Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine

Background Remote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention. Methods We analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial. Results The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p < 0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95% of HF hospitalisations occurring within the following seven days. Conclusions A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review

Comparison of commercial DNA preparation kits for the detection of Brucellae in tissue using quantitative real-time PCR

<p>Abstract</p> <p>Background</p> <p>The detection of Brucellae in tissue specimens using PCR assays is difficult because the amount of bacteria is usually low. Therefore, optimised DNA extraction methods are critical. The aim of this study was to assess the performance of commercial kits for the extraction of <it>Brucella </it>DNA.</p> <p>Methods</p> <p>Five kits were evaluated using clinical specimens: QIAamp™ DNA Mini Kit (QIAGEN), peqGold™ Tissue DNA Mini Kit (PeqLab), UltraClean™ Tissue and Cells DNA Isolation Kit (MoBio), DNA Isolation Kit for Cells and Tissues (Roche), and NucleoSpin™ Tissue (Macherey-Nagel). DNA yield was determined using a quantitative real-time PCR assay targeting IS<it>711 </it>that included an internal amplification control.</p> <p>Results</p> <p>Kits of QIAGEN and Roche provided the highest amount of DNA, Macherey-Nagel and Peqlab products were intermediate whereas MoBio yielded the lowest amount of DNA. Differences were significant (p < 0.05) and of diagnostic relevance. Sample volume, elution volume, and processing time were also compared.</p> <p>Conclusions</p> <p>We observed differences in DNA yield as high as two orders of magnitude for some samples between the best and the worst DNA extraction kits and inhibition was observed occasionally. This indicates that DNA purification may be more relevant than expected when the amount of DNA in tissue is very low.</p

Comparison of diagnostic tests for the detection of Brucella spp. in camel sera

<p>Abstract</p> <p>Background</p> <p>Brucellosis in livestock causes enormous losses for economies of developing countries and poses a severe health risk to consumers of dairy products. Little information is known especially on camel brucellosis and its impact on human health. For surveillance and control of the disease, sensitive and reliable detection methods are needed. Although serological tests are the mainstay of diagnosis in camel brucellosis, these tests have been directly transposed from cattle without adequate validation. To date, little information on application of real-time PCR for detection of <it>Brucella </it>in camel serum is available. Therefore, this study was performed to compare the diagnostic efficiency of different serological tests and real-time PCR in order to identify the most sensitive, rapid and simple combination of tests for detecting <it>Brucella </it>infection in camels.</p> <p>Findings</p> <p>A total of 895 serum samples collected from apparently healthy Sudanese camels was investigated. Sudan is a well documented endemic region for brucellosis with cases in humans, ruminants, and camels. Rose Bengal Test (RBT), Complement Fixation Test (CFT), Slow Agglutination Test (SAT), Competitive Enzyme Linked Immunosorbant Assay (cELISA) and Fluorescence Polarization Assay (FPA) as well as real-time PCR were used. Our findings revealed that <it>bcsp31 </it>kDa real-time PCR detected <it>Brucella </it>DNA in 84.8% (759/895) of the examined samples, of which 15.5% (118/759) were serologically negative. Our results show no relevant difference in sensitivity between the different serological tests. FPA detected the highest number of positive cases (79.3%) followed by CFT (71.4%), RBT (70.7%), SAT (70.6%) and cELISA (68.8%). A combination of real-time PCR with one of the used serological tests identified brucellosis in more than 99% of the infected animals. 59.7% of the examined samples were positive in all serological tests and real-time PCR. A subpopulation of 6.8% of animals was positive in all serological tests but negative in real-time PCR assays. The high percentage of positive cases in this study does not necessarily reflect the seroprevalence of the disease in the country but might be caused by the fact that the camels were imported from brucellosis infected herds of Sudan, accidentally. Seroprevalence of brucellosis in camels should be examined in confirmatory studies to evaluate the importance of brucellosis in this animal species.</p> <p>Conclusion</p> <p>We suggest combining <it>bcsp31 </it>real-time PCR with either FPA, CFT, RBT or SAT to screen camels for brucellosis.</p

Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine

BackgroundRemote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention.MethodsWe analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial.ResultsThe machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p &lt; 0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95% of HF hospitalisations occurring within the following seven days.ConclusionsA machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables us to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based grey and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in grey and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.</p

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients

INTRODUCTION: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. METHODS & RESULTS: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. CONCLUSION: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables us to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based grey and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in grey and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders

Chlamydiaceae and Chronic Diseases : Clinical Implications and Host-Cell Gene Expression in a Model of Interferon-gamma-Induced Persistence

