Weight loss induced only by exercise is frequently less than expected, possibly because of compensatory changes in energy intake and/or energy expenditure. The purpose of the Examination of Mechanisms (E-MECHANIC) of Exercise-Induced Weight Compensation trial is to examine whether increased energy intake and/or reduced spontaneous activity or energy expenditure (outside of structured exercise) account for the less than expected, exercise-associated weight loss.2 to ≤45 kg/m2) men and women ages 18 to 65 years. The exercise dose will be supervised and tightly controlled in an exercise training laboratory. The primary outcome variables are energy intake, which will be measured using doubly labeled water (adjusted for change in energy stores) and laboratory-based food intake tests, and the discrepancy between expected weight loss and observed weight loss. Secondary outcomes include changes in resting metabolic rate (adjusted for change in body mass), activity levels (excluding structured exercise) and body composition. In an effort to guide the development of future interventions, the participants will be behaviorally phenotyped and defined as those who do compensate  or do not compensate .E-MECHANIC is a three-arm, 6-month randomized (1:1:1) controlled trial. The two intervention arms are exercise doses that reflect current recommendations for (1) general health , about 900 kcal/wk) and (2) weight loss . The third arm, a nonexercise control group, will receive health information only. The sample will include a combined total of 198sedentary, overweight or obese .ClinicalTrials.gov ID:  The prevalence of obesity is increasing worldwide. It has become an epidemic and is associated with many chronic diseases ,2. Althoet al. examined the role of supervised exercise in weight loss over the course of 16 months in college-aged men and women [The potential explanations for the failure of exercise training to produce the expected weight loss in long-term studies include poor exercise regimen adherence, reduced spontaneous activity, reduced resting metabolic rate and/or increase in energy  intake. The results of well-controlled studies do not suggest that poor adherence is the source of the discrepancy between actual and expected weight loss. For example, Donnelly nd women . The reqnd women -6.et al. noted that a large amount of variability in the degree to which people increase energy intake in response to exercise. Hence, people can be categorized as “compensators” and “noncompensators,” in this regard; yet, little is known about the characteristics of these groups or the mechanisms responsible for compensation [et al. emphasized the need to examine weight compensation in response to exercise and develop individualized interventions in order to prevent less-than-expected weight loss [Researchers who have examined engaging in exercise for weight loss have found equivocal results showing less-than-expected changes in body weight, particularly when associated with high doses of exercise, with relatively higher interindividual and between-sexes variability . The mecensation . In accoght loss . These rPhysical Activity Guidelines Advisory Committee Report[The US Department of Health and Human Services ee Report, in conjee Report. The 200ee Report. AlthougAn important limitation of the extant literature is the overall short duration of the exercise interventions and the general lack of accuracy of measurements of energy balance  . FurtherThe study protocol was approved by the Institutional Review Board of the Pennington Biomedical Research Center . The ClinicalTrials.gov identifier is NCT01264406.The E-MECHANIC study is a three-arm, 6-month randomized controlled trial (RCT). The selected exercise doses reflect current recommendations for (1) general health , or approximately 800 to 1,000 kcal/wk) and (2) weight loss  . A nonexdiff) differs between the two exercise groups (8 KKW vs. 20 KKW) and between the control and 20-KKW groups and (3) to determine whether change in energy intake mediates Wt Lossdiff.The primary objectives of the E-MECHANIC trial are (1) to test whether energy intake increases in response to either or both of two doses of exercise (8 KKW vs. 20 KKW), (2) to test whether the discrepancy between observed and expected weight loss  to determine whether change in resting metabolic rate ) and activity levels (excluding structured exercise) differ across treatment groups; (2) to determine whether change in body composition differs by exercise dose; and (3) to characterize participants who compensate  and compare them with those who do not compensate .2 to ≤45 kg/m2). The exclusion criteria are current consumption of more than 14 alcoholic drinks per week, smoking within the past 6 months, pregnancy, having been pregnant within the past 6 months, breastfeeding, history of weight loss surgery ), current participation in a weight loss program, medical condition such as diabetes and cardiovascular disease  and inability to complete the study within the designated time frame because of plans to move out of the study area.The recruitment goal in the E-MECHANIC trial is a combined total of 198 men and women. The inclusion criteria are being sedentary  and being overweight or obese (BMI ranging from ≥25 kg/mdiff. Energy intake will be measured using two methods: doubly labeled water (DLW) and laboratory-based food intake tests. The secondary outcomes are changes in RMR, objectively measured activity levels (excluding structured exercise) and body composition. The primary assessments will occur at baseline and at week 24, with identical procedures followed at both assessment periods, and a truncated assessment period at week 4 to allow for identification of potential short-term changes in the study outcomes. Table The E-MECHANIC trial will have three assessment periods during which procedures to collect data for the primary and secondary study outcomes will be carried out. The primary outcomes are 1) energy intake and (2) Wt Loss energy iThe trial will include a multistage screening process prior to participant enrollment consisting of a 3- to 4-week period that includes 1 week of run-in. Following an initial web and/or telephone screen to determine eligibility and provide the potential participants with a brief description of the study, the potential participants will be scheduled for an orientation session where detailed information about the study will be provided. The detailed information will include the number and type of assessments, the length and nature of the exercise training and the time commitment required to complete the study. Next, potential participants will undergo three run-in visits meant to reduce attrition by allowing them to determine if they will be able to schedule the study into their weekly routine. All three run-in sessions must be completed within the 1-week period to satisfy the eligibility criteria. During screening, informed consent will be obtained from each eligible participant by study staff. Staff members will also verify that eligible participants are not currently meeting public health physical activity guidelines   by havinAfter being screened into the trial, participants will complete the comprehensive baseline assessment and will be randomly assigned to one of the exercise arms (8 KKW or 20 KKW) or to the control group (nonexercise arm). Participants will be enrolled at a 2:1 female/male ratio and randomized separately to one of the three intervention groups at a 1:1:1 ratio to obtain an equal number of women and men in each treatment group. Participants who drop out  will not be replaced. Participants in the 8-KKW and 20-KKW intervention groups will continue to attend exercise sessions during week 4 and week 24 assessment periods. Participants who have not met the intervention energy expenditure goals by week 24 will be allowed to exercise up to 3 weeks after the week 24 assessment period.The intervention will commence within 48 hours after a participant’s randomization and span 24 weeks.Participants assigned to the control group will receive multimedia health information twice weekly by text messaging or e-mail throughout the study period. The health information covers a variety of topics, including stress management and the benefits of eating fruits and vegetables -19. Mont2peak). All exercise training will occur on a treadmill. The exercise prescription will initially be based on peak oxygen uptake determined by giving participants a graded treadmill exercise test. In brief, the treadmill speed will start at 2.4 mph on a level grade for 2 minutes, and then increases in speed and/or grade will be applied to produce about a 1 metabolic equivalent of task (MET) increase in workload every 2 minutes until volitional fatigue is reached. During exercise training sessions, the speed and grade of the treadmill will be altered to keep the participants within their target heart rate range (65% to 85% of VO2peak), and the participants will be allowed to select their exercise intensity within that range. On the basis of speed, grade, participant’s weight and standard American College of Sports Medicine (ACSM) equations, energy expenditure will be calculated in real time and the length of each exercise session will be adjusted to meet the daily caloric goal [In the 8-KKW and 20-KKW groups, the exercise intensity for each participant will be set at a target heart rate associated with 65% to 85% of peak oxygen uptake  while exercising and appropriately adjust exercise time. Within the first week of the exercise intervention and once every 2 to 4 weeks thereafter for the remainder of the intervention, trained personnel will measure caloric expenditure rate using a metabolic cart while the participant is walking on a treadmill at a predetermined speed and grade. The metabolic cart data will be used to (1) verify the accuracy of the ACSM equations on an individualized basis, (2) adjust the participant’s daily exercise time to account for changes in metabolic or biomechanical efficiency that may occur due to the exercise training and (3) ensure that calories carried over from previous weeks remain equivalent throughout the study. By correcting the daily exercise time, we can precisely increase or decrease the amount of calories expended per session to match the prescribed weekly exercise AEE.During exercise training sessions, exercise intensity will be monitored by using a heart rate signal from a Polar transmitter placed around the participant’s chest  and recorded every 5 minutes along with the participant’s subjective rating of perceived exertion using the Borg Rating of Perceived Exertion scale. Participants will be allowed to read, listen to music, watch television or socialize with study staff while they exercise. The training program consists of a 3-minute warm-up at a standard intensity level, and then the prescribed training intensity will be initiated. We expect that most participants randomized to the 8-KKW group will be capable of exercising at their required dose during the first week of exercise. Participants randomized to the 20-KKW group may not be able to complete their assigned dose of exercise immediately upon entry into the study. In the 20-KKW group, the exercise dose will progress from 8 KKW during the first week, to 14 KKW during the second week and ultimately to the full prescription (20 KKW) during the third week. By the end of the third week, participants will be expected to perform 100% of their weekly exercise dose for 3 or 4 days in the 8-KKW group and for 3 to 5 days in the 20-KKW group.Table During the E-MECHANIC trial, we will rely on two complementary, state-of-the-art (“gold standard”) methods to measure energy intake: DLW and laboratory-based food intake tests. Energy intake will be measured twice during 2-week periods at baseline (days −14 to 0) and week 24 (days 160 to 174) with DLW, which