Coronary computed tomography angiography in primary care patients with chest pain or dyspnea - a cross-sectional study.

Aims: Coronary Computed Tomography Angiography (CCTA) is recommended as a first-line investigation to exclude significant coronary artery stenosis in case of low to intermediate pre-test probability (PTP). The aim was to investigate CCTA findings in relation to the PTP of patients referred directly from primary health care centres.

Methods/results: In this retrospective cohort study consecutive primary care CCTA referrals in a Swedish county 1st of June 2021 until 30th Nov.

Clinically available predictors of obstructive sleep apnoea requiring treatment in type 2 diabetes patients in primary care.

Obstructive sleep apnoea is a common yet frequently underdiagnosed condition in patients with type 2 diabetes, particularly in primary care. Early detection is important, as untreated sleep apnoea may contribute to worsened metabolic control and increased cardiovascular risk. This study evaluated 164 patients with type 2 diabetes and found that 75% had obstructive sleep apnoea, with 31% requiring treatment for moderate to severe cases.

Proteogenomic analysis reveals Arp 2/3 complex as a common molecular mechanism in high risk pancreatic cysts and pancreatic cancer.

Pancreatic cysts, particularly intraductal papillary mucinous neoplasms (IPMNs), pose a potential risk for progressing to pancreatic cancer (PC). This study investigates the genetic architecture of benign pancreatic cysts and its potential connection to PC using genome-wide association studies (GWAS). The discovery GWAS identified significant genetic variants associated with benign cysts, specifically the rs142409042 variant near the OPCML gene.

Multiomics biomarkers were not superior to clinical variables for pan-cancer screening.

Background: Cancer screening tests are considered pivotal for early diagnosis and survival. However, the efficacy of these tests for improving survival has recently been questioned. This study aims to test if cancer screening could be improved by biomarkers in peripheral blood based on multi-omics data.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality.

Background: Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality.

Methods: CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

Background: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1,453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

Background: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data.

Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases.

Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs.

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets.

Background: Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery.

Trends and factors related to adolescent pregnancies: an incidence trend and conditional inference trees analysis of northern Nicaragua demographic surveillance data.

Background: We aimed to identify the 2001-2013 incidence trend, and characteristics associated with adolescent pregnancies reported by 20-24-year-old women.

Methods: A retrospective analysis of the Cuatro Santos Northern Nicaragua Health and Demographic Surveillance 2004-2014 data on women aged 15-19 and 20-24. To calculate adolescent birth and pregnancy rates, we used the first live birth at ages 10-14 and 15-19 years reported by women aged 15-19 and 20-24 years, respectively, along with estimates of annual incidence rates reported by women aged 20-24 years.

[Clinical translation of genomic medicine].

Recent technical developments and early clinical examples support that precision medicine has potential to provide novel diagnostic and therapeutic solutions for patients with complex diseases, who are not responding to existing therapies. Those solutions will require integration of genomic data with routine clinical, imaging, sensor, biobank and registry data. Moreover, user-friendly tools for informed decision support for both patients and clinicians will be needed.

Assessing the Multiple Dimensions of Poverty. Data Mining Approaches to the 2004-14 Health and Demographic Surveillance System in Cuatro Santos, Nicaragua.

We identified clusters of multiple dimensions of poverty according to the capability approach theory by applying data mining approaches to the Cuatro Santos Health and Demographic Surveillance database, Nicaragua. Four municipalities in northern Nicaragua constitute the Cuatro Santos area, with 25,893 inhabitants in 5,966 households (2014). A local process analyzing poverty-related problems, prioritizing suggested actions, was initiated in 1997 and generated a community action plan 2002-2015.

Digital twins to personalize medicine.

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh.

Introduction: WHO has set a goal to reduce the prevalence of stunted child growth by 40% by the year 2025. To reach this goal, it is imperative to establish the relative importance of risk factors for stunting to deliver appropriate interventions. Currently, most interventions take place in late infancy and early childhood.

PSICA: Decision trees for probabilistic subgroup identification with categorical treatments.

Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments.