Botulinum toxin injections during the COVID-19 epidemic: A retrospective chart review

BACKGROUND AND AIM: Clinical services were severely affected globally during the COVID-19 pandemic. This study aimed to characterize the clinical experience of using botulinum toxin (BTX) injections during the COVID-19 pandemic. Method(s): This is a retrospective chart review of patients who received BTX injections from April 2019 to January 2022. Result(s): A total of 105 patients received an BTX injections, out of which 76 (72.4%) were men. The mean age of the patients was 47.9 +/- 15.1 years. The most common indication for receiving BTX injections was dystonia (n = 79;75.2%), followed by hemifacial spasm (n = 22;21%) and miscellaneous movement disorders (n = 4;3.8%). Focal dystonia (n = 45;57%) was the most frequent form of dystonia, followed by segmental dystonia (n = 24;30%). The percentage of generalized dystonia and hemidystonia was 12% and 1%, respectively. Cervical dystonia (44.4%), blepharospasm (17.8%), and writer's cramp (15.6%) were the most frequent forms of focal dystonia. The miscellaneous group included four patients (3.8%) with trigeminal neuralgia, Holmes tremor, dystonic tics, and hemimasticatory spasm. The mean ages of patients in the dystonia, hemifacial spasm, and the miscellaneous groups were 47.7 +/- 14.9 years, 49.2 +/- 14.0 years, and 44.2 +/- 26.0 years, respectively. The mean BTX dose was 131.6 +/- 104.1 U. The mean BTX doses for the dystonia group, hemifacial spasm, and the miscellaneous group were 158.7 +/- 105.3 U, 40.1 +/- 11.3 U, and 100.0 +/- 70.7 U, respectively. Conclusion(s): Most patients in our cohort had dystonia, followed by hemifacial spasm. Among the patients with dystonia, most had focal dystonia, with cervical dystonia being the most common movement disorder. The data obtained in our study is important to increase awareness of the effectiveness of BTX injections in patients with chronic disorders.Copyright © 2023 Annals of Movement Disorders.

Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains

Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no such systems. In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19. Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. To address the scarcity of cross-lingual training data in emergent domains, we present a method utilizing automatic translation, alignment, and filtering to produce English-to-all datasets. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting. We illustrate the capabilities of our system with examples and release all code necessary to train and deploy such a system1 © 2023 Association for Computational Linguistics.

Multiverse: Multilingual Evidence for Fake News Detection.

The rapid spread of deceptive information on the internet can have severe and irreparable consequences. As a result, it is important to develop technology that can detect fake news. Although significant progress has been made in this area, current methods are limited because they focus only on one language and do not incorporate multilingual information. In this work, we propose Multiverse-a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches. Our hypothesis that cross-lingual evidence can be used as a feature for fake news detection is supported by manual experiments based on a set of true (legit) and fake news. Furthermore, we compared our fake news classification system based on the proposed feature with several baselines on two multi-domain datasets of general-topic news and one fake COVID-19 news dataset, showing that (in combination with linguistic features) it yields significant improvements over the baseline models, bringing additional useful signals to the classifier.

Cross-lingual transfer learning for COVID-19 outbreak alignment

The spread of COVID-19 has become a significant and troubling aspect of society in 2020. With millions of cases reported across countries, new outbreaks have occurred and followed patterns of previously affected areas. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. It is useful to ask, can we utilize this knowledge in one country to model the outbreak in another? To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro and micro text features, we train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions. © ACL 2020.All right reserved.

Does Fake News in Different Languages Tell the Same Story? An Analysis of Multi-level Thematic and Emotional Characteristics of News about COVID-19.

Fake news is being generated in different languages, yet existing studies are dominated by English news. The analysis of fake news content has focused on lexical and stylometric features, giving little attention to semantic features. A few studies involving semantic features have either used them as the inputs to classifiers with no interpretations, or treated them in isolation. This research aims to investigate both thematic and emotional characteristics of fake news at different levels and compare them between different languages for the first time. It extends a state-of-the-art topic modeling technique to extract news topics and introduces a divergence measure to assess the importance of thematic characteristics for identifying fake news. We further examine associations of the thematic and emotional characteristics of fake news. The empirical findings have implications for developing both general and language-specific countermeasures for fake news.

