Panoramic Radiography in the Evaluation of the Relationship of Maxillary Molar Teeth and Maxillary Sinuses on the Deep Learning Models Improved with the Findings Obtained by Cone Beam Computed Tomography.

BACKGROUND: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imaging methods can be used. AIM: The aim of this study was to evaluate the diagnostic performance of deep learning (DL) applications that assess the relationship of the MSF to the first maxillary molar teeth (fMMT) and second maxillary molar teeth (sMMT) on PRs with data confirmed by cone beam computed tomography (CBCT). METHODS: A total of 2162 fMMT and sMMT were included in this retrospective study. The contact relationship of teeth with MSF was compared among DL methods. RESULTS: DL methods, such as GoogLeNet, VGG16, VGG19, DarkNet19, and DarkNet53, were used to evaluate the contact relationship between MMT and MSF, and 85.89% accuracy was achieved by majority voting. In addition, 88.72%, 81.19%, 89.39%, and 83.14% accuracy rates were obtained in right fMMT, right sMMT, left fMMT, and left sMMT, respectively. CONCLUSION: DL models showed high accuracy values in detecting the relationship of fMMT and sMMT with MSF.

Heart Sound Localization in Respiratory Sound Based on a New Computationally Efficient Entropy Bound.

A new entropy bound with low computational complexity for differential Shannon entropy estimation with kernel density approach is proposed in this study, which is based on defining a bound for the Kullback-Leibler divergence between two Gaussian mixture models. The proposed entropy bound is derived to provide computational efficiency without decreasing the accuracy in detecting heart sound segments in respiratory sound. It is shown both theoretically and experimentally that using the proposed bound in an adaptive threshold-based detection method gives very similar performance compared to that obtained by a nonparametric kernel based approach, while its computational cost is much lower. The performance of the proposed method is shown and compared with the three methods in the literature by means of experiments utilizing a database of 20 subjects. The results show that the false negative rate values for the proposed method are 1.45±1.50 % and 1.98±1.81 % for low and medium flow rates, respectively. These average values are similar to the results obtained by the alternative methods. Moreover, the average elapsed time of the proposed method for a piece of data with a length of 20 s is 0.05 s, which is significantly lower than that of the other methods.

A robust method for online heart sound localization in respiratory sound based on temporal fuzzy c-means.

This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, the logarithmic energy features are calculated. Then, these features are used to classify the respiratory sound as heart sound (HS containing lung sound) and non-HS (only lung sound) by the TFCM algorithm. The TFCM is the modified version fuzzy c-means (FCM) algorithm. While the FCM algorithm uses only the local information about the current frame, the TFCM algorithm uses the temporal information from both the current and the neighboring frames in decision making. To measure the detection performance of the proposed method, several experiments have been conducted on a database of 24 healthy subjects. The experimental results show that the average false-negative rate values are 0.8 ± 1.1 and 1.5 ± 1.4 %, and the normalized area under detection error curves are 0.0145 and 0.0269 for the TFCM method in the low and medium respiratory flow rates, respectively. These average values are significantly lower than those obtained by FCM algorithm and by the other compared methods in the literature, which demonstrates the efficiency of the proposed TFCM algorithm. On the other hand, the average elapsed time of the TFCM for a data with length of 0.2 ± 0.05 s is 0.2 ± 0.05 s, which is slightly higher than that of the FCM and lower than those of the other compared methods.

Age group classification and gender detection based on forced expiratory spirometry.

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

Robust heart sound detection in respiratory sound using LRT with maximum a posteriori based online parameter adaptation.

This paper investigates the utility of a likelihood ratio test (LRT) combined with an efficient adaptation procedure for the purpose of detecting the heart sound (HS) with lung sound and the lung sound only (non-HS) segments in a respiratory signal. The proposed detection method has four main stages: feature extraction, training of the models, detection, and adaptation of the model parameter. In the first stage, the logarithmic energy features are extracted for each frame of respiratory sound. In the second stage, the probabilistic models for HS and non-HS segments are constructed by training Gaussian mixture models (GMMs) with an expectation maximization algorithm in a subject-independent manner, and then the HS and non-HS segments are detected by the results of the LRT based on the GMMs. In the adaptation stage, the subject-independent trained model parameter is modified online using the observed test data to fit the model parameter of the target subject. Experiments were performed on the database from 24 healthy subjects. The experimental results indicate that the proposed heart sound detection algorithm outperforms two well-known heart sound detection methods in terms of the values of the normalized area under the detection error trade-off curve (NAUC), the false negative rate (FNR), and the false positive rate (FPR).

