Second and fourth (2D:4D) digit ratio in heroin and cannabis addicted patients.

The etiology of addiction has not yet been fully elucidated. The ratio between the length of the second and fourth fingers (2D:4D ratio) has been linked with prenatal androgen concentrations, but also with addictive behvaiors. Aim: The present study aimed to evaluate the differences of 2D:4D ratio of individuals with cannabis and heroin addiction by examining them together with the control group. A total of sixty two male patients (33 opiate use disorder and 29 cannabis use disorder) with substance use disorder and the twenty-nine healthy controls were included in the present investigation. We obtained the lengths of 2D and 4D of the subjects by using sensitive calipers and calculated the 2D:4D. Heroin-addicted patients had lower 2D:4D ratio in in the right hand (significant difference between control group) (p < 0.001), there was no significant difference found between heroin-cannabis (p = 0.242) and control-cannabis 2D:4D ratios (p < 0.06). In the left hand, it was significant between the heroin-control groups (p < 0.037) and the cannabis-control groups (p < 0.023), while it was not significant between the heroin-cannabis groups (p = 1). In conclusion, we suggest that heroin-and cannabis addicted patients seem to have a lower ratio of 2D:4D compared to healthy control subjects. Our findings can be considered promising as to whether prenatal hormonal factors are important in the etiopathogenesis of addiction.

Assessing the effect of selenium on cyclin D1 level and nuclear factor kappa b activity in NIH/3T3 fibroblast cells at 2100 MHz electromagnetic field exposure.

Although there are numerous studies on the health impacts of electromagnetic field (EMF) of mobile phone operation frequency 2100 MHz, the published works present contradicting results. Long-term exposure to mobile phone frequencies has unclear health hazards. Therefore, it is important to investigate the molecular mechanism of possible biological effects in mobile phone exposure and to determine the corresponding biological markers. Towards this end, this study was designed to assess the effect of 200 nM selenium (Se) on cell viability% [trypan blue], cell cycle biomarker [cyclin D1] and the transcription factor [nuclear factor kappa b (NF-κB)] in NIH/3T3 fibroblast cells when exposed to 2100 MHz mobile phone frequency. When 2100 MHz EMF was exposed to NIH/3T3 fibroblast cells, the cell viability% was reduced, whereas cyclin D1 level and NF-kB activity increased. Also we show that Se supplementation decreases the effects of 2100 MHz EMF on these parameters. Although future studies will be required to investigate the biological effects of EMF emitted by mobile phones, the results obtained here provide an insight into the molecular mechanisms and specifically underlying selenium's protective effect against 2100 MHz EMF exposure.

Comparison of Microwave Hyperthermia Applicator Designs with Fora Dipole and Connected Array.

In microwave hyperthermia tumor therapy, electromagnetic waves focus energy on the tumor to elevate the temperature above its normal levels with minimal injury to the surrounding healthy tissue. Microwave hyperthermia applicator design is important for the effectiveness of the therapy and the feasibility of real-time application. In this study, the potential of using fractal octagonal ring antenna elements as a dipole antenna array and as a connected array at 2.45 GHz for breast tumor hyperthermia application was investigated. Microwave hyperthermia treatment models consisting of different fractal octagonal ring antenna array designs and a breast phantom are simulated in COMSOL Multiphysics to obtain the field distributions. The antenna excitation phases and magnitudes are optimized using the global particle swarm algorithm to selectively increase the specific absorption rate at the target region while minimizing hot spots in other regions within the breast. Specific absorption rate distributions, obtained inside the phantom, are analyzed for each proposed microwave hyperthermia applicator design. The dipole fractal octagonal ring antenna arrays are comparatively assessed for three different designs: circular, linear, and Cross-array. The 16-antenna dipole array performance was superior for all three 1-layer applicator designs, and no distinct difference was found between 16-antenna circular, linear, or cross arrays. Two-layer dipole arrays have better performance in the deep-tissue targets than one-layer arrays. The performance of the connected array with a higher number of layers exceeds the performance of the dipole arrays in the superficial regions, while they are comparable for deep regions of the breast. The 1-layer 12-antenna circular FORA dipole array feasibility as a microwave hyperthermia applicator was experimentally shown.

