The Life in BALANCE (LIB) study is a pilot translational study modeling the Diabetes Prevention Program (DPP) intensive lifestyle coaching intervention among an underserved, high-risk population: American Indians/Alaska Natives (AI/ANs) living in a large urban setting .2) at increased risk for developing type 2 diabetes (HbA1c > 5.4 (36 mmol/mol) < 6.4 percent (46 mmol/mol) participated in the program between April and December, 2011. Study participants completed a 16 week intensive lifestyle coaching intervention. In addition to obtaining qualitative data regarding opportunities and challenges of applying the lifestyle intervention for AI/AN participants in an urban setting, clinical data, including BMI, waist circumference, blood pressure, fasting blood glucose, and blood lipids , were collected.A total of 22 overweight/obese AI/ANs (age, 39.6 ± 10.4 years; BMI, 34.1 ± 6.3 kg/mOnly 12 of the 22 participants remained in the LIB program at the final post-program follow-up. Participants demonstrated significant decreased waist circumference and elevated HDL cholesterol. Triglycerides manifested the highest percentage change without statistical significance. No significant change was observed in blood pressure or fasting blood glucose.LIB participants’ improvements in BMI, waist circumference, HDL cholesterol and triglycerides suggests type 2 diabetes prevention programs aimed at urban AI/ANs show significant potential for reducing the risk of developing type 2 diabetes among this underserved and high risk community. Qualitative data suggest the main challenge for type 2 diabetes prevention specific to this population is a need for improved community outreach strategies. Type 2 diabetes is a global public health crisis affecting all socioeconomic classes and all races . MinoritThe Life in BALANCE (LIB) project was a community-based participatory research project piloting a translational study that modeled the Diabetes Prevention Program (DPP) intensive lifestyle coaching intervention among an urban AI/AN population. The purposes of the study were to: 1) describe the translational type 2 diabetes prevention program specifically modified for an urban AI/AN population; 2) utilize the results of the pilot project to inform larger future type 2 diabetes prevention programs targeting urban AI/AN populations through diet modification and increased physical activity.2; and a HbA1c level between 5.4% (36 mmol/mol) and 6.4% (46 mmol/mol) from a whole blood capillary blood sample. Participants with major illness or using medication known to interfere with glucose tolerance were excluded from this study. Qualified screened participants were informed about the LIB study and those who expressed an interest in and were able to meet the demands of participation in the LIB type 2 diabetes pilot prevention project received a 16-week type 2 diabetes prevention core curriculum and regular follow-up. The intensive interventions accentuated a nutritionally balanced portion control weight loss plan including: weight-loss curriculum, meal planning, fat gram and calorie counting, portion size, and food content education. Target measures for participants were directed towards caloric and fat gram profiles based upon individual goals. Core group sessions were primarily conducted at the Las Vegas Indian Center (a community-based information and resource center) and scheduled on a weekly basis in order to accommodate participant availability as much as possible. On a few occasions sessions took place at a different venue in order to emphasize an educational goal from the curriculum. These included restaurants and a fitness center to illustrate core lessons. LIB Native lifestyle coaches received centralized training that included: required reading, instructional videotapes, observation of trained personnel, and audio/video taped practice sessions and resource core review.The LIB project targeted urban AI/AN residents in Las Vegas, Nevada at increased risk for developing type 2 diabetes. Participants were recruited through community screenings, media announcements and referrals from other participants and/or project members. The inclusion criteria for participant eligibility included: being 21 years of age or older; AI/AN self-identification; a BMI ≥ 25 kg/mParticipant demographic, lifestyle and dietary information was obtained before and after the completion of the LIB lifestyle core curriculum using a baseline survey and a post-participation survey. Participants fell into three groups, those who completed all 16 sessions of the LIB Program Curriculum, and who provided the baseline, post-curriculum and final measures and remained in contact after a period of self-maintenance , those who completed all 16 session of the LIB Program Curriculum and provided the baseline and post-curriculum measures , and those who discontinued at various points in the program and only provided baseline measures . Clinical measures included body weight, BMI, waist circumference, blood pressure, blood lipids, fasting blood glucose, and HbA1c, and were collected before and after the completion of the LIB lifestyle core curriculum and the end of post-program follow-up when possible. All clinical procedures were followed standardized protocols and performed by trained clinical specialists.The SPSS statistical package  was used for analysis of the quantitative data . Descriptive statistics was used to summarize the data. The Wilcoxon signed-rank test and the Fisher’s exact test were used to compare the differences of demographic information and baseline clinical measures between participants who completed the program and those who dropped out. The Friedman test was used to analyze the change of clinical measures across the three time points. Intention-to-treat analysis was used to analyze the influence of dropouts. Wilcoxon signed-rank test was conducted as post hoc analysis with a Bonferroni correction applied.The LIB qualitative component used semi-structured one-on-one interviews to understand the experiences of 11 of the program participants who completed the core curriculum (n = 11 out of 15). Subjects who agreed to participate in the qualitative component ranged in age from 23 to 55 years with a mean of 39 years and included 9 women and 2 men. Interviews were transcribed and then coded and analyzed using Atlas.ti Qualitative Data Analysis software. A guideline of ten open-ended questions was used by the interviewer, but participants were encouraged to speak openly about their experiences. All interview comments were coded into condensed phrases  and codes were then organized into non-overlapping categories .2) subjects were enrolled in this pilot study, 10 of them  dropped out of the program before providing the final set of clinical measures (dropout rate: 45.5%). The demographic information and baseline clinical data are summarized in A total of 22 , demonstrated statistically significant improvement after the LIB intensive lifestyle intervention . Twelve Triglycerides showed the largest mean reduction (15.89%), but the change was not statistically significant because of a high variability. Systolic blood pressure decreased more than diastolic blood pressure. Fasting blood glucose and HbA1c showed very limited improvement after the program.The results from the intention-to-treat analysis were highly consistent with those from the original analysis (Data not shown), indicating that positive findings did not result from removing the data of drop-outs. However, the results from the intention-to-treat analysis are not considered in the final results due to the risk of introducing bias and inflating type I error.Eight thematic categories emerged as the most relevant to the aims of the qualitative component, and the most common sets of codes for each (those reported by the greatest number of participants) are listed in Weight and waist circumference are universal parameters in estimating general and abdominal obesity, respectively. Abnormal body fat distribution around the abdomen is highly associated with an increased risk for type 2 diabetes, even if the BMI falls within the normal range . This isHDL cholesterol and triglycerides are two typical lipoproteins in diabetic dyslipidemia, although their responses to lifestyle interventions may differ. A previously conducted 36-month clinical trial found that both diet alone and diet combined with exercise decreased triglyceride levels and increased HDL cholesterol values in participants with metabolic syndrome . Two simMany studies have indicated that hypertension can be relieved through lifestyle intervention, especially in decreasing systolic blood pressure ,14. A smHyperglycemia is one of the primary symptoms in individuals with prediabetes and type 2 diabetes. It is also the most intractable target in type 2 diabetes prevention programs. Diet and exercise are widely perceived to be beneficial for glycemic control. However, conflicting results on the effects of lifestyle intervention in improving hyperglycemia are common in clinical trials. In the present study, fasting blood glucose was not affected by the LIB lifestyle intervention. In addition, no sign of improvement in long-term glycemic control was observed, which was evidenced by stable HbA1c levels. This finding is consistent with a recent study in an overweight and obese female Canadian population, reporting that fasting blood glucose was not affected by diet and exercise regardless of significant weight loss .Moderate weight loss is beneficial in improving insulin resistance . PreviouIs there anything else about your experiences in the Life in Balance Program that you would like to share that could help programs like this in the future?Interviewer: Mm... I think more—you need to get it out there more to the natives... more advertising. I guess you could say reach out more than to just basic—I don’t know—you know, get out there in the communities and look for the people who do need help and stuff. I’m not sure how many natives live here. It would be nice to get them all together, you know, and teach them and stuff… more outreach—26-year-old Navajo female.The aim of the qualitative research component was to understand the specific challenges and successes of participants who completed this study, and not captured by the clinical measures, in order to inform the design of future type 2 diabetes prevention programs geared toward urban AI/ANs. Contrary to one of the major assumptions motivating this study, the most commonly reported barriers to adherence with lifestyle goals for those who finished the LIB Program—family, work, social obligations, time, money—are not unique to urban American Indians. Worldwide meta-analyses of compliance issues in type 2 diabetes intervention programs report strikingly similar issues ,17. For the way I found out [about the program] was one of my friends that works for the-she used to work for the Indian Education Center. She’s the one that emailed me and told me about it and I thought wow I mean you can’t even-going home and trying to get in something like this and learning about diabetes, you only learn when they have the health fairs, you can go to the hospital and find out but some are too much in a hurry… they just don’t realize, you know, how important it is—55-year-old Navajo female.… What did you find interesting about the Life in Balance Program?Interviewer: well, um, the program itself and the goal of the program for Native Americans. This is a huge issue in my family. It’s kind of always on my mind as I get older and um I really think it should be directed towards youth and younger generations, just because it’s such a huge problem in Native American communities—37-year-old Choctaw female.… Although the challenges of lifestyle goal adherence among urban AI/ANs in the current study appear to be very similar to those of other prediabetics from a broad range of ethnic/racial and socioeconomic backgrounds, the value of developing a type 2 diabetes prevention program aimed specifically at urban AI/ANs, and targeting program recruitment and participation based on AI/AN identity, was nevertheless an important factor regarding interest and participation in the LIB program.Some limitations of the study described here should be noted. First, the pilot study was hampered by a small sample size. Even the intention-to-treat analysis indicates that the positive changes may be attributable to the lifestyle intervention itself, but large variability resulting from the small sample size might have masked some significant results. Second, the last follow-up visit was completed at either 8 months or 12 months after the first clinical measure. Theoretically, acquired positive behaviors gradually weaken over time without proper reinforcement. Participants who had an early follow-up visit might behave better than those who had a late follow-up visit, which might have influenced the last round of clinical measures. Third, due to the nature of secondary analysis, some important confounding factors cannot be addressed in this study. For example, social support is crucial in shaping and consolidating individuals’ positive behaviors. Information like marital status was not collected in the LIB pilot study, which might also have helped explain the high dropout rate. Information on lifestyle choices like smoking and drinking was not collected, which also plays a role in increasing metabolic risk related to type 2 diabetes, and which might have attenuated the effects of the LIB program.According to the qualitative information gleaned from interviews with study participants, the main challenge for type 2 diabetes prevention interventions specific to urban American Indians may be locating and reaching this particular population; a population, in spite of suffering from a greater disease burden, has both health challenges and health aspirations that are indistinguishable from other middle class working Americans. It is clear from this study, through the analysis of interview data and the experiences of the research team during the planning stages of the project, that the need for improved recruitment strategies should be a primary focus in the design of future type 2 diabetes prevention programs among urban American Indian populations.Importantly, both this issue and the majority of other issues describing that are not specific to urban American Indians, and can theoretically be at least partially ameliorated by incorporating more technologically savvy tools into the program. For instance, taking advantage of existing or creating customized smart phone applications to keep track of diet and exercise goals may greatly reduce both participant and researcher burden, a potential that has yet to be fully realized in the health sciences in general . The useIn summary, the results indicate that the LIB program reduced the risk for type 2 diabetes and its complications in urban AI/ANs living in Las Vegas and at an increased risk. The current LIB core curriculum is effective in decreasing weight, waist circumference and elevating HDL cholesterol level. Qualitative data from informal interviews suggest that the main challenge for type 2 diabetes prevention research specific to this population is a need for improved community outreach strategies because, in spite of suffering from a disproportionately higher type 2 diabetes burden, urban AI/ANs in this study faced challenges not different from other working class groups living in urban areas. A more technologically savvy program design and recruitment strategy offer many promising and creative solutions to many of these challenges, and may extend the perceived benefit of participating in a type 2 diabetes prevention program with other AI/AN participants. Further research is needed to investigate the effect of an optimized core curriculum in strengthening the efficacy of the LIB program among AI/ANs living in urban environments.


