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- Transmission of electric energy in the form of electromagnetic field, when the energy transfer efficiency is essential, the power being used to energize the receiving equipment.\n
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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"52976",title:"Family Quality of Life in Autism Spectrum Disorders (ASD)",doi:"10.5772/66201",slug:"family-quality-of-life-in-autism-spectrum-disorders-asd-",body:'\nQuality of life (QoL) is a measure of individual well-being, which includes multiple areas of functioning and is increasingly recognized as a necessary construct in developmental disorder studies. The QoL concept is not new. Happiness and well-being have been discussed by Plato and Aristotle in their writings. QoL has been defined in various ways, from ”general conditions for happiness“ [2] to ”positive life experiences“ [3]. Most researchers consider that social indicators, but also the perception of personal well-being should be considered when measuring QoL and that the objective and subjective indicators are two complementary facets that have to be measured separately [1, 4].
\nQoL is a construct that has been used for a wide range of health problems (drug abuse, mental disorders, oncology, geriatrics, cardiovascular disease, etc.), but lacks a coherent theoretical-derived model or a definition generally accepted [5, 6]. Although there is a general consensus on the main characteristics of the construct [7–9], there are still some issues to be clarified for the development of appropriate QoL measurement instruments: the principles that should guide the measurement process and how should be carried the assessment [10, 11].
\nThe concept of family QoL has been studied systematically only in the recent years. Parents’ experience was considered to be a part of the family life and was initially explored as part of the QoL concept. In the late 1990s, there were initiated two large projects on the families’ QoL, one at Kansas University and the other led by a team of researchers from Australia, Canada, and Israel. The main rationale for these studies was the tendency of governments and other funding sources to assign to the families the care of children and adults with disabilities. This trend was especially important for the families who had a member with intellectual disability that required constant care and supervision. Even though most families are willing to assume this role, many difficulties arise and affect their quality of life [12, 13].
\nThe concept of family QoL emerged as a viable alternative to the use of multiple measurements, which most often caused difficulties in design studies and results reporting because these measurements, developed individually, are rarely suitable to produce a global profile of family status. Given the increasing interest in health status or economic well-being of families and community, these issues were included in the instruments that measure family QoL. Although some researchers have conceptualized and measured some aspects of the family QoL in terms of services available for the child or the family [14], most of them developed a multidimensional construct, with a number of domains representing different areas of the family life, the sum of their ratings indicating the overall family quality of life. That view was shared by many researchers, each of them describing a framework for the domains of the family quality of life. The International Family Quality of Life Project [13, 15, 16] developed an instrument that organizes family QoL in nine domains. Another research team [17] proposed an instrument with six areas. The third research team [18, 19] conceptualized, developed, and tested an instrument that evaluates five domains: family relationships, parenting style, emotional well-being, physical and material wellbeing, and disability support.
\nA way to assess families’ perceptions and needs is to investigate the areas in which they function. One of the problems that arise from using these questionnaires is the difficulty to convince other family members than mothers to answer to them. However, overall satisfaction with the family quality of life can be high, even if there are problems in some areas. Satisfaction varies significantly in each assessed area of the family life. This variation may be associated with the intensity of child\'s behavioral problems or family experience. It is also important to note that family members are different in terms of resilience and coping strategies and that they may report high levels in satisfaction, even if they have many difficulties and need support [20]. The concept of family quality of life is a multidimensional construct, involving a variety of physical, economic, and emotional dimensions, which cannot be properly measured by focusing only on the perspective of the mother. In a systematic review on the conceptualization and assessment of the effects on families who have children with disabilities, among the 20 studies evaluated, 14 used assessments focused on family welfare, family adjustment, and functioning. Among these studies, 55% were focused on the maternal perspective, 35% included both parents, and only one study included the siblings of children with disabilities [21].
\nThe use of variables such as stress and depression to study the effect of disability on families has been criticized because these variables do not take an objective view on the families’ experience and do not consider the possibility that many families experience positive effects and adapt well to raising a child with disabilities [22].
\nStress level was studied in relation to parents’ mental health QoL in several studies [23–25]. Lee et al. found stress to be a significant predictor of mental health QoL, after controlling for five demographic variables (e.g., age, education, income, number of children in family, and severity of child with disability). Stress was negatively correlated with all aspects of QoL including physical, psychological, social, and environmental HRQOL [26].
\nIn another study on parents of children with autism spectrum disorders (ASD), Johnson et al. evaluated the relationships between parents’ stress, family functioning, and QoL. They found that parents’ stress had a negative impact on mental health outcomes [27]. These results are supported by other studies and confirm the negative impact of stress on psychological outcomes among parents of children with ASD (e.g., depression, anxiety, and adjustment) [28, 29].
\nSmith et al evaluated the depressive symptoms of the mother of children and adolescents with ASD. A third of them had clinically significant depressive symptoms [30]. Wallace et al. suggested that parents’ depression is associated with the intensity of child repetitive behaviors and their anxiety with child social communication problems [31]. Family history of depression or shyness had the greatest influence on children socialization scores. Parents of children with autism were more likely to be hospitalized for a mental disorder than those in the control group, and depression and personality disorders were more common in mothers than fathers [32].
\nSocial support, family functioning, and parents’ coping mechanisms are factors that mediate the quality of life of parents of children with ASD [33]. High social support is associated with low levels of anxiety, depression, and stress. The high degree of cohesion and adaptability of the family was shown to be protective against the potential negative effects of raising a child with ASD.
\nAnother factors found to be associated with parental QoL are child’s symptom severity and behavioral problems [25, 26, 34]. Benson et al. studied the impact of child ASD symptoms severity on parents’ depression, the results suggesting that both child symptoms severity and parental stress predicted depression and that the effect of child symptom severity on parental depression was partially mediated by stress [23].
\nIn a study that investigated the QoL of the main caregiver of children with ASD, the results suggested that the most important predictors for caregiver’ physical health were the intensity of children behavioral problems and social support and for their mental health, the level of child functional impairment, social support, use of maladaptive coping strategies, and perceived difficulty. The impact on mental health was more important than that on physical health, which was only slightly below the norms for general population [35].
\nCoping refers to the cognitive and behavioral effort of an individual to adapt to a stressful situation [36]. Studies in autism reported that the frequent use of emotion-oriented coping is associated with parents’ low quality of life [37]. In contrast to these findings, Lee et al. found no association between coping strategies and QoL [26].
\nFamily emotional environment, including the expression of different types of emotions, help children to develop emotional competence. A chronically poor emotional expression of parents and high levels of negative emotions have significant implications for children emotion regulation skill development (failure in understanding emotions, poor management of stressful emotional situations, and incorrect social use of emotions). Chronic suppression of emotional experiences is associated with higher subjective distress and on a long term with emotional regulation deficits.
\nThere are some data suggesting that parental capacity to assure their children health and adaptability may be affected by their emotional state and well-being. Parental stress can influence parents’ and children’s coping resources and affect their ability to perform [38, 39].
\nA study that investigated the quality of life of 286 children with ASD, compared with the norms for general population and the population with chronic somatic disorders, reported that the children with ASD had lower scores for the psychosocial, emotional, and social functioning, but comparative to the population with chronic somatic disorders for physical and school functioning. QoL did not significantly correlate with the diagnosis of ASD or the intellectual abilities, but was significantly associated with the internalizing/externalizing problems measured by child Behavior checklist (CBCL), with the repetitive behaviors, social responsiveness, and adaptive behaviors. Given the fact that the socialization difficulties that characterize ASD are not specific to chronic somatic disorders, such as asthma or diabetes, it is not surprising that children with ASD had lower scores on the psychosocial functioning domain [39]. Storch et al. found a stronger correlation between the CBCL scores and quality of life for the internalizing problems [40]. This stronger association with the internalizing problems is not necessarily surprising for children with ASD, given that withdrawal is an important clinical feature in ASD and also a significant part of internalization scores measured by CBCL.
\nStudies on QoL of the caregivers of children with ASD reported different results. Allik et al. found the behavioral problems and not the autism severity as predictors for the mental health of mothers of children with ASD. This study included parents of children with Asperger syndrome and high-functioning autism, which could influence the results related to autism severity, these forms of ASD associating a lower symptoms severity [41].
\nStuart and McGrew reported a protective effect for social support and a negative one for the use of maladaptive coping strategies, on the subjective difficulty reported by the parents of children with autism. The problems in family functioning also contributed to the level of difficulties reported [42]. These results are similar to those described by Altiere and von Kluge, who found a more frequent use of the adaptive coping mechanisms and social support for the parents of children with autism who had better family relationships than for those with family problems or separated [43].
\nFor children with autism, it was demonstrated a strong correlation between their behavioral problems, the autism symptoms severity and parental stress. Osborne and Reed reported that the parental stress levels have a negative influence on the child outcomes after receiving different educational interventions. The negative effects were observed in both cognitive and adaptive behavior acquisitions [44]. The reason why high levels of parental stress have a negative impact on child\'s behavioral problems and are predictive for poor outcomes in therapy has not been elucidated yet. The most plausible explanation is that the parental stress produces changes in the parent\'s ability to adjust and respond to child\'s behavioral problems.
