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 investigate the impact of long non-coding RNA maternal expression gene 3(lncRNA MEG3)on hypoxia/reoxygenation(H/R)-induced cardiomyocyte apoptosis by modulating the miR-202-5p/signal transducer and activator of transcription 3(STAT3)axis.Methods An H/R cell model was constructed and randomly separated into four groups:sh-NC(transfected with NC shRNA),sh-MEG3(transfected with lncRNA MEG3 shRNA),miR-NC(transfected with lncRNA MEG3 shRNA and NC miR),and in-miR-202-5p(transfected with lncRNA MEG3 shRNA and miR-202-5p inhibitor).In addition,the H/R group(nontransfected H/R model cells)and the AC 16 group(normal AC 16 cells)were set.Quantitative real-time PCR method was used to analyze the expression of lncRNA MEG3,miR-202-5p,and STAT3 in cells from each group.CCK-8 method was used to analyze cell viability.Flow cytometry was used to analyze apoptosis.Enzyme-linked immunosorbent,colorimetric,and probe assays were applied to detect the levels of inter-leukin-6(IL-6),tumor necrosis factor α(TNF-α),malondialdehyde(MD A),glutathione peroxidases(GSH-Px),and reactive oxygen spe-cies(ROS).Western blotting was carried out to examine the expression of STAT3,Bcl-2-associated X protein(Bax),B-cell lymphoma-2(Bcl-2),caspase-3,and caspase-9.A dual luciferase assay was used to analyze the relationship between lncRNA MEG3 and miR-202-5p,as well as the relationship between miR-202-5p and STAT3.Results Compared with that in the AC 16 group,the expression of lncRNA MEG3 and STAT3 in cells in the H/R group increased,while the expression of miR-202-5p decreased(P＜0.05).Compared with that in the H/R group and sh-NC group,the expression of lncRNA MEG3 and STAT3 decreased in the sh-MEG3 group,while the expression of miR-202-5p increased(P＜0.05).Compared with that in the sh-MEG3 group and miR-NC group,the expression of lncRNA MEG3 and STAT3 increased in the in-miR-202-5p group,while the expression of miR-202-5p decreased(P＜0.05).Compared with that in the AC 16 group,the apoptosis rate,levels of IL-6,TNF-α,MDA,and ROS,and the expression of STAT3,Bax,cleaved caspase-3,and cleaved caspase-9 increased in the H/R group.In contrast,the cell viability,clone count,levels of superoxide dismutase(SOD)and GSH-Px,and the expression of Bcl-2 decreased(P＜0.05).Compared with that in the H/R group and sh-NC group,the cell viability,clone count,levels of SOD and GSH-Px,and the expression of Bcl-2 increased in the sh-MEG3 group.In contrast,the apoptosis rate,levels of IL-6,TNF-α,MDA,and ROS,and the expression of STAT3,Bax,cleaved caspase-3,and cleaved caspase-9 decreased(P＜0.05).Compared with that in the sh-MEG3 group and miR-NC group,the apoptosis rate,levels of IL-6,TNF-α,MDA,and ROS,and the expression of STAT3,Bax,cleaved caspase-3,and cleaved caspase-9 increased in the in-miR-202-5p group.In contrast,the cell viability,clone count,levels of SOD and GSH-Px,and the expression of Bcl-2 decreased(P＜0.05).There were multiple binding sites between lncRNA MEG3 and miR-202-5p,and between miR-202-5p and STAT3.The luciferase activity was lower in the WT-MEG3+miR-202-5p group than in the WT-MEG3+miR-NC group(P＜0.05).The luciferase activity was lower in the WT-STAT3+miR-202-5p group than in the WT-STAT3+miR-NC group(P＜0.05).Conclusion Knocking down lncRNA MEG3 can downregulate STAT3 by negatively regulating miR-202-5p,inhibiting H/R-induced cardiomyocyte apoptosis.