Mutations in the RPE65 gene are associated with autosomal recessive early onset severe retinal dystrophy. Morphological and functional studies indicate early and dramatic loss of rod photoreceptors and early loss of S-cone function, while L and M cones remain initially functional. The Swedish Briard dog is a naturally occurring animal model for this disease. Detailed information about rod and cone reaction to RPE65 deficiency in this model with regard to their location within the retina remains limited. The aim of this study was to analyze morphological parameters of cone and rod viability in young adult RPE65 deficient dogs in different parts of the retina in order to shed light on local disparities in this disease. In retinae of affected dogs, sprouting of rod bipolar cell dendrites and horizontal cell processes was dramatically increased in the inferior peripheral part of affected retinae, while central inferior and both superior parts did not display significantly increased sprouting. This observation was correlated with photoreceptor cell layer thickness. Interestingly, while L/M cone opsin expression was uniformly reduced both in the superior and inferior part of the retina, S-cone opsin expression loss was less severe in the inferior part of the retina. In summary, in retinae of young adult RPE65 deficient dogs, the degree of rod bipolar and horizontal cell sprouting as well as of S-cone opsin expression depends on the location. As the human retinal pigment epithelium (RPE) is pigmented similar to the RPE in the inferior part of the canine retina, and the kinetics of photoreceptor degeneration in humans seems to be similar to what has been observed in the inferior peripheral retina in dogs, this area should be studied in future gene therapy experiments in this model. Typical clinical signs include profound night blindness, reduced visual field, severely reduced or absent fundus autofluorescence (FAF) and absent rod ERG recordings Several natural occurring as well as genetically engineered animal models exist for RPE65 deficiency. The RPE65 knockout mouse was generated more than a decade ago, proving the function of the RPE65 protein in supplying 11cis retinal to the photoreceptors All animal models have been employed in the development of a gene therapy approach for the treatment of RPE65 deficiency. Especially the canine model proved to be a valuable tool, representing a model of comparable size and with a similarly developed immune system as in humans Gene therapy studies in over 30 patients resulted in significant improvement in light sensitivity and increase in visual field, indicating a rescue effect for rods Similarly, the reason for the discrepancy in the rescue effect between dogs and humans is unknown. In this regard it should be noted that some differences exist in the superior part of the canine retina, which is normally targeted in preclinical trials, compared to the human retina besides obvious differences such as the lack of a macula region or a foveal pit. First, the superior part of the canine retina is attached to non-pigmented RPE and a unique layer is situated behind the RPE, the tapetum lucidum, which reflects incoming light and thus allows a second passage of photons through the outer segments enabling increased light captivity In this study, we investigated for the first time the state of rod and cone viability in the inferior part of the canine retina and compared the data with the situation in the superior part. We analyzed sprouting of rod bipolar and horizontal cells and observed dramatic sprouting events in the inferior peripheral part of affected retinae while other parts of the retina remained silent. In contrast, while analyzing S- and L/M-cone opsin expression, we found a protection of S- cone opsin expression in the inferior part of the retina, but not in the superior part. This was not the case for L/M cone opsin. These observations indicate pronounced differences in rod and cone viability in the inferior part compared to the superior part of the canine retina, which may have important implications with regard to choosing the ideal treatment area in the canine retinae in order to model the human disease.Local Institutional Animal Care and Use Committee (IACUC) approval was not necessary because the animals were euthanized at the end of original study . Eyes from two wild-type and four RPE65 mutant dogs (18–30 months) were used for the study. The animals were euthanized by intravenous injection of pentobarbital sodium . The eyes were enucleated, the anterior part and the lens were removed, and the eyecups were fixed for 15–30 minutes in 4% paraformaldehyde and cryoprotected in graded sucrose .For cryosections, two different pieces of each retina were excised . One froFor flatmount staining, the nasal part of the retina  was exciThe following secondary antibodies were used: donkey anti-rabbit Alexa 488, donkey anti-mouse Alexa 546 , donkey anti-mouse Cy3 and donkey anti-goat Cy3 . All were diluted 1∶500.Confocal images were taken using a confocal microscope  equipped with Argon and Helium-Neon laser. Images for cone morphology in flatmount preparation were done with a Plan-Apochromat 20x/0.8 . The images for sprouting analyses were taken with a Plan-Apochromate 63x/1.4 objective . For image adjustment Photoshop CS 5 extended  was used.The images for the cone quantification were captured with a Zeiss Axioplan 2 microscope equipped with epifluorescence. Images were taken with a Plan Neofluoar 2.5x/0.075 objective  and afterwards merged in Photoshop CS 5 extended manually to create a map of the retina. The retina map was used to keep the orientation on the preparation for imaging the cones with a Plan Neofluoar 10x/0.30 Ph1 objective .In each region, six z-stacks (containing 4 successive images with 1 µm distance each) were analyzed. Three images contained double labeling with CtBP2 and PKC, and 3 images contained double labeling with CtBP2 and Calbindin. Quantification of sprouting was done using data of CtBP2 staining of all six images, resulting in data from 24 images per retina. Quantification was done using the Cell Counter Tool  of Image J . While going through the Z-stack, each clearly visible CtBP2-positive ribbon synapse was counted once. Contrast and brightness of the images were constantly optimized during counting, without altering the original content.Quantification of ONL thickness was performed using LSM Image Browser software  on the same images where sprouting analysis was done. On each image, the ONL thickness was measured three times at 10 µm distance and the mean values was calculated. Scatter plot with regression curve (first order) was calculated using Sigma Plot.2) was counted with the manual counting tool of Photoshop CS 5 extended. For the L/M-cones, because of the 10 times higher number of counts per image,only an area of 10% (0.06 mm2) was counted. The mean value was calculated for the data of the four affected animals at each position. The mean values of all positions in each region were compared to the healthy control animal with a Mann-Whitney rank sum test using Sigma Plot 12 .To compare the cone distribution in healthy and mutated retinae, the retina was divided into 4 regions . For theTo create the heat maps , an inteTo examine rod bipolar and horizontal cell sprouting as well as cone opsin expression, parts of four affected retinae and one healthy control retina were used as shown in Photoreceptors transmit their chemical signal in the outer plexiform layer (OPL) at the ribbon synapses to the second order neurons . The synaptic ribbons of the photoreceptors can be marked with an antibody against C-terminal binding protein 2 (CtBP2) . The rodThe thickness and the organization of the OPL appear to be unaltered in regions 1, 2, and 3 of affected retinae . The conONL thickness depends on the number of photoreceptor nuclei  and lies in the range of 30 to 70 µm in the healthy retina, depending on the location being in the periphery or central, respectively . In affeFollowing these intriguing results on regional disparities in rod bipolar cell sprouting, we analyzed cone opsin expression in the four different regions of the canine retina. Ninety three areas were analyzed on each retina and the numbers of detectable S- and L/M- cones were visualized on heat maps for absolute and relative loss compared to the healthy retina . The num2, with a peak being in the area around the optic disc (1000–1300 per mm2), representing the visual streak in canids (2) (2), but the reduction is not uniform   and 5A,D uniform  and 5. T uniform  and 5B.  uniform .2) that progressively declines towards the periphery (6000–9000 per mm2) (2) are evenly distributed over the analyzed flatmounts, resulting in a general loss of detectable L/M-cones in all 4 regions at about 60% (58–62%).In healthy retinae, similar to S-cone distribution, the highest number of L/M-cones can be found in the two central regions 2 and 3 (10000–15000 per mmper mm2)  and 5. Iper mm2)  and 5. TTo our knowledge, this study analyzes for the first time rod second order neuron sprouting and the cone opsin expression in RPE65 deficient canine retinae with regard to their location within the entire retina. Concerning sprouting of rod second order neuron processes, a phenomenon attributed to ongoing rod degeneration, we observed dramatic activity in the inferior peripheral retina (region 4), while the rest of the retina had significantly less or absent sprouting activity. This sprouting activity correlates with ONL thickness. In addition, we observed a protection of S-cone opsin staining in the inferior central retina (region 3), while L/M cone opsin loss seemed to be independent of the location.Interestingly, even though the canine retina comprises primarily rod photoreceptors (more than 95%), detailed information about the spatial distribution of degeneration kinetics of these cells in the course of RPE65 deficiency are of limited availability. Initially, the disease in dogs was even considered to be stationary, because no significant degeneration was observed in clinical examinations In a study published by Hernandez et al. (2010) mildly increased dendritic arborisations of bipolar cell into the ONL were observed in affected animals, however information about the localization within the retina and presynaptic elements such as CtBP2 staining is was not given It is currently unknown, whether the sprouting is triggered by the photoreceptor terminals (presynaptic element) or by second order neurons (postsynaptic element). As observed in our study in regions 1 and 4, sprouting is also detectable in healthy retina, which was previously shown in human and murine eyes In current human clinical trials, patients have been treated at ages between 5 and 45 years An alternative hypothesis would be that only successfully treated photoreceptors stay alive and all untreated cells degenerate. In this case, ONL thickness reduction in treated areas would stop eventually in contrast to untreated areas. Only long-term studies in humans will answer this question.The second observation made in our study is a less severe reduction of detectable S-cones in the inferior central region (region 3). The same observation was made, albeit to a lesser extent in both peripheral regions (region 1 and 4).