Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers.

We manually annotated 9734 tweets that were posted by users who reported their pregnancy on Twitter, and used them to train, evaluate, and deploy deep neural network classifiers (F1-score=0.93) to detect tweets that report having a child with attention-deficit/hyperactivity disorder (678 users), autism spectrum disorders (1744 users), delayed speech (902 users), or asthma (1255 users), demonstrating the potential of Twitter as a complementary resource for assessing associations between pregnancy exposures and childhood health outcomes on a large scale.

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.

BACKGROUND: Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges. OBJECTIVE: This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used. METHODS: We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies. RESULTS: Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions. CONCLUSIONS: Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

A review of the DTS: Diathermic Syncope® system with a discussion regarding its use for kosher slaughter (shechita).

Over the last several decades an alternative to current methods of stunning cattle has been developed. This system, DTS: Diathermic Syncope®, has been suggested to the Jewish and Muslim communities as a means to achieve pre-cut stunning in conformity with both religious and EU regulations without a need to resort to a derogation that permits an exemption from the EU requirement to pre-stun all animals undergoing slaughter. The developer's contention is that the system induces fainting, and thus should be acceptable to all groups, including the kosher (Jewish) and Halal (Muslim) consumer. A review of the system based on publications and reports from the developer itself suggests that in reality the system selectively heats the brain, leading to an epileptic-type seizure with tonic-clonic phases and unconsciousness lasting several minutes. It does not induce a (benign) faint, and use of the system might cause structural brain damage. Thus, this system is unlikely to be acceptable under Jewish religious law and its animal welfare value can be questioned.

A survey-based research of medical faculty students' experiences on anatomy education during the Covid-19 pandemic process.

PURPOSE: In this study, the purpose was to uncover the views of medical students about online anatomy education adopted during the COVID-19 pandemic period. It was also aimed to determine whether medical school students found online education suitable for anatomy lectures and which materials they desired to use during teaching anatomy practice lectures in this process. METHODS: A survey form that was prepared with the Google Survey application was administered to the Medical Faculty Term 1 and 2 students who received anatomy courses at Istanbul Yeni Yüzyil University in the spring semester of the 2019-2020 academic year. RESULTS: A total of 180 students, 53.89% of whom were 1st graders and 46.11% 2nd graders participated in the study, and 43.89% of the students stated that they found online education suitable for anatomy theoretical courses, and 12.78% for anatomy practice courses. Also, 43.75% of Term 1 and 41.77% of Term 2 students stated that the pandemic negatively affected the teaching of anatomy theoretical courses. It was found that students considered that anatomy practice courses were more affected by the pandemic before and during the pandemic (P<0.001). CONCLUSIONS: This study uncovered that the pandemic process negatively affected anatomy education and students made more use of face-to-face education. We believe that the results obtained in the study will shed light on the views of anatomists on the teaching of anatomy in the online education process.

Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.

BACKGROUND: In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. OBJECTIVE: The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. METHODS: Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out "reported speech" (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. RESULTS: Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state-level geolocations. CONCLUSIONS: We have made the 13,714 tweets identified in this study, along with each tweet's time stamp and US state-level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.

A natural language processing pipeline to advance the use of Twitter data for digital epidemiology of adverse pregnancy outcomes.