Objective To investigate the impact of long non-coding RNA maternal expression gene 3(lncRNA MEG3)on hypoxia/reoxygenation(H/R)-induced cardiomyocyte apoptosis by modulating the miR-202-5p/signal transducer and activator of transcription 3(STAT3)axis.Methods An H/R cell model was constructed and randomly separated into four groups:sh-NC(transfected with NC shRNA),sh-MEG3(transfected with lncRNA MEG3 shRNA),miR-NC(transfected with lncRNA MEG3 shRNA and NC miR),and in-miR-202-5p(transfected with lncRNA MEG3 shRNA and miR-202-5p inhibitor).In addition,the H/R group(nontransfected H/R model cells)and the AC 16 group(normal AC 16 cells)were set.Quantitative real-time PCR method was used to analyze the expression of lncRNA MEG3,miR-202-5p,and STAT3 in cells from each group.CCK-8 method was used to analyze cell viability.Flow cytometry was used to analyze apoptosis.Enzyme-linked immunosorbent,colorimetric,and probe assays were applied to detect the levels of inter-leukin-6(IL-6),tumor necrosis factor α(TNF-α),malondialdehyde(MD A),glutathione peroxidases(GSH-Px),and reactive oxygen spe-cies(ROS).Western blotting was carried out to examine the expression of STAT3,Bcl-2-associated X protein(Bax),B-cell lymphoma-2(Bcl-2),caspase-3,and caspase-9.A dual luciferase assay was used to analyze the relationship between lncRNA MEG3 and miR-202-5p,as well as the relationship between miR-202-5p and STAT3.Results Compared with that in the AC 16 group,the expression of lncRNA MEG3 and STAT3 in cells in the H/R group increased,while the expression of miR-202-5p decreased(P＜0.05).Compared with that in the H/R group and sh-NC group,the expression of lncRNA MEG3 and STAT3 decreased in the sh-MEG3 group,while the expression of miR-202-5p increased(P＜0.05).Compared with that in the sh-MEG3 group and miR-NC group,the expression of lncRNA MEG3 and STAT3 increased in the in-miR-202-5p group,while the expression of miR-202-5p decreased(P＜0.05).Compared with that in the AC 16 group,the apoptosis rate,levels of IL-6,TNF-α,MDA,and ROS,and the expression of STAT3,Bax,cleaved caspase-3,and cleaved caspase-9 increased in the H/R group.In contrast,the cell viability,clone count,levels of superoxide dismutase(SOD)and GSH-Px,and the expression of Bcl-2 decreased(P＜0.05).Compared with that in the H/R group and sh-NC group,the cell viability,clone count,levels of SOD and GSH-Px,and the expression of Bcl-2 increased in the sh-MEG3 group.In contrast,the apoptosis rate,levels of IL-6,TNF-α,MDA,and ROS,and the expression of STAT3,Bax,cleaved caspase-3,and cleaved caspase-9 decreased(P＜0.05).Compared with that in the sh-MEG3 group and miR-NC group,the apoptosis rate,levels of IL-6,TNF-α,MDA,and ROS,and the expression of STAT3,Bax,cleaved caspase-3,and cleaved caspase-9 increased in the in-miR-202-5p group.In contrast,the cell viability,clone count,levels of SOD and GSH-Px,and the expression of Bcl-2 decreased(P＜0.05).There were multiple binding sites between lncRNA MEG3 and miR-202-5p,and between miR-202-5p and STAT3.The luciferase activity was lower in the WT-MEG3+miR-202-5p group than in the WT-MEG3+miR-NC group(P＜0.05).The luciferase activity was lower in the WT-STAT3+miR-202-5p group than in the WT-STAT3+miR-NC group(P＜0.05).Conclusion Knocking down lncRNA MEG3 can downregulate STAT3 by negatively regulating miR-202-5p,inhibiting H/R-induced cardiomyocyte apoptosis.

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.