Childhood mortality remains a pressing problem in rural Kenya, and reducing under-five deaths is a key target of the Sustainable Development Goals. We aim to describe the reduction in under-five mortality in a rural Kenyan community served by the Lwala Community Alliance and factors associated with under-five mortality in this community.A cross-sectional survey containing a complete birth history was administered to a representative sample of the catchment area of the Lwala Community Alliance. Survival analysis techniques were used to describe temporal trends and risk factors related to under-five mortality.1,362 children were included in the study, and 91 children died before the fifth birthday. The most common causes of death among children under five were malaria (19%), respiratory infection (13%), and anemia (11%). The under-five mortality rate was 104.8 per 1,000 live births from 1999 to 2006 and 53.0 per 1,000 after the founding of the Lwala Community Alliance in 2007. Factors associated with under-five mortality included year of birth , multiple-gestation pregnancy , and birth in the long rain season . Birth spacing greater than 18 months was negatively associated with under-five mortality .There was a significant decrease in under-five mortality before and after the presence of the Lwala Community Alliance. Multiple-gestation pregnancies, birth season, and short birth spacing were associated with under-five mortality and provide possible targets to further reduce mortality in the region. This provides both hyper-local data necessary for implementation efforts and generalizable data and sampling methods that may be useful for other implementing organizations in sub-Saharan Africa. The international community has acknowledged children’s right to life for more than 25 years . The MilKenya saw slightly smaller gains in U5M over this time period but still made large strides . HoweverWell-studied and cost-effective interventions are available to reduce child mortality . ScalingData from rural western Kenya suggest that malaria, acute respiratory infection, anemia, and diarrhea are the most common causes of death in children under five ,33. ThesThe Lwala Community Alliance (LCA) is a community-led, multifaceted organization in rural western Kenya working with a community of 30,000 people to advance their own comprehensive wellbeing. Founded in 2007, LCA operates a hospital and clinic with inpatient, outpatient, maternal, and HIV care, as well as an innovative CHW program incorporating traditional birth attendants. An integrated maternal and child health program, entitled Thrive Through 5, attempts to enroll every pregnant woman and child born in the catchment area. LCA also has programming in education, economic development, and public health. Similar integrated models have shown promise in rapidly decreasing child mortality . LCA opeIn 2017, LCA implemented a community-wide survey, designed to be representative of the entire surrounding catchment area, to better understand priority health metrics of the communities LCA serves. This survey provides both hyper-local data necessary for LCA´s implementation efforts as well as generalizable data and sampling methods that may be useful for other implementing organizations working in sub-Saharan Africa.The study was conducted in the North Kamagambo location in the Rongo sub-county, which corresponds to the geographic catchment area of LCA. The population of the catchment area is estimated to be 30,000 among a population of approximately 920,000 in the greater Migori County . The catSample size was determined using U5M as a binary outcome using a binomial test to compare one proportion to a reference value. Using a power of 0.85 and alpha of 0.05, a sample size of 500 was needed to detect a difference of 40 per 1,000 live births from the rate of 82 per 1,000 live births in Nyanza . This asA proportional stratified sampling method was used to give all households equal chance of selection despite differing population densities. The coordinates of all 5,930 households in the catchment area were input into mapping software. Because we used this method for sampling, a household was defined as a house structure. The catchment area was then broken into 11 grid squares based on the number of survey days. The number of households needed in each grid square to reach a total of 500 was determined to be 8.4% of households within each square. On each survey day, the team travelled to the geographic center of a square. A random degree between 0 and 360 was then generated for each interviewer as well as a random series of skip numbers. The interviewer then travelled along the line determined by the degree and visited the appropriate houses using the skip numbers to determine which houses were interviewed. The survey was administered to a random resident over the age of 18 in each household. The two lowest density squares were surveyed on the same day, yielding a total of 10 days of data collection. This sampling method has been described previously, and we have minimized the bias associated with beginning in the center of a town by using the center of an arbitrary geographic grid square .The survey contained more than 270 questions across multiple domains and was modeled on validated tools, including the Demographic and Health Survey  and the Surveys were administered by trained interviewers not otherwise employed by LCA. All were hired from the community and were fluent in English, Dholuo, and Swahili. Surveys were administered on tablets using a customized CommCare  form to reduce data entry errors and ensure appropriate skip logic. Data collection occurred in January 2017 over a two-week period.All children born after January 1, 1999 for whom the respondent was the mother or father were included in the analyses. There were 1,362 children who met these criteria. Among these children, 91 died before their fifth birthday.This study was approved by the Ethics and Scientific Review Committee at Amref Health Africa (No. P290/2016) and the Institutional Review Board at Vanderbilt University Medical Center (No. 161396). Written informed consent was obtained from all participants. Approval was also obtained from the local area chief and Ministry of Health.A preliminary list of variables available in the survey was created based on previous studies of associations with childhood mortality –46,56,57A time-varying indicator was created to analyze time periods relative to the founding of LCA in 2007. This indicator was set to one on January 1, 2007. Kaplan-Meier curves were used to visualize mortality curves before and after this date, and the log-rank test was used to compare these curves. Mortality rates were calculated using the Kaplan-Meier cumulative failure function. All analyses were performed using Stata version 14.2 .The survey was administered to 539 respondents with a response rate of greater than 99%. 428 (79.6%) of these respondents were the mother or father of at least one child born in the last 18 years and were included in this analysis. Among these respondents, there were a total of 1,362 live births with 91 deaths before the fifth birthday. There was a slight female majority with 52% of births being female. There were between 50 and 100 births in each year between 1999 and 2016, and 7 children were born in the first month of 2017 prior to survey administration. There were no missing data with the exception of maternal age for 3 children (0.2%).Descriptive statistics by vital status for all children included in the analyses. N = 1,362 for all values except maternal age. N = 1,359 for maternal age due to missing values for 3 children. Categorical variables are presented as percentages, and continuous variables are presented as median (IQR).Hazard ratios for final model of under-five mortality. The multiple pregnancy variable was included as time-varying and turns to zero for all children at the first birthday. All other variables are not time-varying. Bolded text indicates significance at p < 0.05.The largest risk factor for U5M was multiple-gestation pregnancy . Although only 33 children in the dataset were part of a multiple-gestation pregnancy, 5 died before the fifth birthday (15%). A model containing an indicator for multiple-gestation pregnancy violated the proportional hazards assumption, which is consistent with prior research showing this is a risk factor only in the first year of life . The modThere was a highly significant reduction in mortality with each one year increase in birth year across the observation period . Furthermore, being born in the long rain season (April to June) was associated with an increased likelihood of U5M . A similar, although smaller, effect was seen when analyzing the likelihood of U5M during all rainy seasons compared to dry seasons . This includes the short rain season (October to December) and the long rain season as above. Nearly equal numbers of children were born in the wet and dry period of the year . Being born more than 18 months after the most recent sibling was negatively associated with U5M . Conversely, being born less than 18 months after the closest sibling would translate to an increased likelihood of U5M (HR 2.90).Living in a household with an improved latrine trended towards being associated with lower U5M , while having livestock trended towards an increased likelihood of U5M, though neither reached statistical significance.A smaller model of infant (under-one) mortality using only variables that were significant in the U5M model resulted in similar hazard ratios. Infant mortality appeared to decrease more over time than U5M .a priori hypothesis that child mortality would improve after the establishment of the Lwala Community Alliance and associated hospital. Including this indicator in the regression model from A time-varying binary indicator that turned to one beginning on January 1, 2007 was created based on the This dataset captured 1,362 children born since 1999 in a rural Kenyan community near Lake Victoria. 