BackgroundNon-suicidal self-injury （NSSI） has become a public health concern of wide interest. Adolescents with depressive disorder are at high risk of NSSI behaviors. Previous studies have mostly focused on analyzing factors associated with NSSI behaviors， while research on developing visual models that are intuitive， quantifiable， and suitable for rapid clinical assessment is relatively limited. ObjectiveTo investigate the risk factors for NSSI behaviors in adolescents with depressive disorder， and to construct a nomogram prediction model for the risk of NSSI behaviors occurrence， so as to provide a quantitative tool for clinical rapid assessment of the occurrence risk of NSSI behaviors in this population. MethodsA cross-sectional study was conducted. A total of 448 adolescent patients diagnosed with depressive disorder who attended the Mental Health Department of The Second Hospital， Lanzhou University from February to December 2025 and met the diagnostic criteria for depressive disorder as specified in the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） were enrolled. All participants were randomly divided into the modeling group （n=313） and the validation group （n=135） at a ratio of 7∶3. Assessments were performed using the Self-rating Depression Scale （SDS）， the Self-rating Anxiety Scale （SAS）， the Barratt Impulsiveness Scale-11 （BIS-11）， the Childhood Trauma Questionnaire-short form （CTQ-SF）， and the Pittsburgh Sleep Quality Index （PSQI）. Binary Logistic regression analysis was applied to screen independent predictors of NSSI behaviors in the modeling group， and a nomogram was constructed accordingly. The receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis （DCA） were used to evaluate the discrimination， calibration and clinical utility of the model， respectively. ResultsBinary Logistic regression analysis revealed that younger age， female sex， higher SDS score， higher BIS-11 score， higher CTQ-SF score， and higher PSQI score were risk factors for NSSI behaviors in adolescent patients with depressive disorder （OR=0.816， 2.671， 1.131， 1.030， 1.064， 1.102， P<0.05 or 0.01）. The Hosmer-Lemeshow test of the nomogram prediction model constructed based on these six indicators showed a good fit （χ²=15.144， P=0.056）. Model validation showed that the AUC was 0.895 for the modeling group and 0.891 for the validation group. The calibration curves suggested that the mean absolute errors between the actual values and the predicted values were 0.043 and 0.023， respectively. Decision curve analysis indicated that the model provided a clear net clinical benefit when the risk threshold was greater than 0.11. ConclusionThe nomogram model based on six indicators （age， sex， depressive symptom severity， impulsivity， childhood trauma， and sleep quality） for predicting NSSI risk in adolescent patients with depressive disorder exhibits good discrimination， calibration， and clinical application value. ［Funded by National Natural Science Foundation of China （number， 82560281）； Gansu Provincial Science and Technology Program （number， 25JRRA618）； Gansu Provincial Health Industry Research Project （number， GSWSKY2025-13）； The College Students' Innovation and Entrepreneurship Training Program of the Second Clinical Medical College of Lanzhou University （number， 20250050060）］

Gastric cancer is one of the major diseases threatening human health, with a high incidence and a low early diagnostic rate. There are many bottlenecks encountered during its treatment. Consequently, improving the early diagnostic rate and exploring new therapeutic targets are currently urgent challenges that need to be addressed. Telomerase is undetectable in normal tissues, but it exhibits high specificity and sensitivity in most cancers and has a definite correlation with prognosis. It may serve as a serum tumor marker and prognostic indicator. Human telomerase reverse transcriptase (hTERT) gene polymorphism can regulate the susceptibility of people to gastric cancer, and affect the occurrence, development, proliferation and apoptosis of gastric cancer through its target gene. Substances such as resistin, visfatin, G-quadruplex and methylenedioxyaniline can affect the occurrence and development of gastric cancer by regulating telomerase expression. The mechanism by which hTERT regulates tumor invasion and metastasis is currently unclear, so elucidating its mechanism is of great significance.This paper will review the research progress of this mechanism in recent years.