A greater understanding of this AVM compartmentalization, even if theoretical, would be useful for optimal planning of endovascular and multimodal AVM therapies. We aimed to develop a biomathematical AVM model for theoretical investigations of intranidal regions of increased mean intravascular pressure (Pmean) and flow representing hemodynamic compartments, upon simulated AVM superselective angiography (SSA). We constructed an AVM model as a theoretical electrical circuit containing four arterial feeders (AF1–AF4) and a three-dimensional nidus of 97 interconnected plexiform and fistulous components. We simulated SSA by increases in Pmean in each AF , and then used network analysis to establish resulting increases in Pmean and flow within each nidus vessel. We analyzed shifts in hemodynamic compartments consequent to increasing AF injection pressures. SSA simulated by increases of 10 mm Hg in AF1, AF2, AF3, or AF4 resulted in dissipation of Pmean over 38, 66, 76, or 20% of the nidus, respectively, rising slightly with simultaneous occlusion of other AFs. We qualitatively analyzed shifting intranidal compartments consequent to varying injection pressures by mapping the hemodynamic changes onto the nidus network. Differences in extent of nidus filling upon SSA injections provide theoretical evidence that hemodynamic and angioarchitectural features help establish AVM nidus compartmentalization. This model based on a theoretical AVM will serve as a useful computational tool for further investigations of AVM embolotherapy strategies.There are currently no  A brain arteriovenous malformation (AVM) is a congenital abnormal tangle (nidus) of dilated blood vessels in the brain that directly diverts blood from arteries  to veins (or draining veins [DVs]), and in doing so, bypasses normal brain tissue. The two hallmarks of AVMs are the presence of a nidus and the associated hemodynamic shunting of arterial blood at high pressure through the nidus and directly into DVs, without the resistance normally offered by brain capillaries. In the absence of head trauma, AVMs are the most common cause of brain hemorrhage in young adults.Within any large AVM nidus, a dynamic equilibrium of blood flow exists in separate regions, or compartments, that are each supplied by their own AF . The prein vivo techniques to accurately study hemodynamic compartmentalization within an AVM nidus. No current imaging or recording technique can provide precise hemodynamic information from within the nidus of a brain AVM. Owing to inaccessibility or danger of direct transvascular access of fragile intranidal microvessels, such an assessment is often reduced to measuring blood flow and/or pressure outside the margins of a nidus using microcatheters placed in AFs and DVs during superselective angiography (SSA). Current imaging techniques are also limited by insufficient spatial resolution to allow visualization of individual intranidal microvessels when attempting to interrogate their flow non-invasively.Importantly, there are currently no practical Biomathematical models offer an alternative theoretical approach to analyzing AVM hemodynamics, which may be otherwise difficult to quantify especially within or in close proximity to the nidus. Typically, this has been accomplished in the past using simulated elementary feeding and draining pedicle anatomy and a nidus composed of simple single or multiple array(s) of parallel vessels .For a brief review of SSA and the current knowledge of intranidal hemodynamic compartmentalization, see However, owing to practical constraints imposed by complex biomathematical simulations, all previously reported theoretical AVM models have been in essence extremely rudimentary replicas of true brain AVMs, each containing an AVM nidus composed of only a small number of vessels. This radical oversimplification of AVMs makes it impossible to study hemodynamic compartmentalization within a nidus. The electrical network AVM model we present herein attempts to elucidate qualitative hemodynamics within a theoretical nidus by adopting more representative gross morphological, histological, angiographic, and biomechanical characteristics of brain AVMs than available previously.This more complex model is necessary to address the question of AVM compartmentalization. Compartments are regions of the network each consisting of numerous interconnected vessels. Earlier simple AVM models contained just a few vessels, and therefore have no analogy to intranidal compartments, and there is no way to address the question of hemodynamic compartmentalization. A head-to-head comparison of our model to other earlier models is challenging because most of these prior publications do not release their code or include enough information to rebuild their model, and, most importantly, it is not possible to investigate intranidal compartmentalization with these simpler models because they contain too few vessels. Our goal was not simply to propose a novel and improved model, but also to capitalize on availability of this new, angioarchitecturally more complex and realistic nidus simulator to theoretically investigate an important morphological and hemodynamic feature of AVMs, i.e., nidus compartmentalization.To that end, we present an electrical network model of a theoretical brain AVM constructed with multiple AFs and DVs, and a three-dimensional (3-D) spatially oriented nidus containing a large number (97) of interconnected plexiform and fistulous intranidal vessels. The inclusion of a simulated intranidal fistula is important because fistulas are of larger caliber than plexiform vessels, and therefore carry higher hemodynamic loads than their plexiform counterparts . We use Blood flow within most human vasculature consists of non-Newtonian fluid flowing in a pulsatile nature through viscoelastic, tapered tubes. This requires computationally intensive calculations to simulate with high fidelity. However, since blood flow in AVMs and surrounding vasculature is within thin capillary vessels downstream of high capacitance vessels, turbulence, pulsatility, and changes in viscosity are negligible. AVM hemodynamics can therefore be described sufficiently by the Hagen–Poiseuille equation,r is vessel inner radius, L is vessel length, and η is the blood viscosity (η = 3.5 centiPoise). This is analogous to Ohm’s law, I = V/Rv, with current I = Q, voltage V = ΔP, and vessel resistance Rv = 8 L η/π r4. Despite known limitations in the applicability of the Hagen–Poiseuille equation , this analogy allows simulation of the fluidic network of blood flow within AVM vasculature using established, matrix-based analysis of electrical networks. If pressure, length, and radius are known for all blood vessels, it is possible to calculate blood flow rate throughout the entire AVM. Importantly, we ignored pressure losses at vessel junctions in our analysis. This is a significant assumption, but since we consider intranidal vessels to be thin, the length of the region at each junction that cannot be approximated as a cylinder is tiny. The pressure drop across a junction may therefore be negligible, and although it ideally should be taken into account, we reasonably did not consider this to limit further complexity of our calculations.where Q is vessel flow rate, ΔP is the difference in pressure between the ends of the vessel, mean) values at major points within the extranidal circulatory network were assigned as the arithmetic mean of values published in clinical accounts: mean systemic blood pressure, 74 mm Hg; mean major AF pressure, 47 mm Hg; mean minor AF pressure, 50 mm Hg; mean DV pressure, 17 mm Hg; and mean central venous pressure (CVP), 5 mm Hg. The nidus vessels comprising the plexiform component were held fixed at a radius of 0.05 cm, and the length of each vessel included the incorporation of an arbitrary factor for tortuosity. The intranidal fistula consisted of a direct connection between AF2 and DV2 with branching plexiform vessels, and was maintained at a uniform radius of 0.10 cm. Since there are no values in the literature for the length of the plexiform vessels, they were approximated to be uniform in length (5.0 cm). The fistulous vessel was given a slightly shorter length (4.0 cm) , three draining veins (DV1–DV3), and an arbitrary, randomly chosen nidus angioarchitecture consisting of 97 vessels  interconnected in a 3-D morphology, as shown in  cm) see .This model was a more complex and realistic elaboration on a previously published model by In network analysis, vessels of various radii and lengths are distributed randomly in a dependent manner to resemble a highly disordered frame or network through which fluid will flow. In analogies found in electricity, the circulatory network can be represented by a complex electrical circuit of connected wires with variable resistance through which the current or flow, powered by an electrical power source or pressure gradient, will traverse. Each wire or vessel connection represents a node or a location at which flow converges and diverges. With respect to the AVM, the node resembles the start or end of a vascular branch, e.g., a bifurcation or trifurcation, within the vascular bed.v; in the opposite direction it is +QRv. If a source of pressure is traversed in the direction of the pressure increase, the change in pressure is +ΔP and in the opposite direction it is −ΔP. This yields a system of linear equations relating flow, pressure, and resistance. These equations were represented in matrix form,The flow through the AVM network was calculated according to Kirchhoff’s circuit laws: (1) the algebraic sum of the currents at any node must be zero, i.e., flow into a node is equal to flow out of the node; and (2) the algebraic sum of the changes in potential (pressure gradient) encountered in a closed traversal must be zero. Within a current loop, if a resistor is traversed in the direction of the current, the change in pressure is −QRRv is a matrix with elements that are either various vessel resistances or the values ±1. Rv is constructed so that each line of matrix Eq. (2) is an instance of either Kirchhoff’s first law or Kirchhoff’s second law. The flow rates through each vessel were then solved by calculating:where ΔΔP is a column vector that contains zeros for each instance of Kirchhoff’s first law and pressure changes for each instance of Kirchhoff’s second law. These pressure changes are those accumulated over each closed vessel loop in the network. Because the loops are closed, these pressure changes are mostly zero and only non-zero when there is an external source of pressure difference in the loop, such as at the heart. Q is a column vector of all of the vessel flow rates. Rv–1 is the left inverse of Rv. Calculating Rv–1 was a computational bottleneck of our approach, thus limiting the size of our network model to 97 vessels only . Calculating Q subsequently allowed us to calculate ΔP for each vessel by solving the Hagen–Poiseuille equation. We performed all calculations and simulation steps using PythonTM .where mean and flow during SSA were considered to represent vessels in which contrast medium had spread, as experimentally shown by mean and flow within each nidus vessel upon SSA.We then simulated SSA of the AVM nidus by