Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

In order to judge the future development trend of science and technology, plan ahead and lay out the frontier technology fields and directions, China Association of Chinese Medicine(CACM) has launched consultation projects for collecting "major scienti-fic issues and engineering technology difficulties in traditional Chinese medicine(TCM)" for the industry for three consecutive years since 2019. Up to now, 18 projects have been selected as major issues for research, and some experience and achievements have been made. These projects have been applied in important scientific and technological work such as scientific and technological planning and deployment at all levels of national, local, and scientific research institutions, the selection and cultivation of major national scientific and technological projects, and the construction of innovation bases, giving full play to the role of the think tank advisory committee of CACM. This study reviewed the selection of major issues for the first time, systematically combed its application in the national layout of science and technology, and put forward the existing problems and improvement suggestions, aiming to provide new ideas for further improving the selection of major issues and research direction, providing a theoretical basis and decision support for the national scientific and technological layout in the field of TCM, and promoting scientific and technological innovation to facilitate the high quality development of TCM.
Medicine, Chinese Traditional ; Inventions ; China ; Drugs, Chinese Herbal

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

Rhamnose synthase (RHM) is a key enzyme in the biosynthesis of uridine diphosphate rhamnose (UDP-Rha), reversibly converting uridine diphosphate-glucose (UDP-Glc) into UDP-Rha in the presence of NADH or NADPH. In this research, yeast extract (YE) was used to stimulate Sorbus aucuparia suspension cells. Based on a previous study of the transcriptome database of S. aucuparia suspension cells, two RHMs were cloned from S. aucuparia and named SaRHM1 (GenBank No.: MK213340) and SaRHM2 (GenBank No.: MK213341). The SaRHM1 gene contained a 2 007 bp open reading frame (ORF) encoding a polypeptide of 668 amino acids with a molecular weight of 75.25 kD, and a theoretical isoelectric point (pI) of 7.24. The SaRHM2 gene contained a 2 040 bp ORF encoding a polypeptide of 679 amino acids with a molecular weight of 76.26 kD and pI of 6.41. Bioinformatic analysis indicated that SaRHM1 and SaRHM2 contained two special sequences of GxxGxxG/A and YxxxK. Multiple sequence alignments and phylogenetic trees show that SaRHM1 and SaRHM2 have high sequence similarity with other plant species of RHMs. The results of enzyme activity assays in vitro revealed that both recombinant SaRHM1 and SaRHM2 are able to convert UDP-Glc into UDP-Rha. SaRHMs displayed maximum activity at 40 ℃ and a pH of 8 and 9, respectively. The K_m values of SaRHM1 and SaRHM2 for UDP-Glc were 212.4 ± 56.70 and 361.0 ± 63.74 μmol·L^-1, respectively, with V_max values of 235.5 ± 18.98 and 516.5 ± 22.30 nmol·min^-1·μg^-1, respectively. This study reports the cloning and sequencing of RHMs from S. aucuparia and verifies their function, which likely provide rhamnose donors for the subsequent biosynthesis of rhamnosides.

With the rapid development of comprehensive health industry, the demand for Chinese medicinal materials is increasing. There is also a growing demand for land for the cultivation of Chinese medicinal materials.Based on the analysis of the demand characteristics of planting habitats for Chinese medicinal herbs, this paper finds that compared with the cultivated environment, the wild environment is more conducive to the improvement of the quality and stress resistance of medicinal plants. The eco-planting for Chinese medicinal materials is the only way to achieve high quality, efficient and sustainable production of traditional Chinese medicine. Therefore, according to the habitat distribution characteristics of wild medicinal plants, combined with the current situation of land resource utilization in China and the increasing demand for land for Chinese herbal medicine cultivation, the land use strategy of Chinese herbal medicine ecological agriculture was proposed. ① To vigorously develop underwood planting and change the existing field cultivation mode. ② To make full use of mountainous areas and barren slopes to carry out wild planta tending or planting imitates wild condition. ③ According to the development law and biological characteristics of medicinal plants, the land resources should be developed and used rationally according to local conditions.This can not only meet the requirements of the specific growth environment of Chinese medicinal materials, realize the sustainable development of the Chinese medicinal materials industry, but also increase the economic income of people in mountainous areas, provide scientific and effective solutions for the land use of Chinese medicinal materials, and also have important significance for the protection of wild Chinese medicinal materials.
Agriculture ; China ; Drugs, Chinese Herbal ; Materia Medica ; Medicine, Chinese Traditional ; Plants, Medicinal

Objective:To observe the therapeutic effect of Kesuting syrups and Keqing capsules, which have the function of promoting lung and resolving phlegm, on a mouse model combining disease and syndrome of human coronavirus pneumonia with cold-dampness pestilence attacking lung. Method:The therapeutic effects of Kesuting syrups (the doses of 22, 11 mL·kg^-1) and Keqing capsules (the doses of 1.155, 0.577 5 g·kg^-1) on this model were evaluated by the inflammatory changes of lung tissue, the expression of viral nucleic acid, the contents of inflammatory factors [interleukin (IL)-6, IL-10, tumor necrosis factor (TNF)-α and interferon-γ (IFN-γ)], and the percentages of immune cells in peripheral blood (CD4⁺ T cells, CD8⁺ T cells and B cells). Result:Compared with the model group, high- and low-dose groups of Keqing capsules and Kesuting syrups could significantly reduce the inflammatory damage in the lung tissues of mice, Keqing capsules could significantly increase the percentages of CD4⁺ T cells, CD8⁺ T cells and B cells in peripheral blood, Keqing capsules and Kesuting syrups could reduce the expression levels of IL-6, IL-10, TNF-α and IFN-γ, inhibit the viral load in lung tissue, as well as improve the pathogenic manifestations of lung tissue. Conclusion:As the first-line drugs for novel coronavirus pneumonia, Keqing capsules and Kesuting syrups have significant therapeutic effect on the mouse model combining disease and syndrome of human coronavirus pneumonia with cold-dampness pestilence attacking lung, and the mechanism may be related to regulating immune function and reducing cytokine storm.