The Harvest of a Lateral Segment of Costal Cartilage for Treating Nasal Deformities.

BACKGROUND: Although costal cartilage has many uses and is a reliable source of cartilage for rhinoplasty procedures, donor-site complications may arise with conventional harvesting techniques. The present report reports a novel technique of harvesting costal cartilage using a specially designed scalpel and studies the use of the harvested cartilage in the reconstruction of secondary nasal deformities in patients with cleft lips. METHODS: Ten patients (7 females and 3 males) with nasal deformities secondary to cleft lip underwent rhinoplasty using this new technique at the Department of Oral and Maxillofacial Surgery, Second Hospital of Hebei Medical University, China, between May 2011 and December 2013. Clinical outcomes were evaluated with a follow-up period of 6 to 30 months. RESULTS: The new technique successfully corrected primary nasal deformities, including flat nasal tip, short columella, flaring alae, and asymmetrical nostrils. Surgeons and patients assessed the outcome to be either good or satisfactory. Patients experienced transient discomfort at the wound site but there were no major complications (such as wound infection, dehiscence, exposure, graft extrusion, and pulmonary involvement). CONCLUSIONS: The novel technique can harvest a lateral segment of costal cartilage for use in the reconstruction of nasal deformities secondary to cleft lip in a one-stage procedure, with minimal donor-site morbidity.

A novel genomic-clinicopathologic nomogram to improve prognosis prediction of hepatocellular carcinoma.

There is a lack of precise and clinical accessible model to predict the prognosis of hepatocellular carcinoma (HCC) in clinic practice currently. Here, an inclusive nomogram was developed by integrating genomic markers and clinicopathologic factors for predicting the outcome of patients with HCC. A total of 365 samples of HCC were obtained from the Cancer Genome Atlas (TCGA) database. The LASSO analysis was carried out to identify HCC-related mRNAs, and the multivariate Cox regression analysis was used to construct a genomic-clinicopathologic nomogram. As results, 9 mRNAs were finally identified as prognostic indicators, including RGCC, CDH15, XRN2, RAB3IL1, THEM4, PIF1, MANBA, FKTN and GABARAPL1, and used to establish a 9-mRNA classifier. Additionally, an inclusive nomogram was built up by combining the 9-mRNA classifier (P < 0.001) and clinicopathologic factors including age (P = 0.006) and metastasis (P < 0.001) to predict the mortality of HCC patients. Time-dependent receiver operating characteristic, index of concordance and calibration analyses indicated favorable accuracy of the model. Decision curve analysis suggested that appropriate intervention according to the established nomogram will bring net benefit when threshold probability was above 25%. The genomic-clinicopathologic model could be a reliable tool for predicting the mortality, helping determining the individualized treatment and probably improving HCC survival.