The androgen receptor (AR) signaling axis plays a critical role in the development, function and homeostasis of the prostate. The classical action of AR is to regulate gene transcriptional processes ...via AR nuclear translocation, binding to androgen response elements on target genes and recruitment of, or crosstalk with, transcription factors. Prostate cancer initiation and progression is also uniquely dependent on AR. Androgen deprivation therapy remains the standard of care for treatment of advanced prostate cancer. Despite an initial favorable response, almost all patients invariably progress to a more aggressive, castrate-resistant phenotype. Considerable evidence now supports the concept that development of castrate-resistant prostate cancer (CRPC) is causally related to continued transactivation of AR. Understanding the critical events and complexities of AR signaling in the progression to CRPC is essential in developing successful future therapies. This review provides a synopsis of AR structure and signaling in prostate cancer progression, with a special focus on recent findings on the role of AR in CRPC. Clinical implications of these findings and potential directions for future research are also outlined.
Aging Differences in Ethnic Skin Vashi, Neelam A; de Castro Maymone, Mayra Buainain; Kundu, Roopal V
The Journal of clinical and aesthetic dermatology
9, Številka:
1
Journal Article
Recenzirano
Aging is an inevitable and complex process that can be described clinically as features of wrinkles, sunspots, uneven skin color, and sagging skin. These cutaneous effects are influenced by both ...intrinsic and extrinsic factors and often are varied based on ethnic origin given underlying structural and functional differences. The authors sought to provide updated information on facets of aging and how it relates to ethnic variation given innate differences in skin structure and function. Publications describing structural and functional principles of ethnic and aging skin were primarily found through a PubMed literature search and supplemented with a review of textbook chapters. The most common signs of skin aging despite skin type are dark spots, loss of elasticity, loss of volume, and rhytides. Skin of color has many characteristics that make its aging process unique. Those of Asian, Hispanic, and African American descent have distinct facial structures. Differences in the concentration of epidermal melanin makes darkly pigmented persons more vulnerable to dyspigmentation, while a thicker and more compact dermis makes facial lines less noticeable. Ethnic skin comprises a large portion of the world population. Therefore, it is important to understand the unique structural and functional differences among ethnicities to adequately treat the signs of aging.
Abstract
A positive follow-up blood culture for methicillin-resistant Staphylococcus aureus (MRSA) while on seemingly appropriate therapy is a common and ominous development. However, the definition ...and management of persistent MRSA bacteremia is unstandardized. In this Opinion Paper, we identify the presence of bacteremia for > 1 calendar day as a “worry point” that should trigger an intensive diagnostic evaluation to identify metastatic infection sites. Next, we define the duration of MRSA bacteremia that likely constitutes antibiotic failure and outline a potential management algorithm for such patients. Finally, we propose pragmatic clinical trial designs to test treatment strategies for persistent MRSA bacteremia.
In recent years, there has been a significant increase in the incidence of depression, which is related to, inter alia, the COVID-19 pandemic. Depression can be fatal if not treated or treated ...inappropriately; is the main cause of suicide attempts. The disease is multifactorial, and pharmacotherapy often fails to bring satisfactory results. Therefore, more and more importance is attached to the natural healing substances and nutrients contained in food. They can significantly affect the process of therapy and prevention of depressive disorders. A proper diet plays a very important role in the prevention of depression and can be a valuable addition to psychological and pharmacological treatment. In turn, an inadequate diet may reduce the effectiveness of antidepressants or may increase their side effects, leading to life-threatening symptoms. The work is a review of the literature on the pathogenesis of the development and treatment of depression, with particular emphasis on dietary supplements and the role of nutrition in the prevention and treatment of depressive disorders.
Abstract Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass ...cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody–antigen interactions docking and affinity maturation.
Abstract How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery ...and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.
Abstract With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite ...their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.
Abstract Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and ...time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing and graph neural network. Considering the rapid advance on computer vision, using the molecular image to enable AI appears to be a more intuitive and effective approach since each chemical substance has a unique visual representation. In this paper, we provide the first survey on image-based molecular representation for drug development. The survey proposes a taxonomy based on the learning paradigms in computer vision and reviews a large number of corresponding papers, highlighting the contributions of molecular visual representation in drug development. Besides, we discuss the applications, limitations and future directions in the field. We hope this survey could offer valuable insight into the use of image-based molecular representation learning in the context of drug development.
Abstract Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features ...of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering– and deep learning–based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
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