However, understanding what determines an individual's dietary preferences and how to change his/her behavior remains limited. The aim of the present study was to evaluate associations between dietary intake and SOC in adults.Favorable dietary habits promote health, whereas unfavorable habits link to various chronic diseases. An individual's "Diet intake was recorded by an 84-item semi-quantitative food frequency questionnaire and SOC was measured by the 13-item Antonovsky questionnaire in 2,446 men and 2,545 women (25–74 years old) from the population based northern Sweden MONICA screening in 1999.Intakes of energy, total and saturated fat, ascorbic acid, sucrose, and servings of fruits, vegetables, cereals, and sweets correlated with SOC among women, whereas intakes of total and saturated fat, ascorbic acid, fiber, and alcohol, and servings of fruits, vegetables, bread, bread and cereals, fish, and potatoes correlated with SOC among men. With a few exceptions, intakes of these nutrients/foods were significantly explained by SOC quartile scores in linear GLM models. Both women and men classified into the highest SOC quartile had significantly higher age-BMI-education standardized mean intakes of vegetables than those in the lowest quartiles. Women in the highest SOC quartile also had higher intake of fruits but lower intakes of energy, total and saturated fat, sucrose, and sweets.Projection to latent structures (PLS) multivariate modeling of intakes of the 84 food items and food aggregates simultaneously on SOC scores supported low SOC to coincide with a presumably less health promoting dietary preference, e.g. intake of pizza, soft drinks, candies, sausages for main course, hamburgers, mashed potato, chips and other snacks, potato salad, French fries, whereas men and women with high SOC scores were characterized by e.g. high intake of rye crisp whole meal bread, boiled potato, vegetables, berries, and fruits.Both men and women in the highest, as compared with the lowest, SOC score quartile reported more "healthy" food choices. Dietary habits for individuals in the lowest SOC quartile therefore may render a higher risk for various endemic diseases. Favorable dietary habits promote health, whereas unfavorable habits are linked to development of various chronic diseases, such as cardiovascular diseases, type-2 diabetes and dental caries. An excess intake of energy, excess refined sugars and saturated fatty acids, and too little fibers and antioxidants can contribute to the development of chronic diseases -4. In geSense of coherence" (SOC), and "generalized resistance resources" (GRRs), such as income, education, ego strength, knowledge, which would provide energy to combat various stressors, and thus influential factors in the salutogenic model. Central to sense of coherence are comprehensibility (the cognitive component), manageability , and meaningfulness . Individuals with low SOC scores are reported to have a higher frequency of various diseases [i) higher sucrose intake in adolescents with low SOC scores [ii) lower ability to change dietary habits and lose weight in over-weight individuals with moderate to low SOC scores [iii) better blood sugar control in type-2 diabetics with high SOC scores, whereas those with low scores have poorer sugar balance [An individual's dietary pattern is largely set by cultural traditions and availability, but both physiological and psychological influences have been described . Howeverdiseases -13, somediseases -17. An aC scores , (ii) loC scores , and , as well as a new cross-sectional sample of 2,500 randomly selected people, were invited. In total 6,000 individuals (71.8 %) participated. For the present paper all individuals aged 75 years or older (n = 206) were excluded.2 (m). BMI was grouped as <20 underweight, 20–24.9 normal weight, 25 – 26.9 moderate overweight, 27 – 29.9 overweight, and ≥ 30 obese. Special forms for questions about food intake and sense of coherence were added.In the MONICA project various cardiovascular risk factors, including weight, height and education level, were monitored. Education was grouped into three levels: primary school (≤ 9 years), secondary school (10–12 years) and university education (≥ 13 years). Body mass index (BMI) was calculated as the ratio between weight (kg) and heightSense of coherence was monitored using the 13-item questionnaire by Antonovsky ,19. PartThe subjects were requested to complete a self-administered, semi-quantitative and optically readable food frequency questionnaire . FrequenThe reported frequencies of consumption were converted to number of intakes per day, and energy and nutrient intakes were calculated by multiplying these frequencies by portion size and energy or nutrient content from a food composition database from the Swedish National Food Administration. The energy and nutrient contents were calculated using the software MAT's (Rudans Lättdata, Sweden).Participants who had more than 10% missing answers were excluded. Single missing answers in sections where normally only one of several options is consumed frequently, such as type of milk, were not grounds for exclusion. Nutrient intake could not be estimated if portion size indication was missing.t-tests, and differences between more groups, i.e., age, BMI and education level groups, by ANOVA. P-values ≤ 0.05 were considered significant. Dietary intake and reporting thereof are highly influenced by gender, age, BMI, and educational level [i) type I and (ii) type-III sum of square estimates, where (i) corresponds to a univariate regression, i.e., only the SOC score had entered the model, and (ii) corresponds to a multiple regression, i.e., all independent variables  were included in the model.Data were analyzed separately for men and women using the SAS System for Windows (Release 8.02, SAS Institute, Cary, NC). Dietary variables were logarithmically transformed to improve normality. Univariate Pearson correlation coefficients were calculated between ln-transformed diet measures and SOC score. Differences between means for men and women were tested with Student's al level . Therefo2, Q2 and VIP, were generated as previously described [2- and Q2-values give the capacity of the X matrix to explain (R2) and predict  the variance of the Y matrix. The relative importance of each x-variable for the correlation structure among X and Y is given as a VIP-value (Variables of Importance in the Projection); VIP-values >1.0 are influential and VIP-values ≥ 1.5 highly influential.To evaluate food selection pattern in individuals in relation to SOC score, PLS multivariate projection to latent structures was applied . PLS is escribed . The R2-In total 2,446 men and 2,545 women answered the SOC- and food-frequency-questionnaires in such a way that the inclusion criteria were met. This corresponded to 86.6% of the 25–74 year old participants in the northern Sweden MONICA 1999 study.In the present cohort SOC scores varied from 23 to 91 points Fig. . Mean scUnivariate correlation analyses between SOC scores and daily intake of nutrients or servings per week in food groups revealed significant associations among women for intakes of energy , total and saturated fat (g/week), ascorbic acid (mg/week), sucrose (g/week), and servings/week of fruits, vegetables, cereals, and sweets (data not shown). Among men significant correlations were seen for daily intake of total and saturated fat (g/day), ascorbic acid (mg/day), fiber (g/day), and alcohol (g/day), and servings of fruits, vegetables, bread, bread and cereals, fish, and potatoes (data not shown). Except for ascorbic acid and cereal intake in women, and bread, bread and cereals, and potato intake in men, variations in intakes of these foods/nutrients were significantly explained by SOC quartile scores in linear GLM models  rendered models which supported low SOC to coincide with a less health promoting dietary preference and vice versa. Thus, in both men and women low SOC scores coincided with high intake of pizza, soft drinks, candies, sausages for main course, hamburgers, mashed potato, chips and other snacks, potato salad, French fries, and traditional broth soaked bread and potato dumplings (Table The present study shows that a high sense of coherence (SOC) is associated with health-promoting food choices, whereas low scores are associated with a less favorable food pattern. SOC score independently contributed in explaining variations in intakes of vegetables in both men and women, and in intakes of saturated fat, sucrose, sweets, and fruits among women, and alcohol intake among men. Correlations for other nutrient/food intakes seen in the initial univariate evaluations, disappeared when tested together with age, BMI and educational level in multiple linear GLM modeling. This indicates that some dietary preferences and food selections are linked to socioeconomic status, whereas others are more bound to the individual's sense of coherence, i.e. his/her personal way of grasping and handling life situations.Although the present results support earlier findings that associate high SOC scores with a more healthy diet -17, the per se may influence SOC. The four cross-sectional MONICA screenings in 1986, 1990, 1994 and 1999 have demonstrated a distinct time trend in dietary intake in northern Sweden [The sense of coherence instrument has been constructed to measure an individual's capacity to cope in a salutogenic way . In accon Sweden . The preNotably, both men and women in the highest, as compared with the lowest SOC score quartile, reported more food choices from the contemporary public message of a healthy life style ,7. In coBoth men and women in the highest, as compared with the lowest, SOC score quartile reported more "healthy" food choices. Dietary habits for individuals in the lowest SOC quartile therefore may render a higher risk for various endemic diseases.The author(s) declare that they have no competing interestsAll participants have contributed to study design and manuscript preparation; IJ has been main author, UL has done data processing, BN has been responsible for SOC, BL for general behavior aspects, and BS is PI for the Northern Sweden MONICA study.SOC scores in gender, age, BMI, education level and SOC-quartile groups. Differences between means for men and women are tested with t-test, and among more groups, i.e. age groups, BMI education level, with ANOVA. ns for p > 0.05Click here for file


This study evaluated the effects of stroke on regulation of cerebral blood flow in response to fluctuations in systemic blood pressure (BP). The autoregulatory dynamics are difficult to assess because of the nonstationarity and nonlinearity of the component signals.We studied 15 normotensive, 20 hypertensive and 15 minor stroke subjects (48.0 ± 1.3 years). BP and blood flow velocities (BFV) from middle cerebral arteries (MCA) were measured during the Valsalva maneuver (VM) using transcranial Doppler ultrasound.R) and BFV (BFVR) signals. The BP-BFV phase shift was calculated as the difference between the phase corresponding to the BPR and BFVR minimum (maximum) values. BP-BFV phase shifts were significantly different between groups. In the normotensive group, the BFVR minimum and maximum preceded the BPR minimum and maximum, respectively, leading to large positive values of BP-BFV shifts.A new technique, multimodal pressure-flow analysis (MMPF), was implemented to analyze these short, nonstationary signals. MMPF analysis decomposes complex BP and BFV signals into multiple empirical modes, representing their instantaneous frequency-amplitude modulation. The empirical mode corresponding to the VM BP profile was used to construct the continuous phase diagram and to identify the minimum and maximum values from the residual BP (BPIn the stroke and hypertensive groups, the resulting BP-BFV phase shift was significantly smaller compared to the normotensive group. A standard autoregulation index did not differentiate the groups. The MMPF method enables evaluation of autoregulatory dynamics based on instantaneous BP-BFV phase analysis. Regulation of BP-BFV dynamics is altered with hypertension and after stroke, rendering blood flow dependent on blood pressure. Noninvasive assessment of cerebral vasoregulation is a major challenge in medical diagnostics and post-stroke care. Dynamic autoregulatory mechanisms adapt cerebral perfusion to spontaneous variations in intracranial and systemic pressure within a few heartbeats. Decline of cerebral blood flow that occurs with normal aging is further potentiated by presence of risk factors for cerebrovascular disease such as hypertension. Cerebral autoregulation is damaged by acute stroke, rendering cerebral blood flow dependent on blood pressure (BP) -3. The dContinuous