In China,the Chinese scientist TU Youyou,winner of the Nobel Prize in Physiology or Medicine and her findings of artemisinin discovered during the 1970s triggered a lot of discussion on developing a biomedical research management strategy in the new era.Traditional management has faced many challenges in the era of Big Science.The University of Cambridge has remarkable achievements in biomedical research and set up an effective management system.Based on the introduction and observations from Cambridge,this article proposes strategies for advancing research management in Chinese universities in our own context.

Objective To evaluate the status of the influence factors of foundational course teachers' academic profession development in medical college.Methods The survey includes academic promotion and academic innovation.In aspect of academic innovation,a total of 68 foundational course teachers in medical college were selected.The subjects were tested by scale of exterior influencing factors on innovative behavior,the assessment scale of innovative behavior and the organizational innovation climate scale,the scientific spirit scale of the scientific and technological talents.The impact of demographic characteristics on academic promotion was also tested.Results In aspect of academic promotion,teachers with different department and education showed statistically significant difference in titles(P＜0.05).The age of teachers' promoted to professor is 40-50 years old,vice professor is 30-40 years old.Generally,it takes 6-10 years to promote to senior titles,and it takes less time for doctors.In aspect of academic innovation,scale was used in 5 points,the lowest 1 point,the highest 5 point.The mean score of the scale of exterior influencing factors on innovative behavior is mission design3.28 ±0.96,innovation investment 2.53±0.74,innovation environment 3.10±0.65.The mean score of the assessment scale of innovative behavior and the organizational innovation climate scale is organization target cognition 3.10 ± 0.74,colleagues relationship 3.44±0.82,team innovation motivation 3.34±0.83.The mean score of the scientific spirit scale of the scientific and technological talents in spiritual science dimension is 3.40±0.67 points.The projects with low scores are distributed in three dimensions of innovation investment,innovation environment and organization target cognition.Conclusions Teachers' academic promotion is related to the department and education.In aspect of academic innovation,teachers' self-evaluation of scientific spirit is higher,the evaluation of the mission design,colleagues relationship,team innovation motivation is also higher,the evaluation of the innovation environment,organization target cognition,innovation investment is lower.Material resources,research cooperation and so on especially should be improved.

Objective To investigate the effect of the administrative intervention on smoking cessation of medical personnel and demonstrate the feasibility and the validity of this approach.Methods Two comparable hospitals were selected,one hospital with 136 smoking medical staff members was used as the intervention group;the other hospital which had 127 smoking medical staff members was used as the control group.We applied administrative intervention and health education to the intervention group and health education only to the control group.The intervention time was 10 months,we used the questionnaire survey before and after intervening to evaluate the smoking rate,the intention to quit smoking,the willingmess to accept the help of quitting and the proportion of those who quitted smoking.Results The smoking rate of intervention group (37.4％) was lower than that of control group (77.2％),the difference was statistically significant (x2=40.99,P＜0.01).The proportion of control group smokers in planning(51.1％) is statistically significant as compared with intervention group (46.18％) (x2=46.18,P＜0.01).The proportion of people who were willing to accept the help from families and friends and smoking cessation counseling in intervention group was significanly higher than that in the control group.(x2=10.04,x2=7.73,x2=7.58;P＜ 0.01).But the proportion of accepting the medicine for quitting smoking was not significantly different (x2=0.16,P＞0.05).The proportion of smokers who wanted to quit smoking on their own willingness in control group was significantly higher than that in intervention group (x2=36.27,P＜0.01).After10 months,61 people (46.6％) in the intervention group succeeded in quitting smoking and 13 (10.6％) people in control group succeeded (x2=28.21,P＜0.01).Conclusion Administrative intervention has feasibility and validity when hospitals take activities for smoking cessation.

Objective:To develope a deep-learning-based auto-segmentation model to segment organs at risk (OARs) in head and neck (H&N) region and compare with atlas-based auto-segmentation software (Smart segmentation).Methods:The auto-segmentation model consisted of classification model and segmentation model based on deep learning neural network. The classification model was utilized to classify CT slices into six categories in the cranio-caudal direction, and then the CT slices corresponding to the categories for different OARs were pushed to the segmentation model respectively. The CT image data of 150 patients were used for auto-segmentation model training and building atlas library in Smart segmentation software. Another 20 patients were used as testing dataset for both auto-segmentation model and Smart segmentation software. Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to evaluate the accuracy of two method, and auto-segmentation time cost was recorded. Paired Student′s t-test or non-parametric Wilcoxon signed-rank test was performed depending on result of normality test. Results:The DSC and HD of auto-segmentation model for brainstem, left eye, right eye, left optic nerve, right optic nerve, left temporal lobe, right temporal lobe, mandible, left parotid and right parotid were 0.88 and 4.41 mm, 0.89 and 2.00 mm, 0.89 and 2.12 mm, 0.70 and 3.00 mm, 0.80 and 2.24 mm, 0.81 and 7.98 mm, 0.84 and 8.82 mm, 0.89 and 5.57 mm, 0.70 and 11.92 mm, 0.77 and 11.27 mm respectively. The results of auto-segmentation model were better than those of Smart segmentation ( t=3.115-7.915, Z=-1.352 to -3.921, P<0.05) except left and right parotids. In addition, the speed of auto-segmentation model was 51.28% faster than that of Smart segmentation. Conclusions:In this study, the deep-learning-based auto-segmentation model demonstrated superior performance in accuracy and efficiency on segmenting OARs in H&N CT images, which was better than Smart segmentation software.

