Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers

Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions, even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent bifurcation or sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. We employ microarray data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data and functional analysis

Slower recovery in space before collapse of connected populations

Slower recovery from perturbations near a tipping point and its indirect signatures in fluctuation patterns have been suggested to foreshadow catastrophes in a wide variety of systems. Recent studies of populations in the field and in the laboratory have used time-series data to confirm some of the theoretically predicted early warning indicators, such as an increase in recovery time or in the size and timescale of fluctuations. However, the predictive power of temporal warning signals is limited by the demand for long-term observations. Large-scale spatial data are more accessible, but the performance of warning signals in spatially extended systems needs to be examined empirically. Here we use spatially extended yeast populations, an experimental system with a fold bifurcation (tipping point), to evaluate early warning signals based on spatio-temporal fluctuations and to identify a novel spatial warning indicator. We found that two leading indicators based on fluctuations increased before collapse of connected populations; however, the magnitudes of the increases were smaller than those observed in isolated populations, possibly because local variation is reduced by dispersal. Furthermore, we propose a generic indicator based on deterministic spatial patterns, which we call ‘recovery length’. As the spatial counterpart of recovery time, recovery length is the distance necessary for connected populations to recover from spatial perturbations. In our experiments, recovery length increased substantially before population collapse, suggesting that the spatial scale of recovery can provide a superior warning signal before tipping points in spatially extended systems.United States. National Institutes of Health (NIH R00 GM085279-02)United States. National Institutes of Health (NIH DP2)Alfred P. Sloan FoundationNational Science Foundation (U.S.

Sharing clinical information across care settings: the birth of an integrated assessment system

Background: Population ageing, the emergence of chronic illness, and the shift away from institutional care challenge conventional approaches to assessment systems which traditionally are problem and setting specific

Time course of airway remodelling after an acute chlorine gas exposure in mice

Accidental chlorine (Cl2) gas inhalation is a common cause of acute airway injury. However, little is known about the kinetics of airway injury and repair after Cl2 exposure. We investigated the time course of airway epithelial damage and repair in mice after a single exposure to a high concentration of Cl2 gas. Mice were exposed to 800 ppm Cl2 gas for 5 minutes and studied from 12 hrs to 10 days post-exposure. The acute injury phase after Cl2 exposure (≤ 24 hrs post-exposure) was characterized by airway epithelial cell apoptosis (increased TUNEL staining) and sloughing, elevated protein in bronchoalveolar lavage fluid, and a modest increase in airway responses to methacholine. The repair phase after Cl2 exposure was characterized by increased airway epithelial cell proliferation, measured by immunoreactive proliferating cell nuclear antigen (PCNA), with maximal proliferation occurring 5 days after Cl2 exposure. At 10 days after Cl2 exposure the airway smooth muscle mass was increased relative to controls, suggestive of airway smooth muscle hyperplasia and there was evidence of airway fibrosis. No increase in goblet cells occurred at any time point. We conclude that a single exposure of mice to Cl2 gas causes acute changes in lung function, including pulmonary responsiveness to methacholine challenge, associated with airway damage, followed by subsequent repair and airway remodelling

Relationship between Plasmodium falciparum malaria prevalence, genetic diversity and endemic Burkitt lymphoma in Malawi

Endemic Burkitt lymphoma (eBL) has been linked to Plasmodium falciparum (Pf) malaria infection, but the contribution of infection with multiple Pf genotypes is uncertain. We studied 303 eBL (cases) and 274 non eBL-related cancers (controls) in Malawi using a sensitive and specific molecular-barcode array of 24 independently segregating Pf single nucleotide polymorphisms. Cases had a higher Pf malaria prevalence than controls (64.7% versus 45.3%; odds ratio [OR] 2.1, 95% confidence interval (CI): 1.5 to 3.1). Cases and controls were similar in terms of Pf density (4.9 versus 4.5 log copies, p = 0.28) and having ≥3 non-clonal calls (OR 2.7, 95% CI: 0.7-9.9, P = 0.14). However, cases were more likely to have a higher Pf genetic diversity score (153.9 versus 133.1, p = 0.036), which measures a combination of clonal and non-clonal calls, than controls. Further work is needed to evaluate the possible role of Pf genetic diversity in the pathogenesis of endemic BL

What we stand for: Reputation platforms in Scandinavian higher education

acceptedVersio

Digital endpoints in clinical trials: emerging themes from a multi-stakeholder Knowledge Exchange event

Background: Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people’s chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person’s health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration. A multi-stakeholder Knowledge Exchange event was organised to facilitate conversations across silos within this research ecosystem.MethodsA survey was sent to an initial list of stakeholders to identify potential discussion topics. Additional stakeholders were identified through iterative discussions on perspectives that needed representation. Co-design meetings with attendees were held to discuss the scope, format and ethos of the event. The event itself featured a cross-disciplinary selection of talks, a panel discussion, small-group discussions facilitated via a rolling seating plan and audience participation via Slido. A transcript was generated from the day, which, together with the output from Slido, provided a record of the day’s discussions. Finally, meetings were held following the event to identify the key challenges for digital endpoints which emerged and reflections and recommendations for dissemination. Results: Several challenges for digital endpoints were identified in the following areas: patient adherence and acceptability; algorithms and software for devices; design, analysis and conduct of clinical trials with digital endpoints; the environmental impact of digital endpoints; and the need for ongoing ethical support. Learnings taken for next generation events include the need to include additional stakeholder perspectives, such as those of funders and regulators, and the need for additional resources and facilitation to allow patient and public contributors to engage meaningfully during the event. Conclusions: The event emphasised the importance of consortium building and highlighted the critical role that collaborative, multi-disciplinary, and cross-sector efforts play in driving innovation in research design and strategic partnership building moving forward. This necessitates enhanced recognition by funders to support multi-stakeholder projects with patient involvement, standardised terminology, and the utilisation of open-source software

Analgesic management of an eight-year-old Springer Spaniel after amputation of a thoracic limb

Analgesic agents were administered perioperatively to an eight-year-old Springer Spaniel undergoing amputation of its right thoracic limb. The amputation was carried out due to a painful, infiltrative and poorly differentiated sarcoma involving the nerves of the brachial plexus. A combination of pre-emptive and multimodal perioperative analgesic strategies was used; including intravenous (IV) infusions of fentanyl, morphine, lidocaine and ketamine

Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies

Expansion microscopy (ExM) enables imaging of preserved specimens with nanoscale precision on diffraction-limited instead of specialized super-resolution microscopes. ExM works by physically separating fluorescent probes after anchoring them to a swellable gel. The first ExM method did not result in the retention of native proteins in the gel and relied on custom-made reagents that are not widely available. Here we describe protein retention ExM (proExM), a variant of ExM in which proteins are anchored to the swellable gel, allowing the use of conventional fluorescently labeled antibodies and streptavidin, and fluorescent proteins. We validated and demonstrated the utility of proExM for multicolor super-resolution (~70 nm) imaging of cells and mammalian tissues on conventional microscopes.United States. National Institutes of Health (1R01GM104948)United States. National Institutes of Health (1DP1NS087724)United States. National Institutes of Health ( NIH 1R01EY023173)United States. National Institutes of Health (1U01MH106011