This study was undertaken to investigate whether chitosan treatments of sprouts of three soybean [Glycine max (L.) Merr.] cultivars (OAC Champion, AC Orford, and AC Proteina) can increase not only concentration of isoflavones in sprouts but also the transcript levels of genes and their subfamilies encoding enzymes at key points of the phenylpropanoid pathway (phenylalanine ammonia‐lyase [E.C. 4.3.1.5], chalcone synthase [E.C. 2.3.1.74], chalcone isomerase [E.C. 5.5.1.6], and chalcone reductase [E.C. 2.3.1.170]), and at a key branch‐point enzyme in isoflavone biosynthesis (isoflavone synthase [E.C. 1.14.13.86]). Chitosan effects on transcript levels of 14 genes differed depending on the cultivar as demonstrated by significant interactions (P < 0.05) between soybean cultivar and chitosan treatment for all genes. For all cultivars, response to chitosan treatments varied significantly depending on the gene. Overall, the greatest response was observed with high molecular weight chitosan. Very‐low‐ and low‐isoflavone cultivars (i.e., AC Orford and OAC Champion) sometimes responded positively to chitosan treatments, while the high‐isoflavone cultivar (i.e., AC Proteina) responded only negatively to chitosan. Chitosan treatments had limited effect on isoflavone concentrations, only reducing glycitein in OAC Champion by 38%. No correlation was found between gene expression and isoflavone concentrations. Differences in isoflavone concentrations were observed between sprouts of the three cultivars; AC Proteina had the highest isoflavone concentration and AC Orford the lowest. Results indicate that although isoflavone concentration and gene expression varied with cultivar, chitosan treatment is not a viable option for increasing isoflavone content in sprouts of cultivars evaluated.

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Chronic pain-related sickness absence is an enormous socioeconomic burden globally.Optimized interventions are reliant on a lucid understanding of the distribution of social insurance benefits and their predictors. This register-based observational study analyzed data for a 7-year period from a population-based sample of 44,241 chronic pain patients eligible for interdisciplinary treatment (IDT) at specialist clinics. Sequence analysis was used to describe the sickness absence over the complete period and to separate the patients into subgroups based on their social insurance benefits over the final 2 years. The predictive performance of features from various domains was then explored with machine learning-based modeling in a nested cross-validation procedure. Our results showed that patients on sickness absence increased from 17% 5 years before to 48% at the time of the IDT assessment, and then decreased to 38% at the end of follow-up. Patients were divided into 3 classes characterized by low sickness absence, sick leave, and disability pension, with eight predictors of class membership being identified. Sickness absence history was the strongest predictor of future sickness absence, while other predictors included a 2008 policy, age, confidence in recovery, and geographical location. Information on these features could guide personalized intervention in the specialized healthcare.Perspective: This study describes sickness absence in patients who visited a Swedish pain specialist interdisciplinary treatment clinic during the period 2005 to 2016. Predictors of future sickness absence are also identified that should be considered when adapting IDT programs to the patient's needs.

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