TXMicroRNAs (miRNAs) regulate mRNA stability and protein expression, and certain miRNAs have been demonstrated to act either as oncogenes or tumor suppressors. Differential miRNA expression signatures have been documented in many human cancers but the role of miRNAs in endometrioid endometrial cancer (EEC) remains poorly understood. This study identifies significantly dysregulated miRNAs of EEC cells, and characterizes their impact on the malignant phenotype. We studied the expression of 365 human miRNAs using Taqman low density arrays in EECs and normal endometriums. Candidate differentially expressed miRNAs were validated by quantitative real-time PCR. Expression of highly dysregulated miRNAs was examined in vitro through the effect of anti-/pre-miRNA transfection on the malignant phenotype. We identified 16 significantly dysregulated miRNAs in EEC and 7 of these are novel findings with respect to EEC. Antagonizing the function of miR-7, miR-194 and miR-449b, or overexpressing miR-204, repressed migration, invasion and extracellular matrix-adhesion in HEC1A endometrial cancer cells. FOXC1 was determined as a target gene of miR-204, and two binding sites in the 3 0 -untranslated region were validated by dual luciferase reporter assay. FOXC1 expression was inversely related to miR-204 expression in EEC. Functional analysis revealed the involvement of FOXC1 in migration and invasion of HEC1A cells. Our results present dysfunctional miRNAs in endometrial cancer and identify a crucial role for miR-204-FOXC1 interaction in endometrial cancer progression. This miRNA signature offers a potential biomarker for predicting EEC outcomes, and targeting of these cancer progression-and metastasis-related miRNAs offers a novel potential therapeutic strategy for the disease.Endometrial cancer is a common cause of gynecological cancer death. The most dominant subtype, endometrioid endometrial cancer (EEC), accounts for >80% of this cancer. Menopause and unopposed estrogenic stimulation are typical risk factors. Patients are generally treated with surgery, radiation, chemotherapy or hormone therapy. Patients with early stage disease have 5-year survival rates over 80%, however, about 15-20% develop metastasis. 1 These patients and those with advanced stage disease or recurrence have poor prognosis due to limitation of effective treatment. 2 Understanding the pathogenesis of this disease may provide insights for the development of novel therapeutic strategies.MicroRNAs (miRNAs) are small noncoding RNA molecules of 19-24 nucleotides that regulate gene expression posttranscriptionally through imperfect base pairing with the 3 0 -untranslated region (3 0 UTR) of target mRNAs, causing transcript degradation and translational inhibition. 3 Approximately 20-30% of all genes are targeted by miRNAs and a single miRNA may target as many as 200 genes. 4 In human cancers, >50% of the miRNA genes are located in chromosomal fragile sites, minimal regions containing loss of heterozygosity, minimal amplicons or common breakpoint regions. 5 DNA ...
Hydrophobic interaction chromatography (HIC) is commonly used to separate protein monomer and aggregate species in the purification of protein therapeutics. Despite being used frequently, the HIC separation mechanism is quite complex and not well understood. In this paper, we examined the separation of a monomer and aggregate protein mixture using Phenyl Sepharose FF. The mechanisms of protein adsorption, desorption, and diffusion of the two species were evaluated using several experimental approaches to determine which processes controlled the separation. A chromatography model, which used homogeneous diffusion (to describe mass transfer) and a competitive Langmuir binary isotherm (to describe protein adsorption and desorption), was formulated and used to predict the separation of the monomer and aggregate species. The experimental studies showed a fraction of the aggregate species bound irreversibly to the adsorbent, which was a major factor governing the separation of the species. The model predictions showed inclusion of irreversible binding in the adsorption mechanism greatly improved the model predictions over a range of operating conditions. The model successfully predicted the separation performance of the adsorbent with the examined feed.
Oligonucleotides containing phosphorothioate (PS) linkages have recently demonstrated significant clinical utility. PS oligonucleotides are manufactured via a solid-phase chain elongation process in which a four-reaction cycle consisting of detritylation, coupling, sulfurization, and failure sequence capping with AcO is repeated. In the capping step, uncoupled sequences are acetylated at the 5'-OH to stop the chain growth and control the levels of deletion, or ( n-1), impurities. Herein, we report that the byproducts of commonly used sulfurization reagents react with the 5'-OH and cap the failure sequences. The standard AcO capping step can therefore be eliminated, and this 3-reaction cycle process affords a higher yield and higher or comparable overall purity compared to the conventional 4-reaction synthesis. This improvement results in reducing the number of reactions from ∼80 to ∼60 for the synthesis of a typical length 20-mer oligonucleotide. For every kilogram of an oligonucleotide intermediate synthesized, > 500 L of reagents and organic solvents is saved, and the E-factor is decreased to <1500 from ∼2000.
