Clinical outcomes during and beyond different COVID-19 critical illness variant periods compared with other lower respiratory tract infections.

BACKGROUND: It is yet to be better understood how outcomes during and after the critical illness potentially differ between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants from other lower respiratory tract infections (LRTIs). We aimed to compare outcomes in adults admitted to an intensive care unit (ICU) with coronavirus disease 2019 (COVID-19) during the Wild-type, Alpha, Delta, and Omicron periods with individuals admitted with other LRTI. METHODS: Population-based cohort study in Stockholm, Sweden, using health registries with high coverage, including ICU-admitted adults from 1 January 2016 to 15 September 2022. Outcomes were in-hospital mortality, 180-day post-discharge mortality, 180-day hospital readmission, 180-day days alive and at home (DAAH), and incident diagnoses registered during follow-up. RESULTS: The number of ICU admitted individuals were 1421 Wild-type, 551 Alpha, 190 Delta, 223 Omicron, and 2380 LRTI. In-hospital mortality ranged from 28% (n = 665) in the LRTI cohort to 35% (n = 77) in the Delta cohort. The adjusted cause-specific hazard ratio (CSHR) compared with the LRTI cohort was 1.33 (95% confidence interval [CI] 1.16-1.53) in the Wild-type cohort, 1.53 (1.28-1.82) in the Alpha cohort, 1.70 (1.30-2.24) in the Delta cohort, and 1.59 (1.24-2.02) in the Omicron cohort. Among patients discharged alive from their COVID-19 hospitalization, the post-discharge mortality rates were lower (1-3%) compared with the LRTI cohort (9%), and the risk of hospital readmission was lower (CSHRs ranging from 0.42 to 0.68). Moreover, all COVID-19 cohorts had compared with the LRTI cohort more DAAH after compared with before the critical illness. CONCLUSION: Overall, COVID-19 critical was associated with an increased hazard of in-hospital mortality, but among those discharged alive from the hospital, less severe long-term outcomes were observed compared with other LRTIs.

Age-specific and genotype-specific carcinogenic human papillomavirus prevalence in a country with a high cervical cancer burden: results of a cross-sectional study in Estonia.

OBJECTIVES: To describe age-specific and type-specific carcinogenic human papillomavirus (HPV) prevalence prior to large-scale effect of HPV vaccines in Estonia and to analyse the risk factors associated with carcinogenic HPV. DESIGN: Cross-sectional study using self-administered questionnaire and self-collected vaginal swabs for detection of HPV infection. SETTING: Estonian Biobank database. PARTICIPANTS: Stratified random sample of women aged 30-33, 57-60 and 67-70 years living in one of the three largest counties in Estonia. Of 3065 women approached, 1347 (43.9%) returned questionnaires and specimens for HPV DNA detection. OUTCOME MEASURES: HPV prevalence and fully adjusted ORs with 95% CIs for risk factors. RESULTS: HPV prevalence was highest among women aged 30-33 years (18.7%; 95% CI 15.8 to 21.9) followed by those aged 67-70 years (16.7%; 95% CI 12.4 to 22.0) and 57-60 years (10.2%; 95% CI 7.8 to 13.3). HPV16 and HPV56 were the most common among women aged 30-33 years (both 4.0%; 95% CI 2.7 to 5.9), and HPV68 was the most common among women aged 57-60 years (2.8%; 95% CI 1.5 to 4.7) and 67-70 years (6.4%; 95% CI 3.6 to 10.4). Vaccination with nonavalent vaccine would have halved the carcinogenic HPV prevalence among women aged 30-33 years. The odds of infection with carcinogenic HPV were higher among women with six or more sexual partners among younger (OR 2.99; 95% CI 1.54 to 5.81) and older (OR 3.80; 95% CI 1.25 to 11.55) women and lower (OR 0.35; 95% CI 0.17 to 0.72) among younger married women. CONCLUSIONS: This study demonstrated U-shaped age-specific genotype profile of carcinogenic HPV prevalence, indicating that public health providers should focus on developing exit strategies for the cervical cancer screening programme in Estonia with a possible extension of HPV testing beyond the current screening age of 65 years. Generalisability of the findings of this study may be affected by the low response rate.

An overview of cervical cancer epidemiology and prevention in the Baltic States.

