Background: Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. Methods: 1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, P53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, PR, CD3, H3K27M, TS, and 1p/19q status. Results: The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0–14 years old (pediatric group), 15–47 years old (youth group), 48–63 years old (middle-aged group), and ≥ 64 years old (elderly group).The proportions of glioblastoma and large tumor size (4–6 cm) increased with age (p = 0.000, p = 0.018, respectively). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p = 0.000, p = 0.017, respectively). Conclusions: Appropriate classifications of the age group for risk stratification are 0–14 years old (pediatric group), 15–47 years old (young group), 48–63 years old (middle age group) and ≥ 64 years old (elderly group). This age group classification is effective in evaluating the risk of glioblastoma in glioma patients.

Osteoporosis (OP) is a highly prevalent orthopedic condition in postmenopausal women and the elderly. Currently, OP treatments mainly include bisphosphonates, receptor activator of nuclear factor kappa‐B ligand (RANKL) antibody therapy, selective estrogen receptor modulators, teriparatide (PTH1‐34), and menopausal hormone therapy. However, increasing evidence has indicated these treatments may exert serious side effects. In recent years, Traditional Chinese Medicine (TCM) has become popular for treating orthopedic disorders. Erxian Decoction (EXD) is widely used for the clinical treatment of OP, but its underlying molecular mechanisms are unclear thanks to its multiple components and multiple target features. In this research, we designed a network pharmacology method, which used a novel node importance calculation model to identify critical response networks (CRNs) and effective proteins. Based on these proteins, a target coverage contribution (TCC) model was designed to infer a core active component group (CACG). This approach decoded the mechanisms underpinning EXD’s role in OP therapy. Our data indicated that the drug response network mediated by the CACG effectively retained information of the component‐target (C‐T) network of pathogenic genes. Functional pathway enrichment analysis showed that EXD exerted therapeutic effects toward OP by targeting PI3K‐Akt signaling (hsa04151), calcium signaling (hsa04020), apoptosis (hsa04210), estrogen signaling (hsa04915), and osteoclast differentiation (hsa04380) via JNK, AKT, and ERK. Our method furnishes a feasible methodological strategy for formula optimization and mechanism analysis and also supplies a reference scheme for the secondary development of the TCM formula.

Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.

// <![CDATA[ $('.header-date').hide();$('#titleAuthors').hide();$('#abstractHeader').hide(); // ]]> Yi Liu1, *, Chao-hu Wang^1,*, Dan-ling Li2, *, Shi-chao Zhang1, Yu-ping Peng1, Jun-xiang Peng1, Ye Song1, Song-tao Qi1, Jun Pan1 1Department of Neurosurgery, Nanfang Hospital of Southern Medical University, Guangzhou, China 2Department of Biometric, college of Public Health of Southern Medical University, Guangzhou, Guangdong, China *These authors contributed equally to this work Correspondence to: Pan Jun, email: panjundu@hotmail.com Song-tao Qi, email: qisongtaosjwk@163.com Keywords: craniopharyngioma, metaplasia, Rathke’s cleft cyst, SE, TREM-1 Received: March 10, 2016 Accepted: June 30, 2016 Published: July 8, 2016

Objective: Understanding the differences of suprasellar papillary and adamantinomatous craniopharyngiomas (PCPs/ACPs) is pivotal for target therapy, surgical strategy or postoperative management. Here, the clinical features, surgical nuances and postoperative hypothalamic outcomes of PCPs were systematically recapitulated. Methods: 24 PCPs and 52 ACPs underwent initial surgery were retrospectively reviewed. Clinical data, quantified third ventricle (3rd V) occupation and optic chiasm distortion were compared, as well as intra-operative findings, operating notes and prognosis. Moreover, analysis of tumor/3rd V relationship and hypothalamic outcomes were also performed. Results: Tumors were more likely to occupies the 3rd V cavity in PCPs. Chiasm distortion of "compressed forward" was the most common pattern (45.8 %) in PCPs, whereas "stretched forward" pattern accounted the highest (42.5 %) in ACPs. Besides, round-shaped with less calcification, duct-like recess, solid consistency, rare subdiaphragmatic invasion, visible lower stalk and improved postoperative visual outcome were more frequently observed in PCPs. The basal membranes of the tumor epithelium and the reactive gliosis were separated by a layer of collagen fibers in most PCPs, which differs from ACPs in the morphological examination of tumor/3rd V floor interface. In daytime sleepiness and memory difficulty, the PCPs showed significantly better outcomes than the ACPs groups, and PCPs suffered less postoperative weight gain (p < 0.05) than ACPs among adult-onset cases. Conclusion: PCPs are different from ACPs regards the clinical features, operative techniques and outcomes. If necessary, PCPs are suggested more amenable to total removal since its less invasiveness to the 3rd V floor and better hypothalamic outcomes.

JOURNAL/nrgr/04.03/01300535-202410000-00026/figure1/v/2026-05-04T143929Z/r/image-tiff Previous studies have shown that growth hormone can regulate hypothalamic energy metabolism, stress, and hormone release. Therefore, growth hormone has great potential for treating hypothalamic injury. In this study, we established a specific hypothalamic axon injury model by inducing hypothalamic pituitary stalk electric lesions in male mice. We then treated mice by intraperitoneal administration of growth hormone. Our results showed that growth hormone increased the expression of insulin-like growth factor 1 and its receptors, and promoted the survival of hypothalamic neurons, axonal regeneration, and vascular reconstruction from the median eminence through the posterior pituitary. Altogether, this alleviated hypothalamic injury-caused central diabetes insipidus and anxiety. These results suggest that growth hormone can promote axonal reconstruction after hypothalamic injury by regulating the growth hormone-insulin-like growth factor 1 axis.