Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs

Nonictal electroencephalographic measures for the diagnosis of functional seizures

Objective: Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians. Methods: The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age=34.76\ub110.55 years, 14 males) and 29 subjects with epilepsy (mean age=38.95\ub113.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches. Results: The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier. Significance: Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma

Investigation of Alzheimer’s Disease EEG Frequency Components with Lempel-Ziv Complexity

. This pilot study applied Lempel-Ziv Complexity (LZC) to 22 resting EEG signals, collected using the 10-20 international system, from 11 patients with Alzheimer’s disease (AD) and 11 age-matched controls. This allowed for frequency band analysis as the EEG signals were first prefiltered with a third order Hamming window in the ranges F to F+WHz with both F and W equal to 1-30Hz respectively. Control subjects were found to have a greater signal complexity than AD patients with statistically significant bands seen at various ranges in all 16 electrodes. The maximum statistical significance (Student’s t test, p<0.01) was increased over the findings with traditional signal filtering techniques allowing the whole range, with a maximum significance of p=3.50e-6 at electrode T4 between 7-18Hz. Electrode F4 also showed significantly high statistically significant differences. The maximum accuracy, both controls and AD patients correctly identified, found with Receiver Operating Characteristic Curves was 95.45% (21 of 22 subjects correctly classified) at T4 (7-18Hz and 7-20Hz), Fp2 (8-32Hz) and F4 (6-21Hz), which is significantly more accurate than the most accurate methods previously applied to this dataset. The beta band (13-30Hz) was found to be most influential in separating the two test groups in this study with the best range suggested to be 5-26Hz, combining traditional theta, alpha and beta bands. These findings suggest pre-filtering has a significant effect on method outcomes and can be successfully tailored to improve the statistical effectiveness of LZC at distinguishing between these two EEG groups. However, more testing is required to investigate the effectiveness at distinguishing other signal dynamics

Parámetros reproductivos y distribución geográfica potencial de las áreas de anidación de Grus canadensis nesiotes (Aves, Gruidae) en Cuba: implicaciones para su conservación

