezed, and a thimble, used to demonstrate a magic trick together. The clown pair behaved like Auguste and Whiteface: the female clown was more dominant, slightly aggressive, bossy and pompous, while the male clown was the foolish, clumsy and more sensitive partner. After the clowns left the room, patients filled out the state measures . Patients subsequently were debriefed about the real aim of the study  and asked not to disclose the use of clowns to other patients until the study was completed. For the last step of the study, they filled out the trait measures .Prior to the study, the local ethics committee approved the study. Consent forms were handed out before and after the experiment. The core of the current study was a surprise visit from a hospital clown pair. The study took place in a separate room in the rehabilitation center, and the procedure was highly standardized. Patients were recruited with the cover story that they were going to participate in an evaluation of patient satisfaction in hospitals. They were also told that a staff member would conduct a routine assessment, which they were to evaluate afterward. Two patients participated in each trial. Patients first filled out the HSEF-General, followed by a baseline assessment of emotional states . Afterward, the clown intervention of a predetermined length took place  with the help of the FACS  were extracted, each containing a studied punch line produced by the clowns followed by the reaction of patients. A certified FACS coderthe FACS . A Duche FACS (7 . The Non FACS (7 . Laughteα = 0.80). A frequency score for Non-Duchenne smiles was built by summing up all Non-Duchenne smiles in the same ten scenes (α = 0.58). The Non-Duchenne smile category was more heterogeneous than the Duchenne smile one, as it comprised different types of Non-Duchenne smiles. A laughter score was built by summing up all four types of laughter vocalizations in ten standardized scenes (α = 0.75). All variables used in the analyses were normally distributed.Three scores were built for use in the analyses. As they were sum scores over ten different standardized scenes during the interaction between the clowns and a patient, Cronbach’s alpha was calculated for each score as a measure of the homogeneity of behaviors during the ten scenes. A frequency score for enjoyment smiles was built by summing up all Duchenne smiles in ten scenes, which showed high internal consistency (SD = 3.76) during the 10 scenes. The percentage of Duchenne smiles among all smiles was 76.29%. The minimum was zero Duchenne smiles; the maximum was 16 . The minimum of Non-Duchenne smiles was zero; the maximum was eight . Patients on average laughed 3.58 times during the ten scenes (SD = 4.14) with a maximum of 15 laughter vocalizations. Thirty percent of patients did not produce any laughter vocalizations during the selected scenes.Patients on average smiled 8.92 times , and positive affect was high . Patients enjoyed participating in the study to a high extent , and 81.5% stated that they felt better after the clown visit. The frequency of Duchenne smiles was positively correlated with funniness of the clown visit , global positive feelings , transcendence  and the joy of participating in the study , and negatively correlated with global negative feelings after the clown visit . The frequency of Non-Duchenne smiles was negatively correlated with the joy of participating in the study , transcendence , feeling better after the clown visit (r = -0.33), and positively correlated to unease . Laughter vocalizations were positively correlated with Duchenne smiles , transcendence , amusement , funniness of the clown visit , and feeling better after the clown visit .Negative affect after the clown visit was very low , and ten patients with the highest scores to group 2 (high trait cheerful). A 2 × 2 repeated measures ANOVA with trait cheerfulness (high vs. low) and type of smile (Duchenne smile vs. Non-Duchenne smile) was computed for the frequency of smiling. Results are displayed inF = 31.58, p < 0.001, F = 6.90, p < 0.05, F = 2.84, p = 0.11. However, there was a numerical trend toward higher levels of Duchenne smiles in the high trait cheerful group  than in the low trait cheerful group . An independent samples t-test confirmed that the two groups significantly differed in their frequency of Duchenne smiles, t(18) = -2.68, p < 0.05. No difference was found for Non-Duchenne smiles, t(18) = -0.30, p = 0.77.Patients showed more Duchenne smiles than Non-Duchenne smiles, t(31) = -2.35, p < 0.05, higher funniness ratings of the clowns, t(31) = -2.82, p < 0.01, higher levels of transcendence , t(28) = -1.75, p = 0.09, and a lower level of unease, t(28) = 3.09, p < 0.01, than individuals low in trait cheerfulness. The two groups did not differ in their general preference for clown performances, t(31) = -1.54, p = 0.14, and laughter, t(18) = -0.70, p = 0.49.Individuals high in trait cheerfulness reported higher positive feelings, Humor interventions have been used frequently in research to increase happiness and lower depression in various settings and samples, including hospital clown interventions  is a core component of a (successful) clown-patient interaction. Hospital clown interventions apparently elicit feelings that go beyond the typical humor response, such as feelings of connection, liberation, appreciation or playfulness. In this sense, the present research validates studies using self-report measures , subjects try to mask or suppress the expression of Schadenfreude . The clown pair used in this study had a rather playful, interactive, hilarity-based style, while other clowns work in a more sensitive, insightful and composed way . Also, cenfreude . It woulDespite the limitations, the results promote the use of hospital clown interventions for the enhancement of a positive emotional state in patients in need of care, but also point out the relevance of accounting for individual differences in recipients of the interventions. It is much to be hoped that this will stimulate future studies in that other researchers also combine objective and subjective assessment methods to get a clearer picture of the variety and uniqueness of emotional responses of patients during a hospital clown intervention. Furthermore, this knowledge can be used by organizations that train clowns to raise the awareness of signs that help explaining the success and failure of hospital clown interventions in their work in hospitals to prevent unwanted side effects such as the induction of negative emotions and rejection. Research demonstrates that emotional expressivity may be a reliable sign of cooperative tendency in humans , indicatThis study was carried out in accordance with the recommendations of “Swiss Psychological Association”; and the Ethics Committee of the Department of Psychology, University of Zurich, with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki.SA conceived and designed the work and was also involved in data collection, data analysis and interpretation, drafting the article, critical revision of the article, and final approval of the published version.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.


Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate actual movements. Accordingly, the predictions provided here might help to design and implement control strategies in the region. Additionally, the methodology here may be used to estimate contact networks for other livestock systems when only incomplete information regarding animal movements is available. Indeed,pulation . Nodes mpulation , 10. SNApulation –13. For pulation –16. Idenpulation , 17. Simpulation .The US swine industry is characterized by large numbers of documented pig movements within and between states and regions. From 1970 to 2001, the number of pigs moved from one state to another (or from Canada to the US) increased from 30 to 50 million . WhereasAnimal movements are only partially regulated in the US, and no source provides complete information on such movements. For example, the United State Department of Agriculture, through the animal disease traceability program, collects information on movements of cattle, bison, equines, sheep and goats, swine, and poultry, only when movements cross state boundaries, except when livestock are moved to slaughter facilities or chicks moved from hatcheries . The lacTo elucidate the role of network structure in the spread of swine diseases, the relation between PRRS manifestation and animal movements between farms  was assessed in two counties in Minnesota . A positWe used two complementary sets of data to construct our model. The first dataset, referred to as the network building data set, included information on pig movements related to two counties being used to fit the model, whereas the second dataset included information on sites located within the broader RCP-N212 area, which was used for prediction purposes. The first dataset included information collected in two counties that were geographically located within the boundaries of the second dataset; however, the two datasets were collected separately. The first data set was based on surveys conducted with owners, managers, and veterinarians on farms and at market sites located in Stevens and Rice counties, Minnesota in 2006 . Animal The second data set, referred to as RCP-N212, contained information on geographical location, owner, and type of site for premises enrolled in the RCP-N212. This data set contains roughly 38% of total swine premises with 100 or more animals located in Minnesota . Data wein- and out-degree, calculated as the number of pig movements received or sent by a specific site to or from other sites. Betweenness, defined as the number of directed paths that pass through a given site, when the shortest paths between other pairs of sites are traced (r) for a mixing matrix was used, as defined by elsewhere   .network density, calculated as the fraction of movements that are present in the network relative to the total number possible; (2) clustering coefficient, used as a measure of cohesiveness and defined as the probability that two sites that are linked to a common site are also linked to each other; (3) diameter, calculated as the largest distance between two sites in the network, where distance is the shortest path between two sites; and (4) the mean path length, calculated as the mean length of the shortest paths connecting two sites  wo sites , 30.ggplot2  that contained all possible origin-destination pairs of sites within each network. Per each possible pair of sites, we assigned a dichotomous outcome  variable  indicating whether or not the animal movement has occurred between that pair. We used the geographical location of each site to estimate the pairwise Euclidean distance (kilometers) between farms, and generated a dichotomous variable  indicating whether or not each pair had a common owner. Additionally, we generated 25 dummy variables, each denoting a possible pair of site types , being 1 if the pair site type combination was true and 0 otherwise.caret package , a  process , 43. We  package  in R  versus observed movements for both, unbalanced and balanced testing datasets by using their confusion matrixes. As result, we compared the accuracy, Kappa statistic, specificity, sensitivity, and the area under the receiver-operating characteristic (ROC) curve. The ROC curve is a graphical method to test predictive performance by contrasting true positive and negative values. The accuracy rate formance . SensitiO(v × nlog(n)), where v is the number of variables and n is the number of observations. This analysis took around 45 min to complete on a standard MacBook Pro®, though other packages such as ranger and random jungle may achieve faster performances for larger data sets and down-sampling can further optimize run times  and 28 variables, thus complexity of the algorithm is given by Finally, using the observed and predicted animal movements, we conducted an SNA to contrast centrality measurements between the observed and predicted network using the non-parametric Kruskal–Wallis test.in- and out-degree per each type of site observed in SN and RN.We used our final model to predict animal movements among sites in the RCP-N212. Using data from RCP-N212, we generated all possible pair combinations among sites located within that RCP area. Similar to the analyses performed with the RF-data, we estimated Euclidean distances (kilometers) between each pair of sites and generated a dichotomous  variable for ownership and 25 dummy variables for possible combinations of types of sites. Acknowledging that movements of animals must also occur to and from sites located out of the RCP-N212 and to avoid overestimations in the number of movements occurring within sites in the RCP, we restricted the number of predicted movements among sites in the RCP-N212 using the maximum values of We summarized the distributions of centrality measures of the predicted network for the entire RCP-N212 and for each of its 34 counties. We used the same metrics as described in Section “The network building data set included 237 sites (220 farms and 17 market sites), of which 33 and 19% were located within Stevens County and Rice County, respectively. The remaining 48% of sites were not located within those counties, but involved animal movements to or from Stevens and Rice  that followed downstream flows, i.e., a vertical structure , although finishers also sent pigs into upstream destinations, including nurseries and farrowing farms , probably to provide replacement animals , there was no statistical difference between the two networks in out-degree (P = 0.31). In contrast, in-degree varied across production types for both networks (P < 0.01 for both), with markets having a higher in-degree  , each shipping animals to three different sites on average, with a maximum of 12. Betweenness did not significantly differ across production types within RN (P = 0.17) but was statistically different among different types of sites in SN (P = 0.02)  than SN (5), though mean path lengths were relatively similar (1.82 for RN and 1.85 for SN). In turn, the distances between sites varied considerably, from less than 1 to more than 1,000 km. The overall mean distance between sites was 111 km, with nurseries and farrowing farms receiving animals from longer distances and boar studs shipping animals to sites located more than 500 km away (Table mtry) estimated was 27 and 20, respectively. We observed a higher κ for the unbalanced dataset, indicating a higher accuracy  corresponded to true movements . RF models for both balanced (similar proportion of class variable “yes” and “no”) and unbalanced datasets used 1,500 trees, and the optimal number of predictors  and predicted  networks based on observed and predicted animal movements from use of the testing dataset, model predictions provided a reasonable approximation of real movements  and in-degree (P = 0.97), whereas values for the out-degree were significantly different (P = 0.02). For the latter, we predicted that, on average, a site would deliver animals to 1.4 sites (SD = 1.3), compared to 1 site (SD = 1.0) observed in the real network.Comparing the observed , in- and out-degree were statistically different across production types in both observed (P < 0.01 for both) and predicted networks . For example, market sites in the observed data received animals from 7.4 different sites, whereas the model predicted receptions from 8.4 different sites. Similarly, whereas the model predicted that a nursery would ship animals into 2.1 farms, observed values indicated 1.7 different farms  for all, except betweenness of market (P = 0.02) and out-degree of finishing farms (P = 0.002).Furthermore, the patterns of connectivity across farm types were qualitatively similar Figure . WhereasP-values of 0.70, 0.05, 0.29, 0.94, respectively)  Figure . Among fin- or out-degree for its production type , and 32% characterized as farrowing farms or nurseries. Only 1.2% of total sites recorded in RCP-N212 were market sites, which were located in only 4 out of the 34 counties in which RCP-N212 sites were located. We generated 688,070 possible origin-destination pairs, and our model predicted that 0.9% of those pairs were likely to move animals between them, using a probability threshold >0.85. However, if the number of likely links for a given farm exceeded the maximum observed in-degree, receiving pigs from 57 sites, whereas farrowing farms, nurseries, and finishers were expected to receive animals (perhaps replacements), on average, from 4, 1, and 2 sites, respectively. On the other hand, the model predicted that nurseries and farrowing farms would ship pigs  to 12 and 10 farms, respectively  animals through, on average, 141.4 km (SD = 75.5 km), whereas nurseries, on average, 113.6 km away (SD = 67.7 km) to sites within the RCP-N212. In turn, farrowing farms and market sites were expected to receive animals from longer distances   sites might provide animal