Background
To explore the effectiveness of preoperative rehabilitation programmes (PreHab) on postoperative physical and psychological outcomes following anterior cruciate ligament reconstruction (ACLR).
Method
A systematic search was conducted from inception to November 2019. Randomised controlled trials (RCTs) published in English were included. Risk of bias was assessed using Version 2 of the Cochrane risk-of-bias tool, and the Grading of Recommendations Assessment system was used to evaluate the quality of evidence.
Results
The search identified 739 potentially eligible studies, three met the inclusion criteria. All included RCTs scored ‘high’ risk of bias.
PreHab in all three RCTs was an exercise programme, each varied in content (strength, control, balance and perturbation training), frequency (10 to 24 sessions) and length (3.1- to 6-weeks).
Statistically significant differences (p < 0.05) were reported for quadriceps strength (one RCT) and single leg hop scores (two RCTs) in favour of PreHab three months after ACLR, compared to no PreHab. One RCT reported no statistically significant between-group difference for pain and function. No RCT evaluated post-operative psychological outcomes.
Conclusion
Very low quality evidence suggests that PreHab that includes muscular strength, balance and perturbation training offers a small benefit to quadriceps strength and single leg hop scores three months after ACLR compared with no PreHab. There is no consensus on the optimum PreHab programme content, frequency and length. Further research is needed to develop PreHab programmes that consider psychosocial factors and the measurement of relevant post-operative outcomes such as psychological readiness and return to sport.
Trial registration
PROSPERO trial registration number. CRD42020162754.
Increasing recombinant protein expression is of broad interest in industrial biotechnology, synthetic biology, and basic research. Codon optimization is an important step in heterologous gene expression that can have dramatic effects on protein expression level. Several codon optimization strategies have been developed to enhance expression, but these are largely based on bulk usage of highly frequent codons in the host genome, and can produce unreliable results. Here, we develop deep contextual language models that learn the codon usage rules from natural protein coding sequences across members of theEnterobacteralesorder. We then fine-tune these models with over 150,000 functional expression measurements of synonymous coding sequences from three proteins to predict expression inE. coli. We find that our models recapitulate natural context- specific patterns of codon usage and can accurately predict expression levels across synonymous sequences. Finally, we show that expression predictions can generalize across proteins unseen during training, allowing forin silicodesign of gene sequences for optimal expression. Our approach provides a novel and reliable method for tuning gene expression with many potential applications in biotechnology and biomanufacturing.
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