Probabilistic graphical model for the evaluation of the emotional and dramatic personality disorders.

Personality disorders are psychological ailments with a major negative impact on patients, their families, and society in general, especially those of the dramatic and emotional type. Despite all the research, there is still no consensus on the best way to assess and treat them. Traditional assessment of personality disorders has focused on a limited number of psychological constructs or behaviors using structured interviews and questionnaires, without an integrated and holistic approach. We present a novel methodology for the study and assessment of personality disorders consisting in the development of a Bayesian network, whose parameters have been obtained by the Delphi method of consensus from a group of experts in the diagnosis and treatment of personality disorders. The result is a probabilistic graphical model that represents the psychological variables related to the personality disorders along with their relations and conditional probabilities, which allow identifying the symptoms with the highest diagnostic potential. This model can be used, among other applications, as a decision support system for the assessment and treatment of personality disorders of the dramatic or emotional cluster. In this paper, we discuss the need to validate this model in the clinical population along with its strengths and limitations.

Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain.

In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.

Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in an Agrometeorological Station in Castile and León, Spain.

This study evaluates the predictive modeling of the daily ambient temperature (maximum, Tmax; average, Tave; and minimum, Tmin) and its hourly estimation (T0h, …, T23h) using artificial neural networks (ANNs) for agricultural applications. The data, 2004-2010, were used for training and 2011 for validation, recorded at the SIAR agrometeorological station of Mansilla Mayor (León). ANN models for daily prediction have three neurons in the output layer (Tmax(t + 1), Tave(t + 1), Tmin(t + 1)). Two models were evaluated: (1) with three entries (Tmax(t), Tave(t), Tmin(t)), and (2) adding the day of the year (J(t)). The inclusion of J(t) improves the predictions, with an RMSE for Tmax = 2.56, Tave = 1.65 and Tmin = 2.09 (°C), achieving better results than the classical statistical methods (typical year Tave = 3.64 °C; weighted moving mean Tmax = 2.76, Tave = 1.81 and Tmin = 2.52 (°C); linear regression Tave = 1.85 °C; and Fourier Tmax = 3.75, Tave = 2.67 and Tmin = 3.34 (°C)) for one year. The ANN models for hourly estimation have 24 neurons in the output layer (T0h(t), …, T23h(t)) corresponding to the mean hourly temperature. In this case, the inclusion of the day of the year (J(t)) does not significantly improve the estimations, with an RMSE = 1.25 °C, but it improves the results of the ASHRAE method, which obtains an RMSE = 2.36 °C for one week. The results obtained, with lower prediction errors than those achieved with the classical methods, confirm the interest in using the ANN models for predicting temperatures in agricultural applications.

Acoustic projection by electro-osmosis.

Electro-osmosis (EO) is a non-traditional pumping and transduction mechanism with the ability to project acoustic energy in fluids. This investigation experimentally validates the influence of zeta potential, a well-studied physical characteristic used for quantifying the efficacy of an EO pump, on generation of sound pressure level. Acoustic signals of discrete frequencies were observed from 130 Hz to over 150 kHz. EO-type projectors are an attractive technology in that it does not contain moving parts, can be fabricated using a variety of materials, is intrinsically resilient to effects of hydrostatic pressure, and may be designed on the micro-electromechanical system scale.

New uses and formulations of glucagon for hypoglycaemia.

Hypoglycaemia is the more frequent complication of insulin therapy and the main barrier to tight glycaemic control. Injectable glucagon and oral intake of carbohydrates are the recommended treatments for severe and non-severe hypoglycaemia episodes, respectively. Nasal glucagon is currently being developed as a ready-to-use device, to simplify severe hypoglycaemia rescue. Stable forms of liquid glucagon could open the field for different approaches for mild to moderate hypoglycaemia treatment, such as mini-doses of glucagon or continuous subcutaneous glucagon infusion as a part of dual-hormone closed-loop systems. Pharmaceutical companies are developing stable forms of native glucagon or glucagon analogues for that purpose.

Evaluation of Markov Models with Discontinuities.

Background. Several methods, such as the half-cycle correction and the life-table method, were developed to attenuate the error introduced in Markov models by the discretization of time. Elbasha and Chhatwal have proposed alternative "corrections" based on numerical integration techniques. They present an example whose results suggest that the trapezoidal rule, which is equivalent to the half-cycle correction, is not as accurate as Simpson's 1/3 and 3/8 rules. However, they did not take into consideration the impact of discontinuities. Objective. To propose a method for evaluating Markov models with discontinuities. Design. Applying the trapezoidal rule, we derive a method that consists of adjusting the model by setting the cost at each point of discontinuity to the mean of the left and right limits of the cost function. We then take from the literature a model with a cycle length of 1 year and a discontinuity on the cost function and compare our method with other "corrections" using as the gold standard an equivalent model with a cycle length of 1 day. Results. As expected, for this model, the life-table method is more accurate than assuming that transitions occur at the beginning or the end of cycles. The application of numerical integration techniques without taking into account the discontinuity causes large errors. The model with averaged cost values yields very small errors, especially for the trapezoidal and the 1/3 Simpson rules. Conclusion. In the case of discontinuities, we recommend applying the trapezoidal rule on an averaged model because this method has a mathematical justification, and in our empirical evaluation, it was more accurate than the sophisticated 3/8 Simpson rule.