(regions 1 and 4). These three regions are those, which are attached to pigmented RPE. This observation seems to be contradictory to what we observed concerning the sprouting events in the rod pathway, which were highest in retinae attached to pigmented RPE. To make things even more complicated, L/M cone opsin reduction seems to be independent of the location within the retina. One explanation would be that there is a protective agent or mechanism that prevents S-cones from dying. This S-cone protective effect has not yet been reported in the literature, partly because this study is the first one to address this question in the entire canine retina. Mowat et al. (2013) compared the photoreceptor density of affected RPE65 dogs at 3 months and 6.5 years of age, and found a reduction of cone and rod labeling between young and old animals A correlation of S cone protection and pigmentation seems to be possible since the pigment in the RPE cell layer has specific functions such as light absorption and antioxidative activity, both rendering it light protective in vitro after transfection of human and murine cone opsins into COS7 cells and also in vivo in Lrat−/− mice cis retinal deprivation, L/M- and S-cone opsins have different aggregation properties causing accumulation of opsin in the endoplasmic reticulum. This causes activation of the unfolded protein response, which may induce apoptosis. It was shown that, compared to L/M-opsin, more S-opsin accumulated in inner segments and OPL and was co-localized with ubiquitin. This observation could also explain the different kinetics of cone opsin loss observed in our study.One possible explanation that is independent of pigmentation are differences in the intracellular stress response in the two cone photoreceptor subtypes, which was demonstrated by Zhang et al (2011) In this study, we demonstrate that in RPE65 deficient dogs rod second order neuron sprouting is heavily increased in the inferior periphery at two years of age and generally increased in areas attached to pigmented RPE. In contrast, we show that S cone opsin expression is not uniformly reduced, as pigmented RPE seems to preserve S-cone opsin but not L/M-cone opsin expression. As the medical field deals with the results of the RPE65 gene therapy, the data obtained in this study add further information on the pathology in the most relevant large animal model and should be considered when designing further preclinical studies.Figure S1Analysis of sprouting events of horizontal cells. Confocal images are projections of 4 considered images of a z-stack. The ribbon synapses are marked with CtBP2 (red) and the horizontal cells are marked with Calbindin (green). The detected sprouting events are highlighted with white circles. Wild type (WT), outer nuclear layer (ONL), outer plexiform layer (OPL), scale 20 µm.(TIF)Click here for additional data file.


The ability to detect small changes in one's visual environment is important for effective adaptation to and interaction with a wide variety of external stimuli. Much research has studied the auditory mismatch negativity (MMN), or the brain's automatic response to rare changes in a series of repetitive auditory stimuli. But recent studies indicate that a visual homolog to this component of the event-related potential (ERP) can also be measured. While most visual mismatch response (vMMR) studies have focused on adult populations, few studies have investigated this response in healthy children, and little is known about the developmental nature of this phenomenon. We recorded EEG data in 22 healthy children (ages 8–12) and 20 healthy adults (ages 18–42). Participants were presented with two types of task irrelevant background images of black and gray gratings while performing a visual target detection task. Spatial frequency of the background gratings was varied with 85% of the gratings being of high spatial frequency  and 15% of the images being of low spatial frequency . Results in the adult group showed a robust mismatch response to deviant (non-target) background stimuli at around 150 ms post-stimulus at occipital electrode locations. In the children, two negativities around 150 and 230 ms post-stimulus at occipital electrode locations and a positivity around 250 ms post-stimulus at fronto-central electrode locations were observed. In addition, larger amplitudes of P1 and longer latencies of P1 and N1 to deviant background stimuli were observed in children vs. adults. These results suggest that processing of deviant stimuli presented outside the focus of attention in 8–12-year-old children differs from those in adults, and are in agreement with previous research. They also suggest that the vMMR may change across the lifespan in accordance with other components of the visual ERP. The human brain is constantly responding to changes in sensory stimuli, even if these changes do not pass into conscious awareness. Mismatch negativity (MMN), or the brain's response to infrequent changes in a series of repetitive stimuli  studies in adults, there is comparatively little research on the vMMR in children. A recent study by Clery et al.  used dynWhile a small number of recent studies, described above, have investigated vMMR in children, specific differences in the mismatch response at various stages in development and across different paradigms are still unclear. In addition, understanding of the neurobiology of developmental differences in vMMR is still in its infancy. In the current study, we aim to further characterize the vMMR in a sample of 8–12-year-old children. This age range is comparable to the age range used in the Clery et al.  study buSD = 5.65); 10 females; 78% right-handed] and 22 healthy children between the ages of 8 and 12 . All participants reported no current, past, or family history of substance abuse, no neurological/neuropsychiatric disorders, no seizure disorder with evidence of seizure activity within the past 12 months, no significant physical impairments or limitations, no history of head trauma or loss of consciousness, and were not currently taking any antipsychotic medications. Participants reported normal or corrected-to-normal vision. One child was excluded from further analysis due to excessive sleepiness during recording, resulting in noisy data.We collected EEG data from 20 healthy adults between the ages of 18 and 42 ; deviant non-target: 93.65 (range 83–100) for adults vs. 74.81 (range 34–97) for children . ERPs were obtained by averaging the baseline corrected EEG epochs for each stimulus category and for each participant.Response latencies and percentage of correct responses to target stimuli were calculated for each subject. All incorrect trials or trials containing responses less than 200 ms and greater than 1000 ms from onset of the target were excluded from further analyses. Continuous EEG data was filtered offline with a 30 Hz (24 dB/octave) zero phase shift Butterworth low-pass filter and visually inspected for movement artifacts. EEG data sets from each participant were corrected for eye-movements using regression analysis as implemented in Neuroscan Edit 4.4  within a specified window after stimulus onset. For P1 and N1, peak windows were determined based on the relevant peak in a visual inspection of grand averages at electrode positions O1, O2, F3, Fz, F4, C3, Cz, and C4. For the occipital channels, P1 peak detection windows for children and adults were defined as 80–150 ms for standard non-target stimuli and deviant non-target stimuli. N1 windows for children and adults were defined as 150–230 ms for standard non-target and deviant non-target stimuli. Since in both groups a negativity at around 150 ms and a positivity at around 230 ms was also clearly visible at frontal electrode positions these peaks were also assessed. For both the child and adult group, the peak detection window for the first positivity was defined as 120–200 ms for standard and deviant non-target stimuli. The first negativity was defined as 200–260 ms for standard and deviant non-target stimuli.MMN was computed by subtracting the ERP to the standard non-target stimulus (HFNT) from the ERP to the deviant non-target stimulus (LFNT). Visual inspection of electrode positions O1 and O2 for both group-averaged difference waves and individual subject data indicated that adult subjects displayed a single negative peak around 150 ms post-stimulus, whereas children displayed two negative peaks. In children, the first negativity occurred at around 150 ms and the second one at around 230 ms. Therefore, in the adult group we detected the vMMM as the most negative peak within a 130–200 ms post-stimulus window, while in the child group we detected the first peak as the most negative peak within 130–200 ms post-stimulus, and the second peak as the most negative peak within 200–275 ms post-stimulus. Since a clear positive peak at around 250 ms was also visible in the children's difference wave at frontal  electrode positions, we also assessed this positive peak within 200–275 ms post-stimulus.T-tests were performed. For between-groups comparisons of ERP peaks repeated measures mixed model ANOVAs were used, with between subject factor Group (child vs. adult) and within-subject factors Stimulus (standard vs. deviant non-target), and Electrode position . If Stimulus effects or interactions with Group were significant, follow-up repeated measures ANOVAs were fit for each group separately.All statistical analyses were performed using SPSS 19 . For behavioral analyses, independent samples t = 2.38, p = 0.022), whereas response accuracy for the standard background target condition did not differ (p > 0.08). All individuals across both groups performed the task