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Margit Kriegbaum,1 Bent Struer Lind,2 Mia Klinten Grand,1 Christen Lykkegaard Andersen1,3 1The Copenhagen Primary Care Laboratory (CopLab) Database, Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; 2Department of Clinical Biochemistry, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark; 3Department of Haematology, University Hospital Copenhagen, Rigshospitalet, Copenhagen, DenmarkCorrespondence: Margit Kriegbaum, Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, 1353 København K, Denmark, Tel +45 35337326, Fax +45 35327629, Email makr@sund.ku.dkBackground: The Copenhagen General Practice Laboratory (CGPL) was founded in 1922 to provide paraclinical analyses to the primary health-care sector in Copenhagen. At the end of 2015, CGPL was closed and the CopLab database was established to make CGPL data available for research.Methods: We isolated tests performed at the CGPL with clinically relevant test results. The database was linked to national registers containing health, social, and demographic information. Results are presented with descriptive statistics showing counts, percentages, medians, and interquartile ranges (IQR).Results: The CopLab database includes 1,373,643 unique individuals from primary care with test results from laboratory analyses of blood/urine/semen as well as cardiac and lung function tests collected by CGPL from greater Copenhagen from 2000 to 2015. The CopLab database holds nearly all test results requested by general practitioners throughout years 2000 to 2015 for residents in the greater Copenhagen area. The median age of the individuals was 51 years and 59.7% were females. Each individual has a median of 4 requisitions. More than 1 million participants are currently alive and living in Denmark and may be followed in national registries such as the Danish National Patient Registry, Laboratory Database, National Prescription Database etc.Keywords: laboratory database, population-based cohort, register studies, clinical biochemistry, epidemiology

Background: Taking notes during learning has benefits both during class (through writing things down to encode information) and after class (by using written notes as external storage for revision). Comparisons of note-taking methods (i.e., using paper or a computer) have mainly shown that paper leads to better learning. However, previous studies have mostly been conducted in laboratory contexts., Aims: The current study investigates university students' perceptions of the efficacity of their own preferred note-taking method, along with the strategies they employ., Sample: Data were collected from 108 university students., Methods: Students answered a questionnaire about their note-taking strategies during initial note-taking (in class) and revision (after class)., Results: The results show that students who take notes on paper do not consider their method to be more effective, but they report engaging in more reformulation and less multitasking. Students who take notes on a computer are more likely to reformat their notes, and thus to reformulate at a later stage. For all students, review sheets are mostly done on paper., Conclusions: These results suggest that although students are not necessarily aware of the benefits of reformulation associated with handwriting on paper during initial note-taking, when revising, they tend to choose handwriting and benefit from reformulation when aiming for deeper processing. Therefore, revision activities remain mainly paper-based., (© 2024 British Psychological Society.)

Recent years have seen a rise in wildfire and extreme weather activity across the globe, which is projected to keep increasing with climate-induced conditions. Air pollution, especially fine particulate matter (PM 2.5 ) concentration, is heavily affected by PM 2.5 emissions from wildfire activity. Paraguay has been historically suffering from fires, with an average of 2.3 million hectares burnt per year during the 2003-2021 period. Annual PM 2.5 concentration in Paraguay is 13.2 μg/m 3 , more than double the recommended by the WHO. We estimate that, historically, almost 40 % of fine air particulates can be attributed to fires. Using a random forest algorithm, we estimate future fire activity and fire related PM 2.5 under different climate change scenarios. With global warming, we calculate that fire activity could increase by up to 120 % by 2100. Annual fire smoke PM 2.5 from fires is expected to increase by 7.7 μg/m 3 by 2100. Under these conditions, Paraguay is expected to suffer an increase in 3500 deaths per year attributable to fire smoke PM 2.5 by 2100. We estimate the economic cost of fire smoke-related mortality by 2100 at US $ 5600 million, equivalent to 2.6 % of Paraguay's GDP, excluding other health- and productivity-related impacts on society., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nicolas Borchers-Arriagada reports financial support was provided by World Bank Group. Paulina Schulz-Antipa reports financial support was provided by World Bank Group. Mariana Conte-Grand reports was provided by World Bank Group., (Copyright © 2024 © 2024 The World Bank. Published by ELSEVIER B.V. Published by Elsevier B.V. All rights reserved.)

Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone., Methods: This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated., Results: MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone., Conclusion: MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results., Clinical Relevance Statement: The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa., Key Points: • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA., (© 2024. The Author(s).)