monitoring of BFV using transcranial Doppler ultrasound enables assessment of dynamic autoregulation from spontaneous BP and BFV fluctuations , and durAccordingly, we: 1) introduce a new method, multimodal pressure flow analysis (MMPF), based on the Hilbert-Huang transformation , to quanStudies were conducted at the Autonomic Nervous System Laboratory at the Department of Neurology at The Ohio State University and at the SAFE  Laboratory at the Beth Israel Deaconess Medical Center at Harvard Medical School. All subjects signed informed consent, approved by the Institutional Review Boards. Subjects in the stroke and non-stroke groups were recruited from the Neurology Stroke Service and through advertisement. Demographic characteristics are summarized in Table 15 healthy normotensive subjects (age 40.2 ± 2.0 years, mean ± SE).20 patients with essential hypertension, controlled by antihypertensive medications (age 49.9 ± 2.0 years).15 subjects with the first minor ischemic stroke (18.3 ± 4.5 months after acute onset) (age 53.1 ± 1.6 years) including 9 subjects treated for hypertension (stroke-hypertensive) and 6 subjects who were normotensive (stroke-normotensive) with no BP treatment. Stroke subjects had a documented infarct on MRI or CT affecting <1/3 of the vascular territory with a minor neurological deficit . The side of the lesion was determined by neurological evaluation and confirmed with MRI and CT. The lesion was in the right hemisphere in 4 and in the left hemisphere in 11 subjects. The infarct types and locations were as follows: small vessel infarcts in 13 subjects and large vessel infarcts in 2 subjects. Cortical infarcts were found in 10 subjects and subcortical infarcts in 5 subjects. Normal carotid Doppler ultrasound study was required for participation. Patients with hemorrhagic strokes, clinically important cardiac disease including major arrhythmias, diabetes and any other systemic illness were excluded. All subjects were carefully screened with a medical history, physical and laboratory examination. Subjects with hypertension (with or without stroke) were treated with antihypertensive agents from the following categories (diuretics, beta-adrenergic blockers and angiotensin-converting enzyme inhibitors). Antihypertensive medications were gradually tapered over 3 days and discontinued for 3 days prior to the study. Anticoagulants and other medications that did not affect cardiovascular or autonomic nervous system function were allowed.After instructions and several practice sessions, each subject rested for 5 minutes in the supine position. The subject was then asked to take a breath and expire forcefully through a mouthpiece with a small air-leak, maintaining for 15 seconds a pressure of 40 mm Hg monitored on a pressure gauge connected to the mouthpiece. All data were continuously acquired over the 5 minute period during which BP returned to baseline. The VM was repeated twice and the signal showing the more prominent VM oscillation by visual inspection was selected.2 was measured from a mask using an infrared end tidal volume CO2 monitor (Datex Ohmeda, Madison WI). The right and left MCAs were insonated from the temporal windows, by placing the 2-MHz probe in the temporal area above the zygomatic arch using a transcranial Doppler ultrasonography system (MultiDop X4, DWL Neuroscan Inc, Sterling, VA). Each probe was positioned to record the maximal BFV and fixed at a desired angle using a three-dimensional positioning system attached to the light-metal probe holder. Special attention was given to stabilize the probes, since their steady position is crucial for reliable, continuous BFV recordings. BFV and all cardiovascular analog signals were continuously acquired at 200 Hz and exported at 50 Hz for off-line post-processing. Data were visually inspected and occasional extrasystoles and outlier data points were removed using linear interpolation. Fourier transform of the Doppler shift  was used to calculate BFV. BFVs in the MCA correlate with invasive measurements of blood flow with xenon clearance [The experiments were done in the morning or > 2 hours after the last meal. The electrocardiogram was measured from a modified standard lead II or III using a Spacelab Monitor . Beat-to-beat BP was recorded from a finger with a Finapres device (Ohmeda Monitoring Systems, Englewood CO), which is based on a photoplethysmographic volume clamp method. During the study protocol, BP was verified by arterial tonometry. With finger position at the heart level and temperature kept constant, the Finapres device can reliably track intraarterial BP changes over prolonged periods of time . Respiralearance , laser Dlearance  and posilearance . Since Mlearance .To quantify the dependency between cerebral blood flow and systemic pressure, we developed a novel computational procedure, called multimodal pressure-flow (MMPF) analysis. The MMPF analysis implemented the Hilbert-Huang transformation  techniquR  BP oscillation induced by the VM. We applied the empirical mode decomposition (EMD) technique developed by Huang et al.  The EMD VM signal to calculate its instantaneous phases. This phase was then used as a reference coordinate both for BP and BFV signals. From this point on, the term phase refers to the phase of the BPVM oscillation during the VM. Unlike the Fourier transform, the Hilbert transform does not assume that signals are composed of superimposed sinusoidal oscillations of constant amplitude and frequency. Real-world biological fluctuations, such as BP and BFV, are not stationary and are better described by analytical methods that can quantify variations of amplitude and frequency.In the next step of the MMPF analysis, we applied the Hilbert transform to the BPS(t) = ∑k Sk (t), where Sk are empirical modes that fulfill certain criteria of the original signal [Mathematically, the first two steps of the MMPF algorithm can be summarized in the following way: Any complex signal s(t) can be represented as the superimposition of more basic (simpler) components: l signal . For eacAk (t), and instantaneous phase, φk (t), can be calculated bywhere the Cauchy principal value is taken in the integral. Instantaneous amplitude, VM profile from the first peak at the beginning of the VM to the subsequent BPVM maximum forms a complete phase cycle from 0–360° over 30–40 seconds. We assigned the first peak at the beginning of the VM to phase 0°, the BPVM minimum during the VM to phase 180° and the subsequent BPVM maximum to phase 360°. To quantify the relationships between BP and BFV signals, we measured the BP-BFV phase shift, defined as the difference between the phase at the BPR minimum (maximum) value and the phase at the BFVR minimum (maximum) value. We have calculated phase values for all data points in this interval and, in principle, we can measure the phase shift at all point of the VM cycle. However, for simplicity, we only calculated the phase shift at the minimum and maximum of these two signals for statistical analysis. Since these BP-BFV phase shifts reflect dynamical changes in peripheral and cerebral vascular tone over the course of the VM, they can be used as a sensitive index of cerebral autoregulation dynamics in normal and pathological conditions.The BPWe also assessed autoregulation using a standard index, calculated using the second-order differential equation model proposed by Tiecks et al This modR and BFVR values and phases. Two-way analysis of variance was used for side-to-side comparisons of BFV between groups (JMP-5.0 SAS Institute, Cary, NC). For the group comparisons, we used the BFVR in the right and left MCAs for the normotensive and hypertensive groups compared to the BFVR in the stroke-side and non-stroke side MCA in the stroke group. Age was different between the groups (p < 0.003). However, age and stroke subtypes had no significant effects when included as co-variants in the analysis. Data are presented as mean ± SE.We used one-way analysis of variance for between-group comparisons of baseline, minimum and maximum BPR and BFVR signals are superimposed on the raw waveforms. For comparison, we show similar signals (the right, non-stroke side, MCA and the left, stroke side, MCA) for a 48 year old woman with the left temporal stroke in Figure R and BFVR for the right (non-stroke side) MCA and for the left (stroke side) MCA are shown in the bottom three panels as a function of the phase. With normal autoregulation, the BFVR changes precede BPR changes over the course of the VM. Therefore, the phase at the BFVR minimum is smaller than the phase at the BPR minimum. (Phase at the BFVR minimum for the right MCA = 129° and the left MCA = 112° vs. the phase at BPR minimum = 178°.) In contrast, with post-stroke cerebral autoregulation, the phase at the BFVR minimum is similar to the phase at the BPR minimum, suggesting that BFV is dependent on blood pressure. (Phase at the BFVR minimum for the non-stroke MCA = 173° and for the stroke MCA = 180° vs. the phase at BPR minimum = 185°).Figure R values and the phases at the BFVR minimum and maximum values for the right MCA in the normotensive and hypertensive groups and for the non-stroke side MCA in the stroke group. Figure R values and the phases for the left MCA in the normotensive and hypertensive group, and the stroke side MCA in the stroke group. Mean BPR values and corresponding phases are shown in panel C. The phase at BFVR minimum was different between groups for the right (non-stroke side) (p = 0.0005) and left (stroke side) (p = 0.004) MCAs. In the normotensive group, the phase at BFVR minimum was shorter than the phase at BPR minimum. In the stroke group, the phase at BFVR minimum was similar to the phase at BPR minimum. The phase at BFVR minimum was greater in the non-stroke (p = 0.002) and stroke (p = 0.03) MCAs, compared to the normotensive group. In the hypertensive group, the phase at BFVR minimum was also greater for the right (p = 0.0007) and left (p = 0.006) MCAs compared to the normotensive group. No significant differences were found between the stroke and hypertensive groups.Figure R minimum was similar between groups (normotensive group = 172.7 ± 3.9°, hypertensive group = 178.6 ± 2.0°, stroke group = 178 ± 1.9°). Average BPR values were higher in the stroke and hypertensive groups compared to the normotensive group at baseline (p = 0.001), at BPR minimum (p = 0.054) and at BPR maximum (p = 0.0001)  Figure . AverageR maximum preceded the phase at BPR maximum  (p = 0.009) and left (stroke side) (p = 0.003) MCAs. In the stroke group, the phase at BFVR maximum was similar to the phase at BPR maximum. The phase was greater for the non-stroke side (p = 0.008) and stroke side (p = 0.03) MCAs, compared to the normotensive group. In the hypertensive group, the phase at BFVR maximum was also greater for the right (p = 0.005) and left (p = 0.009) MCAs compared to the normotensive group. The phase at BPR maximum was similar among groups (normotensive group = 354 ± 4.7°, hypertensive group = 360.1 ± 2.2° stroke group = 364.1 ± 4.3°).In the normotensive group, the phase at BFVm Figure . The phaR and BFVR minimum and between the phases at BPR and BFVR maximum for the right MCA in normotensive and hypertensive groups and for the non-stroke side MCA in the stroke group. The BP-BFV phase shifts at the minimum points were smaller in the stroke and hypertensive groups compared to the normotensive group (p = 0.009). The BP-BFV phase shifts at the maximum points were smaller in stroke and hypertensive groups compared to the normotensive group (p = 0.03). Figure R and BFVR minima was about 60 degrees corresponding to a time difference of about 3.6 seconds. In contrast, in the stroke and hypertensive groups, the time difference between BFV and BP phases was < 0.5 second.Figure The standard autoregulation index was not different between groups and between MCAs in both hemispheres Table . The ratR minima (p = 0.01) and maxima (p = 0.04) were greater in the stroke-normotensive group compared to the normotensive group. For the stroke side MCA, the phases at BFVR minima (p = 0.04) and maxima (NS, p = 0.08) were greater in the stroke-normotensive group compared to the normotensive group. The phases at BFVR minima and maxima were not different between the stroke-hypertensive and hypertensive groups for both MCAs. No significant differences were found between the stroke and non-stroke side MCA.In a subset analysis, we separated stroke-normotensive (N = 6) and stroke-hypertensive subjects (N = 9) and compared them to the non-stroke normotensive and hypertensive groups. For the non-stroke side MCA, the phases at BFVR and BFVR amplitudes were computed for each data sample to construct a continuous phase diagram. The BPR and BFVR profiles were similar over the course of the VM, but the phase relationships were different. The autoregulation indices, calculated using the standard methods, did not differentiate the groups.This study introduces a new technique, based on the Hilbert-Huang transformation ,18, termR minimum and maximum were smaller than BPR phases, reflecting an active vasoregulatory process. With delayed or impaired autoregulation, BFV becomes synchronized with blood pressure. In the stroke group, the BFVR and BPR phase diagrams were similar, suggesting that cerebral blood flow was entrained by systemic BP. In the stroke and hypertensive groups, BFV phases at the BFVR minimum and maximum were greater compared to the normotensive group. The BP-BFV phase shifts were smaller in the stroke and hypertensive group, compared to healthy controls.Cerebral vasoregulation compensates for rapid BP and BFV transitions over the course of the VM. A sudden and parallel increase in intrathoracic  and cereMethods evaluating dynamic cerebral autoregulation that use the Fourier transform-based coherence and transfer functions assume a linear relationship between stationary signals. Coherence, phase and gain derived from the transfer function of spontaneous BP and BFV fluctuations have been used to assess autoregulation. These analyses have shown a significant phase lead of cerebral BFV with respect to systemic BP ,20,21. HPrevious studies indicated bilateral impairment of the dynamics of pressure autoregulation after an acute ,3 and suThere are several limitations of this study: 1) The MMPF method was implemented for the VM, which is widely used for clinical autonomic testing. The VM allows noninvasive evaluation of pressure autoregulation, and testing can be completed in less than 5 minutes. However, the VM requires active patient cooperation, and may not be advisable in acute stroke settings where change in intracranial pressure should be avoided. 