increasing the intravascular pressure  by 10, or 20, or 30 mm Hg separately within each of the four AFs supplying the AVM. These elevations in pressure owing to the injections are typical of those measured in small vessels of experimental animals when using microcatheters . Downstrmean and flow within each nidus vessel prior to SSA injections, we then used these baseline simulations to calculate the percentage increases in intravascular Pmean (ΔP%) and flow (ΔFlow%) in each nidus vessel after SSA through each AF, with and without occlusion of all other AFs. We analyzed the observed shifts in hemodynamic compartments consequent to increasing AF injection pressures by mapping the hemodynamic changes onto a graphic representation of the nidus network using graded color scales to indicate hemodynamic values.Therefore, having first conducted hemodynamic simulations with unperturbed AF pressures to determine the baseline PNext, we conducted a limited parameter sensitivity analysis of the model. We systematically altered two biophysical parameters in our model (vessel length and radius) to study the effects of a possible range of normal variations in AVM biophysical parameters on the behavior of this model and its fidelity to physiological reality. We randomly varied vessel length and radius 1,000 times throughout the entire network, sampling from a uniform distribution of values centered on the “typical” values listed in We also investigated if our findings pertaining to intranidal compartmentalization were dependent on the particular single nidus configuration we had studied, or if we could observe similar trends in compartmentalization in nidus networks of different geometries. Thus, we 100 times randomly varied the connections of 50% of the 97 AVM vessels. For each of these distinct morphologies, we simulated baseline state hemodynamics and SSA at each AF.mean through the plexiform vessels of the nidus ranged from <1 to 103 mm Hg with a mean of 21.6 mm Hg, while Pmean ranged from <1 to 4.1 mm Hg with a mean of 2.9 mm Hg through the fistulous vessels  for each intranidal vessel. The average ΔP% values and sizes of compartments occurring after all SSAs, with and without occlusion of all other AFs, are seen in Superselective angiography through each AF resulted in the dissipation of intravascular PUpon simulating each SSA , we observed a bimodal distribution in the range of ΔP% throughout vessels of the entire nidus, as seen in mean above baseline (ΔP%) superimposed onto the nidus network graphically revealed the changes in overall sizes of intranidal hemodynamic compartments with increasing injection pressures after SSA through each AF. As an example, the ΔP% values and sizes of intranidal compartments occurring after SSA, without and with occlusion of all other AFs, for injections at 20 mm Hg are illustrated graphically in Color maps of the changes in Pmean through the nidus ranged from 11.1 to 32.4 mm Hg. Flow rate through the plexiform vessels of the nidus ranged from <1 to 92 mL/min, while flow ranged from 22.5 to 181 mL/min through the fistulous vessels. Pmean through the plexiform vessels of the nidus ranged from <1 to 145 mm Hg, while Pmean ranged from <1 to 6.2 mm Hg through the fistulous vessels. All of these ranges were physiologically realistic. In 90% of the network geometries, during SSA we observed distributions in ΔP% that had a statistically significant bimodal nature .As a departure from the single realization of the AVM network described above, our investigation of 100 versions of the AVM network, each with 50% of vessels randomly reconnected to different intranidal nodes, illustrated the robust and reproducible behavior of this particular model regardless of its angioarchitecture. In these 100 networks, blood flow through the AVM nidus ranged from 512 to 813 mL/min, and PAs further validation, we repeated four times the same SSA simulation yielding the bimodal distribution of ΔP% depicted in Taken together, this was clear evidence that our observation of intranidal compartmentalization was not strongly network dependent or owing to a particular choice of network geometry, but instead, was a general feature of our AVM network regardless of its internal architecture, thus reflecting the consistent clinical angiographic observation of intranidal compartments within large human AVMs.The many advantages and limitations of theoretical AVM modeling based on electric network analysis have been discussed and commented upon in great detail previously by This model is of one single 3-D configuration, albeit an advance on prior modeling, and is constructed with implementation of average biophysical and hemodynamic parameters. In fact, values within a much wider range of AVM physiological and morphological parameters could have also been used, and would have likely yielded different outcomes. However, we did perform a limited parameter sensitivity analysis to confirm the authentic behavior of the AVM model when varying some of its biophysical inputs. A full sensitivity analysis of all AVM parameters  was beyoWe do not wish to overstate the capability of this current model. Although many experimentally or clinically determined biophysical and hemodynamic parameters have been implemented in the model , nonetheless the absence of concrete data to use for the nidus portion of the model, e.g., to accurately state length of nidus vessels, plays precisely into the “black box” concept that an AVM nidus represents. We used approximated values for nidus vessel length similar to that used by For now, and despite the many limitations of current AVM modeling capabilities, we believe our study offers a theoretical AVM model that at least allows a qualitative appreciation of intranidal compartmentalization in a more rigorous manner than possible previously. The use of this model to help theoretically understand the behavior of AVM compartments is attractive in terms of its simplicity, intuitive familiarity , its implemented anatomic features, and the ease of its computer simulations. The visual display of our results in color-coded graphics superimposed on an image of an AVM nidus without a full need for mathematical comprehension of the computer simulations is an additional appealing feature. This model will be of benefit to clinical and research neuroscientists engaged in study and management of patients with AVMs.per se) are likely responsible for observed AVM hemodynamic compartmentalization (see also Applications of this novel 3-D AVM model to simulate SSA reveal that a combination of angioarchitectural features and hemodynamic equilibrium within the nidus (and not the injection pressures through AFs see also . ImportaAll datasets generated for this study are included in the manuscript/TM conceived the study. All authors designed the experiments, interpreted and discussed the results, and reviewed, approved, and commented on the manuscript. TM and HD supervised the experiments. MJ and TM performed the experiments and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In this work, we study the environmental and operational factors that influence airborne transmission of nosocomial infections. We link a deterministic zonal ventilation model for the airborne distribution of infectious material in a hospital ward, with a Markovian multicompartment SIS model for the infection of individuals within this ward, in order to conduct a parametric study on ventilation rates and their effect on the epidemic dynamics. Our stochastic model includes arrival and discharge of patients, as well as the detection of the outbreak by screening events or due to symptoms being shown by infective patients. For each ventilation setting, we measure the infectious potential of a nosocomial outbreak in the hospital ward by means of a summary statistic: the number of infections occurred within the hospital ward until end or declaration of the outbreak. We analytically compute the distribution of this summary statistic, and carry out local and global sensitivity analysis in order to identify the particular characteristics of each ventilation regime with the largest impact on the epidemic spread. Our results show that ward ventilation can have a significant impact on the infection spread, especially under slow detection scenarios or in overoccupied wards, and that decreasing the infection risk for the whole hospital ward might increase the risk in specific areas of the health‐care facility. Moreover, the location of the initial infective individual and the protocol in place for outbreak declaration both form an interplay with ventilation of the ward. Our results explore the interplay between ward ventilation, location of patients, ward overoccupancy, and outbreak detection management.2.SI model for the infection spread among individuals in a hospital setting. The combined model can be thought of as an adaptation of the Wells–Riley model , p is the pulmonary ventilation (breathing) rate (m3/s), and q is the unit of infection (quantum) as described by Riley et al.  {S(t):t≥0}, where the random variable S(t) represents the number of susceptible individuals at time t, and the aim is to compute transient probabilities pS(t)=P(S(t)=S) for any S∈Ω={0,1,⋯,N}, where N is the number of individuals in the population. These probabilities satisfy the Kolmogorov differential equations:Noakes and Sleigh  propose y et al. . In a stInterevent times are assumed to be exponentially distributed and the stochastic process is simulated using the Gillespie algorithm  there are in any zone at any given time, computed in a specialized manner  of the number of infectives  within each ventilation zone at the hospital ward. In Section SIS model with detection, to evaluate the infection spread dynamics within the hospital ward until detection of the outbreak. Instead of carrying out stochastic simulations, we present an exact approach for analyzing a summary statistic of the outbreak: the total number of infections occurring until the outbreak ends or is detected and declared. This exact approach does not only allow us to compute this quantity of interest, but it also allows one to carry out a sensitivity analysis on the model parameters, so that the impact that different characteristics of the ventilation setting has on this summary statistic can be evaluated.We note here that Equation 2.2.i are discharged at rate γi, so that γi−1 amounts to the average length of stay (LOS) of patients in zone i. Discharges are immediately replaced by new admitted patients, a reasonable approximation for hospital wards under high demand ,⋯,IM(t)):t≥0}, where Ij(t) represents the number of infective individuals in zone j at time t≥0, defined over the space of states S={:ij∈{0,⋯,Nj},j∈{1,⋯,M}}∪{Δ}=C∪{Δ}. Nj is the total number of patients in zone j, leading to N=∑j=1MNj patients in the hospital ward. State Δ represents that the nosocomial outbreak has been detected and declared by health‐care workers by the first detection of an infected patient in the hospital ward. We note that absorbing state  represents the end of the outbreak , due to patients' discharge . We consider Δ also as an absorbing state in this process, since we are only interested in the dynamics of the process until the end or declaration of the outbreak, and the transitions  is a function of the state , representing the per capita infection rate of susceptible individuals in zone j when we have  infective individuals within the ward, computed from Equation ∈C.This leads to a multicompartment SIS epidemic model that can be described as a CTMC 3.