is used to obtain an accurate measure of daily total energy expenditure (TEE). During a period of energy balance, TEE is equal to energy intake ,22. WhenBecause DLW cannot quantify macro- and micronutrient intake , we will quantify energy and nutrient intake in controlled conditions using laboratory-based food intake tests. The strengths of this method include reliability  of food intake measurements and the ability to empirically test the effect of interventions on food intake . Food anad libitum sandwiches, potato chips, cookies, water and choice of artificially sweetened or sugar-sweetened soda or tea. Participants will then return 5.5 hours after the start of their lunch to complete their dinner meal, which will consist of a buffet meal of 18 high- and low-fat foods as previously described [The food intake tests will occur in a laboratory under controlled conditions at baseline (day −4), week 4 (day 28) and week 24 (day 170). On the morning of food intake testing, participants will be instructed to consume a provided 190-kcal nutrition bar between 7:00 AM and 8:00 AM. Each participant will consume the same breakfast item on all three food intake test days. Participants will enter the laboratory between 11:00 AM and 12:00 PM to complete a test lunch, which will consist of escribed . Week 24escribed . We willObserved weight loss will be calculated based on the difference in body weight from baseline to follow-up. Expected weight loss will be calculated using two methods. The first method assumes that 1 kg of weight represents 7,700 kcal of energy. Calories expended during supervised exercise is divided by 7,700 kcal to derive expected weight loss  . The secet al. [Indirect calorimetry will be used to measure RMR on day −14 and day 160. Energy expenditure will be adjusted for change in BMI, as outlined by Martin et al. ,38. Eachet al. .SenseWear armbands will be used to measure physical activity and sedentary behavior at multiple time points throughout the study, including screening, the 2-week DLW period during baseline, week 4 and week 24. The armband is a small device that fits on the upper arm and has five types of sensors that continuously record data: a two-axis accelerometer, two galvanic skin response sensors, a heat flux sensor, a skin temperature sensor and a near-body temperature sensor. Body weight, height, handedness, age, sex and smoking status will be used in the calculation of energy expenditure, which will be carried out using proprietary software. The armband also records the length of time that it was worn on the body, which will allow us to detect noncompliance with data collection procedures. Activity data will be summated per day and displayed in 1-minute epochs, allowing data from days with poor compliance to be imputed or eliminated from the statistical analyses. Participants will be instructed to wear the armband 24 hours/day and remove it while showering, bathing or swimming. The participants randomized to one of the two exercise groups will be instructed to wear the armband during the exercise sessions. If the participant is <95% compliant with wearing the armband during screening or week 4 of testing, they will be asked to wear the armband for an additional 7 days. During the midpoint of baseline (day −7) and week 24 of testing (day 167), the participant’s armband will be checked for compliance. If found to be noncompliant, the participant will be counseled by study staff about the importance of wearing the armband as directed. Participants can be excluded prior to randomization for armband noncompliance.In addition to quantifying duration on the body, the SenseWear armband records key aspects of sedentary and active behaviors, including components of posture allocation, such as (1) number of minutes lying down per day, (2) number of minutes of sleep per day, (3) number of steps taken per day and (4) minutes per day spent in activities of different intensities . The armband quantifies activity intensity on the basis of MET cut points; for example, ≤3.0 METs is classified as sedentary behavior. The armband also quantifies (1) TEE, (2) RMR, (3) activity energy expenditure (AEE) (4) mean MET cut point and (5) minutes per day spent in physical activity. Importantly, the armband quantifies aspects of active and sedentary behaviors not captured by other methods, namely, DLW and RMR. The armbands will be used to quantify important changes in active and sedentary behaviors in response to exercise (between the 8- and 20-KKW groups and between the control and 20-KKW groups). These armband data will also be used to determine if there have been different changes in active and sedentary behaviors in response to exercise between compensators and noncompensators.Extensive validity data on the SenseWear armband have been published. These data show that the armband could be used to estimate TEE more accurately than DLW over the course of 10 days in free-living conditions  and alsoMeasures of body composition, including fat and lean mass, will be assessed by dual-energy X-ray absorptiometry (DXA)  at baseline (day −14 and day 0) and week 24 (day 160 and day 174). Lean mass measurements taken with DXA are used to adjust RMR for alterations in body composition . MeasureA secondary objective of the E-MECHANIC trial is to phenotype the behavior of participants in order to characterize compensators and noncompensators. This has been identified previously as an area in need of further study . We careVisual Analogue Scales (VAS) are administered to measure subjective ratings of appetite . SpecifiThe following instruments will be used to asses constructs associated with eating attitudes and behaviors: (1) the Multifactorial Assessment of Eating Disorders Symptoms (MAEDS), (2) the Eating Inventory (EI), (3) the Food Preference Questionnaire (FPQ), (4) the Food Craving Inventory (FCI) and (5) the Yale Food Addiction Scale (YFAS). The MAEDS is a 56-item measure of eating-disordered behavior that assesses depression, binge eating, purgative behavior, fear of fatness, restrictive eating and avoidance of forbidden foods ,49. The To measure constructs related to the propensity for physical activity and attitudes about general health, a number of valid questionnaires will be administered to participants. First, the tendency to participate in movement throughout the day will be assessed using the Activity Temperament Questionnaire ,54. Secodiff. In our power analyses, we will make the following assumptions: (1) power ≥0.90 and above is ideal; (2) the significance level under the null hypothesis will be set at α = 0.05; (3) the power analyses will be based on the sample size expected at the end of the study ; and (4) null hypotheses will be tested against two-directional  alternative hypotheses. We will rely on conservative estimates of effect size and liberal estimates of variance, as well as the assumption that 1 kg of weight represents 7,700 kcal energy, in our power analyses [The power analyses will focus on the primary outcomes, that is, energy intake (measured with DLW and laboratory-based food intake tests) and Wt Lossanalyses . We expe = 0.05;  the poweThe power analyses for the two methods by which energy intake will be measured rely on the effect sizes given in Table diff comparisons.The effect sizes for the calculations and the power analyses are summarized in Table Analytic data will be archived in an electronic file. Prior to locking the file against further change, we will inspect all data for completeness, proper range and internal consistency. This data-cleaning process will include mock analyses by an analyst blinded to intervention group assignment. Any changes made will be documented in permanent records. Statistical analyses will be performed only after careful consideration is given to the assumptions underlying the statistical methods employed, using model diagnostics such as quantile plots of studentized residuals, component-plus-residual plots and examination of leverage points and outliers.P-values when appropriate. All statistical analyses will be performed using SAS version 9.3 statistical software .First, baseline participant characteristics  will be summarized for each intervention group as counts and percentages for categorical variables and as means and 95% confidence intervals for continuous variables. The statistical significance of differential longitudinal changes in response to interventions will be assessed by employing repeated-measures mixed-effects models with maximum likelihood estimation and Kenward-Roger adjustment to the degrees of freedom of the relevant test statistic. The three interventions will be compared across assessment times . Intervention, race, sex and assessment times will be taken as fixed effects. Participants within groups will be considered as having random effects. Covariates such as age and baseline assessments will be included in preliminary models and retained in the final analytic models if warranted. The model covariance structure  across time will be investigated to enhance the efficiency of the statistical tests. The results for each outcome variable will be summarized as least-squares means and 95% confidence intervals for each intervention group across the assessment times. Model residuals will be tested to see if distributions are approximately Gaussian and data transformations  will be performed if needed. Group differences in baseline characteristics will be tested, and corrective steps, such as poststratification and addition of model covariates, will be taken if necessary. Linear contrasts on least-squares means will be used to test pairwise differential mean changes in each outcome measure from baseline to subsequent assessment times between the three intervention groups. Null hypotheses will be tested against two-directional alternatives at the nominal 0.05 significance level using Tukey-Kramer–adjusted compensators will be defined as those who fail to lose the expected amount of weight during the exercise trial and noncompensators will be defined as those who meet or exceed the expected amount of weight loss during the trial. Differences between the groups on the following variables will be tested: (1) baseline values derived from self-report instruments, (2) baseline energy intake measures  and (3) baseline activity levels (quantified by SenseWear armband readings). In addition, differences between compensators and noncompensators will be evaluated for sex and race disparities, and compensators and noncompensators will be compared with respect to changes in outcomes from baseline to week 24.Second, we will characterize compensators vs. noncompensators, where diff. We will follow the methods of Baron and Kenny [diff is predicted by group, with grouping variables coded as 8 KKW vs. 20 KKW and control vs. 20 KKW. Subsequently, we will test whether change in energy intake predicts Wt Lossdiff. Assuming that the results are significant, we will then determine whether group significantly predicts Wt Lossdiff independently of energy intake. If group does not independently predict Wt Lossdiff, then the relationship between group and Wt Lossdiff may be attributable to change in energy intake. That is, we will then conclude that change in energy intake mediated the relationship between group and Wt Lossdiff. If the relationship between group and Wt Lossdiff is reduced after accounting for change in energy intake, then partial mediation will have been demonstrated.As an initial step in a mediator analysis, we will test whether changes in energy intake mediate or partially mediate the relationship between group assignment (exercise dose) and Wt Lossnd Kenny  and deteWeight gain and obesity are the consequence of positive energy balance, which occurs when caloric intake exceeds calories expended . PhysicaThe E-MECHANIC study will fill a major gap in the current literature. We