Endocannabinoids (eCBs) are endogenous lipid mediators involved in a variety of physiological, pharmacological, and pathological processes. While activation of the eCB system primarily induces inhibitory effects on both GABAergic and glutamatergic synaptic transmission and plasticity through acting on presynaptically expressed CB1 receptors in the brain, accumulated information suggests that eCB signaling is also capable of facilitating or potentiating excitatory synaptic transmission in the hippocampus. Recent studies show that a long-lasting potentiation of excitatory synaptic transmission at Schaffer collateral (SC)-CA1 synapses is induced by spatiotemporally primed inputs, accompanying with a long-term depression of inhibitory synaptic transmission (I-LTD) in hippocampal CA1 pyramidal neurons. This input timing–dependent long-lasting synaptic potentiation at SC-CA1 synapses is mediated by 2-arachidonoylglycerol (2-AG) signaling triggered by activation of postsynaptic N-methyl-d-aspartate receptors, group I metabotropic glutamate receptors (mGluRs), and a concurrent rise in intracellular Ca²⁺. Emerging evidence now also indicates that 2-AG is an important signaling mediator keeping brain homeostasis by exerting its anti-inflammatory and neuroprotective effects in response to harmful insults through CB1/2 receptor-dependent and/or -independent mechanisms. Activation of the nuclear receptor protein peroxisome proliferator-activated receptor-γ apparently is one of the important mechanisms in resolving neuroinflammation and protecting neurons produced by 2-AG signaling. Thus, the information summarized in this review suggests that the role of eCB signaling in maintaining integrity of brain function is greater than what we thought previously.

Objective: The World Cancer Research Fund (WCRF) and the American Institute for Cancer Research (AICR) publish recommendations for cancer prevention. The present study aimed to estimate the association between adherence to these cancer-specific prevention recommendations and subsequent development of cancer in a prospective cohort.Design: A composite adherence score was constructed based on questionnaire data to reflect overall adherence to WCRF/AICR lifestyle-related recommendations on body fatness, physical activity, diet and alcoholic drinks. Multivariable Cox proportional hazard regression models were used to assess the association (hazard ratio; 95 % CI) between the adherence score and risk of developing cancer.Setting: Alberta’s Tomorrow Project, a prospective cohort study.Participants: Men and women (n25 100, mean age at enrolment 50·5 years) recruited between 2001 and 2009 with no previous cancer diagnosis were included in analyses.Results: Cancer cases (n2066) were identified during a mean follow-up of 11·7 years. Participants who were most adherent to the selected WCRF/AICR recommendations (composite score: 4–6) were 13 % (0·87; 0·78, 0·98) less likely to develop cancer compared with those who were least adherent (composite score: 0–2). Each additional recommendation met corresponded to a 5 % (0·95; 0·91, 0·99) reduction in risk of developing cancer. When stratified by sex, the associations remained significant for women, but not for men.Conclusions: Adherence to lifestyle-related cancer prevention recommendations was associated with reduced risk of developing cancer over the follow-up term in this Canadian cohort.

An Individual Survival Distribution (ISD) models a patient's personalized survival probability at all future time points. Previously, ISD models have been shown to produce accurate and personalized survival estimates (for example, time to relapse or to death) in several clinical applications. However, off-the-shelf neural-network-based ISD models are usually opaque models due to their limited support for meaningful feature selection and uncertainty estimation, which hinders their wide clinical adoption. Here, we introduce a Bayesian-neural-network-based ISD (BNN-ISD) model that produces accurate survival estimates but also quantifies the uncertainty in model's parameter estimation, which can be used to (1) rank the importance of the input features to support feature selection and (2) compute credible intervals around ISDs for clinicians to assess the model's confidence in its prediction. Our BNN-ISD model utilized sparsity-inducing priors to learn a sparse set of weights to enable feature selection. We provide empirical evidence, on 2 synthetic and 3 real-world clinical datasets, that BNN-ISD system can effectively select meaningful features and compute trustworthy credible intervals of the survival distribution for each patient. We observed that our approach accurately recovers feature importance in the synthetic datasets and selects meaningful features for the real-world clinical data as well, while also achieving state-of-the-art survival prediction performance. We also show that these credible regions can aid in clinical decision-making by providing a gauge of the uncertainty of the estimated ISD curves.

• Mammography is an effective approach for early detection of breast cancer. • Established longitudinal patterns of adherence to breast cancer screening recommendation. • Having a family doctor can encourage compliance to breast cancer screening recommendation. • Current smokers were more likely to be non-regular screeners.

The high-resolution imaging of three-dimensional structures in biological tissues is essential for biomedical research. The development of light microscopies and various fluorescent labeling techniques provides powerful tools in his regard. However, optical imaging depth suffers from strong light attenuation due to the inherent heterogeneity of biological tissues and light absorption. Tissue optical clearing technology provides a distinct solution for deep imaging with high resolution. In recent years, various tissue optical clearing methods have emerged. This chapter will first review recent and up-to-date in vitro tissue optical clearing techniques from the perspective of the end-users based on sample features in terms of size and age, and then shows the results of a comprehensive evaluation of the performances of some typical clearing methods in various intact mouse organs. It is expected to be helpful for researchers in choosing the most suitable clearing method from a variety of protocols to meet their specific needs.

IntroductionAlberta’s Tomorrow Project (ATP) is a province-wide cohort study of cancer and chronic diseases in Canada. Approximately 55,000 Albertans aged 35-69 years with no history of cancer, other than non-melanoma skin cancer, had joined the ATP. Nearly all (>96%) ATP participants consented to data linkage to administrative healthcare databases. Objectives and ApproachTo validate chronic conditions self-reported by the ATP participants, individual-level data of a total of 52,851 ATP participants were linked to Alberta Health (AH) administrative datasets using Personal Health Number. Cases of chronic conditions, including diabetes, cardiovascular diseases and gastrointestinal disorders, were identified by algorithms for disease diagnosis using International Classification of Diseases (ICD) codes in administrative healthcare data. Cases were identified as “prevalent” if the index date of diagnosis was before or within 6 months of ATP enrollment date. Self-reported chronic conditions were validated by the cases identified as “prevalent” using the linked AH datasets. ResultsAmong the ATP cohort, 804 participants self-reported ever being diagnosed with myocardial infarction (MI), 451 with stroke, 2,907 with diabetes, 566 with ulcerative colitis, 362 with Crohn’s disease and 112 with liver cirrhosis before or at the time of ATP enrollment. Using the linked AH administrative data, 339 participants were identified as prevalent cases with MI, 200 with stroke, 2,702 with diabetes, 208 with ulcerative colitis, 283 with Crohn’s disease and 55 with liver cirrhosis. The sensitivity and specificity of self-reporting having chronic conditions by the ATP participants were 71.1% and 97.0% for MI, 44.4% and 99.3% for stroke, 78.1% and 98.4% for diabetes, 73.6% and 99.2% for ulcerative colitis, 65.7% and 99.7% for Crohn’s disease and 50.9% and 99.8% for liver cirrhosis. Conclusion/ImplicationsBy linking with administrative healthcare data, we were able to validate the accuracy of self-reporting chronic conditions in ATP participants. The sensitivity of self-reporting was acceptable for MI, diabetes, ulcerative colitis and Crohn’s disease, but low for stroke and liver cirrhosis. The specificity was high for all chronic conditions.