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Efficient Light-Induced Phase Transitions in Halogen-Bonded Liquid Crystals.

Chemistry of materials : a publication of the American Chemical Society

Fernandez-Palacio F, Poutanen M, Saccone M, Siiskonen A, Terraneo G, Resnati G, Ikkala O, Metrangolo P, Priimagi A.
PMID: 27917024
Chem Mater. 2016 Nov 22;28(22):8314-8321. doi: 10.1021/acs.chemmater.6b03460. Epub 2016 Oct 06.

Here, we present a new family of light-responsive, fluorinated supramolecular liquid crystals (LCs) showing efficient and reversible light-induced LC-to-isotropic phase transitions. Our materials design is based on fluorinated azobenzenes, where the fluorination serves to strengthen the noncovalent interaction with...

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Fernandez-Palacio F, Poutanen M, Saccone M, et al. Efficient Light-Induced Phase Transitions in Halogen-Bonded Liquid Crystals. Chem Mater. 2016;28(22):8314-8321doi: 10.1021/acs.chemmater.6b03460.
Fernandez-Palacio, F., Poutanen, M., Saccone, M., Siiskonen, A., Terraneo, G., Resnati, G., Ikkala, O., Metrangolo, P., & Priimagi, A. (2016). Efficient Light-Induced Phase Transitions in Halogen-Bonded Liquid Crystals. Chemistry of materials : a publication of the American Chemical Society, 28(22), 8314-8321. https://doi.org/10.1021/acs.chemmater.6b03460
Fernandez-Palacio, Francisco, et al. "Efficient Light-Induced Phase Transitions in Halogen-Bonded Liquid Crystals." Chemistry of materials : a publication of the American Chemical Society vol. 28,22 (2016): 8314-8321. doi: https://doi.org/10.1021/acs.chemmater.6b03460
Fernandez-Palacio F, Poutanen M, Saccone M, Siiskonen A, Terraneo G, Resnati G, Ikkala O, Metrangolo P, Priimagi A. Efficient Light-Induced Phase Transitions in Halogen-Bonded Liquid Crystals. Chem Mater. 2016 Nov 22;28(22):8314-8321. doi: 10.1021/acs.chemmater.6b03460. Epub 2016 Oct 06. PMID: 27917024; PMCID: PMC5127694.
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[Dictionary of magnetic imaging].

Duodecim; laaketieteellinen aikakauskirja

Haltiala T, jääskinen J, Lamminen A, Miettunen R, Poutanen VP, Raininko R, Sepponen R, Suoranta H, Haarala R.
PMID: 1366296
Duodecim. 1992;108(8):827-9.

No abstract available.

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Haltiala T, jääskinen J, Lamminen A, et al. Magneettikuvauksen sanastoa. [Dictionary of magnetic imaging]. Duodecim. 1992;108(8):827-9
Haltiala, T., jääskinen, J., Lamminen, A., Miettunen, R., Poutanen, V. P., Raininko, R., Sepponen, R., Suoranta, H., & Haarala, R. (1992). Magneettikuvauksen sanastoa. [Dictionary of magnetic imaging]. Duodecim; laaketieteellinen aikakauskirja, 108(8), 827-9.
Haltiala, T, et al. "Magneettikuvauksen sanastoa." [Dictionary of magnetic imaging]. Duodecim; laaketieteellinen aikakauskirja vol. 108,8 (1992): 827-9.
Haltiala T, jääskinen J, Lamminen A, Miettunen R, Poutanen VP, Raininko R, Sepponen R, Suoranta H, Haarala R. Magneettikuvauksen sanastoa. [Dictionary of magnetic imaging]. Duodecim. 1992;108(8):827-9. Finnish. PMID: 1366296.
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Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice.

Frontiers in oncology

Merisaari H, Laakso H, Liljenbäck H, Virtanen H, Aronen HJ, Minn H, Poutanen M, Roivainen A, Liimatainen T, Jambor I.
PMID: 34123770
Front Oncol. 2021 May 26;11:583921. doi: 10.3389/fonc.2021.583921. eCollection 2021.

PURPOSE: To evaluate fitting quality and repeatability of four mathematical models for diffusion weighted imaging (DWI) during tumor progression in mouse xenograft model of prostate cancer.METHODS: Human prostate cancer cells (PC-3) were implanted subcutaneously in right hind limbs of...

