Improving somatic health for outpatients with severe mental illness: the development of an intervention

Objective: Patients with severe mental illness (SMI) suffer from more somatic illness than the general population. Possible causes are side effects of neuropsychiatric medication, genetic vulnerability, insufficient health care and lifestyle. This co-morbidity is potentially reversible and augments the costs for health care and diminishes quality of life. Screening on symptoms and risks of somatic diseases and coordination of care are proposed to improve SMI-patients' somatic health status. Methods: A clinical facility was started to improve the somatic health status of patients in an outpatient centre in southern Netherlands. This outpatient centre was added to the specialized care for severe and enduring SMI. The intervention consisted of the inventarisation of side-effects and the detection of gaps in health care provision for 72 patients. This was based on interviewing the patients, laboratory screening, collecting information from their general practitioner and pharmacy. A list was compiled of possible diagnosis and health risks, and a plan of action was made for the treatment. Healthcare consumption, quality of life and general functioning were assessed to analyze cost-effectiveness. Evaluations were performed with the psychiatric care team on the process. Results: Mean annual cost of GP's and medical specialist's consultations were E492. There existed a negative relation between EQ5D VAS and the number of self reported chronic diseases. Conclusion: The authors conclude that the procedure is well feasible, but should be set up in close collaboration with all health care professionals of these patients to make tailor made solutions possible

Unraveling the effects of acute inflammation on pharmacokinetics: a model-based analysis focusing on renal glomerular filtration rate and cytochrome P450 3A4-mediated metabolism

UNLABELLED\nMETHODS\nRESULTS\nCONCLUSION\nBACKGROUND AND OBJECTIVES: Acute inflammation caused by infections or sepsis can impact pharmacokinetics. We used a model-based analysis to evaluate the effect of acute inflammation as represented by interleukin-6 (IL-6) levels on drug clearance, focusing on renal glomerular filtration rate (GFR) and cytochrome P450 3A4 (CYP3A4)-mediated metabolism.\nA physiologically based model incorporating renal and hepatic drug clearance was implemented. Functions correlating IL-6 levels with GFR and in vitro CYP3A4 activity were derived and incorporated into the modeling framework. We then simulated treatment scenarios for hypothetical drugs by varying the IL-6 levels, the contribution of renal and hepatic drug clearance, and protein binding. The relative change in observed area under the concentration-time curve (AUC) was computed for these scenarios.\nInflammation showed opposite effects on drug exposure for drugs eliminated via the liver and kidney, with the effect of inflammation being inversely proportional to the extraction ratio (ER). For renally cleared drugs, the relative decrease in AUC was close to 30% during severe inflammation. For CYP3A4 substrates, the relative increase in AUC could exceed 50% for low-ER drugs. Finally, the impact of inflammation-induced changes in drug clearance is smaller for drugs with a larger unbound fraction.\nThis analysis demonstrates differences in the impact of inflammation on drug clearance for different drug types. The effects of inflammation status on pharmacokinetics may explain the inter-individual variability in pharmacokinetics in critically ill patients. The proposed model-based analysis may be used to further evaluate the effect of inflammation, i.e., by incorporating the effect of inflammation on other drug-metabolizing enzymes or physiological processes.Pharmacolog

Влияние параметров одномассной системы с упругими ограничителями на характер ее колебаний

У статті розглянуто одномасну систему з пружними обмежувачами. Побудовано області існування різних режимів коливань системи, а також визначено вплив параметрів системи на межі цих областей.A one-mass system with elastic constraints is studied. Areas of existing of different oscillation modes are built. Also an influence of system parameters on limits of these areas is determined

Recognizing differentiating clinical signs of CLN3 disease (Batten disease) at presentation

Purpose To help differentiate CLN3 (Batten) disease, a devastating childhood metabolic disorder, from the similarly presenting early-onset Stargardt disease (STGD1). Early clinical identification of children with CLN3 disease is essential for adequate referral, counselling and rehabilitation. Methods Medical chart review of 38 children who were referred to a specialized ophthalmological centre because of rapid vision loss. The patients were subsequently diagnosed with either CLN3 disease (18 patients) or early-onset STGD1 (20 patients). Results Both children who were later diagnosed with CLN3 disease, as children who were later diagnosed with early-onset STGD1, initially presented with visual acuity (VA) loss due to macular dystrophy at 5-10 years of age. VA in CLN3 disease decreased significantly faster than in STGD1 (p = 0.01). Colour vision was often already severely affected in CLN3 disease while unaffected or only mildly affected in STGD1. Optic disc pallor on fundoscopy and an abnormal nerve fibre layer on optical coherence tomography were common in CLN3 disease compared to generally unaffected in STGD1. In CLN3 disease, dark-adapted (DA) full-field electroretinogram (ERG) responses were either absent or electronegative. In early-onset STGD1, DA ERG responses were generally unaffected. None of the STGD1 patients had an electronegative ERG. Conclusion Already upon presentation at the ophthalmologist, the retina in CLN3 disease is more extensively and more severely affected compared to the retina in early-onset STGD1. This results in more rapid VA loss, severe colour vision abnormalities and abnormal DA ERG responses as the main differentiating early clinical features of CLN3 disease