BACKGROUND: In the United States, 17% of pregnancies end in fetal loss: miscarriage or stillbirth. Preterm birth affects 10% of live births in the United States and is the leading cause of neonatal death globally. Preterm births with low birthweight are the second leading cause of infant mortality in the United States. Despite their prevalence, the causes of miscarriage, stillbirth, and preterm birth are largely unknown. OBJECTIVE: The primary objectives of this study are to (1) assess whether women report miscarriage, stillbirth, and preterm birth, among others, on Twitter, and (2) develop natural language processing (NLP) methods to automatically identify users from which to select cases for large-scale observational studies. METHODS: We handcrafted regular expressions to retrieve tweets that mention an adverse pregnancy outcome, from a database containing more than 400 million publicly available tweets posted by more than 100,000 users who have announced their pregnancy on Twitter. Two annotators independently annotated 8109 (one random tweet per user) of the 22,912 retrieved tweets, distinguishing those reporting that the user has personally experienced the outcome ("outcome" tweets) from those that merely mention the outcome ("non-outcome" tweets). Inter-annotator agreement was κ = 0.90 (Cohen's kappa). We used the annotated tweets to train and evaluate feature-engineered and deep learning-based classifiers. We further annotated 7512 (of the 8109) tweets to develop a generalizable, rule-based module designed to filter out reported speech-that is, posts containing what was said by others-prior to automatic classification. We performed an extrinsic evaluation assessing whether the reported speech filter could improve the detection of women reporting adverse pregnancy outcomes on Twitter. RESULTS: The tweets annotated as "outcome" include 1632 women reporting miscarriage, 119 stillbirth, 749 preterm birth or premature labor, 217 low birthweight, 558 NICU admission, and 458 fetal/infant loss in general. A deep neural network, BERT-based classifier achieved the highest overall F1-score (0.88) for automatically detecting "outcome" tweets (precision = 0.87, recall = 0.89), with an F1-score of at least 0.82 and a precision of at least 0.84 for each of the adverse pregnancy outcomes. Our reported speech filter significantly (P < 0.05) improved the accuracy of Logistic Regression (from 78.0% to 80.8%) and majority voting-based ensemble (from 81.1% to 82.9%) classifiers. Although the filter did not improve the F1-score of the BERT-based classifier, it did improve precision-a trade-off of recall that may be acceptable for automated case selection of more prevalent outcomes. Without the filter, reported speech is one of the main sources of errors for the BERT-based classifier. CONCLUSION: This study demonstrates that (1) women do report their adverse pregnancy outcomes on Twitter, (2) our NLP pipeline can automatically identify users from which to select cases for large-scale observational studies, and (3) our reported speech filter would reduce the cost of annotating health-related social media data and can significantly improve the overall performance of feature-based classifiers.

A novel variation of the Stroop task reveals reflexive supremacy of peripheral over gaze stimuli in pro and anti saccades.

A long-standing controversy in social attention debates whether gaze-of-another induces reflexive shifts of one's own attention. In attempting to resolve this controversy, we utilized a novel Stroop task, the PAT Stroop, in which pro- and anti-saccade (PAT) responses are made to competing gaze and peripheral stimuli. The first experiment demonstrated a "Stroop effect" for peripheral stimuli, i.e. peripheral distractors interfered with gaze triggers, but gaze distractors did not interfere with peripheral triggers. These results were replicated in the second experiment, which also negated the possibility that the mere display and practice of the "clean PAT" influenced the results. Thus, the use a new PAT Stroop task demonstrated reflexive supremacy of peripheral stimuli over gaze stimuli. This novel variant of the Stroop task demonstrated similar characteristics to the classic color naming Stroop - i.e. an asymmetrical pattern, and again showed the utility and versatility of stoop-like tasks in probing mental tasks.

Seroprevalence of hepatitis E virus in dromedary camels, Bedouins, Muslim Arabs and Jews in Israel, 2009-2017.

Hepatitis E virus (HEV) is an emerging cause of viral hepatitis worldwide. Recently, HEV-7 has been shown to infect camels and humans. We studied HEV seroprevalence in dromedary camels and among Bedouins, Arabs (Muslims, none-Bedouins) and Jews and assessed factors associated with anti-HEV seropositivity. Serum samples from dromedary camels (n = 86) were used to determine camel anti-HEV IgG and HEV RNA positivity. Human samples collected between 2009 and 2016 from >20 years old Bedouins (n = 305), non-Bedouin Arabs (n = 320) and Jews (n = 195), were randomly selected using an age-stratified sampling design. Human HEV IgG levels were determined using Wantai IgG ELISA assay. Of the samples obtained from camels, 68.6% were anti-HEV positive. Among the human populations, Bedouins and non-Bedouin Arabs had a significantly higher prevalence of HEV antibodies (21.6% and 15.0%, respectively) compared with the Jewish population (3.1%). Seropositivity increased significantly with age in all human populations, reaching 47.6% and 34.8% among ⩾40 years old, in Bedouins and non-Bedouin Arabs, respectively. The high seropositivity in camels and in ⩾40 years old Bedouins and non-Bedouin Arabs suggests that HEV is endemic in Israel. The low HEV seroprevalence in Jews could be attributed to higher socio-economic status.