will accomplish this by examining the role of exercise in weight loss using optimal measures of all aspects of energy balance . To the best of our knowledge, no studies to date have been designed specifically to assess the effect of exercise dose on energy intake and expected versus actual weight loss. We expect that our findings in this trial will have significant public health implications because we will thoroughly evaluate whether and why current exercise recommendations for weight loss do not promote the level of weight loss expected based on the amount of exercise performed. We anticipate that the results of the E-MECHANIC study will provide important information related to the effects of exercise on energy intake and body weight by elucidating the mechanisms underlying exercise-induced weight compensation. Additionally, the participants in the E-MECHANIC study will be overweight and obese men and women, who are thus at risk for the negative health conditions associated with excess adiposity, namely, CVD and diabetes. Thus, the study sample precisely captures sedentary adults who may benefit most from regular exercise. These individuals are likely to be prescribed exercise regimens based on current guidelines. We anticipate that the results of the E-MECHANIC trial will clearly identify energy balance targets to prevent compensation and promote effective weight loss and management.This trial is currently recruiting participants and was approximately 79% complete as of June 2014.ACSM: American College of Sports Medicine; AEE: Activity energy expenditure; ATQ: Activity Temperament Questionnaire; BMA: Body Morph Assessment; BMI: Body mass index; CBC: Complete blood count; CBS: Current body size; CHBS: Compensatory Health Beliefs Scale; CVD: Cardiovascular disease; DLW: Doubly labeled water; DXA: Dual-energy X-ray absorptiometry; ECG: Electrocardiography; EE: Energy expenditure; EI: Eating Inventory; FNDDS: Food and Nutrient Database for Dietary Studies; HIPAA: Health Insurance Portability and Accountability Act; HR: Heart rate; IBS: Ideal body size; KKW: Kilocalories per kilogram of body weight per week; MAEDS: Multifactorial Assessment of Eating Disorders Symptoms; MET: Metabolic Equivalent of Task; mph: Miles per hour; OR: Orientation; PSQI: Pittsburgh Sleep Quality Inventory; RBS: Realistic body size; RCT: Randomized controlled trial; RI: Run-in; RMR: Resting metabolic weight; SV: Screening visit; TEE: Total energy expenditure; USDA: US Department of Agriculture; VAS: Visual Analogue Scale; YFAS: Yale Food Addiction Scale.The authors declare that they have no competing interests.CAM was responsible for data collection and analysis and manuscript writing. WDJ was responsible for the conception and design of the study, data collection and analysis and manuscript writing. CPE was responsible for the conception and design of the study and critical revision of the manuscript. JCR was responsible for the conception and design of the study, data collection and analysis and critical revision of the manuscript. CTL and NMJ were responsible for the conception and design of the study and manuscript writing. SC and MH were responsible for data collection and analysis and critical revision of the manuscript. TSC and CKM were responsible for the conception and design of the study and manuscript writing. All authors will be responsible for conducting the trial. All authors read and approved the final version of the manuscript.


Lungs and liver are the most common sites of colorectal cancer metastases after regional lymph nodes, but metastases to unusual sites are reported. Heterotopic bone formation in soft tissues from colorectal cancer is a rare metastatic occurrence.We present the case of a 29-year-old Caucasian man affected by colon adenocarcinoma with multiple soft tissue metastases visualized by magnetic resonance imaging, computed tomography scan and scintigraphic bone scan. We suppose that these findings can be due to the fact that the tracer is concentrated in the ossification nidus of soft metastases.Our experience suggests that, in the presence of colon adenocarcinoma, a bone scan could be a sensible tool to highlight bone lesions or heterotopic bone nidus in soft tissues and that any subcutaneous lesion should be resected to avoid underestimating a potential malignancy. The clinical presentation of colorectal cancer is usually related to abdominal symptoms, even if peculiar presentations are reported , 2, and A 29-year-old Caucasian man, in good health until February 2010, underwent several diagnostic clinical examinations for dizziness and headache. He was therefore visited by different specialists who, assuming an inner ear disorder, recommended vestibular tests with no pathological results.While he was undergoing all the tests to investigate the origin of his headache, he noticed two oval hard subcutaneous masses in his left lower limb, one in his thigh and the other in the lower third of his leg. According to his doctor, he underwent a specific ultrasound examination which demonstrated the presence of nodular hyperechoic inhomogeneous lesions with posterior attenuation and the cleavage plane with the bone surface could not be evaluated.For this reason a magnetic resonance imaging of his leg was performed and showed that the oval structure presented a diffusely hyperintense signal on T1-weighted (1.8×1cm) in his peroneal muscles, which was connected by a narrow bridge (4×2mm) to the extensor digitorum longus and the bone surface was preserved.He then underwent surgical exeresis of the fusiform lesion in the peroneal muscle of his left leg.Histology demonstrated macroscopically a grey-brown lesion (3×1.5×1cm); after it was cut a whitish mass was observed with well-defined margins and yellowish streaking. Microscopic examination showed the presence of large necrotic areas of infiltration of striated muscle and bone tissue which was marked by alcian blue on the margin of the resection. The pattern of the fusiform mass was that of an unspecified adenocarcinoma. There were no deposits of mucin; these elements could not suggest the origin so his lung was then suspected as the source.Immunohistochemical examination was mandatory. The neoplastic cells were negative for thyroid transcription factor 1 (TTF-1) and cytokeratin (CK) 7, whereas they were positive for carcinoembryonic antigen (CEA) and CK20 and the protein encoded by the caudal type homeobox 2 (CDX2), therefore suggesting a colonic source contrary to the initial suspicion of lung adenocarcinoma.He underwent blood tests which showed in particular neutrophilia, increased alpha amylase levels (162U/L), lipase 247UI/L, fibrinogen (589mg/dL), CEA 270 (<5ng/mL), tissue plasminogen activator>4000 (<85U/L), carbohydrate antigen (CA) 19–9 >210 (<37IU/mL), beta-human chorionic gonadotropin 0.60 (<5mU/mL), CA72-4>100 (<37U/mL), neuron-specific enolase 29 (<10ng/mL) and CK fragment>62 (<3.3ng/mL).He then underwent a whole body computed tomography Figure  (CT) scaMeanwhile, he underwent a whole body scintigraphic scan Figure  that shoAfterwards he started chemotherapy and whole brain irradiation. Despite the treatment, he worsened fast and became aphasic and his relatives decided to stop treatment. He died 15 days later after a cardiac arrest.Heterotopic bone formation is more likely to occur with gastrointestinal cancers and their metastases, especially the mucin-secreting variety, rather than other epithelial tumors , 10, 13 et al. in 2005, a metastatic intramuscular mass removed from the suprapubic area of a 56-year-old man with rectal adenocarcinoma turned out at histology to be a lesion without muscular tissue made of nidus of mature bone tissue with osteoblasts and osteocytes alternated with necrotic areas and deposits of mucin with no bone marrow formation or cartilage tissue. This suggests that malignant tumors secreting mucin can produce a substance which stimulates the differentiation of mesenchymal cells into osteoblasts and that neoplastic necrosis provides a nidus for metaplastic ossification [et al. describe the case of a 69-year-old man with adenocarcinoma of the transverse colon that developed heterotopic ossification in a metastatic axillary lymph node. Immunostaining for alkaline phosphatase revealed a significant concentration of this enzyme in these cells and, to a lesser degree, on the apical membrane of the glandular cells of the adenocarcinoma adjacent to the ossification centers, concluding that alkaline phosphatase is intimately associated with bone formation under these pathologic conditions [Recent studies suggest that the transformation of pluripotent mesenchymal cells into osteoblasts causes the formation of bone tissue in skeletal muscular tissue , 12. It fication , 15. Rannditions .et al. describe a case of two unusual metastatic lesions, one in the soft tissues of the chin and the other in the psoas muscle in a man with an adenocarcinoma of the rectum. Immunostaining of the first lesion was positive for CEA and CK20 but negative for CK7 [Hobdy  for CK7 .et al. reported the presence of bone morphogenetic proteins (BMP), specifically BMP5 and BMP6, in the cytoplasm of tumor cells and weak staining in osteoblast-like cells adjacent to newly formed bone. BMP2 and BMP4 are less expressed in tumor cells, osteoblast-like cells and stromal cells [In the present case, traditional histology was not conclusive and immunostaining was positive for CEA, CK20 and CDX2 and negative for TTF-1 and CK7. In a study on heterotopic ossification from colon adenocarcinoma, Imai al cells . These ial cells .et al. [A new BMP (BMP9) has been recently identified and shown to be the most potent BMP . Leblancet al.  first demTc-MDP) and subsequent acquisitions of total body images after about 2 hours. The principal bonding mechanism of phosphate and phosphonates on the bone is chemical absorption which occurs through chemical bonds in the bending and dislocation on the surface of the hydroxyapatite crystals, while tin and 99mTc are hydrolyzed and bound to the bone either separately or together in the form of tin dioxide and Tc-dioxide. The large surfaces of hydrated hydroxyapatite, present in the growth centers or in the metabolically active bone lesions, facilitate a greater chemical absorption and therefore show a greater capacity to concentrate the radiopharmaceutical. Increased uptake of the radiopharmaceutical occurs in areas involved by pathological phenomenon: the most important factor is represented by the blood flow which carries the radiopharmaceutical ; a second factor is an increase of metabolic activity in correspondence with the bone lesions .The concomitant bone scintigraphic positivity of metastasis in the soft tissue is also worthy of note. Bone scintigraphy is performed by intravenous injection of technetium-99m-methylene diphosphonate (99The hypervascularization of multiple soft tissue metastases without macro- or microcalcification evidenced by CT scan, their intense uptake in bone scintigraphy and positivity for CK20 and CEA, are suggestive of the presence of heterotopic bone formation in soft tissue from colon adenocarcinoma.Unfortunately, the reported case must be interpreted in the light of certain limitations. We do not have the histologic data to prove that the tissue is heterotopic bone rather than dystrophic bone formation. Scintigraphic bone uptake was not totally unambiguous as showed in other studies . NeverthOur experience suggests that, in the presence of colon adenocarcinoma, a bone scan could be a sensible tool to discover not only bone lesions but also possible heterotopic bone nidus in soft tissue. Furthermore, any subcutaneous lesion should be resected as soon as possible to avoid underestimating a potential malignancy .Written informed consent was obtained from the patient’s next of kin for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.


Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings. Much of our knowledge about neural computation in humans has been informed by data collected through carefully controlled experiments in laboratory conditions. Likewise, the success of Brain-Computer Interfaces , have required ground truth labels  as a technique for observing human neural activity is particularly attractive. Its spatial and temporal resolution offers measurements of temporal dynamics inaccessible by functional magnetic resonance imaging (fMRI) and spatial resolution unavailable to extracranial electroencephalography (EEG). Cortical surface ECoG is accomplished less invasively than with penetrating electrodes , while speech detection was 75% accurate (range of 67–83%).Figure 8).Unsupervised decoding of neural recordings was performed by hierarchical clustering of power spectral features of the multi-electrode ECoG recordings. Because the subjects' behavior on each day varied widely, both across days for the same subject and across subjects  of video and audio recordings for each subject day. The labels were acquired for 2-min segments of data distributed randomly throughout the 24-h day.The entirely automated neural decoding performed very well in the validation for all subjects on the categories of movement, speech and rest. Table To assess the significance of the automated labels' accuracy, we compared the F1 scores on each day to F1 scores of randomly shuffled labels. The shuffled labels preserved the relative occurrence of labels and gave an unbiased estimate of chance performance. Figure Our automated neural decoding system is able to predict behavior categories for many days after the initial clustering and correlation with video. To assess the stability of ECoG cluster annotations over time, we clustered the ECoG recordings on one day of data  and annotated them automatically with the simultaneously acquired videos and audio. Next, we tested these annotated ECoG clusters on 3 subsequent days without access to the video and audio data. Figure The performance in test day 1 is lower than on test days 2 and 3. This difference may be due to chance or to some systematic differences between day 4 post surgery and the rest of the clinical evaluation period.Another way to assess the neural decoder discovered through clustering and automated annotation is to examine the neural patterns identified in this unsupervised approach. We mapped these patterns by projecting the centroids of annotated clusters back to feature space. Next, the feature space in electrode coordinates on the brain were averaged within frequency bands, including those typically of interest to studies of human ECoG.Figure These features are largely consistent with known functions of human cortical areas and ECoG phenomena, as well as the existing ECoG literature on motor activation  electrodes implanted subdurally for presurgical clinical epilepsy monitoring at Harborview Medical Center. The study was approved by University of Washington's Institutional Review Board's human subject division; all subjects gave their informed consent and all methods were carried out in accordance with the approved guidelines.Electrode grids were constructed of 3-mm-diameter platinum pads spaced at 1 cm center-to-center and embedded in silastic (AdTech). Electrode placement and duration of each patient's recording were determined solely based on clinical needs. The number of electrodes ranged from 70 to 104, arranged as grids of 8 × 8, 8 × 4, 8 × 2 or strips of 1 × 4, 1 × 6, 1 × 8. Figures Video and audio were recorded simultaneously with the ECoG signals and continuously throughout the subjects' clinical monitoring. The video was recorded at 30 frames per second at a resolution of 640 × 480 pixels. Generally, video was centered on the subject with family members or staff occasionally entering the scene. The camera was also sometimes adjusted throughout the day by hospital staff; for instance, the camera may be centered away during bed pan changes and returned to the patient afterwards. Videos To generate a set of ground-truth labels so that we may assess the performance of our automated algorithms, we performed manual annotation of behavior aided by ANVIL Kipp,  on a smaFor automated video analysis, we first detected salient features for each frame using Speeded Up Robust Features (SURF), which detects and encodes interesting feature points throughout the frame. The amount of motion in each frame was determined by matching the magnitude of change in these feature points across successive frames. Since the subject was the only person in the frame a majority of the time, we are able to determine the subject's approximate movement levels. This approach detected gross motor movements of the arms, torso and head, as well as some finer movements of the face and mouth during speaking. To detect speech, we measured the power averaged across human speech frequency levels (100–3500 Hz) from fast Fourier transformed audio data.11.We assessed the performance of the automated algorithms by comparison to manual annotations. The manual annotations for each behavior were binary  whereas the automated speech and movement levels were analog values. Therefore, the agreement was computed after applying a threshold to the automated movement and speech levels. To clarify, the threshold is what average pixel movement and speech frequency power we considered to be actual speech and movement by the algorithm. We used a small section of videos from a few subject and found movement thresholds that correlated with gross hand-level movement and sound thresholds that included comprehensible speech. These are determined by human visual inspection, on videos, which was consistent across all subjects. This was empirically determined to be an average movement of 1.1 pixels and a mean power of 3 × 10All ECoG recording was bandpassed filtered between 0.1 and 160 Hz to reduced noise. The filtered signal was then converted into a set of power spectral features using short-time Fourier transform using non-overlapping 2-s windows. Each 24-h recording was thus separated into 43,200 samples in time.Because subjects engaged in a variety of activities throughout the day, there is no particular frequency band that would be solely useful for clustering. We considered power at a range of frequencies between 1–52 Hz for each electrode, binning every 1.5 Hz of power for a total of 35 features per electrode per 2-s window. At 82 to 106 electrodes per subject, this process resulted in 2870 to 3710 features for each 2-s window of recording. To normalize the data, we transformed the binned powers levels at each frequency bin for each channel by computing the Z-Score. The dimensionality of the feature space was then reduced with principal component analysis (PCA), and the cumulative fraction of variance explained as a function of the number of PCA modes for each subject day is shown in Figure k = 20 clusters using euclidean distance measures. Next, we segregate the data points into the single cluster with the most number of data points and all the rest of the clusters. This procedure produces the first level of the hierarchical clustering, which now has two clusters. Next, for level L of the clustering, this procedure is repeated for each cluster from level L−1 using k = 20/L (k floored to the largest previous integer). Again, the single cluster with the most number of data points is separated from the rest of the clusters, so that at level L, we end up with 2L clusters. This process of recursive k-means clustering and aggregation is stopped when there are fewer than 100 data points in each cluster, or when L = 10. In this manuscript, we focused on analyzing annotated clusters in levels 1–4.We used the 50-dimensional principal component power spectral features of the ECoG data as features for our hierarchical k-means clustering, a variant of Lamrous et al.'s divisive hierarchical clustering method Lamrous and Taileb . The hie11 (arbitrary units).Results from the clustering analysis were automatically annotated using movement and speech levels. Each type of unsupervised analysis produced time series at different temporal resolutions, so they were all first consolidated into a mean analog value for non-overlapping 16-s windows. For ECoG, we counted how many 2-s windows within each 16-s were assigned to a particular cluster at the target hierarchy level. That is, a score was given for each 16-s window, which was the count of how many 2-s windows within were assigned to the given cluster. For movement and speech detection, we considered what fraction of the 2-s windows within the larger 16-s window exceeded a threshold value, empirically determined by visual inspection to be an average movement of 1.1 pixels and a mean sound power of 3 × 10r correlation between each of the ECoG clusters with the movement and speech levels. The “movement” and “speech” labels were assigned to clusters for which the correlation was the highest for each behavior. If movement and speech both correlated best with the same cluster, the label was assigned to the second best cluster for the activity type that had a lower correlation. The “rest” label was assigned to the cluster with the largest negative correlation with both movement and speech.After consolidation into windows of 16 s, we computed the Pearson We performed this annotation assignment for clustering levels 1–4  for visible and audible behaviors. The hand annotations were distributed randomly throughout each day of each patient. The automated results were compared to manual labels using 16 s windows within the manually annotated times. Each 16 s window is determined to contain an activity if the activity is annotated within any point in the window. Since different clusters have different baseline levels, we determined that the cluster detects the activity if its level is at or above the 25th percentile over the day. Using the manual labels as ground truth, accuracy and F1 scores were computed. The F1 score is the harmonic mean of precision and recall.To determine the statistical significance of the F1 scores compared to chance, we generated shuffled labels by changing the timing of the ground truth labels of each activity, without changing their overall relative frequency. This shuffling was repeated over 1000 random iterations to determine the distribution of F1 scores assuming chance, and the true F1 score was compared against these shuffled F1 scores. We report the percentile of the true F1 scores for all subject days.For each annotated cluster, we projected the centroid values of the cluster back to brain coordinates. The centroids values are 50-dimensional vectors in PCA space, reduced from power spectral features of all recording electrodes. The inverse PCA transform using the original PCA basis projects the centroid back to brain coordinates, where the relative power in each frequency bin is available at each electrode. Note that because of the Z-Score normalization step before computing the original PCA basis, this back-projection reproduces Z-Score values, not voltages. These Z-Scores can be separately averaged according to frequency bins of interesting bands, including a low frequency band  and a high-frequency band  as show in Figure https://github.com/nancywang1991/pyESig2.git.The code developed in Python for processing and all analyses presented in this paper are openly available at NW conceived the experiments with the help of JDO and JGO; NW and RR conducted the experiment; NW, RR, and BB performed the analysis and wrote the manuscript, with input from all authors.This research was support by the Washington Research Foundation (WRF), the National Institutes of Health (NIH) grants NS065186, 2K12HD001097-16 and 5U10NS086525-03, and award EEC-1028725 from the National Science Foundation (NSF).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.


