Project description:The term 'biologging' refers to the use of miniaturized animal-attached tags for logging and/or relaying of data about an animal's movements, behaviour, physiology and/or environment. Biologging technology substantially extends our abilities to observe, and take measurements from, free-ranging, undisturbed subjects, providing much scope for advancing both basic and applied biological research. Here, we review highlights from the third international conference on biologging science, which was held in California, USA, from 1 to 5 September 2008. Over the last few years, considerable progress has been made with a range of recording technologies as well as with the management, visualization, integration and analysis of increasingly large and complex biologging datasets. Researchers use these techniques to study animal biology with an unprecedented level of detail and across the full range of ecological scales-from the split-second decision making of individuals to the long-term dynamics of populations, and even entire communities. We conclude our report by suggesting some directions for future research.
Project description:Recently, artificial exosomes have been developed to overcome the challenges of natural exosomes, such as production scalability and stability. In the production of artificial exosomes, the incorporation of membrane proteins into lipid nanostructures is emerging as a notable approach for enhancing biocompatibility and treatment efficacy. This study focuses on incorporating HEK293T cell-derived membrane proteins into liposomes to create membrane-protein-bound liposomes (MPLCs), with the goal of improving their effectiveness as anticancer therapeutics. MPLCs were generated by combining two key elements: lipid components that are identical to those in conventional liposomes (CLs) and membrane protein components uniquely derived from HEK293T cells. An extensive comparison of CLs and MPLCs was conducted across multiple in vitro and in vivo cancer models, employing advanced techniques such as cryo-TEM (tramsmission electron microscopy) imaging and FT-IR (fourier transform infrared spectroscopy). MPLCs displayed superior membrane fusion capabilities in cancer cell lines, with significantly higher cellular uptake. Additionally, MPLCs maintained their morphology and size better than CLs when exposed to FBS (fetal bovine serum), suggesting enhanced serum stability. In a xenograft mouse model using HeLa and ASPC cancer cells, intravenous administration of MPLCs MPLCs accumulated more in tumor tissues, highlighting their potential for targeted cancer therapy. Overall, these results indicate that MPLCs have superior tumor-targeting properties, possibly attributable to their membrane protein composition, offering promising prospects for enhancing drug delivery efficiency in cancer treatments. This research could offer new clinical application opportunities, as it uses MPLCs with membrane proteins from HEK293T cells, which are known for their efficient production and compatibility with GMP (good manufacturing practice) standards.
Project description:Metal-organic frameworks are a class of porous solids that exhibit intriguing flexibility under stimuli, leading often to reversible giant structural changes upon guest adsorption. DUT-49(Cu) and MIL-53(Cr) are fascinating flexible MOFs owing to their guest-induced breathing and negative gas adsorption behaviors respectively. Molecular simulation is one of the most relevant tools to examine these phenomena at the atomistic scale and gain a unique understanding of the physics behind them. Although molecular dynamics and Monte Carlo simulations are widely used in the field of porous materials, these methods hardly consider the structural deformation of a soft material upon guest adsorption. In this work, a cutting-edge osmotic molecular dynamics approach is developed to consider simultaneously the fluid adsorption process and material flexibility. We demonstrate that this newly developed computational strategy offers a unique opportunity to gain unprecedented molecular insights into the flexibility of this class of materials.
Project description:Globally, glaucoma is a leading cause of visual impairment and vision loss, emphasizing the critical need for early diagnosis and intervention. This research explores the application of deep learning for automated glaucoma diagnosis using retinal fundus photographs. We introduce a novel cross-sectional optic nerve head (ONH) feature derived from optical coherence tomography (OCT) images to enhance existing diagnostic procedures. Our approach leverages deep learning to automatically detect key optic disc characteristics, eliminating the need for manual feature engineering. The deep learning classifier then categorizes images as normal or abnormal, streamlining the diagnostic process. Deep learning techniques have proven effective in classifying and segmenting retinal fundus images, enabling the analysis of a growing number of images. This study introduces a novel mixed loss function that combines the strengths of focal loss and correntropy loss to handle complex biomedical data with class imbalance and outliers, particularly in OCT images. We further refine a multi-task deep learning model that capitalizes on similarities across major eye-fundus activities and metrics for glaucoma detection. The model is rigorously evaluated on a real-world ophthalmic dataset, achieving impressive accuracy, specificity, and sensitivity of 100%, 99.8%, and 99.2%, respectively, surpassing state-of-the-art methods. These promising results underscore the potential of our deep learning algorithm for automated glaucoma diagnosis, with significant implications for clinical applications. By simultaneously addressing segmentation and classification challenges, our approach demonstrates its effectiveness in accurately identifying ocular diseases, paving the way for improved glaucoma diagnosis and early intervention.
