Spatial and temporal facies evolution of a Lower Jurassic carbonate platform, NW Tethyan margin (Mallorca, Spain)

The variety of depositional facies of a Lower Jurassic carbonate platform has been investigated on the island of Mallorca along a transect comprising six stratigraphic profiles. Twenty-nine facies and sub-facies have been recognized, grouped into seven facies associations, ranging in depositional environment from supratidal/terrestrial and peritidal to outer platform. Spatial and temporal (2D) facies distribution along the transect reflects the evolution of the carbonate platform with time showing different facies associations, from a broad peritidal platform (stage 1) to a muddy open platform (stage 2), and finally to a peritidal to outer carbonate platform (stage 3). Stage 1 (early Sinemurian to earliest late Sinemurian) corresponds to a nearly-flat peritidal-shallow subtidal epicontinental platform with facies belts that shifted far and fast over the whole study area. The evolution from stage 1 to stage 2 (late Sinemurian) represents a rapid flooding of the epicontinental shallow platform, with more open-marine conditions, and the onset of differential subsidence. During stage 3 (latest Sinemurian), peritidal and shallow-platform environments preferentially developed to the northeast (Llevant Mountains domain) with a rapid transition to middle-outer platform environments toward the northwest (Tramuntana Range domain). Stages 1 and 3 present facies associations typical of Bahamian-type carbonates, whereas stage 2 represents the demise of the Bahamian-type carbonate factory and proliferation of muddy substrates with suspension-feeders. The described platform evolution responded to the interplay between the initial extensional tectonic phases related to Early Jurassic Tethyan rifting, contemporaneous environmental perturbations, and progressive platform flooding related to the Late Triassic–Early Jurassic worldwide marine transgression and associated accommodation changes

DELFOS-drug efficacy leveraging forked and specialized networks-benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity

Motivation: Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can display severe side effects, compromising patient well-being. Therefore, novel strategies for identifying the optimal set of compounds to treat each tumor have become an important research topic in recent decades. Results: To address this challenge, we developed a novel drug response prediction algorithm called Drug Efficacy Leveraging Forked and Specialized networks (DELFOS). Our model learns from multi-omics data from over 65 cancer cell lines, as well as structural data from over 200 compounds, for the prediction of drug sensitivity. We also evaluated the benefits of incorporating single-cell expression data to predict drug response. DELFOS was validated using datasets with unseen cell lines or drugs and compared with other state-of-the-art algorithms, achieving a high prediction performance on several correlation and error metrics. Overall, DELFOS can effectively leverage multi-omics data for the prediction of drug responses in thousands of drug–cell line pairs

SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning

In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories

Membrane proteins structures: A review on computational modeling tools

Membrane proteins (MPs) play diverse and important functions in living organisms. They constitute 20% to 30% of the known bacterial, archaean and eukaryotic organisms' genomes. In humans, their importance is emphasized as they represent 50% of all known drug targets. Nevertheless, experimental determination of their three-dimensional (3D) structure has proven to be both time consuming and rather expensive, which has led to the development of computational algorithms to complement the available experimental methods and provide valuable insights

The Central Role of Non-Structural Protein 1 (NS1) in Influenza Biology and Infection

Influenza (flu) is a contagious viral disease, which targets the human respiratory tract and spreads throughout the world each year. Every year, influenza infects around 10% of the world population and between 290,000 and 650,000 people die from it according to the World Health Organization (WHO). Influenza viruses belong to the Orthomyxoviridae family and have a negative sense eight-segment single-stranded RNA genome that encodes 11 different proteins. The only control over influenza seasonal epidemic outbreaks around the world are vaccines, annually updated according to viral strains in circulation, but, because of high rates of mutation and recurrent genetic assortment, new viral strains of influenza are constantly emerging, increasing the likelihood of pandemics. Vaccination effectiveness is limited, calling for new preventive and therapeutic approaches and a better understanding of the virus-host interactions. In particular, grasping the role of influenza non-structural protein 1 (NS1) and related known interactions in the host cell is pivotal to better understand the mechanisms of virus infection and replication, and thus propose more effective antiviral approaches. In this review, we assess the structure of NS1, its dynamics, and multiple functions and interactions, to highlight the central role of this protein in viral biology and its potential use as an effective therapeutic target to tackle seasonal and pandemic influenza

