Empirical tests of natural selection-based evolutionary accounts of ADHD: a systematic review

ADHD is a prevalent and highly heritable mental disorder associated with significant impairment, morbidity and increased rates of mortality. This combination of high prevalence and high morbidity/mortality seen in ADHD and other mental disorders presents a challenge to natural selection-based models of human evolution. Several hypotheses have been proposed in an attempt to resolve this apparent paradox. The aim of this study was to review the evidence for these hypotheses.We conducted a systematic review of the literature on empirical investigations of natural selection-based evolutionary accounts for ADHD in adherence with the PRISMA guideline. The PubMed, Embase, and PsycINFO databases were screened for relevant publications, by combining search terms covering evolution/selection with search terms covering ADHD.The search identified 790 records. Of these, 15 full-text articles were assessed for eligibility, and three were included in the review. Two of these reported on the evolution of the seven-repeat allele of the ADHD-associated dopamine receptor D4 gene, and one reported on the results of a simulation study of the effect of suggested ADHD-traits on group survival. The authors of the three studies interpreted their findings as favouring the notion that ADHD-traits may have been associated with increased fitness during human evolution. However, we argue that none of the three studies really tap into the core symptoms of ADHD, and that their conclusions therefore lack validity for the disorder.This review indicates that the natural selection-based accounts of ADHD have not been subjected to empirical test and therefore remain hypothetical

Experiences of the Flipped Classroom method Does it make students more motivated?

The aim of this paper is to highlight use of the flipped classroom method, and how teachers perceive this teaching practice. More specific the research focus on whether the teachers’ experience that the model leads to increased motivation in the students learning process. The background for the research is generated from qualitative interviews with teachers, and the empirical data obtained is from semi-structured interviews with these informants. The results show that the flipped classroom method in fact did increase participation and cooperation, which in turn generated motivation and willing students. The teachers got more time for guidance of each student, which provided more solid knowledge on each student’s academic level

"I did not intend to stop. I just could not stand cigarettes any more." A qualitative interview study of smoking cessation among the elderly

<p>Abstract</p> <p>Background</p> <p>Every year, more than 650,000 Europeans die because they smoke. Smoking is considered to be the single most preventable factor influencing health. General practitioners (GP) are encouraged to advise on smoking cessation at all suitable consultations. Unsolicited advice from GPs results in one of 40-60 smokers stopping smoking. Smoking cessation advice has traditionally been given on an individual basis. Our aim was to gain insights that may help general practitioners understand why people smoke, and why smokers stop and then remain quitting and, from this, to find fruitful approaches to the dialogue about stopping smoking.</p> <p>Methods</p> <p>Interviews with 18 elderly smokers and ex-smokers about their smoking and decisions to smoke or quit were analysed with qualitative content analysis across narratives. A narrative perspective was applied.</p> <p>Results</p> <p>Six stages in the smoking story emerged, from the start of smoking, where friends had a huge influence, until maintenance of the possible cessation. The informants were influenced by "all the others" at all stages. Spouses had vital influence in stopping, relapses and continued smoking. The majority of quitters had stopped by themselves without medication, and had kept the tobacco handy for 3-6 months. Often smoking cessation seemed to happen unplanned, though sometimes it was planned. With an increasingly negative social attitude towards smoking, the informants became more aware of the risks of smoking.</p> <p>Conclusion</p> <p>"All the others" is a clue in the smoking story. For smoking cessation, it is essential to be aware of the influence of friends and family members, especially a spouse. People may stop smoking unplanned, even when motivation is not obvious. Information from the community and from doctors on the negative aspects of smoking should continue. Eliciting life-long smoking narratives may open up for a fruitful dialogue, as well as prompting reflection about smoking and adding to the motivation to stop.</p

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring