Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 13 entries
Sorted by: Best Match Show Resources per page
Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis.

Cancer research

Xie W, Reder NP, Koyuncu CF, Leo P, Hawley S, Huang H, Mao C, Postupna N, Kang S, Serafin R, Gao G, Han Q, Bishop KW, Barner LA, Fu P, Wright JL, Keene CD, Vaughan JC, Janowczyk A, Glaser AK, Madabhushi A, True LD, Liu JT.
PMID: 34853071
Cancer Res. 2021 Dec 01; doi: 10.1158/0008-5472.CAN-21-2843. Epub 2021 Dec 01.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted 3D glandular structures via visual inspection of a...

Cite
Xie W, Reder NP, Koyuncu CF, et al. Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer Res. 2021;doi: 10.1158/0008-5472.CAN-21-2843.
Xie, W., Reder, N. P., Koyuncu, C. F., Leo, P., Hawley, S., Huang, H., Mao, C., Postupna, N., Kang, S., Serafin, R., Gao, G., Han, Q., Bishop, K. W., Barner, L. A., Fu, P., Wright, J. L., Keene, C. D., Vaughan, J. C., Janowczyk, A., Glaser, A. K., Madabhushi, A., True, L. D., & Liu, J. T. (2021). Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer research, . https://doi.org/10.1158/0008-5472.CAN-21-2843
Xie, Weisi, et al. "Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis." Cancer research vol. (2021). doi: https://doi.org/10.1158/0008-5472.CAN-21-2843
Xie W, Reder NP, Koyuncu CF, Leo P, Hawley S, Huang H, Mao C, Postupna N, Kang S, Serafin R, Gao G, Han Q, Bishop KW, Barner LA, Fu P, Wright JL, Keene CD, Vaughan JC, Janowczyk A, Glaser AK, Madabhushi A, True LD, Liu JT. Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer Res. 2021 Dec 01; doi: 10.1158/0008-5472.CAN-21-2843. Epub 2021 Dec 01. PMID: 34853071.
Copy Download .nbib Format: NLM
Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis.

Cancer research

Xie W, Reder NP, Koyuncu CF, Leo P, Hawley S, Huang H, Mao C, Postupna N, Kang S, Serafin R, Gao G, Han Q, Bishop KW, Barner LA, Fu P, Wright JL, Keene CD, Vaughan JC, Janowczyk A, Glaser AK, Madabhushi A, True LD, Liu JT.
PMID: 34853071
Cancer Res. 2021 Dec 01; doi: 10.1158/0008-5472.CAN-21-2843. Epub 2021 Dec 01.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted 3D glandular structures via visual inspection of a...

Cite
Xie W, Reder NP, Koyuncu CF, et al. Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer Res. 2021;doi: 10.1158/0008-5472.CAN-21-2843.
Xie, W., Reder, N. P., Koyuncu, C. F., Leo, P., Hawley, S., Huang, H., Mao, C., Postupna, N., Kang, S., Serafin, R., Gao, G., Han, Q., Bishop, K. W., Barner, L. A., Fu, P., Wright, J. L., Keene, C. D., Vaughan, J. C., Janowczyk, A., Glaser, A. K., Madabhushi, A., True, L. D., & Liu, J. T. (2021). Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer research, . https://doi.org/10.1158/0008-5472.CAN-21-2843
Xie, Weisi, et al. "Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis." Cancer research vol. (2021). doi: https://doi.org/10.1158/0008-5472.CAN-21-2843
Xie W, Reder NP, Koyuncu CF, Leo P, Hawley S, Huang H, Mao C, Postupna N, Kang S, Serafin R, Gao G, Han Q, Bishop KW, Barner LA, Fu P, Wright JL, Keene CD, Vaughan JC, Janowczyk A, Glaser AK, Madabhushi A, True LD, Liu JT. Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer Res. 2021 Dec 01; doi: 10.1158/0008-5472.CAN-21-2843. Epub 2021 Dec 01. PMID: 34853071.
Copy Download .nbib Format: NLM
Laminin-5 and type I collagen promote adhesion and osteogenic differentiation of animal serum-free expanded human mesenchymal stromal cells.