with at least 95% accuracy. There were no significant differences in mean reaction time between the children and the adults (p > 0.1 for both conditions). These results show that both children and adults performed the task with high accuracy and were focusing their attention onto the center of the monitor.Response accuracy (percentage of correct responses) and reaction times for target conditions are indicated in Table ERPs to standard and deviant non-target background stimuli as well as the difference wave (deviant-standard) for adults are shown in Figure F = 65.49, p < 0.001] in the absence of an interaction effect of Stimulus × Group  or an effect of Stimulus , indicating that the P1 amplitude to both standard and deviant non-target stimuli was larger in children than in adults. Furthermore, a significant Electrode position × Group interaction  was observed, indicating that for the children only, the P1 amplitude to standard and deviant non-target stimuli was larger at electrode position O2 than at electrode position O1. No other effects for P1 amplitude were observed.The repeated measures mixed ANOVA for P1 amplitude at the occipital electrode positions demonstrated a main effect of Group  and a significant main effect of Stimulus  in the absence of a Stimulus × Group interaction . No other effects for P1 peak latency were observed. Since a significant main effect for Stimulus was observed we performed follow-up exploratory within group analyses to test whether the P1 peak latency effect of Stimulus held up for both groups separately.The repeated measures mixed ANOVA for P1 peak latency at occipital electrode positions demonstrated a significant effect of Group  and child  group, indicating that for both groups the P1 to deviant stimuli peaked earlier than the P1 to the standard stimuli.A significant effect for Stimulus, in the absence of any other effect, was observed for both the adult  in the absence of an interaction effect of Stimulus × Group  or an effect of Stimulus . No other effects were observed. This pattern of results indicates that the amplitude of this peak to both standard and deviant non-target stimuli was larger in children than in adults.The repeated measures mixed ANOVA for the first negativity at around 150 ms at the frontal electrode positions demonstrated a main effect of Group , but did show a main effect of Stimulus  and a main effect of Electrode position . No other effects were observed. Since a significant main effect of Stimulus was observed we performed follow-up exploratory within group analyses to test whether the effect of Stimulus held up for both groups separately.The repeated measures mixed ANOVA for peak latency of the negativity at around 150 ms at the frontal electrode positions demonstrated no main effect of Group  in the absence of any other effects, indicating that this negativity peaked earlier in the deviant stimulus condition than in the standard stimulus condition.In the adult group, a significant effect of Stimulus was observed , but a significant effect of Electrode position  was observed. These results indicate that the latency of the negativity peak did not differ enough between standard and deviant non-target stimuli to reach significance, whereas it did peak earlier at electrode channel F3 than at electrode channel F4.In the child group, no significant effect of Stimulus was observed, . However, a significant main effect of Stimulus  and a trend for a Stimulus × Group interaction  effect was observed. No other effects for N1 amplitude were observed. Since a significant main effect for Stimulus and a trend for a Stimulus × Group interaction were observed, we performed follow-up exploratory within group analyses to test whether the N1 effect of Stimulus held up for both groups separately.The repeated measures mixed ANOVA for N1 amplitude at the occipital electrode positions didn't show a significant effect of Group  and Electrode location  were observed, indicating that the N1 amplitude to deviant non-target stimuli was larger than the amplitude to standard non-target stimuli and that the amplitude on electrode position O2 was larger than the amplitude on electrode position O1.In the adult group, significant effects of Stimulus  in the absence of any other effect, indicating that the N1 to both standard and deviant non-target stimuli peaked later in the children than in the adults.The repeated measures mixed ANOVA for N1 latency at the occipital electrode positions demonstrated a main effect of Group , and significant effect of Stimulus . No other effects were observed. This pattern of results indicates that the peak amplitude at around 230 ms to both standard and deviant non-target stimuli was larger in children than in adults. Since a significant main effect of Stimulus was observed, we performed follow-up exploratory within group analyses to test whether the effect of Stimulus held up for both groups separately.The repeated measures mixed ANOVA for the positivity at around 230 ms at the frontal electrode positions demonstrated a main effect of Group  was observed, in the absence of any other effects, indicating that the amplitude of the positivity at around 230 ms to deviant non-target stimuli was larger than the amplitude to standard non-target stimuli.In the adult group, a significant effect of Stimulus  was observed, in the absence of any other effects, indicating that the amplitude of the positivity at around 230 ms to deviant non-target stimuli was larger than the amplitude to standard non-target stimuli.In the child group, a significant effect of Stimulus  in the absence of any other effect, indicating that the positivity at around 230 ms to both standard and deviant non-target stimuli peaked later in the children than in the adults.The repeated measures mixed ANOVA for latency of the positivity at around 230 ms at the frontal electrode positions demonstrated a main effect of Group  in the absence of any other effects.The repeated measures within (adult) group ANOVA for the amplitude of the negativity occurring at around 150 ms against the average baseline activity at occipital electrode positions demonstrated a main effect of Stimulus  in the absence of any other effects. This was also the case for the second negativity {main effect of Stimulus: }.The repeated measures within (child) group ANOVA for the amplitude of the first negativity against the average baseline activity at occipital electrode positions demonstrated a main effect of Stimulus  in the absence of any other effects.The repeated measures within (adult) group ANOVA for the amplitude of the positivity at around 250 ms against the average baseline activity at frontal electrode positions demonstrated a main effect of Stimulus  in the absence of any other effects.The repeated measures within (child) group ANOVA for the amplitude of the positivity at around 250 ms against the average baseline activity at frontal electrode positions demonstrated a main effect of Stimulus  between groups. No other effects for amplitude or latency were observed.Although the (first) negative difference wave1 at around 250 ms at the frontal electrode positions demonstrated a main effect of Group , in the absence of any other effects, indicating that the amplitude of this positivity at around 250 ms was larger in the child group than in the adult group. No other effects for amplitude or latency were observed.The repeated measures mixed ANOVA for the amplitude of the positivity occurring in the difference waveThis study investigated the vMMR in healthy children as compared to healthy adults using a simple visual target detection task, during which task irrelevant gratings of high and low spatial frequencies were presented in the background. We found a robust vMMN in the difference wave (deviant non-target stimuli – standard non-target stimuli) occurring around 150 post-stimulus over occipital electrode positions in the adult group, and two occipital negativities in the children, the first one occurring at around 150 ms and a second one at around 230 ms. We also observed a positivity at frontal and central electrode positions at around 250 ms in both groups. This study confirms previous research investigating vMMN in healthy adults and is one of the first to investigate this difference wave in children aged 8–12 years old. The results indicate that both children and adults respond to the occurrence of rare task irrelevant visually deviant stimuli, although this response is still developing in healthy children ages eight to twelve and may be quite different in this age group in terms of morphology  and topography  compared to typically-developing adults.Our results differ from previous work by Clery et al. , in whicA limitation of this study is that it could be argued that stimulus effects from the use of low frequency gratings as deviant stimuli may account for the vMMN seen here. Spatial frequency deviance has been previously studied by Heslenfeld , where dThere were also differences in other ERP components between adults and children: as seen previously in the literature, early components, particularly the amplitude of the P1 was larger in children and both the P1 and N1 peaked later in children. Batty and Taylor  also notThis study adds to the limited pool of studies investigating vMMR in children. Due to the preliminary nature of this study, and aware of the developing cognitive system and accompanying changes in ERPs that tend to occur across the lifespan, we chose a limited age range to determine initial differences between children and adults. However, future research should examine other and even narrower age ranges in order to better map the development of vMMR. Our stimuli also probed only one aspect of automatic visual deviancy detection , and future work should investigate other stimulus properties such as color, luminance, and size, to further understand development of the visual deviance response.Considerations for future studies should also include investigating abnormal development of vMMR. Individuals with schizophrenia have been found to exhibit reduced amplitudes of vMMN when compared to healthy controls (Urban et al., Although the above research has demonstrated the usefulness of vMMN as a potential clinical tool, few studies have investigated altered vMMR in disorders affecting children. To our knowledge there have only been two other studies of vMMR in children with neurodevelopmental disorders (Horimoto et al., 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.