An interpretable natural language processing system for written medical examination assessment.

OBJECTIVE: The assessment of written medical examinations is a tedious and expensive process, requiring significant amounts of time from medical experts. Our objective was to develop a natural language processing (NLP) system that can expedite the assessment of unstructured answers in medical examinations by automatically identifying relevant concepts in the examinee responses. MATERIALS AND METHODS: Our NLP system, Intelligent Clinical Text Evaluator (INCITE), is semi-supervised in nature. Learning from a limited set of fully annotated examples, it sequentially applies a series of customized text comparison and similarity functions to determine if a text span represents an entry in a given reference standard. Combinations of fuzzy matching and set intersection-based methods capture inexact matches and also fragmented concepts. Customizable, dynamic similarity-based matching thresholds allow the system to be tailored for examinee responses of different lengths. RESULTS: INCITE achieved an average F1-score of 0.89 (precision = 0.87, recall = 0.91) against human annotations over held-out evaluation data. Fuzzy text matching, dynamic thresholding and the incorporation of supervision using annotated data resulted in the biggest jumps in performances. DISCUSSION: Long and non-standard expressions are difficult for INCITE to detect, but the problem is mitigated by the use of dynamic thresholding (i.e., varying the similarity threshold for a text span to be considered a match). Annotation variations within exams and disagreements between annotators were the primary causes for false positives. Small amounts of annotated data can significantly improve system performance. CONCLUSIONS: The high performance and interpretability of INCITE will likely significantly aid the assessment process and also help mitigate the impact of manual assessment inconsistencies.

Face stimulus eliminates antisaccade-cost: gaze following is a different kind of arrow.

When a person suddenly looks in a certain direction, others seem to shift their attention to the same, looked-at, location. This common observation, that gaze-of-another seems to trigger reflexive shifts of attention within an observer, has been demonstrated in various studies. Yet just how reflexive it truly is, is an on-going controversy. Unlike most studies in which gaze cues were distractors in a cueing paradigm, the current study used gaze cues as triggers in a mixed pro- and anti-saccade task and a Posner-like discrimination task. In a set of two experiments, we investigated whether attention triggered by gaze-of-another differs from attention triggered by peripheral (exogenous) and arrow stimuli. In the first experiment, gaze cues resulted in slowed saccadic responses and in the elimination of the anti-saccade-cost associated with reflexive orienting. Pro-saccades triggered by peripheral cues had significantly fewer errors and shorter reaction times than anti-saccades. However, there was no significant difference between pro and anti-saccades triggered by gaze cues. Thus, counter to expectations, gaze did not produce reflexive shifts of overt attention. The second experiment showed that attention triggered by gaze cues is no different from attention triggered by biologically irrelevant arrow cues. They both eliminated the anti-saccade-cost and displayed prolonged reaction times. However, manual discrimination RTs showed no significant differences between gaze and peripheral cues. Together, these results suggest that neither gaze nor arrow cues trigger reflexive shifts of overt attention.

Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter.