Health Misinformation in the Covid-19 Era - Detecting Misinformation on Bi-lingual Corpora using Lexical Features

Social media use spiked amid the COVID-19 pandemic, resulting in an increase in fake news proliferation, especially health misinformation. Many misinformation detection studies have primarily focused on English texts, and of these, very few have examined linguistic features (syntactic, lexical, and semantic). Lexical features such as number of upper-case letters have been shown to improve misinformation detection in English and non-English texts, however, use of lexical features is still in its infancy, and thus warrants further investigation. Therefore, a novel lexical-based health misinformation detection model is proposed using machine learning techniques, specifically focusing on two languages, namely, English, and standard Malay. A new dataset containing fake and real news were developed from a fact- checking portal and local media, targeting news related to COVID-19. Common natural language processing tasks including filtering, tokenization, stemming etc. and lexical feature extraction were administered prior to data modelling. Evaluation on a dataset containing 1060 fake and real news each show Random Forest to yield the best performance with 99.6% for F-measure and accuracy of 96%, followed closely by Support Vector Machine. A similar observation was noted for the Malay corpus. Improved health misinformation detection was observed when linguistic features were included as part of the model, hence implying that the features can be successfully used in detecting fake news. © 2022 IEEE.

Harnessing Unsupervised Word Translation to Address Resource Inequality for Peace and Health

Research geared toward human well-being in developing nations often concentrates on web content written in a world language (e.g., English) and ignores a significant chunk of content written in a poorly resourced yet highly prevalent first language of the region in concern (e.g., Hindi). Such omissions are common due to the sheer mismatch between linguistic resources offered in a world language and its low-resource counterpart. However, during a global pandemic or an imminent war, demand for linguistic resources might get recalibrated. In this work, we focus on the high-resource and low-resource language pair ⟨ en, hie⟩ (English, and Romanized Hindi) and present a cross-lingual sampling method that takes example documents in English, and retrieves similar content written in Romanized Hindi, the most popular form of Hindi observed in social media. At the core of our technique is a novel finding that a surprisingly simple constrained nearest-neighbor sampling in polyglot Skip-gram word embedding space can retrieve substantial bilingual lexicons, even from harsh social media data sets. Our cross-lingual sampling method obtains substantial performance improvement in the important domains of detecting peace-seeking, hostility-diffusing hope speech in the context of the 2019 India-Pakistan conflict, and in detecting comments encouraging compliance with COVID-19 guidelines. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of 256 pediatric oral and maxillofacial emergency in-patients during the outbreak of COVID-19.

BACKGROUND/AIMS: Pediatric oral and maxillofacial surgeons have faced severe challenges in ward management due to their high risk of exposure during the COVID-19 epidemic. The aim of this study was to analyze and summarize the treatment methods and infection prevention and control measures applied in emergency cases in the Department of Pediatric Oral and Maxillofacial Surgery, Children's Hospital of Chongqing Medical University, during the COVID-19 epidemic. METHODS: In this retrospective study, information was collected from 256 pediatric emergency patients who were treated from January 23, 2020 to August 9, 2021. The patients' data were statistically analyzed according to age, gender, disease and pathogenesis, operation time, and the main treatment applied in pediatric oral and maxillofacial emergency cases during the COVID-19 epidemic. RESULTS: During the epidemic period, 256 pediatric emergency patients were successfully treated. Among them, there were 170 boys and 86 girls. In all, 182 patients were diagnosed with oral or facial lacerations; 43 had jaw fractures; 26 had maxillofacial infections; and five had dento-alveolar fractures. A total of 246 patients underwent surgery under negative pressure with level 3 protection standards. No doctors or patients infected with COVID-19 were found throughout the stury period. CONCLUSIONS: Pediatric oral and maxillofacial emergency in-patients mainly experienced maxillofacial trauma during the COVID-19 epidemic, followed by infection. Effective diagnosis and treatment, and avoidance of COVID-19 infection can be achieved by strictly following epidemic prevention and treatment procedures.

Morphological and Immunopathological Aspects of Lingual Tissues in COVID-19.