91 of these children died before their fifth birthday. We found malaria (19%), respiratory infections (13%), and anemia (11%) to be the most common causes of U5M in our study population, which is consistent with other studies in rural western Kenya and international studies ,33. In aBeing part of a multiple-gestation pregnancy had the strongest association with U5M in our study. Including multiple-gestation pregnancy in the Cox model violated the proportional hazards assumption, because the effect of this variable is almost entirely within the first year of life. This is consistent with both previous analyses of U5M and studies of perinatal mortality in Kenya ,43. MembWe also found a significant effect of birth during the long rain season on mortality. The effect of birth season on mortality in the literature is somewhat controversial. Although studies have found that child mortality peaks in the rainy season, season of birth has generally not been associated with child mortality beyond the first year of life ,61,62. SLonger birth spacing has consistently been associated with lower childhood mortality in widely varying contexts ,57,64–66Interestingly, we did not find a significant effect of maternal age on U5M. A larger proportion (8%) of children born to mothers under age 18 and an additional 8% of children born to mothers greater than age 35 died before their fifth birthday; compared to only 6% of those born to mothers between 18 and 35 years of age. However, both our final model using maternal age as a continuous variable and a separate Cox regression using categories of age did not show a significant association with mortality. This is likely due to the relatively small number of women giving birth at the extremes of age. Similarly, there is some evidence in the literature that access to improved toilet facilities reduces childhood mortality –69. We dA secondary aim of this study was to analyze the possible effect of the presence of the Lwala Community Alliance on child mortality. Mortality curves for both under-one and under-five mortality were significantly different before and after the founding of LCA in 2007. The U5M rate in the pre-2007 era was 104.8 per 1,000 live births compared to 53.0 per 1,000 afterward with similar reductions in under-one mortality. The estimated U5M rate in Nyanza province from 1998–2008 was 149 per 1,000 live births, and from 2004–2014 was 82 per 1,000 ,70. At fWe did not observe a significant change in cause of death before and after the founding of LCA in 2007. This may be due to the relatively small sample size, which makes changes difficult to interpret. It is also likely that a substantial portion of the reduction in mortality is mediated by very high skilled birth attendant and facility delivery rates. These rates were only available for children under five in the sample and thus could not be included in the model. These rates were 89.7% and 87.9%, respectively. This compares very favorably to the rates for the surrounding county in 2014, which were both 53% . FacilitThe primary limitations to the study are the sample size and the cross-sectional design. Our sample of 1,362 children contained only 91 deaths, which limits the number of variables that can be contained in any one model. Following the rule of 10 events per independent variable , we are Additionally, the cross-sectional nature of the survey forces us to rely on the memory of respondents about events up to 18 years ago. However, this is likely minimized by the important nature of the birth and death of children. Additionally, this would be expected to bias towards more recent deaths in the dataset, as they are closer to the time of the survey. This was not the case, and there were more deaths recorded earlier in the study period.The main limitation in our evaluation of LCA is the lack of a direct control group. It is difficult to compare the mortality rates from this study to those found in larger national surveys because both the instruments and the method of calculation differ slightly. This lack of a control group makes it difficult to control for secular trends in mortality rates. However, the fact that mortality dropped by nearly 50% from the pre-2007 period to the post-2007 period in the setting of a constant mortality rate prior to 2007 suggests a large shift around this time period. The only major healthcare change in this rural area during this time period was the presence of LCA, so it is plausible that at least a portion of the reduction in mortality is due to the establishment of this organization and hospital.We describe factors associated with childhood mortality—including multiple-gestation pregnancies, short birth intervals, and birth during rainy seasons—in a rural Kenyan community using survival analyses of complete birth histories. This provides both the hyper-local information needed to improve programming and generalizable conclusions for other organizations working in similar environments.S1 AppendixThe survey was administered on a tablet-based software. Some minor changes from this printed tool were made based on software logistics.(DOCX)Click here for additional data file.S2 AppendixAn additional model that included child gender did not differ substantially in hazard ratios or significance compared to the model presented in the main body of the manuscript.(DOCX)Click here for additional data file.


BC has a strong positive radiative forcing in the atmosphere, as indicated in many previous studies. Here, we show that the decline in surface air temperatures with reduced BC emissions is weaker than would be expected from the magnitude of its instantaneous radiative forcing at the top of the atmosphere (TOA). Climate simulations show that the global mean change in surface air temperature per unit of instantaneous radiative forcing of BC at the TOA is about one-eighth that of sulphate aerosols, which cool the climate through scattering solar radiation, without absorption. This is attributed to the positive radiation budget of BC being largely compensated for by rapid atmospheric adjustment, whereas the radiative imbalance due to sulphate aerosols drives a slow response of climate over a long timescale. Regional climate responses to short-lived species are shown to exhibit even more complex characteristics due to their heterogeneous spatial distributions, requiring further analysis in future studies.Reducing black carbon (BC), i.e. soot, in the atmosphere is a potential mitigation measure for climate change before revealing the effect of reducing anthropogenic carbon dioxide (CO It has been suggested that reducing other greenhouse species with shorter atmospheric lifetimes than CO2 might be a way to mitigate global warming before the effect of CO2 reduction becomes apparent. Most short-lived greenhouse species are harmful to human health, so reducing them is also crucial in the context of air quality and would result in a co-beneficial effect. Such pollutants are referred to by the Climate and Clean Air Coalition (CCAC)1 as short-lived climate pollutants (SLCPs) and consist of black carbon (BC), methane (CH4), tropospheric ozone (O3), and hydrofluorocarbons (HFCs).The Paris Agreement is intended to keep increases in the global mean surface air temperature to below 2 °C relative to preindustrial levels and to make efforts toward limiting that increase to 1.5 °C. Although reducing greenhouse gas emissions is the predominant mitigating measure in achieving that goal, the effect of this measure on global mean surface air temperatures will not be observed over the course of the next few decades, because after being emitted carbon dioxide , a primary precursor of sulphate aerosols.Two significant issues emerge when attempting to achieve co-beneficial effects on climate change and air quality via reducing pollutant emissions. One is that some air pollutants, such as sulphate and nitrate aerosols, cool the atmosphere; simply reducing them would accelerate global warming. This implies that the overall impact of air pollutants in both warming and cooling the climate need to be assessed when seeking a co-beneficial effect. The Intergovernmental Panel on Climate Change (IPCC) considers them as short-lived climate forcers (SLCFs), regardless of whether the radiative forcing, an imbalance of the energy budget affected by a change in their concentration, is positive or negative. An IPCC Assessment Report6. Past and future climate simulations used in the IPCC Assessment Reports8 all included the effects of all major climate forcing agents, however their individual effects on meteorological parameters (e.g. temperature and precipitation) were not assessed. In an attempt to fill this gap, the Precipitation Driver Response Model Intercomparison Project (PDRMIP) recently analysed responses of precipitation and temperature to individual perturbation of some climate forcing agents using both atmosphere-only and ocean-coupled general circulation models5. The PDRMIP protocol included simulations of climate change with extreme perturbations of sulphate and BC aerosol concentrations or emissions. It predicted that increasing present-day concentrations or emissions of BC by even a factor of 10 would result in only small changes in surface air temperatures due to the dominant rapid adjustment of the climate system, including an increase in low-level clouds6. However, assessment of perturbation of each climate forcing agent individually, particularly with such an unrealistic magnitude, is not sufficient to assess the climate response fully over a range of perturbations, including realistic perturbations. This is essential when making scientifically based decisions about potential measures to mitigate SLCPs/SLCFs, as well as about well-mixed greenhouse gases, such as CO2.The other issue is that although BC is associated with a strong positive radiative forcing effect, recent studies have found that the sensitivity of surface air temperatures to BC tends to be weaker than expected2 in the realistic range (zero to twice) relative to the present conditions using a general circulation model coupled with aerosol processes, known as MIROC-SPRINTARS11. Contrary to the previous studies that took multi-model ensembles for each experiment with only one perturbation per species6, an advantage of this single-model approach is that the results are not contaminated with differences in physical representations that vary across models, enabling consistent investigation of the relationship between the energy budget perturbation and the climate response. The MIROC-SPRINTARS calculates changes in meteorological parameters by aerosol-radiation interactions in which aerosols scatter and absorb solar and thermal radiation, and aerosol-cloud interactions in which aerosols alter cloud microphysical properties and affect the radiation budget by acting as cloud condensation and ice nuclei. Here, two simulations are executed to distinguish climate changes due to rapid adjustment and slow response: one with prescribed sea surface temperatures and sea ice and the other coupled with an ocean general circulation model (OGCM) (see Methods).Here, we investigate the quantitative trend in surface air temperatures with multiple perturbed emissions of BC and SO2 and BC from fuel sources. Although the global mean instantaneous radiative forcing values at the TOA simulated in this study 12, in which the MIROC-SPRINTARS is participating. It is important when using a single-model approach to indicate almost linear trends in the instantaneous radiative forcing at the TOA with perturbed emissions for both sulphate and BC aerosols  is eight times larger than that for BC (0.16 °C W−1 m2). This implies that instantaneous radiative forcing, used when assessing the effect on climate of BC, with climate sensitivity implicitly assumed to be the same as that of sulphate, does not serve as a good predictor