In the past decades, we have witnessed dramatic changes in clinical diagnoses and treatments due to the revolutions of genomics and personalized medicine. Undoubtedly we also met many challenges when we use those advanced technologies in drug discovery and development. In this review, we describe when genomic information is applied in personal healthcare in general. We illustrate some case examples of genomic discoveries and promising personalized medicine applications in the area of neurological disease particular. Available data suggest that individual genomics can be applied to better treat patients in the near future. The field of genetics is the study of genes, inheritance and genetic variation, while genomics is the study of the complete DNA sequence in the genome. In recent years, genomics gave birth to a series of other omics that refer to the complete collection of gene derivatives such as proteins, transcripts, or metabolites. Therefore, the broader and more inclusive term “genomics” sometimes refers to all large-scale approaches that are included in “omics”.Personalized medicine refers to the use of individual unique genomic information to optimize patient care. Because, in each individual, the nature of the disease, the onset, the prognosis, and the drug response are different, the effective application of genomic findings to clinical practice becomes our predominant goal.In this review, we describe the important roles of genomic information applied in personal healthcare in general . First, Human genome variation is represented by single-nucleotide polymorphisms (SNPs), copy-number variations (CNVs), insertions and deletions. During the drug discovery phase, information about genetic variations is used to identify signaling pathways as therapeutic targets. This early-stage research tries to understand the disparity between patients and control populations. Genome-wide association study (GWAS) is an efficient method that detects human genome variations and reveals novel genes contributing to disease pathogenesis. Some known drug targets and associated pathways have been identified on the list of GWAS hits. In the NIH policy white paper 2011) [1 [1], 44As we understand, in reality, sometimes the contributions of genetic variants to the phenotypes are very small. The inconsistency in genotypic measurements diminishes the accuracy of genotypes and causes Type II errors in GWAS ,3. ThereXIAP mutation and was saved by sequencing technology. In responding to President Obama’s announcement of a new initiative for precision medicine, the NIH is going to sequence one million human personal genomes. With the development of quality control metrics and standardization for NGS technologies and data analysis [Advances in sequencing technology have reduced the costs to the point where a human genome can now be sequenced at the $1000 level . Therefoanalysis ,15, NGS Dynamic testing is a method to look at an individual’s molecular profile and study the genetic characteristics such as the messenger RNAs (mRNA), MicroRNAs, proteins and metabolites.FLT3 is the gene that is found to correlate most highly with the mixed-lineage leukemia (MLL) subtype. Several hundred studies have identified gene or protein expression profiles that predict clinical outcome and disease recurrence risks. Some are being routinely used for disease diagnosis and approved by the US FDA [The unique pattern of molecular profiling can be used to explain disease heterogeneity and further classify the diseases. e US FDA .HER2, is an example of a biologic therapy for which the diagnostic assay was developed to identify certain cancer patients who will most likely benefit. Trastuzumab was approved for about 10% of HER2-overexpressing patients. Due to the small sample size of the subgroup of patients, without a sensitive and accurate diagnostic assay, the drug might fail at the clinical trial stage. Clopidogrel is an antiplatelet agent used to prevent heart attack and inhibit blood clots in coronary artery disease. CYP2C19 is an important drug-metabolizing enzyme. In 2010, the FDA put a black box warning on Clopidogrel to make patients and healthcare providers aware that patients with low activity of CYP2C19, representing up to 14% of patients, are at high risk of treatment failure and it is recommended that genetic testing is used to screen out those patients. Tetrabenazine is approved to treat Huntington's disease (HD) and is primarily metabolized by CYP2D6. Due to safety concerns caused by high exposure, patients with low activity of CYP2D6 should be treated with lower doses compared with extensive and intermediate metabolizers.Pharmacogenomics refers to the use of complex molecular information from the genome to optimize drug dose or predict drug response, which is a valuable tool in the development of new drugs as well as in improving overall outcomes with current drugs. Hundreds of drug labels have included exposure information and clinical response variability in individuals with certain genotypes . Among tIt generally takes 10–15 years for an experimental drug to go from the lab to US patient use. Only five in 5000 compounds at preclinical testing can enter the phase I stage. Additionally, only one of those five is approved for the market. On average, it costs a company $1.2 billion. Based on the awareness of the drug development and approval process, researchers who are interested in translating genomic discoveries into personalized drug therapy should understand the challenges involved. (1) Drug development is extremely risky. Every year, about 10% of drug candidates can pass preclinical development to get approval. In recent years, almost half of the potential therapeutics failed with late-stage failures. Moreover, pharmaceutical product candidates receive extensive government regulations. Those factors lead to a conservative attitude toward using genomic approaches in pharmaceutical development. (2) Since the cost for the development of an innovative drug is very high and the time to create a successful drug is extremely long, pharmaceutical companies are reluctant to use genomic approaches to limit the indicated patient population unless “one-size-fits-all” development fails. (3) Even after drug approval, the manufacturer might not be able to make much profit by continuing genomic post-marketing studies due to patents’ limited lifespans.There are additional challenges for genomics to be applied in neurological drug development. (1) There is structural and functional heterogeneity of the individual brain. (2) There are difficulties for acquiring quality data from specimens for reliable analysis. Additionally, developing a human brain model is not easy and animal models cannot display the full range of human phenotypes. (3) Neurological evaluations for disease severity or progression are always subjective due to patients’ or investigators’ reports. Better phenotypes are needed to categorize neurological diseases based on biology and etiology.Advances in genome technologies and the recent huge outpouring of genomic information related to neurological diseases have accelerated the convergence of discovery science and clinical medicine. In this section, we summarize some rare disease and complex disease case examples which may have the potential to translate genomics into therapeutics and make personalized medicine possible. DMD gene on the X chromosome such that it no longer produces functional dystrophin protein. Dystrophin is an important structural component within skeletal and cardiac muscle tissue. A deficiency of dystrophin will result in muscle degeneration and premature death. Most patients are wheelchair-dependent by age 12 and the average life expectancy is around 25. There is no cure for DMD, and an ongoing medical need has been recognized by regulatory authorities. Current treatments including corticosteroids are mainly used to control the onset of symptoms [DMD gene consists of 79 exons. A deletion of exon 71 would be considered “in frame” because exons 70 and 72 could still be spliced to allow transcription. However, deletion of exons 48 through 50 would be “out of frame” since 47 and 51 do not splice back together. Recently, a number of experimental therapeutic approaches are being developed that aim to restore the absent dystrophin protein in the muscles. The treatment strategy is to skip