ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection.

OBJECTIVES: COVID-19 (SARS-CoV-2) pandemic has infected hundreds of millions and inflicted millions of deaths around the globe. Fortunately, the introduction of COVID-19 vaccines provided a glimmer of hope and a pathway to recovery. However, owing to misinformation being spread on social media and other platforms, there has been a rise in vaccine hesitancy which can lead to a negative impact on vaccine uptake in the population. The goal of this research is to introduce a novel machine learning-based COVID-19 vaccine misinformation detection framework. STUDY DESIGN: We collected and annotated COVID-19 vaccine tweets and trained machine learning algorithms to classify vaccine misinformation. METHODS: More than 15,000 tweets were annotated as misinformation or general vaccine tweets using reliable sources and validated by medical experts. The classification models explored were XGBoost, LSTM, and BERT transformer model. RESULTS: The best classification performance was obtained using BERT, resulting in 0.98 F1-score on the test set. The precision and recall scores were 0.97 and 0.98, respectively. CONCLUSION: Machine learning-based models are effective in detecting misinformation regarding COVID-19 vaccines on social media platforms.

Diagnosis of hepatocellular carcinoma in cirrhotic patients: a proof-of-concept study using serum micro-Raman spectroscopy.

Hepatocellular carcinoma (HCC) is the third most common cause of cancer death worldwide. The development of novel diagnostic methods is needed to detect tumours at an early stage when patients are eligible for curative treatments. The purpose of this proof-of-concept study was to determine if micro-Raman spectroscopy applied to the sera of cirrhotic patients may be an alternative method for rapidly discriminating patients with and without HCC. Serum samples were collected from 2 groups of patients: cirrhotic patients with HCC (n = 37) and without HCC (n = 34). Two different approaches were used, dried serum drops and freeze-dried serum, and micro-Raman spectra were acquired in the point-mode with a 785 nm laser excitation in the spectral range of 600-1800 cm(-1). Spectra were quality-tested and pre-processed (smoothing, baseline subtraction, vector normalization). Using principal component analysis, the 2 classes, corresponding to cirrhotic patients with and without HCC, could not be differentiated. In contrast, the support vector machine method using the leave-one-out cross validation procedure was able to correctly classify the two groups of patients with an overall rate of accuracy of 84.5% to 90.2% for dried serum drops and 86% to 91.5% for freeze-dried serum. These results are promising and support the concept that serum micro-Raman spectroscopy may become a useful diagnostic tool to detect biomarkers in the field of cancer, as described here for distinguishing between cirrhotic patients with and without HCC.

Improvement of RNA-SIP by pyrosequencing to identify putative 4-n-nonylphenol degraders in activated sludge.

Nonylphenols (NP) have estrogenic potential because of their phenolic ring, but the organisms involved in the degradation of this alkylated phenol remain unidentified. Using 16S ribosomal RNA (rRNA)-based stable isotope probing (SIP) and a new method based on pyrosequencing, we identified the bacteria involved in the degradation of the aromatic ring of [U-ring-(13)C] 4-n-NP in aerobic sludge. The first order degradation rate of 4-n-NP was 5.5d(-1). Single strand conformation polymorphism of density-separated labeled and unlabeled 16S rRNA showed significant differences and enabled selection of four representative fractions for pyrosequencing. Nineteen phylotypes showed a significant enrichment in the heavy fraction in the labeled pulse. The relative abundances of these phylotypes were combined with the RNA concentration of each fraction to yield a simple model of the distribution of each phylotype across the gradient. This model was used to estimate the percentage of labeling for each phylotype. The sequences showing the highest labeling (11%) were closely related to Afipia sp. but represented only 2 % of the RNA in the heavy fraction of the labeled pulse. The sequences representing the largest proportion of the RNA in the heavy fraction were related to Propionibacterium acnes and Frateuria aurantia, which are known to possess enzymes for phenol degradation. The model shows that despite Afipia having the highest (13)C enrichment, other species encoding phenol degradation pathways are responsible for more (13)C incorporation. Last, we showed that some species represent 12% of the total RNA but contain only 1% (13)C above natural abundance.

Cutaneous leishmaniasis in Al-Badarna: a prospective study among school children.

This paper presents the results of a prospective study cutaneous liashmaniasis in 2 schools in Al-Badarna, during the period from July to December 1992. The pupils were interviewed and examined. Positive cases were referred to the Health Unit for treatment. Of 260 pupils 22 (8.46%) were found to be infected with active lesions.