\nThe aim of this study was to assess the mediating role of parents’ irrational beliefs and automatic negative thoughts in the relationship between the children’s emotional/behavioral problems and the parents’ emotional distress. Our goal was also to investigate whether in this population (families of the children with ASD or attention deficit hyperactivity disorder (ADHD)), the potential mediators (irrational cognitions, negative automatic thoughts, and coping strategies) relate significantly with the emotional distress reported and to assess the parents irrational cognitions and negative automatic thoughts as mediators in the relationship between the assessment of the overall family quality of life and their emotional distress [1]. Few studies have been reported on this topic, especially, much less in families of the children with ASD or ADHD.
\nThe results from previous studies [45, 46] showed no differences between the two diagnosis categories regarding the parents’ levels of emotional distress and the overall assessment of family QoL, so analyses were performed on all studied clinical sample.
The data were collected from 114 children aged 2–14 years, diagnosed with ASD or ADHD, according to international diagnosis criteria DSM IV-TR and ICD-10 and from their parents (65 children with ASD and 49 children with ADHD).
\nChildren and adolescents included in the study were recruited between January 2011 and November 2011. They were patients in the Child and Adolescent Psychiatry Clinic from Cluj-Napoca, Romania or included in a therapy program in various specialized centers from Romania (ClujNapoca, Sibiu, Oradea, Arad, and Tg. Jiu). Children diagnosed and treated in this clinic come from all parts of the country and are diverse in terms of socioeconomic status.
\nWe included in the study: boys or girls with ages between 2 and 14 years, with diagnosis of autistic spectrum disorder (ASD) or attention deficit hyperactivity disorder (ADHD), according to DSM IV-TR international criteria; sign consent of parents to be included in the study. We excluded from the study: the children with a known medical condition (heart or lung and chronic disorders); children who suffered accidents or had major stress or in the last 6 months, which may significantly affect the family quality of life; the children placed in foster care [1].
Child Behavior Checklist (CBCL) was used to assess the internalizing/externalizing problems. There are two versions of the instrument, depending on the age of the child: the preschool checklist (CBCL/1½-5) and the school-age version (CBCL/6-18). The CBCL is to be filled in by the parent/caretaker who spends the most time with the child and assesses various problems and child functioning over the past 2 months. It has been one of the most widely used standardized measures for evaluating maladaptive behaviors and emotional problems in children and adolescents. CBCL assesses internalizing (i.e., anxious, depressive, and overcontrolled) and externalizing (i.e., aggressive, hyperactive, noncompliant, and under controlled) behaviors, as well as scores on DSM-IV-related scales (emotional problems, anxiety disorders, ADHD, opposition defiant disorder, and pervasive developmental disorders/somatic problems) [47].
\nTo assess parents’ emotions and emotional regulation strategies, we used the following instruments: profile of affective distress (PAD)—rating scale with 39 items that assesses subjective dimensions of positive and negative emotions (functional and dysfunctional) [47]; white bear suppression inventory (WBSI)—rating scale with 15 items that assesses the use of suppression as a cognitive and emotional coping strategy; self-efficacy scale (SES)—rating scale with 10 items that assesses the perceived self-efficacy; automatic thoughts questionnaire (ATQ)—rating scale with 30 items that measures the negative automatic thoughts frequency; attitudes and beliefs scale—short form (ABSs)—is a global measure of irrationality into adulthood; it has eight items that assess four types of irrational evaluative beliefs (absolutist claims, catastrophic interpretation, low frustration tolerance and negative overall assessment); cognitive-emotional regulation questionnaire (CERQ)—has 36 items and assesses nine cognitive coping strategies that a person uses when experiencing negative events or situations (self-blame; other-blame; rumination; catastrophizing; putting into perspective; positive refocusing; positive reappraisal; acceptance; refocus on planning) [48, 49].
\nFamily quality of life survey (FQOL) was used to assess the family quality of life, instrument designed to assess the quality of life of the families that have one or more members with intellectual or developmental disability. The family quality of life can be approached from different perspectives.
\nThe FQOL has several sections: first section includes the description of the family members; the following nine assess specific areas of family life (health, financial status, family interactions, support from others, and support from services, values influence, career, leisure activities, and integration in community life). Each of these areas has two sections: Section A contains questions of a more general nature and Section B contains questions related to six key concepts for the theoretical construction of the instrument: importance, opportunities, initiative, achievement/current status, stability, and satisfaction. The final section is shorter and is designed to collect the overall impressions regarding the family QoL [50].
\nThe demographics and other data of interest were obtained through a questionnaire containing questions about the patient (age, sex, age at diagnosis, school activities, and treatment) and his family (mother and father’s age, marital status, education level, and occupation).
The study is cross-sectional. Parents and children who agreed to participate to the study received additional information and signed the informed consent. Psychiatric and somatic evaluations were performed for the patients who met the study inclusion and exclusion criteria in order to confirm the diagnosis and detect possible comorbidities. Each child was psychologically evaluated in order to determine the developmental level and symptoms severity. After the clinical interview, the parents/caregivers filled in the CBCL for the assessment of child internalizing/externalizing problems, the emotional assessment rating scales (PAD, WBI, SES, ATQ, ABSs, and CERQ) and the family quality of life questionnaire (FQOL) [1]. Data were supplemented with information from the patients’ charts and other medical documents. For all the children included in the study, we required and obtained the consent to use medical data, ensuring the privacy and subject’s identity protection. The questionnaires were filled in by the mothers for all children included in the study.
Data were collected into a SPSS database (version 17). Analyses were performed to assess the mediating role of parents’ automatic thoughts and irrational beliefs as mediators of the relationship between children’s problems (affective, anxiety, behavioral) and parents’ emotional distress. The same procedure was used to assess the role of parents’ irrational beliefs and automatic thoughts as mediators of the relationship between the global assessment of family QoL and their emotional distress.
\nThe mediation analysis was used following the next steps: first, the mediation criteria were tested according to the following procedure. We tested if the independent variable is a significant predictor of the dependent variable (criterion). In this way, the total effect was identified. Then we tested whether the independent variable is a significant predictor of the mediating variable and if the latter is a significant predictor of the dependent variable. After that, it was tested whether the independent variable predictive value is reduced when it is placed next to the mediating variable in the multiple linear regression equation on the dependent variable. The difference between the total effect and the independent variable predictive value on controlled mediator represents the mediation effect.
\nIts difference from 0 value was tested using the Sobel test, at a probability of the null hypothesis (mediation effect is null) p < 0.05. For the situations where the mediation effect proved to be statistically significant, in the last phase of the analysis, the mediation effect size was calculated. An effect size indicator was used. It represents the proportion of the total effect explained by the mediating variable. This indicator was calculated as: the difference between the total effect and the mediation effect divided by the total effect and multiplied by 100.
The study was conducted in compliance with the international ethical standards set out in the Helsinki Declaration of Human Rights updated. We obtain the approval of the University of Medicine and Pharmacy Cluj-Napoca Ethics Committee to conduct the study. All the parents and children included in the study signed the informed consent and received additional information. Data were used ensuring the privacy and subject’s identity protection.
One hundred and fourteen subjects were enrolled in the study, with ages ranging between 2 and 14 years (M = 6.95 years, SD = 2.67) (see Table 1). In terms of gender distribution, the sample included 84 boys (73.7%) and 30 girls (26.3%), with a clear predominance of male subjects. Sixty-five children (57%) were diagnosed with ASD and 49 with ADHD (43%). After psychological evaluations, 54 (47.37%) children had a proper developmental level, 51 (44.74%) had developmental delay in all areas (cognitive, language, independence, communication), and 9 children had language delay (7.89%). Most children were under psychological or pharmacological therapy.
ASD (N = 65) | ADHD (N = 49) | |
---|---|---|
Child characteristics | ||
Age mean (SD) | 6.46 (2.17) | 7.61 (3.13) |
Gender (% male) | 58.3 | 41.7 |
Developmental level | N (%) | N (%) |
Normal | 15 (23.07) | 39 (79.59) |
Developmental delay | 44 (67.69) | 7 (14.28) |
Language delay | 6 (9.23) | 3 (6.12) |
Pharmacological therapy N (%) | 43 (66.2) | 27 (55.1) |
Psychotherapy N (%) | 60 (92.3) | 24 (48.97) |
Family characteristics | ||
Mothers age mean (SD) | 33.21 (5.58) | 35.46 (6.37) |
Fathers age mean (SD) | 36.79 (5.54) | 37.78 (6.50) |
Marital status N married (%) | 55 (84.6) | 40 (81.6) |
Level of education | N (%) | N (%) |
Less than 12th grade | 4 (6.2) | 8 (16.3) |
Bachelor’s degree | 37 (56.9) | 28 (57.1) |
Graduate degree | 24 (36.9) | 13 (26.6) |
Employment status | ||
N employed (%) | 58 (89.2) | 38 (77.6) |
Sample demographic description.
Out of the 114 participants in the study, 32 (28.07%) were from rural and 82 (71.93%) from urban environment. In both diagnosis groups, most parents were married, with secondary education and employed [1].
\nThe results of our previous studies on the comparison of satisfaction for the nine areas of family quality of life (QoL) among families of children with ASD and ADHD highlighted the lack of statistically significant differences between the two categories of diagnosis in all the domains assessed by FQOL. We also found no statistically significant differences between the two diagnosis groups regarding parents’ emotional distress, meaning that the diagnostic category does not moderate the intensity and/or direction of the relationship between parental emotional distress and the overall assessment of family QoL, so analyses were performed on the whole clinical sample [45, 46].
