Статті в журналах: "Online toxicity"

1

Lashkarashvili, Nineli, and Magda Tsintsadze. "Toxicity detection in online Georgian discussions." International Journal of Information Management Data Insights 2, no. 1 (April 2022): 100062. http://dx.doi.org/10.1016/j.jjimei.2022.100062.

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&NA;. "Online self-reporting to monitor chemotherapy toxicity." Reactions Weekly &NA;, no. 1182 (December 2007): 4. http://dx.doi.org/10.2165/00128415-200711820-00009.

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Lobo, Paula Reyero, Enrico Daga, and Harith Alani. "Supporting Online Toxicity Detection with Knowledge Graphs." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 1414–18. http://dx.doi.org/10.1609/icwsm.v16i1.19398.

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Due to the rise in toxic speech on social media and other online platforms, there is a growing need for systems that could automatically flag or filter such content. Various supervised machine learning approaches have been proposed, trained from manually-annotated toxic speech corpora. However, annotators sometimes struggle to judge or to agree on which text is toxic and which group is being targeted in a given text. This could be due to bias, subjectivity, or unfamiliarity with used terminology (e.g. domain language, slang). In this paper, we propose the use of a knowledge graph to help in better understanding such toxic speech annotation issues. Our empirical results show that 3% in a sample of 19k texts mention terms associated with frequently attacked gender and sexual orientation groups that were not correctly identified by the annotators.

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Salminen, Joni, Sercan Sengün, Juan Corporan, Soon-gyo Jung, and Bernard J. Jansen. "Topic-driven toxicity: Exploring the relationship between online toxicity and news topics." PLOS ONE 15, no. 2 (February 21, 2020): e0228723. http://dx.doi.org/10.1371/journal.pone.0228723.

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Goyal, Nitesh, Ian D. Kivlichan, Rachel Rosen, and Lucy Vasserman. "Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (November 7, 2022): 1–28. http://dx.doi.org/10.1145/3555088.

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Machine learning models are commonly used to detect toxicity in online conversations. These models are trained on datasets annotated by human raters. We explore how raters' self-described identities impact how they annotate toxicity in online comments. We first define the concept of Specialized Rater Pools: rater pools formed based on raters' self-described identities, rather than at random. We formed three such rater pools for this study - specialized rater pools of raters from the U.S. who identify as African American, LGBTQ, and those who identify as neither. Each of these rater pools annotated the same set of comments, which contains many references to these identity groups. We found that rater identity is a statistically significant factor in how raters will annotate toxicity for identity-related annotations. Using preliminary content analysis, we examined the comments with the most disagreement between rater pools and found nuanced differences in the toxicity annotations. Next, we trained models on the annotations from each of the different rater pools, and compared the scores of these models on comments from several test sets. Finally, we discuss how using raters that self-identify with the subjects of comments can create more inclusive machine learning models, and provide more nuanced ratings than those by random raters.

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Machová, Kristína, Marian Mach, and Matej Vasilko. "Recognition of Toxicity of Reviews in Online Discussions." Acta Polytechnica Hungarica 19, no. 4 (2022): 7–26. http://dx.doi.org/10.12700/aph.19.4.2022.4.1.

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Brown, A. "Kidney Toxicity Related to Herbs and Dietary Supplements: Online Table of Kidney Toxicity Cases." Journal of the Academy of Nutrition and Dietetics 116, no. 9 (September 2016): A84. http://dx.doi.org/10.1016/j.jand.2016.06.300.

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Chong, Yun Yu, and Haewoon Kwak. "Understanding Toxicity Triggers on Reddit in the Context of Singapore." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 1383–87. http://dx.doi.org/10.1609/icwsm.v16i1.19392.

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While the contagious nature of online toxicity sparked increasing interest in its early detection and prevention, most of the literature focuses on the Western world. In this work, we demonstrate that 1) it is possible to detect toxicity triggers in an Asian online community, and 2) toxicity triggers can be strikingly different between Western and Eastern contexts.

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Basch, Ethan, David Artz, Dorothy Dulko, Kevin Scher, Paul Sabbatini, Martee Hensley, Nandita Mitra, John Speakman, Mary McCabe, and Deborah Schrag. "Patient Online Self-Reporting of Toxicity Symptoms During Chemotherapy." Journal of Clinical Oncology 23, no. 15 (May 20, 2005): 3552–61. http://dx.doi.org/10.1200/jco.2005.04.275.

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Purpose Tracking symptoms related to treatment toxicity is standard practice in routine care and during clinical trials. Currently, clinicians collect symptom information via complex and often inefficient mechanisms, but there is growing interest in collecting outcome information directly from patients. Patients and Methods The National Cancer Institute Common Terminology Criteria for Adverse Events schema for seven common symptoms was adapted into a Web-based patient-reporting system, accessible from desktop computers in outpatient clinics and from home computers. Eighty patients with gynecologic malignancies beginning standard chemotherapy regimens were enrolled between April and September 2004. During an 8-week observation period, participants were encouraged to log in and report symptoms at each follow-up visit, or alternatively, to access the system from home. Results All patients completed an initial log in. At each subsequent appointment, most enrollees (80% to 85%) reported symptoms using the online system, with a mean of three follow-up visits per patient during the observation period (range, one to six). Sixty of 80 patients (75%) logged in at least once from home. Use was significantly associated with prior Internet experience. Forty-two severe toxicities (grade 3 to 4) entered from home prompted seven clinician interventions. Most patients (96%) found the system useful and would recommend it to others. Conclusion Patients are capable of reporting symptoms experienced during chemotherapy using a Web-based interface. Assessment in the clinical trial setting and comparison of direct patient- versus clinician-based approaches for reporting symptoms and their severity are warranted.

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Basch, E. M., D. Dulko, D. Artz, K. Scher, P. Sabbatini, M. Hensley, N. Mitra, J. Speakman, M. McCabe, and D. Schrag. "Patient online self-reporting of toxicity symptoms during chemotherapy." Journal of Clinical Oncology 23, no. 16_suppl (June 2005): 6025. http://dx.doi.org/10.1200/jco.2005.23.16_suppl.6025.

