STARD3 (START domain containing 3)

Review on STARD3 (START domain containing 3), with data on DNA, on the protein encoded, and where the gene is implicated

LASP1 (LIM, actin binding and SH3 protein)

Review on LASP1 (LIM, actin binding and SH3 protein), with data on DNA, on the protein encoded, and where the gene is implicated

MMP11 (matrix metalloproteinase 11 (stromelysin 3))

Review on MMP11 (matrix metalloproteinase 11 (stromelysin 3)), with data on DNA, on the protein encoded, and where the gene is implicated

Desensitization to carboplatin in low-grade glioma. A revision of 100 treatments in children

info:eu-repo/semantics/publishedVersio

ICON 2019: International Scientific Tendinopathy Symposium Consensus: Clinical Terminology

© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.Background Persistent tendon pain that impairs function has inconsistent medical terms that can influence choice of treatment.1 When a person is told they have tendinopathy by clinician A or tendinitis by clinician B, they might feel confused or be alarmed at receiving what they might perceive as two different diagnoses. This may lead to loss of confidence in their health professional and likely adds to uncertainty if they were to search for information about their condition. Clear and uniform terminology also assists inter-professional communication. Inconsistency in terminology for painful tendon disorders is a problem at numerous anatomical sites. Historically, the term ‘tendinitis’ was first used to describe tendon pain, thickening and impaired function (online supplementary figure S1). The term ‘tendinosis’ has also been used in a small number of publications, some of which were very influential.2 3 Subsequently, ‘tendinopathy’ emerged as the most common term for persistent tendon pain.4 5 To our knowledge, experts (clinicians and researchers) or patients have never engaged in a formal process to discuss the terminology we use. We believe that health professionals have not yet agreed on the appropriate terminology for painful tendon conditions.Peer reviewedFinal Accepted Versio

Deficiency in trefoil factor 1 (TFF1) increases tumorigenicity of human breast cancer cells and mammary tumor development in TFF1-knockout mice

Although trefoil factor 1 (TFF1; previously named pS2) is abnormally expressed in about 50% of human breast tumors, its physiopathological role in this disease has been poorly studied. Moreover, controversial data have been reported. TFF1 function in the mammary gland therefore needs to be clarified. In this study, using retroviral vectors, we performed TFF1 gain- or loss-of-function experiments in four human mammary epithelial cell lines: normal immortalized TFF1-negative MCF10A, malignant TFF1-negative MDA-MB-231 and malignant TFF1-positive MCF7 and ZR75.1. The expression of TFF1 stimulated the migration and invasion in the four cell lines. Forced TFF1 expression in MCF10A, MDA-MB-231 and MCF7 cells did not modify anchorage-dependent or -independent cell proliferation. By contrast, TFF1 knockdown in MCF7 enhanced soft-agar colony formation. This increased oncogenic potential of MCF7 cells in the absence of TFF1 was confirmed in vivo in nude mice. Moreover, chemically induced tumorigenesis in TFF1-deficient (TFF1-KO) mice led to higher tumor incidence in the mammary gland and larger tumor size compared with wild-type mice. Similarly, tumor development was increased in the TFF1-KO ovary and lung. Collectively, our results clearly show that TFF1 does not exhibit oncogenic properties, but rather reduces tumor development. This beneficial function of TFF1 is in agreement with many clinical studies reporting a better outcome for patients with TFF1-positive breast primary tumors

ICON 2019—International Scientific Tendinopathy Symposium Consensus: There are nine core health-related domains for tendinopathy (CORE DOMAINS): Delphi study of healthcare professionals and patients