Grus canadensis nesiotes (grulla cubana) es una subespecie endémica de Cuba que se encuentra en peligro de extinción. A pesar de estar directamente relacionada con los humedales, no existen estudios que contribuyan a su gestión y conservación. Por ello, se registraron parámetros reproductivos de la subespecie durante ocho temporadas reproductivas entre 2005 y 2015 en un humedal de Cuba; asimismo, se modeló y caracterizó la distribución geográfica potencial de las áreas de anidación, y se analizó su representación dentro de las áreas protegidas (AP). Para elaborar el modelo, se utilizaron el algoritmo de máxima entropía y variables de hábitat (100  m de tamaño de píxel). Para caracterizar la distribución potencial, se calculó la superficie ocupada por cada uso de suelo y tipo de vegetación dentro de dicha área de distribución. Se empleó el mismo procedimiento para calcular la superficie de la distribución que está protegida. Se localizaron 151  nidos en herbazales de ciénaga. Se trataba de plataformas simples sobre suelo húmedo o agua; los más grandes se observaron en 2006. El 70% de los nidos tuvieron dos huevos (1,7 huevos/nido) y el 63,5% fueron exitosos con 1,6 polluelos/nido exitoso. El hábitat potencial de anidación es estrecho (242 km2) y se localiza en el centro del humedal. De la distribución prevista, la superficie con alta probabilidad de presencia es del 13,8%. El 60% del herbazal de ciénaga de la zona del estudio estaba comprendido dentro de la distribución potencial, mientras que la proporción de cultivos (1,2%) y pastizales (2,1%) era baja. Las AP gestionadas solo protegen el 39,1% de la distribución potencial de los sitios de anidación y el 12% de las zonas con alta probabilidad. Se proponen tres sitios prioritarios para estudiar la anidación de la subespecie y hacer un seguimiento de la misma. Las medidas de conservación de la subespecie deberían considerar la distribución geográfica potencial de los sitios de anidación dentro y fuera de las AP.Grus canadensis nesiotes (grulla cubana) es una subespecie endémica de Cuba que se encuentra en peligro de extinción. A pesar de estar directamente relacionada con los humedales, no existen estudios que contribuyan a su gestión y conservación. Por ello, se registraron parámetros reproductivos de la subespecie durante ocho temporadas reproductivas entre 2005 y 2015 en un humedal de Cuba; asimismo, se modeló y caracterizó la distribución geográfica potencial de las áreas de anidación, y se analizó su representación dentro de las áreas protegidas (AP). Para elaborar el modelo, se utilizaron el algoritmo de máxima entropía y variables de hábitat (100  m de tamaño de píxel). Para caracterizar la distribución potencial, se calculó la superficie ocupada por cada uso de suelo y tipo de vegetación dentro de dicha área de distribución. Se empleó el mismo procedimiento para calcular la superficie de la distribución que está protegida. Se localizaron 151  nidos en herbazales de ciénaga. Se trataba de plataformas simples sobre suelo húmedo o agua; los más grandes se observaron en 2006. El 70% de los nidos tuvieron dos huevos (1,7 huevos/nido) y el 63,5% fueron exitosos con 1,6 polluelos/nido exitoso. El hábitat potencial de anidación es estrecho (242 km2) y se localiza en el centro del humedal. De la distribución prevista, la superficie con alta probabilidad de presencia es del 13,8%. El 60% del herbazal de ciénaga de la zona del estudio estaba comprendido dentro de la distribución potencial, mientras que la proporción de cultivos (1,2%) y pastizales (2,1%) era baja. Las AP gestionadas solo protegen el 39,1% de la distribución potencial de los sitios de anidación y el 12% de las zonas con alta probabilidad. Se proponen tres sitios prioritarios para estudiar la anidación de la subespecie y hacer un seguimiento de la misma. Las medidas de conservación de la subespecie deberían considerar la distribución geográfica potencial de los sitios de anidación dentro y fuera de las AP.Reproductive parameters and potential geographical distribution of nesting areas of Grus canadensis nesiotes (Aves, Gruidae) in Cuba: conservation implications Grus canadensis nesiotes (Cuban sandhill crane) is an endemic and endangered subspecies from Cuba. Protection of wetland habitats is essential for survival of this species, but studies that could contribute to its management and conservation are lacking. In this study we recorded the reproductive parameters of Grus canadensis nesiotes in eight breeding seasons between 2005 and 2015 in a wetland of Cuba. We modeled and characterized the potential geographical distribution of the nesting areas, analyzing its representation within protected areas. Maximum entropy algorithm and habitat variables were used for modeling (100 m of pixel size). To characterize the potential distribution we calculated each land–use–vegetation within the potential distribution. We used the same procedure to determine the extent of the protected area. A hundred and fifty–one nests were located in marsh grasslands. These nests were simple platforms built on wet soil/water; the largest nests were observed in 2006. Seventy percent of nests had two eggs (1.7 eggs/nest) and 63.5% were successful with 1.6 chicks per successful nest. The potential nesting habitat is a narrow stretch (242 km2) located in the center of the wetland. The area with high probability of presence makes up 13.8% of the predicted distribution. Sixty percent of marsh grassland of the study area was included in the potential distribution, while the proportion of crops (1.2%) and pastures (2.1%) was low. Managed protected areas cover only 39.1% of the potential distribution of the nesting sites and 12% of the high probability areas. We propose three priority sites to study and monitor nesting of the subspecies. Species conservation actions should consider the potential geographical distribution of nesting sites both inside and outside the protected areas

Zileuton™ loaded in polymer micelles effectively reduce breast cancer circulating tumor cells and intratumoral cancer stem cells

Tumor recurrence, metastatic spread and progressive gain of chemo-resistance of advanced cancers are sustained by the presence of cancer stem cells (CSCs) within the tumor. Targeted therapies with the aim to eradicate these cells are thus highly regarded. However, often the use of new anti-cancer therapies is hampered by pharmacokinetic demands. Drug delivery through nanoparticles has great potential to increase efficacy and reduce toxicity and adverse effects. However, its production has to be based on intelligent design. Likewise, we developed polymeric nanoparticles loaded with Zileuton™, a potent inhibitor of cancer stem cells (CSCs), which was chosen based on high throughput screening. Its great potential for CSCs treatment was subsequently demonstrated in in vitro and in in vivo CSC fluorescent models. Encapsulated Zileuton™ reduces amount of CSCs within the tumor and effectively blocks the circulating tumor cells (CTCs) in the blood stream and metastatic spread

Pivotal role of AKT2 during dynamic phenotypic change of breast cancer stem cells

