1,247 research outputs found
Attachment style, loneliness and depression in older age women
This study explores whether attachment, especially avoidant styles, might be a predictor of social support, loneliness and depression in old age
The child attachment style interview (Child-ASI) and depression: preliminary findings
Background: Childhood familial trauma is a major contributor to psychological disorder. One of its key impacts is insecure attachment style, which impedes the development of close relationships. Trauma-related vulnerability is important to assess in primary school but there is a gap in age-appropriate measurement tools. This paper outlines the development of the Child Attachment Style Interview (Child-ASI) suitable for 6 to 11-year olds. This can be utilised on-site to identify damaging effects of trauma experience at an early stage.
Method: The Child-ASI was developed from an existing adolescent/adult measure and tested on a sample of children in primary school (N=42) together with a subsample of those in care (N=20). A checklist of life events and the Mood and Feeling Questionnaire (MFQ) for depression was also administered.
Results: Acceptable inter-rater reliability was found ([kappa]=0.84 for overall attachment style). A factor analysis of subscales produced the expected five factors for classification. Rates of insecure styles (38 per cent in school and 90 per cent in care) were consistent with other studies and related to separation from parent and depression. Logistic regression showed insecure attachment style and negative life events provided the best model for depression.
Conclusion: Preliminary findings indicate the Child-ASI provides an effective way of assessing attachment style in children. Its use is for more intensive investigation of trauma-related interpersonal problems, administered by trained teachers. It is less intrusive than direct trauma assessments and fits with the policy need to identify vulnerability for mental health issues in schools
The challenge of perioperative pain management in opioid-tolerant patients
The increasing number of opioid users among chronic pain patients, and opioid abusers among the general population, makes perioperative pain management challenging for health care professionals. Anesthesiologists, surgeons, and nurses should be familiar with some pharmacological phenomena which are typical of opioid users and abusers, such as tolerance, physical dependence, hyperalgesia, and addiction. Inadequate pain management is very common in these patients, due to common prejudices and fears. The target of preoperative evaluation is to identify comorbidities and risk factors and recognize signs and symptoms of opioid abuse and opioid withdrawal. Clinicians are encouraged to plan perioperative pain medications and to refer these patients to psychiatrists and addiction specialists for their evaluation. The aim of this review was to give practical suggestions for perioperative management of surgical opioid-tolerant patients, together with schemes of opioid conversion for chronic pain patients assuming oral or transdermal opioids, and patients under maintenance programs with methadone, buprenorphine, or naltrexone
Adolescent attachment style in residential care: the attachment style interview and vulnerable attachment style questionnaire
Attachment frameworks are increasingly used to understand human development and used by social workers in care planning for children and young people in care. To date, there have been few assessment tools that social workers can use easily and therefore little sustained use of attachment assessment by practitioners in residential care. This paper describes the use of the Attachment Style Interview (ASI) for adolescents and the Vulnerable Attachment Style Questionnaire (VASQ) self-report in a pilot study of young people in residential care in the UK and Isle of Man. The aim was to test these relatively new measures in practice contexts administered by practitioners, and to determine rates of insecure and disorganised attachment style to compare with other studies. Results show around half of the young people had disorganised (or mixed) attachment styles using either measure, with avoidant attachment styles more common than anxious ones. Secure style was rare. There was some difference in young person and carer ratings, with young people rating slightly less disorganised style and more anxious style, but simplified classifications were similar. The implications for assessing attachment style in residential care to improve identification of interpersonal risk and aid with care planning are discussed
The anandamide analog, Met-F-AEA, controls human breast cancer cell migration via the RHOA/RHO kinase signaling pathway
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Assessing safety functionalities in the design and validation of driving automation
This paper aims to contribute to the comprehensive and systematic safety assessment of Automated Driving Systems (ADSs) by identifying unknown hazardous areas of operation. The current methodologies employed in this domain typically involve estimating the distributions of situational variables based on human-centered field test, crash databases, or expert knowledge of critical values. However, due to the lack of a-priori knowledge regarding the influential factors, their critical ranges, and their distributions, these approaches may not be entirely suitable for the assessment of emerging automated driving technologies. To deal with this challenging problem, here we propose a testing methodology incorporating realistic yet unobserved driving conditions, distinguished by numerous situational variables, so to encompass unknown unsafe conditions comprehensively. Our methodology utilizes stochastic simulation and uncertainty modeling techniques to account for the variability of realistic driving conditions and their impact on ADSs' performances. By doing so, we aim to identify unsafe operational regions and triggering conditions that can lead to hazardous behaviors, thus improving the development and safety of automated driving functions. For our purposes, the Latin Hypercube Sampling technique and the recently proposed PAWN density-based sensitivity analysis method are employed. We apply this methodology for the first time in the specific field of ADSs design and validation, using an exemplificative use case. We discuss and compare the results obtained from our approach with those obtained from a traditional approach
Associations between depression and specific childhood experiences of abuse and neglect: a meta-analysis
Background
Research documents a strong relationship between childhood maltreatment and depression. However, only few studies have examined the specific effects of various types of childhood abuse/neglect on depression. This meta-analysis estimated the associations between depression and different types of childhood maltreatment (antipathy, neglect, physical abuse, sexual abuse, and psychological abuse) assessed with the same measure, the Childhood Experience of Care and Abuse (CECA) interview.