The effect of silicon loss and fabrication tolerance on spectral properties of porous silicon Fabry-Perot cavities in sensing applications.

In this paper, we investigate the effect of non-uniformities (enlargement of current passage, non-equal surface current densities, etc.) in axial as well as transverse directions of a porous silicon Fabry-Perot (FP) cavity as well as loss nature of bulk silicon on spectral properties of this cavity, even that cavity is created with an anisotropic etching process. Without correct and comprehensive characterization of such cavities by incorporating these non-uniformities and inherent lossy nature of a cavity, detection and identification of biological and chemical molecules by that cavity may yield unpredictable and misleading results. From our simulations, we note the following two key points. First, effects of the refractive index and the thickness of microcavity region of a lossless or lossy FP cavity on resonance wavelength is more prevailing than those of first and last layers. Second, the effect of some small loss inside the FP cavity is not detectable by the measurement of resonance wavelength whereas the same influence is noticeable by the measurement of reflectivity. We carried out some measurements from two different regions on the fabricated cavities to validate our simulation results. From a practical point of view in correct detection and/or identification of lossy biological or chemical vapor by FP cavities, we conclude that not only the measurement of resonance wavelength as well as its shift but also the reflectivity value at the resonance wavelength or some specific wavelengths should be utilized.

Online estimation of lower and upper bounds for heart sound boundaries in chest sound using Convex-hull algorithm.

Heart sound localization in chest sound is an essential part for many heart sound cancellation algorithms. The main difficulty for heart sound localization methods is the precise determination of the onset and offset boundaries of the heart sound segment. This paper presents a novel method to estimate lower and upper bounds for the onset and offset of the heart sound segment, which can be used as anchor points for more precise estimation. For this purpose, first chest sound is divided into frames and then entropy and smoothed entropy features of these frames are extracted, and used in the Convex-hull algorithm to estimate the upper and lower bounds for heart sound boundaries. The Convex-hull algorithm constructs a special type of envelope function for entropy features and if the maximal difference between the envelope function and the entropy is larger than a certain threshold, this point is considered as a heart sound bound. The results of the proposed method are compared with a baseline method which is a modified version of a well-known heart sound localization method. The results show that the proposed method outperforms the baseline method in terms of accuracy and detection error rate. Also, the experimental results show that smoothing entropy features significantly improves the performance of both baseline and proposed methods.

Heart sound localization in chest sound using temporal fuzzy C-means classification.

Most of heart sound cancellation algorithms to improve the quality of lung sound use information about heart sound locations. Therefore, a reliable estimation of heart sound localizations within chest sound is a key issue to enhance the performance of heart sound cancellation algorithms. In this paper, we present a new technique to estimate locations of heart sound segments in chest sound using the temporal fuzzy c-means (TFCM) algorithm. In applying the method, chest sound is first divided into frames and then for each frame, the entropy feature is calculated. Next, by means of these features, the TFCM algorithm is applied to classify a chest sound into two classes: heart sound (heart sound containing lung sound) and non-heart sound (only lung sound). The proposed method was tested on the database used in the liteature and experimetal results are compared with the baseline which is a well-known method in the literature. The experimental results show that the proposed method outperforms the baseline method interms of false negative rate (FNR), false positive rate (FPR) and accuracy (ACC).

Nurses' locations as organ transplantation coordinators in the Turkey National Coordination System.