of the temperature response.Changes in surface air temperatures are traditionally and conveniently estimated from instantaneous radiative forcing, which is related to climate sensitivity. Figure −2 in this study, which is comparable to −0.45 W m−2 by all anthropogenic aerosols in the latest IPCC Assessment Report2.The reason for the weak response of the mean surface air temperature to BC perturbation is that instantaneous radiative forcing is largely compensated for by rapid adjustment Table . The com13. This involves opposite effects of cloud responses that buffer and enhance the instantaneous radiative forcing for BC and sulphate cases, respectively. In the latter, the TOA energy imbalance is swelled by the radiative forcing due to the aerosol-cloud interaction and is not fully cancelled by slow response . In the assessment report14, the temperature sensitivities to all the aerosol species per instantaneous radiative forcing are assumed to be the same as that of CO2. However, this assumption, with the weaker temperature sensitivity to BC emission change not considered, tends to overestimate the expected mitigation of global warming by reducing BC emission. Given that BC reduction is mandatory for avoiding an impact on human health, this study proposes that long-lived greenhouse gases and other SLCPs should be reduced more firmly for mitigating global warming.The present results, together with recent multi-model findings2 and shown to be a better predictor of the temperature change. Indeed, the climate sensitivity parameters based on the effective radiative forcing simulated by MIROC-SPRINTARS are 0.43 and 0.31 °C W−1 m2 for sulphate and BC aerosols respectively, which shows a smaller discrepancy than that for instantaneous radiative forcing , and halocarbons, as well as other SLCPs , is an essential mitigation measure in the pursuit of the goals within the Paris Agreement. However, heterogeneous spatial distributions of SLCFs can result in large changes in surface air temperatures in the mid- and high-latitudes of the Northern Hemisphere , soil dust, and sea salt and precursor gases of sulphate including SO2 and dimethyl sulphide) and precipitation flux, etc. These meteorological parameters in MIROC-SPRINTARS change via aerosol-radiation and aerosol-cloud interactions. The simulated transport processes of aerosols and their precursors include emission, advection, diffusion, chemical reactions of sulphur, wet deposition, and dry deposition. Emission fluxes from fuel and biomass burning sources of SO2, BC, and OM were acquired from the Emissions Database for Global Atmospheric Research and Task Force on Hemispheric Transport of Air Pollution (EDGAR-HTAP)15 and Global Fire Emissions Database (GFED)16. Emissions from natural sources, such as soil dust and sea salt, were calculated from the internal parameters of the model. A change in the snow albedo due to deposition of BC and soil dust was parameterised. A detailed model description of MIROC-SPRINTARS is provided in a previous study17.A global ocean-atmosphere general circulation model, MIROC2 and BC from fuel sources, i.e. the EDGAR-HTAP database. The emission scaling factors were only 5 and 10 times for SO2 and BC, respectively, in the PDRMIP model intercomparison experiments18. To understand the climate effects of sulphate and BC aerosols due to the total response to rapid adjustment, both the simulations with prescribed sea surface temperatures (SSTs) and sea ice19 and with an ocean general circulation model (OGCM)11, were executed for each of the perturbed emissions. The results of the simulations with prescribed SSTs and sea ice were defined to represent the rapid adjustment only, and those with the OGCM included both rapid adjustment and slow responses18. The slow response was estimated as the difference between the results from the two simulations. The instantaneous radiative forcing was calculated by a ‘double call’ in which the atmospheric radiation process in the climate model is called twice with and without the aerosol-radiation interaction in a time step using the simulations with prescribed SSTs and sea ice. The simulations are integrated for 15 and 100 years in the prescribed SSTs and sea ice and ocean-coupled experiments, respectively, and the simulated results were analysed for the last 10 and 50 years, respectively. The horizontal resolution of all simulations was T85 (approximately 1.4° by 1.4° in longitude and latitude) and the vertical resolution was 40 layers with the hybrid sigma-pressure coordinate.Sensitivity tests were performed with various scaling factors , which are globally homogeneous relative to the standard emission amount of SO


Chronic smoking has been shown to have deleterious effects on brain function and is an important risk factor for ischemic stroke. Reduced cortical excitability has been shown among chronic smokers compared with non-smokers to have a long-term effect and so far no study has assessed the effect of smoking on short-term motor learning. Paired associative stimulation (PAS) is a commonly used method for inducing changes in excitability of the motor cortex (M1) in a way that simulates short-term motor learning. This study employed PAS to investigate the effect of chronic cigarette smoking on plasticity of M1. Stimulator output required to elicit a motor-evoked potential (MEP) of approximately 1 mV was similar between the groups prior to PAS. MEP response to single pulse stimuli increased in the control group and remained above baseline level for at least 30 min after the intervention, but not in the smokers who showed no significant increase in MEP size. The silent period was similar between groups at all time points of the experiment. This study suggests that chronic smoking may have a negative effect on the response to PAS and infers that chronic smoking may have a deleterious effect on the adaptability of M1. N-methyl D-aspartate (NMDA) receptors by evoking dopamine release through activation of nicotinic acetylcholine receptors (nACHRs) in the prefrontal cortex [The human sensorimotor cortex is described as being “plastic” which refers to its ability to adapt by reorganizing neural pathways in response to changes or injuries. Reorganization within the motor cortex can be stimulated by repetition of a simple task  and skill cortex . A studyl cortex  found thl cortex ,16 with l cortex . NMDA col cortex ,19. NMDAl cortex . These il cortex .Short-latency inhibition is enhanced, the silent period prolonged, intracortical facilitation reduced and motor-evoked potential (MEP) amplitudes lower in chronic smokers than non-smokers . NeuroplVolunteers were recruited from among the staff and students of the local institution and the surrounding community. The procedure was approved by the Ethics Review Committee for Experimental Research with Human Subjects of the Graduate School of Arts and Sciences, The University of Tokyo, on 9 December 2011 (Project Identification Code: 224-2). This study was conducted in accordance with the statements of the declaration of Helsinki and all volunteers gave written informed consent before taking part in the study. Eleven non-smokers and 10 smokers, all self-reported right-handed males, aged between 27 and 43 years  volunteered for the study. Those describing themselves as chronic smokers also filled in a Fagerström smoking classification questionnaire to assess their tobacco addiction level. Fagerström dependence scores were classified as 0–2 = very low, 3–4 = low, 5 = medium, 6–7 = high and 8–10 = very high dependence . All volExclusion criteria included use of a cardiac pacemaker, metal implants in the head , current use of any medication with contraindications for TMS such as antidepressants which alter cortical excitability, any current or previous neurological, psychiatric or internal disease, current or previous drug dependence (excluding nicotine for smokers), or alcohol abuse .A Magstim 200 stimulator  and a figure-of-eight shaped coil with outer diameters of 9 cm were employed for all testing and PAS procedures. The coil was positioned over the left hand area of the motor cortex, tangentially to the scalp with the handle pointing backwards at an angle of 45° to the sagital plane. The coil was then adjusted slightly to find the optimal position for eliciting an MEP. The target muscle for this experiment was the first dorsal interosseous muscle (FDI) of the right hand. The intensity of stimulator output used for PAS and tests was assessed as the lowest percentage maximum stimulator output (%MSO) required to elicit an MEP of 1 mV peak-to-peak. This was done while subjects performed an isometric abduction of the index finger against a transducer at 10% maximum voluntary force (MVF). This stimulus intensity was then used for all pre- and post-tests as well as the PAS intervention. A block of 20 single TMS pulses was applied prior to PAS to establish baseline corticospinal excitability. This was applied at the predetermined %MSO so that the average MEP size across the 20 stimuli was approximately 1 mV. Blocks of 12 TMS pulses were applied at five-minute intervals for 30 min following PAS. The cortical silent period (CSP) was assessed from the same pre and post recordings and was measured from the point of the onset of the MEP to the point at which the EMG returned to the prestimulus mean . The meaThe PAS protocol used for the present investigation is a novel one. Subjects’ attention during the intervention has been shown to be an important factor for attaining optimal results . PreviouSubjects were comfortably seated in an adjustable upright chair specially designed for TMS with an adjustable frame above the head for holding the coil. First the hand was placed in the manipulandum and secured in position using a Velcro strap. Subjects were then instructed to perform an MVF contraction of the first dorsal interosseous muscle by abducting the index finger as hard as they can against a custom built force plate for three seconds. Subjects were instructed to attempt to abduct the FDI at the distal joint without moving the hand and to maintain the force for three seconds. This MVF measure was then conducted again after one minute rest. The mean of the two measures was taken as the MVF. The hotspot for activation of the FDI was then located while the subject performed a continuous isometric abduction of the index finger at 10% MVF. After the hotspot was located, the coil and the head of the subject were locked into position in the frame. Because the subjects head and the coil were attached to the same frame, their position relative to each other could not change during the experiment. In addition to the frame holding the coil and head in the correct position, a three-dimensional position sensor  was used to track the coil position relative to the head of the subject to ensure the correct coil position was maintained throughout the experiment. Targets for the sensor were attached to a headband worn by the subject and the handle of the coil. These targets were detected using a three-dimensional camera placed about one meter in front of the subject at approximately 50 cm above the level of the head. Any change in the position of the head relative to the coil, were immediately detected by the position sensor which gave immediate feedback to the experimenter via a computer screen. If there were any changes the experimenter could instruct the subject so that he could return to the correct position. The subjects’ right arm was abducted slightly at the shoulder and rested on a table in front of them at slightly above waist level. The right hand of the subject was placed in a manipulandum designed and built in our laboratory for this and similar studies. A transducer was positioned against the distal interphelangeal joint of the right index finger. Electrodes were placed in a tendon-belly arrangement on the right first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles. All EMG data was recorded using a Yokogawa W400 Measuring Station with the WE7000 software package  and stored on a Dell XPS M1210 laptop computer for later offline analyses.t-tests. Fagerström scores for the smokers were simply ranked and recorded. MEP amplitude data were normalised and changes in MEP amplitude following PAS and silent period were compared using a two factor mixed-design analysis of variance (ANOVA) with repeated measures for group  and time  with a significance level set at p < 0.05. Any differences between groups were assessed using Fishers PLSD post-hoc test. Data are expressed as mean ± standard error of the mean (SEM) unless otherwise indicated.Groups were compared for age, %MSO required to evoke motor evoked potentials (MEP) of 1mV and Cotinine scores using Students p < 0.0001) (Cotinine concentration was negligible for the non-smokers (< 0.005 ± 0.0 mg/mL) and typically higher for the smokers (0.746 ± 0.49 mg/mL) ( 0.0001) . The meap = 0.20).The stimulator output level for tests and PAS was set at the %MSO which produced MEPs of approximately 1mV peak-to-peak. The %MSO for the groups were 47 ± 7.1% for smokers and 44 ± 7.8% for controls with a Students t-test showing no statistically significant difference between the groups (p = 0.278). A one-way repeated measures ANOVA comparing pooled force output data from both groups showed no significant difference across time which indicates that force output was unchanged throughout the experiment for all subjects .It is important to assess the force output (10% MVF) of the subjects during both pre- and post-tests to ensure that the task was completed correctly in all experiments. Maximum voluntary force output varied between participants, so that the amount of force produced during the experimental procedure varied accordingly. There were no statistically significant differences between groups for force output across pre and post-tests  and a Fisher’s PLSD post-hoc analysis showed the differences between the groups occurred at 5, 10, 15, 20, 25 and 30 min post-PAS. The control group increased by 30% immediately post-PAS and maintained a greater peak-to-peak MEP size throughout the 30 min post-PAS test period peaking at 10 min with MEPs 56% larger than the pretest (p = 0.044). Conversely, the peak-to-peak MEP size for the smoker group failed to increase substantially immediately after PAS (106% of pre-PAS) and remained less than the baseline level up to 30 min after PAS. The nadir was −24% which occurred 10 min (p = 0.614) after cessation of the PAS intervention  (mean ± SD). A two factor mixed-design ANOVA comparing the groups over time showed a statistically significant interaction between group and time .As can be seen in The present study found that the stimulus intensity required to elicit an MEP of 1 mV was similar between the groups, and in line with the study by Lang et al. . The level of magnetic stimulator output (%MSO) required for eliciting a 1 mV MEP response, although slightly higher for the smokers, was statistically similar between the groups. This demonstrates comparable baseline excitability between the experimental and control groups while the muscle is performing a light isometric contraction, although previous research has found a reduction in cortical excitability at rest in smokers compared with non-smokers . It was Impaired GABA-mediated mechanisms have been observed in clinical contexts, for example coeliac disease, providing further evidence that a disinhibited M1 is involved even in the absence of a clear motor impairment ,40. InteAnother important consideration regarding changes in cortical function is the effect of cognitive functioning on TMS assessments. In recent years, the use of TMS techniques to assist in diagnosis of pathologies, such as dementia, and efforts to improve our understanding of the neurophysiological mechanisms associated with normal ageing and neurodegenerative disorders provides a challenging direction for researchers . For exaThis study does have some limitations. In this study we used a novel approach to PAS. We considered previous protocols and tried this method as it requires the subject to activate the muscle at a higher intensity than other active protocols, while avoiding fatigue, and gave them a task that kept them focused on the intervention in an active manner. While this approach does not appear to be any less effective than previously used methods, it does not seem to be any more effective either. Including rCBF or magnetic resonance imaging (MRI) measures of cortical function may have provided further useful data for this investigation and aided in providing a more comprehensive assessment of the effects of chronic cigarette smoking on cortical function. This study was limited to the motor cortex and provides some insight to these effects. In the present study volunteers were not patients undergoing any treatment for neurological disorders and were not screened for cognitive deficits, nor were they assessed by MRI in order to exclude neurophysiological deficits or radiological lesions. For future studies, researchers should consider including such screening if the equipment is available since neurological disorders may affect results ,42,43. IThis study was carried out to ascertain whether there is a difference in the extent to which the motor cortex responds to paired associative stimulation between healthy chronic smokers and healthy non-smokers of similar age. The non-smokers responded well to PAS and the smokers did not. Indeed the smokers failed to respond at all to the PAS intervention. We have suggested some possible reasons for this result but clearly further research into the effects of chronic cigarette smoking on cortical excitability and the effectiveness of PAS is warranted.


Two vibro-tactile paradigms were administered to the patients to assess conscious function and command following. The first paradigm is called vibrotactile evoked potentials (EPs) with two tactors (VT2), where two stimulators were placed on the patient’s left and right wrist, respectively. The patients were asked to count the rare stimuli presented to one wrist to elicit a P300 complex to target stimuli only. In the second paradigm, namely vibrotactile EPs with three tactors (VT3), two stimulators were placed on the wrists as done in VT2, and one additional stimulator was placed on his/her back. The task was to count the rare stimuli presented to one wrist, to elicit the event-related potentials (ERPs). The VT3 paradigm could also be used for communication. For this purpose, the patient had to count the stimuli presented to the left hand to answer “yes” and to count the stimuli presented to the right hand to answer “no.” All patients except one performed above chance level in at least one run in the VT2 paradigm. In the VT3 paradigm, all 6 stroke patients and 8/9 ALS patients showed at least one run above chance. Overall, patients achieved higher accuracies in VT2 than VT3. LIS patients due to ALS exhibited higher accuracies that LIS patients due to stroke, in both the VT2 and VT3 paradigms. These initial data suggest that controlling this type of BCI requires specific cognitive abilities that may be impaired in certain sub-groups of severely motor-impaired patients. Future studies on a larger cohort of patients are needed to better identify and understand the underlying cortical mechanisms of these differences.Patients with locked-in syndrome (LIS) are typically unable to move or communicate and can be misdiagnosed as patients with disorders of consciousness (DOC). Behavioral assessment scales are limited in their ability to detect signs of consciousness in this population. Recent research has shown that brain-computer interface (BCI) technology could supplement behavioral scales and allows to establish communication with these severely disabled patients. In this study, we compared the vibro-tactile P300 based BCI performance in two groups of patients with LIS of different etiologies: stroke ( The term locked-in syndrome (LIS) was introduced to describe a clinical state of quadriplegia and anarthria due to a disruption of the corticospinal and corticobulbar tracts in the brainstem . The priBased on the severity of motor deficits, three varieties of LIS have been described: classical LIS, in which the patient is unable to move – except for eye movements or blinking – or to speak; incomplete LIS, in which residual voluntary movements in addition to eye movements can be present; and total or complete LIS (CLIS), where patients show total immobility, including lack of voluntary eye movement . PatientDespite the existence of well-defined clinical criteria for the diagnosis of DOC and LIS, differential diagnosis remains challenging and misdiagnosis still occurs. Standardized behavioral scales like the Glasgow Coma-Scale (GCS)  and the Once the diagnosis of LIS has been established, another major challenge with this population is providing them with appropriate devices for communication and environmental management. These tools can increase quality of life and facilitate the assessment of cognitive impairments , which has been described to be often associated with ALS .In this context, brain-computer interface (BCI) systems have been used for decades to establish communication with patients with LIS, usually via the electroencephalogram (EEG) . DiffereIn the current study, we investigated BCI performance in patients with LIS from different etiologies. We explored differences in classification accuracy and EPs using a vibro-tactile based BCI in two sub-groups of LIS due to ALS and stroke. Based on the literature suggesting preserved cognitive abilities in LIS patients from both etiologies , we hypoThe results of this study could help to improve the assessment to detect the presence of consciousness in patients with stroke, ALS and other conditions. These findings may also help to shed light on the differences and clinical characteristics that