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Objective To describe differences between patients with chronic, non-cancer pain (CNCP) who were successfully able to cease full mu agonist chronic opioid analgesic therapy (COAT), and those who exhibited refractory COAT reliance, among those who participated in a multidisciplinary program designed for COAT cessation. Design A retrospective review of electronic medical records (EMR) data was organized for preliminary analysis. Setting A multicenter private practice specializing in CNCP, which received patient referrals from the surrounding geographical area of primary and specialty care offices in Northern California. Subjects Data from 109 patients with CNCP who participated in a multidisciplinary program to cease COAT between the dates of October 2017 to December 2019 were examined. Methods EMR data, pre-COAT cessation, of oral morphine milligram equivalence (MME) and validated questionnaire responses assessing anxiety and fear-based beliefs and behavior, as well as opioid misuse, were extracted and compared between those who successfully ceased COAT and those who did not. Results Patients who were unsuccessful at COAT cessation reported significantly higher Fear Avoidance Beliefs Questionnaire (FAB) scores. No significant differences were found based on incoming MME amounts, Current Opioid Misuse Measure (COMM) or Tampa Scale of Kinesiophobia (TSK) scores. Pain Catastrophizing Scale (PCS) scores showed a split pattern with unclear significance. Conclusions Results suggest that fear avoidance beliefs and behavior, as measured by the FAB, play a significant role in refractory COAT reliance for patients with CNCP. [ABSTRACT FROM AUTHOR]

Reports on the development of a microelectronic packaging for retinal prostheses by researchers from Pasadena, California. Use of a high-density, chip-level integrated interconnect packaging system for bioimplant device construction; Significance of the development of a novel technology for packaging of the intraocular components of the electrical retinal prosthesis system; Advantages of the new approach over other alternative retinal stimulation.

For personalized nutrition in preparation for precision healthcare, we examined the predictors of healthy eating, using the healthy eating index (HEI) and glycemic index (GI), in family-based multi-ethnic colorectal cancer (CRC) families. A total of 106 participants, 53 CRC cases and 53 family members from multi-ethnic families participated in the study. Machine learning validation procedures, including the ensemble method and generalized regression prediction, Elastic Net with Akaike's Information Criterion with correction and Leave-One-Out cross validation methods, were applied to validate the results for enhanced prediction and reproducibility. Models were compared based on HEI scales for the scores of 77 versus 80 as the status of healthy eating, predicted from individual dietary parameters and health outcomes. Gender and CRC status were interactive as additional predictors of HEI based on the HEI score of 77. Predictors of HEI 80 as the criterion score of a good diet included five significant dietary parameters (with intake amount): whole fruit (1 cup), milk or milk alternative such as soy drinks (6 oz), whole grain (1 oz), saturated fat (15 g), and oil and nuts (1 oz). Compared to the GI models, HEI models presented more accurate and fitted models. Milk or a milk alternative such as soy drink (6 oz) is the common significant parameter across HEI and GI predictive models. These results point to the importance of healthy eating, with the appropriate amount of healthy foods, as modifiable factors for cancer prevention.