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Abstract. The accurate prediction of overall survival (OS) is important in clinical decision-making for breast cancer treatment. We developed a model to predict the OS of non-metastatic breast cancer patients in China. This multicenter study included 1844 non-metastatic breast cancer patients who underwent breast cancer surgery between January 2009 and December 2011 in 3 tertiary teaching hospitals in China. Data were collected retrospectively from the database of each hospital. We used univariate and multivariate Cox proportional hazard regression analyses to screen for predictors. A nomogram was developed in the training cohort (from Sun Yat-sen Memorial Hospital [SYSMH]), externally validated in 2 validation cohorts (from the First People's Hospital of Foshan [FPHF] and Sun Yat-sen University Cancer Center (SYUCC)), and compared with CancerMath, a mathematical-based model. We used Receiver Operating Characteristic curves and calibration plots to assess the models. At median follow-ups of 65.9, 68.6, and 66.2 months, the 5-year OS rates were 93.0%, 86.7%, and 91.0% in the SYSMH, FPHF, and SYUCC cohorts, respectively. We identified age, T stage, lymph node status, estrogen receptor, and human epidermal growth factor receptor 2 statuses as significant prognostic factors. A nomogram was developed and externally validated in the FPHF (area under the curve = 0.74) and SYUCC (area under the curve = 0.77) cohorts. Calibration plots showed that the predicted OS was consistent with the actual OS. The nomogram outperformed CancerMath in our study population. In summary, we developed a nomogram to predict survival among non-metastatic breast cancer patientsin China. This nomogram is superior to the CancerMath model in Chinese populations.

Background: Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes. Methods: One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes. Results: The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into ire, Liver ire laming upward, Stomach disharmony, Hyperactivity of ire due to yin defficiency, Heart--kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified. Conclusion: Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical feature co-occurrence patterns, four mutual-exclusion patterns, and one pattern with a single clinical feature variable. [ABSTRACT FROM AUTHOR]

Background BRCA1 plays an essential role in maintaining genome stability. Inherited BRCA1 germline mutation (BRCA1+) is a determined genetic predisposition leading to high risk of breast cancer. While BRCA1+ induces breast cancer by causing genome instability, most of the knowledge is known about somatic genome instability in breast cancer cells but not germline genome instability. Methods Using the exome-sequencing method, we analyzed the genomes of blood cells in a typical BRCA1+ breast cancer family with an exon 13-duplicated founder mutation, including six breast cancer-affected and two breast cancer unaffected members. Results We identified 23 deleterious mutations in the breast cancer-affected family members, which are absent in the unaffected members. Multiple mutations damaged functionally important and breast cancer-related genes, including transcriptional factor BPTF and FOXP1, ubiquitin ligase CUL4B, phosphorylase kinase PHKG2, and nuclear receptor activator SRA1. Analysis of the mutations between the mothers and daughters shows that most mutations were germline mutation inherited from the ancestor(s) while only a few were somatic mutation generated de novo. Conclusion Our study indicates that BRCA1+ can cause genome instability with both germline and somatic mutations in non-breast cells. [ABSTRACT FROM AUTHOR]

Aim: To investigate the outcome of living donor liver transplantation (LDLT) recipients transplanted with small-for-size grafts (SFSGs)., Methods: Between November 2001 and December 2010, 196 patients underwent LDLT with right lobe liver grafts at our center. Recipients were divided into 2 treatment groups: group A with an actuarial graft-to-recipient weight ratio (aGRWR) < 0.8% (n = 45) and group B with an aGRWR ≥ 0.8% (n = 151). We evaluated serum liver function markers within 4 wk after transplantation. We also retrospectively evaluated the outcomes of these patients for potential effects related to the recipients, the donors and the transplantation procedures based upon a review of their medical records., Results: Small-for-size syndrome (SFSS) developed in 7 of 45 patients (15.56%) in group A and 9 of 151 patients (5.96%) in group B (P = 0.080). The levels of alanine aminotransferase and aspartate aminotransferase in group A were higher than those in group B during early period after transplantation, albeit not significantly. The cumulative 1-, 3- and 5-year liver graft survival rates were 82.22%, 71.11% and 71.11% for group A and 81.46%, 76.82%, and 75.50% for group B patients, respectively (P = 0.623). However, univariate analysis of risk factors associated with graft survival in group A demonstrated that the occurrence of SFSS after LDLT was the only significant risk factor affecting graft survival (P < 0.001). Furthermore, multivariate analysis of our data did not identify any additional significant risk factors accounting for poor graft survival., Conclusion: Our study suggests that LDLT recipients with an aGRWR < 0.8% may have liver graft outcomes comparable to those who received larger size grafts. Further studies are required to ascertain the safety of using SFSGs.