the exon to restore a genetic “reading frame”. The functional mRNA is made by splicing out the introns in the pre-mRNA of the dystrophin gene. However, a deletion of exon 50 in the dystrophin gene disrupts the mRNA reading frame and results in the interruption of dystrophin protein production. Antisense knocks out exon 51. The rest of the exons join up together, resulting in a shortened mRNA reading frame but functional dystrophin proteins. If the compounds work, a muscle biopsy should show increased dystrophin expression and patients would remain ambulatory as measured using a six-minute walking distance. Further information can be referred to in DMD draft guidance [DMD is a recessive X-linked disease which affects around one in 3600 males, and females rarely exhibit signs of this disease. This disorder is caused by a mutation in the symptoms . The DMDguidance . The dysAPP) and in the genes for the presenilin 1 and presenilin 2 proteins. Although these three genes reside on different chromosomes, they belong to the same biochemical pathway, which alters the protein production of Aβ and causes neuron death. Individuals with mutations in any of the three genes tend to develop AD before age 65, even as early as in their 30s, while the majority of individuals with AD have late-onset disease, which occurs at age 65 or later. In contrast to EOFAD, risk genes for late-onset AD display a complex interaction pattern that involves not only the genes but also environmental factors. The best known genetic risk factor is the inheritance of the ε4 allele of the apolipoprotein E (APOE). Between 40% and 80% of people with AD possess at least one APOE ε4 allele [APOE ε4 allele is neither necessary nor sufficient for causing AD. The current hypothesis is that APOE ε4 increases a carrier’s risk for hypercholesterolemia, which leads to elevate the accumulation of Aβ. The completion of GWAS has discovered other potential new susceptibility genes during recent years. Lamber et al. [p < 5 × 10−8) with meta-analysis of 74,046 individuals, 11 of which are newly associated with AD. Beecham et al. [APOE genetic risk loci  were associated with clinically defined AD dementia. Jonsson et al. [APP gene that protects against AD and cognitive decline in the elderly without AD. In vitro results showed that the substitution reduced approximately 40% of the formation of amyloidogenic peptides, which further support the involvement of Aβ peptides in AD pathology and BACE1 as a drug target. Five medications are currently used to treat the cognitive problems of AD: four are cholinesterase inhibitors  and the other (memantine) is an N-methyl-d-aspartate receptor antagonist [Alzheimer’s disease is a degenerative brain disease and it is ultimately fatal. Currently, 5.3 million Americans are living with AD. Causes of AD have not been fully understood and genetics may play an important role in disease development. Early-onset familial AD (EOFAD) is a hereditary condition that accounts for up to 5% of all AD patients. The genetic linkage analysis revealed that 1% of AD patients have the disease due to mutations in the amyloid precursor protein  [LRRK2 and SNCA show an autosomal dominant inheritance pattern while PARK2, PARK7 and PINK1 show an autosomal recessive inheritance pattern. Alterations in certain genes, including GBA and UCHL1, do not cause PD but appear to increase the risk of developing the condition in some families. GWAS was used to identify and replicate susceptibility factors for PD. Recently Nalls et al. [PD is one of the most common neurological disorders, affecting around 1% of the population over the age of 65. The motor symptoms of Parkinson’s disease, such as shaking, rigidity, and difficulty with walking and gait, may result from the death of dopamine-generating cells. PD was not considered a genetic disorder; however, around 15% of individuals with PD have a family history of the disease . Familiaherited) . Among ts et al.  conducteMAO-B inhibitors [MAO-B inhibitors (selegiline and rasagiline) increase the level of dopamine in the basal ganglia by blocking monoamine oxidase B enzyme activities [MAO-B inhibitors are used as a monotherapy to improve motor symptoms and reduce fluctuations between on and off periods. Like dopamine agonists, they can delay the need for levodopa in early disease, but they produce more side effects and are less effective than levodopa [SNCA-encoding alpha-synuclein has been confirmed to be associated with PD. Studies found alpha-synuclein protein clumps in the brains of PD patients with toxic effects. Therefore, Affitope is a vaccine that stimulates antibodies to reduce the aggregation of alpha-synuclein in the brain and Phenylbutyrate is being developed for alpha-synuclein clearance from the brain as well [There is no cure for PD. The main families of drugs useful for treating motor symptoms are levodopa, dopamine agonists, and hibitors . Levodophibitors . Dopaminhibitors . MAO-B itivities . MAO-B ilevodopa . Some ot as well . Both trSCN1A and PCDH7 are associated with epilepsy. In routine clinical practice, the variation in treatment response is the most commonly encountered unpredictability and it is possible that individual genomic variation contributes to these phenotypic drug response variations. There are over 20 anti-epileptic drugs (AEDs) and research has validated two genomic associations for AED usage. Individuals who have HLA-B*1502 are more likely to experience a severe skin disorder called Stevens-Johnson syndrome in response to carbamazepine, oxcarbazepine and phenytoin [CYP2C9. The *2 and *3 alleles are associated with decreased metabolism and potential toxicity in North American patients. Therefore, genetic testing is recommended to adjust doses of phenytoin. For patients with low activity of CYP2C9, at least a 50% reduction of the starting dose is recommended, with subsequent maintenance doses adjusted based on response.Epilepsy is a neurological disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behavior, sensations and, sometimes, loss of consciousness . About 1henytoin . GeneticDozens of genes have been found to be associated with various neurological diseases. Our understanding of the genetic etiology will change over time along with the advancement of technologies. Although genomics knowledge is still limited, many clinical institutes have considered to routinely use it in the selection of the ideal treatment for an individual patient. The goal in neurological personalized medicine is to improve currently existing treatments and accelerate future drug therapies. Disease-associated biomarkers will likely advance the translation of scientific discoveries into clinical applications. In addition to behavior and imaging endpoints, some genomics biomarkers can also be used to predict response and follow pathology. The development of disease classifiers could quantify the changes in disease status to improve clinical management and research. The Critical Path Institute has initiated a “Coalition Against Major Diseases (CAMD)”  program The genomics discoveries greatly help us to understand disease mechanisms, which lead to the identification of druggable targets once the mutations are found. However, the bottlenecks along the path of moving neurological disease treatments from the bench to the bedside are numerous and formidable compared with other therapeutic areas. The current missing gaps may be due to the following. (1) Most neurological diseases are complex diseases with lots of gene-lifestyle interactions involved, and they may not have a simple, unique genetic solution. (2) Neurological diseases are chronic and irreversible, which requires long-term disease control and management. (3) The disease-modifying treatment development cost is extremely expensive and double-blind clinical trials generally need 18 months or longer to show treatment effects on slowing disease progression. As highlighted in this paper, what we are certain of is that the ability to understand the clinical features of a patient’s genetic profile and the knowledge of disease mechanisms will facilitate new targeted therapies. As President Obama pointed out in the Precision Medicine Initiative, it calls for continuous efforts to transform information into knowledge which helps neurological patients receive better treatments by using their individual genome characteristics.


Cry1, also show specific circadian expression variation. Overall, our data provide an important chronobiological resource (www.wgpembroke.com/shiny/SCNseq/) and allow us to propose that transcriptional timing in the SCN is gating clock resetting mechanisms.The mammalian suprachiasmatic nucleus (SCN) drives daily rhythmic behavior and physiology, yet a detailed understanding of its coordinated transcriptional programmes is lacking. To reveal the finer details of circadian variation in the mammalian SCN transcriptome we combined laser-capture microdissection (LCM) and RNA-seq over a 24 hr light / dark cycle. We show that 7-times more genes exhibited a classic sinusoidal expression signature than previously observed in the SCN. Another group of 766 genes unexpectedly peaked twice, near both the start and end of the dark phase; this twin-peaking group is significantly enriched for synaptic transmission genes that are crucial for light-induced phase shifting of the circadian clock. 341 intergenic non-coding RNAs, together with novel exons of annotated protein-coding genes, including DOI:http://dx.doi.org/10.7554/eLife.10518.001 The daily cycles of life in mammals are driven by a small region of the brain called the suprachiasmatic nucleus (or SCN). The SCN receives signals from sunlight and other environmental factors to help coordinate most aspects of daily biological activity and behaviour. To work correctly, it is essential that the SCN switches certain genes on and off at exactly the right time. However, many questions remain over the identity of these genes and how their levels of activity change during a 24-hour period.When a gene is active (or “being expressed”), it is used as a template to build the molecules of RNA that are needed to make proteins and to help to control how cells work. Pembroke et al. have now sequenced the RNA molecules made in the SCN of mice  over a 24-hour period. The mice spent half of each day in the light, and half in the dark. This revealed that the expression levels of over a quarter of all the genes that are found in the SCN fluctuate over a 24-hour period. One particular group of genes peak in activity twice a day; Pembroke et al. suggest that these genes are important for controlling how an animal can adjust its body clock to light.Further research is now needed to find out which of the newly discovered fluctuating genes play the most important roles in daily activity rhythms, and which might play a part in disease.DOI:http://dx.doi.org/10.7554/eLife.10518.002 The suprachiasmatic nucleus (SCN) of the hypothalamus is the seat of the principal circadian clock in mammals. Entrained by photic information and other external stimuli, this group of neurons maintains 24-hr rhythms of physiology and behaviour by synchronising molecular oscillators in the brain and peripheral tissues . The traPer2), and the use of experimental samples likely to include a large proportion of the surrounding hypothalamic tissue. The study of such heterogeneous samples will result in reduced amplitudes of oscillations for genes that cycle asynchronously in different brain regions  of the SCN to provide a comprehensive and tissue-specific transcriptomic investigation of the master pacemaker. These data reveal many hundreds of novel cycling transcripts and provide evidence that the temporal variation in a specific group of genes plays a role in modulating light-induced phase resetting of the circadian clock.LCM accurately isolated the entire SCN for transcriptional profiling . Pooled Avp, Vip, Grp and Six6) showed a far greater expression enrichment in the SCN over other brain regions  . Of 10 k regions . In summ-4) and for Gene Ontology (GO) annotations relating to signalling pathways , q < 0.05) of which 4 out of 4 were confirmed using in situ hybridisation (ISH) . These gWe next identified 4569 genes  in over six samples) whose expression oscillated in a sinusoidal manner  accordinProk2, Rorb and Nts, which were also validated by quantitative polymerase chain reaction (qPCR)  suggesting a core set of universally cycling genes  showed no evidence for sinusoidal expression in 12 other tissues, including the whole hypothalamus, based on published circadian expression data  . This inn (qPCR) . Comparin (qPCR) . Six of ng genes . In suppng genes   exhibit a twin-peaking profile in rat SCN similar to that observed in our RNA-seq data , which does not assume a single mode of temporal variation, we identified a single module of 766 genes, which instead of adopting a sinusoidal profile, displayed two peaks of expression at ZT14 and ZT22 . From thseq data . This twseq data , suggestGrin2a/b, Camk2a, Nos1, Ryr2) blocks light-induced phase shifts  of the clock . This ise shifts ; 2010. Me shifts . This sue shifts . Taken te shifts . For exae shifts . More wo3100003L05Rik) whose presence in the SCN was confirmed by ISH at ZT6  loci were identified from the SCN transcriptome data. Considered together, they displayed evidence of significant sequence conservation, had lower expression and fewer exons than protein-coding genes in line with lncRNAs identified in other mammalian tissues , but didH at ZT6  as well H at ZT6 . In addiH at ZT6 , and thuCry1 which appears to represent an alternative transcriptional start site (TSS)  from SCN tissue  . DespiteN tissue . The nov samples , indicatCry1 isoform, which may play important roles in SCN function.In conclusion, this first temporal analysis of the SCN using RNA-sequencing reveals the presence of a twin-peaking transcriptional module with a suspected function in circadian control, and identifies thousands of novel transcripts, including a novel All animal experimentation has been approved by the UK Home Office and the University of Oxford Ethical Review Board. Male C3H/HeH mice at 10–12 weeks of age were group housed in light-tight chambers equipped with light emitting diode (LED) lighting at 150 lx at the cage floor. After 7 days of acclimatisation, mice were singly housed for 7 days prior to tissue harvesting. Brains were removed at one of six Zeitgeber times (ZT) and immediately frozen on dry ice in optimal cutting temperature (OCT) mounting media . Eighteen frozen coronal sections at 15 μM were cut spanning the entire rostral to caudal region of the SCN and mounted onto polyethylene naphthalate (PEN) slides . LCM was carried out from dehydrated, Nissl-stained sections using the PALM system  as previously described . Tissue One SCN, six brain and five liver transcriptome datasets from six independent studies  were downloaded from the Gene Expression Omnibus (GEO) database to allow comparison of expression between tissues. PCA revealed separation of datasets according to tissue rather than experimental factors  suggestiAll sequencing data was processed as follows unless otherwise specified. To retain only high quality reads an in-house script was used to: (i) trim ends of reads with a Phred base quality score below 3, (ii) remove specified over-represented adapter or contaminating sequences, (iii) remove reads with a length below 50 bp De novo transcript assembly was conducted using cufflinks v2.0.2  allowingIdentification of lincRNAs was conducted through the use of the CGAT NGS pipelines rnaseqtranscripts.py and rnaseqlncrna.py . The firSupplementary files 3 and 5.To determine the expression level for each gene in each sample, aligned reads were quantified using either HTSeq  in the brain than in liver and significantly greater expression  in both this study’s SCN dataset and the published SCN dataset  relativeThe R packages JTK_CYCLE v2.1;  and DESeEnrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) Functional Annotation Tool v6.7 . DAVID incorporates Fisher’s exact test and was used to determine significant  GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO terms were passed through REVIGO to prune redundant terms with a semantic similarity of 0.5, 0.7 or 0.9, depending on the length of the GO term . MammaliActB, Gapdh and eight genes which showed the most stable expression in the SCN according to sequencing data . Primer sequences are listed in For validation of results from RNA-seq, qRT-PCR was performed using the BioMark system (Fluidigm) as according to the two-step single-cell gene expression protocol using EvaGreen as described in the Real-Time PCR Analysis User Guide . For the process, 666 ng of starting RNA was used for each sample, which underwent 18 cycles of specific target amplification (STA) followed by a tenfold dilution. SCN CT values were normalised using the housekeeping genes Cry1, total RNA was purified from dissected SCN tissue and used to prepare cDNA . Primers representing previously annotated (5’ GTGAGGAGGTTTTCTTGGAAG 3’) and novel (5’ CTTCTAGGGAATTGCGACTG 3’) exons were used with a common reverse primer (5’ CTGGGAAATACATCAGCTGG 3’) to amplify products by RT-PCR followed by visualisation on an agarose gel and sequencing. The genomic coordinates of the novel exon are: chr10: 85,183,342 to 85,183,832 (mm10) with splicing into the following exon at 85,183,342 or 85,183,510.For RT-PCR of novel exons of Avp (1–490 bp of accession number NM_009732.1), Vip (191–715 bp of NM_011702.1), Scg2 (844–1287 bp of NM_009129.1), Grpr (1287–1741 bp of NM_008177.1) and NONCO7761 . Slide hybridisation and riboprobe detection was carried out as described previously . Regions of the selected transcripts were cloned into pCR4-TOPO  for DIG-labelled riboprobe synthesis: eviously . review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and Aviv Regev as the Senior Editor.Thank you for submitting your work entitled "Temporal transcriptomics suggest that twin-peaking genes reset the clock" for consideration by The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.Summary:Cry1.In this submission (manuscript and on-line tool), Pembroke and colleagues report on a transcriptomic profile and search tool for the suprachiasmatic nuclei (SCN), site of the brain's main circadian pacemaker. They improve on earlier studies of this type by 1) limiting the sampling of the tissue to the SCN itself . 2) by sampling under the physiologically relevant light-dark cycle (as opposed to constant lighting). By combining RNAseq with 1 and 2, they provide exciting new evidence that many other transcripts cycle in a SCN specific manner as do long intergenic non-coding RNAs. Further, they show that some transcripts show two peaks in expression per 24h. Additionally they characterise a novel isoform of the key clock gene, They use established algorithms and analytic tools to identify functionally related groups of genes whose expression cycles and confirm these with qPCR In some instances they also confirm expression via in situ hybridisation. The manuscript is linked to an on-line tool which appears relatively easy to use and allows researchers to interrogate this database. This is a valuable resource, particularly for researchers with limited knowledge/insight into these types of investigations.Essential revisions:1) In their description of the times of rising and falling transcripts, the authors highlight that the peak in some transcripts occurs at time when light cannot shift mouse rhythms and while others peak at times when light can shift the mouse SCN clock. There is somewhat of an over interpretation in this aspect  since mice light-pulsed mice in constant dark will show shifts during the late subjective day or early subjective night. The authors show this is in npy2r and avpr1a, the variability in night (ZT18 or 22) is much higher than at other time points. The authors need to comment on why this is the way it is and also describe further the functional consequences.2) When using the on-line resource, it is clear that the level of variability of some transcripts can be very high. For example, for 3) In 4) For the transcripts showing 2 peaks in expression per 24h, this pattern has been previously observed  and this merits discussion.5) For reporting replicates, could the authors please provide details on the total number of mice used – is it 5 mice=1 data so samples from 15 mice are combined for the average expression profile at a given time point?6) As the authors