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Sher, Sanford P., Carol Aserinsky, James Stuart, and Lois Bird. "Toxicity Test Registry: An Online Entry and Retrieval System." Drug Information Journal 19, no. 1 (January 1985): 49–56. http://dx.doi.org/10.1177/009286158501900108.

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Xia, Yan, Haiyi Zhu, Tun Lu, Peng Zhang, and Ning Gu. "Exploring Antecedents and Consequences of Toxicity in Online Discussions." Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (October 14, 2020): 1–23. http://dx.doi.org/10.1145/3415179.

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Gilda, Shlok, Luiz Giovanini, Mirela Silva, and Daniela Oliveira. "Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversations." Procedia Computer Science 198 (2022): 360–66. http://dx.doi.org/10.1016/j.procs.2021.12.254.

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Sampognaro, Paul, Kathy T. Vo, Megan Richie, Paul D. Blanc, and Kevin Keenan. "Bismuth Subgallate Toxicity in the Age of Online Supplement Use." Neurologist 22, no. 6 (November 2017): 237–40. http://dx.doi.org/10.1097/nrl.0000000000000144.

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15

Reid, Elizabeth, Regan L. Mandryk, Nicole A. Beres, Madison Klarkowski, and Julian Frommel. "Feeling Good and In Control: In-game Tools to Support Targets of Toxicity." Proceedings of the ACM on Human-Computer Interaction 6, CHI PLAY (October 25, 2022): 1–27. http://dx.doi.org/10.1145/3549498.

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Game developers, researchers, and players recognize the harm of toxic behaviour in online games-yet toxicity persists. Players' coping strategies are limited to tools that focus on punishing toxic players (e.g., muting, blocking, reporting), which are inadequate and often misused. To address the needs of players experiencing toxicity, we took inspiration from research in other online spaces that provide support tools for targets of harassment. We iteratively designed and evaluated in-game tools to support targets of toxicity. While we found that most players prefer tools that explicitly address toxicity and increase feelings of control, we also found that tools that solely provide social or emotional support also decrease stress, increase feelings of control, and increase positive affect. Our findings suggest that players may benefit from variety in toxicity support tools that both explicitly address toxicity in the moment and help players cope after it has occurred.

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16

Warner, Jeremy L., Andrew J. Cowan, Aric C. Hall, and Peter C. Yang. "HemOnc.org: A Collaborative Online Knowledge Platform for Oncology Professionals." Journal of Oncology Practice 11, no. 3 (May 2015): e336-e350. http://dx.doi.org/10.1200/jop.2014.001511.

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Future enhancements, including more metadata about drugs and increasingly detailed efficacy and toxicity information, will continue to improve the value of this free source of chemotherapy drug and regimen information.

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17

Vaidya, Ameya, Feng Mai, and Yue Ning. "Empirical Analysis of Multi-Task Learning for Reducing Identity Bias in Toxic Comment Detection." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 683–93. http://dx.doi.org/10.1609/icwsm.v14i1.7334.

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With the recent rise of toxicity in online conversations on social media platforms, using modern machine learning algorithms for toxic comment detection has become a central focus of many online applications. Researchers and companies have developed a variety of models to identify toxicity in online conversations, reviews, or comments with mixed successes. However, many existing approaches have learned to incorrectly associate non-toxic comments that have certain trigger-words (e.g. gay, lesbian, black, muslim) as a potential source of toxicity. In this paper, we evaluate several state-of-the-art models with the specific focus of reducing model bias towards these commonly-attacked identity groups. We propose a multi-task learning model with an attention layer that jointly learns to predict the toxicity of a comment as well as the identities present in the comments in order to reduce this bias. We then compare our model to an array of shallow and deep-learning models using metrics designed especially to test for unintended model bias within these identity groups.

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18

Mutiah, Roihatul, Yen Yen Ari Indrawijaya, Tanaya Jati Dharma, and Jamilah Damaiyanti. "In Silico Study on the Effect of Heliannuol A, B, C, D, E Compounds of Sunflower (Helianthus annuus L.) on Dual PI3K/mTOR (5OQ4) Enzyme." Majalah Obat Tradisional 26, no. 1 (April 7, 2021): 71. http://dx.doi.org/10.22146/mot.57411.

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Heliannuol is a sesquiterpene that has a benzoxepine ring, oxepin. Many derivatives of benzoxepine compounds show anticancer activity by inhibiting the phosphoinositide 3-kinase (PI3K) enzyme. These enzymes play a role in cell proliferation and growth. The study aims to predict the physicochemical properties using Lipinski’s Rule of Five parameters on phosphoinositide 3- kinase (PI3K/Mtor; PDB 5OQ4) enzyme and the toxicity of Heliannuol A, B, C, D, E compounds. The process uses the pkCSM online tool. The validation of receptor 5OQ4 is done using the value parameter RMSD < 2 (Å). Protox online tool dan pkCSM online tool is employed to predict the toxicity using parameter LD50, skin sensitization, Ames toxicity, hepatotoxicity, and toxicity class. The interaction of ligan and enzyme is tested using Molegro Virtual Docker 6.0. Heliannoul A, B, C, D, E compounds fulfill Lipinski’s Rule of Five. The receptor 5OQ4 is known valid using the value of RMSD 0,923 (Å). Heliannuol A, B, C, D, E compounds inhibit Dual PI3K / mTOR enzyme less than Bimiralisib. As a result of the toxicity test of compounds Helliannouls A, B, C, E, and Bimiralisib compounds are included in class 4, while Helliannouls D compounds are included in class 5.

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Carton, Samuel, Qiaozhu Mei, and Paul Resnick. "Feature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 95–106. http://dx.doi.org/10.1609/icwsm.v14i1.7282.

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We present an experimental assessment of the impact of feature attribution-style explanations on human performance in predicting the consensus toxicity of social media posts with advice from an unreliable machine learning model. By doing so we add to a small but growing body of literature inspecting the utility of interpretable machine learning in terms of human outcomes. We also evaluate interpretable machine learning for the first time in the important domain of online toxicity, where fully-automated methods have faced criticism as being inadequate as a measure of toxic behavior.We find that, contrary to expectations, explanations have no significant impact on accuracy or agreement with model predictions, through they do change the distribution of subject error somewhat while reducing the cognitive burden of the task for subjects. Our results contribute to the recognition of an intriguing expectation gap in the field of interpretable machine learning between the general excitement the field has engendered and the ambiguous results of recent experimental work, including this study.