Cost-effectiveness of pediatric bilateral cochlear implantation in Spain.

OBJECTIVES/HYPOTHESIS: To determine the incremental cost-effectiveness of bilateral versus unilateral cochlear implantation for 1-year-old children suffering from bilateral sensorineural severe to profound hearing loss from the perspective of the Spanish public health system. STUDY DESIGN: Cost-utility analysis. METHODS: We conducted a general-population survey to estimate the quality-of-life increase contributed by the second implant. We built a Markov influence diagram and evaluated it for a life-long time horizon with a 3% discount rate in the base case. RESULTS: The incremental cost-effectiveness ratio of simultaneous bilateral implantation with respect to unilateral implantation for 1-year-old children with severe to profound deafness is €10,323 per quality-adjusted life year (QALY). For sequential bilateral implantation, it rises to €11,733/QALY. Both options are cost-effective for the Spanish health system, whose willingness to pay is estimated at around €30,000/QALY. The probabilistic sensitivity analysis shows that the probability of bilateral implantation being cost-effective reaches 100% for that cost-effectiveness threshold. CONCLUSIONS: Bilateral implantation is clearly cost-effective for the population considered. If possible, it should be done simultaneously (i.e., in one surgical operation), because it is as safe and effective as sequential implantation, and saves costs for the system and for users and their families. Sequential implantation is also cost-effective for children who have received the first implant recently, but it is difficult to determine when it ceases to be so because of the lack of detailed data. These results are specific for Spain, but the model can easily be adapted to other countries. LEVEL OF EVIDENCE: 2C. Laryngoscope, 127:2866-2872, 2017.

Markov Influence Diagrams.

Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs-including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis-with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs; i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.

Maximizing fluid delivered by bubble-free electroosmotic pump with optimum pulse voltage waveform.

In generating high electroosmotic (EO) flows for use in microfluidic pumps, a limiting factor is faradaic reactions that are more pronounced at high electric fields. These reactions lead to bubble generation at the electrodes and pump efficiency reduction. The onset of gas generation for high current density EO pumping depends on many parameters including applied voltage, working fluid, and pulse duration. The onset of gas generation can be delayed and optimized for maximum volume pumped in the minimum time possible. This has been achieved through the use of a novel numerical model that predicts the onset of gas generation during EO pumping using an optimized pulse voltage waveform. This method allows applying current densities higher than previously reported. Optimal pulse voltage waveforms are calculated based on the previous theories for different current densities and electrolyte molarity. The electroosmotic pump performance is investigated by experimentally measuring the fluid volume displaced and flow rate.

Vortex chain formation in regions of ion concentration polarization.

The local vortical flow generated inside an ion concentration polarization (ICP) region is evaluated experimentally. The ICP is induced by a patterned nanoporous self-assembling membrane integrated inside a single microchannel. A bottom-view image of the depletion region near the membrane revealed a primary vortex which results from the electric field amplification. A unique perspective of the flow is obtained by imaging the microchannel from its side. This visualization shows for the first time the formation of a chain of three vortices all rotating in the same direction in the depletion region. While observation of multiple vortices has been previously reported, it was in reference to counter rotating vortex pairs and not to the same direction of rotating vortex chain formation. A physical model is proposed which considers a two dimensionally varying concentration profile in the depletion region to account for the formation of multiple vortices rotating in the same direction. The fast rotating primary vortex changes the local concentration in regions adjacent to it, as the advection time scale is much higher than the diffusion time scale. Near the membrane, it moves the low concentration electrolyte from the bottom wall upwards into a higher concentration region. Away from the membrane, it moves the high concentration electrolyte from the middle of the channel downwards into a low concentration region. These local changes in the wall concentration result in a varying slip velocity capable of inducing a secondary vortex. Similarly, this secondary vortex can induce a tertiary one. A numerical simulation is performed using the proposed varying slip velocity model which showed excellent agreement with the experimental observations.

In situ particle zeta potential evaluation in electroosmotic flows from time-resolved microPIV measurements.