Bacteria of the family Chlamydiaceae are obligate intracellular parasites of eukaryotic cells. Coronary artery disease and cerebro-vascular stroke are the most common causes of death worldwide. Chronic diseases like adult-onset asthma or atherosclerosis and trachoma are increasingly attributed to C. pneumoniae and C. trachomatis, respectively. Persistence of these organisms in its respective host is suspected to be the cause of these chronic diseases. In its persistent form, Chlamydiaceae most probably remain in a viable but culture-negative state, in which chlamydicidal drugs are apparently not effective. An improved understanding of the persistence mechanisms will be critical for the development of innovative therapeutic strategies to effectively treat chlamydially induced chronic diseases. In this thesis, the human pathogens C. trachomatis and C. pneumoniae, representing the two genera of the family Chlamydiaceae, were chosen to investigate their interrelationship with the host. To gain a better molecular understanding of the interaction between pathogen and host-cells, a comparative analysis of the gene expression pattern of HeLa-cells after active and IFN-gamma-induced persistent infection with C. pneumoniae was performed using Affymetrix® microchips (HG-U133A). The step towards in situ monitoring is often impeded by too small amounts of sample material. Traditional RNA amplification methods, based on the Eberwine protocol, are often self-limiting due to 3’-biased amplified RNA. New diagnostic tools will be necessary for sensitively detecting Chlamydiaceae during all stages of their developmental cycle and for monitoring chlamydicidal drug therapy. In this thesis, quantitative as well as qualitative CE-marked real-time PCR assays, detecting C. trachomatis DNA from swab, urine and sperm samples, were developed.Bakterien der Familie Chlamydiaceae sind obligat intrazelluläre Parasiten eukaryotischer Zellen. Die koronare Herzkranzgefässerkrankung (KHK) und der ischämische Schlaganfall gehören weltweit zu den häufigsten Todesursachen. Chlamydien werden zunehmend mit chronischen Erkrankungen wie Bronchialasthma bei Erwachsenen und Atherosklerose im Falle von C. pneumoniae oder dem Trachom bei C. trachomatis assoziiert. Dies verstärkt den Verdacht, dass Chlamydien für mehrere Jahre in ihrem Wirt persistieren können und dadurch chronische Erkrankungen auslösen. Im Status der Persistenz liegen die Bakterien metabolisch und morphologisch verändert vor, sind nicht kultivierbar und scheinen mit heutigen Medikamenten nicht therapierbar zu sein. Um durch Chlamydien hervorgerufene chronische Erkrankungen behandeln und heilen zu können, wird die Entwicklung eines tieferen Verständnisses für den Ablauf und die Mechanismen der Persistenz erforderlich sein. In dieser Arbeit wurden die beiden human-pathogenen Erreger Chlamydia trachomatis und Chlamydophila pneumoniae stellvertretend für die beiden Genera (Chlamydia und Chlamydophila) der Familie der Chlamydiaceae untersucht. Um die Pathogenese besser zu verstehen und um Ansatzpunkte für mögliche Therapien chlamydialer Erkrankungen zu schaffen, wurden in dieser Arbeit Affymetrix® Microarrays (HG-U133A) verwendet und die Genexpression von epithelialen HeLa-Zellen nach Infektion mit C. pneumoniae vergleichend analysiert. Der Wechsel von in vitro zu in vivo Modellen gestaltet sich jedoch schwierig, da für Microarray Untersuchungen nicht genügend Untersuchungsmaterial zur Verfügung steht. Herkömmliche RNA Amplifikationsmethoden, die auf dem James Eberwine Verfahren basieren, sind oft selbstlimitierend durch unvollständig amplifizierte RNA. Ein Teilprojekt dieser Arbeit war es deshalb, den Einsatz einer neuen linearen RNA Amplifikationstechnik für C. pneumoniae Persistenz-Studien zu überprüfen. Zusätzlich sind neue diagnostische Anwendungsverfahren notwendig, die es erlauben, Chlamydien während ihres gesamten Entwicklungszyklus hoch sensitiv nachzuweisen und damit den Therapieverlauf zu überwachen. Im Rahmen dieser Arbeit wurden CE-markierte qualitative und quantitative real-time PCR Assays für den Nachweis von C. trachomatis DNA aus Abstrich-, Urin- und Sperma-Proben entwickelt

System-level cortical maturation links to adolescent resilience to adverse life events