BACKGROUND: Turkey is one of the worst countries in the European region for the number of deceased organ donors (3.6) per million people. The aim of this study was to evaluate the location of nurses who have been attached to the National Coordination System as coordinators to increase this number. METHODS: We chose 4 regional coordination centers in which the most brain death cases were reported: Istanbul, Izmir, Antalya, and Ankara. Coordinators were classified according to their occupations. We evaluated the numbers of brain deaths and of donors from 2006 to 2009 in hospitals that have coordinators. RESULTS: The centers that have only nurse organ transplantation coordinators reported 60.3 (total n = 138) of the brain deaths in 2007, 54.8% (n = 97) in 2008, and 47.5% (n = 152) in 2009 in the Istanbul region; 8.4% (n = 131) in 2007, 25% (n = 164) in 2008, and 20% (n = 169) in 2009 in the Izmir region; 42.6% (n = 61) in 2007, 42.6% (n = 56) in 2008, and 32.4% (n = 111) in 2009 in the Antalya region; and 54% (n = 120) in 2007, 45.2% (n = 104) in 2008, and 41.8% (n = 122) in 2009 in the Ankara region. CONCLUSION: Brain death numbers have increased in all regions after the nurses started to work as coordinators. Still many potential donor center have no transplantation coordinator.

The effects of calcium dobesilate on the mechanical function of rat hearts.

AIM: Calcium dobesilate is an angio-protective agent that has positive effects on hemorheological parameters. It decreases blood and plasma viscosity, thrombocyte aggregation, and microvascular hyperpermeability. It is an antioxidant that increases endothelial-derived vasodilator substance secretion. In this experimental study, the effects of calcium dobesilate on myocardial ischemia reperfusion injury were investigated. METHODS: Using the Langendorff setup, 24 adult Wistar albino rat hearts were perfused. MeanP (mean pressure perfusing the coronary arteries), PSP (maximum left ventricle pressure), +dp/dt(max) (change in contraction power over time), -dp/dt(max) (change in relaxing power over time), PP (peak systolic pressure-minimum balloon pressure) and bpm (number of heart beats per minute) were evaluated. The control group (N.=6) was perfused with Tyrode solution alone. The other three groups (N.=6 for each group) were perfused with the Tyrode solution and calcium dobesilate either before ischemia, during the ischemia reperfusion period, or during the reperfusion-only period. RESULTS: The meanP values were significantly higher in groups perfused by calcium dobesilate. For other parameters, calcium dobesilate did not demonstrate a positive effect. CONCLUSIONS: This study showed that calcium dobesilate may have cardio-protective effects in isolated, perfused rat hearts. In hearts perfused by calcium dobesilate, the increase in mean P may be explained by the increase in endothelium-derived vasodilator substances. Further studies are needed to better characterize the myocardial protective effects of calcium dobesilate.

The assessment of the identified substance-abusing adolescent.

When an adolescent has been identified as abusing drugs, alcohol, or both, a complete assessment of this young person must be completed by qualified health professionals before the level of treatment is chosen. With our present state of knowledge of substance-abusing adolescents, this assessment must focus on every sphere of the child's life, not just the quantity and frequency of the drug use. Most substance-abusing young people have experienced deep psychological trauma that must be identified and eventually treated. A family evaluation and psychological testing are crucial to the evaluation of these adolescents. When choosing a treatment facility for a substance-abusing adolescent, it is best to select a treatment program that requires family involvement. An adolescent treatment program should include treatment of both the family and the child, and it must include the goal of abstinence from mood-altering substances as a major component of recovery.

Recognition of depression.

Depression in the terminally ill has never been examined systematically. Frequently depression has been perceived as an inevitable part of illness. The purpose of the present study was to develop an instrument (the Mood Evaluation Questionnaire) to measure depression among terminally ill patients. The MEQ and the Geriatric Depression Scale were completed by 27 hospice patients. A Modified Karnofsky score and index of somatic complaints were obtained. There was no correlation among the Modified Karnofsky, the number of somatic complaints, and the level of depression. However the MEQ and GDS were highly correlated (p < .01). For several reasons, the MEQ appears to be an effective instrument to explore the incidence of depression in the terminally ill.

Narcissism and object relations.

Questionnaire measures of the narcissistic personality disorder can predict healthy and unhealthy self-functioning. That this outcome might support Heinz Kohut's psychoanalytic psychology of the self was tested in a sample of 354 undergraduates. In canonical correlations, factors from the Narcissistic Personality Inventory were associated more strongly with grandiose than with idealizing immaturities in Kohut's bipolar self, while difficulties in interpersonal relationships (i.e., poor object relations) were associated more strongly with idealizing deficits. Zero-order and partial correlational data were congruent with Kohut's hypothesis that self-grandiosity can include elements of both "pathology" and relative mental health, but canonical correlations did not support Kohut's claim that narcissism can be described in a bidimensional self-structure.