2) This study evaluated a population of younger subjects with minor stroke. A larger cohort is needed to determine the effects of ischemic stroke subtypes on the dynamics of autoregulation. 3) Age was different between groups; however, it had no significant effect on BP-BFV relationship in our analysis. Aging and cardiovascular risk factors exert significant but distinct effects on regulation of cerebral blood flow. The vasomotor reactivity to hypercarbia declines with aging and hypertension, while the dynamics of pressure regulation can be preserved . This efMultimodal pressure-flow analysis is a new method that enables evaluation of short nonstationary time-series not suitable for Fourier–based techniques. The MMPF method provides high time and frequency resolution and permits construction of instantaneous phase diagrams on a beat-to-beat basis. This method may be particularly useful as a complementary measure of cerebral autoregulation for the short and nonstationary time series acquired during provocative interventions such as the VM. Application of this method reveals that the regulation of BP-BFV dynamics is altered in both hemispheres after minor stroke, rendering blood flow dependent on blood pressure. Hypertension without stroke is also associated with delayed BP-BFV dynamics.BP = blood pressureR = residual BP calculated by MMPF methodBPVM = empirical mode of BP corresponding to the dominant VM oscillationBPBFV = blood flow velocity in the MCAR = residual BFV calculated by MMPF methodBFVEMD = empirical mode decompositionHHT = Hilbert-Huang TransformMCA = middle cerebral arteryMMPF = multimodal pressure-flow analysisVM = Valsalva maneuverThe author(s) declare that they have no competing interests.VN – designed the study, conducted the experiments, participated in the analysis and wrote the first draft of the manuscript. ACY – contributed to the MMPF development and analysis; LL – conducted the data and statistical analysis. ALG – contributed to the method application, data interpretation and manuscript preparation. LAL – contributed to data interpretation and manuscript preparation. CKP – designed and developed MMPF method, contributed to data analysis and manuscript preparation.


FADD (Fas Associated protein with Death Domain) is a key adaptor molecule transmitting the death signal mediated by death receptors. In addition, this multiple functional protein is implicated in survival/proliferation and cell cycle progression. FADD functions are regulated via cellular sublocalization, protein phosphorylation, and inhibitory molecules. In the present review, we focus on the role of the FADD adaptor in cancer. Increasing evidence shows that defects in FADD protein expression are associated with tumor progression both in mice and humans. Better knowledge of the mechanisms leading to regulation of FADD functions will improve understanding of tumor growth and the immune escape mechanisms, and could open a new field for therapeutic interventions. The FADD gene is located on chromosome 11q13.3 in humans and 7 in mice . For insHuman and mouse FADD genes have the same quite simple organization consisting of two exons  separated by a unique intron of approximately 2 kb. Interestingly, no cap site was reported on the human FADD mRNA [Human and mouse FADD proteins are very similar Figure . They coSince the first role ascribed to FADD was to transmit apoptotic signals through its interaction with death receptors expressed at the cell membrane, it was assumed that FADD protein was exclusively localized in the cytoplasm of the cell. However, a nuclear localization sequence (NLS) and a nuclear export sequence (NES) were recently identified in the human FADD protein Figure , and accin utero at day 12.5 of development, due to underdevelopment, abdominal hemorrhage, and cardiac failure. Moreover, FADD loss of function did not result in a lymphoproliferative disorder as observed in viable Fas mutant mice [The essential role of the FADD molecule was highlighted by generating FADD mutant null mice ,12. Indeant mice ,14. ThesFADD is the main signal transducing intermediate adaptor molecule of several death receptors including Fas, TNF-R1 (tumor necrosis factor receptor 1), DR3 (death receptor 3), TRAIL-R1 (TNF-related apoptosis-inducing ligand, DR4), and TRAIL-R2 (DR5) ,12,15. AControl of FADD recruitment to the DISC can occur following several mechanisms depending on the cell type and the death receptor . The besAll the main death receptors described up to now require FADD adaptor for transmitting their apoptotic signal. Consequently, FADD is a central protein that controls multiple essential cellular processes including cellular homeostasis and elimination of pathological cells, particularly during the course of an immune response.in vivo, and increasing evidences suggest that DEF could be artefactual structures resulting from protein over-expression. As a consequence, the ability of endogenous FADD to aggregate and form DEF in normal situation should be reconsidered.Formation of cytoplasmic death effector filaments (DEF) by oligomerization of DED-containing proteins, including FADD, is responsible for death receptor independent cellular apoptosis . Indeed,-/- mice presented a lack of thymocytes compared to wild type animals, with few or no CD4+ CD8+ double positive thymocytes remaining [-/- T lymphocytes [-/- T lymphocytes entered the cell cycle upon mitogenic stimulation, but died during progression through the cell cycle [Beside being a main death adaptor molecule, FADD is also required for T cell proliferation. The first evidence of this property of FADD came from observations made in chimeric FADD knockout mice. Five-week-old chimeric FADDemaining . Moreoveemaining ,26-30. Tphocytes ,29,30. R-/- lymphocytes, resulting in spontaneous entry and progression through the cell cycle of 10% of freshly isolated FADD-/- T cells (as compared to less than 2% of wild type T cells) [In addition to impairing survival during cell division, FADD deficiency also leads to a dysregulation of the cell cycle machinery . The patT cells) .in vivo [+ CD8+ thymocytes and defective progression through the cell cycle [The mechanisms responsible for FADD regulation of cell cycle progression are not fully understood. However, the phosphorylation of the 194 human and 191 mouse Ser of the protein (Figure in vivo . Replacein vivo , includill cycle ,31.Tumor cells are constantly cycling cells. Although it does not seem to directly affect the cell cycle progression, FADD phosphorylation at Ser 194 sensitizes these cells to reagents that induce G2/M arrest such as the Taxol anticancer drug . In huma+/+ RAG-1-/- mice did not [-/- RAG-1+/+ or FADD-/- RAG-1+/- mice, demonstrating that absence of FADD expression was necessary but not sufficient to induce tumor development in this model [in vivo.The role of the FADD adaptor in cancer was initially demonstrated by generating RAG-1 deficient transgenic mice that target expression of a FADD dominant negative mutant in lymphocytes. With age these mice developed thymic lymphoblastic lymphoma, whereas FADD did not . Moreoveis model . These rUsing a mouse model of thyroid adenoma/adenocarcinoma, we showed spontaneous disappearance of FADD protein expression during the course of tumor development . The so de novo AML treated homogeneously, and found that leukemic cells of 2/3 of patients at diagnosis expressed low or no FADD protein [We recently showed that absence of FADD protein expression in cancer cells is also a relevant phenomenon in human malignancies . We look protein . Moreove protein . Importa protein . As a coThe fact that absence of FADD expression was found in different types of tumor cells both in mice and humans strongly suggested that absence of FADD contributed to tumor development. Indeed, lack of FADD protein can confer numerous advantages on pathological cells, which predominantly result in tumor survival/growth gain Figure .-/- TFC as well as FADD-/- AML cells were resistant to Fas- and TNF-α-mediated apoptosis [in vitro, a process requiring FADD molecule expression [Fas, TRAIL-Rs, TNF-R1, DR3, and potentially other receptors use FADD adaptor for transmitting the death signal. Thus, absence of FADD expression in tumor cells must confer multiple resistance of these cells to death receptor cytotoxicity. In agreement with this assumption, we observed that FADDpoptosis , and unppression -42,44,45As described above, absence of FADD allows co-expression of death receptors and ligands without inducing autocrine or paracrine apoptosis of tumor cells. For instance, although the Fas receptor was expressed at all stages of thyroid tumor development in gsp mice, FasL expression was gained with a high expression in adenomatous/adenocarcinomatous glands . Using t+ FADD- adenomatous/adenocarcinomatous thyroid from gsp mice, as well as on human AML cells, may allow both autocrine and paracrine proliferation of these cells [Since the tumor counter-attack hypothesis cannot apply to all types of cancer cells, one could wonder whether other benefits of death ligand expression by tumor cells exists. Previous reports demonstrated that Fas signaling could lead to proliferation instead of apoptosis, depending on the cell type and the environmental conditions -58. For se cells ,37 (FiguAbsence of FADD expression could confer multiple growth advantages on cancer cells Figure , and is Molecules implicated in FADD regulation of expression also represent new therapeutic targets. However, very little is known about the mechanisms leading to absent/low FADD protein expression in tumor cells. In SCC-9 and SCC-25 carcinoma cell lines, carboplatin upregulated FADD protein expression by increasing FADD mRNA . HoweverFADD is mainly known as a key adaptor molecule for numerous death receptors. However, increasing evidences have shown that FADD is a much more complex molecule implicated in apoptosis, survival, cell cycle progression, and proliferation of the cells. Therefore, FADD plays a central role in the frightening control of cell death and life. As a consequence, a defect in the FADD molecule can contribute to the development of diseases, and particularly cancer. Absence of FADD protein expression is a marker of tumor development in the mouse, and a prognostic factor for poor response to chemotherapy in humans. Since FADD deficiency could contribute to several malignancies, in view of its almost ubiquitous pattern of expression, study of the role of FADD in tumor development, growth, and resistance to treatment, and understanding how the expression of this puzzling molecule is regulated, are targets that merit further investigation.LT conceived the review and drafted the manuscript. GC participated in conceiving the review. AB and GC participated in the preparation of the manuscript. All authors read and approved the final manuscript.