Self-harm is a rare pathology, often seen in psychiatric patients but more frequently in the penitentiary environment. Of the many possible forms of self-harm, foreign bodies (FB) ingestion is by far the most usual in the Romanian prison environment. Our paper aims to present the diagnostic and therapeutic aspects arising as a consequence of digestive tract perforations consequent upon foreign bodies ingestion; we analyze a number of 45 cases which occurred over a 7-year period (2003-2010) in Rahova Penitentiary Hospital. We also examined the surgical particularities of case resolution. Abbreviations: FB – foreign bodies, EEA – end-to-end anastomosis This type of pathology is prevalent in psychiatric patients, but is also often found amongst penitentiary inmates, though the latter is seldom analyzed or presented in the medical literature.Self-harm is usually a non-fatal act by which a person deliberately provokes a lesion or ingests a substance in excess to any therapeutic dose or medical prescription. The act must be non-accidental, but may occur without any clear indication of whether the death wish was present or not . Taking into account the lack of specialized literature when it comes to self-aggression through FB ingestion, the discussion will confine its scope to the present material. According to medical literature, accidental ingestion of foreign bodies is a not infrequent pathology, but deliberate ingestion is rare, usually found amongst prison inmates and psychiatric patients. In the US 1500 deaths/year are due to accidental FB ingestion (2007) , we note that our patients almost never seek qualified medical care in the first days following the ingestion. The vast majority (2/3) see a doctor sometime in the first month after the event, and there were cases in which the interval was around one year. This aspect obviously increases both the number of complications and of surgical interventions. In contrast to a British study where the mean time from FB ingestion to seeking qualified medical care was 12 hours  . Pseudotumoral mass development or transparietal exteriorization of the FB do not represent endoscopic contraindications, but should only play a diagnostic role in such situations. In order to achieve a high success rate when dealing with these cases a number of conditions have to be met simultaneously: 1. such as seeking of medical care quickly (in the literature endoscopic maneuvers were performed in the first 24 to 48 hours after the ingestion); 2. technical and staff capabilities; 3. physical properties  which allow the FB to be manipulated; 4. a reasonable number of ingested FB ; 5. dexterous practitioner; 6. patient consent. As literature data shows, endoscopy can solve up to 10-20% of uncomplicated FB ingestion cases [6]. We found few references to multiple FB ingestion of either medium or small dimensions. In a study of 542 patients with both accidental and deliberate FB ingestion for whom the rate of endoscopic extraction is 19.5%, only 24 patients had ingested foreign bodies larger than 10 cm – and all these cases had a surgical sanction [7].  Endoscopy can be performed in almost all cases, with the notable exception of perforation associated with peritonitis or penetration in a vascular structure [8].  Abdominal ecography can be useful in diagnosing parenchymal penetrations [13]. Although the duodenum is often cited as a preferred impactation zone, it is not considered a complication-prone zone. Impactation zones for a FB exiting the esophagus are the pylorus, Treitz’s angle, the ileo-caecal valve, the rectosigmoidal junction. The literature differs when describing the topography of perforations, with a prevalence of ileal and rectocolic lesions and an occurring ratio of peritonitis between 31-44% . Our data showed that FB even larger than 10 cm can pass through the duodenum, thus contradicting the literature which states that the maximum dimensions allowing pyloric passage are a diameter of 2 cm and a length of 6 cm (10 cm according to some authors) . Our morbidity rate is inferior to that of a 33-case study of accidental ingestions, where it reached a level of 57.6%, but mortalities are comparable [ 11. Conclusions Self-harm is predominantly encountered in the penitentiary environment, FB ingestion being its most frequent manifestation. In contrast to accidental FB ingestion, both the time between ingestion and seeking medical care and the properties of the FB are different in cases of deliberate ingestion, leading to a particular type of lesions. Surgical sanction per se does not imply complex operations, but postoperative evolution may be negatively affected by continued self-aggression, either by repeating the FB ingestion, or by deliberate early parietal and even visceral suture ripping. Better life conditions inside the penitentiary, perfecting ways of efficient communication between prisoners and staff, education and skill-learning amongst the inmates, innovative, civilized and healthy ways of spending free time, respect of inmate rights according to the law, could all lead, in time, to a decrease in the number of self-aggression cases. Disclosures - None


We, and others, have shown that experimenting with cigarettes is a function of both non-genetic and genetic factors. In this analysis we ask: how much of the total risk of experimenting with cigarettes, among those who had not experimented with cigarettes when they enrolled in a prospective cohort, is attributable to genetic factors and to non-genetic factors?Participants , recruited from a large population-based cohort study in Houston, Texas, provided prospective data on cigarette experimentation over three years. Non-genetic data were elicited twice – baseline and follow-up. Participants were genotyped for 672 functional and tagging variants in the dopamine, serotonin and opioid pathways.In the overall model, the adjusted combined non-genetic PAF was 71.2% and the adjusted combined genetic PAF was 58.5%. Among committed never smokers the adjusted combined non-genetic PAF was 67.0% and the adjusted combined genetic PAF was 53.5%. However, among cognitively susceptible youth, the adjusted combined non-genetic PAF was 52.0% and the adjusted combined genetic PAF was 68.4%.Our results suggest there may be differences in genotypes between youth who think they will try cigarettes in the future compared to their peers who think they will not and underscore the possibility that the relative influence of genetic vs. non-genetic factors on the uptake of smoking may vary between these two groups of youth.A clearer understanding of the relative role of genetic vs. non-genetic factors in the uptake of smoking may have implications for the design of prevention programs. DRD2, rs10052016 on SLC6A3, and rs8119844 on SNAP25), two in the serotonin pathway (rs6297 on HTR1B and rs9567732 on HTR2A), and an opioid receptor variant (rs9322451 on OPMR1) – were significantly associated with experimenting with cigarettes OPRM1, SNAP25, HTR1B) were associated with experimentation as were all psychosocial factors. Among susceptible youth older age at baseline, living with a smoker, and three different genes  predicted experimentation Experimenting with cigarettes is a function of both non-genetic and genetic factors In the current analysis we seek to expand our previous work Detailed descriptions of the population-based cohort recruitment methodology from where our study participants were drawn To briefly review the non-genetic variables included in the current analysis, our primary outcome of interest is new experimentation assessed by two items from the Youth Risk Behavior Survey Using new experimentation vs. no experimentation as our outcome and excluding all individuals who had experimented at baseline, we compute the population attributable fraction (PAF) for each factor. The PAF provides an estimate of the percentage of overall risk in a population due to a specific risk factor In OPRM1 (28.9%), being male (22.5%), and cognitively susceptible to smoking (18.3%). In the models stratified on cognitive susceptibility (OPRM1 (31.9%) and being male (26.7%). However, among the cognitively susceptible, the adjusted combined non-genetic PAF was 52.0% and the adjusted combined genetic PAF was 68.4%. In this model, the PAF was greatest for SLC6A3 (25.8%) and older age at baseline (20.3%).In the overall model, the adjusted combined non-genetic PAF was 71.2% and the adjusted combined genetic PAF was 58.5% . In thistibility , among tIn In our study we obtained estimates of PAF for genetic and non-genetic risk factors associated with experimenting with cigarettes using a longitudinal prospective cohort study. Thus, our study design afforded the opportunity to examine incident cases of experimenting with cigarettes rather than prevalent cases. In the overall analysis we found that the influence of the non-genetic factors assessed at baseline accounted for a higher proportion of the risk associated with new experimentation compared to the genetic factors. Overall, 71% of the risk associated with smoking initiation was attributable to the non-genetic factors and 59% to the genes. It should be noted that our participants did report significant changes in the non-genetic risk factors  during the 30-month study period. In a cross-sectional analysis using all variables assessed 30 months after baseline, the association between each of these three variables and being a new experimenter remains significant while controlling for age, gender and the six SNPs (data not shown), which speaks to the stability of the modifiable risk factors during mid to late adolescence.In addition, we calculated the PAF for both genetic and non-genetic risk factors based on the baseline cognitive susceptibility status when the youth enrolled in the study. Of note, at follow-up, rates of experimentation were three times higher among the cognitively susceptible youth compared to the committed never smokers; 45% of the cognitively susceptible youth reported experimentation during the 30 month follow-up period compared to 15% of the committed never smokers The PAF provides an estimate expressed as a percent of the overall risk in a population that is due to the risk factors in question In the current study, we were interested in experimenting with cigarettes, the first step in the smoking trajectory. Accordingly, the candidate SNPs were selected based on their association with sensation seeking tendencies and risk taking behavior because both are associated with smoking initiation Our study has several strengths. The prospective design allowed us to examine incident experimentation reported during follow-up among participants who had not experimented at baseline. This is important because experimentation with cigarettes is the first step in the uptake of smoking. Further, participants were recruited from a population-based cohort Conversely, the main limitation of this study is the lack of an independent replication sample; thus we must consider our findings preliminary. In addition, although we examined a large number of SNPs, we did adjust the significance level for each SNP included in this analysis using a BFDP approach Our results need independent validation, which is not always feasible when studying minority populations, and underscore the possibility that there are differences in genotype between youth who think they will try cigarettes in the future compared to their peers who think they will not try cigarettes. Our results further underscore the possibility that the relative influence of genetic vs. non-genetic factors on the uptake of smoking may vary between these two groups of youth. While we need to be cautious when interpreting the attributable fraction in terms of intervention design


Drug use has an impact on physical and mental health. Quite often, patients attend accident and emergency (A&E) departments due to drug-use-related disorders, and this is why screening and brief intervention for substance use disorders are relevant in emergency room settings. Studies have shown that screening, brief intervention, and referral to treatment (SBIRT) programmes are effective in several medical settings, including A&E departments. Since most of the evidence has been gathered in the US, this study tries to identify the relevance of substance use disorders in a large Spanish A&E Department and the possible need to implement a SBIRT programme. All medical doctors on duty in a given day were asked prospectively about ward consultations attended during their 16 hours-shift. The questionnaire included information on patients’ substance use and if this was related to the medical visit, whether or not they thought the patient needed referral to an addiction specialist and their opinion on what may help them to improve the management of those patients. 194 patients were attended. 22 patients (11.3%) were reported as substance users. Only 2 were visited by the psychiatrist (1%). Alcohol (n=10), cannabis (n=3) and benzodiazepines (n=4) were the most reported drugs (n=10). Practitioners considered that support from an addiction specialist  and specific training in the management of substance users  as the most valuable aids. Availability of screening tools was somewhat less valued . The number of patients who present to the A&E department with a drug related-problem is large, yet these patients are scarcely seen by the psychiatrist. Practitioners see training opportunities in the management of substance users and the availability of addiction specialists as the most valued aids. Regardless, the rate of patients presenting with substance use related disorders warrants further research and suggests the convenience to implement SBIRT programmes in A&E department settings.


