Purpose/Significance To investigate medical students'digital literacy level,and to propose cultivation strategies,so as to provide references for medical universities and colleges to carry out digital literacy education better.Method/Process Through a ques-tionnaire survey,the digital literacy level of 907 medical undergraduates is investigated from 3 dimensions:medical knowledge acquisition and analysis,online learning and security,communication and collaboration.Result/Conclusion The overall level of digital literacy of medical students is high,especially in the use of digital tools for communication and collaboration,but the ability in medical knowledge acquisition and analysis is relatively poor.The digital literacy level of medical students in different grades and majors is different.In order to further improve the digital literacy level of medical students,we should pay attention to creating a personalized digital literacy education mode,constructing a practical digital literacy evaluation system,highlighting the characteristics of digital literacy in the medical field,and strengthening practical education on digital security.

Objective:To explore the effect of narrative nursing on the psychological resilience, self-esteem, and medication compliance of adolescent depression patients.Methods:This study is a quasi-experimental study. From August 2021 to January 2022, 86 adolescent depression patients admitted to the Department of Mental Health at the First Hospital of Shanxi Medical University were selected as the subject by convenience sampling. According to the order of admission time, the patients were divided into a control group and an intervention group, with 43 patients in each group. The control group received routine nursing, while the intervention group received narrative nursing on the basis of routine nursing. We compared the scores of the Self-Rating Depression Scale (SDS), Hamilton Depression Scale (HAMD), Self-Esteem Scale (SES), Connor-Davidson Resilience Scale (CD-RISC), and Morisky Medication Adherence Scale (MMAS-8) before and after intervention between two groups of patients.Results:Before intervention, there was no statistically significant difference in SDS, HAMD, SES, CD-RISC, and MMAS-8 scores between the two groups ( P>0.05). After intervention, the total scores of SDS and HAMD in the intervention group were lower than those before intervention and in the control group, and the toughness, optimism, self-improvement scores and the CD-RISC total score, SES scores and MMAS-8 scores were higher than those before intervention and in the control group, with statistically significant differences ( P<0.05) . Conclusions:Narrative nursing can improve the depressive mood of adolescent depression patients, enhance their psychological resilience, self-esteem, and medication compliance, and can be promoted and applied in clinical practice.

OBJECTIVE To explore the improving effect of luteolin （Lut） on placental dysfunction in rats with gestational diabetes mellitus （GDM） and its potential mechanism based on hedgehog （Hh） signaling pathway. METHODS Twenty female rats were randomly selected as a control group and fed a normal diet. The remaining female rats were fed a high-fat and high-sugar diet for 8 weeks and then caged with male rats. Pregnant rats were administered 35 mg/kg streptozotocin intraperitoneally to establish GDM models. Successfully modeled female rats were randomly allocated to model group， SAG group （Hh signaling pathway activator SAG 50 mg/kg）， Lut low-dose group （Lut 40 mg/kg）， Lut high-dose group （Lut 80 mg/kg）， and Lut high+ITR group （Lut 80 mg/kg+Hh signaling pathway antagonist itraconazole 50 mg/kg）， with 20 rats in each group. Female rats in each drug group were intubated with the corresponding drug solution once a day for 19 days. After the final administration， the serum glucose- fat metabolic parameters （levels of fasting blood glucose and fasting insulin， insulin resistance index）， placental quality， placental permeability [Evan’s blue （EB） content]， and pathological changes in placental tissue were observed. The activities of superoxide dismutase （SOD）， the contents of malondialdehyde （MDA） and reduced glutathione （GSH）， and the protein expressions of Sonic Hh （Shh）， Patched-1 （Ptch1）， Smoothened （Smo） and Gli family zinc finger-1 （Gli1） in placental tissue were detected. HBB_ RESULTS Compared with the control group， rats in the model group showed narrow capillary lumens， perivascular fibrosis in placental tissue， and a significant increase in serum glucose-fat metabolic parameters， placental quality， contents of EB and MDA， while there was a significant decrease in SOD activity， GSH content， and protein expressions of Shh， Ptch1， Smo and Gli1 （P＜0.05）. Compared with the model group， rats in the SAG group， Lut low-dose and high-dose groups had widened capillary lumens， a significant decrease in perivascular fibrosis in placental tissue， serum glucose-fat metabolic parameters， placental qualities， EB and MDA contents， while there was a significant increase in SOD activities， GSH contents， and protein expressions of Shh， Ptch1， Smo and Gli1 （P＜0.05）， with the high-dose group showing no significant difference compared to the SAG group （P＞0.05）. The Hh signaling pathway antagonist itraconazole could significantly reverse the improving effects of Lut on the above indicators （P＜0.05）. CONCLUSIONS Lut can improve glucose metabolism parameters of GDM rats， reduce placental permeability， alleviate pathological damage to placental tissue， and reduce oxidative stress. These effects may be related to the activation of the Hh signaling pathway.

Acid-base dissociation constant (pK_a) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pK_a prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pK_a (multi-fidelity modeling with subgraph pooling for pK_a prediction), a novel pK_a prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pK_a prediction. To overcome the scarcity of accurate pK_a data, low-fidelity data (computational pK_a) was used to fit the high-fidelity data (experimental pK_a) through transfer learning. The final MF-SuP-pK_a model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pK_a achieves superior performances to the state-of-the-art pK_a prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pK_a achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.