In the present work, bioaugmented zinc oxide nanoparticles (ZnO-NPs) were prepared from aqueous fruit extracts of Myristica fragrans . The ZnO-NPs were characterized by different techniques such as X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, ultraviolet (UV) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), dynamic light scattering (DLS), and thermogravimetric analysis (TGA). The crystallites exhibited a mean size of 41.23 nm measured via XRD and were highly pure, while SEM and TEM analyses of synthesized NPs confirmed their spherical or elliptical shape. The functional groups responsible for stabilizing and capping of ZnO-NPs were confirmed using FTIR analysis. The ζ-size and ζ-potential of synthesized ZnO-NPs were reported as 66 nm and −22.1 mV, respectively, via the DLS technique can be considered as moderate stable colloidal solution. Synthesized NPs were used to evaluate for their possible antibacterial, antidiabetic, antioxidant, antiparasitic, and larvicidal properties. The NPs were found to be highly active against bacterial strains both coated with antibiotics and alone. Klebsiella pneumoniae was found to be the most sensitive strain against NPs (27 ± 1.73) and against NPs coated with imipinem (26 ± 1.5). ZnO-NPs displayed outstanding inhibitory potential against enzymes protein kinase (12.23 ± 0.42), α-amylase (73.23 ± 0.42), and α-glucosidase (65.21 ± 0.49). Overall, the synthesized NPs have shown significant larvicidal activity (77.3 ± 1.8) against Aedes aegypti , the mosquitoes involved in the transmission of dengue fever. Similarly, tremendous leishmanicidal activity was also observed against both the promastigote (71.50 ± 0.70) and amastigote (61.41 ± 0.71) forms of the parasite. The biosynthesized NPs were found to be excellent antioxidant and biocompatible nanomaterials. Biosynthesized ZnO-NPs were also used as photocatalytic agents, resulting in 88% degradation of methylene blue dye in 140 min. Owing to their eco-friendly synthesis, nontoxicity, and biocompatible nature, ZnO-NPs synthesized from M. fragrans can be exploited as potential candidates for biomedical and environmental applications.

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The dynamical fluctuations in the rhythms of biological systems provide valuable information about the underlying functioning of these systems. During the past few decades analysis of cardiac function based on the heart rate variability (HRV; variation in R wave to R wave intervals) has attracted great attention, resulting in more than 17000-publications (PubMed list). However, it is still controversial about the underling mechanisms of HRV. In this study, we performed both linear (time domain and frequency domain) and nonlinear analysis of HRV data acquired from humans and animals to identify the relationship between HRV and heart rate (HR). The HRV data consists of the following groups: (a) human normal sinus rhythm (n = 72); (b) human congestive heart failure (n = 44); (c) rabbit sinoatrial node cells (SANC; n = 67); (d) conscious rat (n = 11). In both human and animal data at variant pathological conditions, both linear and nonlinear analysis techniques showed an inverse correlation between HRV and HR, supporting the concept that HRV is dependent on HR, and therefore, HRV cannot be used in an ordinary manner to analyse autonomic nerve activity of a heart.

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

The current study reports advanced, ecofriendly and biosynthesized silver NPs for diverse biomedical and environmental applications using Flammulina velutipes as biosource. In the study, a simple aqueous extract of F. velutipes was utilized to reduce the AgNO3 into stable elemental silver (Ag0) at a nanometric scale. The NPs had average size of 21.4 nm, spherical morphology, and were highly stable and pure. The characterized nanoparticles were exploited for a broad range of biomedical applications including bacteriocidal, fungicidal, leishmanicidal, in vitro antialzheimer’s, antioxidant, anti-diabetic and biocompatibility studies. Our findings showed that F. velutipes mediated AgNPs exhibited high activity against MDR bacterial strains and spore forming fungal strains. All the tested urinary tract infection bacterial isolates, were resistant to non-coated antibiotics but by applying 1% of the synthesized AgNPs, the bactericidal potential of the tested antibiotics enhanced manifolds. The NPs also exhibited dose-dependent cytotoxic potential against Leishmania tropica with significant LC50 of 248 μg ml−1 for promastigote and 251 μg ml−1 for amastigote forms of the parasite. Furthermore, promising antialzheimer and antidiabetic activities were observed as significant inhibition of α-amylase, α-glucosidase, acetylcholinesterase (AChE) and butrylcholineterase (BChE) were noted. Moreover, remarkable biocompatible nature of the particles was found against human red blood cells. The biosynthesized AgNPs as photocatalyst, also resulted in 98.2% degradation of indigo carmine dye within 140 min. Owing to ecofriendly synthesis, biosafe nature and excellent physicochemical properties F. velutipes AgNPs can be exploited as novel candidates for multifaceted biomedical and environmental applications.

ECT is efficacious in the treatment of both the depressed and manic phases of bipolar disorder. While ECT is believed to be equally efficacious in the treatment of mixed affective states, to our knowledge there are no empirical studies on this issue. A chart review study was conducted to compare treatment response and clinical course in three groups of patients who received ECT in a general hospital service: bipolar depressed (n = 38), bipolar manic (n = 5), and bipolar mixed (n = 10), diagnosed by DSM-IV criteria. All three groups showed robust response rates, but the number of days of hospitalization was significantly longer in the mixed group (mean 30, SD 19.0 days) compared with the depressed group (mean 19.0, SD 10.9 days, t = 2.4, p < 0.03). There was a trend for the number of ECT treatments to be greater in the mixed compared with the depressed group. These findings indicate that patients with mixed affective states do respond well to a course of ECT, but the longer hospital stays and greater number of ECT treatments suggest that they may be more difficult to treat with ECT than patients with pure bipolar depression or bipolar mania.

In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.

We identified four missense substitutions in TYR and a single missense substitution in SLC45A2. One missense substitution (p.Arg77Gln) in TYR was found in five different families that originated from the same geographical area and displayed a common haplotype, suggesting a single origin that then spread to different geographical areas of Azad Kashmir, Pakistan.