themselves point out, the fact that they find 7x more oscillating transcripts than Panda 2002 is simply a reflection of improved methodologies, or the fact that their experiments were conducted in LD rather than DD conditions. Some might also arise due to more evolved statistical criteria. Discussion on this point is warranted.7) The claim that half the genes oscillating in the SCN do not oscillate in other tissues is unfortunately marred by the fact that they are comparing their L:D dataset to D:D datasets, as the authors state. This should be discussed. Essential revisions:1) In their description of the times of rising and falling transcripts, the authors highlight that the peak in some transcripts occurs at time when light cannot shift mouse rhythms and while others peak at times when light can shift the mouse SCN clock. There is somewhat of an over interpretation in this aspect  since mice light-pulsed mice in constant dark will show shifts during the late subjective day or early subjective night. The authors show this is in The highest levels of expression of the twin-peaking genes we identified are correlated with well-established susceptibility windows for phase-shifting in rodents. However, as the reviewers correctly state, light is also able to induce shifts at times when our twin-peaking genes do not ‘peak’ (such as at the end of the light phase); in these cases, we suggest that mRNA abundance remains above a particular threshold to facilitate phase shifting, despite not peaking at this time point. To illustrate this hypothesis more clearly, we have amended In addition, we agree that there are undoubtedly complex interactions between photic and non-photic cues that are important for phase adjustment behaviour, and we have now cited additional examples and expanded upon this point in the main text . We do not believe that these specific studies necessarily affect our more general hypothesis that transcript abundance is important for light-responsive phase shifting. Nevertheless, we have toned-down our argument in several places  by acknowledging that more work will be required to determine the link between transcript levels and light-mediated behavioural changes.2) When using the on-line resource, it is clear that the level of variability of some transcripts can be very high. For example, for npy2r and avpr1a, the variability in night (ZT18 or 22) is much higher than at other time points. The authors need to comment on why this is the way it is and also describe further the functional consequences.Our experimental design sought to reduce technical and biological variation in the data by pooling tissue from 5 animals for each individual sample and counterbalancing for factors such as the position of the cage in the light-controlled chamber prior to dissection. We also re-assessed potentially confounding factors such as RNA quality  and the number of sequencing reads/library size obtained. None of these factors was found to be biased towards certain time points or time of day.Avp shows a very small degree of variation in expression at the same time points despite low levels of expression towards the end of the dark phase. In addition, by examining the coefficient of variance (CV) for all genes at each time point, we found that there was not a trend for greater variance of expression during the night as suggested, although it was marginally elevated during the day .Although the two genes mentioned above do show large variation at these time points, they are not representative of the transcriptome as a whole. For example, the related gene Nevertheless, the reviewers raise an interesting point that increased variability at certain times could have a functional consequence. For example, a gene with an elevated CV at a particular time point may not need to be tightly regulated at that specific time; expression may only be required to reach a threshold level to induce its necessary function, with the generation of additional mRNA having no detrimental outcome. Variation may also be dampened at the functional level due to translational efficiency or other molecular feedback mechanisms. Alternatively, a gene might have a reduced CV representing tighter regulation at a particular time point if the precise level of mRNA at that time is functionally important.In summary, from our own RNA-seq data we are reluctant at this stage to come to biological conclusions regarding the variation of single genes based on three biological replicates at each time point, but we are confident that there is no time-dependent technical bias in the dataset that influences our conclusions.3) In Grpr encodes the GRP receptor.4) For the transcripts showing 2 peaks in expression per 24h, this pattern has been previously observed  and this merits discussion.In the text we had mentioned 12-hour harmonics, but we were not aware of these particular studies highlighting ‘twin-peaking’ genes as shown by in situ hybridisation, including isoforms of PKC. We thank the reviewers for this observation and have cited these studies in the text .5) For reporting replicates, could the authors please provide details on the total number of mice used – is it 5 mice=1 data so samples from 15 mice are combined for the average expression profile at a given time point?This is correct; we have now further clarified this point in the Methods (subheading “SCN RNA sample preparation”).6) As the authors themselves point out, the fact that they find 7x more oscillating transcripts than Panda 2002 is simply a reflection of improved methodologies, or the fact that their experiments were conducted in LD rather than DD conditions. Some might also arise due to more evolved statistical criteria. Discussion on this point is warranted. 7) The claim that half the genes oscillating in the SCN do not oscillate in other tissues is unfortunately marred by the fact that they are comparing their L:D dataset to D:D datasets, as the authors state. This should be discussed.We have added additional discussion and citations to address both these important points .


Partial nephrectomy has been underutilized in the United States. We investigated national trends in partial nephrectomy (PN) utilization before and after publication of the American Urological Association (AUA) Practice Guideline for management of the clinical T1 renal mass.We identified adult patients who underwent radical (RN) or PN from November 2007 to October 2011 in the Nationwide Inpatient Sample (NIS). PN prevalence was calculated prior to (11/2007-10/2009) and after Guidelines publication (11/2009-10/2011) and compared the rate of change by linear regression. We also examined the nephrectomy trends in patients with chronic kidney disease (CKD). Statistical analysis included linear regression to determine point-prevalence of PN rates in CKD patients and logistic regression to identify variables associated with PN.2 0.15, p = 0.006): however, the rate of change was not increased after the guidelines. (p = 0.46). Overall rate of PN in patients with CKD did not increase over time .During the study period, 30,944 patients underwent PN and 64,767 RN. The prevalence PN increased from 28.9% in the years prior to guideline release to 35.3% in the years following guideline release with an adjusted odds ratio (OR) of 1.24 . The rate of PN significantly increased throughout the study period (RWe noted a 6.4% absolute increase in PN after release of the AUA guidelines on clinical T1 renal mass was published; however, the rate of increase was not likely associated with guideline release. The rate of PN performed is increasing; however, further investigation regarding medical decision-making surrounding PN is needed. Radical nephrectomy (RN) had been the standard of care for renal malignancy for nearly 4 decades . HoweverIncreased knowledge of chronic cardiac and metabolic sequelae of RN, along with data suggesting oncologic equivalence for partial (PN) compared to radical nephrectomy for SRM, has prompted reconsideration of surgical approaches to renal masses –14. In 2To investigate the potential shorter-term impact of publication of the AUA T1 Renal Mass Guidelines, we analyzed the overall and rate of utilization of PN versus radical nephrectomy (RN) in a national cohort before and after the AUA Guideline release, with a focused analysis of utilization trends of PN in patients with non-dialysis dependent CKD.2, respectively)] in both time periods. Additionally, we investigated patient and external factors associated with performance of RN vs. PN in order to identify potential changes in disparities regarding utilization of nephron sparing surgery and the possible factors associated with patient selection.As the primary outcome, we investigated the proportion and rate of PN performed after the release of the AUA T1 Renal Mass Guidelines compared to prior using a publicly available cross-sectional inpatient database . Patients were divided into two groups consisting of a 2-year span prior to and 2 year after the release of the AUA T1 Renal Mass Guidelines. A secondary outcome is to investigate the rates of RN vs. PN in patients with non-dialysis dependent chronic kidney disease (CKD) , with a median follow-up of 9.3 years, and noted that overall survival with a hazard ratio (HR) of 1.50, testing for non-inferiority was not significant (p = 0.77), and test for superiority was significant (p = 0.03) in favor for RN, though in RCC patients there were no significant differences with respect to overall survival. These findings were presented cautiously given shortcomings in study design and execution , howeverThe criticism for PN utilization of only 35% in this population based cohort is tempered by the overall rise in incidence of PN in the past 4 years as well as the lack of evidence investigating physician clinical decision-making. We identified the largest improvement in PN performed based on hospital volume. In multivariable analysis, patients had a 2 fold increased odds chance of having a PN if the hospital performs at least 25 renal cases  per year confirming results of prior studies using the NIS . VariousWe performed this study not to determine causality of the AUA T1 Renal Mass Guidelines on PN in the United States, but to identify potential opportunities for improvement. Creation of a guideline should only be the beginning of a continuous process of improvement as the ultimate success of depends on critical evaluation over time . In geneLimitations in the data derived from national databases are only as accurate as the codes that have been chosen for the patient. The NIS database does not have pathologic data and therefore may not solely focus on cT1a renal cortical masses. However, the NIS was chosen for its strength in the documentation of the procedure itself as well as the fact that up to 25% of small renal masses have benign histology, and would therefore not be documented in oncology driven databases . MoreovePartial nephrectomy utilization has increased over the 4 years selected for evaluation and should be recognized. However, the lingering question is how many present with amenable tumors and/or various indications for partial nephrectomy and do not have the option offered. One such patient’s are those with CKD, in which a surgical decision could have significant impact on future health in which the urologists is not likely to serve the penalty. Therefore, a concerted effort should be placed on identifying patients who would most benefit from partial nephrectomy and providing that service despite demographic challenges. Additionally, further investigation regarding current clinical decision may provide more immediate problem solving opportunities.We have identified a steady increase in PN over the four-year study period and more PN performed after the publication of the AUA Clinical T1 Renal Mass Treatment Guidelines. However, the rate of PN performed after the guidelines release was not faster indicating that other factors may be influencing the adoption of PN. Additional studies and longer-term follow up are needed to determine the practical effects and clinical significance of evidence-based guidelines.