Evidence-based Shared-Decision-Making Assistant (SDM-assistant) for choosing antipsychotics: protocol of a cluster-randomized trial in hospitalized patients with schizophrenia

BACKGROUND

Choosing an antipsychotic medication is an important medical decision in the treatment of schizophrenia. This decision requires risk-benefit assessments of antipsychotics, and thus, shared-decision making between physician and patients is strongly encouraged. Although the efficacy and side-effect profiles of antipsychotics are well-established, there is no clear framework for the communication of the evidence between physicians and patients. For this reason, we developed an evidence-based shared-decision making assistant (SDM-assistant) that presents high-quality evidence from network meta-analysis on the efficacy and side-effect profile of antipsychotics and can be used as a basis for shared-decision making between physicians and patients when selecting antipsychotic medications.

METHODS

The planned matched-pair cluster-randomised trial will be conducted in acute psychiatric wards (n = 14 wards planned) and will include adult inpatients with schizophrenia or schizophrenia-like disorders (N = 252 participants planned). On the intervention wards, patients and their treating physicians will use the SDM-assistant, whenever a decision on choosing an antipsychotic is warranted. On the control wards, antipsychotics will be chosen according to treatment-as-usual. The primary outcome will be patients' perceived involvement in the decision-making during the inpatient stay as measured with the SDM-Q-9. We will also assess therapeutic alliance, symptom severity, side-effects, treatment satisfaction, adherence, quality of life, functioning and rehospitalizations as secondary outcomes. Outcomes could be analysed at discharge and at follow-up after three months from discharge. The analysis will be conducted per-protocol using mixed-effects linear regression models for continuous outcomes and logistic regression models using generalised estimating equations for dichotomous outcomes. Barriers and facilitators in the implementation of the intervention will also be examined using a qualitative content analysis.

DISCUSSION

This is the first trial to examine a decision assistant specifically designed to facilitate shared-decision making for choosing antipsychotic medications, i.e., SDM-assistant, in acutely ill inpatients with schizophrenia. If the intervention can be successfully implemented, SDM-assistant could advance evidence-based medicine in schizophrenia by putting medical evidence on antipsychotics into the context of patient preferences and values. This could subsequently lead to a higher involvement of the patients in decision-making and better therapy decisions.

TRIAL REGISTRATION

German Clinical Trials Register (ID: DRKS00027316 , registration date 26.01.2022).

Efficient Onboarding and Management of Members in Permissioned Blockchain Networks Utilizing TLS Certificates

Consortia blockchain networks face the issue of expanding their systems to new members. Onboarding processes are often cumbersome, as they require identifying the new participant, manually setting up rights, exchanging key material, and adding information about the new member to the consensus smart contract. Besides that, these processes are time-consuming and scale poorly. Identifying the members might be faulty as the pre-existing members might be deceived by malicious parties claiming to be someone else. This paper proposes a novel methodology to allow the onboarding of new parties without time-intensive off-chain processes. We establish identities of new consortia members by utilizing TLS certificates bound to publicly known domain names. With this identity scheme in place, the network operators can define rules such as only specific parties are allowed to join the network, e.g., only owners of *.edu domains. This methodology scales well, provides for extensive ruling and monitoring, and helps consortia blockchains to grow faster.

Analysis and Application of Verifiable Computation Techniques in Blockchain Systems for the Energy Sector

The energy system is becoming increasingly decentralized. This development requires integrating and coordinating a rising number of actors and small units in a complex system. Blockchain could provide a base infrastructure for new tools and platforms that address these tasks in various aspects—ranging from dispatch optimization or dynamic load adaption to (local) market mechanisms. Many of these applications are currently in development and subject to research projects. In decentralized energy markets especially, the optimized allocation of energy products demands complex computation. Combining these with distributed ledger technologies leads to bottlenecks and challenges regarding privacy requirements and performance due to limited storage and computational resources. Verifiable computation techniques promise a solution to these issues. This paper presents an overview of verifiable computation technologies, including trusted oracles, zkSNARKs, and multi-party computation. We further analyze their application in blockchain environments with a focus on energy-related applications. Applied to a distinct optimization problem of renewable energy certificates, we have evaluated these solution approaches and finally demonstrate an implementation of a Simplex-Optimization using zkSNARKs as a case study. We conclude with an assessment of the applicability of the described verifiable computation techniques and address limitations for large-scale deployment, followed by an outlook on current development trends.

Modeling aspects of the language of life through transfer-learning protein sequences

BACKGROUND

Predicting protein function and structure from sequence is one important challenge for computational biology. For 26 years, most state-of-the-art approaches combined machine learning and evolutionary information. However, for some applications retrieving related proteins is becoming too time-consuming. Additionally, evolutionary information is less powerful for small families, e.g. for proteins from the Dark Proteome. Both these problems are addressed by the new methodology introduced here.

RESULTS

We introduced a novel way to represent protein sequences as continuous vectors (embeddings) by using the language model ELMo taken from natural language processing. By modeling protein sequences, ELMo effectively captured the biophysical properties of the language of life from unlabeled big data (UniRef50). We refer to these new embeddings as SeqVec (Sequence-to-Vector) and demonstrate their effectiveness by training simple neural networks for two different tasks. At the per-residue level, secondary structure (Q3 = 79% ± 1, Q8 = 68% ± 1) and regions with intrinsic disorder (MCC = 0.59 ± 0.03) were predicted significantly better than through one-hot encoding or through Word2vec-like approaches. At the per-protein level, subcellular localization was predicted in ten classes (Q10 = 68% ± 1) and membrane-bound were distinguished from water-soluble proteins (Q2 = 87% ± 1). Although SeqVec embeddings generated the best predictions from single sequences, no solution improved over the best existing method using evolutionary information. Nevertheless, our approach improved over some popular methods using evolutionary information and for some proteins even did beat the best. Thus, they prove to condense the underlying principles of protein sequences. Overall, the important novelty is speed: where the lightning-fast HHblits needed on average about two minutes to generate the evolutionary information for a target protein, SeqVec created embeddings on average in 0.03 s. As this speed-up is independent of the size of growing sequence databases, SeqVec provides a highly scalable approach for the analysis of big data in proteomics, i.e. microbiome or metaproteome analysis.

CONCLUSION

Transfer-learning succeeded to extract information from unlabeled sequence databases relevant for various protein prediction tasks. SeqVec modeled the language of life, namely the principles underlying protein sequences better than any features suggested by textbooks and prediction methods. The exception is evolutionary information, however, that information is not available on the level of a single sequence.

1,25-Dihydroxyvitamin D3 prevents the decrease of bone mineral appositional rate in rats with inflammation-mediated osteopenia (IMO)

We have studied the effects of 1,25-dihydroxyvitamin D3 (1,25(OH)2D3) on bone mass and bone mineral appositional rate in intact rats and rats with inflammation-mediated osteopenia (IMO), where osteoblast number and mineral appositional rate are decreased. 1,25(OH)2D3 prevents IMO-specific bone loss when given in a daily dose of 25 ng per rat, but does not when given in higher doses. The hormone was effective, when given over the complete duration of the experiment (21 days), but not when given over shorter time periods (7 and 14 days, respectively). 1,25(OH)2D3 prevents IMO-dependent reduction in mineral appositional rate and leads to an only moderate increase in intact rats. We conclude, that 1,25(OH)2D3 is more effective in stimulating mineral appositional rate in rats with IMO where mineral apposition is impaired.