Orthopedic reviews

Mittag F, Falkenberg EM, Janczyk A, Götze M, Felka T, Aicher WK, Kluba T.
PMID: 23589764
Orthop Rev (Pavia). 2012 Dec 11;4(4):e36. doi: 10.4081/or.2012.e36. Print 2012 Nov 02.

Mesenchymal stromal cells (MSC) are differentiation competent cells and may generate, among others, mature osteoblasts or chondrocytes in vitro and in vivo. Laminin-5 and type I collagen are important components of the extracellular matrix. They are involved in a...

Cite
Mittag F, Falkenberg EM, Janczyk A, et al. Laminin-5 and type I collagen promote adhesion and osteogenic differentiation of animal serum-free expanded human mesenchymal stromal cells. Orthop Rev (Pavia). 2012;4(4):e36doi: 10.4081/or.2012.e36.
Mittag, F., Falkenberg, E. M., Janczyk, A., Götze, M., Felka, T., Aicher, W. K., & Kluba, T. (2012). Laminin-5 and type I collagen promote adhesion and osteogenic differentiation of animal serum-free expanded human mesenchymal stromal cells. Orthopedic reviews, 4(4), e36. https://doi.org/10.4081/or.2012.e36
Mittag, Falk, et al. "Laminin-5 and type I collagen promote adhesion and osteogenic differentiation of animal serum-free expanded human mesenchymal stromal cells." Orthopedic reviews vol. 4,4 (2012): e36. doi: https://doi.org/10.4081/or.2012.e36
Mittag F, Falkenberg EM, Janczyk A, Götze M, Felka T, Aicher WK, Kluba T. Laminin-5 and type I collagen promote adhesion and osteogenic differentiation of animal serum-free expanded human mesenchymal stromal cells. Orthop Rev (Pavia). 2012 Dec 11;4(4):e36. doi: 10.4081/or.2012.e36. Print 2012 Nov 02. PMID: 23589764; PMCID: PMC3626307.
Copy Download .nbib Format: NLM
Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19.

Journal of clinical pathology

Koelzer VH, Grobholz R, Zlobec I, Janowczyk A.
PMID: 34518361
J Clin Pathol. 2021 Sep 13; doi: 10.1136/jclinpath-2021-207768. Epub 2021 Sep 13.

AIMS: The transition from analogue to digital pathology (DP) in Switzerland has coincided with the COVID-19 crisis. The Swiss Digital Pathology Consortium conducted a national survey to assess the experience of pathologists in dealing with the challenges of the...

Cite
Koelzer VH, Grobholz R, Zlobec I, et al. Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19. J Clin Pathol. 2021;doi: 10.1136/jclinpath-2021-207768.
Koelzer, V. H., Grobholz, R., Zlobec, I., Janowczyk, A. (2021). Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19. Journal of clinical pathology, . https://doi.org/10.1136/jclinpath-2021-207768
Koelzer, Viktor Hendrik, et al. "Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19." Journal of clinical pathology vol. (2021). doi: https://doi.org/10.1136/jclinpath-2021-207768
Koelzer VH, Grobholz R, Zlobec I, Janowczyk A. Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19. J Clin Pathol. 2021 Sep 13; doi: 10.1136/jclinpath-2021-207768. Epub 2021 Sep 13. PMID: 34518361; PMCID: PMC8440121.
Copy Download .nbib Format: NLM
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.

Journal of pathology informatics

Janowczyk A, Madabhushi A.
PMID: 27563488
J Pathol Inform. 2016 Jul 26;7:29. doi: 10.4103/2153-3539.186902. eCollection 2016.

BACKGROUND: Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g.,...

