Platelet thromboxane inhibition by low-dose aspirin in polycythemia vera: Ex vivo and in vivo measurements and in silico simulation.

Low-dose aspirin is currently recommended for patients with polycythemia vera (PV), a myeloproliferative neoplasm with increased risk of arterial and venous thromboses. Based on aspirin pharmacodynamics in essential thrombocythemia, a twice-daily regimen is recommended for patients with PV deemed at particularly high thrombotic risk. We investigated the effects of low-dose aspirin on platelet cyclooxygenase activity and in vivo platelet activation in 49 patients with PV, as assessed by serum thromboxane (TX) B2 and urinary TXA2 /TXB2 metabolite (TXM) measurements, respectively. A previously described pharmacokinetic-pharmacodynamic in silico model was used to simulate the degree of platelet TXA2 inhibition by once-daily (q.d.) and twice-daily (b.i.d.) aspirin, and to predict the effect of missing an aspirin dose during q.d. and b.i.d. regimens. Serum TXB2 averaged 8.2 (1.6-54.7) ng/ml and significantly correlated with the platelet count (γ = 0.39) and urinary TXM (γ = 0.52) in multivariable analysis. One-third of aspirin-treated patients with PV displayed less-than-maximal platelet TXB2 inhibition, and were characterized by significantly higher platelet counts and platelet-count corrected serum TXB2 than those with adequate inhibition. Eight patients with PV were sampled again after 12 ± 4 months, and had reproducible serum TXB2 and urinary TXM values. The in silico model predicted complete inhibition of platelet-derived TXB2 by b.i.d. aspirin, a prediction verified in a patient with PV with the highest TXB2 value while on aspirin q.d. and treated short-term with a b.i.d. regimen. In conclusion, one in three patients with PV on low-dose aspirin display less-than-maximal inhibition of platelet TXA2 production. Serum TXB2 measurement can be a valuable option to guide precision dosing of antiplatelet therapy in patients with PV.

Frequency response in splicing regulation under mRNA auto-depletion control.

Nowadays, there exists a huge literature about stochastic model of transcriptional and translational control in gene networks. However, results related to post-transcriptional regulation via splicing and its connection with transcriptional and translational regulation are almost missing in the current literature and only related to the steady state moments investigation. Nowadays, it is becoming of paramount importance the need for modeling post-transcriptional regulation via splicing especially for DNA viruses or retroviruses. However, there exists only few studies in the literature about splicing regulation and none of them investigate its behavior in the frequency domain that can unveil important features of dynamical stochastic systems that cannot be revealed by the sole steady state moment investigation. The aim of this work is to theoretically investigate a simple gene network subject to splicing regulation with negative feedback control, implemented through mRNA auto-depletion under a frequency domain perspective. This study showed the pivotal role of the burst size, enhancing the noise power spectrum, as well as the splicing conversion rates capable to increase and decrease the noise power spectrum in the pre-mRNA and mRNA, respectively, for high values of conversion rates. Importantly, it shows the capability of the mRNA autodepletion control to modulate the noise as a frequency-dependent amplifying control as a function of the negative feedback strengths.

Physiologically based modelling of the antiplatelet effect of aspirin: A tool to characterize drug responsiveness and inform precision dosing.

A computational approach involving mathematical modeling and in silico experiments was used to characterize the determinants of extent and duration of platelet cyclooxygenase (COX)-1 inhibition by aspirin and design precision dosing in patients with accelerated platelet turnover or reduced drug bioavailability. To this purpose, a recently developed physiologically-based pharmacokinetics (PK) and pharmacodynamics (PD) model of low-dose aspirin in regenerating platelets and megakaryocytes, was used to predict the main features and determinants of platelet COX-1 inhibition. The response to different aspirin regimens in healthy subjects and in pathological conditions associated with alterations in aspirin PK (i.e., severely obese subjects) or PD (i.e., essential thrombocytemya patients), were simulated. A model sensitivity analysis was performed to identify the main processes influencing COX-1 dynamics. In silico experiments and sensitivity analyses indicated a major role for megakaryocytes and platelet turnover in determining the extent and duration of COX-1 inhibition by once-daily, low-dose aspirin. They also showed the superiority of reducing the dosing interval vs increasing the once-daily dose in conditions of increased platelet turnover, while suggested specific dose adjustments in conditions of possible reduction in drug bioavailability. In conclusion, the consistency of our model-based findings with experimental data from studies in healthy subjects and patients with essential thrombocythemia supports the potential of our approach for describing the determinants of platelet inhibition by aspirin and informing precision dosing which may guide personalized antithrombotic therapy in different patient populations, especially in those under-represented in clinical trials or in those associated with poor feasibility.