Adjustable CSF valves have been developed by several manufacturers. These valves are more expensive, but have an advantage in that the operating pressure of the valve can be altered by the use of an external magnet. We have used data collected by the UK Shunt Registry to assess the effectiveness of these valves in reducing valve replacement for under and over-drainage using a case-control design.The UK Shunt Registry contains data on over 55,000 CSF shunt-related procedures. Our data suggests that primary factors involved in shunt revision are patient age, diagnosis and the number of revisions a patient has had. Procedures were identified where an adjustable valve was used in a ventriculoperitoneal shunt. Of these 3,682 had an accurate diagnosis and age entered and we were able to determine the exact number of shunt revisions. A database search was performed for procedures matched for these risk factors but using a fixed-pressure valve. Matches were found for 3,262 procedures. The was dataset was further divided by the age of patient. There were 966 matched pairs aged 70 and over, 1569 matched pairs aged 17 to 69 and 727 matched pairs aged 16 and under The cumulative valve revison rate (CVRR) for up to 5 years was calculated. Only revisions where the valve was replaced for a given reason of under- or over-drainage were considered. A Logrank test was used for comparison of CVRR between adjustable and fixed-pressure valves.In the over 70 age group, adjustable valves performed significantly better (P<0.05) from week 16 up to the 5 years of calculation. In the 17-69 age groups there was a significant lower CVRR from week 7 up to 5 years. However in the younger age group (0 to 16 years) there was no significant improvement in CVRR with an adjustable valve.Our data suggest that adjustable valves may be effective in overcoming problems due to incorrect pressure selection in adult patients. However, although appears to be no significant advantage in using an adjustable valve in the majority of paediatric patients.


To identify a simple decision tree using early symptom change to predict response to atypical antipsychotic therapy in patients with  chronic schizophrenia.Data were pooled from moderately to severely ill patients (n = 1494) from 6 randomized, double-blind trials (N = 2543). Response was defined as a ≥30% reduction in Positive and Negative Syndrome Scale (PANSS) Total score by Week 8 of treatment. Analyzed predictors were change in individual PANSS items at Weeks 1 and 2. A decision tree was constructed using classification and regression tree (CART) analysis to identify predictors that most effectively differentiated responders from non-responders.A 2-branch, 6-item decision tree was created, producing 3 distinct groups. First branch criterion was a 2-point score decrease in at least 2 of 5 PANSS positive items (Week 2). Second branch criterion was a 2-point score decrease in the PANSS excitement item (Week 2). "Likely responders" met the first branch criteria; "likely non-responders" did not meet first or second criterion; "not predictable" patients did not meet the first but did meet the second criterion. Using this approach, response to treatment could be predicted in most patients (92%) with high positive predictive value (79%) and high negative predictive value (75%). Predictive findings were confirmed through analysis of data from 2 independent trials.Using a data-driven approach, we identified decision rules using early change in the scores of selected PANSS items to accurately predict longer-term treatment response or non-response to atypical antipsychotic therapy. This could lead to development of a simple quantitative evaluation tool to help guide early treatment decisions.This is a retrospective, non-intervention study in which pooled results from 6 previously published reports were analyzed; thus, clinical trial registration is not required. Over 66% of patients with chronic schizophrenia who are started on atypical antipsychotics for treatment of moderate to severe symptoms will fail to show moderate improvement after 3 months of treatment, and 1 in 3 will fail to show even minimal improvement . When trUntil recently, scientists and clinicians believed that at least 3 to 4 weeks of treatment were needed for patients to experience clinically meaningful symptomatic improvement in response to antipsychotic therapy. It has now been convincingly shown that a substantial amount of improvement occurs within 2 weeks or less of initiating treatment -8. FailuTo date, studies assessing early symptom improvement ,2,9,10 aamount of change in which of the fewest PANSS symptom measures at the earliest time in treatment is most predictive of response or non-response at Week 8 of treatment. Once a decision tree was created, we tested its validity by applying it to predict response in 2 independent studies with similar designs and patient populations.In this study we used data from 6 randomized, double-blind clinical trials of atypical antipsychotic medications for treatment of moderately to severely ill patients with chronic schizophrenia to develop a simple decision tree employing early symptom improvement to predict longer-term response to treatment. Specifically, we used classification and regression tree (CART) analysis ,15 to idData from patients who met criteria for moderate to severe illness (n = 1494) were pooled from 6 randomized, double-blind trials of at least 8 weeks duration that compared atypical antipsychotics in the treatment of adult patients with chronic schizophrenia (N = 2543). In 5 of the studies, olanzapine was compared to another atypical antipsychotic: risperidone in 2 trials ,17; ziprAll participants met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revised (DSM-IV TR) criteria for schizophrenia, schizophreniform disorder, or schizoaffective disorder, with the majority of patients having been diagnosed with schizophrenia. The mean patient age was between 35 and 45 years, mean duration of illness was >10 years, and the group mean PANSS Total scores were ≥75. Patients in 3 of the studies ,19,20 haData from the 6 pooled studies is hereafter referred to as the "learning data set." The learning data set was used to create the decision tree, which was intended for future use in predicting response to treatment for similar patients.Once the decision tree had been developed, it was validated using 2 independent studies. The first was a 12-week prospective trial to assess whether early symptom change in response to risperidone could predict subsequent clinical and functional outcomes in patients with schizophrenia . PatientAll study protocols were approved by the respective ethical review boards at participating study sites and were conducted in accordance with Good Clinical Practice and the Declaration of Helsinki guidelines. All patients or their legal guardians gave written informed consent prior to undergoing any study procedure or receiving any study treatment. The 6 pooled studies and 2 validation studies are summarized in Table 1-7, a 30-item rating instrument of positive, negative, and general psychopathology symptoms (each item scored from 1 = absent to 7 = severe) and the Clinical Global Impression-Severity (CGI-S) scale [In studies from the learning data set and the 2 trials used to assess the validity of the final model, patients had been evaluated at baseline and at all subsequent visits  using the PANSSS) scale , a cliniS) scale , a 21-it1-7 Total score by Week 8 of treatment, a threshold chosen to reflect symptom improvement that was better than "minimal"[Response was defined as a ≥30% reduction from baseline in PANSSWe used CART analysis to determine which individual components of symptom improvement (input variables) were most predictive of longer-term response. In CART analysis, a set of rules is developed for dividing a large heterogeneous population into smaller, more homogeneous groups with respect to a particular target variable . For thiTo ensure that our results were not driven by any one study or compound, we applied the model to data from the learning data set stratified by study and by compound, and calculated the positive predictive value (PPV), negative predictive value (NPV), percent not predictable, and percentage of patients who were misidentified  for each subgroup.1-7 Total score -- had not unduly affected the final tree, we repeated the analysis using thresholds for response of ≥20% and ≥40% improvement. Finally, we calculated PPV, NPV, and percentage not predictable using the same degree of change in PANSS items from the final model, but with timing of that change defined by week of treatment  to ensure that a reasonable time point to assess early improvement had been chosen.To ensure that our Week 8 threshold for response -- ≥30% improvement in PANSSTo assess the degree to which the criteria identified at Week 2 could accurately predict response beyond Week 8, we applied the model to data from Weeks 12 and 24 that were pooled from the 5 studies in the learning dataset lasting ≥24 weeks.To assess the external validity of the tree, we applied it to data from 2 independent studies with similar patient populations and calculated the resulting PPV, NPV, percentage not predictable, and percentage of patients misidentified.Baseline characteristics of likely responder, likely non-responder, and not predictable groups were compared using analysis of variance (ANOVA) and the Cochran-Mantel-Haenszel test accounting for study effect. Visit-wise mean PANSS Total scores were compared by group using ANOVA adjusted for study at each time point. As a validation of results obtained using PANSS scores, we analyzed by group the distribution of CGI-S categories  at each visit using Fisher's exact test. Changes in QLS score from baseline to Week 8 were compared between groups using ANOVA adjusted for study effect.Exploratory analyses consistently showed that the key predictors of response were whether a patient had at least a 2-point drop in any 1 of the following 5 items from the PANSS Positive subscore: 1-Delusions; 2-Conceptual Disorganization; 3-Hallucinatory Behavior; 6-Suspiciousness; and 23-Unusual Thought Content. A single composite variable was identified as the optimal first partition splitting-variable: at least a 2-point drop in at least 2 of these items.The final decision tree for predicting longer-term response to treatment based on early symptom improvement involved 2 branches and 3 predicted outcome groups: likely responders, likely non-responders, and not predictable Figure . At the The decision tree applied to the learning data set is illustrated in Figure Week 8 response was predicted more accurately by improvement in individual PANSS items at Week 2 than by improvement at Week 1 or by baseline characteristics. We also investigated the predictive value of the CGIS score at Week 2 as a single variable for predicting later response. A 2-point drop in CGI-S at Week 2 resulted in a PPV of 83% and an NPV of only 56%. Since only a small proportion of patients (5.7%) had this level of improvement by Week 2, we also calculated the predictive characteristics of a 1-point drop in CGI-I score, a level of improvement achieved by 49% of patients. The results -- a PPV of 67% and a NPV of 60% -- indicated that this degree of change in this variable was a far less reliable predictor of later response than change in a cluster of PANSS Positive symptoms at that same point in time.With regard to differences across studies Table , the ranA sensitivity analysis using ≥20% and >40% thresholds for response at Week 8 resulted in similar trees produced using a >30% threshold, except that only a 1-point improvement in each of the identified PANSS items was needed when the