| 1. |
- Bergmeister, Konstantin D, et al.
(författare)
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Peripheral nerve transfers change target muscle structure and function
- 2019
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Ingår i: Science advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- Selective nerve transfers surgically rewire motor neurons and are used in extremity reconstruction to restore muscle function or to facilitate intuitive prosthetic control. We investigated the neurophysiological effects of rewiring motor axons originating from spinal motor neuron pools into target muscles with lower innervation ratio in a rat model. Following reinnervation, the target muscle's force regenerated almost completely, with the motor unit population increasing to 116% in functional and 172% in histological assessments with subsequently smaller muscle units. Muscle fiber type populations transformed into the donor nerve's original muscles. We thus demonstrate that axons of alternative spinal origin can hyper-reinnervate target muscles without loss of muscle force regeneration, but with a donor-specific shift in muscle fiber type. These results explain the excellent clinical outcomes following nerve transfers in neuromuscular reconstruction. They indicate that reinnervated muscles can provide an accurate bioscreen to display neural information of lost body parts for high-fidelity prosthetic control.
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| 2. |
- Drude, Natascha Ingrid, et al.
(författare)
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Planning preclinical confirmatory multicenter trials to strengthen translation from basic to clinical research : a multi-stakeholder workshop report
- 2022
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Ingår i: Translational Medicine Communications. - : Springer Nature. - 2396-832X. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Clinical translation from bench to bedside often remains challenging even despite promising preclinical evidence. Among many drivers like biological complexity or poorly understood disease pathology, preclinical evidence often lacks desired robustness. Reasons include low sample sizes, selective reporting, publication bias, and consequently inflated effect sizes. In this context, there is growing consensus that confirmatory multicenter studies -by weeding out false positives- represent an important step in strengthening and generating preclinical evidence before moving on to clinical research. However, there is little guidance on what such a preclinical confirmatory study entails and when it should be conducted in the research trajectory. To close this gap, we organized a workshop to bring together statisticians, clinicians, preclinical scientists, and meta-researcher to discuss and develop recommendations that are solution-oriented and feasible for practitioners. Herein, we summarize and review current approaches and outline strategies that provide decision-critical guidance on when to start and subsequently how to plan a confirmatory study. We define a set of minimum criteria and strategies to strengthen validity before engaging in a confirmatory preclinical trial, including sample size considerations that take the inherent uncertainty of initial (exploratory) studies into account. Beyond this specific guidance, we highlight knowledge gaps that require further research and discuss the role of confirmatory studies in translational biomedical research. In conclusion, this workshop report highlights the need for close interaction and open and honest debate between statisticians, preclinical scientists, meta-researchers (that conduct research on research), and clinicians already at an early stage of a given preclinical research trajectory.
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| 3. |
- Frommlet, Florian, et al.
(författare)
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Selecting predictive biomarkers from genomic data
- 2022
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 17:6 6
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Tidskriftsartikel (refereegranskat)abstract
- Recently there have been tremendous efforts to develop statistical procedures which allow to determine subgroups of patients for which certain treatments are effective. This article focuses on the selection of prognostic and predictive genetic biomarkers based on a relatively large number of candidate Single Nucleotide Polymorphisms (SNPs). We consider models which include prognostic markers as main effects and predictive markers as interaction effects with treatment. We compare different high-dimensional selection approaches including adaptive lasso, a Bayesian adaptive version of the Sorted L-One Penalized Estimator (SLOBE) and a modified version of the Bayesian Information Criterion (mBIC2). These are compared with classical multiple testing procedures for individual markers. Having identified predictive markers we consider several different approaches how to specify subgroups susceptible to treatment. Our main conclusion is that selection based on mBIC2 and SLOBE has similar predictive performance as the adaptive lasso while including substantially fewer biomarkers.
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| 4. |
- Lachmann, Jon, et al.
(författare)
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A subsampling approach for Bayesian model selection
- 2022
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Ingår i: International Journal of Approximate Reasoning. - : Elsevier BV. - 0888-613X .- 1873-4731. ; 151, s. 33-63
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Tidskriftsartikel (refereegranskat)abstract
- It is common practice to use Laplace approximations to decrease the computational burden when computing the marginal likelihoods in Bayesian versions of generalised linear models (GLM). Marginal likelihoods combined with model priors are then used in different search algorithms to compute the posterior marginal probabilities of models and individual covariates. This allows performing Bayesian model selection and model averaging. For large sample sizes, even the Laplace approximation becomes computationally challenging because the optimisation routine involved needs to evaluate the likelihood on the full dataset in multiple iterations. As a consequence, the algorithm is not scalable for large datasets. To address this problem, we suggest using stochastic optimisation approaches, which only use a subsample of the data for each iteration. We combine stochastic optimisation with Markov chain Monte Carlo (MCMC) based methods for Bayesian model selection and provide some theoretical results on the convergence of the estimates for the resulting time-inhomogeneous MCMC. Finally, we report results from experiments illustrating the performance of the proposed algorithm.
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