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As America's opioid crisis has become an "epidemic of epidemics," Ohio has been identified as one of the high burden states regarding fentanyl-related overdose mortality. This study aims to examine changes in the availability of fentanyl, fentanyl analogs, and other non-pharmaceutical opioids on cryptomarkets and assess relationship with the trends in unintentional overdoses in Ohio to provide timely information for epidemiologic surveillance. Cryptomarket data were collected at two distinct periods of time: (1) Agora data covered June 2014-September 2015 and were obtained from Grams archive; (2) Dream Market data from March-April 2018 were extracted using a dedicated crawler. A Named Entity Recognition algorithm was developed to identify and categorize the type of fentanyl and other synthetic opioids advertised on cryptomarkets. Timelagged correlations were used to assess the relationship between the fentanyl, fentanyl analog and other synthetic opioid-related ads from cryptomarkets and overdose data from the Cincinnati Fire Department Emergency Responses and Montgomery County Coroner's Office. Analysis from the cryptomarket data reveals increases in fentanyl-like drugs and changes in the types of fentanyl analogues and other synthetic opioids advertised in 2015 and 2018 with potent substances like carfentanil available during the second period. The time-lagged correlation was the largest when comparing Agora data to Cincinnati Emergency Responses 1 month later 0.84 (95% CI 0.45, 0.96). The time-lagged correlation between Agora data and Montgomery County drug overdoses was the largest when comparing synthetic opioid-related Agora ads to Montgomery County overdose deaths 7 months later 0.78 (95% CI 0.47, 0.92). Further investigations are required to establish the relationship between cryptomarket availability and unintentional overdose trends related to specific fentanyl analogs and/or other illicit synthetic opioids.

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Dendrimers have been used as a vehicle to develop the antimicrobial properties of textile fabrics. We have taken advantage of the large number of functional groups present in the regular and highly branched threedimensional architecture of dendrimers. In this study, the poly(amidoamine) (PAMAM) G-3 dendrimer was modified to provide antimicrobial properties. Following a procedure similar to what is suggested in the literature, PAMAM (G3) with primary amine end groups was converted into ammonium functionalities. The modification was then confirmed by FTIR and 13 C-NMR analysis. Dendrimers have unique properties owing to their globular shape and tunable cavities, this allows them to form complexes with a variety of ions and compounds; and also act as a template to fabricate metal nanoparticles. AgNO 3 -PAMAM (G3) complex as well as a MesoSilver-PAMAM (G3) complex were formed and these modified dendrimers were characterized by a UVVisible spectrophotometer to study the complex formation. Modified dendrimers were applied to the Cotton/Nylon blend fabric. SEM and EDX analysis were performed to study the dispersion of silver nanoparticles onto the fabric. An antimicrobial test of the treated-fabric against Staphylococcus aureus exhibited significant biocidal activities for each type of modified-dendrimer.

To minimize the accelerating amount of time invested on the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. Results: In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. Towards this, we model the relation extraction problem in a multi-task learning (MTL) framework, and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text. The fundamental notion of MTL is to simultaneously learn multiple problems together by utilizing the concepts of the shared representation. Additionally, we also generate the highly efficient single task model which exploits the shortest dependency path embedding learned over the attentive gated recurrent unit to compare our proposed MTL models. The framework we propose significantly improves over all the baselines (deep learning techniques) and single-task models for predicting the relationships, without compromising on the performance of all the tasks.

In recent past, social media has emerged as an active platform in the context of healthcare and medicine. In this paper, we present a study where medical user's opinions on healthrelated issues are analyzed to capture the medical sentiment at a blog level. The medical sentiments can be studied in various facets such as medical condition, treatment, and medication that characterize the overall health status of the user. Considering these facets, we treat analysis of this information as a multi-task classification problem. In this paper, we adopt a novel adversarial learning approach 1 for our multi-task learning framework to learn the sentiment's strengths expressed in a medical blog. Our evaluation shows promising results for our target tasks.