Rotationally Adjustable Hyperthermia Applicators: A Computational Comparative Study of Circular and Linear Array Applicators.

Microwave breast hyperthermia (MH) aims to increase the temperature at the tumor location with minimal change in the healthy tissue. To this end, the specific absorption rate (SAR) inside the breast is optimized. The choice of the MH applicator design is important for a superior energy focus on the target. Although hyperthermia treatment planning (HTP) changes for every patient, the MH applicator is required to be effective for different breast models and tumor types. The linear applicator (LA) is one of the previously proposed applicator designs with linearly arranged antennas; however, it suffers from low focusing ability in certain breast regions due to its unsymmetrical geometrical features. In this paper, we propose to radially adjust the LA to obtain alternative excitation schemes without actually changing the applicator. Antipodal Vivaldi antennas were utilized, and the antenna excitations were optimized with particle swarm optimization (PSO). The comparison of the rotated and the fixed linear applicator, between 12-antenna circular and linear applicators, and finally, between a 24-antenna circular applicator are provided. Within the 12 rotation angles and two target locations that were analyzed, the 135° axially rotated linear applicator gave a 35% to 84% higher target-to-breast SAR ratio (TBRS) and a 21% to 28% higher target-to-breast temperature ratio (TBRT) than the fixed linear applicator. For the deep-seated target, the 135° rotated linear applicator had an 80% higher TBRS and a 59% higher TBRT than the 12-antenna circular applicator, while the results were comparable to the 24-antenna circular applicator.

Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia.

Microwave hyperthermia (MH) requires the effective calibration of antenna excitations for the selective focusing of the microwave energy on the target region, with a nominal effect on the surrounding tissue. To this end, many different antenna calibration methods, such as optimization techniques and look-up tables, have been proposed in the literature. These optimization procedures, however, do not consider the whole nature of the electric field, which is a complex vector field; instead, it is simplified to a real and scalar field component. Furthermore, most of the approaches in the literature are system-specific, limiting the applicability of the proposed methods to specific configurations. In this paper, we propose an antenna excitation optimization scheme applicable to a variety of configurations and present the results of a convolutional neural network (CNN)-based approach for two different configurations. The data set for CNN training is collected by superposing the information obtained from individual antenna elements. The results of the CNN models outperform the look-up table results. The proposed approach is promising, as the phase-only optimization and phase-power-combined optimization show a 27% and 4% lower hotspot-to-target energy ratio, respectively, than the look-up table results for the linear MH applicator. The proposed deep-learning-based optimization technique can be utilized as a protocol to be applied on any MH applicator for the optimization of the antenna excitations, as well as for a comparison of MH applicators.

Single-slice microwave imaging of breast cancer by reverse time migration.

PURPOSE: Microwave imaging of breast cancer is considered and a new microwave imaging prototype including the imaging algorithm, the antenna array, and the measurement configuration is presented. The prototype aims to project the geometrical features of the anomalies inside the breast to a single-slice image at the coronal plane depending on the complex dielectric permittivity variation among the tissues to aid the diagnosis. METHODS: The imaging prototype uses a solid cylindrical dielectric platform, where a total of 24 optimized Vivaldi antennas are embedded inside to form a uniform circular antenna array. The center of the platform is carved to create a hollow part for placement of the breast and the multistatic, microwave scattering parameters are collected with the antenna array around the hollow center. The dielectric platform further enhances the microwave impedance matching against the breast fat tissue and preserves the vertical polarization during the measurements. In the imaging phase, a computationally efficient inverse electromagnetic scattering method-reverse time migration (RTM)-is considered and adapted in terms of scattering parameters to comply with the actual measurements. RESULTS: The prototype system is experimentally tested against tissue-mimicking breast phantoms with realistic dielectric permittivity profiles. The reconstructed single-slice images accurately determined the locations and the geometrical extents of the tumor phantoms. These experiments not only verified the microwave imaging prototype but also provided the first experimental results of the imaging algorithm. CONCLUSIONS: The presented prototype system implementing the RTM method is capable of reconstructing single-slice, nonanatomical images, where the hotspots correspond to the geometrical projections of the anomalies inside the breast.

