Vasoconstrictive responses of the cephalic vein during first-time cardiac implantable electronic device placement

Background: During cardiac implantable electronic device (CIED) implantation procedures cardiac leads have been mostly introduced transvenously. The associated injury to the selected vessel and adjacent tissues may induce reflex vasoconstriction. The aim of the study was to assess the incidence of cephalic vein (CV) vasoconstriction during first-time CIED implantation.Materials and methods: Of the 146 evaluated first-time CIED implantation procedures conducted in our centre in 2016, we selected those during which CV vasoconstriction was recorded. We focused on the stage of the procedure involving CV cutdown and/or axillary vein (AV)/subclavian vein (SV) puncture for lead insertion. Only cases documented via venography were considered.Results: Vasoconstriction was observed in 11 patients (5 females and 6 males, mean age 59.0 ± 21.2 years). The presence of this phenomenon affected the stage of CIED implantation involving cardiac lead insertion to the venous system, in severe cases, requiring a change of approach from CV cutdown to AV/SV puncture. The extent of vasoconstriction front propagation was limited to the nearest valves. Histological examinations of collected CV samples revealed an altered spatial arrangement of myocytes in the tunica media at the level of leaflet attachment.Conclusions: Cephalic vein vasoconstriction is a rare phenomenon associated with accessing the venous system during first-time CIED implantation. The propagation of CV constriction was limited by the location of the nearest valves

Morphometric parameters of cardiac implantable electronic device (CIED) pocket walls observed on device replacement

Background: The final stage of a conventional de-novo cardiac implantable electronic device (CIED) implantation procedure with transvenous lead insertion involves the formation of a pocket by tissue separation superficial to the pectoralis major muscle in the right or left infraclavicular region, where the device is subsequently placed. Over time, a scar “capsule” is formed around the CIED as a result of normal biological remodelling. Materials and methods: The purpose of this study was to analyse the structure and present the variations of CIED capsules observed during device replacement. The nature and extent of this local tissue remodelling, which had occurred from the time of device implantation to its replacement in 2016 (10 ± 3.1 years), was analysed in 100 patients (mean age 77.1 ± 14.5 years), including 45 women and 55 men. Results: The most prevalent types of “capsules” (70% of cases) were those with similar thickness of both walls or a slightly thicker posterior (< 1.0 mm) than anterior wall (< 0.5 mm). The second most common capsule type (23% of cases) was characterised by a significantly thicker posterior wall of scar tissue (> 1.0 mm). The third group of capsules was characterised by various degrees of wall calcification (7% of cases). Conclusions: The extent and nature of scar tissue structure in the CIED pocket walls seem to correlate with the relative position of cardiac lead loops with respect to the device itself; where the more extensive scarring is likely to result from pocket wall irritation in the capsule formation phase due to lead movements underneath the device. The group of cases with calcified capsules was characterised by “old” device pockets (> 13 years) and the oldest population (patients in their 80s and 90s)

A mathematical model of low grade gliomas treated with temozolomide and its therapeutical implications.

Low grade gliomas (LGGs) are infiltrative and incurable primary brain tumours with typically slow evolution. These tumours usually occur in young and otherwise healthy patients, bringing controversies in treatment planning since aggressive treatment may lead to undesirable side effects. Thus, for management decisions it would be valuable to obtain early estimates of LGG growth potential. Here we propose a simple mathematical model of LGG growth and its response to chemotherapy which allows the growth of LGGs to be described in real patients. The model predicts, and our clinical data confirms, that the speed of response to chemotherapy is related to tumour aggressiveness. Moreover, we provide a formula for the time to radiological progression, which can be possibly used as a measure of tumour aggressiveness. Finally, we suggest that the response to a few chemotherapy cycles upon diagnosis might be used to predict tumour growth and to guide therapeutical actions on the basis of the findings

Elliptic differential equations: theory and numerical treatment

This book simultaneously presents the theory and the numerical treatment of elliptic boundary value problems, since an understanding of the theory is necessary for the numerical analysis of the discretisation. It first discusses the Laplace equation and its finite difference discretisation before addressing the general linear differential equation of second order. The variational formulation together with the necessary background from functional analysis provides the basis for the Galerkin and finite-element methods, which are explored in detail. A more advanced chapter leads the reader to the theory of regularity. Individual chapters are devoted to singularly perturbed as well as to elliptic eigenvalue problems. The book also presents the Stokes problem and its discretisation as an example of a saddle-point problem taking into account its relevance to applications in fluid dynamics

Estimates of the onsets of malignant transformation:<i>t</i>_<i>OMT</i> (black solid line), <i>t</i>_{<i>OMT</i>,<i>S</i>} (blue dotted line) and <i>t</i>_{<i>OMT</i>,<i>L</i>} (red dashed-dotted line) for different values of diffusion rate <i>D</i>.

<p>The initial tumour cell densities and other parameters’ values are taken as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179999#pone.0179999.g001" target="_blank">Fig 1</a>.</p

Characteristics of patients selected in the study.

<p>Characteristics of patients selected in the study.</p

Evolution of LGGs diameter—Results based on the simulations of FKE Eq (6) (black solid line), analytic equation of radius evolution Eq (11) (red dashed-dotted line) due to Skellam model (7) and asymptotic behaviour of radius as <i>t</i> → 0 Eq (16) (blue dotted line).

<p>The vertical dashed line denotes the time when malignant transformation was confirmed histopathologically. The model parameters and initial conditions were the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179999#pone.0179999.g003" target="_blank">Fig 3</a> for patients selected in this study.</p

Model parameters fitted for each patient and errors of fits.

<p>Model parameters fitted for each patient and errors of fits.</p

Typical parameter values for system (1).

<p>Typical parameter values for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179999#pone.0179999.e003" target="_blank">system (1)</a>.</p