replacement  to upstream sites. Because outbreaks of diseases, such as PRRS, are also common in downstream sites , movemenvia contaminated vehicles , such mixing might facilitate spread disease vehicles , 49. Thuvehicles , and sitvehicles .As mentioned previously, we used information available from two small, county-based networks to estimate parameters for predictions of animal movements that closely fit observed movements as judged by standard statistical tests. We were able to validate our predictions within SN and RN. The parameters generated by our model were used to predict animal movements between sites over a larger area, i.e., RCP-N212. This is essentially an out-of-sample prediction. As data on actual animal movements were not available for RCP-N212, we cannot directly test the accuracy of our predictions for the larger network. The results for larger network appear reasonable in that they are consistent with the topology of the SN and RN networks, and these results may be useful in helping understand actual (but unobservable) animal movements in Minnesota. We believe that such out-of-sample prediction is warranted for the scale of RCP-N212, given that farms within this program are similar to the farms in SN and RN in terms of geography, demography, and management. However, predictions that would rely on more extensive extrapolation  would not be appropriate given the scope of our sampling. In addition, the value of being able to assemble a full network may not be to target individual farms, but rather to capture possible regional patterns in connectivity.Based on comparisons between observed, county-level data and regional-level model predictions, such as the distribution of movement distances and general attributes of the network, we believe that our predicted full network for RCP-N212 has structural features that are similar to the partial data used to estimate the probability of movement between farms. Thus, our findings appear reasonable and provide insight to better understand animal movement patterns within the RCP-N212. This, in turn, may help farmers design private strategies for sanitary management, as well as aid policy-makers in structure-based decisions. However, given inherent limitations to predictive modeling, we acknowledge that our results may provide only general insights about movement patterns, thus additional work must be done before strong conclusions can be made regarding the utility of the predictions achieved in this way.in- and out-degree. As a result our movement predictions within the RCP-N212 were conservative, showing a lower density in the expected network (0.4%) than in the two observed networks , although some metrics might have been overestimated, such as out-degree for nurseries. This is because there are very few nursery farms in this region, and many of the finishing farms are actually sourcing for pigs from other states outside of RCP-N212. However, our algorithm restricted their choice of nurseries to those within RCP-N212, perhaps leading to a false inflation of their out-degree. Future work should expand to larger geographic regions that encompass all stages of production, capturing movements between states. We anticipate that we could improve model predictions by obtaining information regarding contract relationships and animal movements among Minnesota sites and suppliers located outside RCP-N212.The RCP-N212 covers 34 out of 87 counties in Minnesota, accounting for 38% of the total swine facilities with 100 or more heads in the state. Because farm distribution is heterogeneous within Minnesota, with a greater number of farms toward the south , it is rin- and out-degrees overlap with areas where spatial and temporal aggregations of PRRS have occurred , may be incorporated into a disease transmission model that will help to evaluate the effectiveness of prevention and control strategies in a region, with the ultimate objective of mitigating the impact of endemic disease and hypothetical epidemic incursions. The approach here may also be applied to other regions and production systems, where information on animal movements is only partially regulated, thus improving decision-makers’ ability to plan and implement disease surveillance and control activities.All the authors have met the four criteria described at the guidelines: PVD designed and data interpretation, revised and approved the version to be published, and agreed to be accountable for all aspects of the work. KV and LJ designed, revised and approved the version to be published, and agreed to be accountable for all aspects of the work. SW data interpretation, revised and approved the version to be published, and agreed to be accountable for all aspects of the work. AP designed, revised and approved the version to be published, and agreed to be accountable for all aspects of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Eight families, with high (~50 μg/dL), medium (~20 μg/dL), and low blood levels (~10 μg/dL), were selected from 60 families. The main sources of exposure to lead for children were food and house-dust, and those for pregnant women were soil, respirable dust, and food. LIR was determined by inductively coupled plasma quadrupole mass spectrometry (ICP-QMS) with a two sigma uncertainty of ±0.03%. The LIR of mothers and newborns was similar. In contrast, surma, and to a larger extent petrol, exhibited a negligible contribution to both the child’s and mother’s blood Pb. Household wet-mopping could be effective in reducing Pb exposure. This intake assessment could be replicated for other developing countries to identify sources of lead and the burden of lead exposure in the population.Lead (Pb) in petrol has been banned in developed countries. Despite the control of Pb in petrol since 2001, high levels were reported in the blood of pregnant women and children in Pakistan. However, the identification of sources of Pb has been elusive due to its pervasiveness. In this study, we assessed the lead intake of pregnant women and one- to three-year-old children from food, water, house dust, respirable dust, and soil. In addition, we completed the fingerprinting of the Pb isotopic ratios (LIR) of petrol and secondary sources (food, house-dust, respirable dust, soil,  Lead (Pb) exposure causes an estimated 0.6% of the global burden of disease, predominantly occurring in developing countries . Every yLead has contaminated the environment including food, soil, water, and air, mainly through its usage in petrol. The decrease in the usage of lead in primary sources such as petrol has substantially reduced the population exposure. However, lead exposure is still excessive in several developing countries ,6,7,8,9.204Pb, 206Pb 207Pb, and 208Pb. The most common is 208Pb (52%), followed by 206Pb (24%), 207Pb (23%), and 204Pb (1%). Of these, three isotopes  are produced by the radioactive decay of 238U, 235U, and 232Th, respectively. 204Pb is the only primordial stable isotope. Thus, the abundance of Pb isotopes in a sample depends on concentration of U, Th, and primordial Pb in the source and the time elapsed since their formation . This method is validated by the United States Environmental Protection Agency (USEPA) for an in vitro assay used for estimating lead relative bioavailability (RBA) in environmental media .The extraction fluid used was 0.4 M glycine , adjusted to a pH of 1.50 ± 0.05 using trace metal grade concentrated hydrochloric acid. Soil samples (200 mg) were mixed with the extraction fluid to a solid-to-fluid ratio of 1/100 (mass per unit volume) in a 25 mL lead-free tube. Samples were extracted at 37 °C, at 30 rpm in a BR-40LF bio-shaker  for one hour, ensuring that the pH was maintained at 1.5 ± 0.5. The extracts were filtered with a 0.45 μm cellulose acetate disk filter (33 mm diameter) and the filtered samples were stored at 4 °C. The samples were analyzed by ICP-MS Agilent 7500cx in NIES .The lead concentrations in blood, food, water, petrol, and engine lubricant, as well as the extraction fluids for bio-accessible lead from environmental media , were determined using inductively coupled plasma-mass spectrometry (ICP-MS). The ICP-MS Agilent 7500cx  method was performed in The National Institute for Environmental Studies (NIES), Japan.The measurement for lead was carried out by the calibration curve method using a lead standard solution  and a thallium standard solution  for an internal standard. The lower limit detection of lead was 0.001 ng/mL (ppb).The test for quality control was performed by using commercial reference samples: National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) 995c, Toxic Metals in Caprine Blood  for blood analysis; National Metrology Institute of Japan  Certificated Reference Materials (CRM) 7202-b, Trace Elements in River Water (Elevated Level)  for water analysis. The recovery of lead for the blood and water analysis methods was 95.7%, and 92.9%, respectively.Energy dispersive X-ray fluorescence spectrometry (EDXRF) was conducted by the Industrial Technology Center of Tochigi Prefecture, using an JSX-3100RII element analyzer  to determine the lead concentrations of house dust and respirable dust.For analysis, house dust was placed in a specific plastic cup with thin film sample supports of PROLENE 4.0 microns  and it was pressed by hand using a pestle. House dust samples were analyzed for 240 s (live time) under an air-condition using an X-ray lamp voltage of 50 kV, an auto lamp current, a 7 mm collimator, and a Pb filter. The measurement for lead was carried out by the calibration curve method equipped in the instrument. Samples for the calibration curve were prepared by cellulose, powder , and NIST SRM 2583.For analysis, deposited respirable dust on the filter was placed on the measurement stage with the PROLENE film to prevent the contamination of the detecting