Importance: It is important to understand the magnitude and distribution of the economic burden of prescription opioid overdose, abuse, and dependence to inform clinical practice, research, and other decision makers. Decision makers choosing approaches to address this epidemic need cost information to evaluate the cost effectiveness of their choices. Objective: To estimate the economic burden of prescription opioid overdose, abuse, and dependence from a societal perspective. Design, Setting, and Participants: Incidence of fatal prescription opioid overdose from the National Vital Statistics System, prevalence of abuse and dependence from the National Survey of Drug Use and Health. Fatal data are for the US population, nonfatal data are a nationally representative sample of the US civilian noninstitutionalized population ages 12 and older. Cost data are from various sources including health care claims data from the Truven Health MarketScan Research Databases, and cost of fatal cases from the WISQARS (Web-based Injury Statistics Query and Reporting System) cost module. Criminal justice costs were derived from the Justice Expenditure and Employment Extracts published by the Department of Justice. Estimates of lost productivity were based on a previously published study. Exposure: Calendar year 2013. Main Outcomes and Measures: Monetized burden of fatal overdose and abuse and dependence of prescription opioids. Results: The total economic burden is estimated to be $78.5 billion. Over one third of this amount is due to increased health care and substance abuse treatment costs ($28.9 billion). Approximately one quarter of the cost is borne by the public sector in health care, substance abuse treatment, and criminal justice costs. Conclusions and Relevance: These estimates can assist decision makers in understanding the magnitude of adverse health outcomes associated with prescription opioid use such as overdose, abuse, and dependence.

State prescription drug monitoring programs are promising tools to rein in the epidemic of prescription opioid overdose. We used data from a national survey to assess the effects of these programs on the prescribing of opioid analgesics and other pain medications in ambulatory care settings at the point of care in twenty-four states from 2001 to 2010. We found that the implementation of a prescription drug monitoring program was associated with more than a 30 percent reduction in the rate of prescribing of Schedule II opioids. This reduction was seen immediately following the launch of the program and was maintained in the second and third years afterward. Effects on overall opioid prescribing and prescribing of non-opioid analgesics were limited. Increased use of these programs and the adoption of new policies and practices governing their use may have contributed to sustained effectiveness. Future studies are needed to evaluate the policies’ comparative effectiveness.

OBJECTIVES: We investigated the relationship between parental incarceration history and young adult physical and mental health outcomes using Wave 1 and Wave 4 data from the National Longitudinal Study of Adolescent Health. METHODS: Dependent variables included self-reported fair/poor health and health diagnoses. The independent variable was parental incarceration history. Cross-tabulations and logistic regression models were run. RESULTS: Positive, significant associations were found between parental incarceration and 8 of 16 health problems (depression, posttraumatic stress disorder, anxiety, cholesterol, asthma, migraines, HIV/AIDS, and fair/poor health) in adjusted logistic regression models. Those who reported paternal incarceration had increased odds of 8 mental and physical health problems, whereas those who reported maternal incarceration had increased odds of depression. For paternal incarceration, with the exception of HIV/AIDS, larger associations were found for mental health (odds ratios range 1.43–1.72) as compared with physical health (odds ratios range 1.26–1.31) problems. The association between paternal incarceration and HIV/AIDs should be interpreted with caution because of the low sample prevalence of HIV/AIDs. CONCLUSIONS: This study suggests exposure to parental incarceration in childhood is associated with health problems in young adulthood. Extant literature suggests underlying mechanisms that link parental incarceration history to poor outcomes in offspring may include the lack of safe, stable, nurturing relationships and exposure to violence. To prevent poor health in offspring of the incarcerated, additional studies are needed to (1) confirm the aforementioned associations and (2) assess whether adverse experiences and violence exposure in childhood mediate the relationship between parental incarceration history and offspring health problems.

In 100 primary colorectal carcinomas, we demonstrate by array comparative genomic hybridization (aCGH) that 33% show DNA copy number (DCN) loss involving PARK2 , the gene encoding PARKIN, the E3 ubiquitin ligase whose deficiency is responsible for a form of autosomal recessive juvenile parkinsonism. PARK2 is located on chromosome 6 (at 6q25–27), a chromosome with one of the lowest overall frequencies of DNA copy number alterations recorded in colorectal cancers. The PARK2 deletions are mostly focal (31% ∼0.5 Mb on average), heterozygous, and show maximum incidence in exons 3 and 4. As PARK2 lies within FRA6E, a large common fragile site, it has been argued that the observed DCN losses in PARK2 in cancer may represent merely the result of enforced replication of locally vulnerable DNA. However, we show that deficiency in expression of PARK2 is significantly associated with adenomatous polyposis coli ( APC ) deficiency in human colorectal cancer. Evidence of some PARK2 mutations and promoter hypermethylation is described. PARK2 overexpression inhibits cell proliferation in vitro. Moreover, interbreeding of Park2 heterozygous knockout mice with Apc ^Min mice resulted in a dramatic acceleration of intestinal adenoma development and increased polyp multiplicity. We conclude that PARK2 is a tumor suppressor gene whose haploinsufficiency cooperates with mutant APC in colorectal carcinogenesis.

With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline. Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently. PyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html .

Dietary fiber has a long history in the intervention study of hyperlipidemia. In this review, current understandings of structures, sources, and natures of various kinds of dietary fibers (DFs) were analyzed first. Available evidences for the use of different varieties of DFs in the lipid‐lowering action both in vitro and in vivo were subsequently classified, including both soluble ones, such as glucans, pectins, and gums, and insoluble ones, including arabinooxylans and chitosans, in order to draw a primary conclusion of their dose and molecular weight relationship with lipid‐lowering effect. Their potential mechanisms, especially the related molecular mechanism of protective action in the treatment and prevention of hyperlipidemia, were summarized at last. Five major mechanisms are believed to be responsible for the antihyperlipidemic benefits of DFs, including low levels of energy, bulking effect, viscosity, binding capacity, and fermentation thus ameliorating the symptoms of hyperlipidemia. From the molecular level, DFs could possibly affect the activities of HMG‐CoA reductase, LDL receptors, CYP7A1, and MAPK signaling pathway as well as other lipid metabolism‐related target genes. In summary, dietary fibers could be used as alternative supplements to exert certain lipid‐lowering effects on humans. However, more clinical evidence is needed to strengthen this proposal and its fully underlying mechanism still requires more investigation.