Comment on "The European response to the WHO call to eliminate cervical cancer as a public health problem"

Funding Information: This work was supported through grant EMP416 from the EEA (European Economic Area) and Norway Grants.publishersversionPeer reviewe

Cervical Cancer in the Baltic States : Can Intelligent and Personalized Cancer Screening Change the Situation?

Copyright © 2022 Mindaugas Stankūnas, Kersti Pärna, Anna Tisler, Anda Ķīvīte-Urtāne, Una Kojalo, Jana Zodzika, Nicholas Baltzer, Jan Nygard, Mari Nygard, Anneli Uuskula. Published by Vilnius University Press.The three Baltic States (Estonia, Latvia, and Lithuania) are among the European Union countries with the highest incidence and mortality rates for cervical cancer. In order to tackle this public health challenge, there is an urgent need to implement more advanced and effective methods in cervical cancer prevention in Baltic countries. Nationwide cervical cancer screening programs in the Baltic States commenced in 2004-2009. While the organized screening programs in these countries differ in some relevant details (target age groups, screening interval), the underlying principles and problems, barriers are universal. However, the outcomes of present screening programs are unsatisfactory. In addition, universal screening programs are extremely costly. There is a potential need for more intelligent and personalized cervical cancer screening program. In 2019 the project "Towards elimination of cervical cancer: intelligent and personalized solutions for cancer screening" (2020-2023) was developed with the main objective - to develop improved and personalized cancer screening methods within a sustainable health care system. It is expected, that more sophisticated cervical cancer screening model will be implemented in Estonia, Latvia, and Lithuania, and will have a positive impact to epidemiology of cervical cancer and public health in general.publishersversionPeer reviewe

R.ROSETTA: an interpretable machine learning framework.

Funder: Uppsala Universitet; doi: http://dx.doi.org/10.13039/501100007051Funder: Polska Akademia Nauk; doi: http://dx.doi.org/10.13039/501100004382Funder: Uppsala UniversityBACKGROUND: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components. RESULTS: We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case-control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes. CONCLUSIONS: R.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables

A two-step workflow based on plasma p-tau217 to screen for amyloid β positivity with further confirmatory testing only in uncertain cases

Cost-effective strategies for identifying amyloid-β (Aβ) positivity in patients with cognitive impairment are urgently needed with recent approvals of anti-Aβ immunotherapies for Alzheimer’s disease (AD). Blood biomarkers can accurately detect AD pathology, but it is unclear whether their incorporation into a full diagnostic workflow can reduce the number of confirmatory cerebrospinal fluid (CSF) or positron emission tomography (PET) tests needed while accurately classifying patients. We evaluated a two-step workflow for determining Aβ-PET status in patients with mild cognitive impairment (MCI) from two independent memory clinic-based cohorts (n = 348). A blood-based model including plasma tau protein 217 (p-tau217), age and APOE ε4 status was developed in BioFINDER-1 (area under the curve (AUC) = 89.3%) and validated in BioFINDER-2 (AUC = 94.3%). In step 1, the blood-based model was used to stratify the patients into low, intermediate or high risk of Aβ-PET positivity. In step 2, we assumed referral only of intermediate-risk patients to CSF Aβ42/Aβ40 testing, whereas step 1 alone determined Aβ-status for low- and high-risk groups. Depending on whether lenient, moderate or stringent thresholds were used in step 1, the two-step workflow overall accuracy for detecting Aβ-PET status was 88.2%, 90.5% and 92.0%, respectively, while reducing the number of necessary CSF tests by 85.9%, 72.7% and 61.2%, respectively. In secondary analyses, an adapted version of the BioFINDER-1 model led to successful validation of the two-step workflow with a different plasma p-tau217 immunoassay in patients with cognitive impairment from the TRIAD cohort (n = 84). In conclusion, using a plasma p-tau217-based model for risk stratification of patients with MCI can substantially reduce the need for confirmatory testing while accurately classifying patients, offering a cost-effective strategy to detect AD in memory clinic settings

R.ROSETTA: an interpretable machine learning framework.

Funder: Uppsala Universitet; doi: http://dx.doi.org/10.13039/501100007051Funder: Polska Akademia Nauk; doi: http://dx.doi.org/10.13039/501100004382Funder: Uppsala UniversityBACKGROUND: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components. RESULTS: We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case-control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes. CONCLUSIONS: R.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables

Association of Phosphorylated Tau Biomarkers With Amyloid Positron Emission Tomography vs Tau Positron Emission Tomography

