Designing one-compartment H2O2 fuel cell using electroactive phenalenyl-based [Fe2(hnmh-PLY)3] complex as the cathode material.

The sustainable chemical energy of H2O2 as a fuel and an oxidant in an advantageous single-compartment fuel cell design can be converted into electric energy, which requires molecular engineering to design suitable cathodes for lowering the high overpotential associated with H2O2 reduction. The present work covers the synthesis and structural characterization of a novel cathode material, [FeIII2(hnmh-PLY)3] complex, 1, designed from a PLY-derived Schiff base ligand (E)-9-(2-((2-hydroxynaphthalen-1-yl)methylene)hydrazineyl)-1H-phenalen-1-one, hnmh-PLYH2. Complex 1, when coated on the surface of a glassy carbon electrode (GC-1) significantly catalyzed the reduction of H2O2 in an acidic medium. Therefore, a complex 1 modified glassy carbon electrode was employed in a one-compartment H2O2 fuel cell operated in 0.1 M HCl with Ni foam as the corresponding anode to produce a high open circuit potential (OCP) of 0.65 V and a peak power density (PPD) of 2.84 mW cm-2. CV studies of complex 1 revealed the crucial participation of two Fe(III) centers for initiating H2O2 reduction, and the role of coordinated redox-active PLY units is also highlighted. In the solid state, the π-conjugated network of coordinating (hnmh-PLY) ligands in complex 1 has manifested interesting face-to-face π-π stacking interactions, which have helped the reduction of the complex and facilitated the overall catalytic performance.

Iron Dioxygen Adduct Formed during Electrochemical Oxygen Reduction by Iron Porphyrins Shows Catalytic Heme Dioxygenase Reactivity.

Heme dioxygenases oxidize the indole ring of tryptophan to kynurenine which is the first step in the biosynthesis of several important biomolecules like NAD, xanthurenic acid, and picolinic acid. A ferrous heme dioxygen adduct (or FeIII-O2•-) is the oxidant, and both the atoms of O2 are inserted in the product and its catalytic function has been difficult to emulate as it is complicated by competing rapid reactions like auto-oxidation and/or formation of the µ-oxo dimer. In situ resonance Raman spectroscopy technique, SERRS-RDE, is used to probe the species accumulated during electrochemical ORR catalyzed by site-isolated imidazole-bound iron porphyrin installed on a self-assembled monolayer covered electrode. These in situ SERRS-RDE data using labeled O2 show that indeed a FeIII-O2•- species accumulate on the electrode during ORR between -0.05 and -0.30 V versus Ag/AgCl (satd. KCl) and is reduced by proton coupled electron transfer to a FeIII-OOH species which, on the other hand, builds up on the electrode between -0.20 and -0.40 V versus Ag/AgCl (satd. KCl). This FeIII-OOH species then gives way to a FeIV═O species, which accumulates at -0.50 V versus Ag/AgCl (satd. KCl). When 2,3-dimethylindole is present in the solution and the applied potential is held in the range where FeIII-O2•- species accumulate, it gets oxidized to N-(2-acetylphenyl)acetamide retaining both the oxygens from O2 mimicking the reaction of heme dioxygenases. Turnover numbers more than 104 are recorded, establishing this imidazole-bound ferrous porphyrin as a functional model of heme dioxygenases.

A closed-shell phenalenyl-based dinuclear iron(III) complex as a robust cathode for a one-compartment H2O2 fuel cell.

Closed-shell phenalenyl (PLY) systems are increasingly becoming more attractive as building blocks for developing promising catalysts and electroactive cathode materials, as they have tremendous potential to accept electrons and participate in redox reactions. Herein, we report a PLY-based dinuclear [FeIII2(hmbh-PLY)3] complex, 1, and its utility as a cathode material in a H2O2 fuel cell. Complex 1 was synthesized from a new Schiff base ligand, (E)-9-(2-(2-hydroxy-3-methoxybenzylidene)hydrazineyl)-1H-phenalen-1-one, hmbh-PLYH2, designed using a PLY precursor, Hz-PLY. The newly derived ligand and complex 1 were characterized by various analytical techniques, including single-crystal X-ray diffraction (SCXRD). The cyclic voltammetry (CV) study revealed that complex 1 undergoes five electron reductions under an applied electric potential. When the electroactive complex 1 was employed as a cathode in a membrane-less one-compartment H2O2 fuel cell, with Ni foam as the corresponding anode, the designed fuel cell exhibited an exceptionally high peak power density (PPD) of 2.41 mW cm-2, in comparison with those of all the previously reported Fe-based molecular complexes. DFT studies were performed to gain reasonable insights into the two-electron catalytic reduction (pathway I) of H2O2 by the Fe-center of complex 1 and to explore the geometries, energetics of the electrocatalyst, reactive intermediates and transition states.

Development and validation of a predictive model for the diagnosis of rheumatic heart disease in low-income countries based on two cross-sectional studies.