Cancer stem cells (CSC); Dynamic phenotype; Epithelial-to-mesenchymal transition (EMT)Células madre cancerosas (CSC); Fenotipo dinámico; Transición epitelial a mesenquimal (EMT)Cèl·lules mare canceroses (CSC); Fenotip dinàmic; Transició epitelial a mesenquimal (EMT)Therapeutic resistance seen in aggressive forms of breast cancer remains challenging for current treatments. More than half of the patients suffer from a disease relapse, most of them with distant metastases. Cancer maintenance, resistance to therapy, and metastatic disease seem to be sustained by the presence of cancer stem cells (CSC) within a tumor. The difficulty in targeting this subpopulation derives from their dynamic interconversion process, where CSC can differentiate to non-CSC, which in turn de-differentiate into cells with CSC properties. Using fluorescent CSC models driven by the expression of ALDH1A 1(aldehyde dehydrogenase 1A1), we confirmed this dynamic phenotypic change in MDA-MB-231 breast cancer cells and to identify Serine/Threonine Kinase 2 (AKT2) as an important player in the process. To confirm the central role of AKT2, we silenced AKT2 expression via small interfering RNA and using a chemical inhibitor (CCT128930), in both CSC and non-CSC from different cancer cell lines. Our results revealed that AKT2 inhibition effectively prevents non-CSC reversion through mesenchymal to epithelial transition, reducing invasion and colony formation ability of both, non-CSC and CSC. Further, AKT2 inhibition reduced CSC survival in low attachment conditions. Interestingly, in orthotopic tumor mouse models, high expression levels of AKT2 were detected in circulating tumor cells (CTC). These findings suggest AKT2 as a promising target for future anti-cancer therapies at three important levels: (i) Epithelial-to-mesenchymal transition (EMT) reversion and maintenance of CSC subpopulation in primary tumors, (ii) reduction of CTC and the likelihood of metastatic spread, and (iii) prevention of tumor recurrence through inhibition of CSC tumorigenic and metastatic potential.This work was funded by Fondo de Investigaciones Sanitarias (FIS) from ISCIII, Spanish ministry of Economy and Competitiveness, grant PI17/02242 co-financed by The European Regional Development Fund (FEDER); AC15/00092 grant (Target4Cancer project) from Euro-NanoMed II and PENTRI project, financed by Marato TV3, and EvoNano project, funded by European Union's Horizon 2020 FET Open programme under grant agreement. No. 800983. JSR was supported by a post-doctoral grant from Asociacion Espanola Contra el Cancer (AECC)

How robust are value judgements of health inequality aversion? Testing for framing and cognitive effects

Background: Empirical studies have found that members of the public are inequality averse and value health gains for disadvantaged groups with poor health many times more highly than gains for better off groups. However, these studies typically use abstract scenarios that involve unrealistically large reductions in health inequality, and face-to-face survey administration. It is not known how robust these findings are to more realistic scenarios or anonymous online survey administration. Methods: This study aimed to test the robustness of questionnaire estimates of inequality aversion by comparing the following: (1) small versus unrealistically large health inequality reductions; (2) population-level versus individual-level descriptions of health inequality reductions; (3) concrete versus abstract intervention scenarios; and (4) online versus face to face mode of administration. Fifty-two members of the public participated in face-to-face discussion groups, while 83 members of the public completed an online survey. Participants were given a questionnaire instrument with different scenario descriptions for eliciting aversion to social inequality in health. Results: The median respondent was inequality averse under all scenarios. Scenarios involving small rather than unrealistically large health gains made little difference in terms of inequality aversion, as did population-level rather than individual-level scenarios. However, the proportion expressing extreme inequality aversion fell 19 percentage points when considering a specific health intervention scenario rather than an abstract scenario, and was 11-21 percentage points lower among online public respondents compared to the discussion group. Conclusions: Our study suggests that both concrete scenarios and online administration reduce the proportion expressing extreme inequality aversion but still yield median responses implying substantial health inequality aversion

Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy

The purpose of this study has been to describe motor activity data obtained by using wrist-worn actigraphs in patients with schizophrenia and major depression by the use of linear and non-linear methods of analysis. Different time frames were investigated, i.e., activity counts measured every minute for up to five hours and activity counts made hourly for up to two weeks. The results show that motor activity was lower in the schizophrenic patients and in patients with major depression, compared to controls. Using one minute intervals the depressed patients had a higher standard deviation (SD) compared to both the schizophrenic patients and the controls. The ratio between the root mean square successive differences (RMSSD) and SD was higher in the schizophrenic patients compared to controls. The Fourier analysis of the activity counts measured every minute showed that the relation between variance in the low and the high frequency range was lower in the schizophrenic patients compared to the controls. The sample entropy was higher in the schizophrenic patients compared to controls in the time series from the activity counts made every minute. The main conclusions of the study are that schizophrenic and depressive patients have distinctly different profiles of motor activity and that the results differ according to period length analysed