| 1. |
- Carlén, Kristina, et al.
(author)
-
Sense of coherence predicts adolescent mental health
- 2020
-
In: Journal of Affective Disorders. - : Elsevier. - 0165-0327 .- 1573-2517. ; 274, s. 1206-1210
-
Journal article (peer-reviewed)abstract
- Background: Strong sense of coherence (SOC) has been shown to predict good mental health among adults whereas its predictive value in adolescence is unclear. This life-course oriented prospective study explores whether SOC predicts mental health in a three-year follow-up. Methods: The data is part of the ongoing ‘Finnish Family Competence Study’ launched in 1986 in southwestern Finland (baseline n = 1287). The outcome variable was adolescent's mental health at 18 years of age, measured on the Development and Well-Being Assessment (DAWBA) scale. The main predictor was Antonovsky's SOC score (1987) measured at the age of 15. A total of 498 adolescents were included in the present analyses. Poisson regression was used by univariate and multivariable models using the parents’ age and socioeconomic status and adolescents’ gender as covariates. Results: Multivariable analysis showed that a one-unit increase in SOC decreased the relative risk of a DAWBA-based diagnosis by 4 % (RR [95% CI] 0.96 [0.94–0.98], p < 0.001). Limitations: Typical of very long follow-up, as in our study of nearly two decades, a substantial proportion of the original population-based cohort was lost to follow-up weakening the representability of our cohort. Conclusions: Sense of coherence is a useful and clinically sensitive tool to predict mental health in adolescence. The easily administered, coping-oriented SOC questionnaire is an appropriate instrument in screening for adolescents who would benefit from supportive measures to strengthen their mental well-being.
|
|
| 2. |
- Arnoldussen, Ilse A. C., et al.
(author)
-
Adiposity is related to cerebrovascular and brain volumetry outcomes in the RUN DMC study
- 2019
-
In: Neurology. - : Wolters Kluwer. - 0028-3878 .- 1526-632X. ; 93:9, s. e864-e878
-
Journal article (peer-reviewed)abstract
- OBJECTIVE: Adiposity predictors, body mass index (BMI), waist circumference (WC), and blood leptin and total adiponectin levels were associated with components of cerebral small vessel disease (CSVD) and brain volumetry in 503 adults with CSVD who were ≥50 years of age and enrolled in the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort (RUN DMC).METHODS: RUN DMC participants were followed up for 9 years (2006-2015). BMI, WC, brain imaging, and dementia diagnoses were evaluated at baseline and follow-up. Adipokines were measured at baseline. Brain imaging outcomes included CSVD components, white matter hyperintensities, lacunes, microbleeds, gray and white matter, hippocampal, total brain, and intracranial volumes.RESULTS: Cross-sectionally among men at baseline, higher BMI, WC, and leptin were associated with lower gray matter and total brain volumes, and higher BMI and WC were associated with lower hippocampal volume. At follow-up 9 years later, higher BMI was cross-sectionally associated with lower gray matter volume, and an obese WC (>102 cm) was protective for ≥1 lacune or ≥1 microbleed in men. In women, increasing BMI and overweight or obesity (BMI ≥25 kg/m2 or WC >88 cm) were associated with ≥1 lacune. Longitudinally, over 9 years, a baseline obese WC was associated with decreasing hippocampal volume, particularly in men, and increasing white matter hyperintensity volume in women and men.CONCLUSIONS: Anthropometric and metabolic adiposity predictors were differentially associated with CSVD components and brain volumetry outcomes by sex. Higher adiposity is associated with a vascular-neurodegenerative spectrum among adults at risk for vascular forms of cognitive impairment and dementias.
|
|
| 3. |
- Javeed, Ashir, 1989-, et al.
(author)
-
Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
- 2023
-
In: Applied Sciences. - : MDPI. - 2076-3417. ; 13:8
-
Journal article (peer-reviewed)abstract
- Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a (Formula presented.) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model ((Formula presented.) _RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model (Formula presented.) _RF achieved the highest accuracy of 94.59%. The proposed model (Formula presented.) _RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model (Formula presented.) _RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module ((Formula presented.)).
|
|
| 4. |
- Slijper, Angelique, et al.
