Trends in premature avertable mortality from non-communicable diseases for 195 countries and territories, 1990–2017: A population-based study

Background: The reduction by a third of premature non-communicable disease (NCD) mortality by 2030 is the ambitious target of Sustainable Development Goal (SDG) 3.4. However, the indicator is narrowly defined, including only four major NCDs (cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases) and only for people aged 30–70 years. This study focuses on premature avertable mortality from NCDs—premature deaths caused by NCDs that could be prevented through effective public policies and health interventions or amenable to high-quality health care—to assess trends at global, regional, and national levels using estimates from the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2017. Methods: We reviewed existing lists of NCD causes of death that are either preventable through public health policies and interventions or amenable to health care to create a list of avertable NCD causes of death, which was mapped to the GBD cause list. We estimated age-standardised years of life lost (YLL) per 100 000 population due to premature avertable mortality from NCDs, avertable NCD cause clusters, and non-avertable NCD causes by sex, location, and year and reported their 95% uncertainty intervals (UIs). We examined trends in age-standardised YLL due to avertable and non-avertable NCDs, assessed the progress of premature avertable mortality from NCDs in achieving SDG 3.4, and explored specific avertable NCD cause clusters that could make a substantial contribution to overall trends in premature mortality. Findings: Globally, premature avertable mortality from NCDs for both sexes combined declined −1·3% (95% UI −1·4 to −1·2) per year, from 12 855 years (11 809 to 14 051) in 1990 to 9008 years (8329 to 9756) in 2017. However, the absolute number of avertable NCD deaths increased 49·3% (95% UI 47·3 to 52·2) from 23·1 million (22·0–24·1) deaths in 1990 to 34·5 million (33·4 to 35·6) in 2017. Premature avertable mortality from NCDs reduced in every WHO region and in most countries and territories between 1990 and 2017. Despite these reductions, only the Western Pacific and European regions and 25 countries (most of which are high-income countries) are on track to achieve SDG target 3.4. Since 2017, there has been a global slowdown in the reduction of premature avertable mortality from NCDs. In 2017, high premature avertable mortality from NCDs was clustered in low-income and middle-income countries, mainly in the South-East Asia region, Eastern Mediterranean region, and African region. Most countries with large annual reductions in such mortality between 1990 and 2017 had achieved low levels of premature avertable mortality from NCDs by 2017. Some countries, the most populous examples being Afghanistan, the Central African Republic, Uzbekistan, Haiti, Mongolia, Turkmenistan, Pakistan, Ukraine, Laos, and Egypt, reported both an upward trend and high levels of premature avertable mortality from NCDs. Cardiovascular diseases, cancers, and chronic respiratory diseases have been the main drivers of the global and regional reduction in premature avertable mortality from NCDs, whereas premature mortality from substance use disorders, chronic kidney disease and acute glomerulonephritis, and diabetes have been increasing. Interpretation: Worldwide, there has been a substantial reduction in premature avertable mortality from NCDs, but progress has been uneven across populations. Countries vary substantially in current levels and trends and, hence, the extent to which they are on track to achieve SDG 3.4. By accounting for premature avertable mortality while avoiding arbitrary age cutoffs, premature avertable mortality from NCDs is a robust, comprehensive, and actionable indicator for quantifying and monitoring global and national progress towards NCD prevention and control. Funding: None

Rewriting Abstract Structures: Materialization Explained Categorically

The paper develops an abstract (over-approximating) semantics for double-pushout rewriting of graphs and graph-like objects. The focus is on the so-called materialization of left-hand sides from abstract graphs, a central concept in previous work. The first contribution is an accessible, general explanation of how materializations arise from universal properties and categorical constructions, in particular partial map classifiers, in a topos. Second, we introduce an extension by enriching objects with annotations and give a precise characterization of strongest post-conditions, which are effectively computable under certain assumptions

“Surprise” and the Bayesian Brain: Implications for Psychotherapy Theory and Practice