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Xiong, Guoli, Zhenxing Wu, Jiacai Yi, Li Fu, Zhijiang Yang, Changyu Hsieh, Mingzhu Yin, et al. "ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties." Nucleic Acids Research 49, W1 (April 24, 2021): W5—W14. http://dx.doi.org/10.1093/nar/gkab255.

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Abstract Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that absorption, distribution, metabolism, excretion and toxicity (ADMET) should be evaluated as early as possible. In silico ADMET evaluation models have been developed as an additional tool to assist medicinal chemists in the design and optimization of leads. Here, we announced the release of ADMETlab 2.0, a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules (751 substructures). A multi-task graph attention framework was employed to develop the robust and accurate models in ADMETlab 2.0. The batch computation module was provided in response to numerous requests from users, and the representation of the results was further optimized. The ADMETlab 2.0 server is freely available, without registration, at https://admetmesh.scbdd.com/.

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Dessì, Danilo, Diego Reforgiato Recupero, and Harald Sack. "An Assessment of Deep Learning Models and Word Embeddings for Toxicity Detection within Online Textual Comments." Electronics 10, no. 7 (March 25, 2021): 779. http://dx.doi.org/10.3390/electronics10070779.

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Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task.

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Andonova, Lily, Maya Georgieva, and Alexander Zlatkov. "VIRTUAL TOXICITY AND DRUG LIKENESS OF NEWLY SYNTHESIZED METHYLXANTHINES WITH N1 ARYLPIPERAZINE MOIETY." CBU International Conference Proceedings 6 (September 25, 2018): 1001–6. http://dx.doi.org/10.12955/cbup.v6.1285.

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Aiming to obtain preliminary information on the toxicity, stability and pharmacokinetic behavior of a group of 12 methylxanthines, containing an arylpiperazine moiety at N1, we applied three virtual methods for prediction. The online hazard-screening tool-PBT profiler was used for toxicity evaluation. The pharmacokinetic behavior and drug like properties of the tested compounds were predicted by two online platforms: Molinspiration Cheminformatics and OSIRIS web-based server. The PBT-profiler tool determined, that the investigated compounds are soil persistent, do not bioaccumulate in the food chain and with the exception of the structures containing bulky bi-phenyl substituents all other molecules are of moderate toxicity. All tested compounds meet Lipinski’s Rule of Five border conditions and have high values for drug likeness and drug score, which makes them suitable for future optimizations.

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23

Christiansen, Rasmus Lübeck, Lars Dysager, Christian Rønn Hansen, Henrik Robenhagen Jensen, Tine Schytte, Christina Junker Nyborg, Anders Smedegaard Bertelsen, et al. "Online adaptive radiotherapy potentially reduces toxicity for high-risk prostate cancer treatment." Radiotherapy and Oncology 167 (February 2022): 165–71. http://dx.doi.org/10.1016/j.radonc.2021.12.013.

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Elad, Tal, Ronen Almog, Sharon Yagur-Kroll, Klimentiy Levkov, Sahar Melamed, Yosi Shacham-Diamand, and Shimshon Belkin. "Online Monitoring of Water Toxicity by Use of Bioluminescent Reporter Bacterial Biochips." Environmental Science & Technology 45, no. 19 (October 2011): 8536–44. http://dx.doi.org/10.1021/es202465c.

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Shen, Cuihua, Qiusi Sun, Taeyoung Kim, Grace Wolff, Rabindra Ratan, and Dmitri Williams. "Viral vitriol: Predictors and contagion of online toxicity in World of Tanks." Computers in Human Behavior 108 (July 2020): 106343. http://dx.doi.org/10.1016/j.chb.2020.106343.

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Machová, Kristína, Marián Mach, and Kamil Adamišín. "Machine Learning and Lexicon Approach to Texts Processing in the Detection of Degrees of Toxicity in Online Discussions." Sensors 22, no. 17 (August 27, 2022): 6468. http://dx.doi.org/10.3390/s22176468.

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This article focuses on the problem of detecting toxicity in online discussions. Toxicity is currently a serious problem when people are largely influenced by opinions on social networks. We offer a solution based on classification models using machine learning methods to classify short texts on social networks into multiple degrees of toxicity. The classification models used both classic methods of machine learning, such as naïve Bayes and SVM (support vector machine) as well ensemble methods, such as bagging and RF (random forest). The models were created using text data, which we extracted from social networks in the Slovak language. The labelling of our dataset of short texts into multiple classes—the degrees of toxicity—was provided automatically by our method based on the lexicon approach to texts processing. This lexicon method required creating a dictionary of toxic words in the Slovak language, which is another contribution of the work. Finally, an application was created based on the learned machine learning models, which can be used to detect the degree of toxicity of new social network comments as well as for experimentation with various machine learning methods. We achieved the best results using an SVM—average value of accuracy = 0.89 and F1 = 0.79. This model also outperformed the ensemble learning by the RF and Bagging methods; however, the ensemble learning methods achieved better results than the naïve Bayes method.

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Munasinghe, B. M., Nishanthan Subramaniam, Nimalan Srisothinathan, and B. D. W. Jayamanne. "An online study of knowledge and practices of local anesthetic systemic toxicity among doctors in Sri Lanka." Anaesthesia, Pain & Intensive Care 26, no. 1 (February 2, 2022): 69–74. http://dx.doi.org/10.35975/apic.v26i1.1770.

