The effect of triptorelin and leuprolide on the level of sex hormones in girls with central precocious puberty and its clinical efficacy analysis

Efficacy and safety of leuprorelin 3-month depot (11.25 mg) for idiopathic central precocious puberty treatment of Chinese girls: a single-center retrospective study

OBJECTIVES

This study aimed to evaluate the efficacy and safety of 3-month leuprorelin acetate (3-month LA, 11.25 mg) for the treatment of idiopathic central precocious puberty (ICPP) in Chinese girls.

METHODS

We conducted a single-center retrospective study in China on 28 girls with ICPP who received at least one year of 3-month LA treatment. Data from anthropometry, biochemistry, bone age (BA), and pelvic ultrasonography were assessed before and every 6 months during medication.

RESULTS

At CPP diagnosis, the mean chronological age (CA) was 7.8±0.8 years, with bone age advancement (BA-CA) of 1.5±0.8 years. After treatment initiation, growth velocity decreased significantly from 8.5±1.6 cm/year to 5.8±1.1 cm/year at month 12 (p<0.001). GnRH-stimulated peak LH ≤3IU/L, the primary efficacy criterion, was observed in 27 out of 28 (96.4 %) children at month 3. Basal estradiol <20 pg/mL was achieved by all 28 girls (100 %) at month 6 and remained stable at month 12. Basal follicle-stimulating hormone (FSH) decreased from 4.1±3.5 to 1.7±0.9 (p<0.001), and basal LH was also significantly reduced from 3.3±6.5 to 0.7±0.8 (p=0.035) at month 12. The mean predicted adult height (PAH) at treatment initiation was 152.7±5.8 cm, it increased significantly to 157.5±5.5 cm (p=0.007) after one-year treatment. Pubertal development was slowed in most patients, and in some cases, it was even reversed. Only one patient (3.6 %) reported local intolerance.

CONCLUSIONS

Three-month leuprorelin acetate is a safe and effective treatment for suppressing the pituitary-gonadal axis and restoring impaired adult height in Chinese girls.

A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism

With the development of research on the complex aetiology of many diseases, computational drug repositioning methodology has proven to be a shortcut to costly and inefficient traditional methods. Therefore, developing more promising computational methods is indispensable for finding new candidate diseases to treat with existing drugs. In this paper, a model integrating a new variant of message passing neural network and a novel-gated fusion mechanism called GLGMPNN is proposed for drug-disease association prediction. First, a light-gated message passing neural network (LGMPNN), including message passing, aggregation and updating, is proposed to separately extract multiple pieces of information from the similarity networks and the association network. Then, a gated fusion mechanism consisting of a forget gate and an output gate is applied to integrate the multiple pieces of information to extent. The forget gate calculated by the multiple embeddings is built to integrate the association information into the similarity information. Furthermore, the final node representations are controlled by the output gate, which fuses the topology information of the networks and the initial similarity information. Finally, a bilinear decoder is adopted to reconstruct an adjacency matrix for drug-disease associations. Evaluated by 10-fold cross-validations, GLGMPNN achieves excellent performance compared with the current models. The following studies show that our model can effectively discover novel drug-disease associations.