Health-Related Quality of Life (HRQoL) measures are becoming increasingly used in clinical trials as primary outcome measures. Investigators are now asking statisticians for advice on how to analyse studies that have used HRQoL outcomes.HRQoL outcomes, like the SF-36, are usually measured on an ordinal scale. However, most investigators assume that there exists an underlying continuous latent variable that measures HRQoL, and that the actual measured outcomes (the ordered categories), reflect contiguous intervals along this continuum.t-test and linear regression that assume Normality and constant variance may not be appropriate. For this reason, conventional statistical advice would suggest that non-parametric methods be used to analyse HRQoL data. The bootstrap is one such computer intensive non-parametric method for analysing data.The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis such as the t-tests, linear regression, summary measures and General Linear Models.We used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters, in four datasets (which illustrate the different aspects of study design). We then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes. The standard methods included t-test and linear regression are robust to the violations of assumptions that HRQoL data are likely to cause . While particular to our datasets, these findings are likely to generalise to other HRQoL outcomes, which have discrete, bounded and skewed distributions. Future research with other HRQoL outcome measures, interventions and populations, is required to confirm this conclusion.Overall, in the datasets we studied, using the SF-36 outcome, bootstrap methods produce results similar to conventional statistical methods. This is likely because the  Health Related Quality of Life (HRQoL) measures are now frequently used in clinical trials and health services research, both as primary and secondary endpoints . InvestiHRQoL measures such as the Short Form (SF)-36, Nottingham Health Profile (NHP) and European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 are described in Fayers and Machin  and are This ordinal scaling of HRQoL measures means they generate data with discrete, bounded and non-standard distributions, which may lead to several problems in determining sample size and analysing the data ,3. SinceThe bootstrap ,5 is a dt-tests; multiple regression/analysis of covariance (ANCOVA) models fitted via ordinary least squares (OLS); response feature analysis or summary measures such as the Area Under the Curve (AUC) [Conventional methods of analysis of HRQoL outcomes are extensively described in Fayers and Machin  and Fairve (AUC)  and Geneve (AUC) ] fitted ve (AUC) .The remainder of this paper is structured into the following sections. The SF-36 HRQoL outcome is briefly described in Section 2. Section 2 also describes how the bootstrap can be used for hypothesis testing and confidence interval estimation. Section 2 ends with a description of the four example datasets. The results of conventional methods of analysis and bootstrap methods are compared in Section 3. Other issues, such as withdrawals and study sizes are discussed in Section 4. The final section (5) ends with a summary and conclusions.to pull oneself up by one's bootstraps". The phrase is thought to be based on one of the eighteenth century Adventures of Baron Munchausen by Rudolph Erich Raspe. The Baron had fallen to the bottom of a deep lake. Just when it looked like all was lost, he thought to pick himself up by his own bootstraps [Page 5, [The term bootstrap derives from the phrase "[Page 5, ]!bootstrap sample, and each provides an estimate of the parameter of interest, e.g. mean. The "with replacement" means that any observation can be sampled more than once in each bootstrap sample. It is important because sampling without replacement would simply give a random permutation of the original data, with many statistics such as the mean being exactly the same [Page 115, [The basic idea of the bootstrap involves repeated random sampling with replacement from the original data, to produce random samples of the same size of the original sample, each of which is known as a The bootstrap is mainly used as a method for assessing statistical accuracy i.e. SE, biases and CIs. Throughout this paper we shall use the observed value of the test statistic or parameter estimate as our best guess at the true value of the unknown parameter or statistic. For example, if we are interested in estimating the population mean (from a random sample) it may seem that the best estimator of the mean of the population is the mean of all the bootstrap estimates. This is turns out not to be the case as the mean of the all the bootstrap means is biased. The observed sample mean, from the original data, is always the best estimate of the population mean. The same result applies for other statistics such as the median and regression coefficients.th to the 975th largest values. This is known as the percentile method and although it is an obvious choice, it is not the best method for bootstrapping confidence intervals, because it can have a bias, which one can estimate and correct for. This leads to methods such as the preferred bias corrected and accelerated (BCa) method [a bootstrap confidence intervals using the bootstrap procedure in STATA v8 [Suppose we wish to calculate a 95% confidence interval for a mean HRQoL from a sample. We take a random sample, with replacement from this data, of the same size as the original sample, and calculate the mean HRQoL of the data, in this bootstrap random sample. We do this repeatedly, a large number of times, say 1000. So we now have 1000 bootstrap samples, and 1000 estimates of the sample mean, one from each bootstrap sample. If these 1000 bootstrap sample means are ordered in increasing value, a bootstrap 95% confidence interval for the mean would be from the 25) method ,5. UsingSTATA v8 ., where  and  are the lower and upper bounds of the interval respectively, can be described by its length and shape,According to Efron and Tibshirani [Page 180, ] each in. The standard Normal based intervals are symmetrical about , and hence have shape = 1.00. Shape is a measure of skewness of the CI about the point estimate. A shape > 1.00, implies the CI is positively skewed, with a long tail to the right, whereas shape < 1.00 implies the CI is negatively skewed.'Shape' measures the symmetry of the interval about the point estimate test statistic and a null distribution for the data under the null hypothesis. Given these, we generate bootstrap values of the test statistic under the null distribution for the data and estimate the achieved significance level (ASL) by calculating the proportion of the bootstrap values of the test statistics, which are greater than or equal to the observed value of the test statistic from the original data.Bootstrap methods can also be used for hypothesis testing. The two quantities that we must choose when carrying out a bootstrap hypothesis test are a Several bootstrap test statistics are available for comparing the distribution of sample data in two independent groups. In considering a bootstrap hypothesis for comparing the two means, there is no compelling reason to assume equal variances and so we do not make this assumption. We used a bootstrap test statistic for comparing two means that use only the assumption of a common mean, under the null hypothesis [Page 224, [case and model  resampling.Standard errors and CIs for regression coefficients can also be obtained using bootstrap methods. Two different approaches are possible, y = a + bx, where y is the outcome variable and x is a predictor or explanatory variable, a is the intercept and b is the slope or gradient of the line, with n (x, y) pairs of HRQoL observations. Then case-based resampling involves drawing a bootstrap sample of size n, with replacement from these n pairs. Ordinary least squares (OLS) are then used to estimate the regression coefficients for this bootstrap sample of paired cases. Again we do this repeatedly, say 1000 times, so we now have 1000 bootstrap samples and 1000 estimates of the regression coefficients, one from each bootstrap sample. The standard error of these estimated coefficients is simply the standard deviation of these 1000 estimates. As before we can calculate BCa confidence intervals for these estimated regression coefficients.For example with the simple linear model, x, y) pairs have been drawn by random sampling from a population. However, case based resampling is less appealing if the x values were controlled for in some way, perhaps by the design of the study. In this situation the alternative model or residual based procedures could be used.Case-based resampling may be entirely natural for situations where it is plausible that the  to generate new bootstrap values for the outcome variable. Ordinary least squares are then used to estimate the new bootstrap regression coefficients, for this bootstrap sample. This process  is repeated lots of times to estimate standard errors and confidence intervals for the regression coefficients from the bootstrap samples.parametric bootstrap" when the residuals from a parametric model are bootstrapped to give estimates of the standard error of the parameters. There is considerable debate about which form of resampling is more appropriate. Both forms of resampling can easily be implemented in STATA [Thus model based resampling is an example of the "in STATA  and S-PLin STATA . We now The SF-36 is one of the most commonly used HRQoL measures in the world today. It contains 36 questions measuring health across eight different dimensions – physical functioning (PF), role limitation because of physical health (RP), social functioning (SF), vitality (VT), bodily pain (BP), mental health (MH), role limitation because of emotional problems (RE) and general health (GH). Responses to each question within a dimension are combined to generate a score from 0 to 100, where 100 indicates "good health" . Thus, tThere now follows a brief description of the four datasets which are used throughout the rest of this paper. These datasets illustrate the use of HRQoL outcomes across a variety of study designs. There are three types of study: observational , two group randomised controlled trial (RCT) and longitudinal RCT .Intervention (n = 311) or Control (n = 312) groups. The intervention consisted of up to 10 home visits in the first postnatal month of up to three hours duration by a community postnatal support worker (CPSW). The main outcomes were HRQoL as measured by the SF-36 at six weeks postnatally. This study is unusual since no baseline HRQoL assessment was made. It was felt that it was inappropriate to assess HRQoL just prior to or immediately after childbirth.This RCT aimed to establish the relative cost-effectiveness of postnatal support in the community compared to the usual care provided by community midwives. Six hundred and twenty-three postnatal women were allocated at random to t-test and Mann-Whitney tests) and bootstrap hypothesis tests. We will also compare standard Normal theory (t-test) based CIs with their bootstrap BCa equivalent.Our analysis is based on the 495 responders to the six-week postnatal questionnaire who completed all 36 items of the SF-36. This sample consisted of 241 women in the Control group and 254 women in the Intervention group. We will use this data to illustrate methods for simple two group cross-sectional comparisons of HRQoL scores using conventional (e.g. Surgery and 112 patients attending Rheumatology outpatient clinics. At the six-month follow-up assessment, 211 patients returned the questionnaire (109 and 102 in the Surgery and Rheumatology groups respectively). The data used here are based on the 211 patients returning both assessments.The aim of this longitudinal observational study was to