OBJECTIVE:To examine how the weight of a patient affects both the attitudes that physicians hold as well as the treatments that they intend to prescribe. DESIGN: In a six-cell randomized design, physicians evaluated a medical chart of a male or female patient, depicted as either average weight, overweight or obese, who presented with a migraine headache. SUBJECTS: A total of 122 physicians affiliated with one of three hospitals located in the Texas Medical Center of Houston completed the experiment. MEASUREMENTS: Using a standard medical procedure form, physicians indicated how long they would spend with the patient and which of 41 medical tests and procedures they would conduct. They also indicated their affective and behavioral reactions to the patient. RESULTS: The weight of a patient significantly affected how physicians viewed and treated them. Although physicians prescribed more tests for heavier patients, F(2, 107) ¼ 3.65, P < 0.03, they simultaneously indicated that they would spend less time with them, F(2, 107) ¼ 8.38, P < 0.001, and viewed them significantly more negatively on 12 of the 13 indices. CONCLUSION: This study reveals that physicians continue to play an influential role in lowering the quality of healthcare that overweight and obese patients receive. As the girth of America continues to increase, continued research and improvements in the quality of such healthcare deserve attention.

Although serum prostate specific antigen (PSA) is a well-established diagnostic tool for prostate cancer (PCa) detection, the definitive diagnosis of PCa is based on the information contained in prostate needle biopsy (PNBX) specimens. To define the proteomic features of PNBX specimens to identify candidate biomarkers for PCa, PNBX specimens from patients with PCa or benign prostatic hyperplasia (BPH) were subjected to comparative proteomic analysis. 2-DE revealed that 52 protein spots exhibited statistically significantly changes among PCa and BPH groups. Interesting spots were identified by MALDI-TOF-MS/MS. The 2 most notable groups of proteins identified included latent androgen receptor coregulators and FKBP4] and enzymes involved in mitochondrial fatty acid b-oxidation (DCI and ECHS1). An imbalance in the expression of peroxiredoxin subtypes was noted in PCa specimens. Furthermore, different post-translationally modified isoforms of HSP27 and HSP70.1 were identified. Importantly, changes in FLNA(7-15), FKBP4, and PRDX4 expression were confirmed by immunoblot analyses. Our results suggest that a proteomics-based approach is useful for developing a more complete picture of the protein profile of PNBX specimen. The proteins identified by this approach may be useful molecular targets for PCa diagnostics and therapeutics. ' 2007 Wiley-Liss, Inc.

BackgroundAnalysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behind the low vaccine coverage and come up with corresponding strategies to improve vaccine uptake.ObjectiveTo propose a machine learning system that is able to extract comprehensive public sentiment on HPV vaccines on Twitter with satisfying performance.MethodWe collected and manually annotated 6,000 HPV vaccines related tweets as a gold standard. SVM model was chosen and a hierarchical classification method was proposed and evaluated. Additional feature sets evaluation and model parameters optimization was done to maximize the machine learning model performance.ResultsA hierarchical classification scheme that contains 10 categories was built to access public opinions toward HPV vaccines comprehensively. A 6,000 annotated tweets gold corpus with Kappa annotation agreement at 0.851 was created and made public available. The hierarchical classification model with optimized feature sets and model parameters has increased the micro-averaging and macro-averaging F score from 0.6732 and 0.3967 to 0.7442 and 0.5883 respectively, compared with baseline model.ConclusionsOur work provides a systematical way to improve the machine learning model performance on the highly unbalanced HPV vaccines related tweets corpus. Our system can be further applied on a large tweets corpus to extract large-scale public opinion towards HPV vaccines.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0120-6) contains supplementary material, which is available to authorized users.

BackgroundAs one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially concerns on social media, is of significant importance for HPV vaccination promotion.MethodsIn this study, we leveraged a hierarchical machine learning based sentiment analysis system to extract public opinions towards HPV vaccines from Twitter. English tweets containing HPV vaccines-related keywords were collected from November 2, 2015 to March 28, 2016. Manual annotation was done to evaluate the performance of the system on the unannotated tweets corpus. Followed time series analysis was applied to this corpus to track the trends of machine-deduced sentiments and their associations with different days of the week.ResultsThe evaluation of the unannotated tweets corpus showed that the micro-averaging F scores have reached 0.786. The learning system deduced the sentiment labels for 184,214 tweets in the collected unannotated tweets corpus. Time series analysis identified a coincidence between mainstream outcome and Twitter contents. A weak trend was found for “Negative” tweets that decreased firstly and began to increase later; an opposite trend was identified for “Positive” tweets. Tweets that contain the worries on efficacy for HPV vaccines showed a relative significant decreasing trend. Strong associations were found between some sentiments (“Positive”, “Negative”, “Negative-Safety” and “Negative-Others”) with different days of the week.ConclusionsOur efforts on sentiment analysis for newly approved HPV vaccines provide us an automatic and instant way to extract public opinion and understand the concerns on Twitter. Our approaches can provide a feedback to public health professionals to monitor online public response, examine the effectiveness of their HPV vaccination promotion strategies and adjust their promotion plans.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0469-6) contains supplementary material, which is available to authorized users.

Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug–disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed from http://clinicalnlptool.com/cdr. The online CD-REST demonstration system is available at http://clinicalnlptool.com/cdr/cdr.html.Database URL: http://clinicalnlptool.com/cdr; http://clinicalnlptool.com/cdr/cdr.html