Background: The absence of any agreed-upon tendon health-related domains hampers advances in clinical tendinopathy research. This void means that researchers report a very wide range of outcome measures inconsistently. As a result, substantial synthesis/meta-analysis of tendon research findings is almost futile despite researchers publishing busily. We aimed to determine options for, and then define, core health-related domains for tendinopathy. Methods: We conducted a Delphi study of healthcare professionals (HCP) and patients in a three-stage process. In stage 1, we extracted candidate domains from clinical trial reports and developed an online survey. Survey items took the form: ‘The ‘candidate domain’ is important enough to be included as a core health-related domain of tendinopathy’; response options were: agree, disagree, or unsure. In stage 2, we administered the online survey and reported the findings. Stage 3 consisted of discussions of the findings of the survey at the ICON (International Scientific Tendinopathy Symposium Consensus) meeting. We set 70% participant agreement as the level required for a domain to be considered ‘core’; similarly, 70% agreement was required for a domain to be relegated to ‘not core’ (see Results next). Results: Twenty-eight HCP (92% of whom had >10 years of tendinopathy experience, 71% consulted >10 cases per month) and 32 patients completed the online survey. Fifteen HCP and two patients attended the consensus meeting. Of an original set of 24 candidate domains, the ICON group deemed nine domains to be core. These were: (1) patient rating of condition, (2) participation in life activities (day to day, work, sport), (3) pain on activity/loading, (4) function, (5) psychological factors, (6) physical function capacity, (7) disability, (8) quality of life and (9) pain over a specified time. Two of these (2, 6) were an amalgamation of five candidate domains. We agreed that seven other candidate domains were not core domains: range of motion, pain on clinician applied test, clinical examination, palpation, drop out, sensory modality pain and pain without other specification. We were undecided on the other five candidate domains of physical activity, structure, medication use, adverse effects and economic impact. Conclusion: Nine core domains for tendon research should guide reporting of outcomes in clinical trials. Further research should determine the best outcome measures for each specific tendinopathy (ie, core outcome sets)

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

BACKGROUND: Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. METHODS: The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. RESULTS: It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. CONCLUSION: The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources

A comparison of pharmacoepidemiological study designs in medication use and traffic safety research

In order to explore how the choice of different study designs could influence the risk estimates, a case–crossover and case–time–control study were carried out and their outcomes were compared with those of a traditional case–control study design that evaluated the association between the exposure to psychotropic medications and the risk of having a motor vehicle accident (MVA). A record-linkage database availing data for 3,786 cases and 18,089 controls during the period 2000–2007 was used. The study designs (i.e., case–crossover and case–time–control) were derived from published literature, and the following psychotropic medicines were examined: antipsychotics, anxiolytics, hypnotics and sedatives, and antidepressants, stratified in the two groups selective serotonin reuptake inhibitors (SSRIs) and other antidepressants. Moreover, in order to further investigate the effects of frequency of psychoactive medication exposure on the outcomes of the case–crossover analysis, the data were also stratified by the number of defined daily doses (DDDs) and days of medication use in the 12 months before the motor vehicle accident. Three-thousand seven-hundred fifty-two cases were included in this second part of the case–crossover analysis. The case–crossover design did not show any statistically significant association between psychotropic medication exposure and MVA risk [e.g., SSRIs—Adj. OR = 1.00 (95 % CI: 0.69–1.46); Anxiolytics—Adj. OR = 0.95 (95 % CI: 0.68–1.31)]. The case–time–control design only showed a borderline statistically significant increased traffic accident risk in SSRI users [Adj. OR = 1.16 (95 % CI: 1.01–1.34)]. With respect to the stratifications by the number of DDDs and days of medication use, the analyses showed no increased traffic accident risk associated with the exposure to the selected medication groups [e.g., SSRIs, <20 DDDs—Adj. OR = 0.65 (95 % CI: 0.11–3.87); SSRIs, 16–150 days—Adj. OR = 0.55 (95 % CI: 0.24–1.24)]. In contrast to the above-mentioned results, our recent case–control study found a statistically significant association between traffic accident risk and exposure to anxiolytics [Adj. OR = 1.54 (95 % CI: 1.11–2.15)], and SSRIs [Adj. OR = 2.03 (95 % CI: 1.31–3.14)]. Case–crossover and case–time–control analyses produced different results than those of our recent case–control study (i.e., case–crossover and case–time–control analyses did not show any statistically significant association whereas the case–control analysis showed an increased traffic accident risk in anxiolytic and SSRI users). These divergent results can probably be explained by the differences in the study designs. Given that the case–crossover design is only appropriate for short-term exposures and the case–time–control design is an elaboration of this latter, it can be concluded that, probably, these two approaches are not the most suitable ones to investigate the relation between MVA risk and psychotropic medications, which, on the contrary, are often use chronically