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Background: Inadvertent over-dosage or intravascular injections may still lead to systemic toxicity. Local anesthetic systemic toxicity (LAST) could be potentially life threatening. This study focused on the current knowledge and practices in use of LA by the doctors in Sri Lanka and their ability to detect and manage an event of LAST. Methodology: A descriptive cross-sectional study was conducted among doctors in Sri Lanka using an online self-administered questionnaire based on AAGBI guidelines (2010). Descriptive statistics were analyzed by cross-tabulations and presented as numbers and percentages using IBM-SPSS 25. Results: The response rate was 60% out of 600 doctors. Majority were males (58%) while 45% of the respondents were anesthetists. Ultrasound was used by 47.4% during specific LA use. The majority (74%) considered total body weight for dose calculations. Around 50% of the respondents identified bupivacaine as the most cardiotoxic. The majority (77%) utilized some form of monitoring and were knowledgeable on identification, prevention and initial management of LAST. Approximately 45% identified Intralipid (ILE) as the definitive treatment of LAST, out of which, 66.8% knew the correct dose, 77.2% and 26.5%, the availability and the location of the stored drug, respectively. Conclusion: The basic knowledge about LAST was satisfactory among the respondents. A statistically significant difference on knowledge on maximum safe doses of LA, ILE in established LAST, its dosage and the availability was identified between anesthetists and non-anesthesia doctors; and postgraduate trainees and the rest of the doctors. Overall, significant lapses were noted with regard to the use of total body weight for dose calculations, use of ultrasound during LA administration and dosage, availability and storage of the definitive therapy and ILE. Abbreviations: LA - Local anesthetics; LAST - Local anesthetic systemic toxicity; ILE - Intralipid Key words: Local Anesthetic Systemic Toxicity; LAST, cardiac toxicity, Intralipid Citation: Munasinghe BM, Subramaniam N, Srisothinathan N, Jayamanne BDW. An online study of knowledge and practices of local anesthetic systemic toxicity among doctors in Sri Lanka. Anaesth. pain intensive care 2022;26(1):69-74. DOI: 10.35975/apic.v26i1.1770 Received: August 27, 2021, Reviewed: October 05, 2021, Accepted: October 10, 2021

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Varsou, Dimitra-Danai, Georgia Melagraki, Haralambos Sarimveis, and Antreas Afantitis. "MouseTox: An online toxicity assessment tool for small molecules through Enalos Cloud platform." Food and Chemical Toxicology 110 (December 2017): 83–93. http://dx.doi.org/10.1016/j.fct.2017.09.058.

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Ugurluer, Gamze, Banu Atalar, Teuta Zoto Mustafayev, Gorkem Gungor, Gokhan Aydin, Meric Sengoz, Ufuk Abacioglu, Mustafa Bilal Tuna, Ali Riza Kural, and Enis Ozyar. "Magnetic resonance image-guided adaptive stereotactic body radiotherapy for prostate cancer: preliminary results of outcome and toxicity." British Journal of Radiology 94, no. 1117 (January 1, 2021): 20200696. http://dx.doi.org/10.1259/bjr.20200696.

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Objective: Using moderate or ultra-hypofractionation, which is also known as stereotactic body radiotherapy (SBRT) for treatment of localized prostate cancer patients has been increased. We present our preliminary results on the clinical utilization of MRI-guided adaptive radiotherapy (MRgRT) for prostate cancer patients with the workflow, dosimetric parameters, toxicities and prostate-specific antigen (PSA) response. Methods: 50 prostate cancer patients treated with ultra-hypofractionation were included in the study. Treatment was performed with intensity-modulated radiation therapy (step and shoot) technique and daily plan adaptation using MRgRT. The SBRT consisted of 36.25 Gy in 5 fractions with a 7.25 Gy fraction size. The time for workflow steps was documented. Patients were followed for the acute and late toxicities and PSA response. Results: The median follow-up for our cohort was 10 months (range between 3 and 29 months). The median age was 73.5 years (range between 50 and 84 years). MRgRT was well tolerated by all patients. Acute genitourinary (GU) toxicity rate of Grade 1 and Grade 2 was 28 and 36%, respectively. Only 6% of patients had acute Grade 1 gastrointestinal (GI) toxicity and there was no Grade ≥ 2 GI toxicity. To date, late Grade 1 GU toxicity was experienced by 24% of patients, 2% of patients experienced Grade 2 GU toxicity and 6% of patients reported Grade 2 GI toxicity. Due to the short follow-up, PSA nadir has not been reached yet in our cohort. Conclusion: In conclusion, MRgRT represents a new method for delivering SBRT with markerless soft tissue visualization, online adaptive planning and real-time tracking. Our study suggests that ultra-hypofractionation has an acceptable acute and very low late toxicity profile. Advances in knowledge: MRgRT represents a new markerless method for delivering SBRT for localized prostate cancer providing online adaptive planning and real-time tracking and acute and late toxicity profile is acceptable.

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Rogowski, Paul, Rieke von Bestenbostel, Franziska Walter, Katrin Straub, Lukas Nierer, Christopher Kurz, Guillaume Landry, et al. "Feasibility and Early Clinical Experience of Online Adaptive MR-Guided Radiotherapy of Liver Tumors." Cancers 13, no. 7 (March 26, 2021): 1523. http://dx.doi.org/10.3390/cancers13071523.

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Purpose: To assess the feasibility and early results of online adaptive MR-guided radiotherapy (oMRgRT) of liver tumors. Methods: We retrospectively examined consecutive patients with primary or secondary liver lesions treated at our institution using a 0.35T hybrid MR-Linac (Viewray Inc., Mountain View, CA, USA). Online-adaptive treatment planning was used to account for interfractional anatomical changes, and real-time intrafractional motion management using online 2D cine MRI was performed using a respiratory gating approach. Treatment response and toxicity were assessed during follow-up. Results: Eleven patients and a total of 15 lesions were evaluated. Histologies included cholangiocarcinomas and metastases of neuroendocrine tumors, colorectal carcinomas, sarcomas and a gastrointestinal stroma tumor. The median BED10 of the PTV prescription doses was 84.4 Gy (range 59.5–112.5 Gy) applied in 3–5 fractions and the mean GTV BED10 was in median 147.9 Gy (range 71.7–200.5 Gy). Online plan adaptation was performed in 98% of fractions. The median overall treatment duration was 53 min. The treatment was feasible and successfully completed in all patients. After a median follow-up of five months, no local failure occurred and no ≥ grade two toxicity was observed. OMRgRT resulted in better PTV coverage and fewer OAR constraint violations. Conclusion: Early results of MR-linac based oMRgRT for the primary and secondary liver tumors are promising. The treatment was feasible in all cases and well tolerated with minimal toxicity. The technique should be compared to conventional SBRT in further studies to assess the advantages of the technique.