Previous analysis on the relationship between the child affective, anxiety, and behavioral problems and the overall assessment of the family QoL revealed no significant relationship for both category of diagnosis, TSA and ADHD.
\nThe parents included in the study showed high emotional distress mean scores compared with the general population norms (M = 56.21, SD = 19.64). Analyses performed separately for the two diagnosis categories revealed no statistically significant differences (t (112) = −1.08, p > 0.05) on the level of distress reported.
\nThe scores for negative dysfunctional emotions were average to high when compared with the general population norms (M = 25.1, SD = 10.9), 40% of the parents reporting high and very high levels of negative dysfunctional emotions. Analyses performed separately for the two diagnosis categories revealed no statistically significant differences (t (112) = −1.29, p > 0.05), suggesting that for the studied sample, increased levels of negative dysfunctional emotions and emotional distress, may be due to child developmental problems, but they are not specific to diagnosis.
\nAnalysis of variance (ANOVA) revealed no statistically significant differences between the three groups (non-clinical, sub-clinical, and clinical) for any aspect of parents’ emotional distress measured in the study: emotional distress total score (F = 0.40, p > 0.05), negative dysfunctional emotion total score (F = 0.16, p > 0.05), worry/anxiety dysfunctional emotion score (F = 0.14, p > 0.05), respectively, sadness/depression dysfunctional emotion score (F = 0.16, p > 0.05) (see Table 2).
Variable | Lot | N | Mean (M) | Standard deviation (SD) |
---|---|---|---|---|
Emotional distress total score | Nonclinic | 67 | 55.22 | 19.86 |
Subclinic | 25 | 55.92 | 18.82 | |
Clinic | 22 | 59.59 | 20.41 | |
Total | 114 | 56.21 | 19.64 | |
Negative dysfunctional emotion total score | Nonclinic | 67 | 24.80 | 11.31 |
Subclinic | 25 | 24.84 | 10.66 | |
Clinic | 22 | 26.31 | 10.70 | |
Total | 114 | 25.10 | 10.98 | |
Worry/anxiety dysfunctional emotion score | Nonclinic | 67 | 11.44 | 6.54 |
Subclinic | 25 | 11.24 | 4.53 | |
Clinic | 22 | 12.09 | 4.17 | |
Total | 114 | 11.52 | 5.71 | |
Sadness/depression dysfunctional emotion score | Nonclinic | 67 | 13.35 | 5.56 |
Subclinic | 25 | 13.60 | 6.55 | |
Clinic | 22 | 14.22 | 7.26 | |
Total | 114 | 13.57 | 6.09 |
Central tendency and dispersion indicators of parents’ emotional distress in relation with the intensity of children affective problems.
Comparative analysis of the parents’ emotional distress scores in the three groups (nonclinical, subclinical, and clinical), highlighted a lack of statistically significant differences, as follows: emotional distress total score (F = 0.37, p > 0.05), negative dysfunctional emotion total score (F = 0.39, p > 0.05), worry/anxiety dysfunctional emotion score (F = 0.03, p > 0.05), and sadness/depression dysfunctional emotion score (F = 1.05, p > 0.05) (see Table 3).
Variable | Lot | N | Mean (M) | Standard deviation (SD) |
---|---|---|---|---|
Emotional distress total score | Nonclinic | 58 | 54.68 | 17.75 |
Subclinic | 30 | 57.30 | 20.16 | |
Clinic | 26 | 58.38 | 23.28 | |
Total | 114 | 56.21 | 19.64 | |
Negative dysfunctional emotion total score | Nonclinic | 58 | 24.20 | 10.33 |
Subclinic | 30 | 25.86 | 10.62 | |
Clinic | 26 | 26.23 | 12.90 | |
Total | 114 | 25.10 | 10.98 | |
Worry/anxiety dysfunctional emotion score | Nonclinic | 58 | 11.41 | 6.47 |
Subclinic | 30 | 11.76 | 4.84 | |
Clinic | 26 | 11.50 | 4.98 | |
Total | 114 | 11.52 | 5.71 | |
Sadness/depression dysfunctional emotion score | Nonclinic | 58 | 12.79 | 4.78 |
Subclinic | 30 | 14.10 | 6.05 | |
Clinic | 26 | 14.73 | 8.35 | |
Total | 114 | 13.57 | 6.09 |
Central tendency and dispersion indicators of parents’ emotional distress in relation with the intensity of children anxiety problems.
Comparative analysis of parents’ emotional distress scores in the three groups (nonclinical, subclinical, and clinical) showed a lack of statistically significant differences for the level of emotional distress total score (F = 0.44, p > 0.05), negative dysfunctional emotion total score (F = 0.28, p > 0.05), worry/anxiety dysfunctional emotion score (F = 0.23, p > 0.05), and sadness/depression dysfunctional emotion score (F = 0.48, p > 0.05) (see Table 4) [1].
Variable | Lot | N | Mean (M) | Standard deviation (SD) |
---|---|---|---|---|
Emotional distress total score | Nonclinic | 61 | 55.80 | 21.05 |
Subclinic | 22 | 53.77 | 16.89 | |
Clinic | 31 | 58.77 | 18.86 | |
Total | 114 | 56.21 | 19.64 | |
Negative dysfunctional emotion total score | Nonclinic | 61 | 25.04 | 12.16 |
Subclinic | 22 | 23.81 | 8.53 | |
Clinic | 31 | 26.12 | 10.23 | |
Total | 114 | 25.10 | 10.98 | |
Worry/anxiety dysfunctional emotion score | Nonclinic | 61 | 11.73 | 6.90 |
Subclinic | 22 | 10.77 | 3.89 | |
Clinic | 31 | 11.64 | 4.07 | |
Total | 114 | 11.52 | 5.71 | |
Sadness/depression dysfunctional emotion score | Nonclinic | 61 | 13.31 | 6.11 |
Subclinic | 22 | 13.04 | 5.07 | |
Clinic | 31 | 14.48 | 6.76 | |
Total | 114 | 13.57 | 6.09 |
Central tendency and dispersion indicators of parents’ emotional distress in relation with the intensity of children behavioral problems.
Assuming that the high levels of parental emotional distress is generated among other factors, by the child developmental disorder, we investigated whether in this population (parents of children diagnosed with ASD or ADHD), the potential mediators (irrational beliefs, negative automatic thoughts, coping strategies) relate statistically significant to the reported emotional distress.
\nParents of children included in the study registered high levels of negative automatic thoughts (M = 30.15, SD = 10.39) (more than 69.1% of the general population). For the irrational beliefs measured by ABSs, the mean scores were also very high (M = 11.29, SD = 3.54). Low to medium levels of self-efficacy were reported (M = 30.22, SD = 4.61) (>6.7 of the general population).
\nTable 5 shows the correlations of parents’ automatic thoughts, irrational beliefs, perceived self-efficacy, and suppression coping strategy with their emotional distress scores.
Automatic negative thoughts (ATQ) | Irrational beliefs (ABSs) | Self-efficacy (SES) | Suppression coping strategy (WBSI) | |
---|---|---|---|---|
Emotional distress total score | 0.48** | 0.41** | −0.40** | 0.38** |
Negative dysfunctional emotion total score | 0.49** | 0.39** | −0.37** | 0.36** |
Worry/anxiety dysfunctional emotion score | 0.37** | 0.30** | −0.28** | 0.31** |
Sadness/depression dysfunctional emotion score | 0.53** | 0.43** | −0.41** | 0.36** |
The correlation of parents’ emotional distress scores with their automatic thoughts, irrational beliefs, perceived self-efficacy and use of suppression coping strategy.
Correlation significant at p < 0.01.
Both automatic negative thoughts and irrational beliefs correlate positively and statistically significant with all emotional distress scores, the intensity varying from medium to high. The relationship is positive, meaning that when the intensity of the negative automatic thoughts increases, the emotional distress increases also. In terms of explanatory value, negative automatic thoughts explain between 13% (R2 = 0.13 for worry/anxiety dysfunctional emotion score) and 28% (R2 = 0.28 for sadness/depression dysfunctional emotion score) of the emotional distress variance.
\nFor the irrational beliefs, the relationship is also positive, meaning that when the level of irrationality increases, the emotional distress increases also. The determination coefficients obtained from the square of the correlation coefficients indicate that irrationality explains between 9% (R2 = 0.09 for worry/anxiety dysfunctional emotion scores) and 18% (R2 = 0.18 for sadness/depression dysfunctional emotion score) of the emotional distress variance.
\nThe perceived self-efficacy correlated with all emotional distress scores with values between −0.28 and −0.41. The negative relationship means that, if the self-efficacy reported by parents is higher, the emotional distress will be lower. In terms of explanatory value, the perceived self-efficacy explains between 7.8% (R2 = 0.078 for worry/anxiety dysfunctional emotion score) and 16.8% (R2 = 0.168 for sadness/depression dysfunctional emotion score) of the emotional distress variance.
\nThe suppression coping strategy measured by WBSI correlated with all emotional distress scores with values between r = 0.31 and r = 0.38, statistically significant at p < 0.01. The mean scores (M = 48.51, SD = 11.79) did not exceed the critical cut-off (M > 60).The use of suppression coping strategy (WBSI) explains between 9 (R2 = 0.09 for worry/anxiety dysfunctional emotion score) and 14% (R2 = 0.14 for the emotional distress total score) of the emotional distress variance.
