Trigeminal Neuralgia Secondary to Intracranial Lesions: A Prospective Series of 17 Consecutive Cases

Object: The purpose of the study was to assess the role of Gamma Knife surgery (GKS) in secondary trigeminal neuralgia (TN) caused by space-occupying lesions. Methods: From July 2010 till January 2015, 17 patients had GKS for secondary TN caused by intracranial lesions. The primary outcome was tumor control. The secondary outcomes were the alleviation of pain and the eventual secondary effects. Covariates were the age, duration of symptoms, duration till alleviation etc. Results: The mean age in this series was 63.3 years (range 39-79). The mean follow-up period was 1.85 years (range 0.5-3). Nine (52.9%) were meningiomas, five (29.4%) trigeminal schwannomas, two (11.8%) brain metastases and one (5.9%) arteriovenous malformation (AVM). Eight were located on the right side and nine on the left side. The mean duration of TN was 13.5 months (range 0.5-160). Follow-up was available for 16 patients (94.1%). Pain alleviation appeared after a mean time of 4.6 months (1-11) in 15 patients (88.2%). Five (29.4%) patients completely stopped medication in a mean time of 7 months (range 1-13) and three (17.6%) decreased it at half of the initial doses. No patient developed new hypoesthesia or other cranial nerve complication. The marginal doses for meningiomas and trigeminal schwannomas were 12 Gy (12-14), for metastasis 20 (20-20) and for AVM 24 Gy. The mean target volume was 1.84 cc (range 0.12-8.10). The mean prescription isodose volume was 2.65 cc (0.19-11.90). The mean maximal diameter was 19.9 (range 9-36). The mean number of isocenters was 14.2 (4-27). The mean duration was 76.9 minutes (range 25-172). The mean conformity, selectivity, Paddick and gradient index were: 0.99 (range 0.955-1), 0.701 (range 0.525-0.885), 0.694 (range 0.525-0.885) and 2.904 (range 2.654-3.371). At last follow-up, tumor decreased in 10 (58.8%) patients, was stable in 6 (35.3%) and increased in one (5.9%), the latest at 6 months. Conclusions: Gamma Knife surgery is safe and effective in treating intracranial lesions presenting with secondary TN. The initial pain freedom response was close to 90%, while having no secondary effect. Pain alleviation is achieved even in the absence of a volume variation of the lesions.

Multidetector-row computed tomography in the preoperative diagnosis of intestinal complications caused by clinically unsuspected ingested dietary foreign bodies: a case series emphasizing the use of volume rendering techniques

Objective The present study was aimed at describing a case series where a preoperative diagnosis of intestinal complications secondary to accidentally ingested dietary foreign bodies was made by multidetector-row computed tomography (MDCT), with emphasis on complementary findings yielded by volume rendering techniques (VRT) and curved multiplanar reconstructions (MPR). Materials and Methods The authors retrospectively assessed five patients with surgically confirmed intestinal complications (perforation and /or obstruction) secondary to unsuspected ingested dietary foreign bodies, consecutively assisted in their institution between 2010 and 2012. Demographic, clinical, laboratory and radiological data were analyzed. VRT and curved MPR were subsequently performed. Results Preoperative diagnosis of intestinal complications was originally performed in all cases. In one case the presence of a foreign body was not initially identified as the causal factor, and the use of complementary techniques facilitated its retrospective identification. In all cases these tools allowed a better depiction of the entire foreign bodies on a single image section, contributing to the assessment of their morphology. Conclusion Although the use of complementary techniques has not had a direct impact on diagnostic performance in most cases of this series, they may provide a better depiction of foreign bodies' morphology on a single image section.

Image guided critical screw placement precision in the cervical spine : important or incidental? : results of a series of 91 consecutive cases using OVEM

Study design: A retrospective study of image guided cervical implant placement precision. Objective: To describe a simple and precise classification of cervical critical screw placement. Summary of Background Data: "Critical" screw placement is defined as implant insertion into a bone corridor which is surrounded circumferentially by neurovascular structures. While the use of image guidance has improved accuracy, there is currently no classification which provides sufficient precision to assess the navigation success of critical cervical screw placement. Methods: Based on postoperative clinical evaluation and CT imaging, the orthogonal view evaluation method (OVEM) is used to classify screw accuracy into grade I (no cortical breach), grade la (screw thread cortical breach), grade II (internal diameter cortical breach) and grade III (major cortical breach causing neural or vascular injury). Grades II and III are considered to be navigation failures, after accounting for bone corridor / screw mismatch (minimal diameter of targeted bone corridor being smaller than an outer screw diameter). Results: A total of 276 screws from 91 patients were classified into grade I (64.9%), grade la (18.1%), and grade II (17.0%). No grade III screw was observed. The overall rate of navigation failure was 13%. Multiple logistic regression indicated that navigational failure was significantly associated with the level of instrumentation and the navigation system used. Navigational failure was rare (1.6%) when the margin around the screw in the bone corridor was larger than 1.5 mm. Conclusions: OVEM evaluation appears to be a useful tool to assess the precision of critical screw placement in the cervical spine. The OVEM validity and reliability need to be addressed. Further correlation with clinical outcomes will be addressed in future studies.