Characterization of Open-Ended Coaxial Probe Sensing Depth with Respect to Aperture Size for Dielectric Property Measurement of Heterogeneous Tissues.

The open-ended coaxial probe (OECP) method is frequently used for the microwave dielectric property (DP) characterization of high permittivity and conductivity materials due to inherent advantages including minimal sample preparation requirements and broadband measurement capabilities. However, the OECP method is known to suffer from high measurement error. One well-known contributor to the high error rates is tissue heterogeneity, which can potentially be managed through the selection of a probe with a proper sensing depth (SD). The SD of the OECP is dependent on many factors including sample DPs and probe aperture diameter. Although the effects of sample DPs on SD have been investigated to some extent in the literature, the probe aperture diameters, particularly small diameters, have not been fully explored. To this end, the SDs of probes with three different apertures (0.5, 0.9 and 2.2 mm-diameters) were analyzed in this study. Probes' SDs were first investigated with simulations using a double-layered sample configuration (skin tissue and olive oil). Next, experiments were performed using a commercial OECP with a 2.2 mm aperture diameter. The SD was categorized based on 5%, 20% and 80% DP change. Among these threshold values, a 5% DP change was selected as the benchmark for SD categorization. The findings suggest that probes with a smaller aperture size and correspondingly smaller SD should be utilized when measuring the DPs of thin and multilayered samples, such as healthy and diseased skin tissues, to increase the measurement accuracy.

Classification of rat mammary carcinoma with large scale in vivo microwave measurements.

Mammary carcinoma, breast cancer, is the most commonly diagnosed cancer type among women. Therefore, potential new technologies for the diagnosis and treatment of the disease are being investigated. One promising technique is microwave applications designed to exploit the inherent dielectric property discrepancy between the malignant and normal tissues. In theory, the anomalies can be characterized by simply measuring the dielectric properties. However, the current measurement technique is error-prone and a single measurement is not accurate enough to detect anomalies with high confidence. This work proposes to classify the rat mammary carcinoma, based on collected large-scale in vivo S[Formula: see text] measurements and corresponding tissue dielectric properties with a circular diffraction antenna. The tissues were classified with high accuracy in a reproducible way by leveraging a learning-based linear classifier. Moreover, the most discriminative S[Formula: see text] measurement was identified, and to our surprise, using the discriminative measurement along with a linear classifier an 86.92% accuracy was achieved. These findings suggest that a narrow band microwave circuitry can support the antenna enabling a low-cost automated microwave diagnostic system.

The effect on the anxiety level of the social support given to multiple sclerosis patients.

PURPOSE: This is a descriptive study aiming at determining the effect on the anxiety level of the social support given to multiple sclerosis (MS) patients. DESIGN AND METHODS: This study was conducted with 123 MS patients. The data were collected the Multidimensional Scale of Perceived Social Support (MSPSS), the Hamilton Anxiety Rating Scale (HAM-A). RESULTS: The Multidimensional Scale of Perceived Social Support total score average was 57.18 ± 18.5, and the HAM-A total score average was 20.33 ± 10.42. A negative weak significant relationship was found between the MSPSS total score average and the HAM-A total score and its sub-dimensions. PRACTICE IMPLICATIONS: MS nurses should plan their anxiety management accordingly by giving holistic care and evaluating social support mechanisms.

