OBJECTIVE To investigate the current career development status and existing bottlenecks among graduates of clinical pharmacist training bases， and to provide evidence for optimizing the training system， enhancing trainees’ job competency， and promoting the sound development of the clinical pharmacy discipline. METHODS Graduates from a clinical pharmacist training base in a tertiary class A hospital were selected as survey subjects. A six-dimensional questionnaire was designed using “Wenjuanxing” platform， distributed and collected via a mobile APP and E-mail. The survey analyzed participants’ basic characteristics， the impact of training on their professional roles， as well as their satisfaction regarding professional sentiment， competence， career status， professional recognition， the current state of the clinical pharmacist workforce， and the teaching model of the training base. The reliability of the questionnaire responses was assessed using Cronbach’s α coefficient. RESULTS A total of 164 questionnaires were distributed， and 147 valid responses were recovered， yielding a valid response rate of 89.63%. The Cronbach’s α coefficient for the questionnaire content was 0.943. The surveyed trainees were primarily employed at general hospitals （81.63%） and tertiary class A hospitals （72.11%）， with women constituting the majority （79.59%）. Their ages were concentrated between 26 and 35 years old （63.27%）， and most of them were supervising pharmacists（59.86%）. After training， 96.60% of graduates returned to their original institutions， and the proportion working in clinical pharmacist positions increased from 52.38% to 84.35%. Trainees’ overall satisfaction rate with the training model was 84.35%. However， their satisfaction with innovation and research capacity in clinical pharmacy was only 42 .18%， the satisfaction with salary and benefits was 35.37%， and the satisfaction with both occupational recognition and rationality of staffing was less than 50%. CONCLUSIONS Clinical pharmacist training bases can effectively promote trainees’ career transition， enhance job competency， and stabilize the talent pool among trainees. Nonetheless， the profession still faces bottlenecks such as relatively weak research capacity， low compensation， insufficient professional recognition， and unreasonable staffing. These issues require a multi-pronged， collaborative approach involving the optimization of training systems， the enhancement of career support， and the improvement of industry policies.

Metabolic-bariatric surgery(MBS)has become an important treatment for pathological obesity and metabolic diseases.However,common postoperative nutritional complications—such as protein-energy malnutrition,iron deficiency anemia,and vitamin B12 deficiency—significantly affect patients' long-term prognosis.Traditional nutritional management models rely on static monitoring and standardized supplementation,which are insufficient to address individual variability and dynamic postoperative changes.Artificial intelligence(AI),through integrating multimodal data(such as biochemical indicators,imaging information,and wearable device monitoring)and intelligent modeling,offers new approaches for dynamic monitoring,risk prediction,and personalized intervention.Based on literature from 2017 to 2025,this article systematically evaluates the application of AI in perioperative nutritional management for MBS,covering key technologies including machine learning,deep learning,and natural language processing.It also analyzes current challenges in clinical translation,such as data fragmentation,lack of model interpretability,and limited long-term validation.In the future,enhanced multi-center collaboration,the development of standardized databases,and explainable models will be essential to advancing nutritional management in MBS from empirical practice to precision medicine.

Metabolic-bariatric surgery(MBS)has become an important treatment for pathological obesity and metabolic diseases.However,common postoperative nutritional complications—such as protein-energy malnutrition,iron deficiency anemia,and vitamin B12 deficiency—significantly affect patients' long-term prognosis.Traditional nutritional management models rely on static monitoring and standardized supplementation,which are insufficient to address individual variability and dynamic postoperative changes.Artificial intelligence(AI),through integrating multimodal data(such as biochemical indicators,imaging information,and wearable device monitoring)and intelligent modeling,offers new approaches for dynamic monitoring,risk prediction,and personalized intervention.Based on literature from 2017 to 2025,this article systematically evaluates the application of AI in perioperative nutritional management for MBS,covering key technologies including machine learning,deep learning,and natural language processing.It also analyzes current challenges in clinical translation,such as data fragmentation,lack of model interpretability,and limited long-term validation.In the future,enhanced multi-center collaboration,the development of standardized databases,and explainable models will be essential to advancing nutritional management in MBS from empirical practice to precision medicine.