should be considered with each patient group and underline the importance of a multimodal approach – using stimuli from different sensory modalities – to evaluate non-responsive patients.This retrospective study included data acquired in LIS patients at the University of Palermo, Italy (PA) and by the French Association of Locked-In Syndrome (ALIS) in Paris, France, as part of other studies previously published . For theTable 1 reports the patients’ demographic data. We included a convenience sample of six stroke patients  with a disease duration between 4 and 19 years (median = 10), and nine ALS patients  with a disease duration between 2, 3, and 12 years (median = 7). The difference between gender and age was tested using a Chi-Square-Test (significance = p < 0.05). There was no difference in age and gender between the two groups.The mindBEAGLE system  was used for all data collection and real-time feedback. The system uses active gel-based EEG electrodes connected to a biosignal amplifier . The amplifier has a 24-bit resolution and a high oversampling rate to increase the signal-to-noise ratio. The amplifier is connected to the computer via USB and sends the data in real-time at a sampling rate of 256 Hz. The EEG signal is presented on a monitor for quality inspection during the measurement, and the data are stored in floating point format for later data analysis.The recorded EEG data were filtered between 48 and 50 Hz using a notch filter. Afterward the data were bandpass filtered between 0.1 and 30 Hz to remove baseline shifts and eliminate most of the EEG artifacts. Eight electrodes were used for the recording, placed on the Fz, C3, Cz, C4, CP1, CPz, CP2, and Pz position according to the extended international 10–20 electrode system. The reference electrode was fixed on the right earlobe and the ground electrode was mounted on the forehead.Two P300 oddball paradigms were used: vibrotactile EPs with two tactors (VT2) and vibrotactile EPs with three tactors (VT3). Both paradigms presented 480 stimuli per run, with 60 groups of 8 stimuli. In both paradigms, the patient was instructed via earbuds to silently count vibrotactile pulses to either the left or right wrist. The left and right wrists had an equal chance of being chosen pseudo-randomly as the “target” wrist. All vibrotactile stimuli lasted 100 ms, with a 100 ms delay between stimuli. Both paradigms required about 2.5 min per run and were designed to elicit an oddball P300 to stimuli delivered to the target wrist only.In the VT2 paradigm, the two tactors were placed on the left and right wrists. Each of the 60 groups of eight stimuli per run contained one target and seven non-target stimuli, presented in pseudorandom order. Thus, the target to non-target ratio was 1:7.In the VT3 paradigm, an additional (third) tactor was placed on a third location on the patient’s body. For the ALS patients, the third tactor was placed on the upper part of the back. For the stroke patients, the third tactor was placed on the neck. The position of the third tactor can be arbitrary, since it acts as a distractor. The other two tactors were fixed on the right and left wrists. In VT3, each sequence of eight stimuli included one stimulus to the left wrist, one stimulus to the right wrist, and six stimuli to the third tactor, in pseudorandom order. Thus, each sequence of eight stimuli also contained one target, like the VT2 paradigm, but six of the seven non-targets were meant as “distractor” stimuli that could never be designated as the target. The runtime for both VT modes was 2.5 min for one run.In addition to these two paradigms to assess patients, we also explored communication using the VT3 paradigm. The experimenter asked yes/no questions and the patient was asked to answer by counting the stimuli on either the left or right wrist. One question can be answered after 120 stimuli, which requires 38 s. The system only selects YES or NO if the result is significant and presents no response otherwise. This result is presented to the experimenter via the monitor. Each patient was asked 10 questions. The communication was considered reliable if the patient could accurately answer at least 7/10 questions.Table 1. The total number of runs per session was limited to five per participant, to address concerns with possible fatigue or discomfort.Each patient participated in one experimental session that included one VT3 communication run and at least one run (each) of VT2 and VT3 assessment. Some patients participated in additional VT2 and/or VT3 runs, as shown in For both paradigms, data segments of -100–600 ms around each stimulus were extracted. Each of these single trials was baseline corrected and averaged. Trials in which the EEG signal amplitude exceeded ±100 μV were rejected from the EP and classifier calculation. The EPs were visually inspected. For the classification, the data were classified using the linear discriminant analysis (LDA), which resulted in a classification accuracy between 0 and 100%. This result showed how well the data could be discriminated using the classifier. In one assessment run, 60 sequences of tactile stimuli were presented to the patient. Each sequence contained eight trials in randomized order, in which one trial was the target trial and seven were non-target trials. This resulted in 480 trials total . After removal of artifact trials, the ratio between non-target and target trials was set to 7:1 again. Trials in both pools were shuffled and split 50:50 into training and testing data. The training data were used to create an LDA classifier and tested on the testing data as follows: seven non-target trials and one target trial were randomly chosen and the LDA score was calculated for each of the trials. The trial with the highest LDA score was classified as target trial. If the classified target trial was the real target trial, the test classification was correct (100%), otherwise incorrect (0%). This step was repeated 1000 times, resulting in a classification plot.p < 0.05.Brain-computer interface performance was considered above chance when the classification accuracy was higher than 23% based on binomial distribution . Non-parTable 1 summarizes data obtained from all patients. All patients except one showed at least one run above chance in the VT2 paradigm. In the VT3 paradigm , all 6 stroke patients and 8/9 ALS patients showed at least one run above chance. The ninth ALS patient (patient A2) attained at best 12.4% accuracy. Overall, patients achieved higher accuracies in VT2 than VT3. The accuracy observed during VT2 was higher in ALS patients than in stroke patients, with a median accuracy of 98%  and 32.8% , respectively (p < 0.05). For VT3, ALS patients also achieved a higher accuracy than stroke patients, with a median classification accuracy of 82%  and 22% , respectively (p < 0.01). The results are reported in Figure 1.All patients performed the VT2 and VT3 paradigms, either once or multiple times. All patients participated in one communication run with 10 questions. None of the stroke patients could reliably communicate with the system. The classifier did not get any wrong answers for any of the stroke patients, but it did provide between 4 and 10 “undecided” answers.For ALS patients, 8/10 patients could answer at least 7/10 questions accurately, while 2 were not able to reliably communicate with the system .Figure 2 presents examples with classification accuracy and the EPs on the Cz electrode from patients A9 and S3.All EPs were visually inspected. With the VT2 paradigm, all patients showed a high P300 complex. Figures 3 and 4.With the VT3 paradigm, 4/10 ALS patients  showed a high P300 or other components of the P300 complex, whereas none of the stroke patients did. These results can be seen in The aim of this study was to compare performance with vibro-tactile BCI paradigms in patients with LIS resulting from ALS or stroke. Our hypothesis was that both groups would perform equally well using a vibro-tactile based BCI. Our results show higher performance in ALS than stroke patients, which might reflect the different pathological mechanisms underlying the LIS in each group.The first explanation for the difference in both paradigms is the possible presence of reduced tactile sensitivity in patients with LIS due to stroke. Paterson and Grabois reported abnormalities of sensitivity in 34 of 62 patients (54%)  with LISWe also observed lower accuracies in all patients during the consecutive runs except one in the ALS group. This could partly be explained by an increased fatiguability influenced by the lesion site of the stroke . Even thFigure 4 show that the classifier could attain high accuracy with fewer than 30 groups of eight trials for most ALS patients, which suggests that shorter runs may be feasible.Patient A9’s performance improved considerably between the two VT3 runs , which could be a short-term learning effect. The results from all other patients may reflect fatigue or an absence of learning effects, since the VT3 task entails a more challenging discrimination task. Further studies could explore whether this accuracy reduction correlates with mental exhaustion or other factors and develop new paradigms that might be less tiring. During visual inspection of the EPs, all patients showed a P300 complex during VT2, but four ALS patients elicited a high P300 or other signal during VT3, whereas when none of the stroke patients did. If the high classification accuracies observed in the four ALS patients correspond with the EPs observed, the BCI system also found some additional components in the signal to produce high classification accuracy in the other patients. As VT3 is an active task, and therefore more cognitively demanding, the data suggest that there are some underlying cortical activities in ALS contributing to a high classification accuracy even if the EPs did not exhibit robust differences based on visual inspection of averaged data.While the approach used here is often called a P300 BCI in the literature, many of the participants produced target vs. non-target differences in other ERP components of the P300 complex, notably the N100 and P200. Other articles have also noted that so-called P300 BCIs often rely on non-P300 components within the P300 complex . Future studies should elucidate these differences to design BCI paradigms that consider the underlying disease pathology, so as to best tailor BCIs accordingly for each patient.ZL and RS were mainly responsible for data collection which occurred in collaboration with SL and VLB. AH was mainly responsible for data analysis and manuscript preparation. The remaining authors were responsible for study design and manuscript preparation.The authors declare that the hard- and software were made by g.tec medical engineering GmbH. Authors AH, RO, and CG are employed by g.tec. SL is on the scientific advisory board of g.tec. The remaining 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.