lower threshold was used.Of the 1008 patients pooled from the 5 studies lasting ≥24 weeks, 41% and 43% of patients demonstrated a 40% reduction in PANSS Total score at Weeks 12 and 24, respectively. When model criteria were used to predict response at Weeks 12 and 24, the PPVs were 71% and 51%, respectively and the NPVs were 86% and 82%, respectively. These results were only slightly less robust than those observed with the learning dataset (PPV = 79% and NPV = 75%).The percentage of patients who demonstrated a ≥30% response by Week 8 in the 2 independent validation studies was 37.1% for Kinon et al. , which wPositive predictive value and NPV using the described 1-branch and 2-branch decision tree models, and the percentage of patients in the learning data set who were not predictable are shown by week of treatment in Figure Baseline characteristics and illness severity for likely responder, likely non-responder, and not predictable groups are shown in Table Visit-wise mean PANSS Total score for patients in the learning data set are shown in Figure We used CART analysis on data pooled from 6 large, double-blind, randomized atypical antipsychotic therapy trials to develop a simple 2-branch decision tree that uses the amount of improvement in 6 PANSS items after 2 weeks of treatment to predict the likelihood of longer-term response to continued use of the same treatment by Week 8. When the model was applied to the data set from which it was created, we identified likely responders with 79% accuracy, likely non-responders with 75% accuracy, and a small group of patients (8%) in whom a prediction could not be made. In analyses of the 3 predicted response groups, patients identified as likely responders were more commonly female and had a shorter duration of illness than likely non-responders. They were also more severely ill at baseline across a spectrum of symptom domains, and showed significantly more rapid and more pronounced improvement over time.At the first branch of the decision tree, patients were assessed for improvement in a composite variable consisting of 5 of the PANSS Positive items. A separate analysis of individual PANSS items showed strong correlation between these 5 items, lending strength to our model. Several other prediction studies have identified early improvement in positive symptoms as the primary predictor of later response and non-response. Correll et al  evaluateFinding that early improvement in positive symptoms accurately predicted later improvement confirmed results of earlier studies and extended these results by specifying how early improvement can be assessed and how much improvement is required to accurately predict later response. We focused on a population of patients with chronic schizophrenia who were moderately to severely ill, with predominantly positive symptoms. This describes the majority of patients who come to medical attention and who are initiated or re-initiated on antipsychotic treatment, or who have their current medication changed or adjusted. Also, antipsychotics are most effective in treating positive symptoms, so a model that predicts later response based on early response is likely to include them. Whether this model applies to patients without prominent positive symptoms will be an interesting area for future research.Our decision tree was strengthened and stabilized by the large number of patients and the diversity of studies pooled to create our data set. Although each of the studies had a similar design and similar enrollment criteria, they were carried out at both U.S. and international sites, included 5 different atypical antipsychotics, and were completed over an 11-year period. An analysis of model performance by study and by compound suggested that the creation of the model had not been driven by any one study or any one compound, but rather was a reasonable reflection of its component parts.Many strategic decisions are made during a CART analysis, and the final usefulness of a classification tree depends heavily on the skill and judgment of the statisticians, scientists, and clinicians who determine the input variables and prune the output. We chose changes in PANSS item scores obtained at Weeks 1 and 2 as input variables based on prior work with prediction models. Our analysis of PPV and NPV by study week illustrates the trade-offs inherent in choosing the time for early assessment. While PPV stayed relatively stable regardless of the time point chosen, NPV improved as treatment time passed. However, in the clinical setting, delaying a treatment decision in order to improve NPV translates into additional suffering for some patients who will not improve on present therapy, so we included only very early time points as input variables. We also chose individual PANSS items as input variables rather than the PANSS Total score. The practicality of using a tool consisting of only 6 questions rather than administering the entire 30-question PANSS cannot be overemphasized.Pruning is another area where investigator input is required. A decision tree that is too complex will likely reflect the idiosyncrasies of the data set used to create it, while a tree that is too simple may not fit any data set very well. In constructing this decision tree, we considered pruning back to the first branch, at which point the NPV was 72%. By including the additional branch, we improved the NPV to 75%, but in doing so, added another level of complexity to what could have been a single-question algorithm. This balance between complexity and increased predictive value must be weighed by the practicing clinician.We found that our model was more accurate in predicting response at Week 8 based on response at Week 2 than change in the CGI-I at Week 2. Accurate prediction of later response may require a careful assessment of the current severity of a few distinct symptoms rather than a summary measure of change over 2 weeks that includes consideration of the patient's history, psychosocial circumstances, symptoms, behavior, and functioning.Constructing a decision tree using CART analysis requires having access to a very large database with a consistent set of patients , consistent study design , and consistent clinical measurements . With the appropriate database, CART analysis is a powerful method for answering clinical questions. It is non-parametric; that is, no assumptions are made regarding the underlying distribution of variables, and it permits consideration of both continuous and categorical data. It can handle data sets with large numbers of input variables, each with many possible values; identify significant high-order interactions among variables; detect and quantify non-linear relations; determine which predictors can be dropped because they contain redundant information; and handle missing values through a process of "surrogate substitution." The tree can be simplified because the response variables are, for the most part, conditionally independent of variables not in the tree. CART analysis is limited in that it can be unstable; that is, a small change in the input variables or a fresh data sample can lead to construction of a very different classification tree. In addition, CART analysis is limited in that it can account for only one response variable ,15.The answer provided by CART analysis (the percent likelihood of response) is in line with the mindset of most clinicians. After initiating treatment, clinicians routinely evaluate their patients, make judgments about their progress, and decide whether they should continue treatment or make a change. This decision tree simply provides evidence-based tools, an algorithm, and associated PPV and NPV, to help quantify the decision-making process. Clinicians must still actively consider the unique presentation of each patient; for example, a patient with a high probability of failure as determined by the algorithm may benefit from continuing the treatment if they have already failed several previous medications and a patient with a high probability of success may have treatment discontinued due to an intolerable adverse event.We recognize that when outcomes have been categorized as dichotomous (response/non-response), other approaches such as logistic regression can be used to create a predictive model. The strengths of CART compared with logistic regression are worth noting in this setting. First, CART analysis offers a great deal of flexibility because it requires very few assumptions about the data. Due to its natural branching process, CART is able to handle many dozens of potential predictor variables as well as potential interactions between predictors and non-linear effects of individual predictors, and missing data. CART also has algorithms that account for missing data amongst the predictors; whereas with logistic regression, entire observations are eliminated if a single predictor is missing. Furthermore, at the end of a CART analysis, subgroups (responders and non-responders) are specifically defined due to the variable selection and cut-offs created by the methodology. With logistic regression, significant predictors are identified, but the coefficients do not automatically define subgroups of patients. Finally, our experience suggests that the stepwise, dichotomous approach that is used with a decision tree more closely mimics clinical decision-making than use of odds ratios which indicate association, but do not clearly direct treatment decisions.Two patient populations identified in our analysis deserve special attention from a clinical standpoint: first, the 8% of patients in whom a prediction could not be made and second, the 24% who were misidentified. The decision criteria laid out in this model allow for a category of patients  in whom the prediction of later response is no better than a coin toss. Fortunately, this population is relatively small, <10% in 14 of the 15 analysis subsets . Of those patients in the learning data set who were misidentified, the majority was identified as non-responders, but eventually did respond. The clinical ramifications of changing treatment when no change is needed versus continuing use of an ineffective treatment must be carefully weighed by all those involved.Despite having developed this model using response data from Week 8, the strength of the model was diminished only slightly when used to predict response at Weeks 12 and 24. Further research is needed to see if a model derived using response data following longer-term treatment data might have greater accuracy in predicting response beyond Week 8.Currently, this decision tree is appropriately used only for adult patients with chronic schizophrenia who are moderately to severely ill. It needs to be tested in other patient populations, such as those with chronic schizophrenia who are only mildly ill, and in patients with first episode psychosis. In addition, it needs to be validated in a prospective study design and to be assessed for inter-rater reliability. It is possible that introduction of additional variables, functional measurements or patients' perception of benefit, for example, would have altered the final decision tree. Further model building using data from different populations and different input variables is needed to establish the stability of this particular decision tree and to perhaps construct a tree with even better predictive characteristics and user-friendliness. In addition, were a tool such as this decision tree to be developed for use in the clinical setting, input on clear and effective phrasing would be needed from clinicians. For example, would verbal descriptions such as "substantial improvement" or numeric descriptions such as "a 2-point drop" be most preferred?We used CART analysis of a large pooled data set to construct a simple decision tree that was found to be predictive of later response/non-response based on improvement in 6 individual PANSS items over 2 weeks of treatment. These findings could serve as a foundation for development of a clinically relevant evaluation tool for guiding treatment decisions early in the course of atypical antipsychotic drug therapy.All authors are employees of Eli Lilly and Company and/or one of its subsidiaries, and all authors own stock in Eli Lilly and Company.Authors SR and LC conceived of and designed this study. Author LC performed the statistical analysis. Author SR helped to draft the manuscript. All authors substantially contributed to interpretation of the data and critically revised the manuscript, and read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/11/23/prepub


