Post-pandemic modeling of COVID-19: Waning immunity determines recurrence frequency

There are many factors in the current phase of the COVID-19 pandemic that signal the need for new modeling ideas. In fact, most traditional infectious disease models do not address adequately the waning immunity, in particular as new emerging variants have been able to break the immune shield acquired either by previous infection by a different strain of the virus, or by inoculation of vaccines not effective for the current variant. Furthermore, in a post-pandemic landscape in which reporting is no longer a default, it is impossible to have reliable quantitative data at the population level. Our contribution to COVID-19 post-pandemic modeling is a simple mathematical predictive model along the age-distributed population framework, that can take into account the waning immunity in a transparent and easily controllable manner. Numerical simulations show that under static conditions, the model produces periodic solutions that are qualitatively similar to the reported data, with the period determined by the immunity waning profile. Evidence from the mathematical model indicates that the immunity dynamics is the main factor in the recurrence of infection spikes, however, irregular perturbation of the transmission rate, due to either mutations of the pathogen or human behavior, may result in suppression of recurrent spikes, and irregular time intervals between consecutive peaks. The spike amplitudes are sensitive to the transmission rate and vaccination strategies, but also to the skewness of the profile describing the waning immunity, suggesting that these factors should be taken into consideration when making predictions about future outbreaks.

Modeling surface pH measurements of oocytes

The transport of gases across cell membranes plays a key role in many different cell functions, from cell respiration to pH control. Mathematical models play a central role in understanding the factors affecting gas transport through membranes, and are the tool needed for testing the novel hypothesis of the preferential crossing through specific gas channels. Since the surface pH of cell membrane is regulated by the transport of gases such as CO₂and NH₃, inferring the membrane properties can be done indirectly from pH measurements. Numerical simulations based on recent models of the surface pH support the hypothesis that the presence of a measurement device, a liquid-membrane pH sensitive electrode on the cell surface may disturb locally the pH, leading to a systematic bias in the measured values. To take this phenomenon into account, it is necessary to equip the model with a description of the micro-environment created by the pH electrode. In this work we propose a novel, computationally lightweight numerical algorithm to simulate the surface pH data. The effect of different parameters of the model on the output are investigated through a series of numerical experiments with a physical interpretation.

Common gene variants within 3'-untranslated regions as modulators of multiple myeloma risk and survival

We evaluated the association between germline genetic variants located within the 3'-untranlsated region (polymorphic 3'UTR, ie, p3UTR) of candidate genes involved in multiple myeloma (MM). We performed a case-control study within the International Multiple Myeloma rESEarch (IMMEnSE) consortium, consisting of 3056 MM patients and 1960 controls recruited from eight countries. We selected p3UTR of six genes known to act in different pathways relevant in MM pathogenesis, namely KRAS (rs12587 and rs7973623), VEGFA (rs10434), SPP1 (rs1126772), IRF4 (rs12211228) and IL10 (rs3024496). We found that IL10-rs3024496 was associated with increased risk of developing MM and with a worse overall survival of MM patients. The variant allele was assayed in a vector expressing eGFP chimerized with the IL10 3'-UTR and it was found functionally active following transfection in human myeloma cells. In this experiment, the A-allele caused a lower expression of the reporter gene and this was also in agreement with the in vivo expression of mRNA measured in whole blood as reported in the GTEx portal. Overall, these data are suggestive of an effect of the IL10-rs3024496 SNP on the regulation of IL10 mRNA expression and it could have clinical implications for better characterization of MM patients in terms of prognosis.

Challenges of upgrading craft workforce into Construction 4.0: framework and agreements

Craft workforce is the main productive factor in traditional construction. Construction 4.0 visions are based on automation and digitalisation, meaning that human site activities will require/stipulate changes. The extent to which manual tasks done by humans in construction will be replaced is uncertain. This might vary considering the context or type of work. Construction 4.0 includes craft workforce activities, as these can benefit from technology, fostering digital transformation in the short/medium term. The research scope is workforce–innovation–management using data from job sites. A framework is developed based on data future use involving electronic performance monitoring, building information modelling, smart contracts and artificial intelligence. A systematic scoping review is developed to identify legal/ethical issues in connection to technological aspects. The discussion and findings focus on General Data Protection Regulation compliance to apply the proposed framework. Optimised human–machine-controlled environments must be ethically managed by pre-established collective agreements and must rely on each worker’s awareness and consent. The findings suggest that the human aspects if improperly addressed could result in a bottleneck of digital transformation advances. Along with the framework, the paper provides a step-by-step, streamlined review of the regulations and requirements that need to be considered when implementing electronic monitoring of workers.

