Impact of real-time optical coherence tomography and angiographic coregistration on the percutaneous coronary intervention strategy.

Background: The use of optical coherence tomography (OCT) with angiographic coregistration (ACR) during percutaneous coronary intervention (PCI) for procedural decision-making is evolving; however, large-scale data in real-world practice are lacking. Aims: Our study aims to evaluate the real-time impact of OCT-ACR on clinician decision-making during PCI. Methods: Patients with angiographic diameter stenosis >70% in at least one native coronary artery were enrolled in the study. The pre- and post-PCI procedural strategies were prospectively assessed after angiography, OCT, and ACR. Results: A total of 500 patients were enrolled in the study between November 2018 and March 2020. Among these, data related to 472 patients with 483 lesions were considered for analysis. Preprocedural OCT resulted in a change in PCI strategy in 80% of lesions: lesion preparation (25%), stent length (53%), stent diameter (36%), and device landing zone (61%). ACR additionally impacted the treatment strategy in 34% of lesions. Postprocedural OCT demonstrated underexpansion (15%), malapposition (14%), and tissue/thrombus prolapse (7%), thereby requiring further interventions in 30% of lesions. No further change in strategy was observed with subsequent postprocedural ACR. Angiographic and procedural success was achieved in 100% of patients, and the overall incidence of major adverse cardiovascular events at 1 year was 0.85%. Conclusions: The outcomes reflect the real-time impact of OCT-ACR on the overall procedural strategy in patients undergoing PCI. ACR had a significant impact on the treatment strategy and was associated with better clinical outcomes at 1 year after index PCI. OCT-ACR has become a practical tool for improving outcomes in patients with complex lesions.

Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.

Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.

Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

BACKGROUND: Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models. METHODS: Retrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model. RESULTS: There were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality. DISCUSSION: We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

Exosome loaded immunomodulatory biomaterials alleviate local immune response in immunocompetent diabetic mice post islet xenotransplantation.

Foreign body response (FBR) to biomaterials compromises the function of implants and leads to medical complications. Here, we report a hybrid alginate microcapsule (AlgXO) that attenuated the immune response after implantation, through releasing exosomes derived from human Umbilical Cord Mesenchymal Stem Cells (XOs). Upon release, XOs suppress the local immune microenvironment, where xenotransplantation of rat islets encapsulated in AlgXO led to >170 days euglycemia in immunocompetent mouse model of Type 1 Diabetes. In vitro analyses revealed that XOs suppressed the proliferation of CD3/CD28 activated splenocytes and CD3+ T cells. Comparing suppressive potency of XOs in purified CD3+ T cells versus splenocytes, we found XOs more profoundly suppressed T cells in the splenocytes co-culture, where a heterogenous cell population is present. XOs also suppressed CD3/CD28 activated human peripheral blood mononuclear cells (PBMCs) and reduced their cytokine secretion including IL-2, IL-6, IL-12p70, IL-22, and TNFα. We further demonstrate that XOs mechanism of action is likely mediated via myeloid cells and XOs suppress both murine and human macrophages partly by interfering with NFκB pathway. We propose that through controlled release of XOs, AlgXO provide a promising new platform that could alleviate the local immune response to implantable biomaterials.

Controlled Release of Stem Cell Secretome Attenuates Inflammatory Response against Implanted Biomaterials.

Inflammatory response against implanted biomaterials impairs their functional integration and induces medical complications in the host's body. To suppress such immune responses, one approach is the administration of multiple drugs to halt inflammatory pathways. This challenges patient's adherence and can cause additional complications such as infection. Alternatively, biologics that regulate multiple inflammatory pathways are attractive agents in addressing the implants immune complications. Secretome of mesenchymal stromal cells (MSCs) is a multipotent biologic, regulating the homeostasis of lymphocytes and leukocytes. Here, it is reported that alginate microcapsules loaded with processed conditioned media (pCM-Alg) reduces the infiltration and/or expression of CD68+ macrophages likely through the controlled release of pCM. In vitro cultures revealed that alginate can dose dependently induce macrophages to secrete TNFα, IL-6, IL-1ß, and GM-CSF. Addition of pCM to the cultures attenuates the secretion of TNFα (p = 0.023) and IL-6 (p < 0.0001) by alginate or lipopolysaccharide (LPS) stimulations. Mechanistically, pCM suppressed the NfκB pathway activation of macrophages in response to LPS (p < 0.0001) in vitro and cathepsin activity (p = 0.005) in response to alginate in vivo. These observations suggest the efficacy of using MSC-derived secretome to prevent or delay the host rejection of implants.

Trauma-intrusive hallucinations and the dissociative state.

