Objective:To explore the application effectiveness of the large language model DeepSeek in the cultivation of clinical thinking skills for medical laboratory technology students.Methods:A non-randomized controlled study was conducted. In the 2024-2025 academic year, two classes of second-year medical laboratory technology students from Hubei University of Chinese Medicine were selected and divided into a DeepSeek-assisted teaching group (Class A, n=53) and a traditional teaching control group (Class B, n=53), totaling 106 students. Both groups followed a 20-week problem-based learning (PBL) framework with identical teaching content, instructors, and class hours. Class A utilized DeepSeek via the"Learning Pass AI"platform for case diagnosis reasoning, prompt construction training, test plan formulation, and result analysis, while Class B received traditional PBL instruction. Paired t-tests were used to compare pre-and post-teaching scores in clinical thinking skills, AI interaction literacy, and prompt construction in Class A. Independent samples t-tests and chi-square ( χ2) tests were used to evaluate differences in case reasoning scores, etiology analysis accuracy, and teaching satisfaction between groups. Structured questionnaires supplemented the evaluation of model-assisted teaching processes. Results:The comparison of pre-and post-teaching scores in Class A showed that post-teaching scores significantly improved in clinical thinking skills[(4.02±0.45) points vs. (3.09±0.50) points, t=2.23)] and AI interaction literacy [(4.62±0.41) points vs. (3.27±0.54) points, t=2.18]. Compared to Class B, Class A demonstrated superior performance in case reasoning scores [(81.1±3.8) points vs.(74.3±4.2) points, t=8.97], etiology analysis accuracy [94.3% (50/53) vs. 81.1% (43/53), χ2=4.29], and teaching satisfaction [(95.6±3.2)points vs. (82.6±4.8) points, t=11.86] ( P<0.05). The results of questionnaires indicated that during model application, the prompt construction improved in logic [(2.85±0.58) points to (4.25±0.50) points, t=14.23, P<0.01] and innovation [(2.60±0.53) points to (4.05±0.46) points, t=11.57, P<0.05], but question clarity (77.4%, 41/53) and medical terminology accuracy (43.4%, 23/53) remained primary shortcomings. Conclusion:Integrating large language models into AI-teacher collaborative learning pathways can effectively promote students′ autonomous inquiry and clinical reasoning skills, thereby enhancing medical laboratory technology students′ clinical thinking skills.

Objective:To explore the application effectiveness of the large language model DeepSeek in the cultivation of clinical thinking skills for medical laboratory technology students.Methods:A non-randomized controlled study was conducted. In the 2024-2025 academic year, two classes of second-year medical laboratory technology students from Hubei University of Chinese Medicine were selected and divided into a DeepSeek-assisted teaching group (Class A, n=53) and a traditional teaching control group (Class B, n=53), totaling 106 students. Both groups followed a 20-week problem-based learning (PBL) framework with identical teaching content, instructors, and class hours. Class A utilized DeepSeek via the"Learning Pass AI"platform for case diagnosis reasoning, prompt construction training, test plan formulation, and result analysis, while Class B received traditional PBL instruction. Paired t-tests were used to compare pre-and post-teaching scores in clinical thinking skills, AI interaction literacy, and prompt construction in Class A. Independent samples t-tests and chi-square ( χ2) tests were used to evaluate differences in case reasoning scores, etiology analysis accuracy, and teaching satisfaction between groups. Structured questionnaires supplemented the evaluation of model-assisted teaching processes. Results:The comparison of pre-and post-teaching scores in Class A showed that post-teaching scores significantly improved in clinical thinking skills[(4.02±0.45) points vs. (3.09±0.50) points, t=2.23)] and AI interaction literacy [(4.62±0.41) points vs. (3.27±0.54) points, t=2.18]. Compared to Class B, Class A demonstrated superior performance in case reasoning scores [(81.1±3.8) points vs.(74.3±4.2) points, t=8.97], etiology analysis accuracy [94.3% (50/53) vs. 81.1% (43/53), χ2=4.29], and teaching satisfaction [(95.6±3.2)points vs. (82.6±4.8) points, t=11.86] ( P<0.05). The results of questionnaires indicated that during model application, the prompt construction improved in logic [(2.85±0.58) points to (4.25±0.50) points, t=14.23, P<0.01] and innovation [(2.60±0.53) points to (4.05±0.46) points, t=11.57, P<0.05], but question clarity (77.4%, 41/53) and medical terminology accuracy (43.4%, 23/53) remained primary shortcomings. Conclusion:Integrating large language models into AI-teacher collaborative learning pathways can effectively promote students′ autonomous inquiry and clinical reasoning skills, thereby enhancing medical laboratory technology students′ clinical thinking skills.

Objective To develop a high-performance liquid chromatography-diode array detector (HPLC-DAD) method for determination of cinnamic acid in the decoction of cinnamon, and investigate the effect of different storage temperature and time for the stability of cinnamic acid. Methods An HPLC- DAD method was established. Separation was performed on an Agilent Zorbax C₁₈ column (4.6 mm×250 mm, 5 μm) with 0.1% formic-acetonitrile acid water solution (60:40) as the mobile phase by isocratic elution. The flow rate was 1.0 ml/min, the temperature of column was 25 ℃, the injection volume was 5 μl, the detective UV wave length was 275 nm. The decoction were stored under refrigerated temperature (4 ℃) ambient temperature (25 ℃) and high temperature (40 ℃). The cinnamic acid was detected after 0, 1, 3, 7, 14, 21, 30 d. Results Cinnamic acid was successfully separated by this method, with good linear relationship between 10.21-204.20 μg/ml. The precision, repeatability, stability and recovery were good. Compared with the zero day, the content of cinnamic acid in the decoction of cinnamon decreased significantly (P<0.01) after 21 days and 30 days of ambient temperature storage and after 14 days, 21 days and 30 days of high temperature storage, but no significant change was found in the other groups (P>0.05). Conclusion This HPLC-DAD method had good stability and repeatability. Cinnamic acid was stable in the decoction of cinnamon for 30 days under refrigerated temperature.

Specialty English of Medical Laboratory is a comprehensive and overlapping discipline and plays a major role in multiple medical courses, including Life Science, Clinical Medicine and Laboratory Medicine.However, its teaching practices are limited due to the extensive contents, scattered basic knowledge points, complexities of language learning processes and high demanding of sophisticated teaching skills.Since internet technology′s breakout and the web-based education expanding in the past decade, personalized flipped classroom has drawn considerable attention in the education field and became a hop-spot in teaching practice system.In the present study, the concepts of flipped classroom and basic characteristics of Specialty English of Medical Laboratory were elaborated, as well as the reconstructing teaching processes, exploring the concrete plan for teachers and students in undergraduate program.