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Wong, Andrew, and Monique Mun. "A Case of Kratom Overdose in a Pediatric Patient." Case Reports in Psychiatry 2020 (August 13, 2020): 1–2. http://dx.doi.org/10.1155/2020/8818095.

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Kratom is a synthetic opioid that is federally unregulated and thus available for purchase through online retail and smoke shops in most states. Due to its availability, there is concern for misuse in the pediatric population. There is existing literature describing toxicity of kratom in adults; yet, to the best of our knowledge, there are no cases describing kratom toxicity in the pediatric population. Thus, we present the case of kratom overdose in a pediatric patient.

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Yu, Dengbin, Junfeng Zhai, Changyu Liu, Xueping Zhang, Lu Bai, Yizhe Wang, and Shaojun Dong. "Small Microbial Three-Electrode Cell Based Biosensor for Online Detection of Acute Water Toxicity." ACS Sensors 2, no. 11 (October 30, 2017): 1637–43. http://dx.doi.org/10.1021/acssensors.7b00484.

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Gurung, Anup, Woo-Chang Kang, Beom-Soo Shin, Ju Sik Cho, and Sang-Eun Oh. "Development of an Online Sulfur-Oxidizing Bacteria Biosensor for the Monitoring of Water Toxicity." Applied Biochemistry and Biotechnology 174, no. 7 (September 25, 2014): 2585–93. http://dx.doi.org/10.1007/s12010-014-1210-x.

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Zhang, Xiaoyuan, and Yu Liu. "Potential toxicity and implication of halogenated byproducts generated in MBR online‐cleaning with hypochlorite." Journal of Chemical Technology & Biotechnology 95, no. 1 (October 15, 2019): 20–26. http://dx.doi.org/10.1002/jctb.6199.

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Yadav, Neha, Reshu Madan Sanan, Shefali Phogat, Nisha Yadav, Nupur Dabas, and Pankaj Ritwal. "Private Practitioners Knowledge of Local Anaesthetic Systemic Toxicity (LAST)." Journal of Evolution of Medical and Dental Sciences 10, no. 41 (October 11, 2021): 3571–76. http://dx.doi.org/10.14260/jemds/2021/724.

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BACKGROUND Local anaesthetics (LAs) by definition are drugs that reversibly block the transmission of a nerve impulse, causing reversible absence of pain sensations without affecting consciousness. As a complication of local anaesthetic administration, a condition called as local anaesthetic systemic toxicity (LAST) can occur which affects central nervous system (CNS) and cardiovascular system (CVS). Local anaesthetic systemic toxicity is a very serious condition that can cause death. In LAST, blood levels of anaesthetic agent exceed the maximum dose, usually as a result of multiple injections or an accidental injection into a vein. The main objective of this survey was to assess the knowledge of LAST among private practitioners and to spread awareness about this common but often neglected complication of local anaesthetic administration. METHODS A survey was conducted with a questionnaire comprising of 15 questions for 251 private practitioners (registered in Indian Dental Association, Gurugram). The survey was mailed to the participants and the response was collected through online mode only. Using Excel program, the results were organized in graphs and tables, and presented as descriptive statistics. RESULTS Of the 251 surveys sent online, 151 were completed, with a survey completion rate of 60 %. On an average, only 39.9 % of the participating dentists had adequate knowledge of local anaesthetic systemic toxicity. Based on the survey, only 3.3 % of participating dentists knew how to use lipid treatment and 55.6 % had no idea about lipid treatment. CONCLUSIONS If LAST occurs, the key is to recognize it immediately and institute appropriate management. Based on the survey, there is a need to create awareness about local anaesthetic systemic toxicity and its management among dentists. KEY WORDS Local Anaesthesia, Last, Complication, Toxicity, Overdose

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Udugade, Swati, Harshal Tare, Babaso Udugade, Vijaykumar Wakale, and Chetan Pulate. "in silico Analysis of 4-((1-(3-Nitrophenyl)-5-oxo-3-phenyl-4,5-dihydro-1H-pyrazol-4- yl)methyl)benzoic Acid: An Emerging 3-CLpro Non-peptidic Inhibitors for COVID-19." Asian Journal of Organic & Medicinal Chemistry 7, no. 1 (2022): 137–42. http://dx.doi.org/10.14233/ajomc.2022.ajomc-p376.

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Existing study involves effort to forecast absorption, distribution, metabolism, excretion, toxicity and polypharmacological profile of 4-((1-(3-nitrophenyl)-5-oxo-3-phenyl-4,5-dihydro-1H-pyrazol-4-yl)- methyl)benzoic acid (NPOPPBA), a 3CLpro non-peptidic inhibitors with the aid of by means of in silico methods. In the beginning, PASS online computational software’s utilized to investigate pharmacological action of NPOPPBA. Followed by, Swiss ADME online tool utilized to estimate of physical parameters, chemical properties, log P, solubility, absorption, distribution, metabolism, excretion, drug like property and medicinal chemistry. Lastly, XUNDRUG eMolTox online tool utilized to forecast toxicity. End result of PASS online prediction tool confirmed that NPOPPBA may be used as Fusarinine-C ornithinesterase inhibitor, which may be beneficial in most cancers treatment; Swiss ADME end outcome confirmed molecule may orally absorbable but not able to pass lipophilic membrane of brain and hence will not able to show undesirable effect centrally. Observations of bioavailability study shows NPOPPBA may be taken into consideration as a drug like because it shows all parameters falls inside red location of graph. The log P become observed about 3.7 signifying NPOPPBA may absorb on oral administration, solubility in water was found to be poor demonstrating need of attempts to enhance it in formulation development. This molecule can also additionally inhibits CYP2C19 which performs an essential function in metabolism of drugs live omeprazole, which are utilized to cure of gastrointestinal disorder and need to take precaution in the course of use of proton pump inhibitors. It is also CYP2C9 inhibitor therefore due care need to be taken for drugs undergoing phase I metabolism. XUNDRUG online resource outcomes confirmed hepatic and nephron toxicity possibility of NPOPPBA. Here from this existing analysis, it may be confirmed that the beneficial absorption, distribution, metabolism, excretion, drug like property and easy in synthesis of current molecule recommended that NPOPPBA may be an amazing medicinal agent in upcoming COVID-19 treatment.

