1,721,024 research outputs found
Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
Full text linkIntegrating Artificial Intelligence (AI) into Precision Medicine (PM) is redefining healthcare, enabling personalized treatments tailored to individual patients based on their genetic code, environment, and lifestyle. AI’s ability to analyze vast and complex datasets, including genomics and medical records, facilitates the identification of hidden patterns and correlations, which are critical for developing personalized treatment plans. Unsupervised Learning (UL) is particularly valuable in PM as it can analyze unstructured and unlabeled data to uncover novel disease subtypes, biomarkers, and patient stratifications. By revealing patterns that are not explicitly labeled, unsupervised algorithms enable the discovery of new insights into disease mechanisms and patient variability, advancing our understanding of individual responses to treatment. However, the integration of AI into PM presents some challenges, including concerns about data privacy and the rigorous validation of AI models in clinical practice. Despite these challenges, AI holds immense potential to revolutionize PM, offering a more personalized, efficient, and effective approach to healthcare. Collaboration among AI developers and clinicians is essential to fully realize this potential and ensure ethical and reliable implementation in medical practice. This review will explore the latest emerging UL technologies in the biomedical field with a particular focus on PM applications and their impact on human health and well-being
An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors
Full text linkThe Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction
Molecular Origins of the Mendelian Rare Diseases Reviewed by Orpha.net: A Structural Bioinformatics Investigation
Full text linkThe study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the pathogenic missense variants found in the 2515 proteins involved in Mendelian rare diseases (MRDs), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRDs showed different mutation site distributions at different three-dimensional protein depths. We then highlighted the depth-dependent effects of pathogenic variants for the 20,061 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD
Leveraging proteomics in orphan disease research: pitfalls and potential
No full textIntroduction: The term ‘orphan diseases’ includes conditions meeting prevalence-based or commercial viability criteria: they affect a small number of individuals and are considered an unviable market for drug development. Proteomics is an important technology to study them, providing information on mechanisms and evolution, biomarkers, and effects of therapeutic interventions. Areas covered: Herein, we review how proteomics and bioinformatic tools could be applied to the study of rare diseases and discuss pitfalls and potential. Expert opinion: Research in the field of rare diseases has to face many challenges, and implementation plans should foresee highly specialized collaborative consortia to create multidisciplinary frameworks for data sharing, advancing research, supporting clinical studies, and accelerating drug development. The integration of different technologies will allow better knowledge of disease pathophysiology, and the inclusion of proteomics and other omics technologies in this context will be pivotal to this aim. Several aspects of rare diseases, often perceived as limiting factors, might actually be advantages for a precision medicine approach: the limited number of patients, the collaboration with patient societies, and the availability of curated clinical registries could allow the development of homogeneous clinical databases and ultimately a better control over the data to be analyzed
Three-Dimensional Quantitative Structure–Activity Relationship Study of Transient Receptor Potential Vanilloid 1 Channel Antagonists Reveals Potential for Drug Design Purposes
Full text linkTransient receptor potential vanilloid 1 (TRPV1) was reported to be a putative target for recovery from chronic pain, producing analgesic effects after its inhibition. A series of drug candidates were previously developed, without the ability to ameliorate the therapeutic outcome. Starting from previously designed compounds, derived from the hybridization of antagonist SB-705498 and partial agonist MDR-652, we performed a virtual screening on a pharmacophore model built by exploiting the Cryo-EM 3D structure of a nanomolar antagonist in complex with the human TRPV1 channel. The pharmacophore model was described by three pharmacophoric features, taking advantage of both the bioactive pose of the antagonist and the receptor exclusion spheres. The results of the screening were implemented inside a 3D-QSAR model, correlating with the negative decadic logarithm of the inhibition rate of the ligands. After the validation of the obtained 3D-QSAR model, we designed a new series of compounds by introducing key modifications on the original scaffold. Again, we determined the compounds’ binding poses after alignment to the pharmacophoric model, and we predicted their inhibition rates with the validated 3D-QSAR model. The obtained values resulted in being even more promising than parent compounds, demonstrating that ongoing research still leaves much room for improvement