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Merisaari H, Laakso H, Liljenbäck H, et al. Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice. Front Oncol. 2021;11:583921doi: 10.3389/fonc.2021.583921.
Merisaari, H., Laakso, H., Liljenbäck, H., Virtanen, H., Aronen, H. J., Minn, H., Poutanen, M., Roivainen, A., Liimatainen, T., & Jambor, I. (2021). Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice. Frontiers in oncology, 11583921. https://doi.org/10.3389/fonc.2021.583921
Merisaari, Harri, et al. "Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice." Frontiers in oncology vol. 11 (2021): 583921. doi: https://doi.org/10.3389/fonc.2021.583921
Merisaari H, Laakso H, Liljenbäck H, Virtanen H, Aronen HJ, Minn H, Poutanen M, Roivainen A, Liimatainen T, Jambor I. Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice. Front Oncol. 2021 May 26;11:583921. doi: 10.3389/fonc.2021.583921. eCollection 2021. PMID: 34123770; PMCID: PMC8188898.
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Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees.

CMAJ open

Gutierrez JM, Volkovs M, Poutanen T, Watson T, Rosella LC.
PMID: 34933880
CMAJ Open. 2021 Dec 21;9(4):E1223-E1231. doi: 10.9778/cmajo.20210036. Print 2021.

BACKGROUND: The COVID-19 pandemic has led to an increased demand for health care resources and, in some cases, shortage of medical equipment and staff. Our objective was to develop and validate a multivariable model to predict risk of hospitalization...

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Gutierrez JM, Volkovs M, Poutanen T, et al. Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees. CMAJ Open. 2021;9(4):E1223-E1231doi: 10.9778/cmajo.20210036.
Gutierrez, J. M., Volkovs, M., Poutanen, T., Watson, T., & Rosella, L. C. (2021). Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees. CMAJ open, 9(4), E1223-E1231. https://doi.org/10.9778/cmajo.20210036
Gutierrez, Jahir M, et al. "Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees." CMAJ open vol. 9,4 (2021): E1223-E1231. doi: https://doi.org/10.9778/cmajo.20210036
Gutierrez JM, Volkovs M, Poutanen T, Watson T, Rosella LC. Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees. CMAJ Open. 2021 Dec 21;9(4):E1223-E1231. doi: 10.9778/cmajo.20210036. Print 2021. PMID: 34933880; PMCID: PMC8695533.
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Docetaxel chemotherapy response in PC3 prostate cancer mouse model detected by rotating frame relaxations and water diffusion.

NMR in biomedicine

Laakso H, Ylä-Herttuala E, Sierra A, Jambor I, Poutanen M, Liljenbäck H, Virtanen H, Merisaari H, Aronen H, Minn H, Roivainen A, Liimatainen T.
PMID: 33543563
NMR Biomed. 2021 Apr;34(4):e4483. doi: 10.1002/nbm.4483. Epub 2021 Feb 04.

MRI is a common method of prostate cancer diagnosis. Several MRI-derived markers, including the apparent diffusion coefficient (ADC) based on diffusion-weighted imaging, have been shown to provide values for prostate cancer detection and characterization. The hypothesis of the study...

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Laakso H, Ylä-Herttuala E, Sierra A, et al. Docetaxel chemotherapy response in PC3 prostate cancer mouse model detected by rotating frame relaxations and water diffusion. NMR Biomed. 2021;34(4):e4483doi: 10.1002/nbm.4483.
Laakso, H., Ylä-Herttuala, E., Sierra, A., Jambor, I., Poutanen, M., Liljenbäck, H., Virtanen, H., Merisaari, H., Aronen, H., Minn, H., Roivainen, A., & Liimatainen, T. (2021). Docetaxel chemotherapy response in PC3 prostate cancer mouse model detected by rotating frame relaxations and water diffusion. NMR in biomedicine, 34(4), e4483. https://doi.org/10.1002/nbm.4483
Laakso, Hanne, et al. "Docetaxel chemotherapy response in PC3 prostate cancer mouse model detected by rotating frame relaxations and water diffusion." NMR in biomedicine vol. 34,4 (2021): e4483. doi: https://doi.org/10.1002/nbm.4483
Laakso H, Ylä-Herttuala E, Sierra A, Jambor I, Poutanen M, Liljenbäck H, Virtanen H, Merisaari H, Aronen H, Minn H, Roivainen A, Liimatainen T. Docetaxel chemotherapy response in PC3 prostate cancer mouse model detected by rotating frame relaxations and water diffusion. NMR Biomed. 2021 Apr;34(4):e4483. doi: 10.1002/nbm.4483. Epub 2021 Feb 04. PMID: 33543563.
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Comparative Analysis of the Effects of Long-Term 3,5-diiodothyronine Treatment on the Murine Hepatic Proteome and Transcriptome Under Conditions of Normal Diet and High-Fat Diet.

