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- Khatun, M, et al.
(författare)
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Decidualized endometrial stromal cells present with altered androgen response in PCOS
- 2021
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1, s. 16287-
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Tidskriftsartikel (refereegranskat)abstract
- Hyperandrogenic women with PCOS show disrupted decidualization (DE) and placentation. Dihydrotestosterone (DHT) is reported to enhance DE in non-PCOS endometrial stromal cells (eSCCtrl); however, this has not been assessed in PCOS cells (eSCPCOS). Therefore, we studied the transcriptome profile of non-decidualized (non-DE) and DE eSCs from women with PCOS and Ctrl in response to short-term estradiol (E2) and/or progesterone (P4) exposure with/without (±) DHT. The non-DE eSCs were subjected to E2 ± DHT treatment, whereas the DE (0.5 mM 8-Br-cAMP, 96 h) eSCs were post-treated with E2 and P4 ± DHT, and RNA-sequenced. Validation was performed by immunofluorescence and immunohistochemistry. The results showed that, regardless of treatment, the PCOS and Ctrl samples clustered separately. The comparison of DE vs. non-DE eSCPCOS without DHT revealed PCOS-specific differentially expressed genes (DEGs) involved in mitochondrial function and progesterone signaling. When further adding DHT, we detected altered responses for lysophosphatidic acid (LPA), inflammation, and androgen signaling. Overall, the results highlight an underlying defect in decidualized eSCPCOS, present with or without DHT exposure, and possibly linked to the altered pregnancy outcomes. We also report novel factors which elucidate the mechanisms of endometrial dysfunction in PCOS.
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- Modhukur, V, et al.
(författare)
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Machine Learning Approaches to Classify Primary and Metastatic Cancers Using Tissue of Origin-Based DNA Methylation Profiles
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:15
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Tidskriftsartikel (refereegranskat)abstract
- Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types.
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- Kask, Jan, et al.
(författare)
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Anorexia nervosa in males : excess mortality and psychiatric co-morbidity in 609 Swedish in-patients
- 2017
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Ingår i: Psychological Medicine. - 0033-2917 .- 1469-8978. ; 47:8, s. 1489-1499
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Anorexia nervosa (AN) is a psychiatric disorder with high mortality.METHOD: A retrospective register study of 609 males who received hospitalized care for AN in Sweden between 1973 and 2010 was performed. The standardized mortality ratios (SMRs) and Cox regression-derived hazard ratios (HRs) were calculated as measures of mortality. The incidence rate ratios (IRRs) were calculated to compare the mortality rates in patients with AN and controls both with and without psychiatric diagnoses.RESULTS: The SMR for all causes of death was 4.1 [95% confidence interval (CI) 3.1-5.3]. For those patients with psychiatric co-morbidities, the SMR for all causes of death was 9.1 (95% CI 6.6-12.2), and for those without psychiatric co-morbidity, the SMR was 1.6 (95% CI 0.9-2.7). For the group of patients with alcohol use disorder, the SMR for natural causes of death was 11.5 (95% CI 5.0-22.7), and that for unnatural causes was 35.5 (95% CI 17.7-63.5). The HRs confirmed the increased mortality for AN patients with psychiatric co-morbidities, even after adjusting for confounders. The IRRs revealed no significant difference in mortality patterns between the AN patients with psychiatric co-morbidity and the controls with psychiatric diagnoses, with the exceptions of alcohol use disorder and neurotic, stress-related and somatoform disorders, which seemed to confer a negative synergistic effect on mortality.CONCLUSION: Mortality in male AN patients was significantly elevated compared with the general population among only the patients with psychiatric co-morbidities. Specifically, the presence of alcohol and other substance use disorders was associated with more profound excess mortality.
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- Kask, Kristiina, et al.