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Doolin, Jim, Daniel Aaron Roberts, Christina Cibotti, Scott M. Devlin, Holly Dowling, Virginia Seery, Noah Xavier Tocci, et al. "Electronic monitoring of patient-reported adherence and symptoms on oral chemotherapy at an academic and a community site." Journal of Clinical Oncology 36, no. 30_suppl (October 20, 2018): 283. http://dx.doi.org/10.1200/jco.2018.36.30_suppl.283.

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283 Background: Monitoring of toxicity and adherence is often lacking for patients recently started on oral chemotherapy. National guidelines recommend active outreach to patients within a week after treatment start. We developed an online tool to actively reach out to patients newly started on oral chemotherapy at one academic medical center and community practice. Methods: A multi-disciplinary team, including patients, developed an online oral chemotherapy adherence, symptom, and financial toxicity assessment tool within REDCap. We implemented this tool for new oral chemotherapy prescriptions in May 2018 in the gastrointestinal oncology group of an academic medical center and a general community practice. To quantify the impact of this tool on symptom management, we completed a retrospective analysis of patients receiving new oral chemotherapy prescriptions at these same sites, in the 13 months immediately preceding clinical implementation of the online tool, May 2017 to May 2018. Results: In the pre-intervention historical cohort (n = 58) the median time to first symptom assessment by a clinician was 7 days (range 1 – 41 days, SD 7 days), median time to identifying a new or worsening symptom was 10 days (range 1-55 days, SD 10 days), and median time to clinical action regarding a new or worsening symptom was 10 days (range 1-104, SD 20 days). Our first intervention patient used the online tool in May 2018 to report symptoms of “nausea and fatigue,” 4 days after starting oral chemotherapy. This resulted in an oncology clinical nurse calling the patient to review symptom management by phone. Conclusions: The median time to first symptom assessment in our historical control cohort is 7 days, with standard deviation of 7 days, suggesting potential room for improvement. Thus far, the online tool has been completed by one patient. Further data will be reported regarding the uptake of this tool, the tool’s impact on quality measures, and patient reported symptoms, adherence, and financial toxicity.

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Bersani, Francesco Saverio, Ornella Corazza, Gabriella Albano, Giuseppe Valeriani, Rita Santacroce, Flaminia Bolzan Mariotti Posocco, Eduardo Cinosi, et al. "25C-NBOMe: Preliminary Data on Pharmacology, Psychoactive Effects, and Toxicity of a New Potent and Dangerous Hallucinogenic Drug." BioMed Research International 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/734749.

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Introduction. The use of novel psychoactive substances (NPSs) has rapidly increased as well as their online availability. The aim of this paper is to provide a comprehensive review of the nature and the risks associated with 25C-NBOMe, which has recently appeared in the drug market.Methods. A systematic analysis of the scientific literature and a qualitative assessment of online and media resources (e.g., e-newsgroups, chat-rooms, and e-newsletters) in 10 languages were carried out.Results. 25C-NBOMe is sold online as legal LSD or as research chemical with different designations such as “Boom,” “Pandora,” “Holland film,” or “N-bomb.” It is a partial agonist of 5-HT2A receptors. It is usually ingested orally/sublingually and, less commonly, nasally, through injection, vaginally, rectally, and smoked. Its effects include sublingual numbing, stimulation, “body high,” hallucinations, dissociation, and anxiety. 25C-NBOMe presents high risk of overdoses; acute toxicity and fatalities have been reported.Conclusions. 25C-NBOMe consumption represents an emerging phenomenon with potential harmful effects. Its use is increased by its online availability at low costs. Health and other professionals should be informed about this new trend of substance use.

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JITĂREANU, Alexandra, Ioana-Cezara CABA, Adriana TRIFAN, Silvica PĂDUREANU, and Luminița AGOROAEI. "Triticum aestivum Assay - A Useful Tool for Environmental Monitoring and Toxicity Assessment." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 47, no. 4 (December 4, 2019): 1005–18. http://dx.doi.org/10.15835/nbha47411349.

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The present review summarizes the literature data regarding the application of Triticum aestivum assay as an alternative method for toxicity assessment of environmental pollutants or potential therapeutic agents. Plant bioassays present several advantages among other biological assays (simplicity, low cost, rapid test activation, a wide array of assessment endpoints). They present a good correlation with animal and human cells models, and are a reliable tool for genotoxicity assessment. Furthermore, in the context of toxicology guidelines that promote the substitution of assays using animal models with other bioassays, genotoxicity assays using higher plants models have gained in popularity. The present review focuses on three major aspects regarding Triticum aestivum assay - its utility in environmental pollution monitoring, its application in genotoxicity assessment studies, and its application in phytotoxicity evaluation of nanomaterials. ********* In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 4, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue. *********

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Gandhare, B., S. Kavimani, and B. Rajkapoor. "Acute and Subacute Toxicity Study of Methanolic Extract of ceiba pentandra (Linn.) Gaertn. on Rats." Journal of Scientific Research 5, no. 2 (April 22, 2013): 315–24. http://dx.doi.org/10.3329/jsr.v5i2.11800.

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The purpose of toxicity testing is to provide adequate database to make decisions concerning the toxicology properties of chemical and commercial products. In some situations, the purpose is to decide whether a material will be safe. Under the conditions of expected use in other situations, the objective is to establish the safe limits in condition of use. The methanolic extract of ceiba pentandra was evaluated for acute and subacute toxicity in adult Wistar rats. For the study of acute toxicity, the methanol extract of roots of C. pentandra (MCP) was given separately in various doses (50, 500, 1000, 2000 mg/kg) by oral route. The results showed no signs of toxicity such as general behavior change, mortality, or change in gross appearance of internal organs. Subacute toxicity was studied by daily oral doses 100, 400 and 750 mg/kg (low dose, intermediate dose and high dose) orally for 28 days. The results showed no abnormalities in treated groups as compared to the controls. Although significantly different, all of the values were within normal limits. Neither gross abnormalities nor histopathological changes were observed.Keywords: Acute toxicity; Subacute toxicity; ceiba pentandra.© 2013 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi: http://dx.doi.org/10.3329/jsr.v5i2.11800 J. Sci. Res. 5 (2), 315-324 (2013)

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Almerekhi, Hind, Haewoon Kwak, and Bernard J. Jansen. "Investigating toxicity changes of cross-community redditors from 2 billion posts and comments." PeerJ Computer Science 8 (August 18, 2022): e1059. http://dx.doi.org/10.7717/peerj-cs.1059.

