Background/Objectives: Elevated body temperature is a well-established biomarker of infection, increased disease risk, and adverse health outcomes. However, the relationship between resting body temperature and long-term survival in older individuals is complex. Emerging evidence suggests that higher basal body temperature is associated with reduced survival and accelerated aging in non-obese older adults. This study aimed to compare body temperatures across different age groups in hospitalized older adults. Methods: Data were retrospectively collected from 367 physically healthy residents of a mental health center. Longitudinal data from 142 individuals (68 men and 74 women), aged 45 to 70 years and monitored continuously over 25 years, were compared with cross-sectional data from 225 individuals (113 men and 112 women) who underwent periodic clinical examinations with temperature measurements. The cross-sectional sample was stratified into four survival categories. Resting oral temperatures were measured under clinical conditions to ensure protocol consistency. Age-related changes in both sexes were evaluated using standard regression analysis, Student's t-tests, ANOVA, and Generalized Linear Models. Results: Longitudinal analysis revealed an increase in body temperature with age among women, while cross-sectional analysis showed that long-lived residents generally had lower body temperatures compared to their shorter-lived counterparts. Conclusions: These findings support the hypothesis that lower lifetime steady-state body temperature is associated with greater longevity in physically healthy older adults. However, further research is needed to determine whether the lower body temperature observed in long-lived individuals is linked to specific health advantages, such as enhanced immune function, absence of detrimental factors or diseases, or a reduced metabolic rate potentially influenced by caloric restriction.
- Publication type
- Journal Article MeSH
Množství dostupných dat, která jsou relevantní pro podporu klinického rozhodování, roste mnohem rychleji, než naše schopnost je analyzovat a interpretovat. Proto dosud není plně využit potenciál dat přispět ke stanovení správné diagnózy, terapie a prognózy jednotlivého pacienta. Měřená data mohou zajistit konkrétní přínos pro konkrétního pacienta, což však platí jen v případě, že jejich biostatistická analýza je provedena spolehlivě a pečlivě. To vyžaduje řešit výzvy, které se mohou jevit nesrozumitelnými pro nestatistiky. Cílem tohoto článku je diskutovat principy statistické analýzy velkých dat ve výzkumu i rutinních aplikacích v klinické medicíně, se zvláštním zřetelem na specifické aspekty psychiatrie. Biostatistická analýza dat ve speciálním oboru vyžaduje své specifické přístupy a odlišné zkušenosti oproti jiným klinickým oblastem, jak dokládají komplikace při analýze psychiatrických dat. Analýza velkých dat v psychiatrickém výzkumu i rutinních aplikacích je velmi vzdálena pouhé servisní činnosti využívající standardní metody mnohorozměrné statistiky a/nebo strojového učení.
The amount of available data relevant for clinical decision support is rising not only rapidly but at the same time much faster than our ability to analyze and interpret them. Thus, the potential of the data to contribute to determining the diagnosis, therapy and prognosis of an individual patient is not appropriately exploited. The hopes to obtain benefit from the data for an individual patient must be accompanied by a reliable and diligent biostatistical analysis which faces serious challenges not always clear to non-statisticians. The aim of this paper is to discuss principles of statistical analysis of big data in research and routine applications in clinical medicine, focusing on particular aspects of psychiatry. The paper brings arguments in favor of the idea that the biostatistical analysis of data in a specialty field requires different approaches and different experience compared to other clinical fields. This is illustrated by a description of common complications of the analysis of psychiatric data. Challenges of the analysis of big data in both psychiatric research and routine practice are explained, which are far from a routine service activity exploiting standard methods of multivariate statistics and/or machine learning. Important research questions, which are important in the current psychiatric research, are presented and discussed from the biostatistical point of view.
Uvádíme přehled metod lícování neboli registrace 2D (obrazových) a 3D (objemových) diskrétních dat. Registrací rozumíme nalezení geometrické transformace mezi dvěma soubory diskrétních dat, která ztotožní pozici, orientaci a velikost korespondujících objektů obou souborů. V biomedicíněje aktuální při srovnávání objektů v čase (např. sledování léčby nádoru) nebo při jejich sledování různými senzory (např. integrace dat z různých lékařských zobrazovacích zařízeni) nebo při rekonstrukci 3D objektů ze sériových řezů v mikroskopii a podobně.
We present a short survey of image/volume registration techniques. Registration represents determination of coefficients of geometrical transformation between two images/volumes in order to get corresponding objects into the same position, orientation and scale. In biomedicine this is actual when one compares object(s) during a time period (e.g. tumour treatment observation) or by the use of different sensors (e.g. different modality data fusion). Also, registration is a prerequisite for 3D reconstruction and visualisation of objects from serial optical slices captured by a microscope, etc.
1 online zdroj
- MeSH
- Databases, Genetic MeSH
- Genomics * MeSH
- Publication type
- Periodical MeSH
- Conspectus
- Obecná genetika. Obecná cytogenetika. Evoluce
- NML Fields
- lékařská informatika
- genetika, lékařská genetika
Článek obsahuje základní informace o parametrech a výpočtu datového auditu mamografického screeningu. Jsou zde shrnuty vstupní komponenty auditu i vzorce pro výpočet jednotlivých položek. Z dostupné literatury byly shromážděny zkušenosti ze zahraničních screeningových programů a porovnány s podmínkami v České republice. Rozebrána jsou některá specifika českého programu a diskutovány jsou problémy, které tato specifika přinášejí.
The article contains basic information about parameters and data-audit calculation procedure of screening for breast cancer. There are included the input audit-components as well as formulas for calculation of each individual entry. Valuable experience from international screening programme were taken from available literature and compared to conditions in the Czech Republic. There are analysed some specificities of the Czech programme in this article as well as discussion is taken on commonly coming problems.
- MeSH
- Carcinoma diagnosis prevention & control MeSH
- Humans MeSH
- Mammography MeSH
- Breast Neoplasms economics prevention & control MeSH
- Mass Screening economics methods MeSH
- Data Collection MeSH
- Sensitivity and Specificity MeSH
- Quality Indicators, Health Care MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Review MeSH
A new test of the proportional hazards assumption in the Cox model is proposed. The idea is based on Neyman's smooth tests. The Cox model with proportional hazards (i.e. time-constant covariate effects) is embedded in a model with a smoothly time-varying covariate effect that is expressed as a combination of some basis functions (e.g., Legendre polynomials, cosines). Then the smooth test is the score test for significance of these artificial covariates. Furthermore, we apply a modification of Schwarz's selection rule to choosing the dimension of the smooth model (the number of the basis functions). The score test is then used in the selected model. In a simulation study, we compare the proposed tests with standard tests based on the score process.
1 online zdroj
- MeSH
- Genomics * MeSH
- Publication type
- Periodical MeSH
- Conspectus
- Obecná genetika. Obecná cytogenetika. Evoluce
- NML Fields
- genetika, lékařská genetika
The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust correlation coefficient based on the least weighted squares regression with data-adaptive weights. We compare various dimensionality reduction methods on three real data sets. To investigate the influence of noise or outliers on the data, we perform the computations also for data artificially contaminated by severe noise of various forms. The experimental results confirm the robustness of the method with respect to outliers.