Male mice emit ultrasonic vocalizations (USVs) towards females during male–female interaction. It has been reported that USVs of adult male mice have the capability of attracting females. Although the waveform pattern of USVs is affected by genetic background, differences among strains with respect to USV and the effects of these differences on courtship behavior have not been analyzed fully. We analyzed USV patterns, as well as actual social behavior during USV recording, in 13 inbred mouse strains, which included laboratory and wild-derived strains. Significant effects of strain were observed for the frequency of USV emission, duration, and frequency of the waveform category. Principal component (PC) analysis showed that PC1 was related to frequency and duration, and PC2–4 were related to each waveform. In the comparison of USV patterns and behaviors among strains, wild-derived KJR mice displayed the highest scores for PC2–4, and female mice paired with KJR males did not emit rejection-related click sounds. It is assumed that the waveforms emitted by KJR males have a positive effect in male–female interaction. Therefore, we extracted waveforms in PC2–4 from the USV recordings of KJR mice to produce a sound file, "HIGH2-4". As a negative control, another sound file ("LOW2-4") was created by extracting waveforms in PC2-4 from strains with low scores for these components. In the playback experiments using these sound files, female mice were attracted to the speaker that played HIGH2-4 but not the speaker that played LOW2-4. These results highlight the role of strain differences in the waveforms of male USVs during male–female interaction. The results indicated that female mice use male USVs as information when selecting a suitable mate. Social communication between conspecific animals is one of the behaviors that are essential for survival and breeding. A number of reports have shown that mice use ultrasonic vocalization (USV) in several social contexts  from the Committee for Animal Care and Use in NIG.+, KJR, MSM, NJL, PGN2, and SWN, +, and MSM) have been established as inbred strains after 20 generations of brother–sister mating at the National Institute of Genetics . CAST/Ei was obtained from The Jackson Laboratory, USA. BALB/cAnN (BALB/c) was obtained from the National Institutes of Health (NIH), USA. Given that JF1 mice are known to have an auditory disability, we used a spontaneous revertant, JF1-s+. As a consequence, the mice have a black coat color and normal auditory perception. Mice of the strains C57BL/6JJcl (B6) and MCH were purchased from CLEA Japan, Inc. . The B6 mice were bred at the NIG and used for the experiments. Females of hybrid mice MCH, produced by crossing four different inbred strains that originated in ICR outbred stock, were used for social interactions for recording the ultrasound signals. All animals were maintained at the NIG under a 12 h light/dark cycle (light from 8:00 to 20:00) in a temperature-controlled room (23±2°C).Ultrasound emissions from male mice of 13 inbred strains  and recorder . The microphone was positioned approximately 10 cm above the cage that contained the mice. At the same time, the mice were recorded with a digital video camera .Two-month-old, male mice were mated with female mice of their own strain for 1 month. After 1 month, the male mice were housed individually for 2 days. Female mice of MCH that were older than 10 weeks were injected with pregnant mare serum gonadotropin (PMSG) to control the sexual cycle. On the test day , each male mouse was transferred to a small cage (12×20 cm) with its wood chip bedding , and then an MCH female mouse was introduced into the small cage. Immediately after the female had been introduced, recording of sound and video was started. Sounds and movies were recorded for a maximum of 15 min. Recording was stopped 3 min after the male started vocalizing, but if vocalization was not present, then recording was terminated after 15 min. During this test, intromission and ejaculation were not observed, because the session was a maximum of 15 min long. If the male mouse did not emit USV, it was returned to its cage, together with a female of same strain. One or a few weeks later (minimum 1 week), this male mouse was tested again. Recording was performed during the late part of the light phase, 14:00–18:00 pm.The sound data obtained were analyzed using SAS Lab Pro (Avisoft). Spectrograms were generated by a fast Fourier transformation . The call duration and frequency at the start point, center point, end point, maximum point, and minimum point were calculated for each individual call. From these data, we assigned the call to a waveform category, as defined by Male–female interactions were scored from the video data for the number of occurrences of each of the following types of behavior: grooming, sniffing of genitals, body sniffing in both sexes; attacking (biting and kicking) and mounting by males; and avoidance behavior and clicks in females. These behaviors were observed for 3 min from the first USV.Devocalization of males was performed to verify the absence of USV in females under our test conditions. All animals were anesthetized with sodium pentobarbital. Male mice from the B6 strain, which vocalize frequently, were operated on, and the inferior laryngeal nerve was bilaterally sectioned. Another group of male mice were operated on in the same manner but the inferior laryngeal nerve was not sectioned; these were used as sham-operated control mice We created the USV file HIGH2-4 by cutting and pasting recordings of USVs emitted by KJR mice, which is the highest scoring strain of PC2–PC4, on the basis of high factor loadings for PC2–PC4 (>0.4). Another USV file (LOW2-4) was created by extracting waveforms of USVs on the basis of characteristics of PC2–PC4 from the low score strains, B6, BFM/2 and BALB/c. We created the white-noise file by extracting background noise that did not contain any USVs emitted by mice.We used 10-week-old MCH females for the playback experiment. Two days before the test, female MCH mice were injected with PMSG to control the estrous cycle. Subsequently, 24 hours before the test, the mice were transferred into the test room. The test box  consisted of three compartments, a neutral zone (15×20 cm) and sound zones 1 and 2 (20×10 cm each) http://www.R-project.org). ANOVA was used to determine the effect of strain on each USV parameter . MANOVA was used to determine the effect of strain on waveform categories. Pearson's chi-square test was used to test the effect of strain on the frequency of the USVs. PCA was used to analyze the structure of the USVs. For PCA, each data point was standardized at mean 0, variance 1. Variables with missing data were omitted, and variables that showed high correlation (R>0.9) were combined into one variable. We analyzed components responsible for more than 5% of the variance. For cluster analysis, a distance matrix was calculated by the Euclidean method and the dendrogram was constructed by the Ward method.All statistical analysis was performed using the R package  We performed PCA to simplify the differences among strains, using variables that had shown a significant strain effect in the ANOVA. In the analysis, highly correlated variables (R>0.9) were combined into one variable. More than 90% of the variance in the data was explained by principal components (PC) 1 to 5 (PC1–PC5) . We inteIn the second stage of this study, we investigated the role of the differences in USV patterns with respect to male–female social interaction. ANOVA revealed no significant effect of strain on any of the behavioral components including the positive behavior of females toward males , a domesticated strain, is much more complex than that of the wild strain, the white-rumped munia (Lonchura striata). This complexity of male songs in domesticated birds is thought to result from artificial selection for reproductive advantage There were major differences between laboratory mice and wild-derived mice with respect to call latency and percentage of mice who emitted USVs. The results indicated that laboratory strains emitted USVs much more readily than wild-derived strains. In song birds, clear differences in singing pattern have been reported between wild and domestic birds In this study, we characterized differences among 13 inbred strains with respect to the waveform patterns of male USVs, as well as the social behavior of males and females during the recording of the USVs. By analyzing video images as well as sound data, we found that clicks emitted by females correlated with avoidance behavior in the females in response to the males. We speculate that this avoidance behavior in females results from a composite effect of USV and other behavior during male–female interaction. As a consequence, the causes of avoidance behavior exhibited in females should be considered carefully. In spite of these considerations, it is important to examine the contribution that differences in USV patterns among strains make to male–female interaction in order to understand some of the cues involved in the interaction.In this study, we demonstrated that particular patterns of USV waveforms have a role in attracting females, although other types of waveform do not. As the correlation between click and PC1 (duration and frequency) was weak, the duration and frequency may not influence the preference of females to a large extent. To clarify this point further, we will need to create artificial USV files which are different only in waveform and not in duration and frequency. Such a playback experiment using these different artificial USV files may generate more direct results for female preference based on waveforms. Thus, the present results indicate an important role of strain differences in USV patterns with respect to the rate of success of males when searching for a mate. A recent report showed that different patterns of USV waveforms induce unique patterns of response in the neurons of the inferior colliculus, even if the waveforms of USVs are very similar to each other It has been reported that mice tend to avoid inbreeding through discrimination on the basis of USVs In this study, we investigated USVs during male–female social interaction behavior. Our results did not indicate any role for USVs in the actual mating behavior of mice. It has been reported that bird song changes the physiological state and mating behavior of female birds, and thus contributes to mating success Figure S1Behavior during male–female interactions. A. Behavior of males. B. Behavior of females. Data indicate means ± standard error.(PDF)Click here for additional data file.Table S1Criteria of classification for ultrasonic vocalization patterns.(PDF)Click here for additional data file.Table S2P-values from ANOVA for variables of each waveform category.(PDF)Click here for additional data file.