Thyroid : official journal of the American Thyroid Association

Lietzow J, Golchert J, Pietzner M, Völker U, Poutanen M, Ohlsson C, Homuth G, Köhrle J.
PMID: 33637021
Thyroid. 2021 Jul;31(7):1135-1146. doi: 10.1089/thy.2020.0160. Epub 2021 Apr 07.

No abstract available.

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Lietzow J, Golchert J, Pietzner M, et al. Comparative Analysis of the Effects of Long-Term 3,5-diiodothyronine Treatment on the Murine Hepatic Proteome and Transcriptome Under Conditions of Normal Diet and High-Fat Diet. Thyroid. 2021;31(7):1135-1146doi: 10.1089/thy.2020.0160.
Lietzow, J., Golchert, J., Pietzner, M., Völker, U., Poutanen, M., Ohlsson, C., Homuth, G., & Köhrle, J. (2021). Comparative Analysis of the Effects of Long-Term 3,5-diiodothyronine Treatment on the Murine Hepatic Proteome and Transcriptome Under Conditions of Normal Diet and High-Fat Diet. Thyroid : official journal of the American Thyroid Association, 31(7), 1135-1146. https://doi.org/10.1089/thy.2020.0160
Lietzow, Julika, et al. "Comparative Analysis of the Effects of Long-Term 3,5-diiodothyronine Treatment on the Murine Hepatic Proteome and Transcriptome Under Conditions of Normal Diet and High-Fat Diet." Thyroid : official journal of the American Thyroid Association vol. 31,7 (2021): 1135-1146. doi: https://doi.org/10.1089/thy.2020.0160
Lietzow J, Golchert J, Pietzner M, Völker U, Poutanen M, Ohlsson C, Homuth G, Köhrle J. Comparative Analysis of the Effects of Long-Term 3,5-diiodothyronine Treatment on the Murine Hepatic Proteome and Transcriptome Under Conditions of Normal Diet and High-Fat Diet. Thyroid. 2021 Jul;31(7):1135-1146. doi: 10.1089/thy.2020.0160. Epub 2021 Apr 07. PMID: 33637021.
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Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.

JAMA network open

Ravaut M, Harish V, Sadeghi H, Leung KK, Volkovs M, Kornas K, Watson T, Poutanen T, Rosella LC.
PMID: 34032855
JAMA Netw Open. 2021 May 03;4(5):e2111315. doi: 10.1001/jamanetworkopen.2021.11315.

IMPORTANCE: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.OBJECTIVE: To develop and validate a population-level machine learning model for predicting...

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Ravaut M, Harish V, Sadeghi H, et al. Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. JAMA Netw Open. 2021;4(5):e2111315doi: 10.1001/jamanetworkopen.2021.11315.
Ravaut, M., Harish, V., Sadeghi, H., Leung, K. K., Volkovs, M., Kornas, K., Watson, T., Poutanen, T., & Rosella, L. C. (2021). Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. JAMA network open, 4(5), e2111315. https://doi.org/10.1001/jamanetworkopen.2021.11315
Ravaut, Mathieu, et al. "Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes." JAMA network open vol. 4,5 (2021): e2111315. doi: https://doi.org/10.1001/jamanetworkopen.2021.11315
Ravaut M, Harish V, Sadeghi H, Leung KK, Volkovs M, Kornas K, Watson T, Poutanen T, Rosella LC. Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. JAMA Netw Open. 2021 May 03;4(5):e2111315. doi: 10.1001/jamanetworkopen.2021.11315. PMID: 34032855; PMCID: PMC8150694.
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Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.

NPJ digital medicine

Ravaut M, Sadeghi H, Leung KK, Volkovs M, Kornas K, Harish V, Watson T, Lewis GF, Weisman A, Poutanen T, Rosella L.
PMID: 33580109
NPJ Digit Med. 2021 Feb 12;4(1):24. doi: 10.1038/s41746-021-00394-8.

Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health...