Talent evaluation is an important part of the institutional mechanism for talent development. Based on the stakeholder theory, this study identified and defined the stakeholders in the evaluation of talents in public hospitals, and analyzed the needs of each party and their relationship with each other, and then built the evaluation path of talents in public hospitals. The stakeholders in the evaluation of talents in public hospitals included the government, society, hospitals and talents themselves. The strategic needs of talents of the government and the public welfare needs of talents of the society were transmitted to hospitals through policy orientation and functional positioning. Hospitals formed the evaluation implementation mechanism based on its own development needs through the establishment of talent evaluation index database and the setting of representative indexes of " field-oriented". Finally, the effectiveness of talent evaluation can be strengthened through the independent selection of the main evaluation categories and representative indicators by individual talents, as well as the personalized setting of relevant indicator weights by hospitals, in order to provide feedback on the evaluation needs of talents for the value of expertise. Constructing a talent evaluation system for public hospitals based on demand factors using a stakeholder theory analysis framework can provide reference for the reform of talent evaluation in public hospitals.

Objective:To detect gene mutations in patients with mild phenotypes of neurofibromatosis type 1 (NF1) .Methods:From June 2017 to June 2020, 5 probands with mild phenotypes of NF1 only involving the skin and their family members were collected from Department of Dermatology, Fengxian Institute of Dermatosis Prevention and Treatment in Shanghai. Pedigree investigation was performed to evaluate the clinical phenotypes of NF1. The second-generation targeted gene sequencing combined with Sanger sequencing was performed to detect and verify pathogenic mutations.Results:All the 5 probands presented with only skin lesions, including café-au-lait spots, freckles, neurofibromas, but no other systemic involvement. A total of 5 mutations were identified in different exons of the NF1 gene in the 5 families, including 1 large-fragment deletion mutation (hg38: chr17:31327199-31335928 del 8 730 bp) , 1 splicing mutation (C.7970+1G>T) , 1 insertion mutation (C.3011_3012insTATG, p.N1004fs*) , 1 deletion mutation (C.1754_1757delTAAC, p.T586Vfs*18) , and 1 nonsense mutation (c.C503G, p.S168X) , and the first 3 above mentioned mutations were previously unreported novel mutations.Conclusion:Five mutations were identified in the 5 families with mild phenotypes of NF1, including 3 novel mutations, which expand the mutational spectrum of NF1.

Objective:To provide a basis for policy support of Sino-foreign cooperation in the area of COVID-19, through exploring the composition, integration and main path of Sino-foreign scientific research cooperation in this field.Methods:Based on bibliometrics theory and the core database of Web of Science, Sino-foreign cooperative literatures in COVID-19 research area were screened to construct the keyword co-occurrence network. VOSviewer 1.6.16 software was used for cluster analysis, internal and external connectivity measurement and path exploration.Results:In COVID-19 research area, there were 61 countries, each of which had published more than 10 papers in cooperation with China. The proportion of Sino-US cooperative papers was the highest, accounting for 48.0%. The collaborative research field was divided into five clusters: basic medicine, clinical medicine, public mental health, epidemiology, and environmental impacts. The internal connectivity rate of basic medicine cluster was the highest(60.2%), and the lowest was epidemiology cluster(20.1%). The integration degree of basic medicine cluster and clinical medicine cluster was high, and the number of connecting nodes accounted for 27.7% and 21.9% in the two clusters. Through path analysis, a collaborative research main path of " emergent transmission-infection mechanism-clinical characteristics-anxiety" had been formed in the field.Conclusions:Sino-foreign scientific and technological cooperation in the field of COVID-19 was still in the early stage of development, but the research clusters had initially constructed. Some clusters showed high convergence and integration, and formed a main path of the collaborative research field. In order to realize the in-depth development of research in this field and the full utilization of cooperation resources, the health management department can formulate accurate health policies of scientific and technological cooperation for the development stages of different clusters according to the main path.

Objective:The personnel composition factors and impact which influenced research performance of clinic departments were explored, to optimize personnel structure for further references on enhance the hospital scientific achievements.Methods:The research performance scores and personnel overall composition of 75 clinic departments in the year of 2017 were retrospectively analyzed. Principal component regression was used to analyze the correlation between research performance and personnel composition.Results:KMO was 0.635, P<0.001, the model had statistical significance. Four common factors identified were: middle-age females, young masters, senior master supervisors and doctors & doctoral supervisors. Accumulative variance contribution rate was 87.19%. After reducing equations, doctor degrees ( β=124.164), intermediate professional title ( β=123.573), Age≥40 ( β=102.149) and doctoral supervisors ( β=95.309) were four most influential factors on research performance of clinic departments. Conclusions:The selection of common factors should be comprehensively considered, combining with multiple criterions and professional significance. A team structure based on middle-aged and young people led by doctoral supervisors, and with backbones of doctoral degree personnel will help to improve the scientific research performance of clinical departments.