Objectives: We developed a questionnaire-based risk-scoring system to identify children at risk for rheumatic heart disease (RHD) in rural India. The resulting predictive model was validated in Nepal, in a population with a similar demographic profile to rural India. Methods: The study involved 8646 students (mean age 13.0 years, 46% boys) from 20 middle and high schools in the West Midnapore district of India. The survey asked questions about the presence of different signs and symptoms of RHD. Students with possible RHD who experienced sore throat and joint pain were offered an echocardiogram to screen for RHD. Their findings were compared with randomly selected students without these symptoms. The data were analyzed to develop a predictive model for identifying RHD. Results: Based on our univariate analyses, seven variables were used for building a predictive model. A four-variable model (joint pain plus sore throat, female sex, shortness of breath, and palpitations) best predicted the risk of RHD with a C-statistic of 0.854. A six-point scoring system developed from the model was validated among similarly aged children in Nepal. Conclusions: A simple questionnaire-based predictive instrument could identify children at higher risk for this disease in low-income countries where RHD remains prevalent. Echocardiography could then be used in these high-risk children to detect RHD in its early stages. This may support a strategy for more effective secondary prophylaxis of RHD.

A natural language processing approach to categorise contributing factors from patient safety event reports.

OBJECTIVES: The objective of this study was to explore the use of natural language processing (NLP) algorithm to categorise contributing factors from patient safety event (PSE). Contributing factors are elements in the healthcare process (eg, communication failures) that instigate an event or allow an event to occur. Contributing factors can be used to further investigate why safety events occurred. METHODS: We used 10 years of self-reported PSE reports from a multihospital healthcare system in the USA. Reports were first selected by event date. We calculated χ2 values for each ngram in the bag-of-words then selected N ngrams with the highest χ2 values. Then, PSE reports were filtered to only include the sentences containing the selected ngrams. Such sentences were called information-rich sentences. We compared two feature extraction techniques from free-text data: (1) baseline bag-of-words features and (2) features from information-rich sentences. Three machine learning algorithms were used to categorise five contributing factors representing sociotechnical errors: communication/hand-off failure, technology issue, policy/procedure issue, distractions/interruptions and lapse/slip. We trained 15 binary classifiers (five contributing factors * three machine learning models). The models' performances were evaluated according to the area under the precision-recall curve (AUPRC), precision, recall, and F1-score. RESULTS: Applying the information-rich sentence selection algorithm boosted the contributing factor categorisation performance. Comparing the AUPRCs, the proposed NLP approach improved the categorisation performance of two and achieved comparable results with baseline in categorising three contributing factors. CONCLUSIONS: Information-rich sentence selection can be incorporated to extract the sentences in free-text event narratives in which the contributing factor information is embedded.

Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing.

A report published in 2000 from the Institute of Medicine revealed that medical errors were a leading cause of patient deaths, and urged the development of error detection and reporting systems. The field of radiation oncology is particularly vulnerable to these errors due to its highly complex process workflow, the large number of interactions among various systems, devices, and medical personnel, as well as the extensive preparation and treatment delivery steps. Natural language processing (NLP)-aided statistical algorithms have the potential to significantly improve the discovery and reporting of these medical errors by relieving human reporters of the burden of event type categorization and creating an automated, streamlined system for error incidents. In this paper, we demonstrate text-classification models developed with clinical data from a full service radiation oncology center (test center) that can predict the broad level and first level category of an error given a free-text description of the error. All but one of the resulting models had an excellent performance as quantified by several metrics. The results also suggest that more development and more extensive training data would further improve future results.

Using Community Detection Techniques to Identify Themes in COVID-19-Related Patient Safety Event Reports.

OBJECTIVES: The COVID-19 pandemic has transformed how healthcare is delivered to patients. As the pandemic progresses and healthcare systems continue to adapt, it is important to understand how these changes in care have changed patient care. This study aims to use community detection techniques to identify and facilitate analysis of themes in patient safety event (PSE) reports to better understand COVID-19 pandemic's impact on patient safety. With this approach, we also seek to understand how community detection techniques can be used to better identify themes and extract information from PSE reports. METHODS: We used community detection techniques to group 2082 PSE reports from January 1, 2020, to January 31, 2021, that mentioned COVID-19 into 65 communities. We then grouped these communities into 8 clinically relevant themes for analysis. RESULTS: We found the COVID-19 pandemic is associated with the following clinically relevant themes: (1) errors due to new and unknown COVID-19 protocols/workflows; (2) COVID-19 patients developing pressure ulcers; (3) unsuccessful/incomplete COVID-19 testing; (4) inadequate isolation of COVID-19 patients; (5) inappropriate/inadequate care for COVID-19 patients; (6) COVID-19 patient falls; (7) delays or errors communicating COVID-19 test results; and (8) COVID-19 patients developing venous thromboembolism. CONCLUSIONS: Our study begins the long process of understanding new challenges created by the pandemic and highlights how machine learning methods can be used to understand these and similar challenges. Using community detection techniques to analyze PSE reports and identify themes within them can help give healthcare systems the necessary information to improve patient safety and the quality of care they deliver.

