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Glioblastoma (GBM) is the most common primary brain malignancy in adults, yet survival outcomes remain poor. First line treatment is well established, however disease invariably recurs and improving prognosis is challenging. With the aim of personalizing therapy at recurrence, we have established a high content screening (HCS) platform to analyze the sensitivity profile of seven patient-derived cancer stem cell lines to 83 FDA-approved chemotherapy drugs, with and without irradiation.Seven cancer stem cell lines were derived from patients with GBM and, along with the established cell line U87-MG, each patient-derived line was cultured in tandem in serum-free conditions as adherent monolayers and three-dimensional neurospheres. Chemotherapeutics were screened at multiple concentrations and cells double-stained to observe their effect on both cell death and proliferation. Sensitivity was classified using high-throughput algorithmic image analysis.Cell line specific drug responses were observed across the seven patient-derived cell lines. Few agents were seen to have radio-sensitizing effects, yet some drug classes showed a marked difference in efficacy between monolayers and neurospheres. In vivo validation of six drugs suggested that cell death readout in a three-dimensional culture scenario is a more physiologically relevant screening model and could be used effectively to assess the chemosensitivity of patient-derived GBM lines.The study puts forward a number of non-standard chemotherapeutics that could be useful in the treatment of recurrent GBM, namely mitoxantrone, bortezomib and actinomycin D, whilst demonstrating the potential of HCS to be used for personalized treatment based on the chemosensitivity profile of patient tumor cells.

Neurons and glial cells in the developing brain arise from neural progenitor cells (NPCs). Nestin, an intermediate filament protein, is thought to be expressed exclusively by NPCs in the normal brain, and is replaced by the expression of proteins specific for neurons or glia in differentiated cells. Nestin expressing NPCs are found in the adult brain in the subventricular zone (SVZ) of the lateral ventricle and the subgranular zone (SGZ) of the dentate gyrus. While significant attention has been paid to studying NPCs in the SVZ and SGZ in the adult brain, relatively little attention has been paid to determining whether nestin-expressing neural cells (NECs) exist outside of the SVZ and SGZ. We therefore stained sections immunocytochemically from the adult rat and human brain for NECs, observed four distinct classes of these cells, and present here the first comprehensive report on these cells. Class I cells are among the smallest neural cells in the brain and are widely distributed. Class II cells are located in the walls of the aqueduct and third ventricle. Class IV cells are found throughout the forebrain and typically reside immediately adjacent to a neuron. Class III cells are observed only in the basal forebrain and closely related areas such as the hippocampus and corpus striatum. Class III cells resemble neurons structurally and co-express markers associated exclusively with neurons. Cell proliferation experiments demonstrate that Class III cells are not recently born. Instead, these cells appear to be mature neurons in the adult brain that express nestin. Neurons that express nestin are not supposed to exist in the brain at any stage of development. That these unique neurons are found only in brain regions involved in higher order cognitive function suggests that they may be remodeling their cytoskeleton in supporting the neural plasticity required for these functions.

BackgroundHIV infections disproportionately impact Latino gay and bisexual men (GBM) in the United States. Pre-Exposure Prophylaxis (PrEP) is a proven prevention strategy that can help reduce new HIV infections in this population. Unfortunately, PrEP adoption and persistence among Latino GBM remain low. The added benefits of using PrEP experienced by Latino GBM can provide important insights to inform the development of PrEP messaging to motivate this population to explore and consider PrEP as an appropriate and acceptable HIV prevention tool.MethodsWe conducted in-depth interviews with Latino GBM PrEP users to explore positive feelings and emotions, and additional benefits gained from using PrEP. Data were analyzed using thematic analysis.ResultsA total of 29 Latino GBM completed the study interview. The average age of participants was 30 years, and the mean length of time using PrEP was 17.1 months. Five themes were constructed from the data representing the additional benefits gained by Latino GBM PrEP users, and included: (1) reduced fear, anxiety, and stress about HIV, HIV testing, and sex; (2) feeling empowered and in control of their HIV risk; (3) greater awareness of sexual risk behaviors and sexual health; (4) greater sexual exploration and pleasure, and comfort having condomless sex; and (5) a greater connection to community and a feeling of contributing to the elimination of HIV.ConclusionsThe added benefits identified in this study represent a range of social, emotional, and psychological benefits that Latino GBM experience while using PrEP. They speak to the complementary benefits that PrEP can bring to Latino GBM who decide to use the medication, that go beyond HIV prevention. These findings can inform the development of future PrEP messaging to help improve motivation for PrEP uptake and persistent use among Latino GBM.

