Enhanced electron transfer mediated detection of hydrogen peroxide using a silver nanoparticle–reduced graphene oxide–polyaniline fabricated electrochemical sensor

The current study aims at the development of an electrochemical sensor based on a silver nanoparticle–reduced graphene oxide–polyaniline (AgNPs–rGO–PANI) nanocomposite for the sensitive and selective detection of hydrogen peroxide (H₂O₂). The nanocomposite was fabricated by simple in situ synthesis of PANI at the surface of rGO sheet which was followed by stirring with AEC biosynthesized AgNPs to form a nanocomposite. The AgNPs, GO, rGO, PANI, rGO–PANI, and AgNPs–rGO–PANI nanocomposite and their interaction were studied by UV-vis, FTIR, XRD, SEM, EDX and XPS analysis. AgNPs–rGO–PANI nanocomposite was loaded (0.5 mg cm⁻²) on a glassy carbon electrode (GCE) where the active surface area was maintained at 0.2 cm² for investigation of the electrochemical properties. It was found that AgNPs–rGO–PANI–GCE had high sensitivity towards the reduction of H₂O₂ than AgNPs–rGO which occurred at −0.4 V vs. SCE due to the presence of PANI (AgNPs have direct electronic interaction with N atom of the PANI backbone) which enhanced the rate of transfer of electron during the electrochemical reduction of H₂O₂. The calibration plots of H₂O₂ electrochemical detection was established in the range of 0.01 μM to 1000 μM (R² = 0.99) with a detection limit of 50 nM, the response time of about 5 s at a signal-to-noise ratio (S/N = 3). The sensitivity was calculated as 14.7 μA mM⁻¹ cm⁻² which indicated a significant potential as a non-enzymatic H₂O₂ sensor.

The current study aims at the development of an electrochemical sensor based on a silver nanoparticle–reduced graphene oxide–polyaniline (AgNPs–rGO–PANI) nanocomposite for the sensitive and selective detection of hydrogen peroxide (H₂O₂).     

Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning

Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived from patterns of word co-occurrence in text corpora. These studies have helped map out semantic representation across a distributed brain network spanning temporal, parietal, and frontal cortex. However, it remains unclear whether activation patterns within regions reflect unified representations of sentence-level meaning, as opposed to superpositions of context-independent component words. This is because models have typically represented sentences as “bags-of-words” that neglect sentence-level structure. To address this issue, we interrogated fMRI activation elicited as 240 sentences were read by 14 participants (9 female, 5 male), using sentences encoded by a recurrent deep artificial neural-network trained on a sentence inference task (InferSent). Recurrent connections and nonlinear filters enable InferSent to transform sequences of word vectors into unified “propositional” sentence representations suitable for evaluating intersentence entailment relations. Using voxelwise encoding modeling, we demonstrate that InferSent predicts elements of fMRI activation that cannot be predicted by bag-of-words models and sentence models using grammatical rules to assemble word vectors. This effect occurs throughout a distributed network, which suggests that propositional sentence-level meaning is represented within and across multiple cortical regions rather than at any single site. In follow-up analyses, we place results in the context of other deep network approaches (ELMo and BERT) and estimate the degree of unpredicted neural signal using an “experiential” semantic model and cross-participant encoding.SIGNIFICANCE STATEMENT A modern-day scientific challenge is to understand how the human brain transforms word sequences into representations of sentence meaning. A recent approach, emerging from advances in functional neuroimaging, big data, and machine learning, is to computationally model meaning, and use models to predict brain activity. Such models have helped map a cortical semantic information-processing network. However, how unified sentence-level information, as opposed to word-level units, is represented throughout this network remains unclear. This is because models have typically represented sentences as unordered “bags-of-words.” Using a deep artificial neural network that recurrently and nonlinearly combines word representations into unified propositional sentence representations, we provide evidence that sentence-level information is encoded throughout a cortical network, rather than in a single region.     

Comparison of 68Ga-DOTANOC PET/CT with cardiac MRI in patients with clinical suspicion of cardiac sarcoidosis

Annals of Nuclear Medicine - 68Ga-DOTA-NaI-octreotide (DOTANOC) is a promising new alternative to 18F-fluorodeoxyglucose (FDG) for imaging inflammation in cardiac sarcoidosis. The aim of the study...     

Recognizing and improving patient nonadherence to statin therapy

Despite landmark trials demonstrating the benefits of statin therapy for lipid lowering and in the primary and secondary prevention of acute coronary events, many patients do not adhere to medication regimens. Although incremental gains have been made in lowering the absolute levels of total serum cholesterol in the general population, only one third of treated patients are achieving their lipid goals, with fewer than 20% of patients with cardiovascular disease at their target lipid goals. Only half of patients continue taking statins prescribed to them at 6 months, and only 30% to 40% continue taking them at 1 year. Predictors of poor adherence to statins are described, such as female gender and low socioeconomic status. Approaches that are physician focused and patient centered, such as frequent follow-up and serum lipid testing and better education of patients about cardiovascular disease, are suggested to offset a major impediment to achieving the full therapeutic outcomes promised by clinical trials.