COVID-19, a recently emerged disease caused by SARS-CoV-2 infection, can present with different degrees of severity and a large variety of signs and symptoms. The oral manifestations of COVID-19 often involve the tongue, with loss of taste being one of the most common symptoms of the disease. This study aimed to detect SARS-CoV-2 RNA and assess possible morphological and immunopathological alterations in the lingual tissue of patients who died with a history of SARS-CoV-2 infection. Sixteen cadavers from 8 SARS-CoV-2 positive (COVID-19+) and 8 negative (COVID-19-) subjects provided 16 tongues, that were biopsied. Samples underwent molecular analysis through Real-Time RT-PCR for the detection of SARS-CoV-2 RNA. Lingual papillae were harvested and processed for histological analysis and for immunohistochemical evaluation for ACE2, IFN-γ and factor VIII. Real-Time RT-PCR revealed the presence of SARS-CoV-2 RNA in filiform, foliate, and circumvallate papillae in 6 out of 8 COVID-19+ subjects while all COVID-19- samples resulted negative. Histology showed a severe inflammation of COVID-19+ papillae with destruction of the taste buds. ACE2 and IFN-γ resulted downregulated in COVID-19+ and no differences were evidenced for factor VIII between the two groups. The virus was detectable in most COVID-19+ tongues. An inflammatory damage to the lingual papillae, putatively mediated by ACE2 and IFN-γ in tongues from COVID-19+ cadavers, was observed. Further investigations are needed to confirm these findings and deepen the association between taste disorders and inflammation in SARS-CoV-2 infection.

Cross-lingual COVID-19 Fake News Detection

The COVID-19 pandemic poses a great threat to global public health. Meanwhile, there is massive misinformation associated with the pandemic which advocates unfounded or unscientific claims. Even major social media and news outlets have made an extra effort in debunking COVID-19 misinformation, most of the fact-checking information is in English, whereas some unmoderated COVID-19 misinformation is still circulating in other languages, threatening the health of less-informed people in immigrant communities and developing countries. In this paper, we make the first attempt to detect COVID-19 misinformation in a low-resource language (Chinese) only using the fact-checked news in a high-resource language (English). We start by curating a Chinese realfake news dataset according to existing fact-checking information. Then, we propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content as much as possible. Empirical results on our dataset demonstrate the effectiveness of CorssFake under the cross-lingual setting and it also outperforms several monolingual and cross-lingual fake news detectors. The dataset is available at https://github.com/YingtongDou/CrossFake. © 2021 IEEE.

Employing machine learning for capturing COVID-19 consumer sentiments from six countries: a methodological illustration

Purpose: This paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets. Design/methodology/approach: The study is based on some 35 million original COVID-19-related tweets. The study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction. Findings: The authors identified more than two million tweets from six countries and categorized them into PESTEL (i.e. Political, Economic, Social, Technological, Environmental and Legal) dimensions. The extracted consumer sentiments and associated emotions show substantial differences across countries. Our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. Based on these insights, several future research directions are proposed. Originality/value: First, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. Second, the authors add to the knowledge on differences in COVID-19-related consumer sentiments in different countries. Third, the authors provide avenues for future research on the analysis of unstructured multi-media posts. © 2021, Bodo B. Schlegelmilch, Kirti Sharma and Sambbhav Garg.

INKAD COVID-19 IntelliSearch: A multilingual search engine for answering questions about COVID-19 in real-time from the scientific literature

Thousands of research papers on COVID-19 have been published since the start of the pandemic. To find relevant information in this vast literature, researchers and healthcare information professionals, spend increasingly more time per search query. In this paper, we present INKAD COVID-19 IntelliSearch, a multilingual search engine that we built to help researchers and healthcare information professionals in finding precise and relevant information from the COVID-19 literature in real-time, while considerably reducing time spent per search query. We used the COVID-19 Open Research Dataset as the main source of papers. The search engine has a BM25 based document retrieval component, and a neural question-answering component returning the exact answer span. The overall system is evaluated against a COVID-19 question-answering test set with different information retrieval and question-answering models. We have made INKAD COVID-19 IntelliSearch accessible online for broader use by researchers and medical information professionals. © 2021 IEEE.

Hope Speech Detection for Dravidian Languages Using Cross-Lingual Embeddings with Stacked Encoder Architecture.