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Ravaut M, Sadeghi H, Leung KK, et al. Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data. NPJ Digit Med. 2021;4(1):24doi: 10.1038/s41746-021-00394-8.
Ravaut, M., Sadeghi, H., Leung, K. K., Volkovs, M., Kornas, K., Harish, V., Watson, T., Lewis, G. F., Weisman, A., Poutanen, T., & Rosella, L. (2021). Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data. NPJ digital medicine, 4(1), 24. https://doi.org/10.1038/s41746-021-00394-8
Ravaut, Mathieu, et al. "Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data." NPJ digital medicine vol. 4,1 (2021): 24. doi: https://doi.org/10.1038/s41746-021-00394-8
Ravaut M, Sadeghi H, Leung KK, Volkovs M, Kornas K, Harish V, Watson T, Lewis GF, Weisman A, Poutanen T, Rosella L. Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data. NPJ Digit Med. 2021 Feb 12;4(1):24. doi: 10.1038/s41746-021-00394-8. PMID: 33580109; PMCID: PMC7881135.
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The HEALTHGRAIN definition of 'whole grain'.

Food & nutrition research

van der Kamp JW, Poutanen K, Seal CJ, Richardson DP.
PMID: 24505218
Food Nutr Res. 2014 Feb 04;58. doi: 10.3402/fnr.v58.22100. eCollection 2014.

Most cereal products, like white bread, pasta, and biscuits, are based on flour after removal of bran and germ, the two parts of grain kernels containing most of the dietary fibre and other bioactive components. In the past decade,...

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van der Kamp JW, Poutanen K, Seal CJ, et al. The HEALTHGRAIN definition of 'whole grain'. Food Nutr Res. 2014;58doi: 10.3402/fnr.v58.22100.
van der Kamp, J. W., Poutanen, K., Seal, C. J., & Richardson, D. P. (2014). The HEALTHGRAIN definition of 'whole grain'. Food & nutrition research, 58. https://doi.org/10.3402/fnr.v58.22100
van der Kamp, Jan Willem, et al. "The HEALTHGRAIN definition of 'whole grain'." Food & nutrition research vol. 58 (2014). doi: https://doi.org/10.3402/fnr.v58.22100
van der Kamp JW, Poutanen K, Seal CJ, Richardson DP. The HEALTHGRAIN definition of 'whole grain'. Food Nutr Res. 2014 Feb 04;58. doi: 10.3402/fnr.v58.22100. eCollection 2014. PMID: 24505218; PMCID: PMC3915794.
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A Healthy Nordic Diet Alters the Plasma Lipidomic Profile in Adults with Features of Metabolic Syndrome in a Multicenter Randomized Dietary Intervention.

The Journal of nutrition

Lankinen M, Schwab U, Kolehmainen M, Paananen J, Nygren H, Seppänen-Laakso T, Poutanen K, Hyötyläinen T, Risérus U, Savolainen MJ, Hukkanen J, Brader L, Marklund M, Rosqvist F, Hermansen K, Cloetens L, Önning G, Thorsdottir I, Gunnarsdottir I, Åkesson B, Dragsted LO, Uusitupa M, Orešič M.
PMID: 26962194
J Nutr. 2015 Apr 01;146(4):662-672. doi: 10.3945/jn.115.220459.

BACKGROUND: A healthy Nordic diet is associated with improvements in cardiometabolic risk factors, but the effect on lipidomic profile is not known.OBJECTIVE: The aim was to investigate how a healthy Nordic diet affects the fasting plasma lipidomic profile in...

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Lankinen M, Schwab U, Kolehmainen M, et al. A Healthy Nordic Diet Alters the Plasma Lipidomic Profile in Adults with Features of Metabolic Syndrome in a Multicenter Randomized Dietary Intervention. J Nutr. 2015;146(4):662-672doi: 10.3945/jn.115.220459.
Lankinen, M., Schwab, U., Kolehmainen, M., Paananen, J., Nygren, H., Seppänen-Laakso, T., Poutanen, K., Hyötyläinen, T., Risérus, U., Savolainen, M. J., Hukkanen, J., Brader, L., Marklund, M., Rosqvist, F., Hermansen, K., Cloetens, L., Önning, G., Thorsdottir, I., Gunnarsdottir, I., Åkesson, B., Dragsted, L. O., Uusitupa, M., & Orešič, M. (2015). A Healthy Nordic Diet Alters the Plasma Lipidomic Profile in Adults with Features of Metabolic Syndrome in a Multicenter Randomized Dietary Intervention. The Journal of nutrition, 146(4), 662-672. https://doi.org/10.3945/jn.115.220459
Lankinen, Maria, et al. "A Healthy Nordic Diet Alters the Plasma Lipidomic Profile in Adults with Features of Metabolic Syndrome in a Multicenter Randomized Dietary Intervention." The Journal of nutrition vol. 146,4 (2015): 662-672. doi: https://doi.org/10.3945/jn.115.220459
Lankinen M, Schwab U, Kolehmainen M, Paananen J, Nygren H, Seppänen-Laakso T, Poutanen K, Hyötyläinen T, Risérus U, Savolainen MJ, Hukkanen J, Brader L, Marklund M, Rosqvist F, Hermansen K, Cloetens L, Önning G, Thorsdottir I, Gunnarsdottir I, Åkesson B, Dragsted LO, Uusitupa M, Orešič M. A Healthy Nordic Diet Alters the Plasma Lipidomic Profile in Adults with Features of Metabolic Syndrome in a Multicenter Randomized Dietary Intervention. J Nutr. 2015 Apr 01;146(4):662-672. doi: 10.3945/jn.115.220459. PMID: 26962194.
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Expression of 17β-Hydroxysteroid Dehydrogenase Type 1 and Type 2, P450 Aromatase, and 20α-Hydroxysteroid Dehydrogenase Enzymes in Immature, Mature, and Pregnant Rats.