evaluate two condition specific and two generic health status questionnaires for measuring HRQoL in patients with Osteoarthritis (OA) of the Knee, and offer guidance to clinicians and researchers in choosing between them. Patients were recruited from two settings, knee surgery waiting listings and rheumatology clinics. Four self-completion questionnaires including the SF-36 were sent to the subjects on two occasions 6 months apart. Two hundred and thirty patients returned the questionnaire at initial assessment, consisting of 118 patients awaiting total knee replacement (TKR) Since there was a difference in the baseline HRQoL and sociodemographic characteristics (age and gender) of the Clinic and Surgery groups, we use this dataset to illustrate multiple regression/ANCOVA methods with follow-up HRQoL as the outcome variable and baseline HRQoL, age, gender and group as covariates. We compare the conventional ordinary least squares (OLS) estimates of standard error (SE) and Confidence Interval (CI) for the group regression coefficient with their bootstrap counterparts.Clinic group) or usual care at home by the district nursing service (Home group). The primary outcome was time to complete ulcer healing over the one-year follow-up. Secondary outcomes included HRQoL as measured by the SF-36 at baseline, three months and 12 months follow-up.The aim of this RCT, with one year of follow-up, was to establish the relative cost-effectiveness of community leg ulcer clinics that use four layer compression bandaging versus usual care provided by district nurses. Two hundred and thirty-three patients with venous leg ulcers were allocated at random to intervention (120) or control group (113). The intervention consisted of weekly treatment with four layer bandaging in leg ulcer clinic (t-test) based CIs with their bootstrap BCa equivalent.We use these data to illustrate the use of summary measures such as the AUC for analysing longitudinal data, using conventional and bootstrap hypothesis tests. We will also compare standard Normal theory  versus placebo plus methotrexate (Placebo) in patients with early severe rheumatoid arthritis (RA). The primary efficacy variable in this study was the attainment of American College of Rheumatology (ACR) criteria for improvement of rheumatoid arthritis. Secondary efficacy variables included patient assessment of health related quality of life (HRQoL).The Early Rheumatoid Arthritis or NAMEIT trial was a 48-week, randomised, double blind study to compare Neoral with methotrexate , Weeks 8, 16, 24, 32, 40, and Week 48 at the end of the study or at the time of premature withdrawal from the trial.Three hundred and six subjects at 48 centres were actually entered into the study. One hundred and fifty-two subjects receiving methotrexate were randomised to the Neoral treatment group and 154 subjects receiving methotrexate were randomised to the Placebo group. Of the 306 subjects randomised, 227 completed the study. Seventy-nine randomised subjects discontinued from the study prior to completion.We use these data to illustrate more complex statistical models for analysing longitudinal data e.g. a marginal GLM fitted with GEEs and compare bootstrap SEs and CIs for the parameters with their conventionally estimated counterparts.Figures t-test  and Mann-Whitney (MW) comparisons of the eight SF-36 dimension scores. If we assume a cut-off of p ≤ 0.05 for statistical significance, then the t-test suggests significant differences on two dimensions of the SF-36: RP and SF. On two other dimensions PF (p = 0.060) and BP (p = 0.065) the p-values are close to the arbitrary cut-off of 0.05, suggesting some differences although these may not be statistically reliable. The results of the MW tests suggest significant differences on four dimensions (PF, RP, BP and SF) of the SF-36. The only major contrast between the interpretation of the results of the MW and t-tests is on the BP and PF dimensions, where the former test suggests a difference and later not.Table p-values from a bootstrap hypothesis tests with the p-values from the standard two sample t-test with equal variances, and the MW test. Although they report quantitatively different p-values, the magnitudes are similar, and if we use a cut-off of p < 0.05 for statistical significance then the qualitative interpretation of the tests is the same. So in this example dataset there appears to be little advantage in using the bootstrap hypothesis tests compared to conventional hypothesis tests, such as the t-test, for testing equality of means.The last column of Table MW test, is that they do not allow for the estimation of confidence intervals for parameters or allow for the adjustment of confounding variables such as baseline covariates. One way to estimate non-parametric CIs is via the bootstrap method. Table t-test  based confidence intervals with the bootstrap BCa ones.A major limitation of non-parametric methods, such as the a CIs is almost symmetric about the point estimate of the mean difference except for the RE dimension, where there is some evidence of asymmetry. So again in this example dataset there appears little advantage in using the bootstrap BCa confidence intervals compared to conventional methods of confidence interval estimation.The estimates and lengths of the CIs are almost identical. Table MW tests suggests a significant difference (in distributions) for the PF, RP, BP and SF dimensions, but the bootstrap and Normal confidence limits for two out of four of these dimensions (PF and BP) includes zero; suggesting no differences in the mean HRQoL between the groups.The bootstrap  confidence intervals are calculated for a characteristic of the distributions (for example mean difference). The groups may have differences in distributions but similar characteristics e.g. mean . For exa0 is true. Whereas when confidence intervals for mean differences between two groups are estimated the resampling is carried out separately for each group. A useful analogy is with the comparison of proportions in two independent groups. Here the standard error for the hypothesis test is different to the standard error of the difference between the observed proportions used for estimating a confidence interval [Page 45, [When a hypothesis is tested using the bootstrap, the resampling is carried out assuming the null hypothesis HPage 45, ].Table We were interested in seeing whether or not there was a difference in HRQoL in OA patients after TKR surgery compared with pharmacologically treated patients. From previous studies using the SF-36 we know that HRQoL varies with age and gender ,20. SincThe analysis involved using OLS to fit the multiple regression model with six month follow-up HRQoL as the outcome variable and age in years at baseline; gender of the patient ; baseline HRQoL and treatment group variable (coded 0 = Clinic, 1 = Surgery) as explanatory covariates. The group regression coefficient estimate represents the difference in six-month follow-up HRQoL between the Rheumatology Clinic and TKR Surgery groups after adjustment for the patient's age, gender and baseline HRQoL. A positive value for the regression coefficient indicates the Surgery group has a better mean HRQoL at six months follow-up than the Clinic group after adjustment for the other covariates.Table The regression analysis suggests that at six month follow-up TKR surgical patients have significantly better HRQoL than Rheumatology treated clinic patients on five dimensions of the SF-36 (PF, BP, GH, V and SF) after adjustment for age, gender and baseline HRQoL. As can be seen from Table Qualitatively all of the intervals from the three methods either include or exclude zero so the interpretation of the group regression coefficient is the same. Therefore, again in this example dataset, there appears to be little advantage in using bootstrap case or model based re-sampling to estimate standard errors and confidence intervals compared to conventional methods of confidence interval estimation from the OLS multiple regression model.We are interested in comparing the HRQoL over the one-year follow-up between the Home and Clinic treated groups. The two groups were well matched at baseline for age, gender and HRQoL, except for the RE dimension of the SF-36, where there was some reliable statistical evidence of a difference (p = 0.052).The overall HRQoL of the leg ulcer patients over the 12-month study period (and three HRQoL assessments) can be summarised by the AUC. If we set the time units for the AUC calculation as a fraction of a year, then an AUC value of 100 implies the leg ulcer patient has been in "good health" for the entire 12-month follow-up period. Conversely an AUC value of 0 implies the leg ulcer patient has been in "poor health" for the entire 12-month follow-up period.t-test, the MW test and the bootstrap hypothesis test.Table 7 See  gives thp-values from the t-test and the ASL from the bootstrap hypothesis tests are very similar. None of the p-values for the eight SF-36 dimensions are less than 0.05. Therefore there is no reliable statistical evidence to suggest a difference in mean AUC between the Clinic and Home treated leg-ulcer patients. Only the results of the MW test on the RE dimension of the SF-36 provide (p = 0.071) any evidence of a difference in AUC distributions between the groups, although even this p-value is not statistically significant using the conventional cut-off of 0.05.The t-test with the bootstrap BCa limits. The lengths of the intervals are very similar, although the bootstrap BCa intervals tend to have a non-symmetric shape. All the estimated CIs include zero, again suggesting no evidence of a difference in mean AUC (HRQoL) between the Clinic and Home group patients in the Leg Ulcer study.The table also contrasts the Normal theory based CI estimates from the In the Early RA study, HRQoL assessment was carried out at 0, 8, 16, 24, 32, 40 and 48 weeks. With seven repeated HRQoL measurements, such as this, the best approach is to model the longitudinal data using GLMs.The modelling of longitudinal data takes into account the fact that successive HRQoL assessments by a particular subject are likely to be correlated. We used a marginal model with the Early RA data and used GEEs to estimate the regression coefficients. Marginal models are appropriate when inferences about the population average are the focus. For example, in a clinical trial the average difference between control and treatment is most important, not the difference for any one individual. In a marginal model, the regression of the response on explanatory variables is modelled separately from the within-person correlation.auto- or serial correlation, which is the strength of the association between successive longitudinal measurements of a single HRQoL variable on the same patient.The marginal model is an extension of the linear regression model used with the OA Knee data. Longitudinal models require the specification of the independent (sometimes termed random) if the off diagonal terms of the auto-correlation matrix are zero. The repeated HRQoL observations on the same subject are then independent of each other, and can be regarded as though they were observations from different individuals. On the other hand, if all the correlations are approximately equal or uniform then the matrix of correlation coefficients is termed exchangeable, or compound symmetric. This means that we can re-order (exchange) the successive observations in any way we choose in our data file without affecting the pattern in the correlation matrix. As the time or lag between successive observations increases, the auto-correlation between the observations decreases. A correlation matrix of this form is said to have an autoregressive structure .Several underlying patterns of the auto-correlation matrix are used in the modelling of HRQoL data. The error structure is Table