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This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the same community, across multiple communities, and over time. As for the toxicity detection performance, the BERT model achieved a 91.27% classification accuracy and an area under the receiver operating characteristic curve (AUC) score of 0.963 and outperformed several baseline machine learning and neural network models. The user behavior toxicity analysis showed that 16.11% of users publish toxic posts, and 13.28% of users publish toxic comments. However, results showed that 30.68% of users publishing posts and 81.67% of users publishing comments exhibit changes in their toxicity across different communities, indicating that users adapt their behavior to the communities’ norms. Furthermore, time series analysis with the Granger causality test of the volume of links and toxicity in user content showed that toxic comments are Granger caused by links in comments.

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Møller, P. K., L. Dysager, U. Bernchou, A. S. Bertelsen, C. Brink, F. Mahmood, H. R. Jensen, et al. "OC-0136 Acute toxicity reported by patients with prostate cancer receiving online MR-guided radiotherapy." Radiotherapy and Oncology 170 (May 2022): S112—S113. http://dx.doi.org/10.1016/s0167-8140(22)02512-9.

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Abdelaziz, Ahmed, Yurii Sushko, Sergii Novotarskyi, Robert Korner, Stefan Brandmaier, and Igor Tetko. "Using Online Tool (iPrior) for Modeling ToxCast™ Assays Towards Prioritization of Animal Toxicity Testing." Combinatorial Chemistry & High Throughput Screening 18, no. 4 (April 30, 2015): 420–38. http://dx.doi.org/10.2174/1386207318666150305155255.

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Segura, Pedro A., Karim Saadi, Alexandra Clair, Marc-André Lecours, and Viviane Yargeau. "Application of XCMS Online and toxicity bioassays to the study of transformation products of levofloxacin." Water Science and Technology 72, no. 9 (July 17, 2015): 1578–87. http://dx.doi.org/10.2166/wst.2015.369.

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We studied the nature and antimicrobial activity of ozonolysis transformation products (OTPs) of levofloxacin (LEV), a frequently detected fluoroquinolone antimicrobial in environmental waters. Two bioassays, the Kirby–Bauer test and the broth microdilution assay, were used to measure changes in the antimicrobial activity of solutions at low LEV to O3 molar ratios (2:1, 2:3 and 1:3) compared to solutions without added O3 (LEV:O3 1:0). The Kirby–Bauer test was not sensitive enough to detect significant differences in the growth inhibition zones in samples LEV:O3 2:1 and LEV:O3 1:0; however, the broth microdilution assay showed that bacterial growth inhibition was significantly lower (P < 0.001) in the solutions exposed to O3. Loss of antimicrobial activity in LEV:O3 2:1 solutions of (48 ± 16)% was in agreement with the concentration decrease of LEV of (36 ± 3)% in those same samples. A method of identification of OTPs using XCMS Online was applied to LEV:O3 2:1 and 1:0 samples and indicated the presence of an OTP of LEV of formula C18H20O5N3F, which was identified as LEV-N-oxide. The molecular structure of this compound was partially confirmed by tandem mass spectrometry experiments. This study showed that even at sub-optimal ozone doses, OTPs of higher antimicrobial activity than LEV were not formed.

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Feng, Shuo, Sifeng Mao, Qiang Zhang, Weiwei Li, and Jin-Ming Lin. "Online Analysis of Drug Toxicity to Cells with Shear Stress on an Integrated Microfluidic Chip." ACS Sensors 4, no. 2 (January 28, 2019): 521–27. http://dx.doi.org/10.1021/acssensors.8b01696.

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Grozav, A. M., S. Ye Deyneka, V. O. Chornous, and V. I. Antoniychuk. "Antimicrobial activity of 5-nitrovinylimidazoles." Chernivtsi University Scientific Herald. Chemistry, no. 818 (2019): 52–58. http://dx.doi.org/10.31861/chem-2019-818-07.

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A rapid development of the microbial resistance against antibiotics is one of the most acute problems occurring at contagious diseases treatment. That is why the search of new highly active antimicrobial drugs is a problem of the constant and significant scientific importance. Using the “Pass Online” software, a series of compounds with potentially high antimicrobial activity/inactivity efficiency has been selected among the derivatives of the 5-carbofunctionalized imidazoles. It was found that the 5-nitrovinylimidazoles should exhibit the highest activity. Nitrofural and Clotrimazole are used commonly in medicine as antimicrobial and antifungal medicines and they were selected as the reference compounds to compare an activity of the compounds involved into this investigation. Five inhibition activity indexes were used to evaluate a potential antimicrobial efficiency of the proposed compounds: an ability to inhibit the ATPase of the lipid transfer; the chloride peroxidase, the phthalate-1,6-beta-glucosidase; the NADPH-peroxidase and the endo-1,6-beta-glucosidase. The results obtained in this investigation have proven that these indexes are quite high for both the proposed novel compounds and for the reference medicines. All theoretical results have also been verified experimentally and all the reported compounds have shown a sufficient antimicrobial efficiency against reference strains of the bacteria Staphylococcus Aureus, Eschericia Coli and the fungi Candida Albicans when applied as solutions with concentrations 0.24-500 μg/L. Therefore, they may have a significant potential for the further investigations. An acute toxicity of the compounds has been evaluated by using the software «GUSAR Online Acute Rat Toxicity Prediction». An additional relevance control of the toxicity evaluation was realized by the toxicity cross-verification with the reference compounds Clotrimazole and Nitrofural. It was found that the reported 5-nitrovinylimidazoles can be classified as the fourth toxicity class chemicals.