Cite
Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inform. 2016;7:29doi: 10.4103/2153-3539.186902.
Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of pathology informatics, 729. https://doi.org/10.4103/2153-3539.186902
Janowczyk, Andrew, and Madabhushi, Anant. "Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases." Journal of pathology informatics vol. 7 (2016): 29. doi: https://doi.org/10.4103/2153-3539.186902
Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inform. 2016 Jul 26;7:29. doi: 10.4103/2153-3539.186902. eCollection 2016. PMID: 27563488; PMCID: PMC4977982.
Copy Download .nbib Format: NLM
Quick Annotator: an open-source digital pathology based rapid image annotation tool.

The journal of pathology. Clinical research

Miao R, Toth R, Zhou Y, Madabhushi A, Janowczyk A.
PMID: 34288586
J Pathol Clin Res. 2021 Nov;7(6):542-547. doi: 10.1002/cjp2.229. Epub 2021 Jul 19.

Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized...

Cite
Miao R, Toth R, Zhou Y, et al. Quick Annotator: an open-source digital pathology based rapid image annotation tool. J Pathol Clin Res. 2021;7(6):542-547doi: 10.1002/cjp2.229.
Miao, R., Toth, R., Zhou, Y., Madabhushi, A., & Janowczyk, A. (2021). Quick Annotator: an open-source digital pathology based rapid image annotation tool. The journal of pathology. Clinical research, 7(6), 542-547. https://doi.org/10.1002/cjp2.229
Miao, Runtian, et al. "Quick Annotator: an open-source digital pathology based rapid image annotation tool." The journal of pathology. Clinical research vol. 7,6 (2021): 542-547. doi: https://doi.org/10.1002/cjp2.229
Miao R, Toth R, Zhou Y, Madabhushi A, Janowczyk A. Quick Annotator: an open-source digital pathology based rapid image annotation tool. J Pathol Clin Res. 2021 Nov;7(6):542-547. doi: 10.1002/cjp2.229. Epub 2021 Jul 19. PMID: 34288586; PMCID: PMC8503896.
Copy Download .nbib Format: NLM
Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2.

European urology focus

Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Shih NNC, Feldman M, Gupta S, McKenney J, Lal P, Madabhushi A.
PMID: 33941504
Eur Urol Focus. 2021 Jul;7(4):722-732. doi: 10.1016/j.euf.2021.04.016. Epub 2021 Apr 30.

BACKGROUND: The presence of invasive cribriform adenocarcinoma (ICC), an expanse of cells containing punched-out lumina uninterrupted by stroma, in radical prostatectomy (RP) specimens has been associated with biochemical recurrence (BCR). However, ICC identification has only moderate inter-reviewer agreement.OBJECTIVE: To...

Cite
Leo P, Chandramouli S, Farré X, et al. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. Eur Urol Focus. 2021;7(4):722-732doi: 10.1016/j.euf.2021.04.016.
Leo, P., Chandramouli, S., Farré, X., Elliott, R., Janowczyk, A., Bera, K., Fu, P., Janaki, N., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T. R., Khani, F., Robinson, B. D., Shih, N. N. C., Feldman, M., Gupta, S., McKenney, J., Lal, P., & Madabhushi, A. (2021). Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. European urology focus, 7(4), 722-732. https://doi.org/10.1016/j.euf.2021.04.016
Leo, Patrick, et al. "Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2." European urology focus vol. 7,4 (2021): 722-732. doi: https://doi.org/10.1016/j.euf.2021.04.016
Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Shih NNC, Feldman M, Gupta S, McKenney J, Lal P, Madabhushi A. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. Eur Urol Focus. 2021 Jul;7(4):722-732. doi: 10.1016/j.euf.2021.04.016. Epub 2021 Apr 30. PMID: 33941504; PMCID: PMC8419103.
Copy Download .nbib Format: NLM
Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.

NPJ precision oncology

Leo P, Janowczyk A, Elliott R, Janaki N, Bera K, Shiradkar R, Farré X, Fu P, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Eklund L, Jambor I, Merisaari H, Ettala O, Taimen P, Aronen HJ, Boström PJ, Tewari A, Magi-Galluzzi C, Klein E, Purysko A, Nc Shih N, Feldman M, Gupta S, Lal P, Madabhushi A.
PMID: 33941830
NPJ Precis Oncol. 2021 May 03;5(1):35. doi: 10.1038/s41698-021-00174-3.