BACKGROUND: Although birth defects are the leading cause of infant mortality in the United States, methods for observing human pregnancies with birth defect outcomes are limited. OBJECTIVE: The primary objectives of this study were (i) to assess whether rare health-related events-in this case, birth defects-are reported on social media, (ii) to design and deploy a natural language processing (NLP) approach for collecting such sparse data from social media, and (iii) to utilize the collected data to discover a cohort of women whose pregnancies with birth defect outcomes could be observed on social media for epidemiological analysis. METHODS: To assess whether birth defects are mentioned on social media, we mined 432 million tweets posted by 112,647 users who were automatically identified via their public announcements of pregnancy on Twitter. To retrieve tweets that mention birth defects, we developed a rule-based, bootstrapping approach, which relies on a lexicon, lexical variants generated from the lexicon entries, regular expressions, post-processing, and manual analysis guided by distributional properties. To identify users whose pregnancies with birth defect outcomes could be observed for epidemiological analysis, inclusion criteria were (i) tweets indicating that the user's child has a birth defect, and (ii) accessibility to the user's tweets during pregnancy. We conducted a semi-automatic evaluation to estimate the recall of the tweet-collection approach, and performed a preliminary assessment of the prevalence of selected birth defects among the pregnancy cohort derived from Twitter. RESULTS: We manually annotated 16,822 retrieved tweets, distinguishing tweets indicating that the user's child has a birth defect (true positives) from tweets that merely mention birth defects (false positives). Inter-annotator agreement was substantial: κ = 0.79 (Cohen's kappa). Analyzing the timelines of the 646 users whose tweets were true positives resulted in the discovery of 195 users that met the inclusion criteria. Congenital heart defects are the most common type of birth defect reported on Twitter, consistent with findings in the general population. Based on an evaluation of 4169 tweets retrieved using alternative text mining methods, the recall of the tweet-collection approach was 0.95. CONCLUSIONS: Our contributions include (i) evidence that rare health-related events are indeed reported on Twitter, (ii) a generalizable, systematic NLP approach for collecting sparse tweets, (iii) a semi-automatic method to identify undetected tweets (false negatives), and (iv) a collection of publicly available tweets by pregnant users with birth defect outcomes, which could be used for future epidemiological analysis. In future work, the annotated tweets could be used to train machine learning algorithms to automatically identify users reporting birth defect outcomes, enabling the large-scale use of social media mining as a complementary method for such epidemiological research.

Apoptosis of colon cancer cells under the effect of geldanamycin derivate.

AIM: The apoptotic effect of geldanamycin derivative may be important for the colorectal cancer therapy. The mechanisms of apoptosis require understanding of the behavior of colon cancer cell line Colo-205 which mimics colon adenocarcinoma. Therefore, the effect of IC50 dose of 17-allylamino-17-demethoxygeldanamycin (17-AAG) on the colon cancer cells in vitro was studied for its anti-apoptotic activity. METHOD: Apoptotic ratio of the Colo-205 cells was determined after 17-AAG application with terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining and apoptosis related genes. Apoptosis signal path related key mitochondrial proteins, cytochrome c, bcl-2, caspase 9 and Apaf-1 expression were examined with RT-PCR method. RESULTS: 17-AAG caused induction of cell death. Apoptotic related genes such as cytochrome-c, Apaf-1 and caspase-9 protein expressions were increased significantly (p < 0.05) and anti-apoptotic bcl-2 expression was decreased significantly (p < 0.05). Our results indicated that the application of 17-AAG on Colo-205 cells showed anticancer effect by the apoptosis due to alteration of apoptotic genes. CONCLUSION: The apoptotic effect of 17-AAG as an natural product for alternative medicine would be very important for the success and quality of life during the treatment of colon carcinoma with the combination of anticancer drugs (Tab. 1, Fig. 2, Ref. 32).

Efficacy and safety of grazoprevir + ribavirin for 12 or 24 weeks in treatment-naïve patients with hepatitis C virus genotype 1 infection.