{0,1,2,⋯}. R is used here as a measure of how well ventilation can act as a preventive infection control strategy that is in place even before the actual detection of the outbreak by health‐care workers occurs and reactive strategies can be implemented.Our interest is in analyzing the infectious potential of an outbreak in a given hospital ward, for different ventilation configurations and outbreak detection hypotheses. We measure the infectious potential in terms of the following discrete random variable:3.1.∈C, our aim is to compute probabilitiesn≥0; that is, the probability distribution of R for some initial state . We can compute these probabilities from a system of linear equations, which is obtained by a first‐step argument. In particular, by proposing notationn≥1 and any ∈C, and with boundary conditions p(0)=1, p(n)=0 for all n≥1; a detailed explanation on how Equations n=0 (n) for any ∈C, n≥0. It works sequentially, computing probabilities p(n) for i1+i2+⋯+iM=I for increasing values of n=0,1,2,⋯ and I=1,2,3,⋯,N.Algorithm 1 in the Appendix computes probabilities 3.2.∂E[R]/∂θ with respect to ventilation parameters θ∈{β0}∪{βQij:i,j∈{1,⋯,M}}∪{Qo,i:i∈{1,⋯,M}}. We note that, for an initial state ∈C, E[R]=∑n=0+∞np(n), so that∂p(n)∂θ can be computed from direct differentiation of Equations θ∈{β0}∪{βQij:i,j∈{1,⋯,M}}∪{Qo,i:i∈{1,⋯,M}} are infection rates λj and probabilities p(n). Thus, we getn≥1, and any ∈C. Partial derivatives ∂p(n)∂θ can then be computed from the equations above by following arguments similar to those in Algorithm 1 in the Appendix. In order to solve these equations, one needs to have in hand values of p(n) (previously computed from Algorithm 1), as well as derivatives ∂λj∂θ. These derivatives can be straightforwardly obtained from Equation Our analysis allows one to identify the most important characteristics of the ventilation scenario, regarding the infectious potential of the outbreak until detection, by means of computing partial derivatives of the form V(θ) represents the element‐by‐element partial derivative of matrix V with respect to parameter θ  patients showing some common symptoms, since for some nosocomial pathogens, associated symptoms are quite common and pass unnoticed ,⋯,IM(t),D(t)):t≥0}, where the increasing variable D(t) amounts to the number of detected patients up to time t≥0. We consider that the outbreak is declared once D(t)=D, and the space of states of this CTMC is given by:Saug=Caug∪Δ, with state Δ representing outbreak declaration . Events occurring in this process, at different rates, are described in Table Thus, our interest in this subsection is to analyze the summary statistic Xaug. For example, Equations ∈Caug, and with boundary conditions p(0)=pΔ(0)=1, and p(n)=pΔ(n)=0 for all n≥1 and 0≤d≤D−1. 1A represents a function that takes value 1 if A is satisfied, and 0 otherwise. An adapted version of Algorithm 1, not reported here for the sake of brevity, allows for an efficient solution of this system.Our arguments in the subsections above can be adapted for process 4.N=18 patients. Ventilation rate over the whole ward was 27m3/ min , which equated to an air change rate of 3 AC/h. Diagrams of the ventilation flows and specific rates within and between each zone for each setting are given in Fig. pi=0.01m3/ min  and qi=0.5 quanta / min  for all patients at all zones and for every ventilation scenario . Alternative outbreak detection and declaration hypotheses are discussed in Sections q in our numerical results is explored in Section In Sections 4.1.R versus different values of the global mixing rate β0, the average time δ−1 at which each infective patient shows symptoms, and for ventilation scenarios A–F. For these results, it is assumed that an infective patient in zone 1a starts the outbreak, and we report in Table E[R] computed for these distributions.In Fig. 0. We note that ventilation setting C has significant extract ventilation at the initially infected zone 1a, so that the airflow is directed from 1a outward to the hospital ward. On the other hand, ventilation setting D represents a well‐mixed ward  with no extract ventilation at zone 1b , which might favor the spread of pathogens from 1a toward other zones within the ward, leading to more infections occurring until outbreak detection.Ventilation setting C can be identified in Fig. δ−1=48 h and β0=27m3/ min , where the long‐tailed distribution of R for these scenarios in Fig. δ−1=48 h. Thus, our results suggest that ventilation of the ward should be of special concern for pathogens that have longer infectious asymptomatic periods, or in hospital wards with more limited surveillance policies. It is also clear that the average individual detection time δ−1 has a higher impact on the infection spread than the specific ventilation setting in the ward, so that outbreak detection seems to dominate ventilation regarding infection spread.In general terms, worse scenarios can be identified for values 4.2.{1a,1b,2a,2b}, for δ−1=12 h and β0=9m3/ min . Corresponding mean values E[R] are reported in Table a and 3b are equivalent to zones 1a and 1b, for all ventilation settings in Fig. b and 2b), ventilation setting B is identified as the best one, while D is identified as the worst one. We note that ventilation setting B has no extract ventilation in zones 1b and 2b , but it directs the airflow instead toward corridor areas. In this setting B, corridor areas have no patients and significant ventilation, with airflow unbalance from bays to corridor areas acting in practice as an infection control measure. Thus, our results suggest that the spread control ability of a given ventilation setting depends on the location of the patient starting the outbreak as well as the airflow direction. However, from results in Fig. In Fig. θ∈{β0,Qo,1a,⋯,Qo,c3,βQ1a,1b,⋯,βQc3,3b}, partial derivatives ∂E[R]/∂θ, and elasticities (∂E[R]/∂θ)·(θ/E[R]) for ventilation settings B and D, δ−1=12 h, β0=9m3/ min  and an infective patient starting the outbreak in zone 1b. We note that while dimensionless elasticities are useful for comparison purposes, they equal zero if parameter θ is zero.When focusing on a particular location for the initial infective, comments above are supported by the sensitivity analysis on the ventilation parameters. For example, in Tables b, since Qo,1b=0. Thus, rates βQ1b,c1, Qo,c1, Qo,c2, and Qo,c3 correspond to significantly large negative elasticities reported in Table E[R]). Global mixing rate β0 has a significant impact (large positive elasticity) favoring disease spread, since increasing the value of β0 represents increasing the rate at which pathogens flow among all zones, instead of flowing specifically toward the extract ventilation areas (corridors in this setting).Regime B requires airflow toward the corridor in order to expel pathogens from zone 1a, 1b, and c1), as well as increasing airflow from 1b to 1a and to c1. This is directly related to the fact that, since there is no extract ventilation in zone 1b, infectious material in this zone can only be expelled by directing it toward adjacent zones 1a and c1.According to results in Table 4.3.R, of infections occurring until the end or detection of the outbreak can be split according to where these infections actually occur asR(j) is the number of infections occurring at bay j. Although probabilities P(R(j)=n) can be analytically computed by adapting arguments in Section 6 stochastic simulations of the process.It is clear that the number, 1)+R(2)+R,where R(E[R]=E[R(1)]+E[R(2)]+E[R(3)] for β0=9m3/ min , δ−1=48 h, ventilation settings A, D, and E, and an infective patient starting the outbreak in zone 1a. Results suggest that epidemic spread can be limited by switching ward ventilation from setting D to A, and further containment is obtained by switching to ventilation setting E. However, infection risk in bay 1 (in terms of E[R(1)]) behaves contrarily; although the global hospital ward infection risk (in terms of E[R]) is lower for setting E, this is at the expense of expelling pathogens from the infected zone 1a toward zones 1b and c1, and thus posing a greater risk to patients in bay 1.In Table 4.4.D=2 patients show symptoms (each after average time δ−1). The corresponding mean values of E[R] are reported in Table As explained in Section δ−1=48 h), we observe in Fig. R. Thus, our model predicts that under slow detection, a two‐output situation can be expected: either the initially infective patient is discharged before infecting any other patient (so that R=0), or this patient infects a second patient, leading to a large outbreak  at rate δj, with δj=δ for all j, and where the outbreak is detected and declared after one  infective patients are detected. This leads to the contribution ∑j=1Mδjij in Equations δ−1, then the outbreak is detected at rate δ, and one needs to replace ∑j=1Mδjij by δ in Equations In Sections R of infections until the end or detection of the outbreak, when the results of this screening  arrive after an average time δ−1∈{4h,8h,12h,24h}. Corresponding mean values of E[R] are reported in Table δ−1=4 h, ventilation has a less significant impact on the nosocomial spread, and low values of E[R] are reported in Table δ−1 in obtaining the screening results. In particular, for δ−1=24 h significant differences in E[R] can be noticed among the different ventilation settings, and a marked bimodality can be observed in Fig. Under this hypothesis, we plot in Fig. 4.6.N=21 and locate one additional patient in each of the {c1,c2,c3} zones. This practice is common in U.K. hospitals during times of high demand. We assume that the outbreak is detected and declared after the first patient shows symptoms, each patient showing symptoms after an average time δ−1=12 h. For interzonal mixing β0=9m3/ min , we report in Table R of infections until the end or detection of the outbreak, for ventilation settings A–F, and for the initial infective patient being located in zones {1a,c1,c2,c3}.In this subsection, our aim is to shed some light on the interplay between ventilation, nosocomial spread, and overoccupancy of the hospital ward. We represent hospital ward overoccupancy by locating three additional patients at the corridor areas; in particular, we set c1 and c3. This is explained by noting that bays 1 and 3 are completely symmetric for all ventilation settings as noted in Section a, results in Table =. For zone 1a, values of E[R] are larger in Table a, more infections during this outbreak should be expected under ward overoccupancy. This might not be only related to having more patients in the ward under overoccupancy (21 instead of 18), but also to the potential of patients in the corridor to act as infection links between bays. For example, under overoccupancy, an infective individual in zone 1a might infect individuals in bay 2 by, as a first step, infecting individuals in the corridor areas. These people then might more easily infect individuals in bay 2, before being discharged, due to being in closer proximity and depending on the particular ventilation setting in place in the ward.We first note that results in Table {c1,c2,c3} in Table a has a larger infectious potential (in terms of E[R]) than individuals located in the corridor when ventilation settings B and E are in place, this is not the case for ventilation settings C, D, and F, and these infectious potentials are comparable under ventilation setting A, which represents a well‐mixed scenario. Our results then indicate that overoccupancy leads in general to higher airborne spread risks, and that this increase can be especially significant depending on the specific ventilation in place.Infection dynamics related to the scenario above highly depend on the particular ventilation setting under study, which can be noticed by inspecting rows corresponding to zones 4.7.q is highly pathogen dependent, ranging from q∼0.01 quanta/min for rhinovirus to q∼10 quanta/min for measles  patients in a given hospital ward could be issued depending on the ventilation in place in the ward. Our model also predicts that decreasing the infection spread risk in the hospital ward can sometimes come at the expense of increasing the risk in particular areas of the ward.λj of the number of infectives in each zone at any given time, and where  represents in fact a state of the CTMC under study. This detailed mathematical construction allows for the analytical computation of summary statistics (such as R in this work), and for carrying out a local sensitivity analysis that allows one to identify the particular factors of each ventilation setting having the most significant impact on the infection spread.Similar models have already been considered in the literature for linking zonal ventilation scenarios with epidemic spread models  to show how our methodology can be easily implemented for a different hospital ward to that in Fig. V, illustrating how this matrix varies with hospital ward structure; and (ii) to show that while some of our conclusions might be hospital ward structure dependent, others seem to be valid for a wide range of hospital ward structures .It is clear that some of our conclusions could be highly dependent on the hospital ward structure, and therefore the flexibility of our methodology comes into play. It can be applied to any hospital ward of interest by appropriately adapting the corresponding ventilation matrix susceptible–exposed–infective–recovered). On the other hand, when analyzing hospital wards with longer average patient LOS, so that individuals may become infected and recover during their stay, SIRS‐type epidemic models would be required to represent the recovery of patients (I→R) before discharge (R→S) occurs. We note here that in principle, the methodology outlined in Section In this article, we go beyond the SI epidemic model in Noakes and Sleigh , proposiM=9 zones with three empty zones and six zones containing three patients each) would lead to an intractable number of equations, and stochastic simulation approaches would prevail.Finally, we note that when carrying out our analysis, the main computational effort lies in solving systems of linear equations, where the number of equations is determined by the number of states of the corresponding CTMC, given by Equation 6.Python) in order to reproduce our numerical results are available López‐García, King, and Noakes (Computer codes (in d Noakes .Fig. S1. Diagrams of the ventilation settings considered, and corresponding ventilation matrices V.Fig. S2. Probability mass function of the number R of infections until the end or detection of the outbreak, for ventilation settings SA‐SD.Table S1. Mean Values of E[R] for Scenarios in Fig. S2Click here for additional data file.


After hydrofluorination of ynesulphonamides in superacid or in the presence of hydrofluoric acid/base reagents, a series of α-fluoroenamides has been synthesised and tested for the inhibition of carbonic anhydrase  isoforms. This study reveals a new, highly selective family of cancer-related transmembrane human (h) CA IX/XII inhibitors. These original fluorinated ureido isosters do not inhibit the widespread cytosolic isoforms hCA I and II and selectively inhibit the transmembrane cancer-related hCA IX and XII, offering interesting new leads for future studies. This is regulated by different players including transmembrane carbonic anhydrases IX and XII (CA IX and CA XII) which are overexpressed in human cancer cells2+ ion of the active site, disrupting the catalytic processN-aryl-benzenesulphamides were found to act as selective nanomolar inhibitors of hCAs IX and XII,18 F-labelled analogues however showed no significant uptake in HT-29 tumors compared to normal organs/tissuesThe elevated metabolic rate of solid cancer tumors leads frequently to acidosis and hypoxia,,in vivo oxidative cleavage of the ureas and to the generation of the corresponding aniline-derived metabolites,,N-sulphonylureas isostersN-sulphonylureas. Therefore, following our seminal contribution on the use of tertiary benzene sulphonamides as selective cancer-related hCAs inhibitors, we considered that fluoroenesulphonamide group could represent an interesting novel selective chemotype and evaluated the activity of this new series against hCA I, hCA II, hCA IX and hCA XII.Considering the recent discovery of the urea derivative SLC-0111 which successfully ended Phase I clinical programmes for the treatment of patients with advanced hypoxic tumors over-expressing the isoforms hCA IX/XIITwo methods were equally used to generate the fluroenamides from their corresponding ynamides, as shown in 2CO3, extracted with ethyl acetate (×3). The combined organic phases were dried (MgSO4) and concentrated in vacuo. Products were isolated by column chromatography on silica gel.To a solution of HF (6 ml) maintained at −50 °C or −78 °C, was added very slowly ynamide derivative (1 mmol). The mixture was magnetically stirred at the same temperature during 5 min. The reaction mixture was then neutralised with water–ice–Nain vacuo. Products were isolated by column chromatography on silica gel. The NMR spectra of the products and their detailed characterisation can be found in literature,To a mixture of hydrofluoric acid and pyridine  maintained at the required temperature was added the starting ynamide (1 mmol). The mixture was magnetically stirred at the same temperature during the required time. The reaction mixture was then neutralised with water–ice–sodium carbonate solution, extracted with dichloromethane (3×). The combined organic phases were dried over anhydrous magnesium sulphate, filtered and concentrated 2 hydration reaction2SO4 , following the CA-catalysed CO2 hydration reaction for a period of 10 s at 25 °C. The CO2 concentrations ranged from 1.7 to 17 mM for the determination of the kinetic parameters and activation constants. For each inhibitor, at least six traces of the initial 5–10% of the reaction have been used for determining the initial rate. The uncatalysed rates were determined in the same manner and subtracted from the total observed rates. Stock solutions of inhibitors (10 mM) were prepared in distilled deionized water and the solution diluted to 1 nM using the assay buffer. Inhibitor and enzyme solutions were pre-incubated together for 15 min (standard assay at room temperature) prior to assay, in order to allow for the formation of the enzyme–inhibitor complex. The inhibition constant (KI), was obtained by considering the classical Michaelis–Menten equation and the Cheng-Prusoff algorithm by using non-linear least squares fitting as reported earlierAn SX.18Mv-R Applied Photophysics  stopped-flow instrument has been used to assay the catalytic activity of various CA isozymes for COα-Fluorenesulphonamide analogue 1 was therefore tested as a carbonic anhydrase inhibitor and found to be inactive toward hCAI and II, a poor micromolar inhibitor of HCAIX but, most interestingly, a nanomolar inhibitor of HCAXII. This result was especially encouraging as it reinforces our initial hypothesis and revealed a very selective inhibitor profile for the α-fluoroenesulphonamide pharmacophore  revealed a strong influence of this substituent on the efficiency and selectivity of the inhibitors. Introduction of a phenanthrene (compound 2) was indeed detrimental to the activity while the presence of a thiophene dramatically modified the inhibitory profile. 1-Thiophenyl-sustituted fluoroenesulphonamide 3 was found to be a micromolar inhibitor for hCA IX and not active for hCA XII and the presence of the heteroaromatic ring, in place of the tolyl group, shifts the inhibitor from a highly selective hCA XII inhibitor to a selective hCAIX inhibitor. These results suggest a non-zinc binding mode of action for these new chemotypes and evidence a variation of binding mode for these inhibitors4 exhibited hCA IX nanomolar inhibition and low micromolar hCA XII inhibition. By increasing the distance between the fluoroenamide ureidoisoster moiety and the hydrophobic phenyl group, while maintaining a linear rigidity thanks to electronic conjugation between π electrons, a dual nanomolar selective inhibitor of hCAIX and hCAXII, compound 5, could be discovered. Previous report of in vivo experiments nicely demonstrated that when silencing hCA IX alone leads to a 40% reduction in xenograft tumor volume, the concomitant inhibition of both transmembrane isoforms IX and XII leads to 85% reduction of tumor growth5 can be considered as an interesting lead compound for further studies in this direction. To further explore the potential of fluoroenamide as a new chemotype for hCA selective inhibitors quest, fluoroenimides 6 and 7 were synthesised and tested. As for previously tested N-sulphonyl analogue 1, compound 6 was shown to be nanomolar selective inhibitor of hCAXII. This result suggests that the α-fluoroolefin core, the ureido isoster, is the essential pharmacophore for these compounds, thus confirming our initial hypothesis. Again, shifting from phenyl to alkyl chains dramatically modify the selectivity of these compounds with compound 7 being now a hCA IX selective inhibitor at the micromolar level.To further explore substituent effect on the inhibitory activity/selectivity of this new class of hCA inhibitors, a brief structure activity relationships study was initiated. Exceptionally, all the tested fluoroenesulphonamides were found to be ineffective as offtarget hCA I and hCA II inhibitors and are selective inhibitor of the tumor associated isoforms IX and XII. Replacement of the phenyl ring on the alkene in On the other hand, considering the substantial interest that bacterial/fungal/protozoan CA inhibition raised ultimatelyThis study reveals a new, highly selective family of cancer-related transmembrane CA inhibitors. The tested α-fluoroenamides ureidoisosters did not inhibit widespread cytosolic isoforms hCA I and II, and selectively inhibited the transmembrane cancer-related ones, hCA IX and XII. The simple modification of the C-substituent of the α-fluoroenesulphonamide and α-fluoroenimide revealed the possibility to either generate selective hCA IX, selective hCA XII or dual hCA IX and hCA XII isoform confirming the strong potential of these new pharmacophores for further studies.