PurposeDisparities persist in HIV infection among Black and Latino men who have sex with men (BLMSM) and Black and Latina transgender women (BLTW). Increasing uptake and subsequent consistent use of pre-exposure prophylaxis (PrEP), an effective biomedical strategy for preventing HIV acquisition, can dramatically reduce HIV incidence in these populations. The purpose of this study was to explore reasons for PrEP discontinuation among BLMSM and BLTW living in Los Angeles County to inform the development of support services for these populations to remain persistent with their PrEP regimen.MethodsIn-depth, semi-structured interviews were conducted with 15 BLMSM and 7 BLTW who reported either temporary or indefinite PrEP discontinuation. A thematic analysis approach was used to analyze qualitative data.ResultsFour themes emerged related to reasons for PrEP discontinuation, including: (1) lower perceived HIV risk related to changes in sexual behavior; (2) structural or logistical barriers (e.g., lapse or loss of health insurance, cost, difficulty navigating complex medical systems); (3) anticipated and experienced medication side effects, with a sub-theme of interactions between PrEP and feminizing hormone medications; and (4) challenges with medication adherence.ConclusionsPrEP is an important prevention tool for BLMSM and BLTW, particularly during periods of heightened HIV risk. However, both individual (e.g., inability to adhere to medication, changes in HIV sexual risk behaviors) and structural/logistical (e.g., loss of insurance, navigating complex medical systems) factors can cause temporary or indefinite PrEP discontinuation. Additional support services, beyond those offered by medical providers, are needed to help BLMSM and BLTW PrEP users overcome barriers to discontinuation and assist them to remain persistent with their PrEP regimen. We describe potential options for support services such as PrEP case management, expanded PrEP navigation services, or text messaging services.

Islet and hepatocyte transplantation are associated with tissue factor-dependent activation of coagulation which elicits instant blood mediated inflammatory reaction, thereby contributing to a low rate of engraftment. The aim of this study was i) to evaluate the procoagulant activity of human adult liver-derived mesenchymal progenitor cells (hALPCs), ii) to compare it to other mesenchymal cells of extra-hepatic (bone marrow mesenchymal stem cells and skin fibroblasts) or liver origin (liver myofibroblasts), and iii) to determine the ways this activity could be modulated. Using a whole blood coagulation test (thromboelastometry), we demonstrated that all analyzed cell types exhibit procoagulant activity. The hALPCs pronounced procoagulant activity was associated with an increased tissue factor and a decreased tissue factor pathway inhibitor expression as compared with hepatocytes. At therapeutic doses, the procoagulant effect of hALPCs was inhibited by neither antithrombin activators nor direct factor Xa inhibitor or direct thrombin inhibitors individually. However, concomitant administration of an antithrombin activator or direct factor Xa inhibitor and direct thrombin inhibitor proved to be a particularly effective combination for controlling the procoagulant effects of hALPCs both in vitro and in vivo. The results suggest that this dual antithrombotic therapy should also improve the efficacy of cell transplantation in humans.

