Healthcare workforce equity for health equity: An overview of its importance for the level of primary health care.

BACKGROUND: Healthcare workforce crises often stem from healthcare workers' inequities. This study provides an overview of the main PHC workforce policy questions related to health equity, offering examples of evidence necessary to support the implementation of policies and strategies that increase equity in the health workforce and access to the PHC workforce and services. METHODS: The equity-related policies in PHC and workforce were linked with the indicators listed in the Global Health Workforce Network Data and Evidence Hub and guidelines for health workforce management. RESULTS: The policy-relevant questions in PHC cover many workforce issues such as the optimal size, equitable distribution, relevant competencies to ensure equitable healthcare access, and equitable approaches for retention, training, recruitment, benefits and incentive schemes and governance. This will require intersectionality evidence of the optimised staffing to PHC workload, that PHC practitioners' training demonstrates evidence-based knowledge aligned with locally relevant expertise. CONCLUSION: Critical for equitable PHC access and health equity is the establishment of efficient measurement of PHC workforce equity and its implications for population health. Using indicators that measure health and workforce equity in research, policy, and practices may improve recruitment and retention, and respond more effectively to the PHC workforce crises.

Inflammatory Status Assessment by Machine Learning Techniques to Predict Outcomes in Patients with Symptomatic Aortic Stenosis Treated by Transcatheter Aortic Valve Replacement.

(1) Background: Although transcatheter aortic valve replacement (TAVR) significantly improves long-term outcomes of symptomatic severe aortic stenosis (AS) patients, long-term mortality rates are still high. The aim of our study was to identify potential inflammatory biomarkers with predictive capacity for post-TAVR adverse events from a wide panel of routine biomarkers by employing ML techniques. (2) Methods: All patients diagnosed with symptomatic severe AS and treated by TAVR since January 2016 in a tertiary center were included in the present study. Three separate analyses were performed: (a) using only inflammatory biomarkers, (b) using inflammatory biomarkers, age, creatinine, and left ventricular ejection fraction (LVEF), and (c) using all collected parameters. (3) Results: A total of 338 patients were included in the study, of which 56 (16.5%) patients died during follow-up. Inflammatory biomarkers assessed using ML techniques have predictive value for adverse events post-TAVR with an AUC-ROC of 0.743 and an AUC-PR of 0.329; most important variables were CRP, WBC count and Neu/Lym ratio. When adding age, creatinine and LVEF to inflammatory panel, the ML performance increased to an AUC-ROC of 0.860 and an AUC-PR of 0.574; even though LVEF was the most important predictor, inflammatory parameters retained their value. When using the entire dataset (inflammatory parameters and complete patient characteristics), the ML performance was the highest with an AUC-ROC of 0.916 and an AUC-PR of 0.676; in this setting, the CRP and Neu/Lym ratio were also among the most important predictors of events. (4) Conclusions: ML models identified the CRP, Neu/Lym ratio, WBC count and fibrinogen as important variables for adverse events post-TAVR.

Number of necessary training examples for Neural Networks with different number of trainable parameters.

In this work, the network complexity should be reduced with a concomitant reduction in the number of necessary training examples. The focus thus was on the dependence of proper evaluation metrics on the number of adjustable parameters of the considered deep neural network. The used data set encompassed Hematoxylin and Eosin (H&E) colored cell images provided by various clinics. We used a deep convolutional neural network to get the relation between a model's complexity, its concomitant set of parameters, and the size of the training sample necessary to achieve a certain classification accuracy. The complexity of the deep neural networks was reduced by pruning a certain amount of filters in the network. As expected, the unpruned neural network showed best performance. The network with the highest number of trainable parameter achieved, within the estimated standard error of the optimized cross-entropy loss, best results up to 30% pruning. Strongly pruned networks are highly viable and the classification accuracy declines quickly with decreasing number of training patterns. However, up to a pruning ratio of 40%, we found a comparable performance of pruned and unpruned deep convolutional neural networks (DCNN) and densely connected convolutional networks (DCCN).

Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures.

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph-structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN-based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multimodal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatiotemporal dynamics in brain networks.

Analysis of eyewitness testimony using electroencephalogram signals.