\nRegarding the cognitive coping strategies used, the scores reported by the parents showed above average use for catastrophizing (M = 9.64, SD = 3.82), average use for self-blame (M = 9.66, SD = 3.06), acceptance (M = 13.51, SD = 3.49), rumination (M = 12.24, SD = 3.52), refocus on planning (M = 15.9, SD = 3.25), positive reappraisal (M = 14.37, SD = 3.82), putting into perspective (M = 14.19, SD = 3.76), and below average for positive refocusing (M = 10.89, SD = 3.91) and blame others (M = 7.05, SD = 2.90).
\nAs seen in Table 6, the reported emotional distress correlated statistically significant with the strategies: self-blame (r = 0.32), rumination (r = 0.20), positive refocusing (r = −0.22), positive reappraisal (r = −0.23), catastrophizing (r = 0.37), and blame others (r = 019). The only coping strategies that correlated with all scores of emotional distress measured were self-blame and catastrophizing strategies. Both registered positive relationships meaning that their frequent use associated with higher emotional distress scores. For the self-blame strategy the intensity was mild. The most intense correlation was obtained for the catastrophizing strategy (r = 0.37). The positive refocusing, positive reappraisal, refocus on planning registered negative relationships with different scores of emotional distress, meaning that their frequent use associated with lower emotional distress scores [1].
CERQ coping strategy | Emotional distress total score | Negative dysfunctional emotion total score | Worry/anxiety dysfunctional emotion score | Sadness/depression dysfunctional emotion score |
---|---|---|---|---|
Self-blame | 0.32** | 0.32** | 0.30** | 0.29** |
Acceptance | 0.07 | 0.05 | 0.06 | 0.03 |
Rumination | 0.20* | 0.15 | 0.13 | 0.15 |
Positive refocusing | −0.22* | −0.18* | −0.07 | −0.26** |
Refocus on planning | −0.10 | −0.14 | −0.02 | −0.23* |
Positive reappraisal | −0.23* | −0.25** | −0.13 | −0.34** |
Putting into perspective | −0.06 | −0.09 | −0.03 | −0.14 |
Catastrophizing | 0.37** | 0.38** | 0.32** | 0.38** |
Blame others | 0.19* | 0.14 | 0.10 | 0.15 |
CERQ coping strategy | Emotional distress total score | Negative dysfunctional emotion total score | Worry/anxiety dysfunctional emotion score | Sadness/depression dysfunctional emotion score |
Correlation of parents’ emotional distress with their coping strategies (CERQ).
Correlation significant at p < 0.01.
Correlations significant at p < 0.05.
This mediation model includes the following variables: overall assessment of family quality of life (M = 3.57, SD = 0.65)—predictor variable, emotional distress—criterion variable and irrational cognitions—the mediator variable (Figure 1).
\n\nMediation model 1 testing included the following steps:
\nStep 1. Testing the relationship between the overall assessment of family quality of life and parents reported emotional distress. Simple linear regression analysis proved overall assessment of family quality of life to be a significant predictor for the emotional distress (B = −0.97, β = −0.35, SE = 2.27, p < 0.05). This represents the total effect of the overall assessment of family quality of life on the parents’ emotional distress.
Step 2. Testing the relationship between the overall assessment of family quality of life and parents irrational beliefs. Simple linear regression analysis showed the overall assessment of family quality of life as significant predictor for parents’ irrational cognitions (B= −1.07, β= −0.23, SE = 0.42, p < 0.05).
Step 3. Testing the simultaneous predictive value of the overall assessment of family quality of life and irrational beliefs on parents’ emotional distress. The multiple linear regression analysis showed the predictive value of parents irrational cognitions on their emotional distress, when controlling for the overall assessment of family quality of life (B = 1.93, β = 0.35, SE = 0.47, p < 0.05). It showed also a reduction in the predictive value of the overall assessment of family quality of life on the parents emotional distress, when controlling for their irrational cognitions (B = −6.98, β = −0.27, SE = 2.18, p < 0.05). The mediation effect (the difference in predictive value), delta B = −9.07 – (−6.98), was tested for statistical significance using the Sobel test. The mediating effect was statistically significant, Z = −2.16, p = 0.03. Next, the mediation effect size was calculated as follows: the ratio between the mediation effect and the total effect, multiplied by the value {[−9.07 – (− 6.98)]/ −9.07} × 100. This indicator shows the proportion of relationship between the overall assessments of family quality and parents’ emotional distress explained by irrational cognitions. The mediation size effect was ES = 23%, which expresses a partial mediation effect [1].
The second mediation model includes three variables: family quality of life— predictor variable, parents’ emotional distress—criterion variable and parents\' automatic negative thoughts—the mediator variable (Figure 2).
\nMediation model 1 diagram.
Mediation model 2 diagram.
Mediation model 2 testing included, as in the previous case, the following steps:
\nStep 1. Testing the relationship between the overall assessment of family quality of life and parents reported emotional distress. Simple linear regression analysis showed overall assessment of family quality of life as significant predictor for the emotional distress (B = −0.97, β = −0.35, SE = 2.27, p < 0.05).
Step 2. Testing the relationship between the overall assessment of family quality of life and parents automatic negative thoughts. Simple linear regression analysis showed the overall assessment of family quality of life as significant predictor of parents automatic negative thoughts (B = −4.50, β = −0.33, SE = 1.21, p < 0.05).
Step 3. Testing the simultaneous predictive value of the overall assessment of family quality of life and automatic negative thoughts on parents’ emotional distress. The multiple linear regression analysis showed the predictive value of parents negative automatic thoughts on their emotional distress, when controlling for the overall assessment of family quality of life (B = 0.78, β = 0.41, SE = 0.16, p < 0.05). It showed also a reduction of the predictive value of the overall assessment of family quality of life on the parents emotional distress, when controlling for their automatic negative thoughts (B = −5.54, β = −0.21, SE = 2.19, p < 0.05). The mediation effect, delta B = −9.07 – (−5.54), was tested with the Sobel test. The mediating effect was statistically significant, Z = −2.95, p = 0.003. ES = effect size was 38.9%, which represents a partial mediator effect. From the total effect of the overall assessment of family quality of life on parents’ emotional distress, 38.9% is explained by their automatic negative thoughts [1].
The impact of an autism spectrum disorder (ASD) diagnosis on families is devastating, particularly because there is no curative pharmacological or psychological treatment.
\nIt has been already proven that having a child with special needs involves child-care-related stress, and less time for parents to fulfill their own needs. Compared to typical children\'s parents, parents of children with autism have reported higher family difficulties and are at greater risk for developing physical or psychological problems. Lee et al. investigated the main predictors of mental health for the parents of children with high-functioning autism and reported that physical health, financial status, and low stress are the most critical. The study underlined the need for more research on the potential stress of these parents and stated the need for services targeting the parents’ mental health, in addition to the therapies addressed to the child problems [26].
\nSome studies have reported that parents’ emotional problems correlated positively with child\'s behavioral problem severity and low ability to communicate functionally. Also, a strong correlation between child behavioral problems, autism symptom severity and parental stress was demonstrated. Khanna et al. found a correlation between the physical QoL dimension and child’s behavioral problems [35]. Tung et al. and McStay et al. reported lower QoL among parents of children with higher levels of externalizing problems [24, 25]. Lower scores on prosocial behaviors and higher scores of hyperactivity and conduct problems indicated lower maternal mental health [51, 52]. Only half of the studies that assessed the impact of child emotional problems on parents QoL indicated a significant correlation between the child’s emotional problems and maternal mental health [52, 53].
\nAlthough recent studies have suggested that there is a link between the level of parents’ emotional distress and the intensity of child internalizing/externalizing problems, the results of this study showed no statistically significant differences between the levels of emotional distress, negative dysfunctional emotions, worry/anxiety dysfunctional emotions, sadness/depression dysfunctional emotions in relation to the intensity of children\'s affective, anxiety and behavioral problems, measured by CBCL. In this view, we believe that child\'s developmental disorders may increase parents’ emotional distress, but it does not differ significantly depending on the diagnosis or child-associated internalizing/externalizing problems.
\nParental stress levels were found to be more pronounced in the parents of children with ASD compared to the parents of children with disabilities or other health problems [54]. Hastings and Johnson reported that increased severity levels of autistic symptoms are associated with higher levels of parental stress [55]. Elevated levels of stress can have a negative impact on parents and can lead to depression, anger, anxiety, and family conflicts [56]. Most studies considered the child with autism the source of stress and the well-being of the other family members as the result, but this relationship is bidirectional, meaning that the family members can influence the children with ASD (e.g., maternal marital stress and depression can influence child behavior) [37]. Another study reported that the most important stressors for the parents of children with ASD are: the permanence of the disorder, lack of acceptance of ASD-associated behaviors by the family members and society and low levels of support available. Among the stressors identified were also included: the financial difficulties due to raising a child with ASD, parents concern about the child\'s future, behavioral problems and parents’ psychological characteristics (perceived self-efficacy, locus of control and coping styles) [57].
\nParents of children included in the study showed higher mean scores for emotional distress and average for the negative dysfunctional emotions, compared with the norms for general population. These results can be explained by the impact of raising a child with a developmental disorder, considering that some children did not received any form of psychological intervention to help parents manage the disorder-associated problems. The increased levels of negative dysfunctional emotions and emotional distress may be due to the child developmental problems, but it was not specific to the diagnosis.