This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D convolutional neural network, respectively, for recognizing actions. Two types of fusion are considered—Decision-level fusion and feature-level fusion. Experiments are conducted using the publicly available dataset UTD-MHAD in which simultaneous video images and inertial signals are captured for a total of 27 actions. The results obtained indicate that both the decision-level and feature-level fusion approaches generate higher recognition accuracies compared to the approaches when each sensing modality is used individually. The highest accuracy of 95.6% is obtained for the decision-level fusion approach.  Human action recognition has been extensively studied in the literature and has already been incorporated into commercial products. There are a wide range of applications of human action recognition including human–machine interface, e.g., ,2,3,4, iVision and inertial sensing modalities have been used individually to achieve human action recognition, e.g., ,21,22,23Furthermore, wearable inertial sensors have been used to achieve human action recognition. Similarly, these sensors are cost-effective and widely available. The main advantage of these sensors include their wearability—Thus, they are not limited to a specific field of view. Often, 3-axis acceleration signals from their accelerometers and 3-axis angular velocity signals from their gyroscopes are used for conducting human action recognition, e.g., ,21,22,23Basically, no sensing modality is perfect: no sensing modality provides the entire information associated with various actions. In previous works on fusing two sensing modalities ,28,29,30The rest of the paper is organized as follows: This section gives a brief description of the dataset utilized and the way the video images and inertial signals are fed into the deep learning models described in The UTD-MHAD dataset was collected by a Kinect camera and a wearable inertial sensor at the same time. The dataset consists of video images with a resolution of 640 × 480 and depth images with a resolution of 320 × 240 at 30 frames per second captured by the Kinect camera. It also consists of 3-axis acceleration and 3-axis angular velocity signals captured at the same time at a sampling rate of 50Hz by the inertial sensor. The details of the inertial sensor are provided in [Each of the actions were performed by eight subjects , and each action was repeated four times. Hence, there are a total of 864 action clips, with three of the data corrupted action clips not included. Each action clip consists of videos, depth videos, skeleton joint positions, and inertial sensor signals. In this work, the videos and the inertial signals are used.For the video data, since different actions have different video clip durations, 32 frames across each video clip are selected to form a consistent 3D input to the 3D convolutional neural network discussed in the next section. To gain computational efficiency, each frame is resized to 320 × 240 pixels. As a result, each action clip has the same spatial and temporal size forming a 320 × 240 × 32 3D video data volume. An example 3D video data volume formed in this manner is shown in For the inertial data, an eight-column input image is formed by combining the 3-axis acceleration signals, the 3-axis angular velocity signals, the overall acceleration signal, and the overall angular velocity signal at a sampling frequency of 50Hz. Thus, input images to the 2D convolutional neural network discussed in the next section are of size 8 × 50. An example inertial signals input image is shown in A 3D convolutional neural network is used to learn the video data for the 27 actions of the UTD-MHAD dataset. The architecture of this model is a simplified version of the model discussed in  and is sFor the inertial data, a 2D CNN is used. As inertial sensor signals are time-series signals, the 2D CNN model developed in  for speeA brief overview of the layers is described next. Interested readers are referred to many references that are available on deep neural networks, for example ,35,36. TBesides the above decision-level fusion approach of combining the decisions of two deep neural networks, one for video and one for inertial sensing, a feature-level fusion approach is also considered in this work. 256 features are extracted from the video network model and the inertial network model dropout layers as indicated in This section presents the results of the experiments conducted to examine the effectiveness of the introduced fusion approach when the two sensing modalities of video images and inertial signals are used simultaneously. A leave-one-out cross validation was performed, meaning that the data from one subject was used for testing, and the data from the remaining seven subjects were used for training. This process was repeated for each of the subjects. Then, an average was taken across the subjects. The average accuracy of the following four approaches were obtained: (1) only using video sensing modality, (2) only using inertial sensing modality, (3) feature-level fusion of video and inertial sensing modalities, and (4) decision-level fusion of video and inertial sensing modalities. The confusion matrix of the video sensing modality only and the inertial sensing modality only are shown in From In this paper, for the first time, the simultaneous utilization of video and inertial sensing modalities were considered within a fusion framework to achieve human action recognition based on deep learning models. The following four approaches were compared in terms of recognition accuracies: using only video data as input to a deep neural network, using only inertial data as input to a deep neural network, using both video data and inertial data as inputs to two deep neural networks within a decision-level fusion framework, and using both video data and inertial data as inputs to two deep neural networks within a feature-level fusion framework. The experiments conducted based on the publicly available UTD-MHAD dataset have shown that the decision-level fusion approach provided the highest recognition accuracy of 95.6%—The fusion of the two sensing modalities exhibited more robust recognition outcome in terms of misclassification errors compared to the situations when each sensing modality was used individually.


Polyurea/polyurethane (PUa/PU) shell microcapsules (MCs), containing high loadings of isophorone diisocyanate (IPDI) in the core, were developed to enable the production of mono-component, eco-friendly and safer adhesive formulations for the footwear industry. IPDI microencapsulation was obtained via oil–in–water (O/W) microemulsion combined with interfacial polymerization. A methylene diphenyl diisocyanate (MDI) compound , with higher reactivity than IPDI and low viscosity, was added to the O phase to competitively contribute to the shell formation, improving its quality. Four different active H sources were tested, aimed at achieving a high encapsulation yield. The successful encapsulation of IPDI was confirmed by Fourier transformed infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA), while the MCs’ morphology and size distribution were assessed by scanning electron microscopy (SEM). The incorporation of a multifunctional isocyanate silane in the O phase, as “latent” active H source, led to the formation of impermeable PUa/PU-silica hybrid shell MCs with more than 60 wt.% of pure encapsulated IPDI. A proof-of-concept study shows high peeling strength and a structural type of failure of the adhesive joint, revealing an effective IPDI release. These new engineered MCs are found to be promising crosslinkers for mono-component adhesives for high demanding applications. Isocyanates have been used for over 60 years in polyurethane (PU) and polychloroprene adhesives formulations, mainly because of their high reactivity and capability to promote a good adhesion. The formation of PUs occurs by the reaction between the extremely reactive polyisocyanates’ NCO groups and any chemical group that contains an active hydrogen (H) atom . CurrentIn this work, the IPDI was encapsulated using an interfacial polymerization method in combination with an oil–in–water (O/W) microemulsion technique. Microencapsulation can be defined as a way of coating small particles, liquid droplets or gas bubbles within a thin film or shell, which protects or isolates the core material from the surrounding environment. Many different active materials such as drugs, enzymes, vitamins, pesticides, flavors, and catalysts have been successfully encapsulated inside shells made from a variety of polymeric and non-polymeric materials, depending on the end use of the encapsulated products. The employed technique involved an emulsification process in combination with interfacial polymerization. The emulsion is obtained through vigorous stirring of two or more immiscible phases composed by at least two liquids, usually in the presence of an emulsifier. Regarding the interfacial polymerization process, it is composed by two steps. During the first step, the diffusion of two different reactive monomers occurs, and they come in contact at the interface, i.e., at the surface of the oil droplets of the emulsion system, leading to the formation of an initial thin polymeric layer shell . During The major efforts already reported for microencapsulation of isocyanate compounds belong to the encapsulation of monomeric isocyanate IPDI, as a healing agent. Yang et al. reported the successful microencapsulation of IPDI, as a healing agent, via interfacial polymerization, with a microencapsulation yield of 70 wt.%, taking into account the amount of IPDI and solvent . In thei® 2500, which consists of a mixture of monomeric and polymeric MDI species, responsible for an acceptable low viscosity of the dispersed phase of the emulsion and, therefore, no solvent was required. It displays significantly higher reactivity than IPDI, favorably competes in the formation of the polymeric shell, enhancing the encapsulated IPDI content, and has not yet been described in the literature for this application. In addition, four different active H sources were tested, namely 3-(2-aminoethylamino) propyltrimethoxysilane (aminosilane) in combination with tetraethyl orthosilicate (TEOS), diethylenetriamine (DETA) and 3-(triethoxysilyl) propyl isocyanate or 3-isocyanatopropyltriethoxysilane (IPES), to achieve a faster polymerization reaction with an increased encapsulation yield, as well as durable (long shelf-life) MCs. The novelty of this work relies on: (i) optimization studies involving multifunctional active H sources and a commercially available high reactivity and low viscosity isocyanate compound (Ongronat® 2500), which eliminates the need for a solvent and increases the encapsulated pure isocyanate content; (ii) the use of aminosilane and TEOS in the synthesis, which not only contributed as an active H source, but also for a PUa/PU-silica hybrid shell; (iii) the addition of a “latent” active H source, isocyanato silane (IPES), which has both a Si-ethoxy (Si–OC2H5) and isocyanato functionality and, which can be placed in the dispersed, organic phase, contrary to common active H sources; and (iv) the application of MCs obtained through interfacial polymerization, containing IPDI, as a crosslinking agent to be applied in the adhesive industry for high demanding applications.In the present work, IPDI was efficiently microencapsulated using a readily commercially available MDI source, OngronatThe work reported in this paper was carried out within the framework of a Technology Platform on Microencapsulation and Immobilization led by A.C. Marques, which deals with the synthesis of MCs, microscaffolds and microspheres with tailored morphologies, organic functionalities and a wide porosity range ,14,15,16® 2500 was purchased from BorsodChem. The isocyanate to be encapsulated, Isophorone diisocyanate , was obtained from Bayer. Regarding the active H source compounds, the 3-(2-aminoethylamino) propyltrimethoxysilane (with 90% purity) and tetraethyl orthosilicate  were purchased from VWR Chemicals; Diethylenetriamine (DETA) (with 99% purity) was obtained from Alfa Aesar; and the 3-isocyanatopropyltriethoxysilane  was purchased from Aldrich Chemistry. The surfactant Gum Arabic (GA) was obtained from Labchem. All chemicals were used as received, without further purification.The MDI compound and shell precursor Ongronat® 2500, present at 30 wt.