Face recognition and related psychological phenomenon have been the subject of neurocognitive studies during last decades. More recently the problem of face identification is also addressed to test the possibility of finding markers on the electroencephalogram signals. To this end, this work presents an experimental study where Brain Computer Interface strategies were implemented to find features on the signals that could discriminate between culprit and innocent. The feature extraction block comprises time domain and frequency domain characteristics of single-trial signals. The classification block is based on a support vector machine and its performance for the best ranked features. The data analysis comprises the signals of a cohort of 28 participants.

Atomic force microscopy analysis of the surface alterations of enamel, dentin, composite and ceramic materials exposed to low oral pH in GERD.

Dental erosion is a significant topic in medical literature, both for gastroenterology and dental medicine. Dental structure loss has a psychosocial and functional significance. The pathogenesis of dental erosion in patients diagnosed with gastroesophageal reflux disease (GERD) characterized by the presence of an acidic oral environment after reflux episodes, is not well understood. The present study was designed to observe the effect of low oral pH in time on natural surfaces including enamel and dentine, but also on materials used in treating these dental destructions such as composites and ceramics. The acidic oral environment was estimated in relation to salivary pH. In the dental laboratory, 5-mm2 and 1-mm composite pieces of thick enamel, dentine, Emax Ceramic and Nexco Ivoclar were cut in order to be analyzed using atomic force microscopy (AFM) and to observe the surface alterations. Gastric acid was collected and mixed with saliva until a pH value of 6.0 was obtained, in which the pieces were immersed for 24, 120, 240 h. Roughness of each surface was calculated at a microstructure and nanostructure level. The results showed significant alterations in enamel and dentine exposed to a lower pH level beginning even at a short immersion time, in comparison with composites and ceramics which had no alterations. In conclusion, multidisciplinary attention should be given to detect and manage acidity of the oral cavity caused by GERD, in order to prevent dental erosion.

A graph neural network framework for causal inference in brain networks.

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model's accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

Tocilizumab reduces the risk of ICU admission and mortality in patients with SARS-CoV-2 infection.

OBJECTIVE: In some patients the immune response triggered by SARS-CoV-2 is unbalanced, presenting an acute respiratory distress syndrome which in many cases requires intensive care unit (ICU) admission. The limitation of ICU beds has been one of the major burdens in the management around the world; therefore, clinical strategies to avoid ICU admission are needed. We aimed to describe the influence of tocilizumab on the need of transfer to ICU or death in non-critically ill patients. METHODS: A retrospective study of 171 patients with SARS-CoV-2 infection that did not qualify as requiring transfer to ICU during the first 24h after admission to a conventional ward, were included. The criteria to receive tocilizumab was radiological impairment, oxygen demand or an increasing of inflammatory parameters, however, the ultimate decision was left to the attending physician judgement. The primary outcome was the need of ICU admission or death whichever came first. RESULTS: A total of 77 patients received tocilizumab and 94 did not. The tocilizumab group had less ICU admissions (10.3% vs. 27.6%, P=0.005) and need of invasive ventilation (0 vs 13.8%, P=0.001). In the multivariable analysis, tocilizumab remained as a protective variable (OR: 0.03, CI 95%: 0.007-0.1, P=0.0001) of ICU admission or death. CONCLUSIONS: Tocilizumab in early stages of the inflammatory flare could reduce an important number of ICU admissions and mechanical ventilation. The mortality rate of 10.3% among patients receiving tocilizumab appears to be lower than other reports. This is a non-randomized study and the results should be interpreted with caution.

Síndrome hemofagocítico asociado a infecciones: no tan infrecuente / Haemophagocytic syndrome associated with infections: not so uncommon

Introducción: El síndrome hemofagocítico (SHF) es un trastorno inmunológico grave caracterizado por una inflamación descontrolada con fracaso multiorgánico. Puede estar desencadenado por infecciones víricas, bacterianas, fúngicas o parasitarias. Se describe nuestra experiencia de SHF asociado a infecciones y se estima su incidencia local. Material y método: Estudio retrospectivo observacional de SHF asociado a infecciones en adultos atendidos en el Servicio de Patología Infecciosa de un hospital universitario durante 5años y revisión de las series publicadas en Europa. Resultados: En 2 mujeres con enfermedad de Crohn, el SHF se asoció a infección por citomegalovirus y a leishmaniosis visceral (mieloma múltiple 1, tumor sólido 2, sin enfermedad evidente 1) en 4 pacientes (3 hombres). Fallecieron 2 enfermos. La incidencia estimada fue 0,58/100.000/año. Las series publicadas son heterogéneas. Conclusiones: El SHF asociado a infecciones debe de ser más frecuente de lo descrito. El entorno geográfico puede influir en las infecciones desencadenantes (en nuestro medio, debe buscarse Leishmania)

