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Over the last decade, therapies targeting immune checkpoints, such as programmed death-1 (PD-1), have revolutionized the field of cancer immunotherapy. However, low response rates and immune-related adverse events remain a major concern. Here, we report that epigallocatechin gallate (EGCG), the most abundant catechin in green tea, inhibits melanoma growth by modulating an immune response against tumors. In vitro experiments revealed that EGCG treatment inhibited interferon-gamma (IFN-γ)-induced PD-L1 and PD-L2 expression and JAK-STAT signaling. We confirmed that this effect was driven by inhibiting STAT1 gene expression and STAT1 phosphorylation, thereby downregulating the PD-L1/PD-L2 transcriptional regulator IRF1 in both human and mouse melanoma cells. Animal studies revealed that the in vivo tumor-inhibitory effect of EGCG was through CD8+ T cells and that the inhibitory effect of EGCG was comparable to anti-PD-1 therapy. However, their mechanisms of action were different. Dissimilar to anti-PD-1 treatment that blocks PD-1/PD-L1 interaction, EGCG inhibited JAK/STAT signaling and PD-L1 expression in tumor cells, leading to the re-activation of T cells. In summary, we demonstrate that EGCG enhances anti-tumor immune responses by inhibiting JAK-STAT signaling in melanoma. EGCG could be used as an alternative treatment strategy to target the PD-L1/PD-L2-PD-1 axis in cancers.

The anti-inflammatory cytokine interleukin-37 (IL-37) plays a key role in inhibiting innate and adaptive immunity. Past results have shown that IL-37 is elevated in human Treg cells compared to other T cell subsets and contributes to enhancing the Treg transcription factor, forkhead box protein P3 (FOXP3). However, it is unknown if ectopic expression of IL-37 in non-Treg CD4+ T cells can lead to the development of Treg phenotype and function. In the present study, we used a PrimeFlow® RNA assay and confirmed elevated IL37 expression in human Treg cells. We then stably transfected the non-Treg CD4+ T cell leukemia cell line, E6 Jurkat cells, with IL37 and found significant induction of the Treg phenotype. These IL-37-expressing Jurkat cells had elevated CTLA-4 and FOXP3 and produced IL-10. In conjunction with the Treg phenotype, IL-37-expressing Jurkat cells suppressed T cell activation/proliferation, comparable to human primary Treg cells. The creation of this stable human Treg-like cell line has the potential to provide further assistance for in vitro studies of human Treg cells, as it is more convenient than the use of primary human Treg cells. Furthermore, it provides insights into Treg cell biology and function.

Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) is an immune checkpoint expressed in regulatory T (Treg) cells and activated T lymphocytes. Despite its potential as a treatment strategy for melanoma, CTLA-4 inhibition has limited efficacy. Using data from The Cancer Genome Atlas (TCGA) melanoma database and another dataset, we found that decreased CTLA4 mRNA was associated with a poorer prognosis in metastatic melanoma. To investigate further, we measured blood CTLA4 mRNA in 273 whole-blood samples from an Australian cohort and found that it was lower in metastatic melanoma than in healthy controls and associated with worse patient survival. We confirmed these findings using Cox proportional hazards model analysis and another cohort from the US. Fractionated blood analysis revealed that Treg cells were responsible for the downregulated CTLA4 in metastatic melanoma patients, which was confirmed by further analysis of published data showing downregulated CTLA-4 surface protein expression in Treg cells of metastatic melanoma compared to healthy donors. Mechanistically, we found that secretomes from human metastatic melanoma cells downregulate CTLA4 mRNA at the post-transcriptional level through miR-155 while upregulating FOXP3 expression in human Treg cells. Functionally, we demonstrated that CTLA4 expression inhibits the proliferation and suppressive function of human Treg cells. Finally, miR-155 was found to be upregulated in Treg cells from metastatic melanoma patients compared to healthy donors. Our study provides new insights into the underlying mechanisms of reduced CTLA4 expression observed in melanoma patients, demonstrating that post-transcriptional silencing of CTLA4 by miRNA-155 in Treg cells may play a critical role. Since CTLA-4 expression is downregulated in non-responder melanoma patients to anti-PD-1 immunotherapy, targeting miRNA-155 or other factors involved in regulating CTLA4 expression in Treg cells without affecting T cells could be a potential strategy to improve the efficacy of immunotherapy in melanoma. Further research is needed to understand the molecular mechanisms regulating CTLA4 expression in Treg cells and identify potential therapeutic targets for enhancing immune-based therapies.

Selecting keywords from Twitter as features to identify events is challenging due to language informality such as acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, We compare and identify the best methods for keyword selection as features to be used for classification purposes. Specifically, we study the aspects affecting keywords as features to identify civil unrest and protests. These aspects include the word count, the word forms such as n-gram, skip-gram and bags-of-words as well as the data association methods including correlation techniques and similarity techniques. To test the impact of the mentioned factors, we developed a framework that analyzed 641 days of tweets and extracted the words highly associated with event days along the same time frame. Then, we used the extracted words as features to classify any single day to be either an event day or a nonevent day in a specific location. In this framework, we used the same pipeline of data cleaning, prepossessing, feature selection, model learning and event classification using all combinations of keyword selection criteria. We used Naive Bayes classifier to learn the selected features and accordingly predict the event days. The classification is tested using multiple metrics, such as accuracy, precision, recall, F-score and AUC. This study concluded that the best word form is bag-of-words with average AUC of 0.72 and the best word count is two with average AUC of 0.74 and the best feature selection method is Spearman's correlation with average AUC of 0.89 and the best classifier for event detection is Naive Bayes Classifier.

The essays in On the Writing of New Testament Commentaries discuss historical, hermeneutical, methodological, literary, and theological questions that shape the writing of commentaries on the books of the New Testament. While these essays honor Grant R. Osborne, they also represent the first sustained effort to systematically address commentary writing in the field of New Testament studies.

Extracting textual features from tweets is a challenging task due to the noisy nature of the content and the weak signal of most of the words used. In this paper, we propose using singular value decomposition (SVD) with clustering to group related words as enhanced signals for textual features in tweets in order to improve the correlation with events. The proposed method applies SVD to the time series vector for each feature to factorize the matrix of feature/day counts, to ensure the independence of the feature vectors. Then, k -means clustering is applied to build a look-up table that maps members of each cluster to the cluster centroid. The look-up table is used to map each feature in the original data to the centroid of its cluster. Then, we calculate the sum of the term-frequency vectors of all features in each cluster to the term-frequency vector of the cluster centroid. To evaluate the method, we calculated the correlations of the cluster centroids with the golden standard record vector before and after summing the vectors of the cluster members to the centroid vector. The proposed method is applied to multiple correlation techniques including the Pearson, Spearman, distance correlation, and Kendal Tao. The experiments also considered the different word forms and lengths of the features including keywords, n grams, skip grams, and bags-of-words. The correlation results are enhanced significantly as the highest correlation scores have increased from 0.22 to 0.70, and the average correlation scores have increased from 0.22 to 0.60.