%, the one to competitively contribute to the MCs’ shell formation. The IPDI is less reactive than Ongronat® 2500, which allows its encapsulation due to the faster reaction of Ongronat® 2500 with the OH groups of the aqueous phase.The oil–in–water (O/W) emulsion system was obtained through vigorous stirring of the isocyanates, as the oil phase, consisting of 6.7 wt.% of the total emulsion system, with the water phase consisting of 95.3 wt.% Milli-Q water and 4.7 wt.% GA, at a stirring speed of 3200 rpm with an Ultra-Turrax  during 10 min at room temperature (RT). The oil phase is composed by two isocyanates, being IPDI, present at 70 wt.%, the one to be encapsulated and OngronatOnce the emulsion was ready, the active H sources  were added, dropwise, to the emulsion system (W phase) while under mechanical stirring at 400 rpm, using a mechanical stirrer Heidolph RZR 2051 control. IPES, on the other hand, was added to the O phase used to prepare the emulsion. The emulsification and stirring rates had been previously optimized for these microencapsulation syntheses. During this procedure, the temperature was increased gradually from RT to the maximum synthesis temperature, listed in It should be noted that these syntheses require a very frequent monitoring of the emulsion quality by means of optical microscopy, in order to control the stability of the emulsion, as well as the maturity degree of the shell.The adopted techniques and methods for the MCs’ characterization include optical microscopy, scanning electron microscopy (SEM), Fourier transformed infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). The photomicrographs obtained from SEM were also used to evaluate the MCs’ size distribution using the software ImageJ. Proof of concept studies were carried out to evaluate the MCs’ performance as encapsulating agents to enable mono-component and self-reactive adhesives.A Kruss, MSZ 5600 optical microscope was used to evaluate the MC’ size, shape and maturity , as well as the stability of the emulsion and droplets’ size during the synthesis procedure.SEM analysis was carried out to evaluate the morphology, size distribution, roughness and porosity of the obtained MCs, using a JEOL JSM7001F  with a FEG-SEM (Field Emission Gun) system. Previously to the analysis, the MCs were placed in a sample holder using conductive adhesive tape with a double face. Afterwards, the samples were coated with a conductive Au/Pd thin film, through sputtering, using a Quorum Technologies sputter coater model Q150T ES.To evaluate the MCs’ size distribution, the images obtained through SEM were imported to the software ImageJ . After calibrating the scale, the contrast of the SEM images was enhanced, and the MCs were highlighted by a thresholding function. After treating the images to remove residual debris and agglomerates, the data for the size distribution of MCs was obtained using the Analyze Particles functionality of the software.® Attenuated Total Reflectance (ATR) accessory. The spectra were achieved with a resolution of 4 cm−1 and data collection of 8 scans.FTIR characterization was used to study the chemical structure of the obtained MCs by identification of specific characteristic groups, in order to assess the presence of certain compounds in the MCs’ shell and core. This technique enabled to confirm the presence of unreacted NCO groups in the MCs’ core, as well as to make a comparative evaluation of the encapsulated NCO content among different syntheses. A PerkinElmer, Spectrum Two, FTIR spectrometer was used, equipped with a Pike Technologies MIRacle−1 using a TGA HITACHI STA 7200.Through TGA analysis, it was possible to quantify the amount of encapsulated compound in the obtained MCs. The analyses were performed under a controlled nitrogen atmosphere, at a temperature increase rate of 10 °C·minThe peeling strength test was essential to evaluate the adhesive effectiveness of the adhesive joint, in the bonding process. Peeling strength is the average load per unit width of the bond, required to separate bonded materials. A separation angle of 180 degrees was applied. A high peeling strength value, i.e., similar to that of the reference sample (adhesive joint with non-encapsulated IPDI), confirms the release of the microencapsulated IPDI content and its effective reaction with the surrounding environment (OH pre-polymer).For this test, MCs with encapsulated IPDI (I MCs) were added to an adhesive formulation base component, OH pre-polymer (having OH groups available to react), supplied by CIPADE S.A., to act as a crosslinker. The quantity of MCs added to the solution was such that the quantity of the encapsulated isocyanate corresponds to 5 wt.% of the OH pre-polymer (base component). The resulting mono-component adhesive material was applied as a homogeneous thin film (0.2 mm of thickness) onto previously carded leather substrates. The film was allowed to dry at RT for 15 min. The substrates were then placed on top of each other, with the adhesive faces in contact and were subjected to the conditions of pressure and temperature used in the footwear industry in the adhesive application process. For this test, infrared radiation and a manual press with electrically heated platens, Carver, were used. The samples were left to stand for 48 h at RT before carrying out the peeling strength test. A universal testing machine Instron 5566 was used at a constant rate of speed of 100 mm/min. Tests using a reference sample, namely the same OH pre-polymer but with non-encapsulated IPDI, were also performed for results comparison.The morphology and size distribution of the MCs were assessed by SEM. By optical microscopy, it was possible to follow a MCs’ shell formation behavior and to conclude about the shortest reaction time, for each synthesis, which allows enough maturity of the shell material to withstand the filtration step without damage of the MCs. In p-values obtained through the Shapiro–Wilk normality test, listed in Through the analysis of the size distribution of the MCs, using the ImageJ software, it was possible to conclude that the particles have an average diameter of 95 ± 7, 81 ± 4 and 82 ± 7 μm for the AT MCs, D MCs and I MCs samples, respectively. For the stirring rate employed (400 rpm), the size of the MCs is herein found to be smaller than that reported in the literature, for instance by Yang et al. ,18,19. I−1, related to the N=C=O bond stretching vibration, which indicates the presence of unreacted NCO groups in the MCs, confirming a successful encapsulation of IPDI. The peaks ascribed to the presence of amine groups can be observed at 3200–3400 cm−1 from N–H stretching of the amine bonds and at 1510 cm−1 from N–H bending. The presence of amine groups in the MCs can be related with the presence of PUa in the shell composition, derived mainly from the reaction of NCO groups with the active H sources used in the synthesis of AT MCs and D MCs, namely aminosilane and DETA, respectively. The carbonyl groups observed at ca. 1715–1730 cm−1 (C=O from urethane) and at 1680–1700 cm−1 (C=O from urea), the C=C groups detected at 1522 cm−1, the C–O–C group at 1214 cm−1 and C-O stretching at ca. 1300 cm−1 confirm the presence of urethane and urea linkages, evidencing that the MCs’ shell is composed by PU and PUa. Moreover, the peak assigned to the carbonyl group of urea is more intense than the same peak related to urethane bonds, which means that the majority of the shell material is indeed polyurea.The FTIR spectra of IPDI and the IPDI loaded MCs were obtained to further reveal both the composition and the IPDI encapsulation effectiveness. From the FTIR spectra, shown in −1, typical of Si–O–Si asymmetric stretching vibrations, confirms the presence of siloxane Si–O–Si moieties in the shell, derived from the silanes employed in the synthesis, in the cases of AT MCs and I MCs. During the synthesis of AT MCs, the aminosilane and TEOS, when incorporated into the aqueous phase, tend to hydrolyze, forming silanol groups which, by polycondensation reactions form siloxane moieties (FTIR peak assignment at ca. 1070 cm−1), and when located at the droplets’ surface, can also react with NCO groups generating urethane moieties. Moreover, the NH groups from the aminosilane tend to react with the NCO groups at the interface of the aqueous/organic phases, originating urea moieties. The presence of silanes, as active H source, is found to result in a PUa/PU–silica hybrid shell material. The siloxane moieties are known to contribute to an improved mechanical strength and thermal resistance of the resulting shell.The peak at 1070 cm® 2500, leading to a fast formation of a PUa shell. Regarding the synthesis of D MCs, at which DETA was added as an active H source, the success of the IPDI´s encapsulation can be explained by the reaction of the amine groups of DETA with –NCO groups of the Ongronat2CH3) groups, and can be used as a crosslinker, contributing to the maturity and cohesiveness of the shell. In fact, once the NCO groups from IPES react with the OH groups from the aqueous phase, this molecule will be kept at the interface of the organic/aqueous phases, where the IPES´s alkoxide groups (OCH2CH3) suffer hydrolysis, forming silanol groups, which will react with the NCO groups from Ongronat® 2500. The fact makes IPES to be considered a “latent” active H source, which also displays NCO functionality. In this sense, one can state that IPES acts as an active H source, which can be added to the organic (isocyanate containing) phase, contrary to most of the active H sources, establishing links with either the organic or the aqueous phases, due to its double organic functionality. The presence of IPES inside the tiny droplets of the emulsion, at a concentration of 25 wt.% in the O phase, results in a significant concentration at the interface of the organic/aqueous phases, rather than if it had been added to the aqueous phase, which accounts for 93.3 wt.