Plasmodium vivax and in Plasmodium ovale malaria, recurrent infection occurs from relapses which begin to emerge from the liver approximately 2 weeks after the primary illness. An important determinant of the interval from starting treatment of a symptomatic infection to the patency of these recurrent infections is the in vivo concentration-response relationship and thus the in vivo MIC. Using mechanistic knowledge of parasite asexual replication and the pharmacokinetic and pharmacodynamic properties of the antimalarial drugs, a generative statistical model was derived which relates the concentration-response relationship to time of reinfection patency. This model was used to estimate the in vivo MIC of chloroquine in the treatment of Plasmodium vivax malaria.The MIC is an essential quantitative measure of the asexual blood-stage effect of an antimalarial drug. In areas of high malaria transmission, and thus frequent individual infection, patients who are treated with slowly eliminated antimalarials become reinfected as drug concentrations decline. In the frequent relapse forms of  Careful characterization of the pharmacokinetic (PK) and pharmacodynamic (PD) properties of antimalarial drugs is necessary for the optimal design of malaria treatment regimens. Many antimalarial drugs have unusual pharmacokinetic properties, with very large apparent distribution volumes and long terminal elimination phases. The resultant protracted residual drug levels allow short-course treatments to be given ; they also provide a declining posttreatment prophylactic effect and thereby a disease-free interval during which malaria infections cannot become patent, and this affects the temporal pattern with which reinfections emerge , 2.in vivo in an individual infection is difficult, as it requires administration of inefficacious doses and serial measurement of submicroscopic parasite densities , all infections emerging from the liver following hepatic schizogony will be partially or totally suppressed; i.e., the asexual cycle (48-h) parasite reduction ratio is greater than 1, so parasite numbers decline. New infections usually emerge from the liver at a biomass of ≈104 to 105 parasites (the progeny of between 1 and 10 successful sporozoites)  –8. As drer cycle , 9–11.tMIC plus 8 days . The precise timing depends on the terminal elimination half-life of the antimalarial drug and the slope of the concentration-response curve. If the drugs used do have liver-stage activity, then the bunching of the initial recurrent infections is delayed accordingly.In a population of individuals in whom times to recurrent infection are measured by the same method , this results in a “bunching up” or temporal clustering of initial recurrent infections reaching patency which are either newly acquired reinfections, relapses , or recrudescences (drug failures) , 13. If 3, and a half-maximal effect (EC50) of 250 μg/liter, with a slope coefficient of the concentration-response curve of 7. The top panels in A hypothetical example illustrates this bunching phenomenon for falciparum malaria. Suppose a drug has a terminal elimination half-life of 6 days, starting concentrations (day 0) of 1,000 μg/liter, a maximum PRR of 10n = 1,440 samples) , and 3% of the drug is cleared slowly .A randomized controlled trial comparing therapeutic options for the treatment of vivax malaria was conducted by the Shoklo Malaria Research Unit on the northwest Thailand-Myanmar border . In the samples) . The modP. vivax does not usually become patent within 4 weeks of treatment. This is a week earlier than reported in the first studies of chloroquine for the treatment of P. vivax . This corresponds to a concentration providing 50% of the maximum reduction of between 90 to 250 μg/liter and a slope coefficient of 5 to 30 , left. AP. vivax .If we make the assumption that chloroquine resistance can be quantified uniquely by a fold change in the MIC value (with no change to the slope parameter), then it is possible to predict how increasing resistance will affect the distribution of relapse intervals following chloroquine treatment. Our model predicts that a doubling of the MIC would result in the median time to relapse occurring approximately 6 days earlier . In a moPlasmodium vivax relapse was studied, but the same approach can be applied to Plasmodium falciparum infections in high-transmission settings provided that enough patients are studied. However, the following design aspects are important for the accuracy of the statistical estimation procedure. (i) There should be sufficient blood sampling to estimate the drug elimination profile. At a minimum this should be on day 7 and on the day of recurrent malaria if residual drug concentrations are still detectable. This depends on the drug studied. (ii) Follow-up should be for long enough to capture recurrent infections. A reasonable aim would be to observe 90% of the recurrences due to bunching. This depends on (a) the transmission rate  or relapse rate (P. vivax) and (b) prior estimates of the pharmacokinetic-pharmacodynamic relationship . (iii) Parasite clearance rates for the primary illness should be documented to estimate the maximum in vivo effect. (iv) Sample size estimations should be performed by simulation using a priori knowledge of the transmission rate or relapse rate, the terminal elimination half-life of the relevant antimalarial drug, and its pharmacodynamic parameters.The MIC of an antimalarial drug is a contextual parameter which is an important measure of drug efficacy and quantifies appropriate dosing. During the process of drug development, phase 2 trials specifically designed to estimate the MIC are already recommended, and their utility has been demonstrated , 5. We sThe antimalarial MIC is an individual value for a particular infection in a particular host. Ideally, in every treated patient antimalarial drug concentrations should exceed the MIC until the last blood-stage parasite has been killed. In practice, background immunity may clear infections which are not eliminated completely by the drug. But in any population background, immunity will vary widely and there will be some patients (notably young children) with little or no immunity. The MIC therefore provides a target allowing evidence-based rational design of treatment regimens . UnfortuP. vivax would not be a suitable candidate.This modeling exercise illustrates one approach to field measurement that could be used in areas of high malaria transmission . It exploits the phenomenon of bunching, whereby infections newly acquired, or in this case relapsing, during and after the period of antimalarial treatment for an incident infection (or in a mass treatment) cluster together in time as they become patent. The precision of individual MIC estimates depends on several factors, including the frequency of patient observations and sampling, the accuracy of the pharmacokinetic estimates, the terminal elimination rate constant, and the frequency of reinfection. The pharmacokinetic estimates for an individual can be derived from as little as a single blood level , with population estimates of the terminal elimination half-life obtained through weekly serial measurements. As many treatment studies involve weekly or less frequent outpatient follow-up and recurrent infections may be asymptomatic, the time to reinfection can be overestimated. If transmission is high, then partial immunity will result in many infections being cleared before they reach patency, and at lower transmission settings there will be substantial interindividual variation in the times when reinfections are acquired. Rapid reinfection  is necessary for the implementation of this methodology. For example, long-latency P. falciparum reinfection rates are high, or patients with high rates of P. vivax relapse, to provide accurate MIC estimates. Thus, the method may be more useful for postregistration dose adjustments  and for the interpretation of resistance. As the MIC reflects both host immunity and parasite susceptibility, it offers a method for assessing the former (by comparison between sites and ages) and calibrating the latter (by comparing in vivo and in vitro assessments from the same site).Although this approach could be incorporated in preregistration drug development, it is more suited to large studies of antimalarial drugs which have already been deployed. Phase 3 antimalarial drug studies evaluating asexual blood-stage activity may not enroll a sufficient number of patients in an appropriate setting, e.g., children, in whom in vivo whole-blood chloroquine MIC for Plasmodium vivax on the Thailand-Myanmar border was approximately 67 μg/liter , which may be compared with a recent in vitro geometric mean ex vivo 50% inhibitory concentration (IC50) estimate of 17.3 μg/liter . Given that whole-blood chloroquine concentrations are 8 to 10 times higher than in plasma samples, and that chloroquine is approximately 50% protein bound, these values are not very different  and so the minority of newly acquired or relapsing infections which do become patent are no different in terms of antimalarial drug susceptibility from those which emerged earlier and were cleared spontaneously. These sources of variability are compounded by variation in the duration of preerythrocytic schizont maturation, variation in the number of successful sporozoites inoculated, and variation in parasite growth rates. Nevertheless, if there are sufficient numbers of observations to observe bunching, MIC estimates can be provided from field studies in which patient follow-up is frequent and sufficiently long to capture recurrences and antimalarial blood concentrations have been measured.https://github.com/jwatowatson/bunching.All statistical analysis and modeling was done with R software (version 3.4.1). Reproducible stand-alone R code and data used for the chloroquine modeling can be found at This section presents the theoretical framework which elucidates the relationship between the temporal pattern of recurrent infections and the MIC, or time to reach MIC. The MIC is the estimated antimalarial drug concentration at which the parasite multiplication rate/cycle is one. If the parasite multiplication rate is 10 per blood-stage asexual cycle, then this would correspond to 90% inhibition of multiplication. This approach to MIC estimation relates to slowly cleared antimalarial drugs . The dynamics of the model show why the first recurrent infections to become patent after the administration of slowly eliminated antimalarial treatments cluster together temporarily and how this bunching effect is partially determined by the MIC.Many antimalarial drugs have multiphasic concentration profiles during the distribution and elimination phases. However, the terminal elimination profile is usually a first-order process characterized by a terminal elimination rate constant which is inversely proportional to the terminal elimination half-life. This varies in a population of treated patients because of population variation in pharmacokinetics.Plasmodium falciparum and P. vivax and are independent of the source of parasitemia, whether a reinfection, a recrudescence, or a relapse in the case of P. vivax.The pharmacokinetic-pharmacodynamic model defines the relationship between concentrations of the drug (as measured in whole blood or plasma) and the asexual parasite reduction ratio per 48-h cycle, i.e., the schizonticidal effect of the drug. Parameter estimation is done by