Computational model of electrode-induced microenvironmental effects on pH measurements near a cell membrane

The mechanism of gas transport across cell membranes remains a topic of considerable interest, particularly regarding the extent to which lipids vs. specific membrane proteins provide conduction pathways. Studies of transmembrane (CO₂) transport often rely on data collected under controlled conditions, using pH-sensitive microelectrodes at the extracellular surface to record changes due to extracellular CO₂ diffusion and reactions. Although recent detailed computational models can predict a qualitatively correct behavior, a mismatch between the dynamical ranges of the predicted and observed pH curves raises the question whether the discrepancy may be due to a bias introduced by the pH electrode itself. More specifically, it is reasonable to ask whether bringing the electrode tip near or in contact with the membrane creates a local microenvironment between the electrode tip and the membrane, so that the measured data refer to the microenvironment rather than to the free surface. Here, we introduce a detailed computational model, designed to address this question. We find that, as long as a zone of free diffusion exists between the tip and the membrane, the microenvironment behaves effectively as the free membrane. However, according to our model, when the tip contacts the membrane, partial quenching of extracellular diffusion by the electrode rim leads to a significant increase in the pH dynamics under the electrode, matching values measured in physiological experiments. The computational schemes for the model predictions are based on semi-discretization by a finite-element method, and an implicit-explicit time integration scheme to capture the different time scales of the system.

Metabolism plays a central role in the cortical spreading depression: Evidence from a mathematical model

The slow propagating waves of strong depolarization of neural cells characterizing cortical spreading depression, or depolarization, (SD) are known to break cerebral homeostasis and induce significant hemodynamic and electro-metabolic alterations. Mathematical models of cortical spreading depression found in the literature tend to focus on the changes occurring at the electrophysiological level rather than on the ensuing metabolic changes. In this paper, we propose a novel mathematical model which is able to simulate the coupled electrophysiology and metabolism dynamics of SD events, including the swelling of neurons and astrocytes and the concomitant shrinkage of extracellular space. The simulations show that the metabolic coupling leads to spontaneous repetitions of the SD events, which the electrophysiological model alone is not capable to produce. The model predictions, which corroborate experimental findings from the literature, show a strong disruption in metabolism accompanying each wave of spreading depression in the form of a sharp decrease of glucose and oxygen concentrations, with a simultaneous increase in lactate concentration which, in turn, delays the clearing of excess potassium in extracellular space. Our model suggests that the depletion of glucose and oxygen concentration is more pronounced in astrocyte than neuron, in line with the partitioning of the energetic cost of potassium clearing. The model suggests that the repeated SD events are electro-metabolic oscillations that cannot be explained by the electrophysiology alone. The model highlights the crucial role of astrocytes in cleaning the excess potassium flooding extracellular space during a spreading depression event: further, if the ratio of glial/neuron density increases, the frequency of cortical SD events decreases, and the peak potassium concentration in extracellular space is lower than with equal volume fractions.

Estimating hemodynamic stimulus and blood vessel compliance from cerebral blood flow data

Several key brain imaging modalities that are intended for retrieving information about neuronal activity in brain, the BOLD fMRI as a foremost example, rely on the assumption that elevated neuronal activity elicits spatiotemporally well localized increase of the oxygenated blood volume, which in turn can be monitored non-invasively. The details of the signaling in the neurovascular unit during hyperemia are still not completely understood, and remain a topic of active research, requiring good mathematical models that are able to couple the different aspects of the signaling event. In this work, the question of estimating the hemodynamic stimulus function from cerebral blood flow data is addressed. In the present model, the hemodynamic stimulus is a non-specific signal from the electrophysiological and metabolic complex that controls the compliance of the blood vessels, leading to a vasodilation and thereby to an increase of blood flow. The underlying model is based on earlier literature, and it is further developed in this article for the needs of the inverse problem, which is solved using hierarchical Bayesian methodology, addressing also the poorly known model parameters.

