Multidisciplinary Exploration of Unknown Oral Lesions With Accompanying Oral Verrucas of the Tongue: A Case Report.

A 64-year-old African American male with a history of hypertension and type II diabetes mellitus presented with unexplained upper lip lacerations after several frequent episodes of hemoptysis. Following the upper lip lacerations were several weeks of intermittent unknown episodic fevers. The patient, challenged by impaired mobility, exhibited an array of symptoms, including severe upper lip pain with lacerations and white patches on the tongue. Laboratory findings indicated thrombocytopenia and anemia, with positive tests for both influenza A and B. Despite completing Tamiflu, the patient experienced recurrent fevers. Imaging revealed gastrointestinal abnormalities, leading to the initiation of nystatin and a multi-antibiotic regimen without significant fever resolution. A subsequent tongue biopsy revealed verruca lesions, and acyclovir was initiated. Despite this, the patient developed lip and facial blisters. Negative results from cytomegalovirus (CMV) deoxyribonucleic acid (DNA) polymerase chain reaction (PCR) prompted a shift in focus to managing persistent fevers, ultimately controlled with naproxen but without discoverable cause. This case underscores the diagnostic challenge posed by unexplained fevers in an elderly patient with oral manifestations. The protracted course and evolving symptoms emphasize the intricacies of managing such cases, highlighting the need for continued investigation and collaboration across medical disciplines in navigating complex clinical scenarios.

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective.

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1-score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID-19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.

UVC-PURGE: A Novel Cost-Effective Disinfection Robot for Combating COVID-19 Pandemic.

During the COVID-19 pandemic, surface disinfection using prevailing chemical disinfection methods had several limitations. Due to cost-inefficiency and the inability to disinfect shaded places, static UVC lamps cannot address these limitations properly. Moreover, the average market price of the prevailing UVC robots is huge, approximately 55,165 USD. In this research firstly, a requirement elicitation study was conducted using a semi-structured interview approach to reveal the requirements to develop a cost-effective UVC robot. Secondly, a semi-autonomous robot named UVC-PURGE was developed based on the revealed requirements. Thirdly, a two-phased evaluation study was undertaken to validate the effectiveness of UVC-PURGE to inactivate the SARS-CoV-2 virus and the capability of semi-autonomous navigation in the first phase and to evaluate the usability of the system through a hybrid approach of SUPR-Q forms and subjective evaluation of the user feedback in the second phase. Pre-treatment swab testing revealed the presence of both Gram-positive and Gram-Negative bacteria at 17 out of 20 test surfaces in the conducted tests. After the UVC irradiation of the robot, the microbial load was detected in only 2 (1D and 1H) out of 17 test surfaces with significant reductions (95.33% in 1D and 90.9% in 1H) of microbial load. Moreover, the usability evaluation yields an above-average SUPR-Q score of 81.91% with significant scores in all the criteria (usability, trust, loyalty, and appearance) and the number of positive themes from the subjective evaluation using thematic analysis is twice the number of negative themes. Additionally, compared with the prevailing UVC disinfection robots in the market, UVC-PURGE is cost-effective with a price of less than 800 USD. Moreover, small form factor along with the real time camera feedback in the developed system helps the user to navigate in congested places easily. The developed robot can be used in any indoor environment in this prevailing pandemic situation and it can also provide cost-effective disinfection in medical facilities against the long-term residual effect of COVID-19 in the post-pandemic era.

Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda.

Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilizes the ML techniques to predict the optimal mode of childbirth and to detect various complications during childbirth. A total of 26 articles (published between 2000 and 2020) from an initial set of 241 articles were selected and reviewed following a Systematic Literature Review (SLR) approach. As outcomes, this review study highlighted the objectives or focuses of the recent studies conducted on pregnancy outcomes using ML; explored the adopted ML algorithms along with their performances; and provided a synthesized view of features used, types of features, data sources and its characteristics. Besides, the review investigated and depicted how the objectives of the prior studies have changed with time being; and the association among the objectives of the studies, uses of algorithms, and the features. The study also delineated future research opportunities to facilitate the existing initiatives for reducing maternal complacent and mortality rates, such as: utilizing unsupervised and deep learning algorithms for prediction, revealing the unknown reasons of maternal complications, developing usable and useful ML-based clinical decision support systems to be used by the expecting mothers and health professionals, enhancing dataset and its accessibility, and exploring the potentiality of surgical robotic tools. Finally, the findings of this review study contributed to the development of a conceptual framework for advancing the ML-based maternal healthcare system. All together, this review will provide a state-of-the-art paradigm of ML-based maternal healthcare that will aid in clinical decision-making, anticipating pregnancy problems and delivery mode, and medical diagnosis and treatment.

COVID-19 and black fungus: Analysis of the public perceptions through machine learning.

While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic "black fungus" has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to analyze public perceptions through sentiment analysis regarding black fungus during the COVID-19 pandemic. To attain the objective, first, a support vector machine (SVM) model, with an average AUC of 82.75%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this SVM model was used to predict the class labels of the public tweets (n = 6477) related to COVID-19 and black fungus. As outcome, this article found public perceptions towards black fungus during COVID-19 pandemic belong mostly to sad (n= 2370, 36.59%), followed by joy (n = 2095, 32.34%), fear (n = 1914, 29.55%) and anger (n = 98, 1.51%). This article also found that public perceptions are varied to some critical concerns like education, lockdown, hospital, oxygen, quarantine, and vaccine. For example, people mostly exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen. Again, it was found that mass people have an ignorance tendency to lockdown, COVID-19 restrictions, and prescribed hygiene rules although the coronavirus and black fungus infection rates broke the previous infection records.

Behçet syndrome manifestations and activity in the United States versus Turkey -- a cross-sectional cohort comparison.

OBJECTIVE: To compare clinical manifestations and activity of Behçet syndrome (BS) in the United States versus Turkey using validated outcome measures. METHODS: Consecutive patients with BS from the US National Institutes of Health (NIH), New York University, and the University of Istanbul were evaluated. Disease activity was measured using the Behçet's Syndrome Activity Scale (BSAS) and the Behçet's Disease Current Activity Form (BDCAF) with quality of life measured by the Behçet Disease Quality of Life (BDQOL) form. One-way ANOVA, t-tests, and multivariate regression analyses were performed. RESULTS: Mean age did not differ between sites; however, more women were seen in the United States versus in Turkey (p < 0.001), and disease duration was longer in the United States (p = 0.02). Organ manifestations were similar for oral and genital ulcers, skin disease, arthralgia, eye disease, and thrombosis. However, more gastrointestinal (p < 0.001) and neurologic disease (p = 0.003) was seen in the United States. BSAS and BDCAF scores were worse in the United States compared to Turkey (p = 0.013 and < 0.001, respectively). Worse mean BDQOL scores were observed at the NIH compared to Istanbul (not significant). Multivariable regression models showed worse scores in ethnically atypical patients for BSAS and BDCAF (p = 0.04 and p = 0.001), American patients for BDCAF (p = 0.01), older age for BDCAF (p = 0.005), and women for BDQOL (p = 0.01). CONCLUSION: Demographic and clinical manifestations of BS differ between sites with higher disease activity in the United States compared to Turkey. Referral patterns, age, sex, ethnicity, and country of origin may be important in these differences. These observations raise the question of whether pathogenic mechanisms differ in Turkish and American patients.