BACKGROUND: Research has supported a model of dissociation mediating the experience of hearing voices in traumatised individuals. AIMS: To further understand this model by examining subtypes of the dissociative experience involved in trauma-intrusive hallucinations. METHOD: The study involved four hospitals, 11 psychiatrists and 69 participants assessed using the Psychotic Symptoms Rating scale, the PTSD Symptoms Scale Interview and the Dissociative Subtype of PTSD Score. RESULTS: In total, 59% (n = 41) of the participants heard voices and they were compared with the 41% (n = 28) who did not. The severity of PTSD symptoms did not predict experience of hearing voices. Regression analysis indicated that two scales of dissociation (derealisation/depersonalisation and loss of awareness) were equally good predictors of the extent of hearing voices. Adding other possible predictors (age of trauma <18, sexual violence) was relevant but did not enhance the prediction. CONCLUSIONS: This research supports the proposal that trauma-intrusive voices are mediated by symptoms of dissociation. The supported model describes general, rather than trauma specific, symptoms of dissociation mediating the experience of hearing voices. The concept of anchoring is discussed and suggests a potential treatment strategy, which could be useful in the clinical management of hearing voices. DECLARATION OF INTEREST: None.

Where pseudo-hallucinations meet dissociation: a cluster analysis.

OBJECTIVES: The possible link between cognitive areas of perception and integration of consciousness was examined using assessments of hallucinations and derealisation/depersonalization. METHODS: Sixty-five subjects in three main diagnostic groups - posttraumatic stress disorder (PTSD), borderline personality disorder (BPD) and schizophrenia - identified by their treating psychiatrist as hearing voices were surveyed regarding characteristics of hallucinations, derealisation/depersonalization, delusions and childhood/adult trauma. RESULTS: A cluster analysis produced two clusters predominantly determined by variables of hallucinations measures, childhood sexual abuse and derealisation/depersonalization scores. CONCLUSIONS: History of childhood trauma and variability in derealisation/depersonalization scores were better predictors of external, negative, uncontrollable voices than diagnosis of BPD or PTSD. The potential links between dissociative states and pseudo-hallucinations are discussed.

What psychiatrists should know about sporadic Creutzfeldt-Jakob disease.

OBJECTIVE: To provide psychiatrists with relevant, up to date information about sporadic Creutzfeldt-Jakob disease. CONCLUSIONS: A 59-year-old bookkeeper presented with psychiatric symptoms in the context of stressors and past history of depression, for which her GP prescribed sertraline and olanzapine. Following a further deterioration in her mental state she was referred to acute psychiatric services, and there found to have dementia and myoclonus, and investigations supported a diagnosis of probable Creutzfeldt-Jakob disease, sporadic type (sCJD). This paper serves to outline the emerging literature challenging the notion that suggests psychiatric symptoms are uncommon in the presentation of sCJD.

Direct inhibition of elastase and matrixmetalloproteinases and stimulation of biosynthesis of fibrillar collagens, elastin, and fibrillins by xanthohumol.

In skin aging there is deterioration of the extracellular matrix's collagen and elastin fibers, from its reduced biosynthesis and increased degradation by elastase and matrixmetalloproteinases (MMPs). Xanthohumol is a flavonoid isolated from the hop plant Humulus lupulus L., with anti-microbial, antioxidant, anti-inflammatory, and anti-carcinogenic properties. The goal of this research was to investigate xanthohumol as an anti-skinaging agent via its beneficial regulation of the extracellular matrix. To this purpose, we examined the direct effect of xanthohumol on the activities of elastase and MMPs (MMPs 1, 2, and 9) and its effect on the expression (protein and/or transcription levels) of collagens (types I, III, and V), elastin, and fibrillins (1 and 2) in dermal fibroblasts. Xanthohumol significantly inhibited elastase and MMP-9 activities from its lowest concentration, and MMP-1 and MMP-2 at its higher concentrations, which implies a greater protective effect on elastin. It dramatically increased the expression of types I, III, and V collagens, and elastin, fibrillin-1, and fibrillin-2 in dermal fibroblasts. The effects were similar to those of ascorbic acid. This is the first report identifying xanthohumol's potential to improve skin structure and firmness: it simultaneously inhibits the activities of elastase/MMPs and stimulates the biosynthesis of fibrillar collagens, elastin, and fibrillins.

A solution to the stochastic point location problem in metalevel nonstationary environments.

This paper reports the first known solution to the stochastic point location (SPL) problem when the environment is nonstationary. The SPL problem involves a general learning problem in which the learning mechanism (which could be a robot, a learning automaton, or, in general, an algorithm) attempts to learn a "parameter," for example, lambda*, within a closed interval. However, unlike the earlier reported results, we consider the scenario when the learning is to be done in a nonstationary setting. For each guess, the environment essentially informs the mechanism, possibly erroneously (i.e., with probability p), which way it should move to reach the unknown point. Unlike the results available in the literature, we consider the fascinating case when the point sought for is itself stochastically moving (which is modeled as follows). The environment communicates with an intermediate entity (referred to as the teacher/oracle) about the point itself, i.e., advising where it should go. The mechanism that searches for the point in turn receives responses from the teacher/oracle, which directs how it should move. Therefore, the point itself, in the overall setting, is moving, i.e., delivering possibly incorrect information about its location to the teacher/oracle. This in turn means that the "environment" is itself nonstationary, which implies that the advice of the teacher/oracle is both uncertain and changing with time-rendering the problem extremely fascinating. The heart of the strategy we propose involves discretizing the space and performing a controlled random walk on this space. Apart from deriving some analytic results about our solution, we also report the simulation results that demonstrate the power of the scheme, and state some potential applications.