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Background: Polysaccharides are important active ingredients in Ophiocordyceps gracilis with many physiological functions. It can be obtained from the submerged fermentation by the anamorph (Paraisaria dubia) of Ophiocordyceps gracilis. However, it was found that the mycelial pellets of Paraisaria dubia were dense and increased in volume in the process of fermentation, and the center of the pellets was autolysis due to the lack of nutrient delivery, which extremely reduced the yield of polysaccharides. Therefore, it is necessary to excavate a fermentation strategy based on morphological regulation for Paraisaria dubia to promote polysaccharides accumulation., Results: In this study, we developed a method for enhancing polysaccharides production by Paraisaria dubia using microparticle enhanced technology, talc microparticle as morphological inducer, and investigated the enhancement mechanisms by transcriptomics. The optimal size and dose of talc were found to be 2000 mesh and 15 g/L, which resulted in a high polysaccharides yield. It was found that the efficient synthesis of polysaccharides requires an appropriate mycelial morphology through morphological analysis of mycelial pellets. And, the polysaccharides synthesis was found to mainly rely on the ABC transporter-dependent pathway revealed by transcriptomics. This method was also showed excellent robustness in 5-L bioreactor, the maximum yields of intracellular polysaccharide and exopolysaccharides were 83.23 ± 1.4 and 518.50 ± 4.1 mg/L, respectively. And, the fermented polysaccharides were stable and showed excellent biological activity., Conclusions: This study provides a feasible strategy for the efficient preparation of cordyceps polysaccharides via submerged fermentation with talc microparticles, which may also be applicable to similar macrofungi., (© 2021. The Author(s).)

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy., (© 2021. The Author(s).)

Background: Whether the 7th edition of American Joint Committee on Cancer (AJCC) TNM staging system (AJCC-7) is a successful revision remains debatable. We aimed to compare the predictive capacity of the AJCC-7 for colorectal cancer with the 6th edition of the AJCC TNM staging system (AJCC-6)., Methods: The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) dataset consisting of 158,483 records was used in this study. We evaluated the predictive capacity of the two editions of the staging system using Harrell's C index and Bayesian Information Criterion (BIC)., Results: There was a significant prognostic difference between patients at stage IIB and IIC (P < 0.001). Stage III patients with similar prognoses were adequately sub-grouped in the same stage according to AJCC-7. The Harrell's C index revealed a value of 0.7692 for AJCC-7, which was significantly better than 0.7663 for AJCC-6 (P < 0.001). BIC analysis provided consistent results (P < 0.001)., Conclusions: This study demonstrates that AJCC-7 is superior to the AJCC-6 staging system in predictive capacity.