The process of fitting marginal models using GEE begins by assuming the simple independence form for the autocorrelation matrix, and fitting the model as if each assessment were from a different patient. Once this model is obtained the corresponding residuals are calculated and these are then used to estimate the autocorrelation matrix assuming it is of the exchangeable (or autoregressive) type. This matrix is then used to fit the model again, the residuals are once more calculated, and the autocorrelation matrix obtained. The iteration process is repeated until the corresponding regression coefficients that are obtained in the successive models converge or differ little on successive occasions .Fayers and Machin [Pages 183–202,  emphasisThe overlapping lines on some of the graphs in Figure The marginal model we used for the Early RA data for analysing the seven HRQoL assessments over time was,Yij = β1 + βBasexBase_i + βAgexAge_i + βSexxSex_i + βTimetij + βGroupxGroup_i + εij,     (2)Yij is the HRQoL at time tij post-baseline; tij is the time of the QoL assessment, in weeks post baseline, of patient i at visit j; xBase_i is the baseline HRQoL assessment for subject i; xAge_i is the age (in years) of subject i at time 0 (baseline); xSex_i is the gender of subject i; xGroup_i is the treatment group  for subject i; β1 is a constant and εij is the residual error.where The marginal regression models were fitted in STATA  using thNone of the interaction term coefficients for the eight SF-36 dimensions were statistically significant (from zero). Thus there was no reliable evidence of a 'Treatment × Time' interaction on any dimension of the SF-36 (p > 0.05), irrespective of the autocorrelation structure. Therefore we will only report the results of the simpler model (2), without the interaction term.The beauty of the marginal model and the GEE methodology is that it is very flexible and can in principle deal with all the observed data from a HRQoL study. The subjects are not required to have exactly the same numbers of assessments, and even the assessments can be made at variable times. The latter allows the modelling to proceed even if a subject misses a HRQoL assessment. So it seems unrealistic and unreasonable to use bootstrap resampling methods for marginal models that can only utilise a balanced data set, with equally spaced QoL assessments. Since we are interested in fitting a marginal model and we are likely to have an unbalanced dataset with unequal observations per subject we used simple bootstrap case-resampling.Figure p-values for the treatment group regression coefficients in Table Table The bootstrap and robust standard errors for the time and group coefficients are different, although the bootstrap SE estimate tends to be the same size or somewhat smaller than its robust counterpart. However both bootstrap and robust SE estimates are of a similar order of magnitude. More importantly, the ratios of the estimated coefficient to its standard error are of similar size.A crude test of statistical significance is to examine this ratio, if it is bigger than 2.0 then the estimated regression coefficient is likely to be significantly different from zero. Table a confidence intervals with the model- based estimates in Table When we compare the bootstrap BCThe use of the bootstrap to estimate SEs and CIs for marginal longitudinal models appears to offer little advantage (in the Early RA data) compared to the conventional robust estimates.In the datasets and outcomes studied, and for the specific conventional analyses we used, we have shown that use of the bootstrap does not lead to different p-values, SE and CI estimates compared to conventional methods. On this basis, we cannot conclude the use of the bootstrap is more appropriate than conventional methods. The explanation for this conclusion and the extent of its generalisability deserve discussion.t-test when applied to three-, four- and five point ordinal scaled data using assigned scores, in sample sizes as small as 20 subjects per group. Sullivan and D'Agostino [t-test on difference scores are robust and produce actual significance levels close to the nominal significance levels.One of the fundamental assumptions we have made, is that there exists an underlying continuous latent variable that measures HRQoL, and that the actual measured outcomes are ordered categories that reflect contiguous intervals along this continuum. If the goal of the analysis is to assess the magnitude of the treatment effect on this ordered outcome, then an appealing approach is to assign numeric scores to the ordered categories and then to compare means between groups using conventional linear regression methods. If interest lies elsewhere, for example in comparing the relative frequencies of cumulative probabilities in the ordered categories between treatments, then other techniques such as the proportional odds model would be more appropriate ,2,21. HeAgostino  have expThe generalisability of the results could be called into question as they only apply to the limited number of datasets studied (four) and the SF-36 outcome. The SF-36 outcome is the most widely used generic HRQoL measure in the world today, so that is one obvious reason to use it . SecondlThe SF-36 is a multi-dimensional outcome with eight dimensions. As described in the Introduction the eight dimensions have a variety of distributions. We believe these distributions are not atypical of other generic HRQoL measures such as the NHP and EORTC QLQ-C30. The distributions we considered were chosen based on our experiences with HRQoL data in a variety of settings. So we believe that our results about the bootstrap may have generalisability to other HRQoL outcomes (besides the SF-36) used in other studies and populations, although strictly speaking our results only apply to the SF-36 outcome and the observed datasets. Hence, we cannot make sweeping generalisations about the impact of the bootstrap on other HRQoL outcomes, used in other studies. Therefore, these results need to be replicated with other HRQoL measures in other datasets and populations.It should be noted that in the all four example datasets there is missing data. We assumed that any missing HRQoL values in these datasets were Missing Completely at Random (MCAR). This means that the probability of the HRQoL response being missing is independent of the scores on the previous observed questionnaires and independent of the current and future scores had they been observed. We have assumed that the reduced dataset represents a randomly drawn sub-sample of the full dataset and the inferences drawn can be considered reasonable. This is a strong assumption and unlikely to hold for missing HRQoL data ,7,23-25.t-test and ANCOVA, for three-, four- and five point ordinal scale data using assigned scores has been demonstrated for sample sizes as small as 20 ["In very small samples even a badly fitting parametric analysis may outperform a non-parametric analysis, by providing less variable results at the expense of a tolerable amount of bias."The various datasets used in this study all had a sample size in excess of 100 patients. Some caution should be used in applying the results to smaller sample sizes. However the robustness of the conventional two-sample ll as 20 ,27. Simpll as 20 ] states,The results with the OA Knee data show that there is little to choose from between the case and model based resampling for the multiple linear regression model for estimating SEs and CIs. Since there was very little difference in the SE and CI estimates from the datasets used, for simplicity one would tend to favour a case based resampling approach. Indeed this was the resampling method for the longitudinal marginal model for the Early RA data.In the longitudinal Early RA for simplicity we used only a simple case based resampling for the marginal model and effectively carried out a stratified random resampling with replacement. That is we sampled with replacement blocks or clusters of each patients' repeated HRQoL responses. In theory, one should be able to use model or residual based resampling for the marginal model. The resampling procedure would be rather complex particularly for autoregressive autocorrelation structures and for unbalanced datasets, with HRQoL assessments at unequally spaced time points. One would have to take into account that the residuals were not independent and uncorrelated, and for the autoregressive correlation structure, that the correlation between residuals within a patient declined over time. This is a very interesting avenue and requires further exploration with other longitudinal datasets.Finally, are the results all that surprising or unexpected? We have shown that the use of bootstrap methods for analysis  appears to offer little advantage compared to standard methods in the four datasets studied.If we assume that there exists an underlying continuous latent variable that quantifies the HRQoL response of interest and that the goal of the analysis is to assess the magnitude of a treatment effect on the HRQoL outcome, by comparing means between groups. Then statistical theory says that if the distribution of the HRQoL data is Normal, so will be the distribution of the sample mean. Much more importantly, even if the distribution of HRQoL data is not Normal, as is frequently the case, that of the sample mean will become closer to a Normal distribution as the sample size gets larger. This is a consequence of the Central Limit Theorem (CLT) [Pages 304-7, ]. The NoThus, if the investigator is planning a large study and the sample mean is an appropriate summary measure of the HRQoL outcome, then pragmatically there is no need to worry about the distribution of the HRQoL outcome and we can use standard methods to estimate sample sizes and analyse the data. Since dramatic effects are unlikely in HRQoL studies using the SF-36 as an outcome, large samples sizes are likely to be required ,3,6. So So our research using the SF-36 HRQoL outcome and the four datasets has shown that bootstrap methods appear to produce p-values, SEs and CIs similar to conventional methods. When the standard and the bootstrap methods agree, we can be more confident about the inference we are making and this is an important use of the bootstrap [Page 118, [t-test and OLS multiple regression are robust to the violations of assumptions that HRQoL data are likely to cause . While particular to our datasets, these findings are likely to generalise to other HRQoL outcomes, which have discrete, bounded and skewed distributions. They may not generalise to HRQoL studies with smaller sample sizes of less than 100 subjects. Future research with other HRQoL outcome measures, interventions and populations, is required to confirm this conclusion.In the datasets we studied, using the SF-36 as an outcome measure, bootstrap methods produce results similar to conventional statistical methods. This is likely because the Table 7 – Leg Ulcer study simple cross-sectional comparison of AUC for Home vs. Clinic GroupsClick here for file


