Human biomonitoring and reference values of urinary 1-hydroxypyrene among Iranian adults population.

Polycyclic aromatic hydrocarbons (PAHs) are one of the most important environmental pollutants. Urinary concentrations of 1-hydropyren metabolites of PAHs have been used as biomarkers of these chemicals' exposure in humans. This cross-sectional study was conducted on 468 healthy Iranian adults over 25 years old and non-smokers in six provinces who were selected based on the clustering method. Fasting urine sampling and body composition and demographic measurements were performed. Urine samples were analyzed by GC-MS. The analysis included descriptive statistics and analytical statistics using multiple linear regression by Python software. 1-Hydroxypyrene was found in 100% of samples, and the mean (Reference Value 95%) concentration of 1-hydroxypyrene was 6.12 (RV 95%: 20) µg/L and 5.95 (21) µg/gcrt. There was a direct relationship between the amount of body composition (body fat, visceral fat), BMI, and age with the urinary concentrations of 1-hydropyren metabolites, and this relationship was significant for BMI with urinary concentrations of 1-hydropyren metabolites (P = 0.045). The amount of 1-hydroxypyrene in healthy Iranian adults has been higher than in similar studies in other countries. These results provide helpful information regarding the exposure of Iranian adults to 1-hydroxypyrene, and these data can be used to supplement the national reference values of human biomonitoring for the interpretation of biomonitoring results.

Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design.

Natural language processing techniques have increased the volume and variety of text data that can be analyzed. The aim of this study was to identify the positive and negative topical sentiments among diet, diabetes, exercise, and obesity tweets. Using a sequential explanatory mixed-method design for our analytical framework, we analyzed a data corpus of 1.7 million diet, diabetes, exercise, and obesity (DDEO)-related tweets collected over 12 months. Sentiment analysis and topic modeling were used to analyze the data. The results show that overall, 29% of the tweets were positive, and 17% were negative. Using sentiment analysis and latent Dirichlet allocation (LDA) topic modeling, we analyzed 800 positive and negative DDEO topics. From the 800 LDA topics-after the qualitative and computational removal of incoherent topics-473 topics were characterized as coherent. Obesity was the only query health topic with a higher percentage of negative tweets. The use of social media by public health practitioners should focus not only on the dissemination of health information based on the topics discovered but also consider what they can do for the health consumer as a result of the interaction in digital spaces such as social media. Future studies will benefit from using multiclass sentiment analysis methods associated with other novel topic modeling approaches.

The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach.

BACKGROUND: Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques. METHOD: Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross-validation, and spatial autocorrelation testing. RESULTS: The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories expenditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML outperformed the GWR method. CONCLUSION: This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens.

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter.

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.

Analysis of Social Media Discussions on (#)Diet by Blue, Red, and Swing States in the U.S.

The relationship between political affiliations and diet-related discussions on social media has not been studied on a population level. This study used a cost- and -time effective framework to leverage, aggregate, and analyze data from social media. This paper enhances our understanding of diet-related discussions with respect to political orientations in U.S. states. This mixed methods study used computational methods to collect tweets containing "diet" or "#diet" shared in a year, identified tweets posted by U.S. Twitter users, disclosed topics of tweets, and compared democratic, republican, and swing states based on the weight of topics. A qualitative method was employed to code topics. We found 32 unique topics extracted from more than 800,000 tweets, including a wide range of themes, such as diet types and chronic conditions. Based on the comparative analysis of the topic weights, our results revealed a significant difference between democratic, republican, and swing states. The largest difference was detected between swing and democratic states, and the smallest difference was identified between swing and republican states. Our study provides initial insight on the association of potential political leanings with health (e.g., dietary behaviors). Our results show diet discussions differ depending on the political orientation of the state in which Twitter users reside. Understanding the correlation of dietary preferences based on political orientation can help develop targeted and effective health promotion, communication, and policymaking strategies.

Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter.

To combat health disinformation shared online, there is a need to identify and characterize the prevalence of topics shared by trolls managed by individuals to promote discord. The current literature is limited to a few health topics and dominated by vaccination. The goal of this study is to identify and analyze the breadth of health topics discussed by left (liberal) and right (conservative) Russian trolls on Twitter. We introduce an automated framework based on mixed methods including both computational and qualitative techniques. Results suggest that Russian trolls discussed 48 health-related topics, ranging from diet to abortion. Out of the 48 topics, there was a significant difference (p-value ≤ 0.004) between left and right trolls based on 17 topics. Hillary Clinton's health during the 2016 election was the most popular topic for right trolls, who discussed this topic significantly more than left trolls. Mental health was the most popular topic for left trolls, who discussed this topic significantly more than right trolls. This study shows that health disinformation is a global public health threat on social media for a considerable number of health topics. This study can be beneficial for researchers who are interested in political disinformation and health monitoring, communication, and promotion on social media by showing health information shared by Russian trolls.