Background: Lyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than 90% of all vector-borne diseases in North America. Objective: In this paper, self-reported tweets on Twitter were analyzed in order to predict potential Lyme disease cases and accurately assess incidence rates in the US. Methods: The study was done in three stages: (1) Approximately 1.3 million tweets were collected and and pre-processed to extract the most relevant Lyme disease tweets with geolocations. The tweets were manually labelled as relevant or irrelevant to Lyme disease using a set of precise keywords, yielding a curated labelled dataset of 77500 tweets. (2) This labelled data set was used to train, validate, and test various combinations of NLP word embedding methods and prominent ML classification models, such as TF-IDF and logistic regression, Word2vec and XGboost, and BERTweet, among others, to identify potential Lyme disease tweets. (3) Lastly, the presence of spatio-temporal patterns in the US over a 10-year period were studied. Results: Preliminary results showed that BERTweet outperformed all tested NLP classifiers for identifying Lyme disease tweets, achieving the highest classification accuracy and F1-score of 90%. There was also a consistent pattern indicating that the West and Northeast regions of the US had a higher tweet rate over time. Conclusions: We focused on the less-studied problem of using Twitter data as a surveillance tool for Lyme disease in the US. Several crucial findings have emerged from the study. First, there is a fairly strong correlation between classified tweet counts and Lyme disease counts, with both following similar trends. Second, in 2015 and early 2016, the social media network like Twitter was essential in raising popular awareness of Lyme disease. Third, counties with a high incidence rate were not necessarily related with a high tweet rate, and vice versa. Fourth, BERTweet can be used as a reliable NLP classifier for detecting relevant Lyme disease tweets.
BACKGROUND Lyme disease is the most prevalent tick-borne disease in the Northern Hemisphere. Delayed treatment can exacerbate symptoms and result in more severe cases, making this condition a major public health concern in the coming years. Additionally, the Lyme disease surveillance system relies on healthcare professionals to report cases, which weakens the system's efficiency in having accurate data since only the cases seeking medical attention are reported. Thus, there is a need to enhance the surveillance tools of Lyme disease using other data sources such as web-data. OBJECTIVE Worldwide Twitter data was analyzed to understand its potential and its limitations as a tool for Lyme disease surveillance. The proposed Twitter data system is primarily a transformer-based classifier that leverages self-reported tweets to identify potential cases of Lyme disease. METHODS We first used approximately 20,000 English tweets collected worldwide from a database with more than 1.3 million tweets related to Lyme disease. Because most Lyme disease tweets are from the US, we selected only 20,000 tweets, from which about 10% represented other countries than the US, to capture more variability across countries. After preprocessing and geolocating the tweets, a set of carefully selected keywords was used to manually label a subset of tweets to classify them as potential or non-Lyme disease cases. Emojis were converted to sentiment words and then used in place of emojis in the tweets. The dataset of labelled tweets was then used to train, validate, and test the performance of three transform-based classifier variants, namely ALBERT, DistilBERT, and BERTweet, to classify the remaining and other new tweets. RESULTS The empirical results showed that BERTweet is the best classifier among all classification models evaluated, with the highest average F1-score of 89.3%, classification accuracy of 90.0%, precision of 97.1%, except for the recall where TF-IDF and k-Nearest Neighbors perform better by 93.2 % against 82.6% for BERTweet. When emojis' expressions were used to enrich the tweet embeddings, the recall score for BERTweet increased by 8%, and DistilBERT had a markedly increased F1-score of 93.8% (+4%) and a classification accuracy of 94.1% (+4%), while ALBERT had a F1-score of 93.1% (5%) and a classification accuracy of 93.9% (+5%). CONCLUSIONS This study revealed several key findings. First, that BERTweet and DistilBERT can serve as robust NLP classifiers to identify self-reported potential cases of Lyme disease. Second, emojis are effective as enrichment features to improve the accuracy of the tweet embedding and the performance of transformer-based classifiers. In particular, the emojis reflecting sadness, empathy, and encouragement can help reduce false negatives. Third, the general awareness of Lyme disease is high in the United States, the United Kingdom, Australia, and Canada as self-reported potential cases of Lyme disease on Twitter from these countries accounted for more than 50% of the collected English tweets, while Lyme disease-related tweets are scarce in countries from Africa and Asia. Finally, the most commonly reported symptoms of Lyme disease are rash, fatigue, fever, and arthritis while symptoms such as borrelial lymphocytoma, palpitations, swollen lymph nodes, neck stiffness, and irregular heartbeat are unusual and rare.
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