Architectural Policy Design: How Policy Makers Try to Shape Policy Feedback Effects When Designing Policies

How do politicians use policies strategically for their own political benefit and to achieve long-term political goals, and how does such strategic behavior in- fluence the design of policies? Answering these questions is crucial for under- standing key dynamics, challenges, limitations and opportunities of public policy making, for explaining strategic choices policy makers make when they design new policies and political struggles they engage in with their oppo- nents

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Machine learning is expected to fuel significant improvements in medical care. To ensure that fundamental principles such as beneficence, respect for human autonomy, prevention of harm, justice, privacy, and transparency are respected, medical machine learning systems must be developed responsibly. Many high-level declarations of ethical principles have been put forth for this purpose, but there is a severe lack of technical guidelines explicating the practical consequences for medical machine learning. Similarly, there is currently considerable uncertainty regarding the exact regulatory requirements placed upon medical machine learning systems. This survey provides an overview of the technical and procedural challenges involved in creating medical machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address these challenges. First, a brief review of existing regulations affecting medical machine learning is provided, showing that properties such as safety, robustness, reliability, privacy, security, transparency, explainability, and nondiscrimination are all demanded already by existing law and regulations - albeit, in many cases, to an uncertain degree. Next, the key technical obstacles to achieving these desirable properties are discussed, as well as important techniques to overcome these obstacles in the medical context. We notice that distribution shift, spurious correlations, model underspecification, uncertainty quantification, and data scarcity represent severe challenges in the medical context. Promising solution approaches include the use of large and representative datasets and federated learning as a means to that end, the careful exploitation of domain knowledge, the use of inherently transparent models, comprehensive out-of-distribution model testing and verification, as well as algorithmic impact assessments

Identification of Novel Functional Inhibitors of Acid Sphingomyelinase

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans

FGF signalling is involved in cumulus migration in the common house spider Parasteatoda tepidariorum

Cell migration is a fundamental component during the development of most multicellular organisms. In the early spider embryo, the collective migration of signalling cells, known as the cumulus, is required to set the dorso-ventral body axis.Here, we show that FGF signalling plays an important role during cumulus migration in the spider Parasteatoda tepidariorum. Spider embryos with reduced FGF signalling show reduced or absent cumulus migration and display dorsoventral patterning defects. Our study reveals that the transcription factor Ets4 regulates the expression of several FGF signalling components in the cumulus. In conjunction with a previous study, we show that the expression of fgf8 in the germ-disc is regulated via the Hedgehog signalling pathway. We also demonstrate that FGF signalling influences the BMP signalling pathway activity in the region around cumulus cells. Finally, we show that FGFR signalling might also influence cumulus migration in basally branching spiders and we propose that fgf8 might act as a chemo-attractant to guide cumulus cells towards the future dorsal pole of the spider embryo

Nusinersen versus Sham Control in Later-Onset Spinal Muscular Atrophy

International audienceBACKGROUND Nusinersen is an antisense oligonucleotide drug that modulates pre-messenger RNA splicing of the survival motor neuron 2 (SMN2) gene. It has been developed for the treatment of spinal muscular atrophy (SMA). METHODS We conducted a multicenter, double-blind, sham-controlled, phase 3 trial of nusinersen in 126 children with SMA who had symptom onset after 6 months of age. The children were randomly assigned, in a 2: 1 ratio, to undergo intrathecal administration of nusinersen at a dose of 12 mg (nusinersen group) or a sham procedure (control group) on days 1, 29, 85, and 274. The primary end point was the least-squares mean change from baseline in the Hammersmith Functional Motor Scale-Expanded (HFMSE) score at 15 months of treatment; HFMSE scores range from 0 to 66, with higher scores indicating better motor function. Secondary end points included the percentage of children with a clinically meaningful increase from baseline in the HFMSE score (>= 3 points), an outcome that indicates improvement in at least two motor skills. RESULTS In the prespecified interim analysis, there was a least-squares mean increase from baseline to month 15 in the HFMSE score in the nusinersen group (by 4.0 points) and a least-squares mean decrease in the control group (by -1.9 points), with a significant between-group difference favoring nusinersen (least-squares mean difference in change, 5.9 points; 95% confidence interval, 3.7 to 8.1; P< 0.001). This result prompted early termination of the trial. Results of the final analysis were consistent with results of the interim analysis. In the final analysis, 57% of the children in the nusinersen group as compared with 26% in the control group had an increase from baseline to month 15 in the HFMSE score of at least 3 points (P< 0.001), and the overall incidence of adverse events was similar in the nusinersen group and the control group (93% and 100%, respectively). CONCLUSIONS Among children with later-onset SMA, those who received nusinersen had significant and clinically meaningful improvement in motor function as compared with those in the control group. (Funded by Biogen and Ionis Pharmaceuticals; CHERISH ClinicalTrials. gov number, NCT02292537.