discretizing into 24-h intervals. Because it is only the schizonticidal effect which is of interest, the setups of the PK-PD model are identical for both k), the drug's maximal effect Lmax , which we designate ρ1/2. Other suitable choices would include the EC50 or the half-maximal inhibitory concentration (IC50). The choice of parameter should reflect best prior knowledge of the corresponding concentration-response curve. We argue that the EC50 or the EC90 will most often be points on the concentration-response with the most accurate a priori knowledge. These are all in vivo measures and have different values when assessed in vitro using different endpoints.In this exercise, the pharmacodynamic outcome was the parasite reduction ratio over the course of one asexual blood-stage life cycle (48 h). This is a positive real number which takes values greater than 1 when the parasite population declines and values less than 1 when the parasite population grows. Framing the problem in this way allows for population dynamics to be characterized with a single continuous outcome. We assume that the antimalarial concentration-effect relationship is a sigmoid function of the log concentration of drug in the blood or plasma. This sigmoid function is parameterized by its linearized slope coefficient .The EC50 is defined as the theoretical concentration of drug at which the parasite reduction ratio is Lmin/2 over the course of one life cycle. This is the concentration causing half the maximal growth per asexual cycle .The ICPD = {Lmax, Lmin, k, ρ1/2}) uniquely define the pharmacodynamic properties of the drug. A commonly used mathematical formula for this sigmoid relationship is given by the logistic function. For example, the 48-h parasite reduction ratio as a function of the logarithm of the drug concentration ρ can be written ask/4. Empirical data, both in vivo and in vitro, can usually give a robust characterization of the central aspect of this curve, i.e., around the ρ1/2 and concentrations at which the effect is close to Lmax   . Howeverifficult . The MICLmin is known: this is the reciprocal of the normal parasite growth rate with negligible drug concentrations. In the estimation algorithm we set Lmin = 0.1 . This value is independent of the drug studied.In the following we assume that Lmax is known for chloroquine and is 103.We also assume that the in vivo MIC using data on intervals to recurrent infection. A Bayesian approach for the estimation of the MIC is appropriate, as there will be informative prior information regarding the key parameters in the model. For example, in vitro dose-response data can provide plausible values for the slope of the dose-response curve, although the EC50 (ρ1/2) will in general not be the same.This section outlines the Bayesian statistical method we use for the estimation of the iT has to be greater than 8 days. This statistical model makes the following assumptions: (i) the drug lacks preerythrocytic activity and (ii) there are no recrudescences; i.e., the time to reinfection T1, …,nT be the times of posttreatment malaria recurrences  for n patients, for a given antimalarial. Let i from treatment up to iT. In practice, these may either be simulated from a population PK model or extrapolated from patient measurements relevant to the terminal phase of the drug . The imputation of the drug concentrations over time is considered a separate inference problem. Thus, the logarithmic concentrations PD and the logarithmic drug concentrations iS be the (unknown) time of hepatic schizont rupture.Let iY conditional on a value of iY is computed by solvingpyrogenic is the threshold total number of blood-stage asexual parasites causing fever in an adult (the pyrogenic density), usually above 108, 104 is the number of parasites emerging from the liver for each inoculated sporozoite, and c indexes the number of parasite asexual cycles after hepatic schizont rupture. If iS occurs too early and the infection is cleared , then iY is set to 0.We define a generative model which computes the time of reinfection  PD.k = 1,..,K samples.Sample i ∈ 1..N:Sample latent variable iS until infection is cleared or total number of parasites goes above pyrogenic threshold.Simulate forward the fate of the infection (discretizing daily) as in Save time of reinfection For subjects L1 distance between observed and simulated data: Compute the Repeat for A.kL < ε.Select all samples Select closest samples.We use the following approximate Bayesian computation (ABC)-type algorithm  to estimF(·) is the prior distribution over merozoite release times: the latent variables In this algorithm, Π is the prior distribution over the parameters t1/2 affect the precision of the posterior estimates of ρMIC. It is easy to augment the statistical model with a hierarchical prior over values of ith patient individual elimination half-life. For example, the terminal elimination half-life of lumefantrine varies between 3 to 6 days, so one option would be a Gaussian prior with a mean of 4.5 days and standard deviation of 0.75 day , chloroquine , or chloroquine plus primaquine (0.5 mg base/kg/day for 14 days). All doses were supervised by the study staff. Informed consent was obtained from all patients. Ethical approval of the study was given by the Mahidol University Faculty of Tropical Medicine Ethics Committee (MUTM 2010-006) and the Oxford Tropical Research Ethics Committee (OXTREC 04-10). See clinicaltrials.gov trial number NCT01074905 , a reinfection from a new mosquito inoculation (termed reinfection), or activation of dormant parasites in the liver known as hypnozoites (termed relapse). In practice, most or all early recurrences are relapses.Subjects were followed daily for supervised drug treatment. Follow-up continued weekly for 8 weeks and then every 4 weeks for a total of 1 year. Sampling for chloroquine levels (capillary whole blood) was done on day 6 (±1 day) and on the day of a recurrence. Recurrent episodes were detected actively at scheduled visits by microscopy (lower limit of detection is circa 20 parasites per μl). Patients were encouraged to come to the clinics between scheduled visits when unwell, so some recurrences would have been detected passively. When recurrence occurred with microscopy-confirmed For the purposes of this analysis, we used only the data from individuals randomized to the artesunate and chloroquine monotherapy arms. Times until relapse combined with estimated PK profiles for chloroquine treated patients are used to estimate the MIC of chloroquine. Times until relapse and total parasite biomass for artesunate monotherapy-treated patients were used to derive a prior distribution over liver schizont rupture times.The data used to estimate the whole-blood MIC of chloroquine consisted of 418 recurrences before day 60 occurring in 137 individuals . The number of recurrences ranged from 1 to 10, with a median number of recurrences of 2. The median time to recurrent infection in this subset of patients treated with chloroquine was 42 days. Patient ages ranged from 1.5 to 55 years (median age was 18 years).To estimate the chloroquine pharmacokinetic profiles of these patients, we pooled data from all chloroquine-treated patients, giving a total of 1,440 PK samples from 438 individuals. The median number of samples per individual was 1.P. vivax parasites depends on the latent time of merozoite release into blood. The following details how total parasite biomass is estimated for each individual and the data from the artesunate monotherapy arm are used to obtain a prior distribution of times of merozoite release.Our PK-PD model of chloroquine and 6 × BV, where BV is the patient's blood volume, estimated as 2.78 liters/m2 for males and 2.44 liters/m2 for females (0.425 × height0.725 × 71.84 × 10−4). The parasite count used is the one recorded on detection of each new episode.Total parasite biomass is estimated as follows: biomass = parasites per microliter × 10 females . Surface4 liver merozoites. The number of days since merozoite release is then estimated as5 merozoites, then the interval is 2 days shorter.Artesunate is a rapidly eliminated drug  and thus has no posttreatment prophylactic effect. Therefore, in patients randomized to artesunate monotherapy, the time (since last treatment) of relapse is indicative of when liver merozoites were released into the blood. We assume that in the absence of drug (or at negligible concentrations) the parasite multiplication rate is 10-fold per 48-h cycle. We assume that each new bloodstream infection is initiated by 10Outlier detection was done for samples taken around day 6 with very low concentrations (below 100 μg/liter). We also removed samples taken after day 60, as we consider recurrent infections only before day 60 to be informative for the MIC. Day 60 was chosen as a qualitative assessment based on the difference in the distribution to time to recurrence between artesunate-treated individuals (no postprophylactic effect) and chloroquine-treated individuals.nlme from package nlme). The concentration of chloroquine at time t is designated ρ(t) and is modeled asp is the proportion defining the biexponential decay and α1 and α2 are the two decay rates. To avoid overfitting, p was assumed to be a fixed effect. The intercept β and the rates α1 and α2 were assumed to be random effects.A mixed-effects biexponential decay model was fitted to these PK samples , the estimated total parasite biomass, and the individual parameter fits from The following data and metadata on all subjects included in this analysis are available at iT (i = 1..418) is generated from a truncated Weibull distribution  where the lower bound is zero and the upper bound is the time of recurrent infection minus 6 days. The parameters of the Weibull distribution were estimated from the artesunate-treated individuals (see above).The latent merozoite release times for each reinfection time 3.The maximum PRR of chloroquine was fixed at 10PD was set as a bivariate normal distribution over the natural logarithm of the slope and the IC50, with means 1.5 and 5.5, respectively. For both parameters the standard deviation was set to 1.5  and plugged into the ABC algorithm.Total parasite biomass corresponding to patency of infection was taken as the individual estimated parasite biomass on detection of episode .We use the ABC algorithm outlined under “Statistical inference” above, with the following specifics.5 iterations of the algorithm. The posterior distributions of the PD parameters are shown in We ran 10


Introduction Individualized risk assessment for venous thromboembolism (VTE) using the Caprini risk score (CRS), coupled with targeted prophylaxis based on the score, is effective in reducing postoperative VTE. Critics contend that using this tool is time consuming for health care providers. We decided to create a patient-completed CRS and conducted a prospective study to compare the scores calculated by a patient with those calculated by a blinded physician for the same patient.Methods In phase 1, we interviewed patients in our deep vein thrombosis (DVT) support group who had a history of thrombosis and included their family members to determine areas of misunderstanding in the original CRS. We created a patient-completed form based on these interviews. In phase 2, we further optimized the questions after a CRS-trained, blinded physician scored 20 hospitalized patients during the pilot study. In the final (third) phase, we measured the agreement level between the new form filled out by the trained physicians and those filled out by the patients. The study was approved by our local institutional review board. Using PASS version 11, we determined that a sample size of 37 individuals achieves a power of 80%, to detect a 0.1 difference between the null hypothesis correlation of 0.5 and the alternative hypothesis correlation of 0.7 using a two-sided hypothesis test with a significance level of 0.05. We tabulated the individuals' answers and categorized the scores by using SPSS version 23 to estimate the kappa value, linear correlation, and the Bland–Altman test. A kappa value greater than 0.8 indicated an “almost perfect agreement.”Results We tested the first patient-completed CRS version (phase 2) in a 20-patient pilot study. A poor agreement was observed with the body mass index (BMI) responses in multiple iterations, and so we excluded the BMI calculation from the final patient-completed CRS form. We recruited 42 patients with an average age of 55, mostly female (45%), who completed less than college education (62%) to fill out the updated CRS form (phase 3). An almost perfect agreement was found for both the individual questions and the overall score comparing physician and patient answers, resulting in a high correlation (r = 0.95). In Bland–Altman, we did not find any trend for extreme values.Conclusion We created and validated a patient-completed CRS form that has an excellent agreement level with the physician-completed form. From the results, the physician only needs to calculate the BMI. The average time for a patient to complete the form was 5 minutes. The average time for the physician to finalize the score was approximately 6 minutes. Implementation studies are needed to assess the correlation of the aggregated score, derived from this form, with the occurrence of perioperative VTE. Perioperative venous thromboembolism (VTE) is a leading preventable cause of morbidity and mortality in surgical patients.The timing and necessity for primary VTE prophylaxis are decisions that are based on balancing both patient- and procedure-specific risks of bleeding and thrombosis. The 2005 Caprini risk score (CRS) weighs individual risk factors to create an aggregate risk score that correlates with postoperative VTE in nonorthopaedic surgical patients. The Caprini model consists of 38 individual risk factors weighted from 1 to 5 points depending on the likelihood of an individual factor to be associated with the development of VTE.A growing body of evidence suggests that patients and their family members want more education regarding prevention, significance, and consequences of VTE.The validation process was divided into three phases.We held a focus group from members of a local deep vein thrombosis (DVT) support group. Patients who had a history of VTE and their family members filled out the existing CRS form (Phase 1). The aim of this exercise was to determine areas of misunderstanding in the original CRS. One of five trained physicians completed the original CRS version on the same patients and transcribed their observations. We used a deductive approach to analyze both sets of responses until data saturation was reached.We prospectively recruited patients admitted to the John Stroger Jr. Hospital from October 2016 through January 2017. We included patients older than 18 years admitted to a medical or surgical unit. We excluded illiterate patients and those with altered mental status, visual disorders, and acquired/congenital cognition impairment.We conducted a pilot study on 20 patients to refine the CRS document (Phase 2). In an interim analysis, we measured the level of agreement between each item of the patient- and the physician-completed CRS to specifically target the problem questions. During the first part of the interview, the patients calculated their patient-completed CRS with no assistance or instructions other than those in the form. Subsequently, a physician scored the CRS for the same patient on the basis of information acquired from a formal patient exam and electronic medical records (EMR). The scoring physician was blinded to the patient's report, and the scores for both patient and physician were compared. Basing on the results of this comparison and the patients' suggestions, we produced the final form .During a 4-week period, we prospectively enrolled patients admitted to the medical and surgical units for the first 48 to 96 hours of admission. The patients completed the form and this was compared with the blinded physician interview. Each physician interviewer underwent a training session on the CRS by one of the senior authors (A.T.) before the validation process was commenced.The institutional review board approved this study and waived signed consent.We calculated Cohen's kappa (k) to quantify the question-to-question agreement between the patient-completed CRS and the physician-completed CRS. The Bland–Altman method was used to evaluate the agreement between the scores calculated by the patients and those by the physician.There were five focus-group sessions with eight participants each in phase 1. The main areas of concern were the presence of the item “age” in multiple sections of the original form, the concept of BMI, the concept of lung disease, and identification of chronology of events in the statement “now or within the past month” in the original form. The form created for the pilot addressed those questions and used multiple strategies to answer more challenging topics, including BMI. We used this form during phase 2 of the study.k < 0.4) in three major areas of the initial form: chronology of prior medical history, interpretation of mobility and ambulation, and accuracy of the BMI. We tested the modifications in the final validation phase.In phase 2 of this study, we recognized consistently poor agreement .Second, the immobility definition in the original CRS was adopted from the Medical Patients with Enoxaparin (MEDENOX) trial;Third, BMI was not accurately calculated based on the information provided by patients in any of the facilitated question iterations. We inquired about the BMI in three different ways: (1) by asking patient their height and weight, (2) providing a chart to calculate the points adjudicated by the CRS, based on their height and weight, and (3) by directly asking their BMI. Therefore, we decided by consensus to remove BMI from the patient-completed CRS in subsequent analyses, and concluded that it should be added in the final intake by the prescribing physician.k: 0.8 (95% CI: 0.65–0.85) andk: 1.00 (standard deviation: 0.00) for the second cutoff value  when CRS was categorized following ACCP guideline recommendations. Because higher levels of postoperative VTE are seen with CRS levels of 8 and 11, we further divided the CRS using these cutoff values.f value . SpearmWe created and validated the first patient-completed version of the most widely used perioperative VTE risk assessment model, which showed excellent correlation. This English form may facilitate the use of the Caprini score to assess nonorthopaedic surgical patients' thrombosis risk in the United States and in other countries where English is used. Providing a clear-cut estimate of thrombosis risk may aid physicians in providing appropriate thrombosis prophylaxis that will reduce the incidence of perioperative symptomatic VTE. Additional evaluation of patient-friendly forms in other languages is underway using the methods in the current study.The concept of scoring VTE risk has led to multiple VTE risk assessment tools including the Kucher score,One of the most successful programs using the score in surgical patients was done by Cassidy and associates in a hospital-wide program using an EMR-based CRS.The unique feature of the Cassidy regimen was prescribing extended prophylaxis for 7 to 10 days for scores from 5 to 8, and 30 days of low-molecular-weight heparin prophylaxis for those with scores of 9 or greater. There is a drop-down menu that indicates reasons for not prescribing chemoprophylaxis such as an increased bleeding risk. During the audit period, 100% compliance was achieved in the low- and moderate-risk groups and 89% compliance in those who were at high risk. The 30-day clinically proven PE rate during the audit period decreased from 1.1 to 0.5%.A recent a meta-analysis involving 14,776 patients in 13 eligible studies examining VTE events was conducted. Eight studies in 7,590 patients contained data for clinically relevant bleeding with and without anticoagulant prophylaxis. These studies examined the benefit of postoperative chemoprophylaxis according to Caprini's scores. The majority of patients received mechanical prophylaxis. VTE risk in patients who did not receive chemoprophylaxis varied between 0.7 and 10.7% according to the Caprini model. Those with higher scores were significantly more likely to suffer VTE events. Patients with scores of 6 or less comprised 75% of the overall 14,776 patients. These patients did not have significant reduction of VTE using chemoprophylaxis. Significant benefit using chemoprophylaxis was seen in those with scores of 7 or more.Although robust evidence supports the efficacy of the Caprini model in identifying surgical patients at a high risk for postoperative VTE, including in the posthospitalization period,The advantages of our study include the fact that most of the study participants had a low educational level, thereby making our results generalizable to the larger population. We developed a solid methodology involving a three-phase validation process, including a qualitative analysis of patient comments. The new form lacks patient-reported BMI which we consider an advantage because of the substantial growing body of evidence supporting inappropriate obesity estimation when BMI calculation is based on patient-reported data.Patient-reported outcomes are reliable and conveniently obtained measures to assist in clinical practice. For instance, in rheumatology, the routine assessment of patient index data 3 (RAPID3)—which quantifies functional capacity, pain, and global status—appears adequate to document status and monitor the effectiveness of therapies in patients with rheumatoid arthritis; it is also considerably easier to obtain than other physician-reported measurements.The implementation of a formal VTE prevention program, by using a validated risk assessment tool, can effectively reduce VTE occurrence. Using a retrospective database, Catterick and Hunt reported the impact of their national VTE risk assessment tool on secondary care in England. Based on their analysis involving approximately 15 million hospital admissions across England in 2011, the authors estimated that 2,000 secondary diagnoses and 1,200 ninety-day readmissions were avoided. Similarly around 940 deaths owing to VTE were avoided in a population of over 53 million in England in 2011 and 2012.We created and validated a patient-completed CRS form that has an excellent agreement level with the physician-completed form. This study demonstrates that the burden of completing this comprehensive risk assessment is decreased by involving the patients in their care. The average time for the patient to complete the form was 5 minutes, and validated by the physician in a few additional minutes (6 minutes). Further studies are necessary, including those in other popular languages, to assess this methodology to efficiently and accurately complete a patients' risk of VTE.