Endocrinology

Akinola LA, Poutanen M, Vihko R, Vihko P.
PMID: 28200169
Endocrinology. 1997 Jul 01;138(7):2886-2892. doi: 10.1210/endo.138.7.5258.

No abstract available.

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Akinola LA, Poutanen M, Vihko R, et al. Expression of 17β-Hydroxysteroid Dehydrogenase Type 1 and Type 2, P450 Aromatase, and 20α-Hydroxysteroid Dehydrogenase Enzymes in Immature, Mature, and Pregnant Rats. Endocrinology. 1997;138(7):2886-2892doi: 10.1210/endo.138.7.5258.
Akinola, L. A., Poutanen, M., Vihko, R., & Vihko, P. (1997). Expression of 17β-Hydroxysteroid Dehydrogenase Type 1 and Type 2, P450 Aromatase, and 20α-Hydroxysteroid Dehydrogenase Enzymes in Immature, Mature, and Pregnant Rats. Endocrinology, 138(7), 2886-2892. https://doi.org/10.1210/endo.138.7.5258
Akinola, Lateef A, et al. "Expression of 17β-Hydroxysteroid Dehydrogenase Type 1 and Type 2, P450 Aromatase, and 20α-Hydroxysteroid Dehydrogenase Enzymes in Immature, Mature, and Pregnant Rats." Endocrinology vol. 138,7 (1997): 2886-2892. doi: https://doi.org/10.1210/endo.138.7.5258
Akinola LA, Poutanen M, Vihko R, Vihko P. Expression of 17β-Hydroxysteroid Dehydrogenase Type 1 and Type 2, P450 Aromatase, and 20α-Hydroxysteroid Dehydrogenase Enzymes in Immature, Mature, and Pregnant Rats. Endocrinology. 1997 Jul 01;138(7):2886-2892. doi: 10.1210/endo.138.7.5258. PMID: 28200169.
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Is a Single Fecal Microbiota Transplant a Promising Treatment for Recurrent .

Open forum infectious diseases

Hota SS, Poutanen SM.
PMID: 29600252
Open Forum Infect Dis. 2018 Feb 23;5(3):ofy045. doi: 10.1093/ofid/ofy045. eCollection 2018 Mar.

No abstract available.

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Hota SS, Poutanen SM. Is a Single Fecal Microbiota Transplant a Promising Treatment for Recurrent . Open Forum Infect Dis. 2018;5(3):ofy045doi: 10.1093/ofid/ofy045.
Hota, S. S., & Poutanen, S. M. (2018). Is a Single Fecal Microbiota Transplant a Promising Treatment for Recurrent . Open forum infectious diseases, 5(3), ofy045. https://doi.org/10.1093/ofid/ofy045
Hota, Susy S, and Poutanen, Susan M. "Is a Single Fecal Microbiota Transplant a Promising Treatment for Recurrent ." Open forum infectious diseases vol. 5,3 (2018): ofy045. doi: https://doi.org/10.1093/ofid/ofy045
Hota SS, Poutanen SM. Is a Single Fecal Microbiota Transplant a Promising Treatment for Recurrent . Open Forum Infect Dis. 2018 Feb 23;5(3):ofy045. doi: 10.1093/ofid/ofy045. eCollection 2018 Mar. PMID: 29600252; PMCID: PMC5868186.
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