Background: Haemophagocytic syndrome (HPS) is a severe immunological disorder characterised by uncontrolled inflammation and multiple organ failure. HPS can be triggered by viral, bacterial, fungal and parasitical infections. We report our experience with infection-related HPS and estimate its local incidence. Material and method: We conducted an observational retrospective study of infection-associated HPS in patients treated in the Department of Infectious Diseases of a university hospital within a 5-year period, as well as a review of the published series in Europe. Results: HPS was associated with infection by cytomegalovirus in 2 women with Crohn's disease and was associated with visceral leishmaniosis in 4 patients (3 men, 1 woman; 1 case of multiple myeloma; 2 cases of solid tumours; 1 case of no apparent disease). Two patients died, and the estimated incidence rate was 0.58/100,000 inhabitants/year. The published series are mixed. Conclusions: Infection-related HPS must be more common than reported. The geographical environment can influence the triggering infections (in our environment, Leishmania should be considered)

Haemophagocytic syndrome associated with infections: Not so uncommon. / Síndrome hemofagocítico asociado a infecciones: no tan infrecuente.

BACKGROUND: Haemophagocytic syndrome (HPS) is a severe immunological disorder characterised by uncontrolled inflammation and multiple organ failure. HPS can be triggered by viral, bacterial, fungal and parasitical infections. We report our experience with infection-related HPS and estimate its local incidence. MATERIAL AND METHOD: We conducted an observational retrospective study of infection-associated HPS in patients treated in the Department of Infectious Diseases of a university hospital within a 5-year period, as well as a review of the published series in Europe. RESULTS: HPS was associated with infection by cytomegalovirus in 2 women with Crohn's disease and was associated with visceral leishmaniosis in 4 patients (3 men, 1 woman; 1 case of multiple myeloma; 2 cases of solid tumours; 1 case of no apparent disease). Two patients died, and the estimated incidence rate was 0.58/100,000 inhabitants/year. The published series are mixed. CONCLUSIONS: Infection-related HPS must be more common than reported. The geographical environment can influence the triggering infections (in our environment, Leishmania should be considered).

Technical Note: A comparison of point set registration methods for electromagnetic tracking.

PURPOSE: High dose rate brachytherapy applies intense and destructive radiation. A treatment plan defines radiation source dwell positions to avoid irradiating healthy tissue. The study discusses methods to quantify any positional changes of source locations along the various treatment sessions. METHODS: Electromagnetic tracking (EMT) localizes the radiation source during the treatment sessions. But in each session the relative position of the patient relative to the filed generator is changed. Hence, the measured dwell point sets need to be registered onto each other to render them comparable. Two point set registration techniques are compared: a probabilistic method called coherent point drift (CPD) and a multidimensional scaling (MDS) technique. RESULTS: Both enable using EMT without external registration and achieve very similar results with respect to dwell position determination of the radiation source. Still MDS achieves smaller grand average deviations (CPD-rPSR: MD = 2.55 mm, MDS-PSR: MD = 2.15 mm) between subsequent dwell position determinations, which also show less variance (CPD-rPSR: IQR = 4 mm, MDS-PSR: IQR = 3 mm). Furthermore, MDS is not based on approximations and does not need an iterative procedure to track sensor positions inside the implanted catheters. CONCLUSION: Although both methods achieve similar results, MDS is to be preferred over rigid CPD while nonrigid CPD is unsuitable as it does not preserve topology.

Hybridizing EMD with cICA for fMRI Analysis of Patient Groups.

Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.

Mining EEG scalp maps of independent components related to HCT tasks.

This work presents an unsupervised mining strategy, applied to an independent component analysis (ICA) of segments of data collected while participants are answering to the items of the Halstead Category Test (HCT). This new methodology was developed to achieve signal components at trial level and therefore to study signal dynamics which are not available within participants' ensemble average signals. The study will be focused on the signal component that can be elicited by the binary visual feedback which is part of the HCT protocol. The experimental study is conducted using a cohort of 58 participants.

A tool to automatically analyze electromagnetic tracking data from high dose rate brachytherapy of breast cancer patients.