AIMS: To inform future Baltic States-specific policy analyses, we aimed to provide an overview of cervical cancer epidemiology and existing prevention efforts in Estonia, Latvia and Lithuania. METHODS: A structured desk review: we compiled and summarized data on current prevention strategies, population demography and epidemiology (high risk human papillomavirus (HPV) prevalence and cervical cancer incidence and mortality over time) for each Baltic State by reviewing published literature and official guidelines, performing registry-based analyses using secondary data and having discussions with experts in each country. RESULTS: We observed important similarities in the three Baltic States: high burden of the disease (high incidence and mortality of cervical cancer, changes in TNM (Classification of Malignant Tumors) stage distribution towards later stage at diagnosis), high burden of high-risk HPV in general population and suboptimal implementation of the preventive strategies as low screening and HPV vaccination coverage. CONCLUSIONS: Cervical cancer remains a substantial health problem in the region and the efforts in addressing barriers by implementing a four-step plan for elimination cervical cancer in Europe should be made. This goal is achievable through evidence-based steps in four key areas: vaccination, screening, treatment, and public awareness.

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

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.

R.ROSETTA: an interpretable machine learning framework.

BACKGROUND: 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.

Studies of liver tissue identify functional gene regulatory elements associated to gene expression, type 2 diabetes, and other metabolic diseases.

BACKGROUND: Genome-wide association studies (GWAS) of diseases and traits have found associations to gene regions but not the functional SNP or the gene mediating the effect. Difference in gene regulatory signals can be detected using chromatin immunoprecipitation and next-gen sequencing (ChIP-seq) of transcription factors or histone modifications by aligning reads to known polymorphisms in individual genomes. The aim was to identify such regulatory elements in the human liver to understand the genetics behind type 2 diabetes and metabolic diseases. METHODS: The genome of liver tissue was sequenced using 10X Genomics technology to call polymorphic positions. Using ChIP-seq for two histone modifications, H3K4me3 and H3K27ac, and the transcription factor CTCF, and our established bioinformatics pipeline, we detected sites with significant difference in signal between the alleles. RESULTS: We detected 2329 allele-specific SNPs (AS-SNPs) including 25 associated to GWAS SNPs linked to liver biology, e.g., 4 AS-SNPs at two type 2 diabetes loci. Two hundred ninety-two AS-SNPs were associated to liver gene expression in GTEx, and 134 AS-SNPs were located on 166 candidate functional motifs and most of them in EGR1-binding sites. CONCLUSIONS: This study provides a valuable collection of candidate liver regulatory elements for further experimental validation.

Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases.

Several Genome Wide Association Studies (GWAS) have reported variants associated to immune diseases. However, the identified variants are rarely the drivers of the associations and the molecular mechanisms behind the genetic contributions remain poorly understood. ChIP-seq data for TFs and histone modifications provide snapshots of protein-DNA interactions allowing the identification of heterozygous SNPs showing significant allele specific signals (AS-SNPs). AS-SNPs can change a TF binding site resulting in altered gene regulation and are primary candidates to explain associations observed in GWAS and expression studies. We identified 17,293 unique AS-SNPs across 7 lymphoblastoid cell lines. In this set of cell lines we interrogated 85% of common genetic variants in the population for potential regulatory effect and we identified 237 AS-SNPs associated to immune GWAS traits and 714 to gene expression in B cells. To elucidate possible regulatory mechanisms we integrated long-range 3D interactions data to identify putative target genes and motif predictions to identify TFs whose binding may be affected by AS-SNPs yielding a collection of 173 AS-SNPs associated to gene expression and 60 to B cell related traits. We present a systems strategy to find functional gene regulatory variants, the TFs that bind differentially between alleles and novel strategies to detect the regulated genes.

Risk stratification in cervical cancer screening by complete screening history: Applying bioinformatics to a general screening population.

Women screened for cervical cancer in Sweden are currently treated under a one-size-fits-all programme, which has been successful in reducing the incidence of cervical cancer but does not use all of the participants' available medical information. This study aimed to use women's complete cervical screening histories to identify diagnostic patterns that may indicate an increased risk of developing cervical cancer. A nationwide case-control study was performed where cervical cancer screening data from 125,476 women with a maximum follow-up of 10 years were evaluated for patterns of SNOMED diagnoses. The cancer development risk was estimated for a number of different screening history patterns and expressed as Odds Ratios (OR), with a history of 4 benign cervical tests as reference, using logistic regression. The overall performance of the model was moderate (64% accuracy, 71% area under curve) with 61-62% of the study population showing no specific patterns associated with risk. However, predictions for high-risk groups as defined by screening history patterns were highly discriminatory with ORs ranging from 8 to 36. The model for computing risk performed consistently across different screening history lengths, and several patterns predicted cancer outcomes. The results show the presence of risk-increasing and risk-decreasing factors in the screening history. Thus it is feasible to identify subgroups based on their complete screening histories. Several high-risk subgroups identified might benefit from an increased screening density. Some low-risk subgroups identified could likely have a moderately reduced screening density without additional risk.