The free energy principle (FEP) has gained widespread interest and growing acceptance as a new paradigm of brain function, but has had little impact on the theory and practice of psychotherapy. The aim of this paper is to redress this. Brains rely on Bayesian inference during which “bottom-up” sensations are matched with “top-down” predictions. Discrepancies result in “prediction error.” The brain abhors informational “surprise,” which is minimized by (1) action enhancing the statistical likelihood of sensory samples, (2) revising inferences in the light of experience, updating “priors” to reality-aligned “posteriors,” and (3) optimizing the complexity of our generative models of a capricious world. In all three, free energy is converted to bound energy. In psychopathology energy either remains unbound, as in trauma and inhibition of agency, or manifests restricted, anachronistic “top-down” narratives. Psychotherapy fosters client agency, linguistic and practical. Temporary uncoupling bottom-up from top-down automatism and fostering scrutinized simulations sets a number of salutary processes in train. Mentalising enriches Bayesian inference, enabling experience and feeling states to be “metabolized” and assimilated. “Free association” enhances more inclusive sensory sampling, while dream analysis foregrounds salient emotional themes as “attractors.” FEP parallels with psychoanalytic theory are outlined, including Freud’s unpublished project, Bion’s “contact barrier” concept, the Fonagy/Target model of sexuality, Laplanche’s therapist as “enigmatic signifier,” and the role of projective identification. The therapy stimulates patients to become aware of and revise the priors’ they bring to interpersonal experience. In the therapeutic “duet for one,” the energy binding skills and non-partisan stance of the analyst help sufferers face trauma without being overwhelmed by psychic entropy. Overall, the FEP provides a sound theoretical basis for psychotherapy practice, training, and research

Mapping established psychopathology scales onto the Hierarchical Taxonomy of Psychopathology (HiTOP)

The Hierarchical Taxonomy of Psychopathology (HiTOP) organizes phenotypes of mental disorder based on empirical covariation, offering a comprehensive organizational framework from narrow symptoms to broader patterns of psychopathology. We argue that established self-report measures of psychopathology from the pre-HiTOP era should be systematically integrated into HiTOP to foster cumulative research and further the understanding of psychopathology structure. Hence, in this study, we mapped 92 established psychopathology (sub)scales onto the current HiTOP working model using data from an extensive battery of self-report assessments that was completed by community participants and outpatients (N = 909). Content validity ratings of the item pool were used to select indicators for a bifactor-(S-1) model of the p factor and five HiTOP spectra (i.e., internalizing, thought disorder, detachment, disinhibited externalizing, and antagonistic externalizing). The content-based HiTOP scales were validated against personality disorder diagnoses as assessed by standardized interviews. We then located established scales within the taxonomy by estimating the extent to which scales reflected higher-level HiTOP dimensions. The analyses shed light on the location of established psychopathology scales in HiTOP, identifying pure markers and blends of HiTOP spectra, as well as pure markers of the p factor (i.e., scales assessing mentalizing impairment and suspiciousness/epistemic mistrust)

A social inference model of idealization and devaluation

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Experimental Evaluation of Gene Silencing As New Therapeutic Option in the Treatment of Gemcitabine-chemoresistant Non-small-cell Lung Cancer

The evolution of technological and therapeutic applications of siRNA since the description of the interference process in 2006 has been extremely rapid and very productive. Currently, at least ten tumor entities and ten viral infections in which siRNA-based therapy might play an auspicious role have been described. Because of the very poor prognosis of NSCLC, we examined and proposed a new therapeutic alternative for the treatment of gemcitabine-resistant lung cancer via siRNA-specific silencing of six important molecules involved in lung carcinogenesis.Methods: One hundred thousand gemcitabine-chemoresistant A549 cell lines were cultured in a humidified atmosphere containing 5 % CO2 at 37°C and were transfected with specific siRNA targeting HIF1, HIF2, STAT3, SRF, E2F1 and Survivin. The relative expression of these molecules was examined via qRT-PCR and the viability of the chemoresistant cells after siRNA transfection was analyzed using a CASY system 72 hours after specific transfection.Results: The relative expression of the examined target molecules was suppressed by up to 73 % after specific transfection, and the CASY system demonstrated a concentration-dependent reduction in the viability of chemoresistant A549 cells of up to 61 %. Therefore the obtained results were significantly better in comparison to the control group.Conclusions: siRNA complexes may induce accurate suppression of various target molecules involved in lung tumor growth, in particular in gemcitabine- chemoresistant adenocarcinoma. Therefore, siRNA-based nanotechnology might represent a productive platform for the development of new chemotherapeutic agents for advanced stages of lung cancer in the context of a personalized multimodality regimen

Dynamic Simulation of a solar tower system with open volumetic receiver - a review on the vICERP project

The paper presents an overview on the modeling and simulation activities of the virtual institute for central receiver power plants (vICERP). Within a three years launch period models and tools for dynamic simulation of central receiver power plants have been developed by the five research institutes involved. The models are based on the Modelica modeling language. Today, models for the heliostat field, the receiver, the air cycle, the thermal storage, and the water-steam cycle are available within the consortium. As a first application, the Solar Tower Jülich technology was used as a reference. Models are validated with real operational data from the Solar Tower Jülich