\nSeveral studies assessed the levels of stress, anxiety, and depression in parents of children with ASD [30, 32]. Stress, coping style, and parental self-efficacy emerged as important factors associated with QoL. The results of a recently published review [58] suggested that parental stress was negatively associated with QoL and also that parents of children with ASD are more likely to experience high levels of stress [59].
\nEmotional or behavioral problems are probably based on the maladaptive and irrational beliefs about selves, world, and life. The results of an unpublished study showed that people with high levels of distress presented more maladaptive cognitions, irrational beliefs, and dysfunctional attitudes, while having a diminished unconditional acceptance of self, when compared to people with low-distress levels [60].
\nThe frequency of negative automatic thoughts on self is theoretically associated with depression. Parents’ mean scores indicated a high level of negative automatic thoughts. For the irrational beliefs measured by ABSs, the mean scores were very high, high level of irrationality being usually associated with emotional distress, anxiety, depression, and various cognitive distortions. Both automatic thoughts and irrational beliefs correlate positively and statistically significant with all measured scores of emotional distress. Self-efficacy represents a person belief that their actions can be/are responsible for the success of a particular activity. Parents reported low to medium levels of self-efficacy. High levels of self-efficacy are positively correlated with unconditional acceptance, optimism, and self-esteem. Studies have shown a negative relation between the levels of anxiety and self-efficacy. It was demonstrated that self-efficacy improved parental well-being [61], but very few studies addressed its impact on parental QoL. Parental self-efficacy in managing their children’s problems and dealing with other family problems was found to be an important factor associated with QoL [53, 58].
\nCoping refers to a person cognitive and behavioral effort to adapt to a stressful situation. Generally, two types of coping strategies are used: problem-oriented (adaptive coping) and emotion-centered (maladaptive coping). Problem-oriented coping strategies address directly the problem causing the stress, while emotion-centered coping involves strategies to reduce the stress created by the problem. Although more studies emphasized the role of coping in parental adjustment, results were conflicting on the specific coping strategies that were associated with QoL due to the variation in the measurements used and in the theoretical conceptualization of coping. Hastings et al. identified four key coping dimensions: active avoidance coping, problem-focused coping, positive coping, and religious/denial coping [62]. Other studies reported that the use of emotion-oriented coping is associated with parents’ low quality of life [30, 63]. A chronic and general trend of using suppression as coping strategy may result in increased frequency of the thoughts that the person tries to avoid, and may be a precursor or a maintenance factor to certain psychopathological conditions. Suppression measured by WBSI correlated to all scores of emotional distress. Further research on the relationship between coping dimensions and QoL is necessary to establish which coping styles are helpful when raising a child with ASD. Future studies should aim at distinguishing between ASD and other conditions when examining parental coping and QoL.
\nThe scores reported by the parents on the cognitive coping strategies measured by CERQ showed above average use for catastrophizing, average use for self-blame, acceptance, rumination, refocus on planning, positive reappraisal, putting into perspective, and below average for positive refocusing and blame others. Parents’ emotional distress correlated significantly with self-blame, rumination, positive refocusing, catastrophizing, and blame others strategies. For the strategies self-blame, rumination, catastrophizing, and blame others, the positive correlation indicates that their frequent use associated with higher emotional distress levels. The positive refocusing, positive reappraisal, refocus on planning strategies registered negative relationships with different scores of emotional distress, meaning that their frequent use associated with lower emotional distress levels.
\nThe study on parents’ irrational beliefs and negative automatic thoughts as mediators of the relationship between the overall assessment of family quality of life and parents’ emotional distress showed that of the total effect of the overall assessment of family quality of life on the parents’ emotional distress, 38.9% is explained by their negative automatic thoughts and 23% by their irrational cognitions. Therefore, correct identification of parents’ irrational cognitions and negative automatic thoughts and their relationship with quality of life (that became the main therapeutic outcome) are essential for adequate family support and therapy efficiency. These results suggest that the development of support services for parents in order to reduce negative automatic thoughts and irrational beliefs could have a beneficial effect on the families’ quality of life and level of distress experienced due to raising a child with developmental disorders.
The study is cross-sectional and the results do not allow a causal relationship deduction. Also, the clinical sample size was relatively small. The instruments were filled in by a parent (mother) and the answers reflect its perception on the family quality of life. The parents were asked to answer the questions considering different periods of time (ranging from 2 weeks to the last 2 months) and the reporting accuracy could be reduced. The tested variables were analyzed in relation to the overall assessment of family quality of life. They may have an impact on certain areas or domains included in the family quality of life assessment (e.g., health, financial well-being, etc.), but not change the overall perceived family quality of life. The mediation analyses were also calculated using the overall assessment of family quality of life scores. The psychometric instruments used do not fully identify the emotional regulation mechanisms and some cognitive or noncognitive strategies may be excluded or not considered. Also, emotions and regulation strategies are difficult to separate, but the instruments used were standardized, validated, and proved to be optimal in previous studies. The results will reflect the experience of families of children with ASD and ADHD who receive specialized services in the Child and Adolescent Psychiatry Clinic from Cluj-Napoca and other specialized centers in the country, and that exclude some families with children diagnosed with ASD or ADHD who do not have health insurance, live in rural areas or the financial status does not allow them access to specialized services.
There were no statistically significant differences between the levels of emotional distress, negative dysfunctional emotions, worry/anxiety dysfunctional emotions, sadness/depression dysfunctional emotions reported by parents, in relation to the intensity of children\'s affective, anxiety and behavioral problems. Increased levels of emotional distress may be due to child developmental problems, but it is not specific to the diagnosis. We found significant correlations between the emotional distress reported by the parents and their automatic negative thoughts and irrational cognitions, the relation intensities varying from medium to high [1]. Significant correlations were obtained also for the relationship between parents’ emotional distress and the coping strategies: self-blame, rumination, positive refocusing, positive reappraisal, blame others, and catastrophizing. The level of parents’ emotional distress proved also to be linked with the use of suppression strategy and self-perceived efficacy. Part of the relationship between the overall assessments of family quality of life with parents’ emotional distress was explained by their irrational beliefs and negative automatic thoughts, the mediating effect being partial. In this view, the specialized services should include also interventions for the parents of children with developmental disorders (ASD, ADHD) [1]. Adaptive coping mechanisms, low levels of perceived distress, irrational cognitions, and negative dysfunctional emotions may become relevant outcomes for parents’ psychological interventions due to their impact on the assessment of the overall family quality of life.
\nFuture studies should investigate more links between the emotional distress, cognitive strategies used, irrational cognitions, self-efficacy, negative automatic thoughts, and the satisfaction with QoL. These studies should be longitudinal, with large clinical samples and involving different pathologies. Thus, the reported quality of life could be more accurately assessed in the processes of establishing and monitoring the therapeutic outcomes.
The authors declare that they have no conflict of interest.
Breast neoplasm is the most common cancer in women, which is originated from mammary epithelial tissue. The age of breast cancer is about 40–60 years old or before and after menopause. The morbidity of breast cancer is showed to be an upward trend year by year [1]. There are many factors that trigger breast cancer; however, the genetic factors only account for 10 and 90% of inducing factors of breast cancer remain to be investigated. SASH1 is a novel tumor suppressor gene, which is located in chromosome 6q24.3 [2] and is expressed in most of human tissues and cells except for lymphocytes and dendritic cells [3]. SASH1 was originally identified as a candidate tumor suppressor gene in breast cancer and colon cancer, regulating tumorigenesis of breast and other solid cancers and the adhesive and migratory behavior of cancer cells in tumor formation [4, 5]. Compared with that in normal breast epithelial tissues, SASH1 is downregulated in 74% of epithelial tissues of breast cancer-affected individuals [4, 6]. Some studies indicate that SASH1 downregulation is associated with tumor metastasis [3, 5]. Other studies indicate downregulated SASH1 promotes metastasis of hepatoma carcinoma cells through Shh signal pathway [7].
IQGAP1 is a scaffolding protein with 189 kDa of molecule weight, which contains multiple protein-interacting domains, such as a calponin homology domain, a polyproline-binding domain, four calmodulin-binding motifs, and a Ras GAP-related domain [8, 9]. The binding players of IQGAP1 proteins are involved in actin, calmodulin, members of the Rho GTPase family (i.e., Rac1 and Cdc42), Rap1, E-cadherin, β-catenin, members of the mitogen-activated protein kinase (MAPK) pathway, and adenomatous polyposis coli [8, 10]. Various basic cellular activities such as cytoskeletal organization, cell-cell adhesion, cell migration, transcription, and signal transduction are mediated by the bindings of IQGAP1 to these proteins [11]. Cell-cell adhesion of epithelial cells is predominantly mediated by E-cadherin and the associated catenin complex [12], which includes α-catenin (102 kDa), β-catenin (92 kDa), and γ-catenin/plakoglobin (83 kDa). β-Catenin combines with E-cadherin, and α-catenin links this E-cadherin/β-catenin complex to the actin cytoskeleton, which is essential for E-cadherin to express its full adhesive function. Remodeling of this adhesive sequence leads to cell detachment or loosening of cell-cell contact, which promotes epithelial cells to move as clusters, and IQGAP1 is involved in the remodeling of the adhesive complexes of epithelial cells [11, 13, 14, 15]. Our previous studies suggest that SASH1 is associated with MAP2K2 to cross talk with ERK1/2-CREB cascade to trigger melanin synthesis in the formation of hyperpigmentation plaques of a kind of dyschromatosis [16]. Importantly, our previous studies also indicate that SASH1 not only bind to G alpha S protein (Gαs) but IQGAP1 to form a novel Gαs-SASH1-IQGAP1-E-cadherin cascade and mutated SASH1(s) which mediate E-cadherin expression through the Gαs-SASH1-IQGAP1-E-cadherin cascade to promote directional migration of melanocytes or melanoma cells [17]. So, it is speculated that this mechanism may also exist in breast cancer cells. Taken above, the associations between SASH1 and IQGAP1 in breast cancer cells and the expression of SASH1, IQGAP1, and E-cadherin were analyzed by immunohistochemistry analyses in 80 cases of the affected individuals of breast cancer. Furthermore, the expression relationship among SASH1, IQGAP1, and E-cadherin and the associations between clinical index of breast cancer patients and the expression of SASH1 and IQGAP1, respectively, were assessed to find out novel interference targets for early prevention of breast cancer metastasis.