element. Respirable dust samples were analyzed for 600 s (live time) under the same condition as for the house dust analysis. The measurement for lead was carried out by the calibration curve method. The standard filters for the calibration curve were prepared by the droplet method.207Pb/206Pb versus 208Pb/206Pb were performed using ICP-QMS Agilent 7500cx in NIES. The instrumental conditions of ICP-QMS for LIR analysis are given in 207Pb/206Pb and 208Pb/206Pb. The extraction of NIST SRM 2583 was analysed for every ICP-QMS measurement for quality control. The value (n = 5) was 0.822 ± 0.002 for 207Pb/206Pb and 2.027 ± 0.003 for 208Pb/206Pb, which agreed with the value measured by the ICP-MS multi-collector .The acid digested solution of blood samples, food, and the extraction of bioaccessible lead from environmental media , was analyzed for a comparison of the lead isotope ratios (LIR). Measurements of the LIR 207Pb/206Pb and 208Pb/206Pb were separately determined for the blood of pregnant women, newborns, and children, as well as for all of the environmental media and food samples of pregnant women and children.The mean and standard error for LIR of We also measured the lead concentration for raw (pre-cooked) and cooked food for common food items to ascertain the contribution of cooking utensils for increasing the lead levels in the food. Five different types of utensil, including commonly used alloys, steel, iron, non-stick utensils, and microwaves, were used for cooking the same food. Three common foods items including lentils , potatoes, and chicken were assessed. The raw and cooked food items were processed in the same manner as the food duplicate samples and were measured for lead contents .4), and soil [in µg/kg body weight/week] was conducted with the following calculation formula, based on the USEPA Exposure Factors Handbook 2011 edition ; DI is the calculated daily intake of food (mg/day) and water (mL/day); IngR is the ingestion rate of house dust : 60 mg/day) and for soil : 50 mg/day); InhR is the inhalation rate of respirable dust : 21.4 m3/day and child (2–< 3 years) 8.9 m3/day); BW is the body weight, kg; multiplied by seven to convert it into a weekly dose. The daily intake of food and water was calculated by the weighed value at sample preparation. For all of the ingestion and breathing rates, we used the data from the USEPA Exposure Factors Handbook 2011 edition , two for a medium blood lead group [~20 µg/dL], and four families for a low blood lead group [~10 µg/dL]. The cord blood lead levels were closer to the blood lead levels of pregnant women  .sr = 0.86; p = 0.007). The cord blood levels were also highly correlated with the one- to three-year-old children  (The lead levels of the pregnant women’s blood were highly correlated (spearman’s ρ) with cord blood lead levels (= 0.007) .The lead content levels of three common food items  were similar before and after cooking using four different utensils, suggesting a minimal contribution of utensils for increasing the lead content in the food. However, the lead concentrations for all food items were lower than the controlled limits .The lead concentrations of the petrol and engine lubricant from gas stations in the neighborhood of the study participants ranged from 0.013 to 0.083 mg/L, well below the control levels of less than 20 mg/L .EDXRF analysis and food and water determined by ICP-MS after total acid digestion by the microwave. As the soil in Karachi could not be transported to Japan, the soil data is missing in this figure. 4) intake, which ranged from 2% to 75% (mean = 27.12%), while the IVBA ranged from 0% to 54% (mean = 20.37%). The percentage contribution of lead by respirable dust was higher for families exposed to lower levels of lead. House dust was also an important source; however, water was contributing a negligible amount of lead intake among pregnant women.The second most important source of lead exposure among pregnant women was respirable dust (PM4) and soil were also important sources of exposure for one- to three-year-old children, as opposed to pregnant women. However, lead from water was contributing a negligible amount to the body burden of lead in the young child similar to the pregnant women. There were no marked differences in the sources of exposure (percentage contribution of lead) for young children among families exposed to high and low lead levels. The children of all selected families were similarly exposed to lead from food, house dust, respirable dust, and soil.The lead intake by various sources among the one- to three-year-old child of the family is described in The bioaccessibility of lead in different sources was calculated in the data presented in Recent studies in Pakistan have shown high blood lead levels in the vulnerable population, including newborns and children ,13,14. TBesides validating the findings of high lead exposure among this population, the study identified that food, house-dust, and respirable dust were the main sources contributing to the lead level in the blood of pregnant women, and food and house-dust contributed the most to the lead level seen among young children. The contribution of dust to the blood lead level is most critical for children aged one to three years, typically with the highest lead levels and greater hand-to-mouth activity . For thePrevious available studies in Pakistan had limitations, as these were purely epidemiological in nature and had identified behavioral and subjective factors ,13. Somesurma/kohl use (eye cosmetic), father’s occupational lead exposure, a parent’s illiteracy, and a child’s habit of hand-to-mouth activity [surma daily, and in households where the mothers took no calcium or less iron supplements during pregnancy [Studies used to identify lead sources are so far based on behavioural studies and the determination of lead levels in samples of petrol, paint, dust, soil, and water, separately. These measurements have been made without objectively linking them to determine the proportion contribution of these sources for blood lead levels. A study in several geographically different locations in Karachi suggested that high blood lead levels were related to vicinity to the main street and intersection, activity . Anotherregnancy . In one regnancy . All of This study is the first food duplicate study in Pakistan and provides information about the oral intake of lead in food. Few studies have conducted the measurement of lead exposure through food. Since the implementation of unleaded gasoline in developed and many developing countries, food may be considered as a major source of secondary exposure. However, due to the unavailability of reliable methods and laboratories, it has not been studied, particularly in developing countries. The proportion of bioaccessible lead from multiple sources and source apportionment using LIR were estimated for pregnant women and young children in Karachi, to identify the important contributors of lead in these vulnerable populations. Food, house-dust and respirable dust were identified as major sources of exposure among pregnant women. Besides food, house dust was identified to contribute to blood lead levels among young children in Pakistan. This investigation informs that regular wet-mopping in the households could be an important intervention for the prevention of exposure to lead. Also, further investigations are needed to identify the contamination sources of food and major foods contributing to lead exposure in this population.surma (particularly) was distinct from the blood LIR of pregnant women, newborns, and young children in most families, indicating that these are not the primary major sources of exposure.We used isotopic analysis by ICP-QMS validated by a ICP-MS multi-collector. Our analysis of ILR was not precise enough to determine the percentage contribution of lead from individual sources. However, LIR of pregnant women’s blood and cord blood were closely related in most families and the child’s blood was more closely related with current environmental sources of exposure such as food, house-dust, and respirable dust. The LIR of petrol (largely) and Pregnant women’s LIR was relatively higher and distinct from all other current sources of lead exposure, suggesting past exposure and the mobilization of lead deposited in bone tissue. Alternatively, pregnant women’s exposure might relate to some other environmental sources which have not been studied in this investigation. However, a strong relation of a newborn’s and child’s blood LIR with current sources clearly indicate that the lead level of pregnant women could only be due to past exposure. The lead deposits in the bone tissues remain in constant exchange with blood and that might be a larger contributor to pregnant women’s blood lead levels, particularly during pregnancy and breast feeding ,26.The uptake of lead per body weight, as determined by IVBA, by young children, was almost three times higher compared to pregnant women B. This iWe further investigated the main source of exposure, i.e., food, for possible sources of contamination. First, we investigated lead contamination through cooking with several cooking utensils in a laboratory in Aga Khan University. The increment of lead after cooking was negligible and the lead concentrations in the food ingredients examined were generally low . It suggTherefore, we suggest that, first, a more systematic surveillance for lead contamination in food and the environment is required in Pakistan. Second, we must delineate possible contamination sources during agricultural and animal farming practices and the processing of various food items in Pakistan.The currently available petrol contains lead levels much lower than the recommended guidelines (<20 mg/L). It is evident that current automobile exhaust gas is not a major lead