Clustering texts together is an essential task in data mining and information retrieval, whose aim is to group unlabeled texts into meaningful clusters that facilitate extracting and understanding useful information from large volumes of textual data. However, clustering short texts (STC) is complex because they typically contain sparse, ambiguous, noisy, and lacking information. One of the challenges for STC is finding a proper representation for short text documents to generate cohesive clusters. However, typically, STC considers only a single-view representation to do clustering. The single-view representation is inefficient for representing text due to its inability to represent different aspects of the target text. In this paper, we propose the most suitable multi-view representation (MVR) (by finding the best combination of different single-view representations) to enhance STC. Our work will explore different types of MVR based on different sets of single-view representation combinations. The combination of the single-view representations is done by a fixed length concatenation via Principal Component analysis (PCA) technique. Three standard datasets (Twitter, Google News, and StackOverflow) are used to evaluate the performances of various sets of MVRs on STC. Based on experimental results, the best combination of single-view representation as an effective for STC was the 5-views MVR (a combination of BERT, GPT, TF-IDF, FastText, and GloVe). Based on that, we can conclude that MVR improves the performance of STC; however, the design for MVR requires selective single-view representations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ahmed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As social media booms, abusive online practices such as hate speech have unfortunately increased as well. As letters are often repeated in words used to construct social media messages, these types of words should be eliminated or reduced in number to enhance the efficacy of hate speech detection. Although multiple models have attempted to normalize out-of-vocabulary (OOV) words with repeated letters, they often fail to determine whether the in-vocabulary (IV) replacement words are correct or incorrect. Therefore, this study developed an improved model for normalizing OOV words with repeated letters by replacing them with correct in-vocabulary (IV) replacement words. The improved normalization model is an unsupervised method that does not require the use of a special dictionary or annotated data. It combines rule-based patterns of words with repeated letters and the SymSpell spelling correction algorithm to remove repeated letters within the words by multiple rules regarding the position of repeated letters in a word, be it at the beginning, middle, or end of the word and the repetition pattern. Two hate speech datasets were then used to assess performance. The proposed normalization model was able to decrease the percentage of OOV words to 8%. Its F1 score was also 9% and 13% higher than the models proposed by two extant studies. Therefore, the proposed normalization model performed better than the benchmark studies in replacing OOV words with the correct IV replacement and improved the performance of the detection model. As such, suitable rule-based patterns can be combined with spelling correction to develop a text normalization model to correctly replace words with repeated letters, which would, in turn, improve hate speech detection in texts., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mansur et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The assessment of examination questions is crucial in educational institutes since examination is one of the most common methods to evaluate students' achievement in specific course. Therefore, there is a crucial need to construct a balanced and high-quality exam, which satisfies different cognitive levels. Thus, many lecturers rely on Bloom's taxonomy cognitive domain, which is a popular framework developed for the purpose of assessing students' intellectual abilities and skills. Several works have been proposed to automatically handle the classification of questions in accordance with Bloom's taxonomy. Most of these works classify questions according to specific domain. As a result, there is a lack of technique of classifying questions that belong to the multi-domain areas. The aim of this paper is to present a classification model to classify exam questions based on Bloom's taxonomy that belong to several areas. This study proposes a method for classifying questions automatically, by extracting two features, TFPOS-IDF and word2vec. The purpose of the first feature was to calculate the term frequency-inverse document frequency based on part of speech, in order to assign a suitable weight for essential words in the question. The second feature, pre-trained word2vec, was used to boost the classification process. Then, the combination of these features was fed into three different classifiers; K-Nearest Neighbour, Logistic Regression, and Support Vector Machine, in order to classify the questions. The experiments used two datasets. The first dataset contained 141 questions, while the other dataset contained 600 questions. The classification result for the first dataset achieved an average of 71.1%, 82.3% and 83.7% weighted F1-measure respectively. The classification result for the second dataset achieved an average of 85.4%, 89.4% and 89.7% weighted F1-measure respectively. The finding from this study showed that the proposed method is significant in classifying questions from multiple domains based on Bloom's taxonomy., Competing Interests: NO authors have competing interests.

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.

Word Sense Disambiguation (WSD) is the task of determining which sense of an ambiguous word (word with multiple meanings) is chosen in a particular use of that word, by considering its context. A sentence is considered ambiguous if it contains ambiguous word(s). Practically, any sentence that has been classified as ambiguous usually has multiple interpretations, but just one of them presents the correct interpretation. We propose an unsupervised method that exploits knowledge based approaches for word sense disambiguation using Harmony Search Algorithm (HSA) based on a Stanford dependencies generator (HSDG). The role of the dependency generator is to parse sentences to obtain their dependency relations. Whereas, the goal of using the HSA is to maximize the overall semantic similarity of the set of parsed words. HSA invokes a combination of semantic similarity and relatedness measurements, i.e., Jiang and Conrath (jcn) and an adapted Lesk algorithm, to perform the HSA fitness function. Our proposed method was experimented on benchmark datasets, which yielded results comparable to the state-of-the-art WSD methods. In order to evaluate the effectiveness of the dependency generator, we perform the same methodology without the parser, but with a window of words. The empirical results demonstrate that the proposed method is able to produce effective solutions for most instances of the datasets used.