Cell and tissue models of alkaptonuria
No full textAlkaptonuria (AKU) is a rare metabolic disease of historical and medical interest. Despite the identification of gene and protein defects leading to the accumulation of homogentisic acid (HGA), little is known on how HGA is transformed into an ochronotic pigment (the hallmark of the disease) leading to a range of clinical manifestations. Major obstacles in tackling the pathological features of AKU are the rarity of biological samples, the invasiveness of sampling techniques and the intrinsic difficulties of studying the pigmented tissues. This review provides an overview of the in vitro and ex vivo cell and tissue models that were recently developed and characterized to fill the above-mentioned gaps in the knowledge of AKU
Fine tuning by protein kinases of CaV1.2 channel current in rat tail artery myocytes
No full textSeventeen compounds, rather selective, direct or indirect inhibitors and activators of PKA, PKG, and PKC, were analysed for effects on vascular CaV1.2 channel current (ICa1.2) by using the patch-clamp technique in single rat tail artery myocytes. The aim was to investigate how PKs regulate ICa1.2 and disclose any unexpected modulation of CaV1.2 channel function by these agents. The cAMP analogues 8-Br-cAMP and 6-Bnz-cAMP partially reduced ICa1.2 in dialysed cells, while weakly increasing it under the perforated configuration. The β-adrenoceptor agonist isoproterenol and the adenylate cyclase activator forskolin concentration-dependently increased ICa1.2; this effect was reversed by PKA inhibitors H-89 and KT5720, but not by PKI 6-22. The cGMP analogue 8-Br-cGMP, similarly to the NO-donor SNP, moderately reduced ICa1.2, this effect being reversed to a slight stimulation under the perforated configuration. Among PKG inhibitors, Rp-8-Br-PET-cGMPS decreased current amplitude in a concentration-dependent manner while Rp-8-Br-cGMPS was ineffective. The non-specific phosphodiesterase inhibitor IBMX increased ICa1.2, while H-89, KT5720, and PKI 6-22 antagonized this effect. The PKC activator PMA, but not the diacylglycerol analogue OAG, stimulated ICa1.2 in a concentration-dependent manner; conversely, the PKCα inhibitor Gö6976 markedly reduced basal ICa1.2 and, similarly to the PKCδ (rottlerin) and PKCε translocation inhibitors antagonised PMA-induced current stimulation. The ensemble of findings indicates that the stimulation of cAMP/PKA, in spite of the paradoxical effect of both 8-Br-cAMP and 6-Bnz-cAMP, or PKC pathways enhanced, while that of cGMP/PKG weakly inhibited ICa1.2 in rat tail artery myocytes. Since Rp-8-Br-PET-cGMPS and Gö6976 appeared to block directly CaV1.2 channel, their docking to the channel protein was investigated. Both compounds appeared to bind the α1C subunit in a region involved in CaV1.2 channel inactivation, forming an interaction network comparable to that of CaV1.2 channel blockers. Therefore, caution should accompany the use of these agents as pharmacological tools to elucidate the mechanism of action of drugs on vascular preparations
Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease
No full textAlkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2-dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum amyloid A (SAA) levels, which are linked with inflammation and oxidative stress and might contribute to disease progression and patients' poor quality of life. Recently, we reported that inflammatory markers (SAA and chitotriosidase) and oxidative stress markers (protein thiolation index) might be disease activity markers in AKU. Thanks to an international network, we collected genotypic, phenotypic, and clinical data from more than 200 patients with AKU. These data are currently stored in our AKU database, named ApreciseKUre. In this work, we developed an algorithm able to make predictions about the oxidative status trend of each patient with AKU based on 55 predictors, namely circulating HGA, body mass index, total cholesterol, SAA, and chitotriosidase. Our general aim is to integrate the data of apparently heterogeneous patients with AKUAKU by using specific bioinformatics tools, in order to identify pivotal mechanisms involved in AKU for a preventive, predictive, and personalized medicine approach to AKU.-Cicaloni, V., Spiga, O., Dimitri, G. M., Maiocchi, R., Millucci, L., Giustarini, D., Bernardini, G., Bernini, A., Marzocchi, B., Braconi, D., Santucci, A. Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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