contamination source. We could not obtain gasoline or alkyl lead used in the past. However, we speculate that similar alkyl lead was added to gasoline in the past, which produced ubiquitous lead contamination that is sustained in the environment. This is supported by studies conducted in western countries which show that emitted lead remains a source of exposure for a longer duration, maybe decades . The leaThere were certain limitations in this analysis which need to be considered. As the samples were collected from one megacity, the study findings can only be applied to this city. However, Karachi is a megacity where approximately 10% of the population of Pakistan resides. Also, being the main harbor of the country, most of the petrol and food are processed and transported up-country from Karachi, so we consider that a similar LIR would be prevalent in other parts of the country. Due to the high cost and time required to conduct the laboratory analysis, for several matrices of triad , as well as for four isotopes, we limited the analysis to eight families. However, the samples were selected from a larger study based on the blood lead levels among pregnant women. The samples chosen were from both high and low levels of lead exposure among the same population exposure range.Nevertheless, the study methodology can be used for determining the sources of lead exposure in similar situations. To the best of our knowledge, it is among the first few studies of this nature which has comprehensively determined the source apportionment and utilized LIR analysis to compare patterns of Pb exposure in blood specimens, food duplicates, and environmental samples in a developing country. The information would provide management strategies for public health action.The study capitalized on the strong collaboration between developing and developed country and we feel that this has been an important strength of this study. The methodology required several sophisticated advanced analyses, which are generally not available in a developing country like Pakistan. The limited capacity has been the major limitations for such studies to be replicated in developing countries.High levels of blood lead were present among pregnant mothers and young children and may induce adverse developmental effects in the newborns and young children. Food, house-dust, and respirable dust among pregnant women and young children were the main contributor of blood Pb in this population. Surma, and to a large extent petrol, are not major contributors of the blood lead levels of mothers and children in Pakistan. Behavioral interventions such as wet-mopping and clean cooking practices may help to control the lead exposure among this population. Therefore, a surveillance of lead contamination is urgently needed to devise countermeasures to reduce environmental lead contaminations.


The Tui-Na and Thai massage techniques have been used widely over a long period in Eastern culture. Tui-Na is a traditional Chinese therapeutic massage technique and has been used since 2700 BC . Thai maThe term “massage” indicates pressing, rubbing, and scrubbing using the hands, and massage therapy is used for injuries, musculoskeletal pain, and correction of body shape. The Tui-Na and Thai massage techniques have been used not only to treat musculoskeletal conditions but also to improve circulation of the blood and metabolism, increase physical fitness, and to treat several diseases , 4. HoweThirty healthy middle-aged men (aged approximately 40 yr) attended the Sports Medicine Laboratory at Mokwon University in Daejeon-si, Korea, for assessment of vascular compliance. They were divided into a Thai massage group (n=10), a Tui-Na massage group (n=10), and a control group (n=10) .Massages were administered three times a week for 40 min each over a period of 8 wk by four massage experts, each with more than 10 yr of experience. For Tui-Na massage, the subjects were positioned straight on the massage table. Hand manipulations, including rolling (Gun Fa), the one-finger pushing method (Yi Zhi Chan Fa), and scrubbing (Ca Fa), were then performed at a frequency of 120 times per minute from the head to the feet, including the neck, shoulders, arms, chest, abdomen, and legs. For Thai massage, the subjects were positioned straight on the massage table. The massage expert then pressed along the Sen path (the energy in China and Prana in India) using the feet, palms, thumbs, and knees, with equal pressure applied to the left and right sides of the body. The massage was performed in the supine and prone positions. The massage concluded in the sitting position, and was followed by stretching. The control subjects were instructed to perform normal daily activities and to avoid certain physical activities.Vascular compliance was assessed by PWV using an approved medical device . Electrocardiography leads applied to the left and right forearms were linked to the device with the subject in the supine position, and PWV measurements were performed when heart rate and blood pressure were confirmed to be stable. PWV was divided by subject height to control for individual differences in vascular length and calculated before and after massage therapy.P<0.05.All data are presented as the mean±standard deviation. One-way analysis of variance was performed to compare the physical characteristics of the subjects and changes in vascular compliance in the left and right forearms between the study groups. Tukey's post hoc test was also performed. All analyses were performed using SPSS ver.18.0 . Statistical significance was set at The changes in vascular compliance in the left and right forearms are presented in P<0.001). However, there were no significant differences in vascular compliance between the Tui-Na and Thai massage groups (P>0.05).Vascular compliance was significantly better on both sides in the Tui-Na and Thai massage groups than in the control group (Massage using either the Tui-Na or the Thai technique has a positive effect on vascular compliance. These massage techniques have similar efficacy.


The World Bank has reported that global smoking rates declined from 2000 to 2012, with the only exception found in males in Sub-Saharan Africa. Sub-Saharan Africa is considered to be in stage one of the tobacco epidemic continuum. To address this problem, school-based programs for smoking prevention are considered cost-effective and promising. Since tobacco prevention programs are influenced by social competence or customs of each country, tobacco prevention programs that have success in Western countries are not always effective in African countries. Therefore, the current study systematically reviewed relevant literature to examine the effects of these types of programs in African countries.Online bibliographic databases and a hand search were used. We included the studies that examined the impact of school-based programs on preventing tobacco use in Africa from 2000 to 2016.th to 9th graders, while the other two were temporary programs. All programs were based on the hypothesis that providing knowledge and/or social skills against smoking would be helpful. All studies utilized smoking or polydrug use rates to compare outcomes before/after intervention. There were no significant differences between intervention and control groups in three studies, with the other three demonstrating only partial effectiveness. Additionally, three studies also examined change of knowledge/attitudes towards smoking as an outcome. Two of these showed significant differences between groups.Six articles were selected. Four were conducted in South Africa and two were performed in Nigeria. Four programs were systematically incorporated into annual curriculums, targeting 8All RCTs studies showed no significant change of smoking-rate by the intervention. The effectiveness of intervention was observed only in some sub-group. The cohort studies showed school-based interventions may be effective in improving knowledge and attitudes about smoking. However, they reported no significant change of smoking-rate by the intervention. Smoking is one of the largest threats to public health. Smoking is associated with nearly one out of every three deaths from cancer, nearly one out of every five deaths from heart disease, and importantly, nine out of ten lung cancer deaths ,2. In hiAdolescence is one of the highest risk developmental phases for the initiation of tobacco use. In Africa, the estimated prevalence of smoking is 9.3% for boys, 3.8% for girls, and 6.6% for both sexes [th grade) [The results of previous studies on the effectiveness of school-based programs in preventing tobacco use among school children are mixed. According to a review by Thomas et al. reported longest follow-up found an overall significant effect with average 12% reducation in starting smoking compared with controls, but no effect for all trials pooled at less than 1 year. However, trials of combined social competence/social influence curricula had a significant effect on smoking prevention at both follow-up periods. While ahttps://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42017056711The protocol for this systematic review was registered in February 20, 2017, in the PROSPERO database: In October 2016, PubMed, Web of Science, SCOPUS, ERIC, PsycINFO, Popline, CINAHL, and CENTAL were used to search for relevant articles. We created the search terms based on a previous review, and then adapted them to all other databases according to vocabulary and syntax of each database.We adapted the search formula to each database style. The basic formula utilized was as follows:school* AND (child* OR adolescen* OR student* OR pupil*) AND  AND (tobacco OR smok*)In addition, we hand-searched five key journals, including School Health Research, Health Education Research, Health Promotion