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive,...

Cite
Leo P, Janowczyk A, Elliott R, et al. Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ Precis Oncol. 2021;5(1):35doi: 10.1038/s41698-021-00174-3.
Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Bera, K., Shiradkar, R., Farré, X., Fu, P., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T. R., Khani, F., Robinson, B. D., Eklund, L., Jambor, I., Merisaari, H., Ettala, O., Taimen, P., Aronen, H. J., Boström, P. J., Tewari, A., Magi-Galluzzi, C., Klein, E., Purysko, A., Nc Shih, N., Feldman, M., Gupta, S., Lal, P., & Madabhushi, A. (2021). Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ precision oncology, 5(1), 35. https://doi.org/10.1038/s41698-021-00174-3
Leo, Patrick, et al. "Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study." NPJ precision oncology vol. 5,1 (2021): 35. doi: https://doi.org/10.1038/s41698-021-00174-3
Leo P, Janowczyk A, Elliott R, Janaki N, Bera K, Shiradkar R, Farré X, Fu P, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Eklund L, Jambor I, Merisaari H, Ettala O, Taimen P, Aronen HJ, Boström PJ, Tewari A, Magi-Galluzzi C, Klein E, Purysko A, Nc Shih N, Feldman M, Gupta S, Lal P, Madabhushi A. Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ Precis Oncol. 2021 May 03;5(1):35. doi: 10.1038/s41698-021-00174-3. PMID: 33941830; PMCID: PMC8093226.
Copy Download .nbib Format: NLM
A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.

Computer methods in biomechanics and biomedical engineering. Imaging & visualization

Janowczyk A, Doyle S, Gilmore H, Madabhushi A.
PMID: 29732269
Comput Methods Biomech Biomed Eng Imaging Vis. 2018;6(3):270-276. doi: 10.1080/21681163.2016.1141063. Epub 2016 Apr 28.

Deep learning (DL) has recently been successfully applied to a number of image analysis problems. However, DL approaches tend to be inefficient for segmentation on large image data, such as high-resolution digital pathology slide images. For example, typical breast...

Cite
Janowczyk A, Doyle S, Gilmore H, et al. A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Comput Methods Biomech Biomed Eng Imaging Vis. 2016;6(3):270-276doi: 10.1080/21681163.2016.1141063.
Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A. (2018). A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer methods in biomechanics and biomedical engineering. Imaging & visualization, 6(3), 270-276. https://doi.org/10.1080/21681163.2016.1141063
Janowczyk, Andrew, et al. "A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images." Computer methods in biomechanics and biomedical engineering. Imaging & visualization vol. 6,3 (2018): 270-276. doi: https://doi.org/10.1080/21681163.2016.1141063
Janowczyk A, Doyle S, Gilmore H, Madabhushi A. A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Comput Methods Biomech Biomed Eng Imaging Vis. 2018;6(3):270-276. doi: 10.1080/21681163.2016.1141063. Epub 2016 Apr 28. PMID: 29732269; PMCID: PMC5935259.
Copy Download .nbib Format: NLM
Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.

European radiology

Shiradkar R, Panda A, Leo P, Janowczyk A, Farre X, Janaki N, Li L, Pahwa S, Mahran A, Buzzy C, Fu P, Elliott R, MacLennan G, Ponsky L, Gulani V, Madabhushi A.
PMID: 32945970
Eur Radiol. 2021 Apr;31(4):2644. doi: 10.1007/s00330-020-07285-8.

No abstract available.