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Payne, Heather Ann, Suneil Jain, Clive Peedell, Albert Edwards, James Andrew Thomas, Prantik Das, Amanda Hansson Hedblom, Emily Woodward, Rhodri Saunders, and Amit Bahl. "Delphi study to identify consensus on patient selection for hydrogel rectal spacer use during radiation therapy for prostate cancer in the UK." BMJ Open 12, no. 7 (July 2022): e060506. http://dx.doi.org/10.1136/bmjopen-2021-060506.

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ObjectivesTo identify consensus on patient prioritisation for rectal hydrogel spacer use during radiation therapy for the treatment of prostate cancer in the UK.DesignDelphi study consisting of two rounds of online questionnaires, two virtual advisory board meetings and a final online questionnaire.SettingRadical radiation therapy for localised and locally advanced prostate cancer in the UK.ParticipantsSix leading clinical oncologists and one urologist from across the UK.InterventionsRectal hydrogel spacer.Primary and secondary outcome measuresNone reported.ResultsThe panel reached consensus on the importance of minimising toxicity for treatments with curative intent and that even low-grade toxicity-related adverse events can significantly impact quality of life. There was agreement that despite meeting rectal dose constraints, too many patients experience rectal toxicity and that rectal hydrogel spacers in eligible patients significantly reduces toxicity-related adverse events. However, as a consequence of funding limitations, patients need to be prioritised for spacer use. A higher benefit of spacers can be expected in patients on anticoagulation and in patients with diabetes or inflammatory bowel disease, but consensus could not be reached regarding patient groups expected to benefit less. While radiation therapy regimen is not a main factor determining prioritisation, higher benefit is expected in ultrahypofractionated regimens.ConclusionThere is a strong and general agreement that all patients with prostate cancer undergoing radical radiation therapy have the potential to benefit from hydrogel spacers. Currently, not all patients who could potentially benefit can access hydrogel spacers, and access is unequal. Implementation of the consensus recommendations would likely help prioritise and equalise access to rectal spacers for patients in the UK.

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Du, Yu, Yasong Chen, Lina Zou, Songqiang Deng, Guanghe Li, and Dayi Zhang. "Monitoring the Activated Sludge Activities Affected by Industrial Toxins via an Early-Warning System Based on the Relative Oxygen Uptake Rate (ROUR) Index." Applied Sciences 9, no. 1 (January 4, 2019): 154. http://dx.doi.org/10.3390/app9010154.

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Shock load from industrial wastewater is known to harm the microbial activities of the activated sludge in wastewater treatment plants (WWTPs) and disturb their performance. This study developed a system monitoring the activated sludge activities based on the relative oxygen uptake rate (ROUR) and explored the influential factors with wastewater and the activated sludge samples collected from a typical WWTP in the Taihu Lake of southern Jiangsu province, China. The ROUR was affected by the concentration of toxic substances, mixed liquid suspended solids (MLSS), hydraulic retention time (HRT) and pH. Higher toxin contents significantly decreased the ROUR and the EC50 value of Zn2+, Ni2+, Cr(VI), Cu2+, and Cd2+ was 13.40, 15.54, 97.56, 12.01, and 14.65 mg/L, respectively. The ROUR declined with the increasing HRT and MLSS above 2000 mg/L had buffering capacities for the impacts of toxic substances to some extent. The ROUR remained stable within a broad range pH (6–10), covering most of the operational pH in WWTPs and behaving as an appropriate indicator for monitoring the shock load. A toxicity model assessing and predicting the ROUR was developed and fitted well with experimental data. Coupling the ROUR monitoring system and toxicity model, an online early-warning system was assembled and successfully used for predicting the toxicity of different potential toxic metals. This study provides a new universal toxicity model and an online early-warning system for monitoring the shock load from industrial wastewater, which is useful for improving the performance of WWTPs.

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Masullo Chen, Gina, Ashley Muddiman, Tamar Wilner, Eli Pariser, and Natalie Jomini Stroud. "We Should Not Get Rid of Incivility Online." Social Media + Society 5, no. 3 (July 2019): 205630511986264. http://dx.doi.org/10.1177/2056305119862641.

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Incivility and toxicity have become concepts du jour in research about social media. The clear normative implication in much of this research is that incivility is bad and should be eliminated. Extensive research—including some that we’ve authored—has been dedicated to finding ways to reduce or eliminate incivility from online discussion spaces. In our work as part of the Civic Signals Initiative, we’ve been thinking carefully about what metrics should be adopted by social media platforms eager to create better spaces for their users. When we tell people about this project, removing incivility from the platforms frequently comes up as a suggested metric. In thinking about incivility, however, we’ve become less convinced that it is desirable, or even possible, for social media platforms to remove all uncivil content. In this short essay, we discuss research on incivility, our rationale for a more complicated normative stance regarding incivility, and what other orientations may be more useful. We conclude with a post mortem arguing that we should not abandon research on incivility altogether, but we should recognize the limitations of a concept that is difficult to universalize.

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Wei, Lesong, Xiucai Ye, Tetsuya Sakurai, Zengchao Mu, and Leyi Wei. "ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning." Bioinformatics 38, no. 6 (January 6, 2022): 1514–24. http://dx.doi.org/10.1093/bioinformatics/btac006.

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Abstract Motivation Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from therapeutic peptides is their toxicity toward human cells, and few available algorithms for predicting toxicity are specially designed for short-length peptides. Results We present ToxIBTL, a novel deep learning framework by utilizing the information bottleneck principle and transfer learning to predict the toxicity of peptides as well as proteins. Specifically, we use evolutionary information and physicochemical properties of peptide sequences and integrate the information bottleneck principle into a feature representation learning scheme, by which relevant information is retained and the redundant information is minimized in the obtained features. Moreover, transfer learning is introduced to transfer the common knowledge contained in proteins to peptides, which aims to improve the feature representation capability. Extensive experimental results demonstrate that ToxIBTL not only achieves a higher prediction performance than state-of-the-art methods on the peptide dataset, but also has a competitive performance on the protein dataset. Furthermore, a user-friendly online web server is established as the implementation of the proposed ToxIBTL. Availability and implementation The proposed ToxIBTL and data can be freely accessible at http://server.wei-group.net/ToxIBTL. Our source code is available at https://github.com/WLYLab/ToxIBTL. Supplementary information Supplementary data are available at Bioinformatics online.

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