International, Tropical Medicine & International Health, and Cochrane Library.Two reviewers (AN and MY) independently searched articles, utilizing identical methodology, and evaluated articles to find relevant studies following pre-established inclusion criteria. Any disagreements between the reviewers were resolved by discussion and, if necessary, by consulting a third reviewer (ST).The title and abstracts of the results generated from the searching database were screened using the following inclusion criteria:Study design: all quantitative study designs, including randomized controlled trials (RCT), non-randomized trials, cohort studies (controlled and uncontrolled), and cross-sectional studies.Study objectives: studies that examined the impact of school-based programs on preventing tobacco use.Participants: students in Grades 1 to 12Location: African countiesProgram: any type of school-based program targeting students to prevent smokingPublishing: studies published in a peer-reviewed journal and written in English from January 1, 2000 to June 30, 2016We examined each of the selected articles from the viewpoint of country, targeted children, and type of intervention. We also evaluated the risk of bias of RCTs or cluster-randomized control trials by the Cochrane Collaboration’s tool for assessing risk of bias . For nonThe PubMed search yielded 549 articles. Searches of Web of Science, ERIC, PsycINFO, Popline, CINAHL, and CENTRAL yielded 173, 13, 206, 51, 19 and 25 articles, respectively. SCOPUS limited the length of search formula. Therefore, we divided the section of the formula concerning country name into three parts and conducted three separate searches. These searches yielded 220, 184, and 155 articles, respectively. We found one article through a hand search. The most common reasons for exclusion were that studies were not prevention studies at schools . Six articles [Selection bias of four RCTs survey –18 was nBias due to confounding of two cohort studies was judged a low risk because of very simple surveys at schools. Bias in selection of two cohort surveys was judged a low risk since selection of participants into the survey did not base on participant characteristics. Bias in classification of two cohort surveys was judged a low risk since intervention groups cleary defined. Bias due to deviation of two cohort surveys was equally judged a serious risk since it is considered a lot of other factors such as children’s life style or human relationship affect the results. Bias due to missing data of two cohort surveys was judged a low risk since the percentage attrition of Raji et al., OdukoyaThe study design was very similar in every study. Therefore, evaluation of risk of bias was similar for all studies.th- to 9th-grade learners. The other two interventions [Four studies –17, 20 wventions ,20 were ventions ,17,18. Tventions . One stuventions  used twoAll six studies utilized smoking or polydrug use rates before and after program implementation as outcome measures. There were no significant differences between intervention and control groups in three studies ,19,20, aThree studies ,19,20 alIn total, only five studies –20 reporIn the study by Resnicow et al. , 36 publth-grade students. The intervention was based on the HW curriculum, and was designed based on the premise that increasing basic life skills, knowledge of the risks associated with substance use/sexual behavior, and enhancing the skills needed to resist substance use/sex is necessary. It was also considered that promoting healthy free time experiences would decrease substance use and sexual behaviors among youth [Smith et al.  randomlyng youth –39. The th-grade students. The intervention was based on the HW curriculum. The program consisted of 18 lessons from 8th to 9th grade. The effectiveness was measured by the lifetime and previous 30-day polydrug use rate, including tobacco. Data were collected at the beginning and end of 8th grade. The results showed no significant effects of the HW program relating to gender, cohort, or treatment for lifetime polydrug use. Females of the intervention group had a smaller increase (32%) in substance use than females in the control group (36%). However, the results of the main effect models for polydrug use in the past 30 days showed no significant difference in analysis of all participants. On the other hand, the rate of polydrug use in the HW program group was significantly lower than the control group among non-users .Tibbits et al.  randomlyMotamedi et al.  randomlyp < 0.001). Attitudes towards smoking also changed significantly. For example, 71.9% of respondents in the study group at baseline felt that cigarettes should not be sold to people less than 18 years old, and this increased to 91.7% post-intervention (p < 0.001). After the intervention, the number of students who had smoked in the last 30 days decreased by 0.6% in the intervention group (7.9% vs. 7.3%), whereas this number increased by 0.1% in the control group (8.8% vs. 8.9%). These differences were not statistically significant.Raji et al.  recruitep < 0.001) compared to the control group. The mean score of attitudes toward smoking was also significantly higher in the intervention group (p < 0.001). After the intervention, the number of students who had smoked in the last 30 days decreased by 1.0% in the intervention group (4.0% vs. 3.0%), whereas this number increased by 0.5% in the control group (3.5% vs. 3.5%). However, these differences were not statistically significant.Odukoya et al.  randomlyThese six selected studies suggest that school-based interventions might have some positive effects on improving knowledge level of smoking and attitudes towards smoking, and partially prevent the increase of smoking prevalence among secondary school students in Africa. However, we could not find robust evidence that school-based interventions decreased smoking prevalence among school children in Africa. Identical to our analysis, a Cochrane review reported that school-based interventions in other areas of the world could not detect a statistically significant decrease in the number of current smokers over time .We must consider why these six studies could not show strong evidence that school-based interventions were effective in reducing smoking prevalence in individual students or populations at large, although selected studies showing improved knowledge and attitudes about smoking and/or increased smoking prevalence. As a reason, the limited quality and inconsistent outcome measurements can be considered as follows.First, the purposes and outcome measurements were inconsistent for each intervention. In the six selected studies, students targeted for intervention were mixed, composed of non-smokers, non-smokers who were likely to initiate smoking, and current smokers. Therefore, interventions attempted to play a role in preventing the initiation of smoking, quitting smoking, and/or reducing tobacco or other drug dangers. Such multiple focuses may weaken the effects for each group of target students. Specific analysis for each type of student may be required to increase the power of interventions.Second, it is possible the training for presenters was not appropriate or not sufficient. Only Resnicow et al. and OdukThird, the interventions did not focus on the modification of various factors that induce smoking behaviors in adolescents. The selected studies showed the different impacts of intervention by gender ,20. PrevFourth, a comprehensive approach may be needed to enhance the effectiveness of interventions. All of the selected six studies created intervention programs only from the perspective of skills-based health education within the FRESH framework. It also may be effective to combine with other approaches including health-related school policy statements, education for community people, and increases in tobacco tax. The Health Promoting Schools Framework provided by the WHO suggests the importance of collaboration with parents and local communities to develop effective intervention programs in schools . The culCurrently, the global trend of smoking is in decline. In light of this, tobacco companies have begun to expand their markets in low- and middle-income countries, capitalizing on economic growth, changing social norms, and population demographics. Africa has lower rates of tobacco taxation, weaker smoke-free policies, and less stringent tobacco advertising restrictions in comparison to high-income countries. In countries such as Africa, school-based anti-smoking intervention programs have many strong points. Since the enrolment rate of primary school increased from 75.2% in 1990 to 99.2% in 2013  in AfricThe limitations of this review are that only a few papers were selected, and those included were only carried out in South Africa and Nigeria. Africa contains extreme diversity in religion, race, and school systems. Therefore, this review may not represent the results for school-based programs on preventing tobacco use in the whole of Africa. Furthermore, there were no “pure prevention cohort” but groups of mixed never-smokers and smokers. Even with these limitations, this study clarified weak points of existing studies, and provided future directions for study design within this field.There were four RCTs studies and two cohort studies of school-based tobacco prevention program in African countries from 2000 to 2016. The all of RCTs studies showed no significant change of smoking-rate by the intervention. The effectiveness of intervention was observed only in some sub-group. The cohort studies showed school-based interventions may be effective in improving knowledge and attitudes about smoking. However, they reported no significant change of smoking-rate by the intervention.S1 FileThis is the PRISMA 2009 checklist.(DOC)Click here for additional data file.


Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose.To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging. A novel spatio-temporal discriminative dictionary learning scheme was proposed to differentiate PsP and TTP, thereby avoiding segmentation of the region of interest. The authors constructed a novel discriminative sparse matrix with the classification-oriented dictionary learning approach by excluding the shared features of two categories, so that the pooled features captured the subtle difference between PsP and TTP. The most discriminating features were then identified from the pooled features by their feature scoring system. Finally, the authors stratified patients with GBM into PsP and TTP by a support vector machine approach. Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were used to assess the robustness of the developed system.The average accuracy and AUC values after ten rounds of tenfold CV were 0.867 and 0.92, respectively. The authors also assessed the effects of different methods and factors  on the performance of their classification system which obtained the best performance.The proposed objective classification system without segmentation achieved a desirable and reliable performance in differentiating PsP from TTP. Thus, the developed approach is expected to advance the clinical research and diagnosis of PsP and TTP. The conventional magnetic resonance imaging (MRI) is unable to distinguish PsP from TTP since these two entities have similar intensity and shapes on MRI. Currently, the only method for distinguishing between PsP and TTP is to perform follow-up MRI examinations based on the changes in the lesion site. It usually takes several months to obtain an accurate diagnosis, which will result in a critical delay for the clinical management of patients. Although the pathological confirmation of PsP may be helpful, brain tumor biopsies are not typically done in clinical practice, since the procedure is invasive, carries increased risk for the given chemoradiotherapy, and may lead to delays in the treatment due to the process of wound healing. Therefore, there is an urgent need to develop novel methods for the early diagnosis of PsP and TTP.Over the last decade, distinguishing between PsP and TTP has been recognized as a significant issue, and many efforts have been devoted to explore genetic biomarkers and imaging features to address this need. Several genetic and molecular markers involved in GBM have been associated with the development of PsP, including MGMT promoter methylation,et al. generated 5 Gy-level co-occurrence matrix texture maps of contrast, energy, entropy, correlation, and homogeneity to differentiate PsP and TTP on MRI scans.Functional MRIs, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and diffusion tensor imaging (DTI), have been extensively investigated for their ability to discriminate between PsP and TTP based on quantitative parameters, such as relative cerebral blood volume (rCBV),Thus, we developed a novel objective classification system using dictionary learning based on longitudinal DTI for effectively differentiating PsP and TTP. DTI measures the directionality of proton movement, which can be used to compute the maps of FA. Since brain tissues with treatment effects have lower FA values compared with recurrent tumors,Dictionary learning and sparse coding is a branch of signal processing and machine learning aimed at discovering a frame in which some of the training data admit a sparse representation.t) to extend 2D spatial patches without relying on an intermediary descriptor. The sparse dictionaries were defined by the 3D spatio-temporal representation of the FA. Second, PsP and TTP display subtle differences in DTI, and their commonly shared features do not contribute to their discrimination. Therefore, we explicitly learned the PsP/TTP-specific dictionaries and the common pattern dictionary and encoded the DTIs using these dictionaries. We then combined the corresponding sparse coefficients of the PsP-specific and TTP-specific dictionaries to construct a new sparse matrix with a strong discriminating power. After that, we extracted features from the sparse matrix using the max.pooling technique, and evaluated and selected the most discriminating features by our feature scoring system.Inspired by these state-of-the-art dictionary learning algorithms, we applied a classification-oriented and spatio-temporal dictionary learning scheme in our classification system. First, we analyzed the longitudinal DTI directly by using the spatio-temporal volume  were collected at the Wake Forest School of Medicine. All of these patients received standard care, including surgical resection followed by concurrent radiotherapy and chemotherapy with temozolomide. The enrolled patients received a similar dose (around 60 Gy) of conformal radiotherapy. Along with radiotherapy and chemotherapy, the patients underwent DTI scans  each two or three months for postsurgical monitoring. The diagnosis of PsP and TTP was based on the follow-up imaging data and clinical experience of the physicians in charge. Biopsies were also performed in seven patients for further confirmation. For each patient, we retrospectively selected successive DTI scans at three time-points. The third time point, i.e., the latest one, was the time when a patient was diagnosed with PsP or TTP. The corresponding clinical record included age, sex, date of surgery, date of failure or progression, pseudoprogression, and date of death.2.B.We computed the FA value for each voxel in DTI using FSL software.2.C.y be a 3D patch of size s × s*t, where s and t are the patch sizes in spatial and temporal dimensions, respectively. This patch y was then converted to the column-vector of length m = s2t, as shown in Fig. y = (ia)i∈ with ia gray level  associated to the pixel i. Each patch is normalized as y and L2-norm. To capture spatio-temporal structure in the proposed scheme, we adjusted s to elastically define the contributions of spatial dimension.The longitudinal DTI for GBM patients provides the dynamic changes of pathology based on FA value, which can be used for the diagnosis of PsP and TTP. Since the amount of longitudinal data is huge, learning a dictionary directly over the entire set of data is difficult. Thus, we adopted a 3D patch-based dictionary learning approach from spatio-temporal volumes. We define a spatio-temporal image patch as a succession of 2D image patches. First, an axial image in a volume with the largest cross-sectional lesion area was selected from each longitudinal DTI. Let 2.D.The difference between PsP and TTP in DTI is subtle. PsP has slightly lower FA values than TTP in the pathological area, and there is considerable similarity between PsP and TTP in normal regions. To capture the fine distinctions, we learned specific dictionaries for PsP and TTP, respectively, to describe the class-specific characteristics. Simultaneously, we learned a shared dictionary to describe the common features of PsP and TTP.N being the size of the training set. Dictionary learning aims to seek a dictionary and the corresponding sparse coefficient so that each patch iy can be sparsely represented by the atoms in this dictionary. The learning process can be described by the following optimization problem: λ1 balances the trade-off between data fidelity and sparsity regularization. The dictionary D and A is denoted as D and coefficients A can be used effectively to represent the data; however, they are not developed for classification tasks.Mathematically, a matrix is composed of spatio-temporal patches N = pN + tN. We denoted the PsP and TTP class as superscript or subscript p and t, respectively. Dp)( and Dt)(. Dp)( and Dt)( and Ds)=ay, we sparsely encoded it by Dps)( and Dts)( with representation coefficients Dts)m1 and d1 are the mean value and standard deviation of a feature in the positive samples, while m0 and d0 are the corresponding statistics in the negative samples.In this formula, Starting from the individual scores, we sequentially added each feature (with DX score from high to low) to form a feature set and tested its predictive performance by tenfold cross-validation. This process yielded a curve of CV accuracy with some top-ranked features. We then identified the best accuracy and the associated features, which would serve as the optimal feature set for next training and testing. The selected features, i.e., atoms with their maximum coefficients, contributed to the discrimination between PsP and TTP.2.G.