The construction of pEGFP-C3-SASH1 recombined vectors was mainly referred to our previous description [17]. IQGAP1 cDNA was obtained from Han Jiahuai Lab, Xiamen University (Xiamen, Fujian, China), and cloned into pcDNA3.0-HA vector. PCR was performed with IQGAP1 cDNA as template using TransTaq® DNA Polymerase High Fidelity (TransGen Biotech, Ltd., Beijing, China) using the following cloning primers of IQGAP1: sense primer, 5’-TAGTCTAGAAT GTCCG CCGCAACGAG-3’(Xba I inserted) and antisense primer, 5′-CCGCTCGAGTTACTTCCCGTAGAACTTTTTG-3′ (Xho I inserted). The amplification conditions were as follows: 95°C 2 min, 95°C 30 s, 58°C 30 s, and 72°C 1 min for 30 cycles and 72°C 5 min and 4°C forever. The recombined vectors were identified by enzyme digestion of endonuclease and CDS of SASH1 and IQGAP1 genes.
Human breast cancer cell lines including SK-BR-3 cells were obtained from the Cell Bank of Chinese Academy of Sciences (Shanghai, China). After several times of passage, cells were used and cultured in Dulbecco’s Modified Eagle’s medium (DMEM) (Gibco, Logan, UT), containing 10% BI fetal bovine serum (Bioind, Israel) and 1% penicillin-streptomycin solution at 37°C with 5% CO2. SK-BR-3 cells were subcultured for three times and cultured to logarithmic growth phase for plasmid transfection. The HA-IQGAP1-pcDNA3.0 and pEGFP-C3-SASH1 were transfected into SK-BR-3 cells according to different combinations using PEI prepared by us. The transfected SK-BR-3 cells were divided into three groups, that is, two single-vector transfection groups and one double-vector transfection group. At 48 h after transfection, the transfected SK-BR-3 cells were lysed and collected for immunoprecipitation assays.
Transfected SK-BR-3 cells were gently washed in PBS three times and then lysed for 25 min using IP-WB lysis buffer (Beyondtime Inc. Ltd., Jiangsu, China) with complete protease inhibitor cocktail per 10-cm dish for 20 min on ice. The cell lysates were transferred to 1.5 ml microcentrifuge tubes. The extracts were centrifuged for 15 min at 12,000 rpm at 4°C. The supernatants were immunoprecipitated using GFP mouse monoclonal antibody (T0005, Affinity Biosciences, Cincinnati, OH, USA) or HA mouse monoclonal antibody (mAb) (Abmart, Shanghai, China) as performed in our previous descriptions [17]. The immunoprecipitates were washed with PBS for three times and subjected to western blotting as previously described [16, 17]. Most of the western blots were mainly performed in our previous reports [17]. The associated HA-IQGAP1 or GFP-SASH1 was detected by western blot along with β-tubulin as loading control. The primary antibodies used in western blot were as follows: anti-GFP, anti-HA, and anti-β-tubulin (10B1) mouse mAb (EarthOx Life Science, Millbrae, CA, USA or Shanghai Genomics, Shanghai, China).
All breast cancer patients who underwent surgery were followed by treatment in accordance with the National Comprehensive Cancer Network clinical practice guidelines. Fresh primary breast cancer tissues and some of the corresponding adjacent tissues were collected from 80 breast ductal carcinoma patients undergoing resection from May 2015 to June 2016 at the Chongqing Cancer Hospital. Histological diagnosis and tumor-node-metastasis staging of cancer were determined in accordance with the American Joint Committee on Cancer manual criteria for breast cancer. Written informed consent regarding tissue and data used for scientific purposes was obtained from all participating patients. The study was approved by the Research Ethics Committees of the affiliated Hospitals of Guizhou Medical University and Chongqing Cancer Hospital. All of the breast cancer cases were diagnosed by pathological examinations (HE staining and immunohistochemistry analyses). In the clinical cases of breast cancer, 26 cases are with lymph node metastasis, 51 cases are without lymph node metastasis, and 3 cases could not acquire the information of lymph node metastasis. Breast tumor diameters of 16 cases were <1 cm, those of 41 cases were 1.1–2 cm, those of 18 cases were 2.1–3 cm, and those of 5 cases were >3 cm. According to WHO histological classification of breast tumors (2003), 80 cases of breast invasive ductal carcinoma were graded histologically in terms of duct formation, nuclear pleomorphism, and mitosis. Among the 80 cases of breast invasive ductal carcinoma, 65 cases were graded into 3 grades: 8 cases belonged to grade I, 47 cases to grade II, and 10 cases to grade III.
The breast cancer tissues obtained from surgical operation were fixed at 4°C in 10% formaldehyde solution for 24 h. The excess fat and other tissues of breast cancer tissues were removed and embedded with paraffin and made into 5 millimeter (mm) tissue sections. The tissue sections (5 mm) were baked at 56°C and dehydrated and subjected to peroxidase blocking. Tissues of human breast cancer and corresponding adjacent tissues were immunohistochemically stained with SASH1 rabbit polyclonal antibody (pAb) (A302-265A-1, Bethyl Laboratories, Inc., Texas, USA, or Novus Biologicals, USA), IQGAP1 rabbit polyclonal antibody (Bethyl Laboratories, Inc., Texas, USA), and E-Cadherin (24E10) Rabbit mAb (#3195, Cell Signaling Technology). Primary antibodies were added and incubated at 37°C and then for overnight at 4°C. After washing three times for 10 min each with TBS, the sections were incubated with horseradish peroxidase-conjugated anti-rabbit and anti-mouse universal secondary antibodies for 30 min at 37°C. Subsequently, the sections were counterstained with hematoxylin mounted, observed, and photographed under the positive position microscope BX51 at a 100× magnification or a 400× magnification. Finally, the stained slides were observed under a microscope, and images were acquired [17]. The experimental protocols were mainly referred to our previous description [17].
According to the staining intensity of tumor cells, the three proteins, SASH1, IQGAP1, and E-cadherin, were scored and divided into four grades: 0 score (−), 1 score (+), 2 score (++), and 3 score (+++). The three proteins were also scored according to positive cells’ percentage of the three proteins and divided into six grades: 0 score (<1%), 1 score (1–20%), 2 score (21–40%), 3 score (41–60%), 4 score (61–80%), 5 score (81–100%). Based on the staining intensity of SASH1, IQGAP1, and E-cadherin, the staining intensity and positive cells’ percentage of three proteins were calculated as in our previous description [16]. Total scores of each visual field were determined by the formula: staining intensity scores of positive cells × scores of positive cells’ percentage = total scores of each view fields.
All of experimental results were repeated for three times and statistically analyzed using SPSS 16.0 statistical software. Chi square test was performed to analyze the IHC results of breast cancer tissues and the relationship between expression of SASH1 and IQGAP1 and clinical indicators. Rank-sum test was used to assess the grading relationship between the SASH1 and E-cadherin and IQGAP1 and E-cadherin, respectively. Spearman correlation coefficient method was used to assess the correlation between expressed scores of SASH1 and E-cadherin and IQGAP1 and E-cadherin, respectively. The data are indicated as mean ± standard error of the mean (SEM), and the difference was statistically significant with P < 0.05. Cartograms were plotted with GraphPad Prism 5.
To identify the associations between SASH1 and IQGAP1, HA-IQGAP1-pcDNA3.0 and pEGFP-C3-SASH1 were constructed and were singly or combinedly transfected into SK-BR-3 cells and immunoprecipitation; western blot (IP-WB) was performed to identify the associations between exogenous SASH1 and exogenous IQGAP1. HA-IQGAP1 and GFP-SASH1 were singly or in pair transfected into SK-BR-3 cells at 48 h posttransfection, the transfected cells were lysed, and HA-IQGAP1 was immunoprecipitated, and the associated GFP-SASH1 was detected by GFP antibody. The associated HA-IQGAP1 and GFP-SASH1 in the immunoprecipitates and cell lysates (input) were confirmed by western blot. Meanwhile, GFP-SASH1 and HA-IQGAP1 were also either single or in pair transfected into SK-BR-3 cells and after 48 h of transfection, the transfected cells were lysed and were GFP-SASH1 was immunoprecipitated and the associated HA-IQGAP1 was detected by HA antibody. The associated GFP-SASH1 and HA-IQGAP1 in the immunoprecipitates and cell lysates (input) were identified by western blot. Finally, our IP-WB analyses confirmed that exogenous SASH1 was associated with exogenous IQGAP1 (Figure 1).
