Decreased Gene Expression of Antiangiogenic Factors in Endometrial Cancer: qPCR Analysis and Machine Learning Modelling.

Endometrial cancer (EC) is an increasing health concern, with its growth driven by an angiogenic switch that occurs early in cancer development. Our study used publicly available datasets to examine the expression of angiogenesis-related genes and proteins in EC tissues, and compared them with adjacent control tissues. We identified nine genes with significant differential expression and selected six additional antiangiogenic genes from prior research for validation on EC tissue in a cohort of 36 EC patients.

Lipoprotein(a) As a Risk Factor in a Cohort of Hospitalised Cardiovascular Patients: A Retrospective Clinical Routine Data Analysis.

Introduction: Lipoprotein(a) (Lp(a)) is a well-recognised risk factor for ischemic heart disease (IHD) and calcific aortic valve stenosis (AVS).

Methods: A retrospective observational study of Lp(a) levels (mg/dL) in patients hospitalised for cardiovascular diseases (CVD) in our clinical routine was performed. The Lp(a)-associated risk of hospitalisation for IHD, AVS, and concomitant IHD/AVS versus other non-ischemic CVDs (oCVD group) was assessed by means of logistic regression.

Models including preoperative plasma levels of angiogenic factors, leptin and IL-8 as potential biomarkers of endometrial cancer.

Background: The diversity of endometrial cancer (EC) dictates the need for precise early diagnosis and pre-operative stratification to select treatment options appropriately. Non-invasive biomarkers invaluably assist clinicians in managing patients in daily clinical practice. Currently, there are no validated diagnostic or prognostic biomarkers for EC that could accurately predict the presence and extent of the disease.

Lipoprotein(a) in Cardiovascular Diseases: Insight From a Bibliometric Study.

Lipoprotein(a) [Lp(a)] is a complex polymorphic lipoprotein comprised of a low-density lipoprotein particle with one molecule of apolipoprotein B100 and an additional apolipoprotein(a) connected through a disulfide bond. The serum concentration is mostly genetically determined and only modestly influenced by diet and other lifestyle modifications. In recent years it has garnered increasing attention due to its causal role in pre-mature atherosclerotic cardiovascular disease and calcific aortic valve stenosis, while novel effective therapeutic options are emerging [apolipoprotein(a) antisense oligonucleotides and ribonucleic acid interference therapy].

The quality of digital health software: Should we be concerned?

The digitalization of healthcare fuelled by advances in technology and the increased prevalence of mobile smart devices and health-related internet of things can offer equitable access to expert-level healthcare globally. Growing demand for telemedicine, mobile health apps, and advanced data analytics have further established their role in a modern information society during the Covid-19 crisis. Digital health is, in essence, powered by software (DHSW), which has to operate in the specific digital health environment characteristics and is therefore highly and intrinsically complex and prone to software defects and faults.

Role of Agile in Digital Public Health Transformation.

The digitalisation of healthcare, fueled by advances in technology and the COVID-19 pandemic can not only empower equitable access to global expert-level healthcare but also make healthcare more patient-centric. Every digital health solution has one common fundamental component: they all run on computing platforms and are powered by complex software. Traditional software development life cycles have often failed in designing complex software; consequently, the agile approach was introduced.

Machine learning on small size samples: A synthetic knowledge synthesis.

Machine Learning is an increasingly important technology dealing with the growing complexity of the digitalised world. Despite the fact, that we live in a 'Big data' world where, almost 'everything' is digitally stored, there are many real-world situations, where researchers are still faced with small data samples. The present bibliometric knowledge synthesis study aims to answer the research question 'What is the small data problem in machine learning and how it is solved?' The analysis a positive trend in the number of research publications and substantial growth of the research community, indicating that the research field is reaching maturity.