Method
A systematic search in scientific databases included use of CECA interview and strict clinical assessment for major depression as criteria. Our meta-analysis utilized Cohen's d and relied on a random-effects model.
Results
The literature search yielded 12 primary studies (reduced from 44), with a total of 4372 participants and 34 coefficients. Separate meta-analyses for each type of maltreatment revealed that psychological abuse and neglect were most strongly associated with the outcome of depression. Sexual abuse, although significant, was less strongly related. Furthermore, the effects of specific types of childhood maltreatment differed across adult and adolescent samples.
Limitations
Our strict criteria for selecting the primary studies resulted in a small numbers of available studies. It restricted the analyses for various potential moderators.
Conclusion
This meta-analysis addressed the differential effects of type of childhood maltreatment on major depression, partially explaining between-study variance. The findings clearly highlight the potential impact of the more “silent” types of childhood maltreatment (other than physical and sexual abuse) on the development of depression
Mercorio, A.; Della Corte, L.; De Angelis, M.C.; Buonfantino, C.; Ronsini, C.; Bifulco, G.; Giampaolino, P. Ovarian Drilling: Back to the Future. Medicina 2022, 58, 1002. https://doi.org/10.3390/ medicina58081002
Polycystic ovary syndrome (PCOS) is the leading cause of anovulatory infertility. The complex metabolic dysregulation at the base of this syndrome often renders infertility management challenging. Many pharmacological strategies have been applied for the induction of ovulation with a non-negligible rate of severe complications such as ovarian hyperstimulation syndrome and multiple pregnancies. Ovarian drilling (OD) is currently being adopted as a second-line treatment, to be performed in case of medical therapy. Laparoscopic ovarian drilling (LOD), the contemporary version of ovarian wedge resection, is considered effective for gonadotropins in terms of live birth rates, but without the risks of iatrogenic complications in gonadotropin therapy. Its endocrinal effects are longer lasting and, after the accomplishment of this procedure, ovarian responsiveness to successive ovulation induction agents is enhanced. Traditional LOD, however, is burdened by the potential risks of iatrogenic adhesions and decreased ovarian reserve and, therefore, should only be considered in selected cases. To overcome these limits, novel tailored and mini-invasive approaches, which are still waiting for wide acceptance, have been introduced, although their role is still not well-clarified and none of them have provided enough evidence in terms of efficacy and safety
Prenyltransferase inhibitors induce apoptosis in proliferating thyroid cells through a p53-independent CrmA-sensitive, and caspase-3-like protease-dependent mechanism.
Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach
Introduction Italy became the second epicenter of the novel coronavirus disease 2019 (COVID-19) pandemic after China, surpassing by far China’s death toll. The disease swept through Lombardy, which remained in lockdown for about two months, starting from the 8th of March. As of that day, the isolation measures taken in Lombardy were extended to the entire country. Here, assuming that effectively there was one case “zero” that introduced the virus to the region, we provide estimates for: (a) the day-zero of the outbreak in Lombardy, Italy; (b) the actual number of asymptomatic infected cases in the total population until March 8; (c) the basic (R0)and the effective reproduction number (Re) based on the estimation of the actual number of infected cases. To demonstrate the efficiency of the model and approach, we also provide a tentative forecast two months ahead of time, i.e. until May 4, the date on which relaxation of the measures commenced, on the basis of the COVID-19 Community Mobility Reports released by Google on March 29. Methods To deal with the uncertainty in the number of the actual asymptomatic infected cases in the total population Volpert et al. (2020), we address a modified compartmental Susceptible/ Exposed/ Infectious Asymptomatic/ Infected Symptomatic/ Recovered/ Dead (SEIIRD) model with two compartments of infectious persons: one modelling the cases in the population that are asymptomatic or experience very mild symptoms and another modelling the infected cases with mild to severe symptoms. The parameters of the model corresponding to the recovery period, the time from the onset of symptoms to death and the time from exposure to the time that an individual starts to be infectious, have been set as reported from clinical studies on COVID-19. For the estimation of the day-zero of the outbreak in Lombardy, as well as of the “effective” per-day transmission rate for which no clinical data are available, we have used the proposed SEIIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March. This was accomplished by solving a mixed-integer optimization problem. Based on the computed parameters, we also provide an estimation of the basic reproduction number R0 and the evolution of the effective reproduction number Re. To examine the efficiency of the model and approach, we ran the simulator to “forecast” the epidemic two months ahead of time, i.e. from March 8 to May 4. For this purpose, we considered the reduction in mobility in Lombardy as released on March 29 by Google COVID-19 Community Mobility Reports, and the effects of social distancing and of the very strict measures taken by the government on March 20 and March 21, 2020. Results Based on the proposed methodological procedure, we estimated that the expected day-zero was January 14 (min-max rage: January 5 to January 23, interquartile range: January 11 to January 18). The actual cumulative number of asymptomatic infected cases in the total population in Lombardy on March 8 was of the order of 15 times the confirmed cumulative number of infected cases, while the expected value of the basic reproduction number R0 was found to be 4.53 (min-max range: 4.40- 4.65). On May 4, the date on which relaxation of the measures commenced the effective reproduction number was found to be 0.987 (interquartiles: 0.857, 1.133). The model approximated adequately two months ahead of time the evolution of reported cases of infected until May 4, the day on which the phase I of the relaxation of measures was implemented over all of Italy. Furthermore the model predicted that until May 4, around 20% of the population in Lombardy has recovered (interquartile range: *10% to *30%)
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