Effects of typical and atypical antipsychotic drugs on gene expression profiles in the liver of schizophrenia subjects

<p>Abstract</p> <p>Background</p> <p>Although much progress has been made on antipsychotic drug development, precise mechanisms behind the action of typical and atypical antipsychotics are poorly understood.</p> <p>Methods</p> <p>We performed genome-wide expression profiling to study effects of typical antipsychotics and atypical antipsychotics in the postmortem liver of schizophrenia patients using microarrays (Affymetrix U133 plus2.0). We classified the subjects into typical antipsychotics (n = 24) or atypical antipsychotics (n = 26) based on their medication history, and compared gene expression profiles with unaffected controls (n = 34). We further analyzed individual antipsychotic effects on gene expression by sub-classifying the subjects into four major antipsychotic groups including haloperidol, phenothiazines, olanzapine and risperidone.</p> <p>Results</p> <p>Typical antipsychotics affected genes associated with nuclear protein, stress responses and phosphorylation, whereas atypical antipsychotics affected genes associated with golgi/endoplasmic reticulum and cytoplasm transport. Comparison between typical antipsychotics and atypical antipsychotics further identified genes associated with lipid metabolism and mitochondrial function. Analyses on individual antipsychotics revealed a set of genes (151 transcripts, FDR adjusted p < 0.05) that are differentially regulated by four antipsychotics, particularly by phenothiazines, in the liver of schizophrenia patients.</p> <p>Conclusion</p> <p>Typical antipsychotics and atypical antipsychotics affect different genes and biological function in the liver. Typical antipsychotic phenothiazines exert robust effects on gene expression in the liver that may lead to liver toxicity. The genes found in the current study may benefit antipsychotic drug development with better therapeutic and side effect profiles.</p

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease.

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology

Reporting incidental coronary, aortic valve and cardiac calcification on non-gated thoracic computed tomography, a consensus statement from the BSCI/BSCCT and BSTI.

Incidental coronary and cardiac calcification are frequent findings on non-gated thoracic CT. We recommend that the heart is reviewed on all CT scans where it is visualised. Coronary artery calcification is a marker of coronary artery disease and it is associated with an adverse prognosis on dedicated cardiac imaging and on non-gated thoracic CT performed for non-cardiac indications, both with and without contrast. We recommend that coronary artery calcification is reported on all non-gated thoracic CT using a simple patient-based score (none, mild, moderate, severe). Furthermore, we recommend that reports include recommendations for subsequent management, namely the assessment of modifiable cardiovascular risk factors and, if the patient has chest pain, assessment as per standard guidelines. In most cases, this will not necessitate additional investigations. Incidental aortic valve calcification may also be identified on non-gated thoracic CT and should be reported, along with ancillary findings such as aortic root dilation. Calcification may occur in other parts of the heart including mitral valve/annulus, pericardium and myocardium, but in many cases these are an incidental finding without clinical significance

Vascular Thrombosis in Severe COVID-19 Requiring Extracorporeal Membrane Oxygenation: A Multicenter Study.

OBJECTIVES: Coronavirus disease 2019 has been reported to be a prothrombotic condition; however, multicenter data comparing this with other viral pneumonias in those requiring extracorporeal membrane oxygenation are lacking. We conducted a multicenter study using whole-body CT to examine the prevalence, severity, and nature of vascular complications in coronavirus disease 2019 in comparison with patients with other viral pneumonias. DESIGN: We analyzed whole-body CT scans for the presence of vascular thrombosis (defined as pulmonary artery thrombus, venous thrombus, systemic arterial thrombus, or end-organ infarct). The severity, distribution, and morphology of pulmonary artery thrombus were characterized. Competing risk cumulative incidence analysis was used to compare survival with discharge. SETTING: Three centers of the English national extracorporeal membrane oxygenation service. PATIENTS: Consecutive patients admitted with either coronavirus disease 2019 or noncoronavirus disease 2019 viral pneumonia admitted from January 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One-hundred thirty-six patients (45.2 ± 10.6 yr old, 39/146 [27%] female) requiring extracorporeal membrane oxygenation support underwent whole-body CT scans at admission. Of these, 86 had coronavirus disease 2019 pneumonia, and 50 had noncoronavirus disease 2019 viral pneumonia. Vascular thrombosis was seen more often in patients with coronavirus disease 2019 (odds ratio, 12.9 [95% CI 4.5-36.8]). In those with coronavirus disease 2019, 57 (73%) demonstrated pulmonary artery thrombus or pulmonary perfusion defects. Eighty-two percent of thrombus exhibited emboli-like morphology. The location of pulmonary artery thrombus and parenchymal perfusion defects was only concordant in 30% of cases. The risk of mortality was higher in those with coronavirus disease 2019 compared with noncoronavirus disease 2019 pneumonia (χ2 = 3.94; p = 0.047). Mortality was no different in coronavirus disease 2019 patients with or without vascular thrombosis (χ2 = 0.44; p = 0.51). CONCLUSIONS: In patients who received extracorporeal membrane oxygenation, coronavirus disease 2019 is associated with a higher prevalence of vascular thrombosis compared with noncoronavirus disease viral pneumonias. The pattern of pulmonary vascular changes suggests concurrent embolic disease and small vessel disease. Despite this, vascular thrombosis was not linked to poorer short-term prognosis in those with coronavirus disease 2019

Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19.

BackgroundDirect evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19.MethodsFor this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes.FindingsPatients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43-6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17-3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [InterpretationRadiotranscriptomic analysis of CT angiography scans introduces a potentially powerful new platform for the development of non-invasive imaging biomarkers. Application of this platform in routine CT pulmonary angiography scans done in patients with COVID-19 produced the radiotranscriptomic signature C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation and responsible for adverse clinical outcomes, which predicts in-hospital mortality and might allow targeted therapy.FundingEngineering and Physical Sciences Research Council, British Heart Foundation, Oxford BHF Centre of Research Excellence, Innovate UK, NIHR Oxford Biomedical Research Centre, Wellcome Trust, Onassis Foundation

Current approaches to the isolation and structural elucidation of active compounds from natural products

The essence of medicinal plants, either as pure compounds or as standardized extracts, provides unlimited opportunities for new drug leads because of the unmatched availability of chemical diversity. Due to an increasing demand for chemical diversity in screening programs, seeking therapeutic drugs from natural products, the isolation of compounds from edible plants has grown. Botanicals and herbal preparations for medicinal usage contain various types of bioactive compounds. The focus of this chapter is on revised current analytical methodologies, which include the extraction, isolation, and identification of bioactive principles in natural products. Chromatographic techniques such as LC and spectroscopic methods like NMR and MS will be discussed.UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de QuímicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones en Productos Naturales (CIPRONA

Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19.

BackgroundDirect evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19.MethodsFor this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes.FindingsPatients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43-6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17-3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [InterpretationRadiotranscriptomic analysis of CT angiography scans introduces a potentially powerful new platform for the development of non-invasive imaging biomarkers. Application of this platform in routine CT pulmonary angiography scans done in patients with COVID-19 produced the radiotranscriptomic signature C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation and responsible for adverse clinical outcomes, which predicts in-hospital mortality and might allow targeted therapy.FundingEngineering and Physical Sciences Research Council, British Heart Foundation, Oxford BHF Centre of Research Excellence, Innovate UK, NIHR Oxford Biomedical Research Centre, Wellcome Trust, Onassis Foundation