Are Smart Homes Adequate for Older Adults with Dementia?

Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users' daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients' habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients' habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users' behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation.

Stochasticity in transcriptional, splicing and translational regulations in time and frequency domains.

Alternative splicing is one of the most important post-transcriptional regulation. Splicing is essential for the expression of most of the human protein coding genes and is associated with several diseases, comprising cancer. It is also strongly used by minor organisms and several viruses. In the past decades, an extensive mathematical literature was developed to model and analyze gene networks under both deterministic and stochastic formalisms. However, such literature is predominantly focused to deal with the modeling of transcriptional and translational regulation, but its extension to comprise post-transcriptional regulation via splicing and its connection with transcriptional and translational regulation is still almost missing in literature. The aim of this work is to theoretically study and complete the knowledge about a general basic open loop and linear modeling scheme of gene expression via alternative splicing and its connection with transcription and translation, under a stochastic dimension. This study showed the pivotal role of the splicing conversion rates capable to both increase or decrease the stochastic noise, as well as their interconnection with the stochastic bursts in gene expression, autocorrelation and noise power spectra. The study also shows when it is important to model the pre-mRNA degradation or, at least, to account for the conversion rate for more than two mRNA isoforms.

Role of E6 in Maintaining the Basal Cell Reservoir during Productive Papillomavirus Infection.

Papillomaviruses exclusively infect stratified epithelial tissues and cause chronic infections. To achieve this, infected cells must remain in the epithelial basal layer alongside their uninfected neighbors for years or even decades. To examine how papillomaviruses achieve this, we used the in vivo MmuPV1 (Mus musculus papillomavirus 1) model of lesion formation and persistence. During early lesion formation, an increased cell density in the basal layer, as well as a delay in the infected cells' commitment to differentiation, was apparent in cells expressing MmuPV1 E6/E7 RNA. Using cell culture models, keratinocytes exogenously expressing MmuPV1 E6, but not E7, recapitulated this delay in differentiation postconfluence and also grew to a significantly higher density. Cell competition assays further showed that MmuPV1 E6 expression led to a preferential persistence of the cell in the first layer, with control cells accumulating almost exclusively in the second layer. Interestingly, the disruption of MmuPV1 E6 binding to MAML1 protein abrogated these phenotypes. This suggests that the interaction between MAML1 and E6 is necessary for the lower (basal)-layer persistence of MmuPV1 E6-expressing cells. Our results indicate a role for E6 in lesion establishment by facilitating the persistence of infected cells in the epithelial basal layer, a mechanism that is most likely shared by other papillomavirus types. Interruption of this interaction is predicted to impede persistent papillomavirus infection and consequently provides a novel treatment target. IMPORTANCE Persistent infection with high-risk HPV types can lead to development of HPV-associated cancers, and persistent low-risk HPV infection causes problematic diseases, such as recurrent respiratory papillomatosis. The management and treatment of these conditions pose a considerable economic burden. Maintaining a reservoir of infected cells in the basal layer of the epithelium is critical for the persistence of infection in the host, and our studies using the mouse papillomavirus model suggest that E6 gene expression leads to the preferential persistence of epithelial cells in the lower layers during stratification. The E6 interaction with MAML1, a component of the Notch pathway, is required for this phenotype and is linked to E6 effects on cell density and differentiation. These observations are likely to reflect a common E6 role that is preserved among papillomaviruses and provide us with a novel therapeutic target for the treatment of recalcitrant lesions.

Modeling Pharmacokinetics of Doxorubicin in Multiple Myeloma Cells.

Doxorubicin (DOXO) is a well-established chemotherapy drug for treatment of different tumors, ranging from breast cancer, melanoma to multiple myeloma (MM). Here, we present a coupled experimental/modeling approach to study DOXO pharmacokinetics in MM cells, investigate its distribution among the extracellular and intracellular compartments during time. Three model candidates are considered and identified. Model selection is performed based on its ability to describe the data both qualitatively and in terms of quantitative indexes. The most parsimonious model consists of a nonlinear structure with a saturation-threshold control of intracellular DOXO efflux by the DOXO bound to the cellular DNA. This structure could explain the hypothesis that MM cells are drug-resistant, likely due to the involvement of P-glycoproteins.The proposed model is able to predict the intracellular (free and bound) DOXO and suggests the presence of a saturation-threshold drug-resistant mechanism.Clinical Relevance- The model can be used to properly understand and guide further experimental setup, e.g., to investigate multiple myeloma cell variability among different cell lines.