Physicians' awareness of their important role in defusing the obesity epidemic has increased. However, the number of family practitioners who treat obesity problems continues to be low. Self-efficacy refers to the belief in one's ability to organize and execute the courses of action required to produce given attainments. Thus, practitioners who judge themselves incapable of managing obesity do not even try. We hypothesized that practitioners' self-efficacy and motivation would be enhanced as a result of participating in an interactive course designed to enrich their knowledge of obesity management.t-test. The interviews were analyzed by qualitative methods.Twenty-nine family practitioners participated in the course, which was accompanied by qualitative interviews. The difference between the physicians' pre-course and post-course appraisals was tested by paired p < 0.0005). A deeper insight on the practitioners' self-efficacy processes was gained through reflection of the practitioners on their self-efficacy during the interviews.Post-course efficacy appraisals were significantly higher than pre-course appraisals (Up-to-date information and workshops where skills, attitudes and social support were addressed were important in making the program effective. In tht survey  in 2000.Guidelines published in 1996 for the management of obesity recommended setting a modest weight loss and weight maintenance, rather than a targeted ideal weight, as goals . Lately,Many chronic health problems are exacerbated by unhealthy behaviors and harmful environmental conditions. From the psychological perspective, healthful lifestyles and environmental conditions may yield large health benefits. The widespread adoption of a healthier lifestyle rather than medical technologies has resulted in a substantial decline in premature mortality and morbidity . The medSelf-efficacy refers to the belief in one's ability to organize and execute the courses of action required to produce a given attainment . Such beSelf-efficacy is measured by the strength of a subject's beliefs in the ability to execute requisite activities. Social cognitive theory distinguishes among three basic processes of personal change: the adoption, general usage and maintenance over time of new behavioral patterns . EfficacTo our knowledge and after a literature search, no intervention studies have yet been conducted to change the self-efficacy of FPs in Israel towards treating obese people. Among other courses for which physicians usually receive credits towards their annual professional training, a course was offered by the Israeli Academic Medical Council. The course was initiated by the Israeli Association of FPs and recommended by the Medical Professional Journal of FPs in Israel. Registration in the course was open to all FPs. The present research was a preliminary study. This being the case, the first group of FPs who attended the new course participated in it. Though that group contained a small number of subjects, the importance of investigating a new program contribution to FPs self-efficacy for future research was evident.The objectives of the course were to enrich the knowledge of FPs with up-to-date information on obesity and to raise their motivation to treat it. The study objective was to determine if an interactive course would raise the self-efficacy of FPs to treat obesity. It was hypothesized that the self-efficacy of FPs would be enhanced as a result of participating in an interactive obesity-treatment course.Twenty-nine FPs  chose to participate in the course along with other Continuing Medical Education (CME) courses. All participants work as FPs in public health care clinics throughout the country.This study was based on a one group, pre-course – post-course test design, without a control group. It was accompanied by qualitative interviews to validate the results of the data analysis.The strength of the efficacy beliefs of the FPs to treat obesity was estimated by using a five point scale Likert type questionnaire containing nine items Table . The subThe Alpha Cronbach for the reliability of the tests was α = 0.88 for the pre-course test and α = 0.90 for the post-course test Table .The following aspects of structure validity were examined: (a) for the content aspect, the items matched the domain concept map. Final version was rewritten on the basis of experts' and researchers' comments. The items were finally presented in an nonsequential order to make subjects think while reading the questions and not relate to earlier answers; and (b) for the substantive aspect, FPs were interviewed regarding their self-efficacy to assure that the questions were clear and did not require rewording. The interview was a 30 min. open interview. A physician was given open questions such as: "Describe your feelings and thoughts of efficacy to treat obesity" or "How the self-control lectured help your efficacy perceptions". The subject was free to speak openly on the issue. The interview was actually a verbal reflection of thoughts and feelings of the subjects. It was recorded and later on transcribed by the researchers. The interview was analyzed by the constant comparative qualitative method of analysis ,21. Evert-test in order to compare pre and post strength of efficacy beliefs for treating obesity.The present study design used paired Participants answered the questionnaire before the start of the course and at the end of the last session, and a few days later participated in an open interview. The course was interactive and contained 12 clinical and psychological lectures given by experts in all subjects relating to obesity Table . Every lFPs filled in clinical report forms and presented the cases they treated and tools they used. The cases were discussed during the course and feedback was given by colleagues and experts. The course supplied the FPs with knowledge, skills and psychological tools such as: food and physical activity diaries, decision making tools, self-evaluation tools, self-report tools, self-monitoring tools, persuading tools, stimulus control tools and counter-conditioning tools, to treat obesity.Medical knowledge and skills were not examined. The course attempted to raise the self-efficacy of FPs by creating a supportive atmosphere, providing feedback, recalling successful experiences, learning from models, bringing patients into the workshops, discussing sociological and psychological problems and allowing reflection of thoughts and emotions. Studies have shown that reflection enhances metacognitive processes such as: self-monitoring, self-evaluation, self-reaction and attribution ,22-25. St-test  and the results of the qualitative analysis indicate that the criteria derived from the interviews matched those of the questionnaire. When speaking openly, FPs addressed the same issues that made up the domain theoretical concept map.Several researchers have suggested that quantitative efforts in the study of self-efficacy should be complemented by qualitative studies aimed at gaining a deeper understanding of attitudes and emotions -24. The The study showed that acquisition of knowledge and skills enable a person to meet personal standards of merit that tend to heighten beliefs of personal efficacy . In judgThe impact of the questions analyzed in this study extends beyond the issues asked by the questionnaire. New insights were gained through qualitative analysis: FPs analyzed the process they experienced during the course. They described how their efficacy beliefs were enhanced through reflection, feedback and the supportive climate of the course. Studies have shown that reflection enhances self-efficacy processes since self-appraisal of efficacy is structured by experience and reflective thought . FPs repIn summary, an effective program of widespread change in health practices includes four major components. The first is informational and intended to increase the physician's awareness and knowledge of the subject. The second involves development of skills needed to translate informed concerns into effective action. The third is aimed at building a robust sense of self-efficacy to support the exercise of control in the face of difficulties that inevitably arise. This is achieved by providing repeated opportunities for guided practice and corrective feedback in applying the skills in simulated situations that people are likely to encounter. The final component involves creating social support for desired changes. The present program contained all these components.The limitations of the study were the small number of participants and the reliance on one motivated group of FPs. These limitations result from the fact that this was the first course organized by the Israeli Association of Family Physicians on the subject of obesity. It was important to study the effect of the program on FPs self-efficacy for future continuing education and research.We recommend studying the effect of interactive courses on the lifestyle and weight loss of FPs. Future research should consider randomized samples from larger courses and analyses of the correlation between course success and actual FPs performance, FPs' self-efficacy and treatment outcomes e.g: BMI change, Lipids and BP. We also recommend studying the differences in obesity treatment outcomes and in general health feelings between patients whose FPs attended obesity courses and patients whose FPs did not.Practical recommendations for Continuing Medical Education planners would be to focus on workshops rather than on lectures, to enhance those processes the FPs felt less efficacious to go through and to have guidance of Endocrinology experts' as well as Psychology and Education specialists to improve communication with patients and their families in an effort to enhance motivation to loose weight. It is also recommended to bring patients to the workshops to reflect on the treatment they have received.The author(s} declare that they have no competing interests.SK and AF conceived and designed the study, participated in the collection, analysis and interpretation of data and drafted the manuscript. SV Participated in the statistical analysis, interpretation of data and draft of the manuscript. SP participated in the design of the study, data collection and interpretation. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


IGFBP3 gene; one upstream of the promoter and one intronic. We previously reported a hot spot of promoter hypermethylation of IGFBP-3 in human hepatocellular carcinomas and derivative cell lines. As the hot spot locates at the putative upstream p53 consensus sequences, these p53 consensus sequences are really functional is a question to be answered.Insulin-like growth factor binding protein (IGFBP)-3 functions as a carrier of insulin-like growth factors (IGFs) in circulation and a mediator of the growth suppression signal in cells. There are two reported p53 regulatory regions in the In this study, we examined the p53 consensus sequences upstream of the IGFBP-3 promoter for the p53 induced expression of IGFBP-3. Deletion, mutagenesis, and methylation constructs of IGFBP-3 promoter were assessed in the human hepatoblastoma cell line HepG2 for promoter activity.In vitro methylation of these p53 consensus sequences also suppressed IGFBP-3 expression. In contrast, the expression of IGFBP-3 was not affected in the absence of p53 overexpression. Further, we observed by electrophoresis mobility shift assay that p53 binding to the promoter region was diminished when methylated.Deletions and mutations of these sequences completely abolished the expression of IGFBP-3 in the presence of p53 overexpression. From these observations, we conclude that four out of eleven p53 consensus sequences upstream of the IGFBP-3 promoter are essential for the p53 induced expression of IGFBP-3, and hypermethylation of these sequences selectively suppresses p53 induced IGFBP-3 expression in HepG2 cells. Insulin-like growth factor binding protein (IGFBP)-3 is a multifunctional protein ferrying insulin-like growth factors (IGFs) in circulation and mediating growth suppression signals in cells. Serum IGFBP-3 protein (< 5000 ng/ml) complexes with IGFs and an acid labile subunit (ALS), to extend the half lives and modulate the bio-availability of IGFs . While aIGFBP-3 is produced in most tissues, but the main site of production is liver. It is produced by non-parenchymal cells  while parenchymal cells (hepatocytes) do not produce it under normal condition . We postet al. were not confirmed until now [et al. indicated. Thus, we postulated that these sites are important for the expression of IGFBP-3 induced by p53. Moreover, we hypothesized that the suppression of apoptosis