Cite
Shiradkar R, Panda A, Leo P, et al. Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. Eur Radiol. 2021;31(4):2644doi: 10.1007/s00330-020-07285-8.
Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (2021). Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European radiology, 31(4), 2644. https://doi.org/10.1007/s00330-020-07285-8
Shiradkar, Rakesh, et al. "Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology." European radiology vol. 31,4 (2021): 2644. doi: https://doi.org/10.1007/s00330-020-07285-8
Shiradkar R, Panda A, Leo P, Janowczyk A, Farre X, Janaki N, Li L, Pahwa S, Mahran A, Buzzy C, Fu P, Elliott R, MacLennan G, Ponsky L, Gulani V, Madabhushi A. Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. Eur Radiol. 2021 Apr;31(4):2644. doi: 10.1007/s00330-020-07285-8. PMID: 32945970.
Copy Download .nbib Format: NLM
Competition between metal-amido and metal-imido chemistries in the alkaline earth series: an experimental and theoretical study of BaNH.

Journal of the American Chemical Society

Janczyk A, Lichtenberger DL, Ziurys LM.
PMID: 16433526
J Am Chem Soc. 2006 Feb 01;128(4):1109-18. doi: 10.1021/ja053473k.

The pure rotational spectrum of BaNH in its X(1)Sigma(+) ground electronic state has been recorded using millimeter/submillimeter direct absorption methods; data for the deuterium and barium 137 isotopomers have been measured as well. The molecules were produced by the...

Cite
Janczyk A, Lichtenberger DL, Ziurys LM. Competition between metal-amido and metal-imido chemistries in the alkaline earth series: an experimental and theoretical study of BaNH. J Am Chem Soc. 2006;128(4):1109-18doi: 10.1021/ja053473k.
Janczyk, A., Lichtenberger, D. L., & Ziurys, L. M. (2006). Competition between metal-amido and metal-imido chemistries in the alkaline earth series: an experimental and theoretical study of BaNH. Journal of the American Chemical Society, 128(4), 1109-18. https://doi.org/10.1021/ja053473k
Janczyk, Alexandra, et al. "Competition between metal-amido and metal-imido chemistries in the alkaline earth series: an experimental and theoretical study of BaNH." Journal of the American Chemical Society vol. 128,4 (2006): 1109-18. doi: https://doi.org/10.1021/ja053473k
Janczyk A, Lichtenberger DL, Ziurys LM. Competition between metal-amido and metal-imido chemistries in the alkaline earth series: an experimental and theoretical study of BaNH. J Am Chem Soc. 2006 Feb 01;128(4):1109-18. doi: 10.1021/ja053473k. PMID: 16433526.
Copy Download .nbib Format: NLM
A Neuro-behavioral Test and Algorithms for Screening and Evaluating Therapy in ADHD.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

Modarres-Zadeh M, Xu X, Jaromczyk A, Senders S.
PMID: 17282742
Conf Proc IEEE Eng Med Biol Soc. 2005;2005:2486-9. doi: 10.1109/IEMBS.2005.1616973.

A new neuro-behavioral test with analysis algorithms has been developed for use in screening individuals for attention deficit hyperactivity disorder (ADHD) and for the quantitative evaluation of ADHD medication/therapy in diagnosed patients. This technique combines a 15 minute auditory-based...

Cite
Modarres-Zadeh M, Xu X, Jaromczyk A, et al. A Neuro-behavioral Test and Algorithms for Screening and Evaluating Therapy in ADHD. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:2486-9doi: 10.1109/IEMBS.2005.1616973.
Modarres-Zadeh, M., Xu, X., Jaromczyk, A., & Senders, S. (2005). A Neuro-behavioral Test and Algorithms for Screening and Evaluating Therapy in ADHD. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 20052486-9. https://doi.org/10.1109/IEMBS.2005.1616973
Modarres-Zadeh, Mohammad, et al. "A Neuro-behavioral Test and Algorithms for Screening and Evaluating Therapy in ADHD." Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference vol. 2005 (2005): 2486-9. doi: https://doi.org/10.1109/IEMBS.2005.1616973
Modarres-Zadeh M, Xu X, Jaromczyk A, Senders S. A Neuro-behavioral Test and Algorithms for Screening and Evaluating Therapy in ADHD. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:2486-9. doi: 10.1109/IEMBS.2005.1616973. PMID: 17282742.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 13 entries