SASH1 is associated with IQGAP1. (A) GFP-SASH1 and HA-IQGAP1 were singly or in pair transfected into SK-BR-3 cells, and at 36 h after transfection, the transfected cells were lysed and collected for IP-WB analyses. HA-IQGAP1 was immunoprecipitated, and the associated GFP-SASH1 was detected by western blot using GFP antibody. GFP-SASH1 and HA-IQGAP1 in the cell lysates (input) were detected by western blot along with β-tubulin with loading control. (B) HA-IQGAP1 and GFP-SASH1 were singly or in pair transfected into SK-BR-3 cells, and at 36 h after transfection, the transfected cells were lysed and collected for IP-WB analyses. GFP-SASH1 was immunoprecipitated, and the associated GFP-SASH1 was detected by western blot using GFP antibody. HA-IQGAP1 and GFP-SASH1 in the cell lysates (input) were analyzed by western blot along with β-tubulin with loading control.
IHC analyses confirmed that the positive staining of SASH1 and IQGAP1 protein was light brown in breast cancer tissues, the cell nucleus was purple, and the distribution of SASH1 and IQGAP1 was located in the same sites of breast cancer tissues. SASH1 and IQGAP1 show the same or similar expression tendency in breast cancer tissues, i.e., low level of SASH1 expression is followed by low level of IQGAP1 expression and high expression of SASH1 is accompanied by high expression of IQGAP1 (Figure 2A). A total of 80 breast cancer tissues were divided into four groups according to the median value of SASH1 and IQGAP1 protein expression scores: SASH1 scores <1.23 were considered as low expression, SASH1 scores ≥1.23 were considered as high expression, IQGAP1 scores <0.78 were maintained as low expression, and SASH1 scores ≥0.78 were maintained as high expression. Statistical analyses indicated that in the 80 cases of breast cancer tissues, cases with low SASH1 expression accounted for 56.3% (45/80) and the cases with low IQGAP1 expression were more than those of high IQGAP1 expression (>65%, P = 0.015) (Figure 2B). And statistical analyses also suggested that in the 80 cases of breast cancer tissues, cases with low IQGAP1 expression accounted for 58.8% (47/80) and the cases with low IQGAP1 expression were more than those of high IQGAP1 expression (>60%, P = 0.011) (Figure 2B). Meanwhile, the IHC detection results of SASH1 and IQGAP1 were scored and analyzed by Spearman correlation analyses, and the scores of SASH1 and IQGAP1 were plotted by GraphPad Prism 5 software. In 80 cases of breast cancer tissues, except for 5 cases, SASH1 scores and IQGAP1 scores in most of cases closely intersected, which indicated that the SASH1 expression and IQGAP1 expression showed significantly positive correlation (r = 0.308, P = 0.004) (Figure 2C).
SASH1 expression in 80 cases of breast cancer tissues which is positively correlated with IQGAP1. (A) The expressions of SASH1 and IQGAP1 in 80 cases of breast cancer tissues were detected by immunohistochemical staining method. The cell nucleus was dyed purple and SASH1 and IQGAP1 were dyed pale brown. The left panels were HE staining, and the middle panels and the right panels were IHC staining of SASH1 and IQGAP1. The figures in upper panels were 100× magnification, and one region in the 100× magnification figures was amplified for 400× and framed in black and showed in the bottom panels. (B) The expressions of SASH1 and IQGAP1 were scored, and the score results of SASH1 and IQGAP1 were plotted with GraphPad Prism 5 and analyzed by χ2 test. The analysis results of SASH1 and IQGAP1 expressions in the left panel indicated that when SASH1 expression was low, the positive percentage of low expressed IQGAP1 was much more than that of high expressed IQGAP1. The expression of SASH1 showed significantly positive correlation with that of IQGAP1 (P = 0.015). And the statistical analyses also suggested that when IQGAP1 expression was low, the positive percentage of low expressed SASH1 was much more than that of high expressed SASH1. The expression of IQGAP1 demonstrated significantly positive correlation with that of SASH1 (P = 0.011). (C) The expressions of SASH1 and IQGAP1 were scored, and the score results of SASH1 and IQGAP1 were plotted with GraphPad Prism 5 and analyzed by Spearman correlation coefficient analyses. Spearman correlation coefficient analyses indicated that except for two score values of SASH1, expression of SASH1 and IQGAP1 showed good similar or same tendency of changes (P = 0.004).
It has been known that the expression of SASH1 and IQGAP1 is associated with tumor metastasis. So, in this study, we further identify the relationship of expression of SASH1 and IQGAP1 with clinical data of breast cancer-affected individuals. Our analyses (Table 1) indicated that in 77 cases of breast cancer with lymph node dissection, the low expression rate of SASH1 protein in lymph node metastasis positive group was slightly higher than that in lymph node metastasis negative group. The low expression rate of IQGAP1 protein was slightly lower than that of the negative lymph node metastasis group, but the difference was not statistically significant (65.4% vs. 54.9%, 57.7% vs. 58.8%, all P value > 0.05). In 80 cases of breast cancer, the low expression rate of SASH1 protein was 50.0, 58.5, 50.0, and 80.0%, respectively, in patients with tumor diameter <1.0 cm, 1.1–2.0 cm, 2.1–3.0 cm, and >3.0 cm. The low expression rates of IQGAP1 protein were 62.5, 56.1, 55.6, and 80.0%, respectively. There were significant differences between the two groups (P < 0.05). In 65 cases of breast cancer with histological grading data, the low expression rates of SASH1 protein in histological grading I, II, and III were 50.0, 55.3, and 70.0%, respectively. The low expression rates of IQGAP1 protein were 75.0, 51.1, and 70.0%, respectively. There were significant differences between groups (P < 0.01).
Clinical parameters | Total | SASH1 | IQGAP1 | P | ||
---|---|---|---|---|---|---|
Low | High | Low | High | |||
Lymph node metastasisa | >0.05 | |||||
Positive | 26 | 17 | 9 | 15 | 11 | |
Negative | 51 | 28 | 23 | 30 | 21 | |
Tumor diameter/cmb | <0.05 | |||||
≤1 | 16 | 8 | 8 | 10 | 6 | |
1.1–2 | 41 | 24 | 17 | 23 | 18 | |
2.1–3 | 18 | 9 | 9 | 10 | 8 | |
>3 | 5 | 4 | 1 | 4 | 1 | |
Histological gradec | <0.01 | |||||
I | 8 | 4 | 4 | 6 | 2 | |
II | 47 | 26 | 21 | 24 | 23 | |
III | 10 | 7 | 3 | 7 | 3 |
Association of SASH1 and IQGAP1 expressions with the clinical parameters of breast cancer patients (n).
N = 77.
N = 80.
N = 65.
SASH1 and IQGAP1 have been identified to be involved in tumor metastasis. And immunohistochemistry (IHC) analyses were performed to detect the expression of E-cadherin in breast cancer tissues and the relevance of E-cadherin with SASH1 and IQGAP1, respectively. IHC analyses indicated that E-cadherin was mainly located in the cytoplasma membrane of breast cancer tissues. According to the positive intensity of E-cadherin staining, E-cadherin protein expression in breast cancer tissues was graded to four grades, and meanwhile the positive intensity of SASH1 protein staining was also graded to four grades. Statistical analyses suggested that expression of SASH1 protein was significantly positive related to that of E-cadherin (r = 0.461, P < 0.001 (Table 2 and Figure 3)). SASH1 and E-cadherin staining intensity was moderately positive staining intensity, respectively, which was defined low expression. And further statistical analyses suggested SASH1 and E-cadherin were downregulated in 77 cases (77/80, 96.25%) of breast cancer tissues. All of these indicated that the low expression of SASH1 and the low expression of E-cadherin protein in breast cancer tissue are in good agreement.
E-cadherin | SASH1 | IQGAP1 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
− | + | ++ | ++ | Totala | − | + | ++ | +++ | Totalb | |
− | 4 | 6 | 6 | 1 | 17 | 8 | 8 | 1 | 0 | 17 |
+ | 6 | 20 | 2 | 0 | 28 | 12 | 12 | 4 | 0 | 28 |
++ | 5 | 25 | 3 | 0 | 33 | 12 | 19 | 2 | 0 | 33 |
+++ | 1 | 0 | 1 | 0 | 2 | 1 | 1 | 0 | 0 | 2 |
Total | 16 | 51 | 12 | 1 | 80 | 33 | 40 | 7 | 0 | 80 |
Correlation of SASH1 and IQGAP1 expressions with E-cadherin expression rankin breast cancer tissues (n, N = 80).
r = 0.461, P < 0.001.
r = 0.454, P < 0.001; by rank-sum test.
SASH1, IQGAP1, and E-cadherin proteins showed consistent changes in the breast cancer tissues. The cell nucleus was dyed purple and SASH1, IQGAP1, and E-cadherin were dyed pale brown, and the magnification is 200×. According to staining intensity of tumor cells and the numbers of positive cells, the immunohistochemical results of SASH1, IQGAP1, and E-cadherin proteins were divided into four grades: negative (−), weakly positive (+), moderately positive (++), and strongly positive (+++). The expression of SASH1 was positively correlated with that of E-cadherin and the expression of IQGAP1 show positive correlation with that of E-cadherin. The cell nucleus was dyed purple, and SASH1, IQGAP1, and, E-cadherin were dyed pale brown, and the magnification is 200×.