mediated by IGFBP-3 due to the promoter hypermethylation will be a possible pathway of hepatocarcinogenesis. To explore this possibility, the functions of the promoter upstream binding sites of p53 were examined precisely in this study.Promoter analysis of IGFBP-3 indicated that the NaB-RE sequence is essential for the sodium butyrate (NaB) induced IGFBP-3 expression , but thentil now . The met2.HepG2 cells were obtained from Japanese Cancer Research Resources Bank (Tokyo, Japan), and maintained in D-MEM supplemented with 10 % FCS (Life Technologies, Tokyo, Japan), antibiotic-antimycotics at 37°C in a humidified atmosphere of 95 % air and 5 % COMlu I-Bgl II fragment to pGL2-Basic  of pGL2-210 in vitro with Sss I methylase (New England Biolabs, Inc., Beverly, MA), and reconstituting the fragment and unmethylated vector , carries a 1.9 kb IGFBP-3 promoter (-1805/+69) in pGL2-Basic (Promega Corp., Madison, WI). A series of deletion mutant constructs, pGL2-270, pGL2-240, pGL2-210, pGL2-180, pGL2-150, pGL2-120, pGL2-90, pGL2-60, pGL2-30, and pGL2-1 containing the indicated fragments upstream of the transcription start site and 60 bp of fragments downstream of the transcription start site, were generated by PCR amplification of the promoter fragment and subsequent subcloning of the e et al. . The pla4 cells/well in 24-well plates. After 24 hours, cells were transfected with 0.25 μg/well of reporter plasmid DNA in serum-containing medium. Forty-eight hours post transfection, cells were washed twice with PBS and collected for luciferase assays. Transfections were performed in quadruplicate and experiments were performed at least two times.HepG2 cells were transiently transfected using the FuGENE 6 transfection reagent according to the manufacturer's instructions . Cells were seeded at a density of 5 × 10Luciferase activities of cell lysates were measured according to the manufacturer's instructions (Promega Corp. Madison, WI) using a liquid scintillation counter (Aloka, LSC-700, Tokyo, Japan). Luciferase activities were normalized for total protein determined using the Bradford Assay (Bio-Rad Laboratories, In., Hercules, CA).Mlu I-Bgl II fragment of pGL-210 were labelled using [α-32P]dCTP by end filling with Klenow fragment, and used for EMSA. Oligonucleotide DNAs (BP3WPSF: 5'-GGCTGCAGCG GGCGTGCGCA CGAGGAGCAG GTGCCCGGGC GAGTCTCGAG CTGCACGCCC CCGAGCTCGG-3', BP3WPSR: 5'-CCGAGCTCGG GGGCGTGCAG CTCGAGACTC GCCCGGGCAC CTGCTCCTCG TGCGCACGCC CGCTGCAGCC-3'), comprising the promoter sequence of -210/-149, were custom-made (Sigma-genosys Japan, Ishikari, Japan), annealed one hour at room temperature, and used as cold competitor in the assay. EMSA was performed in a 20 μl reaction containing 10 mM Tris (pH 7.5), 2.5% Glycerol, 50 mM KCl, 0.1 mM EDTA, 1 mM DTT, and in the presence of 50 ng/μl of double-stranded poly [d(I-C)], 5000 cpm (2 ng) of 32P-labeled probe DNA, and 2.5 μg of H2O2 treated MCF7 nuclear extract . The reaction mixture was incubated at 14°C for 20 min. 20 μl of each reaction mixture was then loaded onto a native 4 % polyacrylamide gel containing 0.5 × Tris-Glycine buffer (25 mM Tris, 190 mM Glycine, 1 mM EDTA pH 8.3), and electrophoresed at 14°C, 100 V for 1 hr. For the supershift assay, a p53 antibody (Ab-2, Oncogene Research Products, San Diego, CA) was used.278 bp of the et al. [We identified a hot spot of promoter hypermethylation in human hepatocellular carcinoma and its cell lines in the promoter upstream region of IGFBP-3 Fig. . As methet al. , but difTo confirm the importance of p53 binding sites between -210 to -150, we abrogated one of p53 binding sites by site-directed mutagenesis . But similar patterns compared to site directed mutation were observed. Although the expression of IGFBP-3 was slightly enhanced in the absence of p53 overexpression in the methylated construct, it was decreased in the presence of p53 overexpression in the methylated construct  were incubated with HWe have indicated that the p53 binding sites upstream of IGFBP-3 promoter are essential for its induction by p53, and that the induction can be suppressed by promoter hypermethylation in the human hepatoblastoma cell line HepG2. A working model of the p53 action in IGFBP-3 promoter upstream binding sites is summarized in Fig. IGFBP3 is a true tumor suppressor gene. IGFBP3 is a gene that is silenced by biallelic hypermethylation or hypermethylation and loss of heterogeneity (LOH) in human hepatocellular carcinoma. As reported recently [Our observations of promoter hypermethylation in human hepatocellular carcinomas and derivative cell lines (12), and the observations in this report strongly support the notion that recently , we haverecently , prostatrecently , kidneysrecently , and lunrecently , so on. recently , TGF-β /recently -25, or aIGFBP-3 is a ubiquitous, multifunctional protein, whose importance as a carrier of IGFs is evident. The absence of gross loss-of-function mutations of IGFBP-3 observed to date likely underscores its functional importance. We hypothesize that IGFBP-3 is a gene whose basal level of expression is essential for cell survival, but upon induction by p53, high levels of expression of IGFBP-3 induces apoptosis. Alternatively, IGFBP-3 may be a gene that is essential for cell survival when induced by growth hormones or IGFs, but functions as an apoptotic mediator when induced by p53.We observed slight base changes within p53 binding sites strongly influenced the induction of IGFBP-3 by p53. As SNPs that change the expression level of IGFBP-3 were within p53 binding sites , and it We identified the MyoD (-195/-186) and WT1 (-164/-156) binding sites as well as p53 binding sites at the hypermethylation hot spot in HCC by promoter analysis using TRANSFAC (v 4.0). As MyoD is a transcription factor that can induce apoptosis, and WT1 is a tumor suppressor gene, the existence of a binding site for these putative regulator genes in the hot spot of the promoter of IGFBP-3 suggest the possibility that these genes also use IGFBP-3 as a mediator of their actions.We conclude that four out of eleven p53 consensus sequences upstream of the IGFBP-3 promoter are essential for the p53 induced expression of IGFBP-3, and hypermethylation of these sequences selectively suppresses p53 induced IGFBP-3 expression in HepG2 cells. As IGFBP-3 functions downstream of many growth suppressors and its growth suppression effects are drastic, and as it is a small-sized secreted protein, the use of IGFBP-3 in tumor therapy will be a promising option.ALS; acid labile subunit, D-MEM; Dulbecco's modification of Eagle's medium DTT; dithiothreitol, EDTA; ethylenediaminetetra-acetic acid, EMSA; electrophoresis mobility shift assay, FCS; fetal calf serum, HCC; hepatocellular carcinoma, IGF; Insulin-like growth factor, IGFBP-3; Insulin-like growth factor binding protein-3, LOH; loss of heterogeneity, NaB; sodium butyrate, NaB-RE (sodium butyrate-responsive region), PBS; phosphate buffered saline, PCR; polymerase chain reaction, TGF-β; transforming growth factor-β, TNF-α; tumor necrosis factor-α, SEM; standard error of mean, SNP; single nucleotide polymorphism.The author(s) declare that they have no competing interests.TH carried out these studies and manuscript preparation, KN and YI participated plasmid construction and reporter assay, TS and HS participated the EMSA, EY helped the array study, TO participated in the design of the study and performed the statistical analysis. NK conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Honeybees signal the location of food sources to their hive- mates using a "dancing" flight pattern. Translating these patterns, scientists learn what bees perceive Experimental psychologists working with humans have a fundamental advantage over scientists studying the behaviour of other animals. This is because human subjects can give a verbal account of their experience. For example, they can report: ‘These two lights of different colour look equally bright’ or ‘This object looks further away than that one’. Such direct reports facilitate studying how information from the sensory periphery, that is, the sense organs that actually interface with the environment, is processed in the brain.Apis), which have a symbolic ‘language’ that nestmates use to communicate with each other about profitable food sources. By eavesdropping on this communication, scientists have recently obtained a unique perspective into the perceptual world of insects.The perceptual world of animals is often very different from that of humans. Many animals have sensory facilities that we humans lack; for example, insects can see ultraviolet and polarised light. But how they actually perceive the world, based on information from their sensory periphery, is often beyond our grasp. Because animals cannot describe their sensations, our access to them is often based on indirect psychophysical tests, where animal performance depends fundamentally on motivation and training method . HoweverHow does the dance language work? A triumphant scout bee returns from the field, and advertises the location of a newly discovered food source to nestmates. To do this, the forager performs a repetitive sequence of movements, the so-called waggle dance, which is one of the most intriguing examples of complex animal behaviour. The successful forager wiggles her abdomen provocatively from side to side, moving forward in a straight line. Then she runs in a half circle to the left, back to her starting point, performs another straight wiggle run along the path of her first, and then circles to the right . This paIn the 1940s, Nobel laureate Karl von Frisch deciphered the code hidden in this seemingly senseless choreography performed on vertical honeycombs in the darkness of the hive (reviewed in decreased by more than 50%! This clearly shows that bee perception of distance cannot solely be based on energy expenditure, since a longer flight that cost more energy was danced as a shorter ‘distance’ in the waggle run.But how do bees measure the flight distance that they communicate so precisely? It was previously thought they do this by measuring the energy used as they fly . HoweverSo what actually drives the bee odometer? Because the landscape bees pass in flight moves more slowly when bees fly at higher altitudes, The quality of information available about the velocity of the passing landscape will depend, of course, on the sensitivity of the eyes. The eyes of bees contain three types of colour receptors, with maximum sensitivity in the ultraviolet, blue, and green domains of the spectrum . Their ePLoS Biology, With so many external variables influencing distance estimation, it seems unlikely that the honeybee odometer would be very robust in natural conditions. Now, as reported in this issue of But there must be something else beside visual cues. Navigation over water, in the near absence of visible ground features, is extremely difficult without a reliable internal instrument measuring travel speed. This is the case even for us humans with sophisticated measuring devices: malfunctioning air speed indicators have been responsible for several airplane crashes into water, for example Birgenair Flight 301 and AeroPeru Flight 603 in 1996. decrease than when there is an increase in nectar quality of the same magnitude. Such asymmetric perception of gains and losses is well known in humans, where it has been linked to risk-aversive behaviour (Spying on honeybee dances can not only tell us about the cues they use for navigation, but also allows insights into the cognitive architecture that governs other aspects of bee behaviour, such as the assessment of flower quality. We've learned that bees prefer high over low nectar concentrations because this is reflected in their dances. When bees find better nectar, they dance more enthusiastically, that is, the number of dance circuits per minute increases . Howeverehaviour . Unfortu