Social media and COVID-19: Characterizing anti-quarantine comments on Twitter.

Social media has become a mainstream channel of communication during the COVID-19 pandemic. While some studies have been developed on investigating public opinion on social media data regarding COVID-19 pandemic, there is no study analyzing anti-quarantine comments on social media. This study has collected and analyzed near 80,000 tweets to understand anti-quarantine social comments. Using text mining, we found 11 topics representing different issues such as comparing COVID-19 and flu and health side effects of quarantine. We believe that this study shines a light on public opinion of people who are against quarantine.

Social media and COVID-19: Can social distancing be quantified without measuring human movements?

The COVID-19 outbreak has posed significant threats to international health and the economy. In the absence of treatment for this virus, public health officials asked the public to practice social distancing to reduce the number of physical contacts. However, quantifying social distancing is a challenging task and current methods are based on human movements. We propose a time and cost-effective approach to measure how people practice social distancing. This study proposes a new method based on utilizing the frequency of hashtags supporting and encouraging social distancing for measuring social distancing. We have identified 18 related hashtags and tracked their trends between Jan and May 2020. Our evaluation results show that there is a strong correlation (p < .05) between our findings and the Google social distancing report.

Cognitive and perceptual functions in patients with occipital lobe epilepsy, patients with migraine, and healthy controls.

This study was conducted to compare cognitive and perceptual functions among patients with occipital lobe epilepsy, patients with migraine, and healthy individuals, in relation to the moderating roles of gender and educational level. Participants included 93 individuals from Mashhad City, Khorasan-e-Razavi province, Iran. A demographic questionnaire and Bender-Gestalt II (BGT-II; Brannigan & Decker, 2003) were used for data collection in this study. Results showed significant group differences for copy, recall, motor, and perceptual subscales of BGT-II in these samples, where patients with occipital lobe epilepsy and patients with migraine having significantly lower scores than healthy individuals. Also, patients with occipital lobe epilepsy had significantly poorer scores in all subscales of the BGT-II in comparison with the patients with migraine. There were no significant differences with regard to gender and educational level when considering dependent variables in the present study.

Exploring diseases and syndromes in neurology case reports from 1955 to 2017 with text mining.

BACKGROUND: A large number of neurology case reports have been published, but it is a challenging task for human medical experts to explore all of these publications. Text mining offers a computational approach to investigate neurology literature and capture meaningful patterns. The overarching goal of this study is to provide a new perspective on case reports of neurological disease and syndrome analysis over the last six decades using text mining. METHODS: We extracted diseases and syndromes (DsSs) from more than 65,000 neurology case reports from 66 journals in PubMed over the last six decades from 1955 to 2017. Text mining was applied to reports on the detected DsSs to investigate high-frequency DsSs, categorize them, and explore the linear trends over the 63-year time frame. RESULTS: The text mining methods explored high-frequency neurologic DsSs and the relationships between them from 1955 to 2017. We detected more than 18,000 unique DsSs and found 10 categories of neurologic DsSs. While the trend analysis showed the increasing trends in the case reports for top-10 high-frequency DsSs, the categories had mixed trends. CONCLUSION: Our study provided new insights into the application of text mining methods to investigate DsSs in a large number of medical case reports that occur over several decades. The proposed approach can be used to provide a macro level analysis of medical literature by discovering interesting patterns and tracking them over several years to help physicians explore these case reports more efficiently.

Wearable activity trackers, accuracy, adoption, acceptance and health impact: A systematic literature review.