Retrospective study of sonographic findings in bone involvement associated with rotator cuff calcific tendinopathy: preliminary results of a case series

Abstract Objective: The present study was aimed at investigating bone involvement secondary to rotator cuff calcific tendonitis at ultrasonography. Materials and Methods: Retrospective study of a case series. The authors reviewed shoulder ultrasonography reports of 141 patients diagnosed with rotator cuff calcific tendonitis, collected from the computer-based data records of their institution over a four-year period. Imaging findings were retrospectively and consensually analyzed by two experienced musculoskeletal radiologists looking for bone involvement associated with calcific tendonitis. Only the cases confirmed by computed tomography were considered for descriptive analysis. Results: Sonographic findings of calcific tendinopathy with bone involvement were observed in 7/141 (~ 5%) patients (mean age, 50.9 years; age range, 42-58 years; 42% female). Cortical bone erosion adjacent to tendon calcification was the most common finding, observed in 7/7 cases. Signs of intraosseous migration were found in 3/7 cases, and subcortical cysts in 2/7 cases. The findings were confirmed by computed tomography. Calcifications associated with bone abnormalities showed no acoustic shadowing at ultrasonography, favoring the hypothesis of resorption phase of the disease. Conclusion: Preliminary results of the present study suggest that ultrasonography can identify bone abnormalities secondary to rotator cuff calcific tendinopathy, particularly the presence of cortical bone erosion.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

Aplicación de algunos modelos de series temporales al mercado de la electricidad en España

Hoy día, todo el mundo tiene un ojo puesto en el Mercado Eléctrico en nuestro país. No existe duda alguna sobre la importancia que tiene el comportamiento de la demanda eléctrica. Una de las peculiaridades de la electricidad que producimos, es que hoy por hoy, no existen aún métodos lo suficientemente efectivos para almacenarla, al menos en grandes cantidades. Por consiguiente, la cantidad demandada y la ofertada/producida deben casar de manera casi perfecta. Debido a estas razones, es bastante interesante tratar de predecir el comportamiento futuro de la demanda, estudiando una posible tendencia y/o estacionalidad. Profundizando más en los datos históricos de las demandas; es relativamente sencillo descubrir la gran influencia que la temperatura ambiente, laboralidad o la actividad económica tienen sobre la respuesta de la demanda. Una vez teniendo todo esto claro, podemos decidir cuál es el mejor método para aplicarlo en este tipo de series temporales. Para este fin, los métodos de análisis más comunes han sido presentados y explicados, poniendo de relieve sus principales características, así como sus aplicaciones. Los métodos en los que se ha centrado este proyecto son en los modelos de alisado y medias móviles. Por último, se ha buscado una relación entre la demanda eléctrica peninsular y el precio final que pagamos por la luz.

Ecological fallacy in a time series context: evidence from the Spanish regional unemployment rates

The ecological fallacy (EF) is a common problem regional scientists have to deal with when using aggregated data in their analyses. Although there is a wide number of studies considering different aspects of this problem, little attention has been paid to the potential negative effects of the EF in a time series context. Using Spanish regional unemployment data, this paper shows that EF effects are not only observed at the cross-section level, but also in a time series framework. The empirical evidence obtained shows that analytical regional configurations are the least susceptible to time effects relative to both normative and random regional configurations, while normative configurations are an improvement over random ones.