Scalable Estimation of Epidemic Thresholds via Node Sampling.

Infectious or contagious diseases can be transmitted from one person to another through social contact networks. In today's interconnected global society, such contagion processes can cause global public health hazards, as exemplified by the ongoing Covid-19 pandemic. It is therefore of great practical relevance to investigate the network transmission of contagious diseases from the perspective of statistical inference. An important and widely studied boundary condition for contagion processes over networks is the so-called epidemic threshold. The epidemic threshold plays a key role in determining whether a pathogen introduced into a social contact network will cause an epidemic or die out. In this paper, we investigate epidemic thresholds from the perspective of statistical network inference. We identify two major challenges that are caused by high computational and sampling complexity of the epidemic threshold. We develop two statistically accurate and computationally efficient approximation techniques to address these issues under the Chung-Lu modeling framework. The second approximation, which is based on random walk sampling, further enjoys the advantage of requiring data on a vanishingly small fraction of nodes. We establish theoretical guarantees for both methods and demonstrate their empirical superiority.

Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen.

The objectives of this study were (1) to predict ruminal pH and ruminal ammonia and volatile fatty acid (VFA) concentrations by developing artificial neural networks (ANN) using dietary nutrient compositions, dry matter intake, and body weight as input variables; and (2) to compare accuracy and precision of ANN model predictions with that of a multiple linear regression model (MLR). Data were collected from 229 published papers with 938 treatment means. The data set was randomly split into a training data set containing 70% of the observations and a test data set with the remaining observations. A series of ANN with a range of 1 to 9 artificial neurons in 1 hidden layer were examined, and the best one was selected to compare with the best-fitted MLR model. The performance of model predictions was evaluated by root mean square errors (RMSE) and concordance correlation coefficients (CCC) using cross-evaluations with 100 iterations. When using the ANN to predict ruminal pH and concentrations of ammonia, total VFA, acetate, propionate, and butyrate, the RMSE were 4.2, 41.4, 20.9, 22.3, 32.9, and 29.7% of observed means, respectively. The RMSE for the MLR were 4.2, 37.8, 18.3, 19.9, 29.8, and 26.6% of the observed means. The CCC for ruminal pH, ruminal concentrations of ammonia, total VFA, acetate, propionate, and butyrate were 0.57, 0.49, 0.45, 0.40, 0.52, and 0.40, using the ANN, and 0.37, 0.48, 0.40, 0.29, 0.43, and 0.35, using the MLR. Evaluations of the MLR and the ANN indicated that these 2 model forms exhibited similar prediction errors, with 4.2, 39.6, 19.6, 21.1, 31.3, and 28.1% of observed means for pH, ammonia, total VFA, acetate, propionate, and butyrate. Although the ANN increased the precision of predictions related to ruminal metabolism, it failed to improve the accuracy compared with the linear regression model.

The effect of temporal aggregation level in social network monitoring.

Social networks have become ubiquitous in modern society, which makes social network monitoring a research area of significant practical importance. Social network data consist of social interactions between pairs of individuals that are temporally aggregated over a certain interval of time, and the level of such temporal aggregation can have substantial impact on social network monitoring. There have been several studies on the effect of temporal aggregation in the process monitoring literature, but no studies on the effect of temporal aggregation in social network monitoring. We use the degree corrected stochastic block model (DCSBM) to simulate social networks and network anomalies and analyze these networks in the context of both count and binary network data. In conjunction with this model, we use the Priebe scan method as the monitoring method. We demonstrate that temporal aggregation at high levels leads to a considerable decrease in the ability to detect an anomaly within a specified time period. Moreover, converting social network communication data from counts to binary indicators can result in a significant loss of information, hindering detection performance. Aggregation at an appropriate level with count data, however, can amplify the anomalous signal generated by network anomalies and improve detection performance. Our results provide both insights on the practical effects of temporal aggregation and a framework for the study of other combinations of network models, surveillance methods, and types of anomalies.

Synthesis of calcium hydrogen phosphate and hydroxyapatite coating on SS316 substrate through pulsed electrodeposition.

The orthopaedic implants for human body are generally made of different biomaterials like stainless steels or Ti based alloys. However, it has been found that from surface properties point of view, none of these materials is attractive for fast tissue or cell growth on the surface of implant. This is one of the most important criteria to assure quick bonding between implant and body tissues vis-à-vis minimum recovery time for the patient. Keeping in view of the above facts, this work involves the pulsed electro-deposition coating of biocompatible hydroxyapatite and its group compounds from a diluted bath of calcium and phosphate salt at various current densities over the biomaterial sheet of SS316. SEM study confirms different morphologies of the coatings at different current densities. Characterization techniques like X-ray diffraction, SEM with EDX and FTIR have been used to confirm the phase and percentage quantity of hydroxyapatite compound in the depositions. This coating can serve as a medium for faster tissue growth over the metallic implants.