IMPORTANCE: The recent proliferation of phosphorylated tau (p-tau) biomarkers has raised questions about their preferential association with the hallmark pathologies of Alzheimer disease (AD): amyloid-β plaques and tau neurofibrillary tangles. OBJECTIVE: To determine whether cerebrospinal fluid (CSF) and plasma p-tau biomarkers preferentially reflect cerebral β-amyloidosis or neurofibrillary tangle aggregation measured with positron emission tomography (PET). DESIGN, SETTING, AND PARTICIPANTS: This was a cross-sectional study of 2 observational cohorts: the Translational Biomarkers in Aging and Dementia (TRIAD) study, with data collected between October 2017 and August 2021, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), with data collected between September 2015 and November 2019. TRIAD was a single-center study, and ADNI was a multicenter study. Two independent subsamples were derived from TRIAD. The first TRIAD subsample comprised individuals assessed with CSF p-tau (p-tau181, p-tau217, p-tau231, p-tau235), [18F]AZD4694 amyloid PET, and [18F]MK6240 tau PET. The second TRIAD subsample included individuals assessed with plasma p-tau (p-tau181, p-tau217, p-tau231), [18F]AZD4694 amyloid PET, and [18F]MK6240 tau PET. An independent cohort from ADNI comprised individuals assessed with CSF p-tau181, [18F]florbetapir PET, and [18F]flortaucipir PET. Participants were included based on the availability of p-tau and PET biomarker assessments collected within 9 months of each other. Exclusion criteria were a history of head trauma or magnetic resonance imaging/PET safety contraindications. No participants who met eligibility criteria were excluded. EXPOSURES: Amyloid PET, tau PET, and CSF and plasma assessments of p-tau measured with single molecule array (Simoa) assay or enzyme-linked immunosorbent assay. MAIN OUTCOMES AND MEASURES: Associations between p-tau biomarkers with amyloid PET and tau PET. RESULTS: A total of 609 participants (mean [SD] age, 66.9 [13.6] years; 347 female [57%]; 262 male [43%]) were included in the study. For all 4 phosphorylation sites assessed in CSF, p-tau was significantly more closely associated with amyloid-PET values than tau-PET values (p-tau181 difference, 13%; 95% CI, 3%-22%; P = .006; p-tau217 difference, 11%; 95% CI, 3%-20%; P = .003; p-tau231 difference, 15%; 95% CI, 5%-22%; P < .001; p-tau235 difference, 9%; 95% CI, 1%-19%; P = .02) . These results were replicated with plasma p-tau181 (difference, 11%; 95% CI, 1%-22%; P = .02), p-tau217 (difference, 9%; 95% CI, 1%-19%; P = .02), p-tau231 (difference, 13%; 95% CI, 3%-24%; P = .009), and CSF p-tau181 (difference, 9%; 95% CI, 1%-21%; P = .02) in independent cohorts. CONCLUSIONS AND RELEVANCE: Results of this cross-sectional study of 2 observational cohorts suggest that the p-tau abnormality as an early event in AD pathogenesis was associated with amyloid-β accumulation and highlights the need for careful interpretation of p-tau biomarkers in the context of the amyloid/tau/neurodegeneration, or A/T/(N), framework

Plasma and CSF biomarkers in a memory clinic: Head-to-head comparison of phosphorylated tau immunoassays

INTRODUCTION: Direct comparisons of the main blood phosphorylated tau immunoassays in memory clinic populations are needed to understand possible differences. METHODS: In the BIODEGMAR study, 197 participants presenting with cognitive complaints were classified into an Alzheimer's disease (AD) or a non-AD cerebrospinal fluid (CSF) profile group, according to their amyloid beta 42/ phosphorylated tau (Aβ42/p-tau) ratio. We performed a head-to-head comparison of nine plasma and nine CSF tau immunoassays and determined their accuracy to discriminate abnormal CSF Aβ42/p-tau ratio. RESULTS: All studied plasma tau biomarkers were significantly higher in the AD CSF profile group compared to the non-AD CSF profile group and significantly discriminated abnormal CSF Aβ42/p-tau ratio. For plasma p-tau biomarkers, the higher discrimination accuracy was shown by Janssen p-tau217 (r = 0.76; area under the curve [AUC] = 0.96), ADx p-tau181 (r = 0.73; AUC = 0.94), and Lilly p-tau217 (r = 0.73; AUC = 0.94). DISCUSSION: Several plasma p-tau biomarkers can be used in a specialized memory clinic as a stand-alone biomarker to detect biologically-defined AD. HIGHLIGHTS: Patients with an Alzheimer's disease cerebrospinal fluid (AD CSF) profile have higher plasma phosphorylated tau (p-tau) levels than the non-AD CSF profile group. All plasma p-tau biomarkers significantly discriminate patients with an AD CSF profile from the non-AD CSF profile group. Janssen p-tau217, ADx p-tau181, and Lilly p-tau217 in plasma show the highest accuracy to detect biologically defined AD. Janssen p-tau217, ADx p-tau181, Lilly p-tau217, Lilly p-tau181, and UGot p-tau231 in plasma show performances that are comparable to their CSF counterparts

Astrocyte reactivity influences amyloid-β effects on tau pathology in preclinical Alzheimer's disease

An unresolved question for the understanding of Alzheimer's disease (AD) pathophysiology is why a significant percentage of amyloid-β (Aβ)-positive cognitively unimpaired (CU) individuals do not develop detectable downstream tau pathology and, consequently, clinical deterioration. In vitro evidence suggests that reactive astrocytes unleash Aβ effects in pathological tau phosphorylation. Here, in a biomarker study across three cohorts (n = 1,016), we tested whether astrocyte reactivity modulates the association of Aβ with tau phosphorylation in CU individuals. We found that Aβ was associated with increased plasma phosphorylated tau only in individuals positive for astrocyte reactivity (Ast+). Cross-sectional and longitudinal tau-positron emission tomography analyses revealed an AD-like pattern of tau tangle accumulation as a function of Aβ only in CU Ast+ individuals. Our findings suggest astrocyte reactivity as an important upstream event linking Aβ with initial tau pathology, which may have implications for the biological definition of preclinical AD and for selecting CU individuals for clinical trials

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives