Determination of HCV genotypes and viral loads in chronic HCV infected patients of Hazara Pakistan

Hepatitis C Virus (HCV) genotype and viral load are two significant predictive variables knowledge of which might persuade treatment decisions. The objective of the present study was to identify the distribution of different HCV genotypes circulating in the study area and to estimate viral load in chronically HCV infected patients. Out of total 305 HCV positive patients, 177 (58%) were males and 128 (42%) were females. Frequency breakup of the HCV positive patients was 169, 69, 38 and 29 from Abbottabad, Mansehra, Haripur and Battagram districts respectively. Out of the total 305 tested serum samples, 255 (83.06%) were successfully genotyped whereas 50 (16.4%) samples were found with unclassified genotypes. Among typable genotypes, 1a accounted for 21 (6.8%) 1b for 14 (4.6%), 2a for 4 (1.31%) 3a for 166 (54.42%) and genotype 3b for (8.19%). Twenty five (8.19%) patients were infected with mixed HCV genotypes. Viral load distribution was classified into three categories based on its viral load levels such as low (< 60, 0000 IU/mL), intermediate (60,0000-80,0000 IU/mL) and high (> 80,0000 IU/mL). The baseline HCV RNA Viral load in HCV genotype 3 infected patients was 50 (26.17%), 46 (24.08%) and 95 (49.73%) for low, intermediate and high categories respectively. For genotypes other than 3, these values for low, intermediate and high viral load categories were 50 (43.85), 35 (30.70) and 29 (25.43) respectively. Pre-treatment viral load in patients with untypable genotype was 19 (38.00%), 5 (20.00%) and 11 (44.00%) for low, intermediate and high viral load categories. Viral load distribution was also categorized sex wise; for males it was 58 (32.76%), 26 (14.68%) and 93 (52.54%) whereas for females it was 40 (31.25%), 34 (26.56%) and 54 (42.18%) for low, intermediate and high viral load respectively. In conclusion HCV genotype 3a is the most prevalent genotype circulating in Hazara Division like other parts of pakistan. Pre-treatment viral load is significantly high (p 0.014) in patients infected with HCV genotype 3 as compared to other genotypes

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

Knowledge, attitudes, and practices among nurses in Pakistan towards diabetic foot

Introduction: Diabetic foot ulcers are a pressing complication of diabetes mellitus. Wound care requires a significant proportion of healthcare resources. It is imperative, therefore, for healthcare professionals to possess sound knowledge of the disease along with a positive attitude to ensure better clinical practice. Our literature search revealed a scarcity of data pertaining to diabetic foot ulcers. Therefore, this study aims to evaluate the knowledge and attitudes of nurses regarding diabetic foot care. Methods: A cross-sectional study design was employed, a pre-validated and pre-tested questionnaire was used to collect data from a sample size of 250 nurses working at two tertiary care hospitals in Karachi, Pakistan. The study was conducted over a period of three months (January to March 2018) and included all nurses who possessed at least one year of clinical experience in diabetic ulcer care. The statistical software employed was SPSS version 19 (IBM Corp., Armonk, NY, US). Non-parametric tests and descriptive statistics were used for data analysis and statistical significance was assumed at a p-value of less than 0.5. Results: Only 54% of the nurses in our study possessed adequate knowledge of diabetic foot ulcers. The mean score of knowledge was 74.9 (±9.5). Macdonald’s standard criteria for learning outcomes was used to gauge the knowledge levels of our study population. Nurses performed best in the domain of ulcer care with 65.3% of the participants possessing good knowledge of the topic. The overall attitude of nurses towards patients with diabetic ulcers was positive. Conclusion: This study highlights important gaps in nurses’ knowledge and sheds light on the lack of evidence-based practice. Poor knowledge can compromise healthcare standards, even with the presence of positive attitudes. Hence, a comprehensive revision of nursing curricula across local tertiary hospitals for allowing nurses to update their knowledge is warrante

6-(4-Nitro­phen­oxy)hexa­nol

The title compound, C12H17NO4, features an almost planar mol­ecule (r.m.s. deviation for all non-H atoms = 0.070 Å). All methyl­ene C—C bonds adopt an anti­periplanar conformation. In the crystal structure the mol­ecules lie in planes parallel to (12) and the packing is stabilized by O—H⋯O hydrogen bonds

Privacy-aware relationship semantics–based XACML access control model for electronic health records in hybrid cloud

