Factors affecting the periapical healing process of endodontically treated teeth

Tissue repair is an essential process that reestablishes tissue integrity and regular function. Nevertheless, different therapeutic factors and clinical conditions may interfere in this process of periapical healing. This review aims to discuss the important therapeutic factors associated with the clinical protocol used during root canal treatment and to highlight the systemic conditions associated with the periapical healing process of endodontically treated teeth. The antibacterial strategies indicated in the conventional treatment of an inflamed and infected pulp and the modulation of the host's immune response may assist in tissue repair, if wound healing has been hindered by infection. Systemic conditions, such as diabetes mellitus and hypertension, can also inhibit wound healing. The success of root canal treatment is affected by the correct choice of clinical protocol. These factors are dependent on the sanitization process (instrumentation, irrigant solution, irrigating strategies, and intracanal dressing), the apical limit of the root canal preparation and obturation, and the quality of the sealer. The challenges affecting the healing process of endodontically treated teeth include control of the inflammation of pulp or infectious processes and simultaneous neutralization of unpredictable provocations to the periapical tissue. Along with these factors, one must understand the local and general clinical conditions (systemic health of the patient) that affect the outcome of root canal treatment prediction

Can Neuroscience Help Predict Future Antisocial Behavior?

Part I of this Article reviews the tools currently available to predict antisocial behavior. Part II discusses legal precedent regarding the use of, and challenges to, various prediction methods. Part III introduces recent neuroscience work in this area and reviews two studies that have successfully used neuroimaging techniques to predict recidivism. Part IV discusses some criticisms that are commonly levied against the various prediction methods and highlights the disparity between the attitudes of the scientific and legal communities toward risk assessment generally and neuroscience specifically. Lastly, Part V explains why neuroscience methods will likely continue to help inform and, ideally, improve the tools we use to help assess, understand, and predict human behavior

Time-course of effects of external beam radiation on [18F]FDG uptake in healthy tissue and bone marrow.

The utility of PET for monitoring responses to radiation therapy have been complicated by metabolically active processes in surrounding normal tissues. We examined the time-course of [18F]FDG uptake in normal tissues using small animal-dedicated PET during the 2 month period following external beam radiation. Four mice received 12 Gy of external beam radiation, in a single fraction to the left half of the body. Small animal [18F]FDG-PET scans were acquired for each mouse at 0 (pre-radiation), 1, 2, 3, 4, 5, 8, 12, 19, 24, and 38 days following irradiation. [18F]FDG activity in various tissues was compared between irradiated and non-irradiated body halves before, and at each time point after irradiation. Radiation had a significant impact on [18F]FDG uptake in previously healthy tissues, and time-course of effects differed in different types of tissues. For example, liver tissue demonstrated increased uptake, particularly over days 3-12, with the mean left to right uptake ratio increasing 52% over mean baseline values (p < 0.0001). In contrast, femoral bone marrow uptake demonstrated decreased uptake, particularly over days 2-8, with the mean left to right uptake ratio decreasing 26% below mean baseline values (p = 0.0005). Significant effects were also seen in lung and brain tissue. Radiation had diverse effects on [18F]FDG uptake in previously healthy tissues. These kinds of data may help lay groundwork for a systematically acquired database of the time-course of effects of radiation on healthy tissues, useful for animal models of cancer therapy imminently, as well as interspecies extrapolations pertinent to clinical application eventually

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

Deepr: A Convolutional Net for Medical Records

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space