A Novel Approach on Microwave Hyperthermia.

Microwave hyperthermia (MH) requires the selective focusing of microwave energy on the targeted region while minimally affecting the healthy tissue. Emerging from the simple nature of the linear antenna arrays, this work demonstrates focusing maps as an application guide for MH focusing by adjusting the antenna phase values. The focusing of the heating potential (HP) on different density breast models is performed via the proposed method using Vivaldi antennas. The effect of the tumor conductivity on the focusing is discussed. As a straightforward approach and utilizing the Vivaldi antennas, the system can be further combined with MH monitoring application.

SAFE: A Novel Microwave Imaging System Design for Breast Cancer Screening and Early Detection-Clinical Evaluation.

SAFE (Scan and Find Early) is a novel microwave imaging device intended for breast cancer screening and early detection. SAFE is based on the use of harmless electromagnetic waves and can provide relevant initial diagnostic information without resorting to X-rays. Because of SAFE's harmless effect on organic tissue, imaging can be performed repeatedly. In addition, the scanning process itself is not painful since breast compression is not required. Because of the absence of physical compression, SAFE can also detect tumors that are close to the thoracic wall. A total number of 115 patients underwent the SAFE scanning procedure, and the resultant images were compared with available magnetic resonance (MR), ultrasound, and mammography images in order to determine the correct detection rate. A sensitivity of 63% was achieved. Breast size influenced overall sensitivity, as sensitivity was lower in smaller breasts (51%) compared to larger ones (74%). Even though this is only a preliminary study, the results show promising concordance with clinical reports, thus encouraging further SAFE clinical studies.

Towards Non-Invasive Diagnosis of Skin Cancer: Sensing Depth Investigation of Open-Ended Coaxial Probes.

Dielectric properties of biological tissues are traditionally measured with open-ended coaxial probes. Despite being commercially available for laboratory use, the technique suffers from high measurement error. This prevents the practical applications of the open-ended coaxial probes. One such application is the utilization of the technique for skin cancer detection. To enable a diagnostic tool, there is a need to address the error sources. Among others, tissue heterogeneity is a major contributor to measurement error. The effect of tissue heterogeneity on measurement accuracy can be decreased by quantifying the probe sensing depth. To this end, this work (1) investigates the sensing depth of the 2.2 mm-diameter open-ended coaxial probe for skin mimicking material and (2) offers a simple experimental setup and protocol for sensing depth characterization of open-ended coaxial probes. The sensing depth characterized through simulation and experiments using two double-layered configurations composed to mimic the skin tissue heterogeneity. Three thresholds in percent increase of dielectric property measurements were chosen to determine the sensing depth. Based on the experiment results, it was concluded that the sensing depth was effected by the dielectric property contrast between the layers. That is, high contrast results in rapid change whereas low contrast results in a slower change in measured dielectric properties. It was also concluded that the sensing depth was independent of frequency between 0.5 to 6 GHz and was mostly determined by the material located immediately at the aperture of the probe.

Towards Accurate Microwave Characterization of Tissues: Sensing Depth Analysis of Open-Ended Coaxial Probes with Ex Vivo Rat Breast and Skin Tissues.

Dielectric properties of biological materials are commonly characterized with open-ended coaxial probes due to the broadband and non-destructive measurement capabilities. Recently, potential diagnostics applications of the technique have been investigated. Although the technique can successfully classify the tissues with different dielectric properties, the classification accuracy can be improved for tissues with similar dielectric properties. Increase in classification accuracy can be achieved by addressing the error sources. One well-known error source contributing to low measurement accuracy is tissue heterogeneity. To mitigate this error source, there is a need define the probe sensing depth. Such knowledge can enable application-specific probe selection or design. The sensing depth can also be used as an input to the classification algorithms which can potentially improve the tissue classification accuracy. Towards this goal, this work investigates the sensing depth of a commercially available 2.2 mm aperture diameter probe with double-layered configurations using ex vivo rat breast and skin tissues. It was concluded that the dielectric property contrast between the heterogeneous tissue components has an effect on the sensing depth. Also, a membrane layer (between 0.4-0.8 mm thickness) on the rat wet skin tissue and breast tissue will potentially affect the dielectric property measurement results by 52% to 84%.