Objective:To explore the combination of study abroad and scientific research effectiveness, improve the " effectiveness concern" related to the selection mechanism for abroad-study, establish an evaluation system combined studying abroad and scientific research effectiveness.Methods:The research was based on 45 local-funded abroad-study programs, and used path analysis to connect main influence factors of studying abroad with scientific achievements, and establish a model.Results:The final path analysis model contained two regression equations: (1) in the equation of SCI articles, adjusted R2=0.600, F=5.698, P<0.001, statistic difference was significant. In this pathway, influence factors on SCI articles were successively Professional Title ( β=0.736, P<0.001), Age ( β=-0.700, P<0.001) and foreign institutions ( β=-0.318, P=0.031); (2) in the equation of Project funding, adjusted R2=0.490, F=7.608, P<0.001, statistic difference was significant. In this pathway, influence factors on project funding were successively SCI Articles ( β=0.409, P=0.010), Study country ( β=-0.342, P=0.015), and Degree ( β=-0.223, P=0.043). Conclusions:According to the path analysis, impacts of studying abroad on scientific research effectiveness were evaluated and detailed more precisely. The research established scientific-effectiveness oriented pathway between assigning and evaluation system, providing support to promote effectiveness of hospital abroad-study scientific outputs, and provide possible reference for local and national governments to optimize international exchange programs.

Objective The research aims to optimize the hospital research performance appraisal ,clarify the scientific characteristics and possible shortages of different clinic departments in various types ,to enhance the effectiveness of research performance appraisal .Methods Descriptive statistics were used to generalize hospital research performance appraisal in 2017 . Hierarchical clustering was used to cluster and analyze performance appraisal characteristics of 75 clinic departments .Results SCI papers (405) ,National projects (181) and National core journal papers (106) took 75 .2％ of total average score .The op-timal solution of the cluster was 6 types for 75 departments and the dendrogram illustrated significant varieties among the 6 types .Six departments' types were academic-conference-oriented ,national-paper-oriented ,SCI-paper-oriented ,advanced-pro-ject-oriented ,provincial/horizontal-project-oriented and attending-conference-oriented .The percentages of the oriented indica-tors that took their total average scores were 59 .4％ ,42 .0％ ,66 .7％ ,57 .0％ ,61 .8％ ,52 .3％ .Conclusions Compared with K-means method ,the results of hierarchical clustering equipped with better characteristics and interpretative power .Research performance appraisal has been further optimized .Departments in different types showed significant characteristics and weak-nesses ,which provides managers with effective guidance on countermeasures .

Objective To analyze the SCI papers output and its impact factors of a hospital during 2013-2017,to provide supports and proofs for scientific research administrators to update incentive policies for stimulate growth of SCI Paper publication.Methods Dynamic sequence and Optimal Scaling Regression methods were used to conduct statistical analysis of the output and impact factors of hospital-published SCI papers.Results The average increased number per year of hospital-published SCI papers output was 30.3 with an annual increase rate of 37.5％,which was 9.5％ higher than the national average.In 2017,the growth rate of a fixed base of hospital-published SCI papers was increased to 257.4 ％,which was 89％ higher than the national rate during the same period.The model was statistically significant (F =10.513,P＜ 0.001,adjusted R2 =0.409) according to the Optimal Scaling Regression analysis of 180 first/corresponding authors out of 516 SCI papers.We found that authors' status as tutor of post-graduate student (P＜0.001),average project fundings (P=0.001),professional titles (P=0.048) and genders (P=0.005) have significant impacts on the output of hospital-published SCI papers (P＜0.05).The sum of the importance of the four factors above was 0.961,namely account for 96.1％ of the whole model.The quantitative score conversion chart shows that doctoral tutors,high average project funding,senior professional titles,and male authors of the SCI papers have higher quantitative scores.Conclusions Tutor status,average project fundings,professional titles and genders could impact the output of SCI papers.Scientific research administrators could adjust incentive policies of SCI papers,based on the science platform construction inclining to doctor tutors,adding matching funds for science and research,bonding professional title appraisals and rising incentives for female researchers on SCI papers,to promote SCI papers output.