Frequency analysis of splicing regulation.

In the past decades, mathematical modelers developed a huge literature to model and analyze gene networks under both deterministic and stochastic formalisms. Such literature is predominantly focused on modeling transcriptional and translational regulation, while the development of proper mathematical frameworks to model and study post-transcriptional regulation via splicing and its connection with transcriptional and translational regulation are almost missing. Nowadays, it is becoming of paramount importance the need for modeling post-transcriptional regulation via splicing especially for bacteria or viruses. However, current literature is focused on investigating splicing regulation at steady state and none of them have the purpose to investigate gene networks behavior in the frequency domain, thus providing only a partial investigation about the system dynamical response. The aim of this work is to theoretically investigate a simple gene network subjects to splicing regulation with/without negative feedback control under a frequency domain perspective. This study showed the pivotal role of the burst size, as well as splicing conversion rates to modulate the noise and the power spectrum response. It also shows an interesting behavior under the frequency domain induced by the merging effect of burst size, splicing conversion rates and negative feedback strength.

Mathematical modelling of SigE regulatory network reveals new insights into bistability of mycobacterial stress response.

BACKGROUND: The ability to rapidly adapt to adverse environmental conditions represents the key of success of many pathogens and, in particular, of Mycobacterium tuberculosis. Upon exposition to heat shock, antibiotics or other sources of stress, appropriate responses in terms of genes transcription and proteins activity are activated leading part of a genetically identical bacterial population to express a different phenotype, namely to develop persistence. When the stress response network is mathematically described by an ordinary differential equations model, development of persistence in the bacterial population is associated with bistability of the model, since different emerging phenotypes are represented by different stable steady states. RESULTS: In this work, we develop a mathematical model of SigE stress response network that incorporates interactions not considered in mathematical models currently available in the literature. We provide, through involved analytical computations, accurate approximations of the system's nullclines, and exploit the obtained expressions to determine, in a reliable though computationally efficient way, the number of equilibrium points of the system. CONCLUSIONS: Theoretical analysis and perturbation experiments point out the crucial role played by the degradation pathway involving RseA, the anti-sigma factor of SigE, for coexistence of two stable equilibria and the emergence of bistability. Our results also indicate that a fine control on RseA concentration is a necessary requirement in order for the system to exhibit bistability.

Stochastic modeling of human papillomavirusearly promoter gene regulation.

High risk forms of human papillomaviruses (HPVs) promote cancerous lesions and are implicated in almost all cervical cancer. Of particular relevance to cancer progression is regulation of the early promoter that controls gene expression in the initial phases of infection and can eventually lead to pre-cancer progression. Our goal was to develop a stochastic model to investigate the control mechanisms that regulate gene expression from the HPV early promoter. Our model integrates modules that account for transcriptional, post-transcriptional, translational and post-translational regulation of E1 and E2 early genes to form a functioning gene regulatory network. Each module consists of a set of biochemical steps whose stochastic evolution is governed by a chemical Master Equation and can be simulated using the Gillespie algorithm. To investigate the role of noise in gene expression, we compared our stochastic simulations with solutions to ordinary differential equations for the mean behavior of the system that are valid under the conditions of large molecular abundances and quasi-equilibrium for fast reactions. The model produced results consistent with known HPV biology. Our simulation results suggest that stochasticity plays a pivotal role in determining the dynamics of HPV gene expression. In particular, the combination of positive and negative feedback regulation generates stochastic bursts of gene expression. Analysis of the model reveals that regulation at the promoter affects burst amplitude and frequency, whereas splicing is more specialized to regulate burst frequency. Our results also suggest that splicing enhancers are a significant source of stochasticity in pre-mRNA abundance and that the number of viruses infecting the host cell represents a third important source of stochasticity in gene expression.

IoT Security Configurability with Security-by-Contract.

