Classification of cognitive impairment using electroencephalography for clinical inspection.

Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.

Electrodermal signal analysis using continuous wavelet transform as a tool for quantification of sweat gland activity in diabetic kidney disease.

Sympathetic innervation of the sweat gland (SG) manifests itself electrically as electrodermal activity (EDA), which can be utilized to measure sudomotor function. Since SG exhibits similarities in structure and function with kidneys, quantification of SG activity is attempted through EDA signals. A methodology is developed with electrical stimulation, sampling frequency and signal processing algorithm. One hundred twenty volunteers participated in this study belonging to controls, diabetes, diabetic nephropathy, and diabetic neuropathy. The magnitude and time duration of stimuli is arrived by trial and error in such a way it does not influence controls but triggers SG activity in other Groups. This methodology leads to a distinct EDA signal pattern with changes in frequency and amplitude. The continuous wavelet transform depicts a scalogram to retrieve this information. Further, to distinguish between Groups, time average spectrums are plotted and mean relative energy (MRE) is computed. Results demonstrate high energy value in controls, and it gradually decreases in other Groups indicating a decline in SG activity on diabetes prognosis. The correlation for the acquired results was determined to be 0.99 when compared to the standard lab procedure. Furthermore, Cohen's d value, which is less than 0.25 for all Groups indicating the minimal effect size. Hence the obtained result is validated and statistically analyzed for individual variations. Thus this has the potential to get transformed into a device and could prevent diabetic kidney disease.

Sweat gland morphology and physiology in diabetes, neuropathy, and nephropathy: a review.

Context: Sweat glands (SGs) play a vital role in thermal regulation. The function and structure are altered during the different pathological conditions.Objective: These alterations are studied through three techniques: biopsy, sweat analytes and electrical activity of SG.Methods: The morphological study of SG through biopsy and various techniques involved in quantifying sweat analytes is focussed on here. Electrical activities of SG in diabetes, neuropathy and nephropathy cases are also discussed, highlighting their limitations and future scope.Results and Conclusion: The result of this review identified three areas of the knowledge gap. The first is wearable sensors to correlate pathological conditions. Secondly, there is no device to look for its structure and quantify its associated function. Finally, therapeutic applications of SG are explored, especially for renal failure. With these aspects, this paper provides information collection and correlates SG with pathologies related to diabetes. Hence this could help researchers develop suitable technologies for the gaps identified.

Assessment of chronic kidney disease using skin texture as a key parameter: for South Indian population.

Periodical monitoring of renal function, specifically for subjects with history of diabetic or hypertension would prevent them from entering into chronic kidney disease (CKD) condition. The recent increase in numbers may be due to food habits or lack of physical exercise, necessitates a rapid kidney function monitoring system. Presently, it is determined by evaluating glomerular filtration rate (GFR) that is mainly dependent on serum creatinine value and demographic parameters and ethnic value. Attempted here is to develop ethnic parameter based on skin texture for every individual. This value when used in GFR computation, the results are much agreeable with GFR obtained through standard modification of diet in renal disease and CKD epidemiology collaboration equations. Once correlation between CKD and skin texture is established, classification tool using artificial neural network is built to categorise CKD level based on demographic values and parameter obtained through skin texture (without using creatinine). This network when tested gives almost at par results with the network that is trained with demographic and creatinine values. The results of this Letter demonstrate the possibility of non-invasively determining kidney function and hence for making a device that would readily assess the kidney function even at home.

Regulation of C. elegans neuronal differentiation by the ZEB-family factor ZAG-1 and the NK-2 homeodomain factor CEH-28.

The C. elegans pharyngeal neuron M4 is a multi-functional cell that acts as a cholinergic motor neuron to stimulate peristaltic pharyngeal muscle contraction and as a neuroendocrine cell secreting neuropeptides and growth factors to affect other cells both inside and outside the pharynx. The conserved transcription factors ZAG-1 and CEH-28 are co-expressed in M4 through most of development, and here we examine how these factors contribute to M4 differentiation. We find ZAG-1 functions upstream of CEH-28 in a branched pathway to activate expression of different sets of M4 differentiation markers. CEH-28 activates expression of the growth factor genes dbl-1 and egl-17, and the neuropeptide genes flp-5 and flp-2, while ZAG-1 activates expression of the serotonin receptor ser-7, as well as expression of ceh-28 and its downstream targets. Other markers of M4 differentiation are expressed normally in both zag-1 and ceh-28 mutants, including the neuropeptide gene flp-21 and the acetylcholine biosynthetic gene unc-17. Unlike ceh-28 mutants, zag-1 mutants completely lack peristaltic muscle contractions resulting from broader defects in M4 differentiation. Despite these defects, neither ZAG-1 nor CEH-28 are terminal selectors of the M4 phenotype, and we suggest they function in a hierarchy to regulate different aspects of M4 differentiation.

CEH-28 activates dbl-1 expression and TGF-ß signaling in the C. elegans M4 neuron.

M4 is a multifunctional neuron in the Caenorhabditis elegans pharynx that can both stimulate peristaltic contractions of the muscles in the pharyngeal isthmus and function systemically to regulate an enhanced sensory response under hypoxic conditions. Here we identify a third function for M4 that depends on activation of the TGF-ß family gene dbl-1 by the homeodomain transcription factor CEH-28. dbl-1 is expressed in M4 and a subset of other neurons, and we show CEH-28 specifically activates dbl-1 expression in M4. Characterization of the dbl-1 promoter indicates that CEH-28 targets an M4-specific enhancer within the dbl-1 promoter region, while expression in other neurons is mediated by separate regulatory sequences. Unlike ceh-28 mutants, dbl-1 mutants do not exhibit M4 synaptic and signaling defects. Instead, both ceh-28 and dbl-1 mutants exhibit morphological defects in the g1 gland cells located adjacent to M4 in the pharynx, and these defects can be partially rescued by M4-specific expression of dbl-1 in these mutants. Identical gland cell defects are observed in sma-6 and daf-4 mutants defective in the receptor for DBL-1, but they are not observed in sma-2 and sma-3 mutants lacking the R-Smads functioning downstream of this receptor. Together these results identify a novel neuroendocrine function for M4 and provide evidence for an R-Smad-independent mechanism for DBL-1 signaling in C. elegans.