On the importance of the heterogeneity assumption in the characterization of reservoir geomechanical properties

The geomechanical analysis of a highly compartmentalized reservoir is performed to simulate the seafloor subsidence due to gas production. The available observations over the hydrocarbon reservoir consist of bathymetric surveys carried out before and at the end of a 10-yr production life. The main goal is the calibration of the reservoir compressibility cM, that is, the main geomechanical parameter controlling the surface response. Two conceptual models are considered: in one (i) cM varies only with the depth and the vertical effective stress (heterogeneity due to lithostratigraphic variability); in another (ii) cM varies also in the horizontal plane, that is, it is spatially distributed within the reservoir stratigraphic units. The latter hypothesis accounts for a possible partitioning of the reservoir due to the presence of sealing faults and thrusts that suggests the idea of a block heterogeneous system with the number of reservoir blocks equal to the number of uncertain parameters. The method applied here relies on an ensemble-based data assimilation (DA) algorithm (i.e. the ensemble smoother, ES), which incorporates the information from the bathymetric measurements into the geomechanical model response to infer and reduce the uncertainty of the parameter cM. The outcome from conceptual model (i) indicates that DA is effective in reducing the cM uncertainty. However, the maximum settlement still remains underestimated, while the areal extent of the subsidence bowl is overestimated. We demonstrate that the selection of the heterogeneous conceptual model (ii) allows to reproduce much better the observations thus removing a clear bias of the model structure. DA allows significantly reducing the cM uncertainty in the five blocks (out of the seven) characterized by large volume and large pressure decline. Conversely, the assimilation of land displacements only partially constrains the prior cM uncertainty in the reservoir blocks marginally contributing to the cumulative seafloor subsidence, that is, blocks with low pressure

Modelling inflammatory biomarker dynamics in a human lipopolysaccharide (LPS) challenge study using delay differential equations

Clinical studies in healthy volunteers challenged with lipopolysaccharide (LPS), a constituent of the cell wall of Gram-negative bacteria, represent a key model to characterize the Toll-like receptor 4 (TLR4)-mediated inflammatory response. Here, we developed a mathematical modelling framework to quantitatively characterize the dynamics and inter-individual variability of multiple inflammatory biomarkers in healthy volunteer LPS challenge studies. Data from previously reported LPS challenge studies were used, which included individual-level time-course data for tumour necrosis factor alpha (TNF-alpha), interleukin 6 (IL-6), interleukin 8 (IL-8) and C-reactive protein (CRP). A one-compartment model with first-order elimination was used to capture the LPS kinetics. The relationships between LPS and inflammatory markers was characterized using indirect response (IDR) models. Delay differential equations were applied to quantify the delays in biomarker response profiles. For LPS kinetics, our estimates of clearance and volume of distribution were 35.7 L h(-1) and 6.35 L, respectively. Our model adequately captured the dynamics of multiple inflammatory biomarkers. The time delay for the secretion of TNF-alpha, IL-6 and IL-8 were estimated to be 0.924, 1.46 and 1.48 h, respectively. A second IDR model was used to describe the induced changes of CRP in relation to IL-6, with a delayed time of 4.2 h. The quantitative models developed in this study can be used to inform design of clinical LPS challenge studies and may help to translate preclinical LPS challenge studies to humans.Pharmacolog

A system pharmacology Boolean network model for the TLR4-mediated inflammatory response in early sepsis

Sepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood. By integrating prior knowledge from literature using mathematical modelling techniques, we aimed to obtain a deeper mechanistic insight into sepsis pathogenesis and to evaluate promising novel therapeutic targets, with a focus on Toll-like receptor 4 (TLR4)-mediated pathways. A Boolean network of regulatory relationships was developed for key immune components associated with sepsis pathogenesis after TLR4 activation. Perturbation analyses were conducted to identify therapeutic targets associated with organ dysfunction or antibacterial activity. The developed model consisted of 42 nodes and 183 interactions. Perturbation analyses suggest that over-expression of tumour necrosis factor alpha (TNF-α) or inhibition of soluble receptor sTNF-R, tissue factor, and inflammatory cytokines (IFN-γ, IL-12) may lead to a reduced activation of organ dysfunction related endpoints. Over-expression of complement factor C3b and C5b led to an increase in the bacterial clearance related endpoint. We identified that combinatory blockade of IFN-γ and IL-10 may reduce the risk of organ dysfunction. Finally, we found that combining antibiotic treatment with IL-1β targeted therapy may have the potential to decrease thrombosis. In summary, we demonstrate how existing biological knowledge can be effectively integrated using Boolean network analysis for hypothesis generation of potential treatment strategies and characterization of biomarker responses associated with the early inflammatory response in sepsis.Biopharmaceutic

Zebrafish larvae as experimental model to expedite the search for new biomarkers and treatments for neonatal sepsis

Neonatal sepsis is a major cause of death and disability in newborns. Commonly used biomarkers for diagnosis and evaluation of treatment response lack sufficient sensitivity or specificity. Additionally, new targets to treat the dysregulated immune response are needed, as are methods to effectively screen drugs for these targets. Available research methods have hitherto not yielded the breakthroughs required to significantly improve disease outcomes, we therefore describe the potential of zebrafish (Danio rerio) larvae as preclinical model for neonatal sepsis. In biomedical research, zebrafish larvae combine the complexity of a whole organism with the convenience and high-throughput potential of in vitro methods. This paper illustrates that zebrafish exhibit an immune system that is remarkably similar to humans, both in terms of types of immune cells and signaling pathways. Moreover, the developmental state of the larval immune system is highly similar to human neonates. We provide examples of zebrafish larvae being used to study infections with pathogens commonly causing neonatal sepsis and discuss known limitations. We believe this species could expedite research into immune regulation during neonatal sepsis and may hold keys for the discovery of new biomarkers and novel treatment targets as well as for screening of targeted drug therapies.Pharmacolog

Automatic quantification of lymphocyte vacuolization in peripheral blood smears of patients with Batten's disease (CLN3 disease)

Quantifying lymphocyte vacuolization in peripheral blood smears (PBSs) serves as a measure for disease severity in CLN3 disease—a lysosomal storage disorder of childhood-onset. However, thus far quantification methods are based on labor-intensive manual assessment of PBSs. As machine learning techniques like convolutional neural networks (CNNs) have been deployed quite successfully in detecting pathological features in PBSs, we explored whether these techniques could be utilized to automate quantification of lymphocyte vacuolization. Here, we present and validate a deep learning pipeline that automates quantification of lymphocyte vacuolization. By using two CNNs in succession, trained for cytoplasm-segmentation and vacuolization-detection, respectively, we obtained an excellent correlation with manual quantification of lymphocyte vacuolization (r = 0.98, n = 40). These results show that CNNs can be utilized to automate the otherwise cumbersome task of manually quantifying lymphocyte vacuolization, thereby aiding prompt clinical decisions in relation to CLN3 disease, and potentially beyond

Semi-physiological Enriched Population Pharmacokinetic Modelling to Predict the Effects of Pregnancy on the Pharmacokinetics of Cytotoxic Drugs

BACKGROUND AND OBJECTIVE: As a result of changes in physiology during pregnancy, the pharmacokinetics (PK) of drugs can be altered. It is unclear whether under- or overexposure occurs in pregnant cancer patients and thus also whether adjustments in dosing regimens are required. Given the severity of the malignant disease and the potentially high impact on both the mother and child, there is a high unmet medical need for adequate and tolerable treatment of this patient population. We aimed to develop and evaluate a semi-physiological enriched model that incorporates physiological changes during pregnancy into available population PK models developed from non-pregnant patient data. METHODS: Gestational changes in plasma protein levels, renal function, hepatic function, plasma volume, extracellular water and total body water were implemented in existing empirical PK models for docetaxel, paclitaxel, epirubicin and doxorubicin. These models were used to predict PK profiles for pregnant patients, which were compared with observed data obtained from pregnant patients. RESULTS: The observed PK profiles were well described by the model. For docetaxel, paclitaxel and doxorubicin, an overprediction of the lower concentrations was observed, most likely as a result of a lack of data on the gestational changes in metabolizing enzymes. For paclitaxel, epirubicin and doxorubicin, the semi-physiological enriched model performed better in predicting PK in pregnant patients compared with a model that was not adjusted for pregnancy-induced changes. CONCLUSION: By incorporating gestational changes into existing population pharmacokinetic models, it is possible to adequately predict plasma concentrations of drugs in pregnant patients which may inform dose adjustments in this population