During High Dose Rate Brachytherapy (HDR-BT) the spatial position of the radiation source inside catheters implanted into a female breast is determined via electromagnetic tracking (EMT). Dwell positions and dwell times of the radiation source are established, relative to the patient's anatomy, from an initial X-ray-CT-image. During the irradiation treatment, catheter displacements can occur due to patient movements. The current study develops an automatic analysis tool of EMT data sets recorded with a solenoid sensor to assure concordance of the source movement with the treatment plan. The tool combines machine learning techniques such as multi-dimensional scaling (MDS), ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA) and particle filter (PF) to precisely detect and quantify any mismatch between the treatment plan and actual EMT measurements. We demonstrate that movement artifacts as well as technical signal distortions can be removed automatically and reliably, resulting in artifact-free reconstructed signals. This is a prerequisite for a highly accurate determination of any deviations of dwell positions from the treatment plan.

A multi-variate blind source separation algorithm.

BACKGROUND AND OBJECTIVE: The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation. METHODS: The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations. RESULTS: The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar. CONCLUSION: The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.

On the use of particle filters for electromagnetic tracking in high dose rate brachytherapy.

Modern radiotherapy of female breast cancers often employs high dose rate brachytherapy, where a radioactive source is moved inside catheters, implanted in the female breast, according to a prescribed treatment plan. Source localization relative to the patient's anatomy is determined with solenoid sensors whose spatial positions are measured with an electromagnetic tracking system. Precise sensor dwell position determination is of utmost importance to assure irradiation of the cancerous tissue according to the treatment plan. We present a hybrid data analysis system which combines multi-dimensional scaling with particle filters to precisely determine sensor dwell positions in the catheters during subsequent radiation treatment sessions. Both techniques are complemented with empirical mode decomposition for the removal of superimposed breathing artifacts. We show that the hybrid model robustly and reliably determines the spatial positions of all catheters used during the treatment and precisely determines any deviations of actual sensor dwell positions from the treatment plan. The hybrid system only relies on sensor positions measured with an EMT system and relates them to the spatial positions of the implanted catheters as initially determined with a computed x-ray tomography.

On the use of multi-dimensional scaling and electromagnetic tracking in high dose rate brachytherapy.

High dose rate brachytherapy affords a frequent reassurance of the precise dwell positions of the radiation source. The current investigation proposes a multi-dimensional scaling transformation of both data sets to estimate dwell positions without any external reference. Furthermore, the related distributions of dwell positions are characterized by uni-or bi-modal heavy-tailed distributions. The latter are well represented by α-stable distributions. The newly proposed data analysis provides dwell position deviations with high accuracy, and, furthermore, offers a convenient visualization of the actual shapes of the catheters which guide the radiation source during the treatment.

Ferulic acid lowers body weight and visceral fat accumulation via modulation of enzymatic, hormonal and inflammatory changes in a mouse model of high-fat diet-induced obesity.

Previous studies have reported on the glucose and lipid-lowering effects of ferulic acid (FA) but its anti-obesity potential has not yet been firmly established. This study investigated the possible anti-obesitogenic effects of FA in mice fed a high-fat diet (HFD) for 15 weeks. To assess the antiobesity potential of FA, 32 male Swiss mice, weighing 20-25 g (n=6-8 per group) were fed a normal diet (ND) or HFD, treated orally or not with either FA (10 mg/kg) or sibutramine (10 mg/kg) for 15 weeks and at the end of this period, the body weights of animals, visceral fat accumulation, plasma levels of glucose and insulin hormone, amylase and lipase activities, the satiety hormones ghrelin and leptin, and tumor necrosis factor-α (TNF-α) and monocyte chemoattractant protein-1 (MCH-1) were analyzed. Results revealed that FA could effectively suppress the HFD-associated increase in visceral fat accumulation, adipocyte size and body weight gain, similar to sibutramine, the positive control. FA also significantly (P<0.05) decreased the HFD-induced elevations in serum lipid profiles, amylase and lipase activities, and the levels of blood glucose and insulin hormone. The markedly elevated leptin and decreased ghrelin levels seen in HFD-fed control mice were significantly (P<0.05) reversed by FA treatment, almost reaching the values seen in ND-fed mice. Furthermore, FA demonstrated significant (P<0.05) inhibition of serum levels of inflammatory mediators TNF-α, and MCH-1. These results suggest that FA could be beneficial in lowering the risk of HFD-induced obesity via modulation of enzymatic, hormonal and inflammatory responses.