The task of hope speech detection has gained traction in the natural language processing field owing to the need for an increase in positive reinforcement online during the COVID-19 pandemic. Hope speech detection focuses on identifying texts among social media comments that could invoke positive emotions in people. Students and working adults alike posit that they experience a lot of work-induced stress further proving that there exists a need for external inspiration which in this current scenario, is mostly found online. In this paper, we propose a multilingual model, with main emphasis on Dravidian languages, to automatically detect hope speech. We have employed a stacked encoder architecture which makes use of language agnostic cross-lingual word embeddings as the dataset consists of code-mixed YouTube comments. Additionally, we have carried out an empirical analysis and tested our architecture against various traditional, transformer, and transfer learning methods. Furthermore a k-fold paired t test was conducted which corroborates that our model outperforms the other approaches. Our methodology achieved an F1-score of 0.61 and 0.85 for Tamil and Malayalam, respectively. Our methodology is quite competitive to the state-of-the-art methods. The code for our work can be found in our GitHub repository (https://github.com/arunimasundar/Hope-Speech-LT-EDI).

Are Oral Mucosal Changes a Sign of COVID-19? A Cross-Sectional Study at a Field Hospital. / ¿Son las alteraciones en la mucosa oral un signo de COVID-19? Estudio transversal en un Hospital de Campaña.

BACKGROUND: Coronavirus disease 19 (COVID-19) has many manifestations, including respiratory, thrombotic, neurologic, digestive, and cutaneous ones. Cutaneous manifestations have been classified into 5 clinical patterns: acro-ischemic (pseudo-chilblain), vesicular, urticarial, maculopapular, and livedoid. Oral manifestations have also been reported, but much less frequently. PATIENTS AND METHODS: We performed a cross-sectional study in which we examined the oral mucosa of 666 patients with COVID-19 at the IFEMA field hospital in Madrid in April 2020. RESULTS: Seventy-eight patients (11.7%) had changes involving the oral mucosa. The most common were transient anterior U-shaped lingual papillitis (11.5%) accompanied or not by tongue swelling (6.6%), aphthous stomatitis (6.9%), a burning sensation in the mouth (5.3%), mucositis (3.9%), glossitis with patchy depapillation (3.9%), white tongue (1.6%), and enanthema (0.5%). Most of the patients also reported taste disturbances. CONCLUSION: COVID-19 also manifests in the oral cavity. The most common manifestations are transient U-shaped lingual papillitis, glossitis with patchy depapillation, and burning mouth syndrome. Mucositis with or without aphthous ulcers or enanthema may also be observed. Any these findings may be key clues to a diagnosis of COVID-19.

Thinking Out-of-the-Box: A Non-Standard Application of Standard Pulse-Oximetry and Standard Near-Infrared Spectroscopy in a COVID-19 Patient.

PURPOSE: Purpose of this report is to describe the feasibility of lingual pulse oximetry and lingual near-infrared spectroscopy (NIRS) in a COVID-19 patient to assess lingual tissue viability after several days of mechanical ventilation in the prone position. MATERIALS & METHODS: In a COVID-19 ICU-patient, the tongue became grotesquely swollen, hardened and protruding from the oral cavity after 20 h of mechanical ventilation uninterrupted in the prone position. To assess the doubtful viability of the tongue, pulse-oximetric hemoglobin O2-saturation (SpO2; Nellcor, OxiMax MAX-NI, Covidien, MA, USA) and NIRS-based, regional tissue O2-saturation measurements (rSO2; SenSmart, Nonin, MN, USA) were performed at the tongue. RESULTS: At the tongue, regular pulse-oximetric waveforms with a pulse-oximetric hemoglobin O2-saturation (SpO2) of 88% were recorded, i.e. only slightly lower than the SpO2 reading at the extremities at that time (90%). Lingual NIRS-based rSO2 measurements yielded stable tissue rSO2-values of 76-78%, i.e. values expected also in other adequately perfused and oxygenated (muscle-) tissues. CONCLUSION: Despite the alarming, clinical finding of a grotesquely swollen, rubber-hard tongue and clinical concerns on the adequacy of the tongue perfusion and oxygenation, our measurements of both arterial pulsatility (SpO2) and NIRS-based tissue oxygenation (rSO2) suggested adequate perfusion and oxygenation of the tongue, rendering non-vitality of the tongue, e.g. by lingual venous thrombosis, unlikely. To our knowledge, this is the first clinical report of lingual rSO2 measurement.