Cybersecurity is one of the biggest challenges in the Internet of Things (IoT) domain, as well as one of its most embarrassing failures. As a matter of fact, nowadays IoT devices still exhibit various shortcomings. For example, they lack secure default configurations and sufficient security configurability. They also lack rich behavioural descriptions, failing to list provided and required services. To answer this problem, we envision a future where IoT devices carry behavioural contracts and Fog nodes store network policies. One requirement is that contract consistency must be easy to prove. Moreover, contracts must be easy to verify against network policies. In this paper, we propose to combine the security-by-contract (S × C) paradigm with Fog computing to secure IoT devices. Following our previous work, first we formally define the pillars of our proposal. Then, by means of a running case study, we show that we can model communication flows and prevent information leaks. Last, we show that our contribution enables a holistic approach to IoT security, and that it can also prevent unexpected chains of events.

Obesity is associated with impaired responsiveness to once-daily low-dose aspirin and in vivo platelet activation.

BACKGROUND: The prevalence and degree of obesity is rising worldwide, increases cardiovascular risk, modifies body composition and organ function, and potentially affects the pharmacokinetics and/or pharmacodynamics of drugs. OBJECTIVES: To investigate the pharmacodynamics of once-daily low-dose aspirin in healthy obese subjects, and to assess whether body weight (BW) and body mass index (BMI) affect the pharmacology of aspirin. PATIENTS/METHODS: Otherwise healthy, obese (BMI > 30 kg/m2 ) subjects were studied before and after 3-4 weeks of 100-mg once-daily aspirin intake. Aspirin pharmacodynamics were assessed according to serum thromboxane (TX) B2 levels measured at 4 hours, 24 hours (i.e., posologic interval) and 48 hours after the last witnessed intake; age-matched and sex-matched non-obese controls were included. A previously calibrated pharmacokinetic/pharmacodynamic in silico model of aspirin was used to fit serum TXB2 data from obese subjects. At baseline, the major urinary TXA2 and prostacyclin metabolites, urinary isoprostane and plasma inflammatory biomarkers were measured. RESULTS: In 16 obese subjects (aged 47 ± 11 years; BMI of 39.4 ± 5.1 kg/m2 ), residual serum TXB2 values between 4 and 48 hours after aspirin intake were increased 3- to 5-fold as compared with controls. At 24 hours, the residual serum TXB2 level was log-linearly associated with body size over a wide range of BMI and BW values, without any apparent threshold. The in silico model predicted that reduced aspirin bioavailability would be inversely related to body size and rescued by 200 mg of aspirin once daily or 85 mg twice daily. Baseline urinary TXA2 metabolite, isoprostane and plasma C-reactive protein levels were significantly increased in obese subjects. CONCLUSIONS: Obesity is associated with impaired aspirin responsiveness, largely because of body size. Impaired inhibition of platelet activation by conventional low-dose aspirin may affect antithrombotic efficacy.

A Model of Acetaminophen Pharmacokinetics and its Effect on Continuous Glucose Monitoring Sensor Measurements.

Some of commercial continuous glucose monitoring (CGM) devices, i.e., minimally-invasive sensors able to measure almost continuously glucose concentration in the subcutaneous tissue, recently received the regulatory approval to be used for making therapeutic decisions in diabetes management. A fundamental requirement for its safe and effective use is represented by the accuracy of CGM measurements. However, despite recent advances in sensors accuracy and reliability, CGM still suffers from inaccuracy problems in presence of pharmacologic interferences, e.g., the common orally administered acetaminophen (APAP), which artificially raises CGM glucose readings for several hours. A model of the artifact induced by APAP on CGM measurements would be useful to design algorithms to compensate such a distortion. The aim of this work is to exploit the data published by previous literature studies to design a model of oral APAP pharmacokinetics and its effect on glucose concentration measured by CGM sensors. Specifically, the developed model was identified on average data of both plasma APAP concentration and the APAP effect on CGM profiles after an oral administration of 1000 mg of APAP. The APAP effect on CGM readings was estimated from the difference observed, in the same study, between the glucose profile measured by a Dexcom G4 Platinum sensor and the plasma glucose concentration. The model was validated by comparing the simulated effect of mealtime APAP administration in CGM measurements of 100 virtual subjects generated by the UVA/Padova Type 1 Diabetes (TID) Simulator vs. the effect observed in a clinical study by Maahs et al. (Diabetes Care, 2015) in 40 TID subjects taking APAP at breakfast. Results suggest that the proposed model is able to reliably describe the mean APAP effect on CGM measurements.