At the MTD of 3.6 mg/kg every 3 weeks, T-DM1 was associated with mild, reversible toxicity and substantial clinical activity in a heavily pretreated population. Phase II and III trials in patients with advanced HER2-positive breast cancer are under way.

Onartuzumab plus erlotinib was associated with improved PFS and OS in the MET-positive population. These results combined with the worse outcomes observed in MET-negative patients treated with onartuzumab highlight the importance of diagnostic testing in drug development.

Bone marrow mesenchymal lineage cells are a heterogeneous cell population involved in bone homeostasis and diseases such as osteoporosis. While it is long postulated that they originate from mesenchymal stem cells, the true identity of progenitors and their in vivo bifurcated differentiation routes into osteoblasts and adipocytes remain poorly understood. Here, by employing large scale single cell transcriptome analysis, we computationally defined mesenchymal progenitors at different stages and delineated their bi-lineage differentiation paths in young, adult and aging mice. One identified subpopulation is a unique cell type that expresses adipocyte markers but contains no lipid droplets. As non-proliferative precursors for adipocytes, they exist abundantly as pericytes and stromal cells that form a ubiquitous 3D network inside the marrow cavity. Functionally they play critical roles in maintaining marrow vasculature and suppressing bone formation. Therefore, we name them marrow adipogenic lineage precursors (MALPs) and conclude that they are a newly identified component of marrow adipose tissue.

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Purpose The phase III OAM4971g study (METLung) examined the efficacy and safety of onartuzumab plus erlotinib in patients with locally advanced or metastatic non-small-cell lung cancer selected by MET immunohistochemistry whose disease had progressed after treatment with a platinum-based chemotherapy regimen. Patients and Methods Patients were randomly assigned at a one-to-one ratio to receive onartuzumab (15 mg/kg intravenously on day 1 of each 21-day cycle) plus daily oral erlotinib 150 mg or intravenous placebo plus daily oral erlotinib 150 mg. The primary end point was overall survival (OS) in the intent-to-treat population. Secondary end points included median progression-free survival, overall response rate, biomarker analysis, and safety. Results A total of 499 patients were enrolled (onartuzumab, n = 250; placebo, n = 249). Median OS was 6.8 versus 9.1 months for onartuzumab versus placebo (stratified hazard ratio [HR], 1.27; 95% CI, 0.98 to 1.65; P = .067), with a greater number of deaths in the onartuzumab arm (130 [52%] v 114 [46%]). Median progression-free survival was 2.7 versus 2.6 months (stratified HR, 0.99; 95% CI, 0.81 to 1.20; P = .92), and overall response rate was 8.4% and 9.6% for onartuzumab versus placebo, respectively. Exploratory analyses using MET fluorescence in situ hybridization status and gene expression showed no benefit for onartuzumab; patients with EGFR mutations showed a trend toward shorter OS with onartuzumab treatment (HR, 4.68; 95% CI, 0.97 to 22.63). Grade 3 to 5 adverse events were reported by 56.0% and 51.2% of patients, with serious AEs in 33.9% and 30.7%, for experimental versus control arms, respectively. Conclusion Onartuzumab plus erlotinib did not improve clinical outcomes, with shorter OS in the onartuzumab arm, compared with erlotinib in patients with MET-positive non-small-cell lung cancer.

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ObjectivesDiabetic cardiomyopathy (DCM), characterized by myocardial structural and functional changes, is an independent cardiomyopathy that develops in diabetic individuals. The present study was sought to investigate the effect of curcumin on modulating DCM and the mechanisms involved.MethodsAn experimental diabetic rat model was induced by low dose of streptozoticin(STZ) combined with high energy intake on rats. Curcumin was orally administrated at a dose of 100 or 200 mg·kg−1·d−1, respectively. Cardiac function was evaluated by serial echocardiography. Myocardial ultrastructure, fibrosis area and apoptosis were assessed by histopathologic analyses. Metabolic profiles, myocardial enzymes and oxidative stress were examined by biochemical tests. Inflammatory factors were detected by ELISA, and interrelated proteins were measured by western blot.ResultsRats with DCM showed declined systolic myocardial performance associated with myocardial hypertrophy and fibrosis, which were accompanied with metabolism abnormalities, aberrant myocardial enzymes, increased AGEs (advanced glycation end products) accumulation and RAGE (receptor for AGEs) expression, elevated markers of oxidative stress (MDA, SOD, the ratio of NADP+/NADPH, Rac1 activity, NADPH oxidase subunits expression of gp91phox and p47phox ), raised inflammatory factor (TNF-α and IL-1β), enhanced apoptotic cell death (ratio of bax/bcl-2, caspase-3 activity and TUNEL), diminished Akt and GSK-3β phosphorylation. Remarkably, curcumin attenuated myocardial dysfunction, cardiac fibrosis, AGEs accumulation, oxidative stress, inflammation and apoptosis in the heart of diabetic rats. The inhibited phosphorylation of Akt and GSK-3β was also restored by curcumin treatment.ConclusionsTaken together, these results suggest that curcumin may have great therapeutic potential in the treatment of DCM, and perhaps other cardiovascular disorders, by attenuating fibrosis, oxidative stress, inflammation and cell death. Furthermore, Akt/GSK-3β signaling pathway may be involved in mediating these effects.

cardiovascular disease (cVD) is the leading cause of death worldwide and a major public health concern. CVD prediction is one of the most effective measures for CVD control. In this study, 29930 subjects with high-risk of CVD were selected from 101056 people in 2014, regular follow-up was conducted using electronic health record system. Logistic regression analysis showed that nearly 30 indicators were related to CVD, including male, old age, family income, smoking, drinking, obesity, excessive waist circumference, abnormal cholesterol, abnormal low-density lipoprotein, abnormal fasting blood glucose and else. Several methods were used to build prediction model including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees, Ada Boost and Random forest. We used the multivariate regression model as a benchmark for performance evaluation (Area under the curve, AUC = 0.7143). The results showed that the Random Forest was superior to other methods with an AUC of 0.787 and achieved a significant improvement over the benchmark. We provided a CVD prediction model for 3-year risk assessment of CVD. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of cVD prediction and treatment in china. Cardiovascular disease (CVD) is a series of diseases involving the circulatory system, including angina pectoris, myocardial infarction, coronary heart disease, heart failure, arrhythmia and else, which is generally related to atherosclerosis. With the social economy development, the population aging and the urbanization acceleration in China, some changes have taken place in national lifestyles, which leading to a rise of CVD prevalence. In 2016, there were more than 290 million cases of CVD in China, and 4.344 million deaths from it, including 2.098 million deaths from stroke and 1.736 million deaths from coronary heart disease, which bringing heavy social and economic burden 1. CVD is a disease that can be prevented and controlled, and early intervention can effectively control its progress 2. In recent years, many achievements have been made in the study of CVD risk prediction model, but the effect of epidemiological risk factors and biomarkers may be different in different populations, the CVD model has certain population specificity. In addition, there has been no study on CVD risk prediction model based on large cohort population in eastern China. At the same time, a large number of the existing CVD prediction models use multivariable regression method to build prediction models in a linear fashion, but it generally exhibit modest predictive performance, especially for certain sub-populations 3,4. Machine learning (ML) such as random forest (RF) can improve the performance of risk predictions by exploiting large data repositories to identify novel risk predictors and more complex interactions between them 3. In this study, we conducted a CVD prediction model research based on a specific c...