Isotype distribution of anticardiolipin antibodies in systemic lupus erythematosus prospective analysis of a series of 100 patients

A prospective study of IgG and IgM isotypes of anticardiolipin antibodies (aCL) in a series of 100 patients with systemic lupus erythematosus was carried out. To determine the normal range of both isotype titres a group of 100 normal control serum samples was studied and a log-normal distribution of IgG and IgM isotypes was found. The IgG anticardiolipin antibody serum was regarded as positive if a binding index greater than 2.85 (SD 3.77) was detected and a binding index greater than 4.07 (3.90) was defined as positive for IgM anticardiolipin antibody. Twenty four patients were positive for IgG aCL, 20 for IgM aCL, and 36 for IgG or IgM aCL, or both. IgG aCL were found to have a significant association with thrombosis and thrombocytopenia, and IgM aCL with haemolytic anaemia and neutropenia. Specificity and predictive value for these clinical manifestations increased at moderate and high anticardiolipin antibody titres. In addition, a significant association was found between aCL and the presence of lupus anticoagulant. Identification of these differences in the anticardiolipin antibody isotype associations may improve the clinical usefulness of these tests, and this study confirms the good specificity and predictive value of the anticardiolipin antibody titre for these clinical manifestations.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

MCMC Analysis Of Classical Time Series Algorithms

Identification of order of an Autoregressive Moving Average Model (ARMA) by the usual graphical method is subjective. Hence, there is a need of developing a technique to identify the order without employing the graphical investigation of series autocorrelations. To avoid subjectivity, this thesis focuses on determining the order of the Autoregressive Moving Average Model using Reversible Jump Markov Chain Monte Carlo (RJMCMC). The RJMCMC selects the model from a set of the models suggested by better fitting, standard deviation errors and the frequency of accepted data. Together with deep analysis of the classical Box-Jenkins modeling methodology the integration with MCMC algorithms has been focused through parameter estimation and model fitting of ARMA models. This helps to verify how well the MCMC algorithms can treat the ARMA models, by comparing the results with graphical method. It has been seen that the MCMC produced better results than the classical time series approach.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

SYNTHESIS AND BIOLOGICAL ACTIVITY OF NEW SERIES OF ORGANOTIN(IV) ESTERS WITH N,N-DIACETYLGLYCINE

A bioactive N,N-diacetylglycine (NNDAG) and new organotin(IV) complexes (OTCs) (1-7) were synthesized. Spectroscopic techniques were employed to characterize NNDAG and OTCs. FTIR was employed to verify N,N protection of glycine by acetyl groups. The disappearance of υ(OH) at 3000-2600 cm-1 showed de-protonation of free ligand. The Δυ 150<200 cm-1 of OTCs 4-7 verified bidentate coordination with tetrahedral geometry. The Δυ of OTCs1 and 3 was <200 cm-1 exhibitingtrans -octahedral geometry while OTC 2 dimer was assigned a unique sinusoidal view. The 1H NMR spectra of OTCs verified their synthesis by de-protonation of NNDAG and no chemical shift was found downfield for carboxylic acid proton. The 13C, 119Sn NMR and Mass spectrometric data also supported FTIR and 1H NMR descriptions. The OTCs 4, 5, 6 and7 (500 ppm) proved twice as active against Escherichia coli as the standard antibiotic enoxacin (1000 ppm). The promising property of the OTCs (4, 5, 6 and7) is clearly due to their tetrahedral. The OTCs 4and 5 exhibited excellent activity against M. minimum and good activity against T. castaneum.LD50 of all the compounds were determined and OTCs4, 5 and 7 were found to be active.

Regime Switching Models for Electricity Time Series that Capture Fat Tailed Distributions

In the power market, electricity prices play an important role at the economic level. The behavior of a price trend usually known as a structural break may change over time in terms of its mean value, its volatility, or it may change for a period of time before reverting back to its original behavior or switching to another style of behavior, and the latter is typically termed a regime shift or regime switch. Our task in this thesis is to develop an electricity price time series model that captures fat tailed distributions which can explain this behavior and analyze it for better understanding. For NordPool data used, the obtained Markov Regime-Switching model operates on two regimes: regular and non-regular. Three criteria have been considered price difference criterion, capacity/flow difference criterion and spikes in Finland criterion. The suitability of GARCH modeling to simulate multi-regime modeling is also studied.

Analysis of Patterns in Electricity Spot Market Time Series

Due to its non-storability, electricity must be produced at the same time that it is consumed, as a result prices are determined on an hourly basis and thus analysis becomes more challenging. Moreover, the seasonal fluctuations in demand and supply lead to a seasonal behavior of electricity spot prices. The purpose of this thesis is to seek and remove all causal effects from electricity spot prices and remain with pure prices for modeling purposes. To achieve this we use Qlucore Omics Explorer (QOE) for the visualization and the exploration of the data set and Time Series Decomposition method to estimate and extract the deterministic components from the series. To obtain the target series we use regression based on the background variables (water reservoir and temperature). The result obtained is three price series (for Sweden, Norway and System prices) with no apparent pattern.