According to the staining intensity of IQGAP1 protein and E-cadherin protein in 80 cases of breast cancer, the staining intensity of E-cadherin and IQGAP1 was divided into four grades. Statistical analyses demonstrated that expression of IQGAP1 protein was significantly positive related to that of E-cadherin (r = 0.454, P < 0.001 (Table 2 and Figure 3)). Staining intensity of IQGAP1 and E-cadherin was moderately positive staining intensity, respectively, which was defined low expression. And further statistical analyses suggested IQGAP1 and E-cadherin were both downregulated in 78 cases (78/80, 97.5%) of breast cancer tissues. All of these indicated that the low expression of IQGAP1 and the low expression of E-cadherin showed better consistency in breast cancer tissue.
Clinical research indicates that occurrence of breast cancer is associated with many factors including genetic factors, environment, and lifestyle. SASH1, a tumor suppressor gene, is downregulated in most of neoplasms. Decrease or deletion of SASH1 expression is closely related to tumor metastasis [4, 5, 18]. It has been reported that the expression of SASH1 protein in osteosarcoma tissues with lung metastasis is significantly lower than that in osteosarcoma tissues without lung metastasis [19]. Upregulated SASH1 can significantly suppress the migration of cervical carcinoma Hela cells, and, in contrast, knockdown of SASH1 significantly results in reduced adhesion ability of human colon cancer SW480 cells and mouse rectal cancer CMT-93 cells and enhanced migration ability of these tumor cells [3]. Downregulation of SASH1 protein expression in thyroid tumor cells may play an important role in thyroid tumor metastasis [20]. SASH1 mRNA is downregulated in primary liver cancer and thyroid cancer [5]. Compared with corresponding normal tissues, SASH1 protein is downregulated in 37 cases among 50 cases of breast cancer tissues and SASH1 expression loss is associated with breast cancer metastasis [4]. All of these studies suggest that expression loss of SASH1 medicates tumor metastasis. In this study, our IHC analyses identified that in 80 cases of breast cancer tissues, low expression of SASH1 protein in 45 cases (45/80 56.3%) was found, which indicated that SASH1 was downregulated in breast cancer.
IQGAP1 proteins are members of the evolutionarily conserved scaffolding protein family and are more widely expressed than other members of the family [21, 22]. IQGAP1 interacts with specific proteins such as actin, calmodulin, Rho GTPase family members, E-cadherin, and β-catenin. The interactions of IQGAP1 with those specific proteins medicate multiple cell activities such as cell scaffold, intercellular adhesion, metastasis, invasion, transcription, and cell signal transduction. For example, the binding of IQGAP1 to β-catenin to form E-cadherin/β-catenin complex inhibits intercellular adhesion of epithelial cells and promotes β-catenin-mediated transcriptional activation [9]. IQGAP1 protein ,which mediates E-cadherin-mediated-intercellular adhesion, is the key molecule in cell polarization and directed migration [23]. IQGAP1 expression is showed to be of prognostic significance in advanced colorectal carcinoma, and a shorter overall survival of colorectal carcinoma patients can be predicted by diffuse expression pattern of IQGAP1 [11]. In this study, IHC analyses indicated, in 80 cases of breast cancer tissues, IQGAP1 protein level was significantly low in 47 cases accounting for 58.8%, which suggested that IQGAP1 was downregulated in breast cancer.
Multiple endocrine neoplasia type 1 (MEN1) is a dominantly inherited tumor syndrome that results from the mutation of the MEN1 gene that encodes protein menin. MEN1 is revealed to bind to IQGAP1 and increases E-cadherin/β-catenin interaction with IQGAP1 and a novel menin-IQGAP1 pathway that controls cell migration and cell-cell adhesion found in endocrine cells [24]. Activated Rac1 and Cdc42 can bind to IQGAP1, and the bindings of IQGAP1 and Rac1 as well as Cdc42 promote cell mobility and polarization [25, 26]. IQGAP1 is both a downstream effector and an upstream activator of Cdc42, where active Cdc42 antagonizes IQGAP1 dissociation of the cell-cell contacts [27, 28]. Cdc42 inhibits IQGAP1’s role in polarized secretion in β-cells or perhaps migration [29]. In this study, IP-WB analyses indicated the protein-protein interactions between SASH1 and IQGAP1. It has been reported that SASH1 expression suppresses cell proliferation and interacts with cytoskeletal proteins, which promotes cell matrix adhesion [3, 4]. Meanwhile, other studies have identified that SASH1 is associated with scaffold proteins and foster tumor migration [3]. Hence, we speculate that the bindings of SASH1 and IQGAP1 co-mediate breast cancer metastasis.
Recurrence or metastasis of breast cancer is the leading cause of breast cancer-related death. It has been identified that epithelial-mesenchymal transition (EMT) plays a pivotal role in tumor metastasis through generation and survival of induced circulating tumor cells [30]. One of the EMT functions is to downregulate and relocate the epithelial cell adhesion protein including the leading actor, E-cadherin [31]. The decreased expression of E-cadherin in breast cancer was associated with high pathological grade, tumor volume enlargement, lymph node metastasis, and distant metastasis and with disease rehabilitation and overall survival time, which indicates that reduced expression or function loss of E-cadherin promotes breast cancer invasion and migration [32]. A dynamic equilibrium of E-cadherin between the E-cadherin-β-catenin-α-catenin complex and the E-cadherin-β-catenin-IQGAP1 complex at sites of cell-cell contact is proposed. The ratio between these two complexes could determine the strength of adhesion [33]. Our previous study found that SASH1 mutations enhanced mutated SASH1 expression, however induced downregulation of E-cadherin in epithelial cells of skin [17]. So it is speculated that there is a connection between SASH1 and E-cadherin. In this study, SASH1 protein level is positively correlated with E-cadherin, and IQGAP1 protein level is also positively correlated with E-cadherin, which also identifies the connection between SASH1 and E-cadherin.
Taken above, we speculate that SASH1 may mediate breast cancer metastasis through a novel SASH1-IQGAP1-E-cadherin signal cascade. When SASH1 and IQGAP1 protein levels in breast cancer tissues and breast cancer cells were low, the protein levels of E-cadherin are also reduced, which causes the reduced cell adhesion ability, the tumor cell ability which is easy to fall off, the enhanced invasion, and the tumor cell metastasis ability to distance. The novel findings about SASH1 will become a novel target to treat breast cancer, which will be conducive to the precision and diversification of breast cancer treatment, effectively improving the prognosis of patients. In this study, we find that low protein levels of SASH1 and IQGAP1 are related to tumor size, and the reduced protein levels of SASH1 and IQGAP1 are associated with tumor grading, which provides a new reference for the rapid diagnosis of tumor grading and tumor size. And our findings on SASH1 and IQGAP1 provide a new and more intuitive basis for determining the operation plan and resection range, judging the curative effect of operation and early detection of tumor metastasis and recurrence. However, in this study we find that low expression levels of SASH1 and IQGAP1 are significantly not related to lymph node metastasis, which presumably can be related to a small sample size of breast cancer tissues. The relationship between SASH1 and IQGAP1 with lymph node metastasis needs to be further investigated.
We thank Central Laboratory at Yongchuan Hospital, Chongqing Medical University and Clinical Research Center, and the Affiliated Hospital, Guizhou Medical University for housing experiments. We thank Zhou Xin and Jiang Xiaoli of the Breast Disease Diagnosis and Treatment Center of Chongqing Cancer Hospital for their active help and cooperation in sample collection. This work was supported partly by the High Level Talent Introduction Project of Affiliated Hospital of Guizhou Medical University (grant numbers: 2018I-1 and I-2017-19) and Guizhou Provincial Science and Technology Department Project (grant number: Qian Ke He LH [2017] 7193). This work was supported by the Yongchuan Hospital Project, Chongqing Medical University (YJYJ201347), Chongqing Education Commission Project (KJ1400201), and Guizhou’s Introduction Project of Million Talents.
No conflict between the authors.
Notes: The chapter text was mainly referred to our article entitled as “SASH1-IQGAP1-E-cadherin signal cascade may regulate breast cancer metastasis” (Tumor. 2017;37(6):633–641) which we published in the Chinese Journal Tumor in June 2017. In this chapter, we rewrite the chapter text according to the suggestions of reviewers.
The figures and tables of this chapter were taken or reedited from the figures and tables of our published article entitled “SASH1-IQGAP1-E-cadherin signal cascade may regulate breast cancer.”
We thanks the Chinese Journal Tumor allow us reuse the Figures and tables in our article entitled as“SASH1-IQGAP1-E-cadherin signal cascade may regulate breast cancer metastasis” (Tumor, 2017, 37(6): 633~641) which were published in the Chinese Journal Tumor. We are allowed to reuse the Figures, Tables and Text of our article entitled as “SASH1-IQGAP1-E-cadherin signal cascade may regulate breast cancer” under the terms of the Creative Commons Attribution License (CC BY) without having to obtain permission provided that the original source of publication.
Dulbecco’s Modified Eagle’s medium epithelial-mesenchymal transition guanine nucleotide-binding protein subunit-alpha isoforms short hematoxylin and eosin staining immunohistochemical IQ motif-containing GTPase activating protein 1 immunoprecipitation-western blot polyethylenimine polyclonal antibody SAM and SH3 domain-containing 1 standard error of the means
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