Introduction: Adolescence is a period of increased brain reorganization that is essential to biological and psychosocial maturation, but also to mental health (Paus et al., 2008). Normative adolescent brain maturation as captured via neuroimaging follows two main modes: 1) conservative strengthening of initially strong connections, or 2) disruptive remodeling, i.e. strengthening of initially weak connections and vice versa (Váša et al., 2020). While adverse experiences and psychopathological processes can alter maturational trajectories (Stenson et al., 2021), adolescent reorganization may also hold potential for flexible adaptation to risk factors. Thus, normative maturation facilitating psychosocial skills may also aid well-being through resilience to adversity.Methods: We analyzed age-related changes in microstructural profile covariance (MPC; Fig. 1A) and resting-state functional connectivity (FC; Fig. 1B) in a longitudinal cohort of individuals aged 14-26 (n=295; 512 scans; 50.8% female). MPC reflects inter-regional similarity of intracortical profiles based on myelin-sensitive magnetic transfer (MT) data sampled at ten cortical depths. We first identified maturational modes by correlating the whole-brain MPC and FC patterns of each region at age 14 with the age-related changes of these patterns (14-26y; computed via edge-wise linear mixed effect models; Fig. 1C). Positive correlations indicate conservative and negative correlations disruptive development (FDR<0.05). Next, we investigated whether observed maturational patterns may contribute to resilience to adverse life events. From the total cohort, we drew a sub-sample (n=281) of individuals who reported adverse life experiences in the past 18 months. Conceptualizing resilience as adaptation to adversity, individuals were matched based on their adversity load and allocated to either high (n=88) or low resilient (n=89) groups based on reported well-being scores (top or bottom 33%, respectively; Fig. 2A). Structural and functional brain maturational modes were contrasted between the two groups via Fisher’s z differences.Results: Our work describes topologically heterogeneous patterns of structural and functional maturational modes (Fig. 1D) and differential associations with resilience. We observed disruptive development of MPC in frontal and parietal cortical areas, and conservative development in sensory, paralimbic, temporal and medial frontal regions. Linking structure to function, we found parallel conservative development in regions involved in sensory- and attention-related processes. Default mode and frontoparietal networks showed both cross-modal disruptive rewiring and a structure-function divergence, in which structure showed conservative but function disruptive developmental patterns (Fig. 1E). Last, we found that individuals who maintain better well-being after exposure to adverse life events showed overall less conservative and more disruptive functional maturational patterns, indicating increased functional network rewiring during development. Effects were smaller for structural patterns and suggested a potential benefit of disruptive MPC development only in regions where change occurs in parallel with functional change (Fig. 2B).Conclusion: Our longitudinal findings show overlapping but distinct patterns of structural and functional reorganization during adolescence. Cross-modal cortical transformations and structure-function decoupling in maturational modes were observed in association and paralimbic cortex, which are known to show protracted plasticity associated with both sociocognitive refinement and psychopathological alterations (Sydnor et al., 2021). Our findings suggest that brain remodeling throughout adolescence is especially pronounced in individuals showing better adaptation to adverse life events, and may thus facilitate resilience. This observation is in line with current psychological constructs of resilience as an adaptive, flexible process (Kalisch et al., 2017)

Faster dynamic auctions via polymatroid sum

We consider dynamic auctions for finding Walrasian equilibria in markets with indivisible items and strong gross substitutes valuation functions. Each price adjustment step in these auction algorithms requires finding an inclusion-wise minimal maximally overdemanded set or an inclusion-wise minimal maximally underdemanded set at the current prices. Both can be formulated as a submodular function minimization problem. We observe that minimizing this submodular function corresponds to a polymatroid sum problem, and using this viewpoint, we give a fast and simple push-relabel algorithm for finding the required sets. This improves on the previously best running time of Murota, Shioura and Yang (ISAAC 2013). Our algorithm is an adaptation of the push-relabel framework by Frank and Miklós (JJIAM 2012) to the particular setting. We obtain a further improvement for the special case of unit-supplies. We further show the following monotonicty properties of Walrasian prices: both the minimal and maximal Walrasian prices can only increase if supply of goods decreases, or if the demand of buyers increases. This is derived from a fine-grained analysis of market prices. We call “packing prices” a price vector such that there is a feasible allocation where each buyer obtains a utility maximizing set. Conversely, by “covering prices” we mean a price vector such that there exists a collection of utility maximizing sets of the buyers that include all available goods. We show that for strong gross substitutes valuations, the component-wise minimal packing prices coincide with the minimal Walrasian prices and the component-wise maximal covering prices coincide with the maximal Walrasian prices. These properties in turn lead to the price monotonicity results