Protein tandem repeats (TRs) are often associated with immunity-related functions and diseases. Since that last census of protein TRs in 1999, the number of curated proteins increased more than seven-fold and new TR prediction methods were published. TRs appear to be enriched with intrinsic disorder and vice versa. The significance and the biological reasons for this association are unknown. Here, we characterize protein TRs across all kingdoms of life and their overlap with intrinsic disorder in unprecedented detail. Using state-of-the-art prediction methods, we estimate that 50.9% of proteins contain at least one TR, often located at the sequence flanks. Positive linear correlation between the proportion of TRs and the protein length was observed universally, with Eukaryotes in general having more TRs, but when the difference in length is taken into account the difference is quite small. TRs were enriched with disorder-promoting amino acids and were inside intrinsically disordered regions. Many such TRs were homorepeats. Our results support that TRs mostly originate by duplication and are involved in essential functions such as transcription processes, structural organization, electron transport and iron-binding. In viruses, TRs are found in proteins essential for virulence.

Clinical decision making regarding the treatment of unruptured intracranial aneurysms (IA) benefits from a better understanding of the interplay of IA rupture risk factors. Probabilistic graphical models can capture and graphically display potentially causal relationships in a mechanistic model. In this study, Bayesian networks (BN) were used to estimate IA rupture risk factors influences. From 1248 IA patient records, a retrospective, single-cohort, patient-level data set with 9 phenotypic rupture risk factors (n=790 complete entries) was extracted. Prior knowledge together with score-based structure learning algorithms estimated rupture risk factor interactions. Two approaches, discrete and mixed-data additive BN, were implemented and compared. The corresponding graphs were learned using non-parametric bootstrapping and Markov chain Monte Carlo, respectively. The BN models were compared to standard descriptive and regression analysis methods. Correlation and regression analyses showed significant associations between IA rupture status and patient's sex, familial history of IA, age at IA diagnosis, IA location, IA size and IA multiplicity. BN models confirmed the findings from standard analysis methods. More precisely, they directly associated IA rupture with familial history of IA, IA size and IA location in a discrete framework. Additive model formulation, enabling mixed-data, found that IA rupture was directly influenced by patient age at diagnosis besides additional mutual influences of the risk factors. This study establishes a data-driven methodology for mechanistic disease modelling of IA rupture and shows the potential to direct clinical decision-making in IA treatment, allowing personalised prediction.

Study design: Single-centre, two-parallel group, methodological randomised controlled trial to assess blinding feasibility.Background: Trials of manual therapy interventions of the back face methodological challenges regarding blinding feasibility and success. We assessed the feasibility of blinding an active manual soft tissue mobilisation and control intervention of the back. We also assessed whether blinding is feasible among outcome assessors and explored factors influencing perceptions about intervention assignment.Methods: On 7–8 November 2022, 24 participants were randomly allocated (1:1 ratio) to active or control manual interventions of the back. The active group (n = 11) received soft tissue mobilisation of the lumbar spine. The control group (n = 13) received light touch over the thoracic region with deep breathing exercises. The primary outcome was blinding of participants immediately after a one-time intervention session, as measured by the Bang blinding index (Bang BI). Bang BI ranges from –1 (complete opposite perceptions of intervention received) to 1 (complete correct perceptions), with 0 indicating ‘random guessing’—balanced ‘active’ and ‘control’ perceptions within an intervention arm. Secondary outcomes included blinding of outcome assessors and factors influencing perceptions about intervention assignment among both participants and outcome assessors, explored via thematic analysis.Results: 24 participants were analysed following an intention-to-treat approach. 55% of participants in the active manual soft tissue mobilisation group correctly perceived their group assignment beyond chance immediately after intervention (Bang BI: 0.55 [95% confidence interval (CI), 0.25 to 0.84]), and 8% did so in the control group (0.08 [95% CI, −0.37 to 0.53]). Bang BIs in outcome assessors were 0.09 (−0.12 to 0.30) and −0.10 (−0.29 to 0.08) for active and control participants, respectively. Participants and outcome assessors reported varying factors related to their perceptions about intervention assignment.Conclusions: Blinding of participants allocated to an active soft tissue mobilisation of the back was not feasible in this methodological trial, whereas blinding of participants allocated to the control intervention and outcome assessors was adequate. Findings are limited due to imprecision and suboptimal generalisability to clinical settings. Careful thinking and consideration of blinding in manual therapy trials is warranted and needed.Trial registration: ClinicalTrials.gov: NCT05822947 (retrospectively registered)

The R package abn is a comprehensive tool for Bayesian Network (BN) analysis, a form of probabilistic graphical model. BNs are a type of statistical model that leverages the principles of Bayesian statistics and graph theory to provide a framework for representing complex multivariate data. They can derive a directed acyclic graph from empirical data to describe the dependency structure between random variables. Additive Bayesian Network (ABN) models extend the concept of generalized linear models, typically used for predicting a single outcome, to scenarios with multiple dependent variables (e.g., Kratzer et al. (2023)). This makes them a powerful tool for understanding complex, multivariate datasets. This package provides routines for structure learning and parameter estimation of ABN models.