http://ibi.zju.edu.cn/software/qtlnetwork.

Cortical representations of visual information are modified by an animal's visual experience. To investigate the mechanisms in mice, we replaced the coding part of the neural activity-regulated immediate early gene Arc with a GFP gene and repeatedly monitored visual experience-induced GFP expression in adult primary visual cortex by in vivo two-photon microscopy. In Arc-positive GFP heterozygous mice, the pattern of GFP-positive cells exhibited orientation specificity. Daily presentations of the same stimulus led to the reactivation of a progressively smaller population with greater reactivation reliability. This adaptation process was not affected by the lack of Arc in GFP homozygous mice. However, the number of GFP-positive cells with low orientation specificity was greater, and the average spike tuning curve was broader in the adult homozygous compared to heterozygous or wild-type mice. These results suggest a physiological function of Arc in enhancing the overall orientation specificity of visual cortical neurons during the post-eye-opening life of an animal.

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LPV/r exposure during late pregnancy was lower compared to postpartum and compared to non-pregnant historical controls. Small amounts of lopinavir cross the placenta. The pharmacokinetics, safety, and effectiveness of increased LPV/r dosing during the third trimester of pregnancy should be investigated.

Pancreatic cancer is characterized by a desmoplastic reaction that creates a dense fibroinflammatory microenvironment, promoting hypoxia and limiting cancer drug delivery due to decreased blood perfusion. Here we describe a novel tumor-stroma interaction that may help explain the prevalence of desmoplasia in this cancer. Specifically, we found that activation of HIF-1α by tumor hypoxia strongly activates secretion of the sonic hedgehog ligand SHH by cancer cells which in turn causes stromal fibroblasts to increase fibrous tissue deposition. In support of this finding, elevated levels of HIF-1α and SHH in pancreatic tumors were determined to be markers of decreased patient survival. Repeated cycles of hypoxia and desmoplasia amplified each other in a feed forward loop that made tumors more aggressive and resistant to therapy. This loop could be blocked by HIF-1α inhibition, which was sufficient to block SHH production and HH signaling. Taken together, our findings suggest that increased HIF-1α produced by hypoxic tumors triggers the desmoplasic reaction in pancreatic cancer, which is then amplified by a feed forward loop involving cycles of decreased blood flow and increased hypoxia. our findings strengthen the rationale for testing HIF inhibitors may therefore represent a novel therapeutic option for pancreatic cancer.

Importance Current prehospital traumatic brain injury guidelines utilize a systolic blood pressure threshold of <90mmHg for treating hypotension (age≥10) based on studies showing higher mortality when blood pressure drops below this level. However, the guidelines also acknowledge the weakness of the supporting evidence. Objective In a statewide, multisystem study of traumatic brain injury, to evaluate whether any statistically supportable systolic pressure-versus-mortality threshold emerges from the data, a priori, without assuming that a cut-point exists. Design Observational evaluation of a large prehospital database established as a part of the Excellence in Prehospital Injury Care (EPIC) Traumatic Brain Injury Study (NIH/NINDS-1R01NS071049). The generalized additive model and logistic regression were utilized to determine the relationship between systolic pressure and probability of death, adjusting for significant/important confounders. Setting The pre-implementation cohort (1/1/2007–3/31/2014) of the EPIC Study. Participants Patients (age≥10) with moderate/severe traumatic brain injury (Barell Matrix-Type 1 and/or International Classification of Disease-9 head region severity ≥3 and/or Abbreviated Injury Scale head-region severity ≥3) and lowest prehospital systolic pressure between 40 and 119mmHg were included. Main Outcome Measure The main outcome measure was in-hospital mortality. Results Among the 3,844 included cases, the model revealed a monotonically-decreasing relationship between systolic pressure and adjusted probability of death across the entire range (40–119mmHg). Each ten-point increase of systolic pressure was associated with a decrease in the adjusted odds of death of 18.8% (aOR=0.812; 95% confidence interval: 0.748–0.883). Thus, the adjusted odds of mortality increase as much for a drop from, say, 110 to 100mmHg as for 90 to 80mmHg, and so on, throughout the range. Conclusions and Relevance We found a linear relationship between lowest prehospital systolic blood pressure and severity-adjusted probability of mortality across an exceptionally wide range. There is no identifiable threshold or inflection point between 40 and 119mmHg. Thus, in traumatic brain injury, the concept that 90mmHg represents a unique or important physiological “cut-point” may be wrong. Furthermore, clinically-meaningful “hypotension” may not be as low as current guidelines suggest. Randomized trials evaluating treatment levels significantly above 90mmHg are needed.