State-of-the-art progress in cloud computing encouraged the healthcare organizations to outsource the management of electronic health records to cloud service providers using hybrid cloud. A hybrid cloud is an infrastructure consisting of a private cloud (managed by the organization) and a public cloud (managed by the cloud service provider). The use of hybrid cloud enables electronic health records to be exchanged between medical institutions and supports multipurpose usage of electronic health records. Along with the benefits, cloud-based electronic health records also raise the problems of security and privacy specifically in terms of electronic health records access. A comprehensive and exploratory analysis of privacy-preserving solutions revealed that most current systems do not support fine-grained access control or consider additional factors such as privacy preservation and relationship semantics. In this article, we investigated the need of a privacy-aware fine-grained access control model for the hybrid cloud. We propose a privacy-aware relationship semantics–based XACML access control model that performs hybrid relationship and attribute-based access control using extensible access control markup language. The proposed approach supports fine-grained relation-based access control with state-of-the-art privacy mechanism named Anatomy for enhanced multipurpose electronic health records usage. The proposed (privacy-aware relationship semantics–based XACML access control model) model provides and maintains an efficient privacy versus utility trade-off. We formally verify the proposed model (privacy-aware relationship semantics–based XACML access control model) and implemented to check its effectiveness in terms of privacy-aware electronic health records access and multipurpose utilization. Experimental results show that in the proposed (privacy-aware relationship semantics–based XACML access control model) model, access policies based on relationships and electronic health records anonymization can perform well in terms of access policy response time and space storage

Deep eutectic solvent coated cerium oxide nanoparticles based polysulfone membrane to mitigate environmental toxicology

In this study, ceria nanoparticles (NPs) and deep eutectic solvent (DES) were synthesized, and the ceria-NP’s surfaces were modified by DES to form DES-ceria NP filler to develop mixed matrix membranes (MMMs). For the sake of interface engineering, MMMs of 2%, 4%, 6% and 8% filler loadings were fabricated using solution casting technique. The characterizations of SEM, FTIR and TGA of synthesized membranes were performed. SEM represented the surface and cross-sectional morphology of membranes, which indicated that the filler is uniformly dispersed in the polysulfone. FTIR was used to analyze the interaction between the filler and support, which showed there was no reaction between the polymer and DES-ceria NPs as all the peaks were consistent, and TGA provided the variation in the membrane materials with respect to temperature, which categorized all of the membranes as very stable and showed that the trend of stability increases with respect to DES-ceria NPs filler loading. For the evaluation of efficiency of the MMMs, the gas permeation was tested. The permeability of CO2 was improved in comparison with the pristine Polysulfone (PSF) membrane and enhanced selectivities of 35.43 (αCO2/CH4) and 39.3 (αCO2/N2) were found. Hence, the DES-ceria NP-based MMMs proved useful in mitigating CO2 from a gaseous mixture

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

A novel chalcone derivative which acts as a microtubule depolymerising agent and an inhibitor of P-gp and BCRP in in-vitro and in-vivo glioblastoma models

<p>Abstract</p> <p>Background</p> <p>Over the past decades, in spite of intensive search, no significant increase in the survival of patients with glioblastoma has been obtained. The role of the blood-brain barrier (BBB) and especially the activity of efflux pumps belonging to the ATP Binding Cassette (ABC) family may, in part, explain this defect.</p> <p>Methods</p> <p>The <it>in-vitro </it>activities of JAI-51 on cell proliferation were assessed by various experimental approaches in four human and a murine glioblastoma cell lines. Using drug exclusion assays and flow-cytometry, potential inhibitory effects of JAI-51 on P-gp and BCRP were evaluated in sensitive or resistant cell lines. JAI-51 activity on <it>in-vitro </it>microtubule polymerization was assessed by tubulin polymerization assay and direct binding measurements by analytical ultracentrifugation. Finally, a model of C57BL/6 mice bearing subcutaneous GL26 glioblastoma xenografts was used to assess the activity of the title compound <it>in vivo</it>. An HPLC method was designed to detect JAI-51 in the brain and other target organs of the treated animals, as well as in the tumours.</p> <p>Results</p> <p>In the four human and the murine glioblastoma cell lines tested, 10 μM JAI-51 inhibited proliferation and blocked cells in the M phase of the cell cycle, via its activity as a microtubule depolymerising agent. This ligand binds to tubulin with an association constant of 2 × 10⁵M^-1, overlapping the colchicine binding site. JAI-51 also inhibited the activity of P-gp and BCRP, without being a substrate of these efflux pumps. These <it>in vitro </it>studies were reinforced by our <it>in vivo </it>investigations of C57BL/6 mice bearing GL26 glioblastoma xenografts, in which JAI-51 induced a delay in tumour onset and a tumour growth inhibition, following intraperitoneal administration of 96 mg/kg once a week. In accordance with these results, JAI-51 was detected by HPLC in the tumours of the treated animals. Moreover, JAI-51 was detected in the brain, showing that the molecule is also able to cross the BBB.</p> <p>Conclusion</p> <p>These <it>in vitro </it>and <it>in vivo </it>data suggest that JAI-51 could be a good candidate for a new treatment of tumours of the CNS. Further investigations are in progress to associate the title compound chemotherapy to radiotherapy in a rat model.</p

Atom Optics Quantum Pendulum

We explain the dynamics of cold atoms, initially trapped and cooled in a magneto-optic trap, in a monochromatic stationary standing electromagnetic wave field. In the large detuning limit the system is modeled as a nonlinear quantum pendulum. We show that wave packet evolution of the quantum particle probes parametric regimes in the quantum pendulum which support classical period, quantum mechanical revival and super revival phenomena. Interestingly, complete reconstruction in particular parametric regime at quantum revival times is independent of potential height.Comment: 14 pages, 7 figure