γ and the trade-off coefficient C. In addition, the AUC was calculated to evaluate the accuracy and robustness of each classifier. The AUC curve was determined based on sensitivity and specificity, which were calculated as We chose the SVM algorithm3.s = 13, dictionary size of pK and tK = 100, sK = 10% × pK, η = 0.15, and σ = e−6. For all analyses, the number of nonzero coefficients was fixed at 10. We conducted ten rounds of tenfold CV to evaluate the average accuracy of performance. Moreover, we calculated the corresponding AUC to assess the overall performance in terms of robustness of this classification system. P-values between different performances were computed using t-tests.We collected data from 35 patients with GBM, including 13 cases of PsP and 22 cases of TTP. The longitudinal DTI analyses with three-time points were registered, and the FA images were calculated ] while the exhaustive searching took too long to be practical [O(2n)].The pooled features were sorted by the DX-score Eq. . The x-aR with default settings. Figure To further illustrate the performance of 28 selected features in discriminating between PsP and TTP, we performed the hierarchical clustering analysis on them using the function heatmap in 3.C.p-values. The average CV accuracies were 0.867, 0.858, and 0.864 with 10, 20, and 60 repetitions, respectively, indicating that our approach has a promising differentiation capability. We also obtained ∼0.9 of AUC values for the performance with various repeated times. In addition, the p-values between performances with different repeated times were all greater than 0.15. Therefore, the differences between performances were insignificant, indicating our classification system is stable.Figure et al.We also analyzed the effects of dictionary and patch sizes on our classification system. Figure η, patch size s, specific dictionary size sK, as shown in supplementary material Figs. S1–S3.Additionally, we also performed the sensitivity analysis on the weight 3.D.3.D.1.T3 was the ultimate diagnostic time point, and T1 and T2 were the previous inspection time points. We defined L3 as the data collected from T1, T2, and T3, L2 from T3 and T2, and L1 from T3 only [Fig. L1, L2, and L3. The average accuracies and AUCs from ten rounds of tenfold CV are shown in Fig. L3 had a better performance than other two groups. Although L3 performed marginally better than L2 in terms of both accuracy and AUC, the difference was insignificant since the p-values were greater than 0.05 [Figs. L1 was 0.825. However, L3 outweighs L1 significantly (p < 0.05). Our analysis indicates that the longitudinal data boost the classification performance.Our classification system was developed based on clinical follow-up scans from three-time points. We also investigated if using the data from one or two time points could obtain a similar performance. We assumed that nly Fig. . We test05 Figs. . AUC val3.D.2.In the computer vision field, several pooling techniques have been used for classification based on dictionary learning. To determine if other pooling techniques have more advantages than the max pooling technique we used, we tested the predictive accuracy of our system with other four pooling methods, including square root of mean squared statistics (Sqrt), mean of absolute values (Mean), summary of absolute values (Sum), and the combination of max, Sqrt, Mean, Sum pooling techniques (Comb). Sqrt, Mean, and Sum were defined as The average accuracies and AUCs from ten rounds of tenfold CV using five pooling techniques are shown in Fig. 3.D.3.Dimensionality reduction is a key step in a classification system. In our system, we applied the feature scoring system3.D.4.y is a 3D patch, m is the length of column-vector, and ia is a FA value associated to the pixel i. Hence, we obtained a feature vector composed by the average FA values of the image patches for each case. Then, we selected the most discriminative features using the feature scoring system. Table Clinically, the longitudinal FA values can convey the diagnostic information for PsP and TTP. In this study, we investigated the classification ability of FA values as immediate features. First, the average FA value, i.e., 3.D.5.s semiautomatic segmentation scheme, as shown in supplementary material Fig. S1.Morphological changes in tumor regions on follow-up MRI have been used as indicators for the development of PsP and TTP. Therefore, we explored the classification performance of morphological features from the longitudinal MRI data for PsP and TTP. We collected data from 17 patients with GBM  who had longitudinal contrast-enhanced T1 Flair MRI scans. First, we segmented the enhancing and necrotic areas using 4.Differentiation of PsP and TTP in GBM patients is still challenging for physicians since PsP can mimic tumor progression at the tumor site or resection margins, and its appearance on MRI is comparable to that of TTP. Genetic biomarkers and imaging features have been explored to distinguish PsP from TTP. However, the previous work has limitations, and the results to date are equivocal. So far, there are no biomarkers or computer-aided diagnosis systems utilized clinically for this purpose. Thus, there is an urgent need for the development of new approaches to improve the accuracy and efficiency of diagnosis. In this study, we developed an objective classification system to distinguish between PsP and TTP based on spatio-temporal dictionary learning with longitudinal DTI. DTI has a promising potential in differentiating the two phenotypes, and longitudinal DTI scans used in the proposed system are analogous to the clinical strategy of follow-up surveillance. To best of our knowledge, it is the first time that a dictionary learning scheme has been used to differentiate between PsP and TTP. Hence, the extracted features used in our classification system are independent on the segmentation of ROI. In addition, we constructed a novel discriminative sparse matrix to exclude features that are shared between PsP and TTP. The ultimate selected features based on the discriminative sparse matrix captured subtle differences between PsP and TTP. Our classification system was confirmed by tenfold CV and AUC analysis.The main contribution of this study is to introduce the use of a dictionary learning scheme for the diagnosis of PsP versus TTP in patients with GBM. The objective classification system based on dictionary learning can directly extract the features from the sparse coefficient matrix by pooling techniques, and hence, it can avoid the segmentation of ROI. In the traditional classification system, the ROI segmentation is a fundamental and critical step, since the classification performance is dependent on the characteristics of ROI, which is subject to the segmentation quality. The lesion segmentation in MRI/DTI is an open area in the research community, in particular for the segmentation of GBM recurrence area, due to the low contrast and multiple lesion areas. The current method of manual identification of ROI for the differentiation of PsP and TTP is labor-intensive, probably unreliable, and often infeasible for large-scale studies. Therefore, a dictionary learning scheme is a rational solution for the differentiation of PsP and TTP.Dp)( and Ds) technique,5.To conclude, we developed an objective classification approach without segmentation to distinguish PsP and TTP. The longitudinal DTI, as a promising imaging approach for characterizing microstructural changes or differences in neuropathology and treatment, was utilized in this method. The spatio-temporal and classification-oriented dictionary learning framework was applied to capture the subtle discriminative characteristics between PsP and TTP. The feature extraction is independent of segmentation of ROI. The results demonstrated that our proposed system can achieve the reliable and accurate classification of PsP and TTP. Therefore, this system will be a useful tool in the clinical diagnosis of PsP and TTP.