(author)
-
Computer game-based upper extremity training in the home environment in stroke persons : a single subject design
- 2014
-
In: Journal of NeuroEngineering and Rehabilitation. - : Biomed Central. - 1743-0003. ; 11:1
-
Journal article (peer-reviewed)abstract
- BACKGROUND: The objective of the present study was to assess whether computer game-based training in the home setting in the late phase after stroke could improve upper extremity motor function.METHODS: Twelve subjects with prior stroke were recruited; 11 completed the study.DESIGN: The study had a single subject design; there was a baseline test (A1), a during intervention test (B) once a week, a post-test (A2) measured directly after the treatment phase, plus a follow-up (C) 16-18 weeks after the treatment phase. Information on motor function (Fugl-Meyer), grip force (GrippitR) and arm function in activity (ARAT, ABILHAND) was gathered at A1, A2 and C. During B, only Fugl-Meyer and ARAT were measured. The intervention comprised five weeks of game-based computer training in the home environment. All games were designed to be controlled by either the affected arm alone or by both arms. Conventional formulae were used to calculate the mean, median and standard deviations. Wilcoxon's signed rank test was used for tests of dependent samples. Continuous data were analyzed by methods for repeated measures and ordinal data were analyzed by methods for ordered multinomial data using cumulative logistic models. A p-value of < 0.05 was considered statistically significant.RESULTS: Six females and five males, participated in the study with an average age of 58 years (range 26-66). FMA-UE A-D (motor function), ARAT, the maximal grip force and the mean grip force on the affected side show significant improvements at post-test and follow-up compared to baseline. No significant correlation was found between the amount of game time and changes in the outcomes investigated in this study.CONCLUSION: The results indicate that computer game-based training could be a promising approach to improve upper extremity function in the late phase after stroke, since in this study, changes were achieved in motor function and activity capacity.
|
|
| 5. |
- Ohlsson, Monica, et al.
(author)
-
Hereditary myopathy with early respiratory failure associated with a mutation in A-band titin
- 2012
-
In: Brain. - : Oxford University Press (OUP). - 0006-8950 .- 1460-2156. ; 135:6, s. 1682-1694
-
Journal article (peer-reviewed)abstract
- Hereditary myopathy with early respiratory failure and extensive myofibrillar lesions has been described in sporadic and familial cases and linked to various chromosomal regions. The mutated gene is unknown in most cases. We studied eight individuals, from three apparently unrelated families, with clinical and pathological features of hereditary myopathy with early respiratory failure. The investigations included clinical examination, muscle histopathology and genetic analysis by whole exome sequencing and single nucleotide polymorphism arrays. All patients had adult onset muscle weakness in the pelvic girdle, neck flexors, respiratory and trunk muscles, and the majority had prominent calf hypertrophy. Examination of pulmonary function showed decreased vital capacity. No signs of cardiac muscle involvement were found. Muscle histopathological features included marked muscle fibre size variation, fibre splitting, numerous internal nuclei and fatty infiltration. Frequent groups of fibres showed eosinophilic inclusions and deposits. At the ultrastructural level, there were extensive myofibrillar lesions with marked Z-disc alterations. Whole exome sequencing in four individuals from one family revealed a missense mutation, g.274375T > C; p.Cys30071Arg, in the titin gene (TTN). The mutation, which changes a highly conserved residue in the myosin binding A-band titin, was demonstrated to segregate with the disease in all three families. High density single nucleotide polymorphism arrays covering the entire genome demonstrated sharing of a 6.99 Mb haplotype, located in chromosome region 2q31 including TTN, indicating common ancestry. Our results demonstrate a novel and the first disease-causing mutation in A-band titin associated with hereditary myopathy with early respiratory failure. The typical histopathological features with prominent myofibrillar lesions and inclusions in muscle and respiratory failure early in the clinical course should be incentives for analysis of TTN mutations.
|
|
| 6. |
- Karlsson, Christina, 1961-, et al.