BackgroundThe study of circulating biomarkers and their association with disease outcomes has become progressively complex due to advances in the measurement of these biomarkers through multiplex technologies. The Least Absolute Shrinkage and Selection Operator (LASSO) is a data analysis method that may be utilized for biomarker selection in these high dimensional data. However, it is unclear which LASSO-type method is preferable when considering data scenarios that may be present in serum biomarker research, such as high correlation between biomarkers, weak associations with the outcome, and sparse number of true signals. The goal of this study was to compare the LASSO to five LASSO-type methods given these scenarios.MethodsA simulation study was performed to compare the LASSO, Adaptive LASSO, Elastic Net, Iterated LASSO, Bootstrap-Enhanced LASSO, and Weighted Fusion for the binary logistic regression model. The simulation study was designed to reflect the data structure of the population-based Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD), specifically the sample size (N = 1000 for total population, 500 for sub-analyses), correlation of biomarkers (0.20, 0.50, 0.80), prevalence of overweight (40%) and obese (12%) outcomes, and the association of outcomes with standardized serum biomarker concentrations (log-odds ratio = 0.05–1.75). Each LASSO-type method was then applied to the TESAOD data of 306 overweight, 66 obese, and 463 normal-weight subjects with a panel of 86 serum biomarkers.ResultsBased on the simulation study, no method had an overall superior performance. The Weighted Fusion correctly identified more true signals, but incorrectly included more noise variables. The LASSO and Elastic Net correctly identified many true signals and excluded more noise variables. In the application study, biomarkers of overweight and obesity selected by all methods were Adiponectin, Apolipoprotein H, Calcitonin, CD14, Complement 3, C-reactive protein, Ferritin, Growth Hormone, Immunoglobulin M, Interleukin-18, Leptin, Monocyte Chemotactic Protein-1, Myoglobin, Sex Hormone Binding Globulin, Surfactant Protein D, and YKL-40.ConclusionsFor the data scenarios examined, choice of optimal LASSO-type method was data structure dependent and should be guided by the research objective. The LASSO-type methods identified biomarkers that have known associations with obesity and obesity related conditions.

Early diagnosis of disease has potential to reduce morbidity and mortality. Biomarkers may be useful for detecting disease at early stages before it becomes clinically apparent. Prostate-specific antigen (PSA) is one such marker for prostate cancer. This article is concerned with modeling receiver operating characteristic (ROC) curves associated with biomarkers at various times prior to the time at which the disease is detected clinically, by two methods. The first models the biomarkers statistically using mixed-effects regression models, and uses parameter estimates from these models to estimate the time-specific ROC curves. The second directly models the ROC curves as a function of time prior to diagnosis and may be implemented using software packages with binary regression or generalized linear model routines. The approaches are applied to data from 71 prostate cancer cases and 71 controls who participated in a lung cancer prevention trial. Two biomarkers for prostate cancer were considered: total serum PSA and the ratio of free to total PSA. Not surprisingly, both markers performed better as the interval between PSA measurement and clinical diagnosis decreased. Although the two markers performed similarly eight years prior to diagnosis, it appears that total PSA performed better than the ratio measure at times closer to diagnosis. The area under the ROC curve was consistently greater for total PSA than for the ratio four and two years prior to diagnosis and at the time of diagnosis.