A computational model integrating brain electrophysiology and metabolism highlights the key role of extracellular potassium and oxygen

The human brain is a small organ which uses a disproportionate amount of the total metabolic energy production in the body. While it is well understood that the most significant energy sink is the maintenance of the neuronal membrane potential during the brain signaling activity, the role of astrocytes in the energy balance continues to be the topic of a lot of research. A key function of astrocytes, besides clearing glutamate from the synaptic clefts, is the potassium clearing after neuronal activation. Extracellular potassium plays a significant role in triggering neuronal firing, and elevated concentration of potassium may lead to abnormal firing patterns, e.g., seizures, thus emphasizing the importance of the glial K⁺ buffering role. The predictive mathematical model proposed in this paper elucidates the role of glial potassium clearing in brain energy metabolism, integrating a detailed model of the ion dynamics which regulates neuronal firing with a four compartment metabolic model. Because of the very different characteristic time scales of electrophysiology and metabolism, care must be taken when coupling the two models to ensure that the predictions, e.g., neuronal firing frequencies and the oxygen-glucose index (OGI) of the brain during activation and rest, are in agreement with empirical observations. The temporal multi-scale nature of the problem requires the design of new computational tools to ensure a stable and accurate numerical treatment. The model predictions for different protocols, including combinations of elevated activation and ischemic episodes, are in good agreement with experimental observations reported in the literature.

An adaptive smoothness regularization algorithm for optical tomography

In diffuse optical tomography (DOT), the object with unknown optical properties is illuminated with near infrared light and the absorption and diffusion coefficient distributions of a body are estimated from the scattering and transmission data. The problem is notoriously ill-posed and complementary information concerning the optical properties needs to be used to counter-effect the ill-posedness. In this article, we propose an adaptive inhomogenous anisotropic smoothness regularization scheme that corresponds to the prior information that the unknown object has a blocky structure. The algorithm updates alternatingly the current estimate and the smoothness penalty functional, and it is demonstrated with simulated data that the algorithm is capable of locating well blocky inclusions. The dynamical range of the reconstruction is improved, compared to traditional smoothness regularization schemes, and the crosstalk between the diffusion and absorption images is clearly less. The algorithm is tested also with a three-dimensional phantom data.

Statistical analysis of metabolic pathways of brain metabolism at steady state

The estimation of metabolic fluxes for brain metabolism is important, among other things, to test the validity of different hypotheses which have been proposed in the literature. The metabolic model that we propose considers, in addition to the blood compartment, the cytosol, and mitochondria of both astrocyte and neuron, including detailed metabolic pathways. In this work we use a recently developed methodology to perform a statistical Flux Balance Analysis (FBA) for this model. The methodology recasts the problem in the form of Bayesian statistical inference and therefore can take advantage of qualitative information about brain metabolism for the simultaneous estimation of all reaction fluxes and transport rates at steady state. By a Markov Chain Monte Carlo (MCMC) sampling method, we are able to provide for each reaction flux and transport rate a distribution of possible values. The analysis of the histograms of the reaction fluxes and transport rates provides a very useful tool for assessing the validity of different hypotheses about brain energetics proposed in the literature, and facilitates the design of the pathways network that is in accordance with what is understood of the functioning of the brain. In this work, we focus on the analysis of biochemical pathways within each cell type (astrocyte and neuron) at different levels of neural activity, and we demonstrate how statistical tools can help implement various bounds suggested by experimental data.

HOME CARE PREVENTS COGNITIVE AND FUNCTIONAL DECLINE IN FRAIL ELDERLY

Today home health care (HHC) programs have been developed in numerous Western countries, in order to answer the questions regarding the care of frail elderly suffering from polypathologies and, therefore, being at high risk of disability. The HHC program of the Israelite Hospital of Rome has been planned as a complementary model, and not as a substitute of hospitalization, being able to offer flexible services, suitable for each elderly patient. The present study has established that taking care of old patients in their home allows us to prevent the deterioration of cognitive performance and functional impairments,as measured by the mini mental state examination (MMSE), the scales of activity of daily living (ADL), and the instrumental activity of daily living (IADL), respectively. We found considerable improvements also in the mood disorders during HHC, as measured by the geriatric depression scale (GDS). All psychometric tests were administered at the beginning of home care and after almost 1 year. Moreover, we formulated some questions regarding the quality of the offered services, and the answers revealed great satisfaction of both the patients and their caregivers.