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Ključne besede: physiotherapy, chronic low back pain, knowledge synthesis, bibliometric analysis
Objavljeno v DKUM: 23.08.2024; Ogledov: 73; Prenosov: 6
Celotno besedilo (2,11 MB)

Opis: 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. Results: In total of 11,767 adult patients, the association with Lp(a) was strongest in the IHD/AVS group (eβ = 1.010, p < 0.001), followed by the IHD (eβ = 1.008, p < 0.001) and AVS group (eβ = 1.004, p < 0.001). With increasing Lp(a) levels, the risk of IHD hospitalisation was higher compared with oCVD in women across all ages and in men aged ≤75 years. The risk of AVS hospitalisation was higher only in women aged ≤75 years (eβ = 1.010 in age < 60 years, eβ = 1.005 in age 60–75 years, p < 0.05). Conclusions: The Lp(a)-associated risk was highest for concomitant IHD/AVS hospitalisations. The differential impact of sex and age was most pronounced in the AVS group with an increased risk only in women aged ≤75 years.
Ključne besede: acute myocardial infarction, aortic valve stenosis, atherosclerosis, cardiovascular diseases, cardiovascular risk, ischemic heart disease, lipoprotein(a), postmenopausal women
Objavljeno v DKUM: 12.06.2024; Ogledov: 137; Prenosov: 11
Celotno besedilo (285,58 KB)
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Opis: Background: The pathogenesis of type 2 diabetes mellitus is complex and still unclear in some details. The main feature of diabetes mellitus is high serum glucose, and the question arises of whether there are other statistically observable dysregulations in laboratory measurements before the state of hyperglycemia becomes severe. In the present study, we aim to examine glucose and lipid profiles in the context of age, sex, medication use, and mortality. Methods: We conducted an observational study by analyzing laboratory data from 506,083 anonymized laboratory tests from 63,606 different patients performed by a regional laboratory in Slovenia between 2008 and 2019. Laboratory data-based results were evaluated in the context of medication use and mortality. The medication use database contains anonymized records of 1,632,441 patients from 2013 to 2018, and mortality data were obtained for the entire Slovenian population. Results: We show that the highest percentage of the population with elevated glucose levels occurs approximately 20 years later than the highest percentage with lipid dysregulation. Remarkably, two distinct inflection points were observed in these laboratory results. The first inflection point occurs at ages 55 to 59 years, corresponding to the greatest increase in medication use, and the second coincides with the sharp increase in mortality at ages 75 to 79 years. Conclusions: Our results suggest that medications and mortality are important factors affecting population statistics and must be considered when studying metabolic disorders such as dyslipidemia and hyperglycemia using laboratory data.
Ključne besede: diabetes, metabolic syndrome, hematological data, aging
Objavljeno v DKUM: 21.05.2024; Ogledov: 204; Prenosov: 11
Celotno besedilo (2,12 MB)
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Opis: Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Ključne besede: XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Objavljeno v DKUM: 14.03.2024; Ogledov: 299; Prenosov: 32
Celotno besedilo (5,28 MB)
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Opis: Biceps femoris is a major propulsor muscle in sprinting and its contraction time negatively correlates to the running speeds of adults. Our aim was to compare age and gender-related correlations between vastus lateralis and biceps femoris contraction times and running speeds during a longitudinal study of 9- to 14-year old children. On a yearly basis we conducted vastus lateralis and biceps femoris tensiomyographic measurements of muscle contraction time and maximal running speeds measured during 7 meter sprints with flying starts using photocells in 107 children (53 boys). Vastus lateralis contraction time was not correlated with the running speed. However, biceps femoris contraction time was negatively correlated with the running speed only in boys after the age of 12.9 years (Pearson r ranges from -0.391 to -0.426; p < 0.002). It was concluded that biceps femoris contraction time is far less correlated with running speed than in adult athletes (Pearson r = -0.60); however, the correlation is gender and age-specific. It seems that the knee flexor and hip extensor, biceps femoris, is not as yet the major determinant of running speed in 9- to 14-year old children at that age.
Ključne besede: skeletal muscles, biceps femoris, vastus lateralis, tensiomyography, pediatrics
Objavljeno v DKUM: 21.07.2017; Ogledov: 2352; Prenosov: 144
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