Objective:To study the genetic variation and distribution characteristics of thalassemia in people of childbearing age in Hubei Province, and to provide clinical basis for the local government decision-making departments to formulate and promote appropriate policies for prevention and control of thalassemia.Methods:Venous blood samples were collected from 44 849 people of childbearing age in hospitals in Hubei Province from May 13, 2019 to August 17, 2021. PCR-flow fluorescence hybridization and PCR+diversion hybridization were used to screen thalassemia genes. Spouses of those who tested positive were also tested for thalassemia genes. When both spouses carried the same type of thalassemia gene, the amniotic fluid of pregnant women was extracted for prenatal diagnosis and followed up.Results:Among the 44 849 people of childbearing age, 2 286 cases were diagnosed as thalassemia gene carriers through genetic testing, and the total detection rate was 5.10% (2 286/44 849). Among them, 1 488 cases were diagnosed as α-thalassemia, and the detection rate was 3.32% (1 488/44 849); 767 cases were diagnosed as β-thalassemia, and the detection rate was 1.71% (767/44 849); 31 cases were diagnosed as α-thalassemia combined with β-thalassemia, and the detection rate was 0.07% (31/44 849). The top three genotypes of α-thalassemia were -α 3.7/αα, -- SEA/αα, and -α 4.2/αα, accounting for 58.06% (864/1 488), 26.14% (389/1 488), and8.74% (130/1 488), respectively. The top three genotypes of β-thalassemia were β IVS-Ⅱ-654/β N, β CD41-42/β N, and β CD17/β N, accounting for 41.72% (320/767), 21.25% (163/767), and 16.04% (123/767), respectively. The main genotypes of α-thalassemia combined with β-thalassemia were -α 3.7/αα complex β IVS-Ⅱ-654/β N and -α 3.7/αα complex β CD41-42/β N, accounting for 29.03% (9/31) and 16.13% (5/31), respectively. A total of 59 people of childbearing age were conducted prenatal diagnosis, among fetus, there were 4 cases of severe thalassemia (2 cases of severe α-thalassemia, 2 cases of severe β-thalassemia), 5 cases of intermediate α-thalassemia, 5 cases of intermediate or severe β-thalassemia, 19 cases of mild thalassemia (8 cases of mild α-thalassemia, 11 cases of mild β-thalassemia), 13 cases of stationary α-thalassemia, and 1 case of stationary α-thalassemia combined with mild β-thalassemia, there were 12 cases without α-thalassemia or β-thalassemia genes. After follow-up, 4 cases of severe thalassemia, 2 cases of intermediate α-thalassemia, and 5 cases of intermediate or severe β-thalassemia were terminated pregnancy by the joint decision of both parents. Conclusions:In Hubei Province, the detection rate of thalassemia is high, and α-thalassemia is the main mutation type. The combination of thalassemia gene screening and prenatal diagnosis is of great significance in reducing the birth rate of children with thalassemia.

Objective:To investigate the clinical value of dynamic detection of lymphocyte subsets and blood cell counts in management of patients with lupus nephritis (LN).Methods:The clinical data of 65 patients with primary LN admitted in Affiliated Hospital of Qingdao University from January 2015 to April 2021 were retrospectively analyzed. According to the stage of disease progression and medications used,LN patients were classified into primary active phase,post-induction therapy phase,and maintenance therapy phase. The changes in lymphocyte subsets were monitored,and the relationship of lymphocyte subsets and blood cell count ratios with lupus activity and infection events was evaluated.Results:The decrease of CD4 +T lymphocyte and NK cell counts were negatively correlated with the activity of systemic lupus erythematosus (SLE)( r=-0.67,-0.33, P<0.01),while CD8 +T lymphocyte,B cell counts,neutrophil/lymphocyte ratio (NLR),platelet/lymphocyte ratio (PLR),and monocyte/lymphocyte ratio (MLR) were positively correlated with the SLE activity( r=0.38,0.26,0.34,0.26,0.29, P<0.05). The area under ROC curve (AUC) of CD4 +T lymphocyte count in predicting the occurrence of infection in LN patients was the highest (0.89); taking 247.50 cell/μl as cutoff value,the sensitivity and specificity were 81.25% and 87.50%,respectively. The combination of CD4 +T lymphocyte with CRP increased the predicting value for the occurrence of infection. Conclusion:Dynamic detection of blood lymphocyte subsets and blood cell counts can reflect SLE activity and the occurrence of infection in LN patients. Among these indicators the CD4 +T lymphocyte has the highest predictive value for the occurrence of infection,and the combination of the CD4 +T lymphocyte count with CRP level can further improve the predicting value.