Training oncology physicians to advise their patients on complementary and integrative medicine: An implementation study for a manual‐guided consultation

Background: The unmonitored use of complementary medicine in patients with cancer can be associated with an increased risk of safety-related issues, such as lower adherence to conventional cancer therapies. Training oncology physicians to advise their patients about the effectiveness and safety of these therapies could improve this situation. Methods: The objective of this study was to develop and pretest a consultation framework that has high potential to be widely implemented. The framework comprises: 1) a systematically developed and tested, manualized, guided consultation; and 2) blended learning training (e-learning and communication skills training workshop) to upskill oncology physicians in advising their patients on complementary and integrative medicine (CIM). For this implementation study, mixed methods were used to develop the manual (literature review, consensus procedure, pilot testing) and the training (questionnaires and interviews with oncology physicians and patients with cancer and an examination of the skills in a setting with standardized patients). Results: The training was tested with 47 oncology physicians from across Germany. The manual-guided consultation (context: general information on the setting and communication techniques; inform: consultation duration and content; capture: previous CIM use; prioritize: focus on consultation; advise: evidence-based CIM recommendations; discuss, advise, accept, or advise against other CIM; concretize advice: summary and implementation; and monitor: documentation) was considered suitable. The structure and time frame (maximum, 20 minutes) of the consultation as well as the training were feasible and well accepted. Conclusions: The current study demonstrates that the KOKON-KTO framework (a German acronym for Competence Network for Complementary Medicine - Consultation Training for Oncology Physicians) is suitable for training oncology physicians. Its implementation can lead to better physician-patient communication about CIM in cancer

A note on q-Euler numbers and polynomials

The purpose of this paper is to construct q-Euler numbers and polynomials by using p-adic q-integral equations on Zp. Finally, we will give some interesting formulae related to these q-Euler numbers and polynomials.Comment: 6 page

A Selberg integral for the Lie algebra A_n

A new q-binomial theorem for Macdonald polynomials is employed to prove an A_n analogue of the celebrated Selberg integral. This confirms the g=A_n case of a conjecture by Mukhin and Varchenko concerning the existence of a Selberg integral for every simple Lie algebra g.Comment: 32 page

Effective and flexible modeling approach to investigate various 3D Talbot carpets from a spatial finite mask

We present an effective modeling approach for a fast calculation of the Talbot carpet from an initially 2-dimensional mask pattern. The introduced numerical algorithm is based on a modified angular-spectrum method, in which it is possible to consider the border effects of the Talbot region from a mask with a finite aperture. The Bluestein’s fast Fourier transform (FFT) algorithm is applied to speed up the calculation. This approach allows as well to decouple the sampling points in the real space and the spatial frequency domain so that both parameters can be chosen independently. As a result an extended three-dimensional Talbot-carpet can be calculated with a minimized number of numerical steps and computation time, but still with high accuracy. The algorithm was applied to various 2-dimensional mask patterns and illumination setups. The influence of specific mask patterns to the resulting field intensity distribution is discussed

Thomae type formulae for singular Z_N curves

We give an elementary and rigorous proof of the Thomae type formula for singular $Z_N$ curves. To derive the Thomae formula we use the traditional variational method which goes back to Riemann, Thomae and Fuchs.Comment: 22 page

Block-Anti-Circulant Unbalanced Oil and Vinegar

We introduce a new technique for compressing the public keys of the UOV signature scheme that makes use of block-anti-circulant matrices. These matrices admit a compact representation as for every block, the remaining elements can be inferred from the first row. This space saving translates to the public key, which as a result of this technique can be shrunk by a small integer factor. We propose parameters sets that take into account several important attacks

Simple deterministic dynamical systems with fractal diffusion coefficients

We analyze a simple model of deterministic diffusion. The model consists of a one-dimensional periodic array of scatterers in which point particles move from cell to cell as defined by a piecewise linear map. The microscopic chaotic scattering process of the map can be changed by a control parameter. This induces a parameter dependence for the macroscopic diffusion coefficient. We calculate the diffusion coefficent and the largest eigenmodes of the system by using Markov partitions and by solving the eigenvalue problems of respective topological transition matrices. For different boundary conditions we find that the largest eigenmodes of the map match to the ones of the simple phenomenological diffusion equation. Our main result is that the difffusion coefficient exhibits a fractal structure by varying the system parameter. To understand the origin of this fractal structure, we give qualitative and quantitative arguments. These arguments relate the sequence of oscillations in the strength of the parameter-dependent diffusion coefficient to the microscopic coupling of the single scatterers which changes by varying the control parameter.Comment: 28 pages (revtex), 12 figures (postscript), submitted to Phys. Rev.

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10 years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate