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- Matikas, A., et al.
(författare)
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Immune function and response to neoadjuvant chemotherapy in hormone receptor positive, HER2-negative breast cancer
- 2017
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Ingår i: Annals of Oncology. - : Oxford University Press. - 0923-7534 .- 1569-8041. ; 28
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundGene expression (GE) signatures and Tumor Infiltrating Lymphocyte (TILs) enumeration have shown promise as predictors of response to neoadjuvant chemotherapy in Hormone Receptor negative (HR-) and HER2+, but not in HR+/HER2- breast cancer (BC). This study aimed to explore their predictive value in HR+/HER2- BC, based on previous work from our group on the association of immune function and chemosensitivity in advanced HR+ BC.MethodsThe PROMIX phase 2 trial enrolled patients with locally advanced HER2- BC to receive six cycles of epirubicin and docetaxel, plus bevacizumab during cycles 3-6. Patients underwent tumor biopsies at baseline and after cycle 2 for GE profiling using DNA microarrays and TIL enumeration according to standard guidelines. Since pathologic complete remission (pCR) is relatively rare in HR+ BC, we also associated an immune gene module score (IMS) and TIL counts with the non-dichotomous variable of decrease in tumor size.ResultsOf the 150 enrolled patients, n = 113 were HR+. For n = 71, both TIL and GE data were available at baseline, while for n = 78 and n = 49 patients longitudinal TIL and GE data at baseline and cycle 2 were available, respectively. At baseline, on both univariate (OR = 2.29, P = 0.037) and multivariate analysis (OR = 2.35, P = 0.044) IMS was associated with pCR, while its association with tumor shrinkage was only apparent on univariate (P = 0.047) and not multivariate analysis (P = 0.061). TIL infiltration >50% (n = 9) was associated with neither pCR (OR = 1.812, P = 0.61) nor tumor shrinkage (P = 0.99). However, decreases in TIL counts in cycle 2 compared with baseline were associated with lesser decreases in tumor size (P = 0.043 for univariate and P = 0.044 for multivariate analysis).
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| 4. |
- Johansson, C, et al.
(författare)
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Interaction by cholestyramine on the uptake of hydrocortisone in the gastrointestinal tract.
- 1978
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Ingår i: Acta medica Scandinavica. - 0001-6101. ; 204:6, s. 509-12
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Tidskriftsartikel (refereegranskat)abstract
- An absolute reduction of the plasma cortisol levels and a delay of the peak concentrations were recorded in 10 healthy subjects, when a bile-sequestering anionic exchange resin, cholestyramine, was given prior to a single oral hydrocortisone dose, indicating that the resin interferes with the uptake of a neutral sterol in the human gastrointestinal tract. The possibility of a direct binding of drug to resin is supported by the affinity of hydrocortisone to cholestyramine in vitro, which was uninfluenced by the presence of sodium taurocholate. Cholestyramine significantly delayed the gastric emptying of a glucose solution, indicating that the resin not only decreases but also delays hydrocortisone absorption. Careful supervision is recommended when treatment with cholestyramine is given concomitant to neutral sterol drugs.
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| 5. |
- Lindholm, Andreas, et al.
(författare)
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Machine learning : a first course for engineers and scientists
- 2022
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Bok (övrigt vetenskapligt/konstnärligt)abstract
- This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning
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