% of the total emulsion mass. The NCO groups from Ongronat® 2500 also react with the aqueous phase, contributing to the formation of the shell material.For the I MCs synthesis, IPES was added to the organic phase. IPES contains a double organic functionality, namely –NCO and ethoxy (OCH−1 assigned to NCO stretching (area in the range of 1926 to 2444 cm−1) and the area of the peak at 1300 cm−1 assigned to C–O stretching, typically related to the PUa/PU shell material, which does not tend to suffer significant changes over time after synthesis of the MCs. The peaks area was calculated using OriginPro 9 software, and the relative encapsulation yields were calculated based on the equation below:Y can be considered as a relative encapsulation yield, which represents an indirect measure of the encapsulation efficiency. Y values are 13.75, 5.84 and 18.48 for AT MCs, D MCs and I MCs, respectively, as reported in ® 2500, leading to a faster shell formation which creates a barrier between the NCO groups from IPDI and the surrounding water medium. Indeed, the incorporation of IPES, as a “latent” active H source, inside the tiny droplets of the microemulsion, is shown to be an effective strategy for the desired high encapsulation yield. Its NCO group is responsible for its concentration at the interface of the organic/aqueous phases, being therefore highly available to react through its double organic functionalities, establishing links with both the isocyanate phase and the aqueous phase. Further calculation was carried out to estimate a relative measure of the isocyanate encapsulation efficiency, by considering the peak at ca. 2260 cmOn the other hand, the remaining active H sources, employed in the synthesis of AT MCs and D MCs, were added to the dispersant (W) phase, which creates a “dilution” effect , requiring some time for them to migrate to the emulsion droplet surface to react with the NCO groups. The aminosilane and TEOS have the advantage, in comparison to DETA, of having more affinity to the oil phase, before the hydrolysis of the alkoxy groups occur, leading to a faster migration to the oil droplets and consequent reaction with the NCO groups, despite the slower reaction kinetics of the silanol groups with the NCO groups in comparison with NH (from DETA). DETA, on the other hand, has an affinity to the aqueous phase and not so much tendency to move toward the isocyanate droplets surface.Shelf-life studies were conducted on the I MCs  to confi® 2500 is much wider than the one observed in the I MCs spectra, and the NCO peak from IPES presents a deviation to larger wavenumbers.The NCO peak shape and wavenumber, observed for the I MCs spectra, when compared to that of the isocyanate species employed in the synthesis, reveals that the encapsulated isocyanate is indeed IPDI, since the NCO peak of the OngronatTGA studies enabled the quantification of the liquid isocyanate in the MCs’ samples. Generally, water and other solvents evaporation occur during a first step, below 100 °C. The second degradation step is ascribed to the decomposition of encapsulated monomeric isocyanate, followed by a third and last step related to the polymeric, or hybrid, shell decomposition. It should be stressed that there is an overlap, resulting from the decomposition of isocyanate compounds , PU and PUa shell material, so there is not an exact borderline to separate them. There are references which mention that the PU decomposition starts around 200 °C ,9,21. InIt should be noted that the thermograms exhibit different slopes between 150 and 300 °C, indicating that possibly there are isocyanates in different stages of polymerization, i.e., monomeric (IPDI and MDI) species and oligomeric (pre-polymeric) species. Regarding the shell, its decomposition starts around 300 °C and two steps can be observed in the thermograms: a first step (300–370 °C) related to the decomposition of soft segments and a second step (above 370 °C) related to the decomposition of hard polymeric segments from PU and PUa parts of the shell . These sAs shown in As discussed before, the peeling strength test was performed as an indirect way to evaluate the behavior of the MCs in terms of isocyanate release, crosslinking effectiveness and resulting adhesion capacity. The adhesive formulation base component (OH pre-polymer), when tested without any isocyanate compound, exhibits an adhesion performance by itself, but it leads to an adhesive type of failure when performing the peeling test. It should be noted that an effective crosslinking, by the reaction with NCO groups, is expected to lead to a structural type of failure of the adhesive joint, which is the one that denotes a stronger adhesion performance. This is critical for the high adhesive performance and durability, desired in the footwear industry. The average load measured per unit width of the bond, at the peeling strength tests, is listed in ® 2500, an isocyanate source that consists of monomeric and polymeric MDI species, and exhibits a significantly higher reactivity than IPDI, contributing for the fast shell formation; and (b) incorporation of a “latent” H active source, isocyanato triethoxysilane (IPES), in the organic phase (tiny droplets of the emulsion), which tends to be mainly concentrated at the interface of the organic/aqueous phases, and establishes links either with the aqueous phase (through its NCO groups) or with the organic phase (through its Si-OH groups). These PUa/PU-silica hybrid shell MCs exhibited approximately the same isocyanate content three months after their synthesis, demonstrating an effective barrier capacity of the shell. Their performance as crosslinkers in adhesive formulations was tested through peeling strength analyses and compared with adhesive joints having the same amount of non-encapsulated IPDI. Samples containing the I MCs were found to exhibit the same peeling strength as the samples with non-encapsulated IPDI, which reveals the release and crosslinking ability of the encapsulated IPDI. Additionally, the I MCs were responsible for a structural failure of the adhesive joint, which illustrates the potential of these MCs as crosslinkers in mono-component, self-reactive, safer and eco-innovative adhesives for highly demanding applications.Perfectly spherical, core–shell and free-flowing MCs, of PUa/PU polymeric shell or PUa/PU-silica hybrid shell, with average diameters ranging from 81 ± 4 to 95 ± 7 μm, were synthesized using an O/W emulsion combined with interfacial polymerization and exhibit a high loading of pure IPDI in the core. It was concluded that an enhanced encapsulation of pure IPDI isocyanate (above 60 wt.% of the MCs’ weight) occurs when the following strategies are used together: (a) mixture of IPDI with a small amount of commercially available Ongronat


Objectives: Ward rounds serve a crucial daily activity in hospitals. Building on the Entrustable Professional Activity “Conducting internal medicine ward rounds” consisting of ten competencies and 25 corresponding activities, this study aims at assessing content and external validity of an observation checklist for this EPA. Methods: A focus group aimed at content validation of the checklist. Five participants therefore evaluated it with respect to comprehensiveness and comprehensibility. To investigate external validity, 14 authentic ward rounds were video-taped and rated by two raters with the adapted observation checklist in terms of the appearance of certain activities in the videos.Results: After some adaptions, participants of the focus group agreed on a checklist consisting of nine competencies, 25 activities and 110 examples of observable behaviours supporting content validity. External validity was studied by using the observation checklist for ratings of ward round videos. The checklist was regarded as a valuable tool to structure observation. Along with the high frequencies of observed behaviour and interrater-reliability, external validity can be assumed. Conclusion: The first scientifically developed comprehensive observation checklist for the EPA conducting a ward round in internal medicine is presented. The checklist is a valuable tool for providing elaborated feedback in undergraduate and graduate medical education. Focussing on multi-institutional validation and the cut offs of the checklist to determine the levels of entrustment are recommended for future research.  Conducting clinical ward rounds is an important daily routine that every physician is expected to perform once he or she is graduated . Ward roWith our previous study , we concbe present and observe, act with direct supervision, act with indirect supervision, act without supervision, and provide supervision. In the undergraduate medical education the fifth level cannot be reached but can be attempted after medical graduation.The EPA locates competences in the context of professional practice and clarifies the tasks and responsibilities of a trainee when conducting ward rounds. Besides, the EPA serves an elaborated and objective overview that provides a basis for feedback and assessment that can then be applied in both undergraduate and graduate medical education . Our previous interview study explored competences of residents and summarized corresponding activities and tasks a graduated resident with ward round responsibilities should be able to carry out . A totalcommunication with the patient, communication with the team, diagnostic analysis and therapy planning, empathy, leadership skills, management of difficult situations and faults, organization skills, professionalism, self-management, and teaching and learning abilities. Each competence was characterized by a set of activities to depict professional practice as detailed and comprehensible as possible. On this basis, the EPA “Conducting an internal medicine ward round” was compiled.However, it remained unclear whether the EPA could be easily implemented as a feasible observation tool for the characteristics of ward rounds conducted at a university hospital. For this purpose, we conducted two separate validation studies aiming for both content and external validation of the EPA observation checklist to making it useful for educational purposes on ward rounds in internal medicine and for further research.The project was driven by two questions:Does the EPA observation checklist represent a comprehensive and comprehensible overview of required ward round competences and activities? Can the observation checklist be applied as comprehensive and feasible observation tool for ward rounds conducted by ward round teams? To be applicable in professional practice, the content of an EPA needs to be expanded by detailed descriptions of observable behaviour and the context for those behaviours . The oriTo ensure content validity of the observation checklist, a focus group was conducted. While focus groups are often used for explorative purposes, they are also employed as a tool for (cross-) validation. Through interactions between participants, the information provided by preliminary data can, in the optimal case, be strengthened and extended .The five participants which reflected the different medical stakeholders involved in ward rounds were chosen for the focus group: four physicians working on an internal ward having work experience in both patient care and academic teaching ; one fourth year medical student who had already gained a substantial ward round routine build within medical studies and clerkships. Due to the complex nature of the observation checklist, participants were given the opportunity to familiarize themselves with the content of the checklist one week ahead of the focus group. For this purpose, they were provided with access to the computer-based environment CASUS which was originally developed as learning platform for problem-based learning in medicine . On the After the preparation phase, the actual focus group took place. It was sub-divided into three parts: introduction phase, discussion of identified competences and activities, application opportunities of the checklist. Both the concept of EPAs and the development of the EPA “Conducting an internal medicine ward round” were recapitulated shortly. Participants were introduced to the observation checklist and the aim of the focus group procedure. They were further provided with the opportunity to ask questions. The following discussion was guided by a pre-structured discussion guideline. Questions focused on the conceptualization and structure of the observation checklist but also on the comprehensibility and appropriateness of the examples. Participants further evaluated whether the checklist as a whole serves a proper representation of ward rounds, hence, a good basis for assessing ward round practice. The final part of the focus group discussion focused on possible areas and settings of checklist application in both undergraduate and graduate education. Video and audio data of the focus group were transcribed and the contributions of each participant were summarized, critically assessed and the observation checklist was refined. To verify that the adaptions made on the basis of participants’ suggestions reflect their true intentions, participants