A time-resolved microPIV method is presented to measure in an EOF the particles zeta potential in situ during the transient start-up of a microdevice. The method resolves the electrophoretic velocity of fluoro-spheres used as tracer particles in microPIV. This approach exploits the short transient regime of the EOF generated after a potential drop is imposed across a microchannel and before reaching quasisteady state. During the starting of the transient regime, the electrophoretic effect is dominant in the center of the channel and the EOF is negligible. By measuring the velocity of the tracer particles with a microPIV system during that starting period, their electrophoretic velocity is obtained. The technique also resolves the temporal evolution of the EOF with three regions identified. The first region occurs before the electroosmotic effect reaches the center of the channel, the second region extends until the EOF reaches steady state, and thereafter is the third region. The two time constants separating these regions are also obtained and compared to the theory. The zeta potential of 860 nm diameter polystyrene particles is calculated for different solutions including borate buffer, sodium chloride, and deionized water. Results show that the magnitudes of the electrophoretic and electroosmotic velocities are in the range of |300| to |700| µm/s for these measurements. The zeta potential values are compared to the well-established closed cell technique showing improved accuracy. The method also resolves the characteristic response time of the EOF, showing small but important deviations from current analytical predictions. Additionally, the measurements can be performed in situ in microfluidic devices under actual working EOF conditions and without the need for calibrations.

Selecting treatment strategies with dynamic limited-memory influence diagrams.

OBJECTIVE: The development of dynamic limited-memory influence diagrams as a framework for representing factorized infinite-horizon partially observable Markov decision processes (POMDPs), the introduction of algorithms for their (approximate) solution, and the application to a dynamic decision problem in clinical oncology. MATERIALS AND METHODS: A dynamic limited-memory influence diagram for high-grade carcinoid tumor pathophysiology was developed in collaboration with an expert physician. Three algorithms, known as single policy updating, single rule updating, and simulated annealing have been examined for approximating the optimal treatment strategy from a space of 10(19) possible strategies. RESULTS: Single policy updating proved intractable for finding a treatment strategy for carcinoid tumors. Single rule updating and simulated annealing both found the treatment strategy that is applied by physicians in practice. CONCLUSIONS: Dynamic limited-memory influence diagrams are a suitable framework for the representation of factorized infinite-horizon POMDPs, and the developed algorithms find acceptable solutions under the assumption of limited memory about past observations. The framework allows for finding reasonable treatment strategies for complex dynamic decision problems in medicine.

Conservative management of postsurgical chylothorax with octreotide.

Postsurgical chylothorax after lung resection has an important associated rate of morbidity and mortality, and a large proportion of cases require re-exploration. The most desirable and least aggressive option is conservative treatment. The use of octreotide seems to be associated with a higher rate of resolution without the need for surgical intervention. We present 4 cases in which this drug showed excellent efficacy and minimal adverse effects in the resolution of postsurgical chylothorax.

Development and validation of a liquid chromatographic method for determination of lacidipine residues on surfaces in the manufacture of pharmaceuticals.

A high-performance liquid chromatographic (HPLC) method for the assay of lacidipine residues in swabs collected from various surfaces involved in drug manufacture is described. The swabbing procedure using two cotton swabs was validated applying a wipe test. An RP-HPLC method, developed to determine low quantities of the drug in the presence of its main impurities, was also validated. To remove drug residues from stainless steel and glass surfaces, the first cotton swab must be soaked preferably in acetonitrile whereas, on vinyl surfaces better results are obtained using methanol. The HPLC method selected involves a C12 column, at 40 degrees C, a mixture of acetonitrile-0.05 M ammonium acetate (88:12, v/v) as a mobile phase and UV detection at 282 nm. Recoveries obtained are strongly dependent on the type of surface tested, being higher on stainless steel. The surface material has also different influence on the drug stability. The method was validated over a range of 0.5-100 microg/400 cm2 and had a detection limit of 0.1 microg/400 cm2.

Development and validation of an LC assay for sumatriptan succinate residues on surfaces in the manufacture of pharmaceuticals.

A high performance liquid chromatographic (HPLC) method for the assay of sumatriptan succinate residues in swabs collected from manufacturing equipment surfaces was developed and validated in order to control a cleaning procedure. The swabbing procedure using two cotton swabs moistened with water was validated applying a wipe-test and a HPLC method developed to determine low quantities of the drug. The HPLC method involves a C18 column at 25 degrees C, a mixture of ammonium phosphate monobasic (0.05 M)-acetonitrile (84:16, v/v) as a mobile phase and UV detection at 228 nm. Using the proposed method, the average recoveries obtained are of 88.5% for vinyl, 94.2% for glass and 95.2% for stainless steel plates with RSD of 5.5 (n=36), 2.3 (n=36), 2.2% (n=36), respectively. The method was successfully applied to the assay of real swab samples collected from the equipment surfaces.