In Vivo Dielectric Properties of Healthy and Benign Rat Mammary Tissues from 500 MHz to 18 GHz.

This work investigates the in vivo dielectric properties of healthy and benign rat mammary tissues in an attempt to expand the dielectric property knowledge of animal models. The outcomes of this study can enable testing of microwave medical technologies on animal models and interpretation of tissue alteration-dependent in vivo dielectric properties of mammary tissues. Towards this end, in vivo dielectric properties of healthy rat mammary tissues and chemically induced benign rat mammary tumors including low-grade adenosis, sclerosing adenosis, and adenosis were collected with open-ended coaxial probes from 500 MHz to 18 GHz. The in vivo measurements revealed that the dielectric properties of benign rat mammary tumors are higher than the healthy rat mammary tissues by 9.3% to 35.5% and 19.6% to 48.7% for relative permittivity and conductivity, respectively. Furthermore, to our surprise, we found that the grade of the benign tissue affects the dielectric properties for this study. Finally, a comparison with ex vivo healthy human mammary tissue dielectric properties revealed that the healthy rat mammary tissues best replicate the dielectric properties of healthy medium density human samples.

Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues.

Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this technology, there is a need to decrease the measurement error which is reported to be more than 30% for an in vivo measurement setting. This work investigates the machine learning (ML) algorithms' ability to decrease the measurement error of open-ended coaxial probe techniques to enable tissue characterization devices. To explore the potential of this technique as a tissue characterization device, performances of multiclass ML algorithms on collected in vivo rat hepatic tissue and phantom dielectric property data were evaluated. Phantoms were used for investigating the potential of proliferating the data set due to difficulty of in vivo data collection from tissues. The dielectric property measurements were collected from 16 rats with hepatic anomalies, 8 rats with healthy hepatic tissues, and in house phantoms. Three ML algorithms, k-nearest neighbors (kNN), logistic regression (LR), and random forests (RF) were used to classify the collected data. The best performance for the classification of hepatic tissues was obtained with 76% accuracy using the LR algorithm. The LR algorithm performed classification with over 98% accuracy within the phantom data and the model generalized to in vivo dielectric property data with 48% accuracy. These findings indicate first, linear models, such as logistic regression, perform better on dielectric property data sets. Second, ML models fitted to the data collected from phantom materials can partly generalize to in vivo dielectric property data due to the discrepancy between dielectric property variability.

Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm.

The proper management of renal lithiasis presents a challenge, with the recurrence rate of the disease being as high as 46%. To prevent recurrence, the first step is the accurate categorization of the discarded renal calculi. Currently, the discarded renal calculi type is determined with the X-ray powder diffraction method which requires a cumbersome sample preparation. This work presents a new approach that can enable fast and accurate classification of discarded renal calculi with minimal sample preparation requirements. To do so, first, the measurements of the dielectric properties of naturally formed renal calculi are collected with the open-ended contact probe technique between 500 MHz and 6 GHz with 100 MHz intervals. Cole-Cole parameters are fitted to the measured dielectric properties with the generalized Newton-Raphson method. The renal calculi types are classified based on their Cole-Cole parameters as calcium oxalate, cystine, or struvite. The classification is performed using k-nearest neighbors (kNN) machine learning algorithm with the 10 nearest neighbors, where accuracy as high as 98.17% is achieved.

Radio-Frequency and Microwave Techniques for Non-Invasive Measurement of Blood Glucose Levels.