Project description:Chemicals transfer from the packaging materials and their dissolution in food and water can create health risks. Due to the costly and time-intensive nature of experimental measurements, employing artificial intelligence (AI) methodologies is beneficial. This research uses five renowned AI-based techniques (namely, long short-term memory, gradient boosting regressor, multi-layer perceptron, Random Forest, and convolutional neural networks) to anticipate chemical migration from packaging materials to the food/water structure, considering variables such as temperature, chemical characteristics, and packaging/food types. The relevance analysis has been employed for monitoring the way that these explanatory variables impact the chemical migration from packaging materials into foods and water. Optimizing the hyperparameters, evaluating the prediction accuracy, and comparing the performance of these AI models reveal that the gradient boosting regressor (GBR) is the superior method for this simulation. The proposed GBR model accurately predicts 1847 experimental datasets, showcasing mean squared error, mean absolute error, root mean squared error, relative absolute error percent, and regressing coefficient, of 0.06, 0.15, 0.24, 6.46%, and 0.9961 respectively. Additionally, implementing a leverage algorithm for outlier detection further affirms the reliability of this modeling study.
Project description:Protease research has undergone a major expansion in the last decade, largely due to the extremely rapid development of new technologies, such as quantitative proteomics and in-vivo imaging, as well as an extensive use of in-vivo models. These have led to identification of physiological substrates and resulted in a paradigm shift from the concept of proteases as protein-degrading enzymes to proteases as key signalling molecules. However, we are still at the beginning of an understanding of protease signalling pathways. We have only identified a minor subset of true physiological substrates for a limited number of proteases, and their physiological regulation is still not well understood. Similarly, links with other signalling systems are not well established. Herein, we will highlight current challenges in protease research.
Project description:Background: Medulloblastoma (MB) is one of the most prevalent embryonal malignant brain tumors. Current classification organizes these tumors into four molecular groups (WNT-activated, SHH-activated and TP53-wild type, SHH-activated and TP53-mutant, and non-WNT/non-SHH). Recently, a comprehensive classification has been established, identifying numerous subgroups, some of which exhibit a poor prognosis. It is critical to establish effective subgrouping methods for accurate diagnosis and patient’s management that strikes a delicate balance between improving outcomes and minimizing the risk of comorbidities. Methods: We evaluated the ability of Nanopore sequencing to provide clinically relevant methylation and copy number profiles of MB. Nanopore sequencing was applied to an EPIC discovery cohort of frozen MB, benchmarked against the gold standard EPIC array, and validated further evaluated on an integrated diagnosis cohort of MB.
Project description:The archaeal RNA polymerase (RNAP) is a double-psi β-barrel enzyme closely related to eukaryotic RNAPII in terms of subunit composition and architecture, promoter elements and basal transcription factors required for the initiation and elongation phase of transcription. Understanding archaeal transcription is, therefore, key to delineate the universally conserved fundamental mechanisms of transcription as well as the evolution of the archaeo-eukaryotic transcription machineries. The dynamic interplay between RNAP subunits, transcription factors and nucleic acids dictates the activity of RNAP and ultimately gene expression. This review focusses on recent progress in our understanding of (i) the structure, function and molecular mechanisms of known and less characterized factors including Elf1 (Elongation factor 1), NusA (N-utilization substance A), TFS4, RIP and Eta, and (ii) their evolution and phylogenetic distribution across the expanding tree of Archaea.
Project description:The development of an aptamer-based therapeutic has rapidly progressed following the first two reports in the 1990s, underscoring the advantages of aptamer drugs associated with their unique binding properties. In 2004, the US Food and Drug Administration (FDA) approved the first therapeutic aptamer for the treatment of neovascular age-related macular degeneration, Macugen developed by NeXstar. Since then, eleven aptamers have successfully entered clinical trials for various therapeutic indications. Despite some of the pre-clinical and clinical successes of aptamers as therapeutics, no aptamer has been approved by the FDA for the treatment of cancer. This review highlights the most recent and cutting-edge approaches in the development of aptamers for the treatment of cancer types most refractory to conventional therapies. Herein, we will review (1) the development of aptamers to enhance anti-cancer immunity and as delivery tools for inducing the expression of immunogenic neoantigens; (2) the development of the most promising therapeutic aptamers designed to target the hard-to-treat cancers such as brain tumors; and (3) the development of "carrier" aptamers able to target and penetrate tumors and metastasis, delivering RNA therapeutics to the cytosol and nucleus.
Project description:Lung cancer remains a formidable global health challenge that necessitates inventive strategies to improve its therapeutic outcomes. The conventional treatments, including surgery, chemotherapy, and radiation, have demonstrated limitations in achieving sustained responses. Therefore, exploring novel approaches encompasses a range of interventions that show promise in enhancing the outcomes for patients with advanced or refractory cases of lung cancer. These groundbreaking interventions can potentially overcome cancer resistance and offer personalized solutions. Despite the rapid evolution of emerging lung cancer therapies, persistent challenges such as resistance, toxicity, and patient selection underscore the need for continued development. Consequently, the landscape of lung cancer therapy is transforming with the introduction of precision medicine, immunotherapy, and innovative therapeutic modalities. Additionally, a multifaceted approach involving combination therapies integrating targeted agents, immunotherapies, or traditional cytotoxic treatments addresses the heterogeneity of lung cancer while minimizing its adverse effects. This review provides a brief overview of the latest emerging therapies that are reshaping the landscape of lung cancer treatment. As these novel treatments progress through clinical trials are integrated into standard care, the potential for more effective, targeted, and personalized lung cancer therapies comes into focus, instilling renewed hope for patients facing challenging diagnoses.