(författare)
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Patients with premenstrual dysphoric disorder have increased startle response across both cycle phases and lower levels of prepulse inhibition during the late luteal phase of the menstrual cycle
- 2008
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Ingår i: Neuropsychopharmacology. - : Springer Science and Business Media LLC. - 0893-133X .- 1740-634X. ; 33:9, s. 2283-2290
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Tidskriftsartikel (refereegranskat)abstract
- Patients with premenstrual dysphoric disorder (PMDD) experience their most intense symptoms during the late luteal phase. The aim of the current study was to compare acoustic startle response and prepulse inhibition in PMDD patients and controls during the follicular and late luteal phases of the menstrual cycle. Following two months of prospective daily ratings on the Cyclicity Diagnoser scale, 30 PMDD patients and 30 asymptomatic controls, between the ages of 20 and 46, were included in the study. The eyeblink component of the acoustic startle reflex was assessed using electromyographic measurements of m. orbicularis oculi. Twenty pulse-alone trials (115 dB 40 ms broad-band white noise) and 40 prepulse-pulse trials were presented. The prepulse stimuli consisted of a 115 dB 40 ms noise burst preceded at a 100 ms interval by 20 ms prepulses that were 72, 74, 78, or 86 dB. PMDD patients had a significantly higher startle response than controls during both phases of the menstrual cycle (p<0.05). PMDD patients exhibited lower levels of prepulse inhibition with 78 dB and 86 dB prepulses compared to control subjects in the luteal (p<0.01) but not in the follicular phase. Whereas control subjects displayed increased PPI during the late luteal phase compared to the follicular phase (p<0.01), PPI magnitude remained unchanged in PMDD patients between cycle phases. Relative to controls, PMDD patients displayed increased startle reactivity across both menstrual cycle phases and deficits in prepulse inhibition of acoustic startle during the late luteal phase. These findings are consistent with an altered response to ovarian steroids among PMDD patients.
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- Kask, Lena, et al.
(författare)
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Proteomic analysis of tissue factor activation
- 2013
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Ingår i: Journal of Thrombosis and Haemostasis. - 1538-7933 .- 1538-7836. ; 11:S2, s. 379-380
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 27. |
- Kask, Nathalie, et al.
(författare)
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Data-driven modelling of fatigue in pelvic floor muscles when performing Kegel exercises
- 2019
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Ingår i: 2019 IEEE 58th Conference On Decision And Control (CDC). - : IEEE. - 9781728113982 ; , s. 5647-5653
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Konferensbidrag (refereegranskat)abstract
- This paper studies how to describe, using a piece-wise linear dynamical model, the short-term effects of fatigue and recovery on the strength of pelvic floor muscles. Specifically, we first adapt a known model that describes short-term fatigue in skeletal muscles to the specific problem of describing fatigue in pelvic floor muscles when performing Kegel exercises, and then propose a strategy to learn the modelSs parameters from field data. In details, we estimate the model parameters using a least squares approach starting from measurement data that has been obtained from three healthy women using a dedicated vaginal pressure sensor array and a connected mobile app which gamifies the Kegel exercising experience. We show that describing the pelvic floor muscles behaviour in terms of short-term fatigue and recovery factors plus learning the associated parameters from data from healthy women leads to the possibility of precisely forecasting how much pressure the players will exert while playing the game. By cross-learning and cross-testing individual models from the three volunteers we also discover that the models need to be individualized: indeed, the numerical results indicate that, generically, using data from one player to model another leads to potentially drastically lower forecasting capabilities.
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| 28. |
- Kask, Nathalie, et al.
(författare)
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Data-driven modelling of fatigue in pelvic floor muscles when performing Kegel exercises
- 2020
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Ingår i: 2019 IEEE 58th Conference on Decision and Control (CDC). - : IEEE. ; , s. 5647-5663
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Konferensbidrag (refereegranskat)abstract
- This paper studies how to describe, using a piecewise linear dynamical model, the short-term effects of fatigue and recovery on the strength of pelvic floor muscles. Specifically, we first adapt a known model that describes short-term fatigue in skeletal muscles to the specific problem of describing fatigue in pelvic floor muscles when performing Kegel exercises, and then propose a strategy to learn the modelřs parameters from field data. In details, we estimate the model parameters using a least squares approach starting from measurement data that has been obtained from three healthy women using a dedicated vaginal pressure sensor array and a connected mobile app which gamifies the Kegel exercising experience. We show that describing the pelvic floor muscles behaviour in terms of short-term fatigue and recovery factors plus learning the associated parameters from data from healthy women leads to the possibility of precisely forecasting how much pressure the players will exert while playing the game. By cross-learning and cross-testing individual models from the three volunteers we also discover that the models need to be individualized: indeed, the numerical results indicate that, generically, using data from one player to model another leads to potentially drastically lower forecasting capabilities.
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