Grazoprevir (GZR) is a second-generation hepatitis C virus NS3/4A protease inhibitor. The aim of this study was to evaluate GZR plus ribavirin (RBV) in patients with HCV GT1 infection. Noncirrhotic, IL28B CC patients with HCV genotype 1 infection were randomized to GZR 100 mg once daily and RBV for 12 or 24 weeks. Patients in the 12-week arm with detectable HCV RNA at treatment week 4 (TW4) had treatment extended to 24 weeks (response-guided therapy, RGT). The primary endpoint was sustained virologic response (SVR12) at follow-up week 12 (HCV RNA <25 IU/mL) in the per-protocol (PP) population (excluding patients with important protocol deviations). Twenty-six patients were randomized and 22 were included in the PP population. SVR12 was 58.3% (7 of 12) and 90% (9 of 10) in the RGT and 24-week arms, respectively. Seven PP patients had virologic failure, including one patient in the 24-week arm who relapsed after follow-up week 12. All three breakthrough patients had wild-type (WT) virus at baseline and developed breakthrough at TW6 or TW12 with Y56H, A156T and D168A/N mutations. Of the five relapse patients, four had WT at baseline (at relapse three had WT and one had V55A and D168A), and one had S122A/T at baseline and S122T at relapse. There were no serious adverse events (AEs), discontinuations due to AEs or grade 3/4 elevations in total and/or direct bilirubin. Grazoprevir plus RBV was associated with a rapid and sustained suppression of HCV RNA. These results support further evaluation of grazoprevir-based regimens (NCT01716156; protocol P039).

Historical epidemiology of hepatitis C virus (HCV) in select countries - volume 2.

Chronic hepatitis C virus (HCV) infection is a leading cause of liver related morbidity and mortality. In many countries, there is a lack of comprehensive epidemiological data that are crucial in implementing disease control measures as new treatment options become available. Published literature, unpublished data and expert consensus were used to determine key parameters, including prevalence, viremia, genotype and the number of patients diagnosed and treated. In this study of 15 countries, viremic prevalence ranged from 0.13% in the Netherlands to 2.91% in Russia. The largest viremic populations were in India (8 666 000 cases) and Russia (4 162 000 cases). In most countries, males had a higher rate of infections, likely due to higher rates of injection drug use (IDU). Estimates characterizing the infected population are critical to focus screening and treatment efforts as new therapeutic options become available.

Strategies to manage hepatitis C virus (HCV) infection disease burden - volume 2.

The hepatitis C virus (HCV) epidemic was forecasted through 2030 for 15 countries, and the relative impact of two scenarios was considered: (i) increased treatment efficacy while holding the treated population constant and (ii) increased treatment efficacy and increased annual treated population. Increasing levels of diagnosis and treatment, in combination with improved treatment efficacy, were critical for achieving substantial reductions in disease burden. In most countries, the annual treated population had to increase several fold to achieve the largest reductions in HCV-related morbidity and mortality. This suggests that increased capacity for screening and treatment will be critical in many countries. Birth cohort screening is a helpful tool for maximizing resources. In most of the studied countries, the majority of patients were born between 1945 and 1985.

The present and future disease burden of hepatitis C virus (HCV) infections with today's treatment paradigm - volume 2.

Morbidity and mortality attributable to chronic hepatitis C virus (HCV) infection are increasing in many countries as the infected population ages. Models were developed for 15 countries to quantify and characterize the viremic population, as well as estimate the number of new infections and HCV related deaths from 2013 to 2030. Expert consensus was used to determine current treatment levels and outcomes in each country. In most countries, viremic prevalence has already peaked. In every country studied, prevalence begins to decline before 2030, when current treatment levels were held constant. In contrast, cases of advanced liver disease and liver related deaths will continue to increase through 2030 in most countries. The current treatment paradigm is inadequate if large reductions in HCV related morbidity and mortality are to be achieved.

The transplantation of solid organs from HIV-positive donors to HIV-negative recipients: ethical implications.

HIV-positive individuals have traditionally been barred from donating organs due to transmission concerns, but this barrier may soon be lifted in the USA in limited settings when recipients are also infected with HIV. Recipients of livers and kidneys with well-controlled HIV infection have been shown to have similar outcomes to those without HIV, erasing ethical concerns about poorly chosen beneficiaries of precious organs. But the question of whether HIV-negative patients should be disallowed from receiving an organ from an HIV-positive donor has not been adequately explored. In this essay, we will discuss the background to this scenario and the ethical implications of its adoption from the perspectives of autonomy, beneficence/non-maleficence and justice.

Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium.

OBJECTIVE: The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results. METHODS: The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events). RESULTS: In total, 29 teams registered, representing 17 countries. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora. CONCLUSION: To facilitate future work, the datasets-a total of 61 353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.