(author)
-
Registered Nurses´ View of Performing Pain Assessment among Persons with Dementia as Consultant Advisors
- 2012
-
In: Open Nursing Journal. - : Bentham Open. - 1874-4346. ; 6, s. 62-70
-
Journal article (peer-reviewed)abstract
- BACKGROUND: Pain assessment in persons with dementia is well known as a challenging issue to professional caregivers, because of these patients´ difficulties in verbalising pain problems. Within municipal dementia care in Sweden, pain assessment has become problematic for registered nurses, as they have entered a new role in their nursing profession, from being clinical practitioners to becoming consultant advisers to other health care staff.AIM: To present municipal registered nurses´ view of pain assessment in persons with dementia in relation to their nursing profession as consultant advisers.METHODS: Purposive sampling was undertaken with 11 nurses invited to participate. Data were collected by focus groups. Qualitative content analysis was used to analyse the data.FINDINGS: Four categories were identified to describe registered nurses´ view of pain assessment: estrangement from practical nursing care, time consuming and unsafe pain documentation, unfulfilled needs of reflection possibilities, and collaboration and coordination.CONCLUSIONS: The performance of pain assessment through a consultant advising function is experienced as frustrating and as an uncomfortable nursing situation. The nurses feel resistance to providing nursing in this way. They view nursing as a clinical task demanding daily presence among patients to enable them to make accurate and safe assessments. However, due to the consultative model, setting aside enough time for the presence seems difficult to accomplish. It is necessary to promote the quality of systematic routines in pain assessment and reflection, as well as developing professional knowledge of how pain can be expressed by dementia patients, especially those with communication difficulties.
|
|
| 7. |
- Bokenberger, Kathleen, et al.
(author)
-
Association between sleep characteristics and incident dementia accounting for baseline cognitive status : A prospective population-based study
- 2017
-
In: The journals of gerontology. Series A, Biological sciences and medical sciences. - : Oxford University Press. - 1079-5006 .- 1758-535X. ; 72:1, s. 134-139
-
Journal article (peer-reviewed)abstract
- Background: While research has shown that sleep disorders are prevalent among people with dementia, the temporal relationship is unclear. We investigated whether atypical sleep characteristics were associated with incident dementia while accounting for baseline cognitive functioning.Methods: Screening Across the Lifespan Twin Study (SALT) participants were 11,247 individuals from the Swedish Twin Registry who were at least 65 years at baseline (1998-2002). Sleep and baseline cognitive functioning were assessed via the SALT telephone screening interview. Data on dementia diagnoses came from national health registers. Cox regression was performed to estimate hazard ratios (HR) for dementia.Results: After 17 years of follow-up, 1,850 dementia cases were identified. Short (≤ 6 hours) and extended (> 9 hours) time-in-bed (TIB) compared to the middle reference group (HR=1.40, 95% CI=1.06-1.85, HR=1.11, 95% CI=1.00-1.24, respectively) and rising at 8:00AM or later compared to earlier rising (HR=1.12, 95% CI=1.01-1.24) were associated with higher dementia incidence. Bedtime, sleep quality, restorative sleep, and heavy snoring were not significant predictors. Findings stratified by baseline cognitive status indicated that the association between short TIB and dementia remained in those cognitively intact at the start.Conclusions: Short and extended TIB as well as delayed rising among older adults predicted increased dementia incidence in the following 17 years. The pattern of findings suggests that extended TIB and late rising represent prodromal features whereas short TIB appeared to be a risk factor for dementia.
|
|
| 8. |
- Tajsharghi, Homa, 1968, et al.
(author)
-
Myosin storage myopathy associated with a heterozygous missense mutation in MYH7.
- 2003
-
In: Annals of neurology. - : Wiley. - 0364-5134 .- 1531-8249. ; 54:4, s. 494-500
-
Journal article (peer-reviewed)abstract
- Myosin constitutes the major part of the thick filaments in the contractile apparatus of striated muscle. MYH7 encodes the slow/beta-cardiac myosin heavy chain (MyHC), which is the main MyHC isoform in slow, oxidative, type 1 muscle fibers of skeletal muscle. It is also the major MyHC isoform of cardiac ventricles. Numerous missense mutations in the globular head of slow/beta-cardiac MyHC are associated with familial hypertrophic cardiomyopathy. We identified a missense mutation, Arg1845Trp, in the rod region of slow/beta-cardiac MyHC in patients with a skeletal myopathy from two different families. The myopathy was characterized by muscle weakness and wasting with onset in childhood and slow progression, but no overt cardiomyopathy. Slow, oxidative, type 1 muscle fibers showed large inclusions consisting of slow/beta-cardiac MyHC. The features were similar to a previously described entity: hyaline body myopathy. Our findings indicate that the mutated residue of slow/beta-cardiac MyHC is essential for the assembly of thick filaments in skeletal muscle. We propose the term myosin storage myopathy for this disease.
|
|
| 9. |
- Javeed, Ashir, 1989-, et al.
(author)
-
Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks
- 2023
-
In: Computers, Materials and Continua. - : Tech Science Press. - 1546-2218 .- 1546-2226. ; 75:2, s. 2491-2508
-
Journal article (peer-reviewed)abstract
- Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.
|
|
| 10. |
|
|
