Family correlates of daughter's and son's locus of control expectancies during childhood

Children who expect they can bring about good outcomes and avoid bad outcomes tend to experience more personal successes. Little is known about factors that contribute to these ‘control expectancies’. The purpose of the present study was to determine whether children's internal control expectancies occur in the context of parents’ internal control expectancies, low family strain, and high family cohesiveness and whether these factors are more strongly related to daughters’ than sons’ control expectancies. A community sample of 85 children aged 9–11 years and their parents (85 mothers; 63 fathers) completed rating scales. Fathers’ more internal control expectancies and mothers’ reports of fewer family strains were associated with daughters’ but not sons’ greater internal control expectancies, and greater family cohesiveness was related to both daughters’ and sons’ internal control orientations. These findings suggest that family factors may contribute to children's, particularly daughters’, development of internal control expectancies.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

The Web of Internet Dependency: Search Results for the Mental Health Professional

Some researchers speculate that as many as 10% of users worldwide suffer from “problematic” use of the internet, possibly stemming from an internet dependency. Research is in its early stages, making it an uncertain issue for mental health professionals. This paper provides: (1) a critical review of research on problematic internet use for the mental health and addictions professional; and (2) practical implications, including suggestions on treatment and prevention, given its uncertain clinical classification.     

IFN-alpha1,8 inhibits tumor-induced angiogenesis in murine angiosarcomas.

Interferon-alpha (IFN-alpha) has proved effective in the treatment of hemangiomas, hemangioblastomas, and Kaposi's sarcoma. To investigate the ability of IFNs to inhibit angiosarcoma, we used two transformed murine endothelial cell lines that form angiosarcomas in vivo. SVR and MS1-VEGF cell lines express oncogenic H-ras or vascular endothelial growth factor (VEGF), respectively. IFN-alpha1,8, which is active against murine and human cells, inhibited SVR and MS1-VEGF proliferation in vitro by 40% at 10(3) U/mL (p = 0.028). In vivo, IFN-alpha1,8 inhibited SVR tumor volume by 71% (p = 0.047) and MS1-VEGF volume by 79% (p = 0.003). Tumor-induced angiogenesis was decreased in SVR tumors by 52% (p = 0.005) and in MS1-VEGF tumors by 58% (p = 0.001). Sera from IFN-alpha1,8-treated mice bearing either SVR or MS1-VEGF tumors demonstrated a 5-fold increase in IP-10/CXCL10 (p = 0.001), an IFN-induced antiangiogenic protein. Both recombinant IP-10 and IFN-alpha1,8 inhibited human umbilical vein endothelial cell (HUVEC) vessel formation in the fibrin gel assay, a three-dimensional culture model of angiogenesis, by 56% at 25 ng/mL and 50% at 1.2 ng/mL, respectively (p < 0.001). An IP-10 blocking antibody restored vessel formation to 80% of untreated controls (p = 0.001). Given the magnitude of the in vivo response, these data suggested that the antitumor effects of IFN-alpha1,8 were likely mediated through angiogenesis inhibition rather than solely by direct inhibition of tumor cell proliferation.     

Effects of acute administration of d-amphetamine and haloperidol on procedural learning in man

The effects of an indirect dopamine-agonist, d-amphetamine, and a non-selective dopamine receptor antagonist, haloperidol, were investigated in normal male volunteers using a between-subjects double-blind design in a procedural learning task, thought mainly to involve unconscious/automatic learning. The results showed: (1) d-amphetamine facilitated response speed, whereas haloperidol inhibited it, in comparison to placebo; (2) the linear increase in procedural learning corresponded with pharmacological manipulation of degree of dopaminergic activity, i.e. subjects given haloperidol showed the least, and subjects given d-amphetamine the greatest, procedural learning. The implications of these findings are discussed in relation to investigation of abnormalities of procedural learning processes in schizophrenia. Received: 28 June 1996/Final version: 2 October 1996