received the revised checklist through the CASUS environment. Each participant was asked to provide feedback and, if necessary, suggest further adaptions. The checklist was finalized when all participants had no further comments. The final checklist is shown in attachment 1 .To assess the checklist’s external validity, it was applied for the observation of authentic video-taped ward rounds conducted by seven ward round teams on internal medicine wards at our university hospital. To avoid strain for real patients, standardized patients were used. Two standardized patient cases were developed by two experienced internists (CL and RS): the first case described a patient experiencing anaphylaxis and the second case comprised a patient with fever of unknown origin. The cases reflected typical diseases in internal medicine and were assumed to be solvable by internists, independent of their subspecialisation. The cases intentionally differed in their complexity to reflect the variance of ward rounds as depending on the severity of a disease. The standardized patients were trained using a detailed role script. A standardized final year medical student joined each ward round team on their daily ward and provided a short patient presentation according to a detailed script ahead of the consultation of the standardized patients.Seven routine ward round teams from different internal medicine wards of our university hospital participated  familiarized themselves with the coding procedure referring to the checklist and the belonging coding instructions. For test evaluation, two raters independently watched two ward round videos and filled out the observation checklist sheet (see attachment 1 ) along the coding instructions. The ratings were compared, discrepancies were discussed and descriptions in the observation list were refined. The two raters independently watched all videotaped patient encounters during the ward rounds and rated all of them according to the activities listed in the EPA checklist. Whenever an exemplary behaviour characteristic for an activity was observed, the corresponding activity was considered as being observed. Accordingly, whenever a rater ticked one activity characteristic for a competence, the respective competence was considered as being observed. Whenever a competence was not required, the rater ticked the option ‘not applicable’ in the checklist. When a competency was required but not observable in a specific situation, no items were ticked. All the other exemplary behaviours listed below the not required competence were excluded from the further comparison of the ratings. Interrater-reliability was calculated across all videos resulting in a Cohen’s Kappa coefficient of 0.73 which is regarded as a sustainable agreement . The focus group discussion lasted 118 minutes and enabled a lively discussion in which each participant was involved and contributed his or her opinion and experience. The participants approved the observation checklist as a useful tool not only for elaborated summative and formative feedback and assessment but also for stimulating individuals’ learning and self-reflection.Participants agreed on nine of the initial competences which are appropriate to picture a typical ward and the corresponding affordances at our university hospital: communication with the patient, communication with the team and leadership skills, diagnostic process and therapy planning, empathy, management of difficult situation and faults and leadership skills, organization skills, professionalism, self-management, and teaching and learning abilities. The participants of the focus group discussion regarded the leadership skills as an important area of competence which cannot be treated separately but should be assessed in light of other ward round competences. Competences, and hence, activities, were mainly linked to team and patient communication and the management of difficult situations and faults. Participants pointed out that variations in the demonstrated competences may arise depending on the ward round goals (patient care vs. teaching), patient characteristics  and composition of the ward round team (number and profession of participants).The participants also made adaptions in the wording and description of some activities. They agreed that the high grade of detail of the provided checklist facilitates a better understanding of the relevant competences and activities and increases the applicability as a tool for feedback and assessment. However, they indicated that this complexity may impede the intuitive application on the round. In the follow-up phase the revised version of the checklist was presented to all participants using the CASUS tool. Regarding each revised competence the respective activities and exemplary behaviours were presented. Individuals had to indicate whether or not he or she agrees on this final version. In case of disagreement, he or she was asked to provide an explanation for the respective reason. The final version of the EPA checklist is shown in attachment 1 . With respect to generalizability a multi-institutional validation in a real life setting would be desirable. For scientific reasons however a standardization of the work place scenario was needed. To assess the external validity of the checklist, we thus chose to videotape real life ward rounds at different hospitals on different wards with different ward round teams. An overview on the duration of the video-taped ward rounds can be seen in Table 2 The research question was, to which extent the checklist is suitable to structure the observation of ward rounds and whether the checklist aligns with typical ward rounds. The observation checklist was applied successfully and 488 behavior patterns representing the 25 ward round activities respective nine competences were coded. Six of the competencies were observed in each ward round video irrespective of the ward round team and case characteristics. Two competences – management of difficult situations and leadership skills, self-management – and their corresponding activities could not be observed in any ward round video. Besides, three activities  could only be observed in some videos. Conducting ward rounds is a key responsibility of a physician once he or she is graduated. Nevertheless, research highlights that both physicians and students struggle in conducting and understanding the purpose of ward rounds. With our previous study , we deveParticipants of the focus group reached consensus that the initially identified competencies picture ward round practice at our university hospital. However, participants highlighted that leadership skills should not be regarded in isolation but be assessed in light of other areas of competency. After some adaptions in the wording and descriptions of activities, participants agreed that the activities and examples for these activities included in the observation checklist are relevant for and comprehensive with respect to describing ward round competence. We conclude that content validity is given. External validity was studied by using the observation checklist for ratings of 14 authentic ward rounds performed by seven internal medicine ward round teams. Raters indicated that the observation checklist provided a valuable tool for structuring observation of the ward round process and that the activities and examples align with professional practice. Along with this insight, the high frequencies of observed behavior associated with the nine ward round competencies support external validity.Difficulties in the application of the observation checklistmanagement of difficult situations and leadership skills and self-management could not be observed. Also activities tied to communication with the team and leadership skills, diagnostic analysis and therapy planning were only observed in some videos. It is likely, that the design of the study impeded the appearance of certain activities. We used standardized cases that ensured comparability of data. However, the cases did not involve critical situations that triggered the handling of disturbances or faults. Besides, we only videotaped parts of the ward round process at one single occasion. Videos included each a patient consultation and the associated preliminary discussion and debriefing. For practical reasons, the preparation and post-processing of the ward rounds were not subject of the video even though they would have provided additional information. To facilitate grounded entrustment decisions on a trainee’s performance, we therefore recommend to observe trainees across multiple ward round occasions enabling him or her to deal with variations in ward rounds and patient characteristics and to also show behavior that is not necessarily relevant for every ward round encounter . F. FmanageIn addition to these design-related limitations, some activities and examples for them were not observable as they refer to an individual’s cognitive processes. Per definition, these activities cannot find their way into the observational checklist . Due to Entrustment decisions strive to inform about a trainee’s ability to master a professional task and to determine the level of required supervision. These decisions are complex in nature and are shaped by several factors such as the trainee, the supervisor, the context, the task and the relationship between trainee and supervisors , 25]. T. T25]. TThis study examines the validity of an observation checklist developed for student education and educational research. By testing a rather theoretical model (EPA) in a practical setting  it constitutes an improvement of already existing checklists in the literature and builds a bridge between theory and practice.The second part of the study addresses the external validation. We chose to standardize the patient cases and to perform checklist rating upon videotapes and not by attendance of the raters during the real-life ward rounds, which might have influenced the behavior of the teams. This enabled us to assess seven different teams under standardized conditions and revealed high quality data. However, in terms of generalizability and with the goal to determine thresholds for the different levels of entrustment, the number of scenarios is far too low. This issue is recommended to be addressed by upcoming studies. The presented observation checklist features a comprehensive compilation of ward round competences and their corresponding activities that were translated into graspable examples for observable behavior. It goes beyond prior attempts , 2] tha tha2] thAs pointed out by the focus group, with its 25 activities and 85 exemplary facets of behavior, the observation checklist is quite extensive and therefore bears the potential to impede the intuitive application in the course of the ward round. Hence, we encourage both trainees and supervisors to familiarize themselves with the checklist before application to maximize educational outcomes. Moreover, as pointed out before, we recommend the implementation of after-round feedback processes that can be enriched with additional information sources (e.g. questionnaires) to enhance reflection on the ward round procedure and to amend learning and professional development. As stressed out by prior research, medical staff does not necessarily possess appropriate teaching competences . SpecifiWe provide a science based observation checklist for the EPA “Conducting an internal medicine ward round”. The checklist was furthermore shown to be a feasible instrument for assessing ward round performance. The implementation of the observation checklist in both the workplace and medical education are desired next steps. Accompanying research should tackle the instrument’s reliability for assessment purposes and its feasibility in the workplace.The authors declare that they have no competing interests.