Investigation Route of the Coronary Patient in the Public Health System in Curitiba, São Paulo and in Incor - IMPACT Study

Background: The investigation of stable coronary artery disease (CAD) and its treatment depend on risk stratification for decision-making on the need for cardiac catheterization and revascularization. Objective: To analyze the procedures used in the diagnosis and invasive treatment of patients with CAD, at the Brazilian Unified Health System (SUS) in the cities of Curitiba, São Paulo and at InCor-FMUSP. Methods: Retrospective, descriptive, observational study of the diagnostic and therapeutic itineraries of the Brazilian public health care system patient, between groups submitted or not to prior noninvasive tests to invasive cardiac catheterization. Stress testing, stress echocardiography, perfusion scintigraphy, catheterization and percutaneous or surgical revascularization treatment procedures were quantified and the economic impact of the used strategies. Results: There are significant differences in the assessment of patients with suspected or known CAD in the metropolitan region in the three scenarios. Although functional testing procedures are most often used the direct costs of these procedures differ significantly (6.1% in Curitiba, 20% in São Paulo and 27% in InCor-FMUSP). Costs related to the procedures and invasive treatments represent 59.7% of the direct costs of SUS in São Paulo and 87.2% in Curitiba. In InCor-FMUSP, only 24.3% of patients with stable CAD submitted to CABG underwent a noninvasive test before the procedure. Conclusion: Although noninvasive functional tests are the ones most often requested for the assessment of patients with suspected or known CAD most of the costs are related to invasive procedures/treatments. In most revascularized patients, the documentation of ischemic burden was not performed by SUS

Decoding Partner Specificity of Opioid Receptor Family

This paper describes an exciting big data analysis compiled in a freely available database, which can be applied to characterize the coupling of different G-Protein coupled receptors (GPCRs) families with their intracellular partners. Opioid receptor (OR) family was used as case study in order to gain further insights into the physiological properties of these important drug targets, known to be associated with the opioid crisis, a huge socio-economic issue directly related to drug abuse. An extensive characterization of all members of the ORs family (μ (MOR), δ (DOR), κ (KOR), nociceptin (NOP)) and their corresponding binding partners (ARRs: Arr2, Arr3; G-protein: Gi1, Gi2, Gi3, Go, Gob, Gz, Gq, G11, G14, G15, G12, Gssh, Gslo) was carried out. A multi-step approach including models' construction (multiple sequence alignment, homology modeling), complex assembling (protein complex refinement with HADDOCK and complex equilibration), and protein-protein interface characterization (including both structural and dynamics analysis) were performed. Our database can be easily applied to several GPCR sub-families, to determine the key structural and dynamical determinants involved in GPCR coupling selectivity

In silico studies targeting G-protein coupled receptors for drug research against Parkinson’s disease

Parkinson's Disease (PD) is a long-term neurodegenative brain disorder that mainly affects the motor system. The causes are still unknown, and even though currently there is no cure, several therapeutic options are available to manage its symptoms. The development of novel anti-parkinsonian agents and an understanding of their proper and optimal use are, indeed, highly demanding. For the last decades, L-3,4-DihydrOxyPhenylAlanine or levodopa (L-DOPA) has been the gold-standard therapy for the symptomatic treatment of motor dysfunctions associated to PD. However, the development of dyskinesias and motor fluctuations (wearing-off and on-off phenomena) associated to long-term L-DOPA replacement therapy have limited its antiparkinsonian efficacy. The investigation for non-dopaminergic therapies has been largelyexplored as an attempt to counteract the motor side effects associated to dopamine replacement therapy. Being one of the largest cell membrane protein families, G-Protein-Coupled Receptors (GPCRs) have become a relevant target for drug discovery focused in a wide range of therapeutic areas, including Central Nervous System (CNS) diseases. The modulation of specific GPCRs potentially implicated in PD, excluding dopamine receptors, may provide promising non-dopaminergic therapeutic alternatives for symptomatic treatment of PD. In this review, we focused on the impact of specific GPCR subclasses, including dopamine receptors, adenosine receptors, muscarinic acetylcholine receptors, metabotropic glutamate receptors, and 5-hydroxytryptamine receptors, on the pathophysiology of PD and the importance of structure- and ligand-based in silico approaches for the development of small molecules to target these receptors