Wearable activity trackers (WAT) are electronic monitoring devices that enable users to track and monitor their health-related physical fitness metrics including steps taken, level of activity, walking distance, heart rate, and sleep patterns. Despite the proliferation of these devices in various contexts of use and rising research interests, there is limited understanding of the broad research landscape. The purpose of this systematic review is therefore to synthesize the existing wealth of research on WAT, and to provide a comprehensive summary based on common themes and approaches. This article includes academic work published between 2013 and 2017 in PubMed, Embase, Scopus, Web of Science, ACM Digital Library, and Google Scholar. A final list of 463 articles was analyzed for this review. Topic modeling methods were used to identify six key themes (topics) of WAT research, namely: (1) Technology Focus, (2) Patient Treatment and Medical Settings, (3) Behavior Change, (4) Acceptance and Adoption (Abandonment), (5) Self-monitoring Data Centered, and (6) Privacy. We take an interdisciplinary approach to wearable activity trackers to propose several new research questions. The most important research gap we identify is to attempt to understand the rich human-information interaction that is enabled by WAT adoption.

Data for efficiency comparison of raw pumice and manganese-modified pumice for removal phenol from aqueous environments-Application of response surface methodology.

Present deadest collection was aimed to evaluate the efficiency of raw pumice (RWP) and Mn-modified pumice (MMP). Response surface methodology (RSM) based on the central composite designs (CCD) was applied to evaluate the effects of independent variables including pH, adsorbents dosage, contact time and adsorbate concentration on the response function and the best response values were predicted. The Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to characterize the adsorbents. Based on acquired data, the maximum efficiency removal of phenol was obtained 89.14% and 100% for raw and Mn-modified pumice respectively. The obtained data showed pH was effective parameter on phenol removal among the different variables. Evaluation of data using isotherms and kinetics models showed the fitted with Langmuir isotherm and pseudo second order kinetic for both adsorbents. According to obtained data was observed that modification of pumice can improve the efficiency removal of phenol to meet the effluent standards.

Design of a remote-control drug delivery implantable chip for cancer local on demand therapy using ionic polymer metal composite actuator.

Since the local, on demand, cancer therapy is a challenging clinical issue today, this paper presents the design, fabrication and characterization of a remotely controlled single reservoir drug delivery chip using Ionic Polymer Metal Composite (IPMC) actuator. Here, Drug release was externally programmed and controlled wirelessly on demand by a communication circuit. The transmitter and receiver circuits were designed to control the release/sealed status remotely even from 7 cm distance while the transmitter and receiver were coupled magnetically. IPMC here was used as the moving cap of the reservoir, that in release mode, lets the drug out on demand with a low received power of 20 mW. The novel simple design could release the whole content of the drug which is remarkable in comparison with the designs which need complicated optimizations of diffuser, nuzzle and IPMC diaphragm pump, leading to an incomplete release. To make sure that there is no leakage in the sealed mode, IPMC was attached to a polydimethylsiloxane (PDMS) support film. Biocompatibility of all the components of the chip were tested by 3-(4,5-dimethylthiazol-2-yl)- 2,5-diphenyltetrazolium bromide (MTT) assay.

Alterations in TSH and Thyroid Hormones following Mobile Phone Use.

OBJECTIVES: In recent years, the widespread use of mobile phones has lead to a public debate about possible detrimental effects on human health. In spite of years of research, there is still a great controversy regarding the possibility of induction of any significant physiological effects in humans by microwave radiations emitted by mobile phones. This study aims to investigate the effects of electromagnetic fields induced by the Global System for Mobile communications (GSM) mobile phones on the Thyroid Stimulating Hormone (TSH) and thyroid hormones in humans. METHODS: 77 healthy university students participated in this study. The levels of T3, T4 and TSH were measured by using appropriate enzyme-linked immunosorbent assay (ELISA) kits (Human, Germany). RESULTS: The average levels of T3, T4 and TSH in students who moderately used mobile phones were 1.25±0.27 ng/ml, 7.76±1.73 µg/dl and 4.25±2.12 µu/l respectively. The levels in the students who severely used mobile phones were 1.18±0.30, 7.75±1.14 and 3.75±2.05 respectively. In non-users, the levels were 1.15±0.27, 8.42±2.72 and 2.70±1.75, respectively. The difference among the levels of TSH in these 3 groups was statistically significant (P<0.05). CONCLUSION: As far as the study is concerned, this is the first human study to assess the associations between mobile phone use and alterations in the levels of TSH and thyroid hormones. Based on the findings, a higher than normal TSH level, low mean T4 and normal T3 concentrations in mobile users were observed. It seems that minor degrees of thyroid dysfunction with a compensatory rise in TSH may occur following excessive use of mobile phones. It may be concluded that possible deleterious effects of mobile microwaves on hypothalamic-pituitary-thyroid axis affects the levels of these hormones.