This paper reviews non-invasive blood glucose measurements via dielectric spectroscopy at microwave frequencies presented in the literature. The intent is to clarify the key challenges that must be overcome if this approach is to work, to suggest some possible ways towards addressing these challenges and to contribute towards prevention of unnecessary 'reinvention of the wheel'.

Medical students' risk perceptions on decreased attention, physical and social risks in using mobile phones and the factors related with their risk perceptions.

The aim of this cross-sectional study is to determine the mobile phone use, risk perception and related factors among the medical students. The study population was 1465 medical students. The dependent variable was the risk perception on mobile phones. The risk perception was assessed in three dimensions: decreased attention, physical risks and social risks. The exploratory factor analysis was used for the success of the scale, and the factor loadings were calculated using the varimax rotation method. Logistic Regression Analysis was applied to determine the factors associated with risk perception. The presented results are based on 1079 students (Access rate = 73.7%). In all dimensions of risk perception, more than half of the medical students had high risk perceptions. Female students and lower social classes had high risk perceptions. An understanding of the determinants and mobile phone utilization characteristics might help in developing effective risk communication to these subgroups.

Machine learning aided diagnosis of hepatic malignancies through in vivo dielectric measurements with microwaves.

In the past decade, extensive research on dielectric properties of biological tissues led to characterization of dielectric property discrepancy between the malignant and healthy tissues. Such discrepancy enabled the development of microwave therapeutic and diagnostic technologies. Traditionally, dielectric property measurements of biological tissues is performed with the well-known contact probe (open-ended coaxial probe) technique. However, the technique suffers from limited accuracy and low loss resolution for permittivity and conductivity measurements, respectively. Therefore, despite the inherent dielectric property discrepancy, a rigorous measurement routine with open-ended coaxial probes is required for accurate differentiation of malignant and healthy tissues. In this paper, we propose to eliminate the need for multiple measurements with open-ended coaxial probe for malignant and healthy tissue differentiation by applying support vector machine (SVM) classification algorithm to the dielectric measurement data. To do so, first, in vivo malignant and healthy rat liver tissue dielectric property measurements are collected with open-ended coaxial probe technique between 500 MHz to 6 GHz. Cole-Cole functions are fitted to the measured dielectric properties and measurement data is verified with the literature. Malign tissue classification is realized by applying SVM to the open-ended coaxial probe measurements where as high as 99.2% accuracy (F1 Score) is obtained.

Determinants of inpatient costs of angina pectoris, myocardial infarction, and heart failure in a university hospital setting in Turkey.

OBJECTIVE: This study aimed to determine the correlates of in-hospital costs for angina pectoris (AP), myocardial infarction (MI), and heart failure (HF) in a university hospital setting. METHODS: This is a retrospective cost-of-illness study using data from the records of patients who were admitted with AP, MI, or HF to Dokuz Eylül University Hospital during 2008. Direct medical costs were calculated from the Social Security Institute perspective using a bottom-up approach. Socio-demographic and clinical information was abstracted from patient files. Costs were presented in Turkish lira (TL). A generalized linear model was used in the multivariate analysis. RESULTS: We included 337 in-patients in total in the study. AP was present in 26.4% (n=89), MI was present in 55.8% (n=188), and HF was present in 17.8% (n=60) of patients. MI was the most costly disease (2760 TL), followed by HF (2350 TL) and AP (1881 TL). The largest proportion of the total cost was formed by medical interventions (27.5%), followed by surgery (22.2%). Presence of DM, smoking, diagnosis of MI, HF, need for intensive care, and resulting in death were strong predictors of treatment costs. CONCLUSION: Both preadmission characteristics of patients (diabetes mellitus, smoking, use of anti-aggregant before admission) and in-patient characteristics (diagnosis, coronary artery bypass grafting, intensive care need, death) predicted the hospital cost of cardiovascular diseases (CVDs) independently. Our results may be used as input for health-economic models and economic evaluations to support the decision-making of reimbursement and the cost-effectiveness of public health interventions in healthcare.