BENZENE-1,3-DIAMIDOETHANETHIOL (BDETH2) AND ITS METAL COMPOUNDS

There is a global need to find a permanent and readily implemented solution to the problem of heavy metal pollution in aqueous environments. A dithiol compound, benzene-1,3-diamidoethanethiol (BDETH2), also known as N,N\u27-bis(2-mercaptoethyl) isophthalamide or N,N\u27-bis(2-mercaptoethyl)-1,3-benzenedicarboxamide, capable of binding divalent metal ions, has been synthesized and characterized. A broad range of BDET-metal compounds, spanning the periodic chart, has been prepared and characterized by IR, MS, EA, Raman, XAFS and TGA. The characteristics of the BDET-M compounds were determined through secondary reactions. In an effort to derivatize BDET-M compounds through alkylalumination a new cyclic compound, 1,3- bis(4,5-dihydrothiazolo)benzene, has been synthesized by refluxing BDETH2 in the presence of AlMe3. Mineral coating studies have been performed and it was found that coating with BDET prevents metal leaching. XPS studies indicated that covalent bonds exist between BDET and metals at the mineral surfaces. BDETH2 is not water soluble and must be used as an ethanolic solution to precipitate metals from water. In an effort to find similar ligands that are water-soluble another dithiol compound, N,N\u27-bis(2-mercaptoethyl)oxalamide (MOA), and a monothiol compound, N-mercaptoethyl-furoylamide (MFA), have been synthesized. Each was found to precipitate Cd, Hg and Pb from water, to varying degrees. Some metal compounds of MOA, MFA and dithiothreitol (DTT), a watersoluble dithiol compound have been prepared and characterized. These compounds provide insight into the properties of the BDET-M compounds. For example, it was shown that insolubility in water is a common feature of thiol compounds and is not unique to BDET-M compounds

A Maximum Likelihood Sequence Equalizing Architecture Using Viterbi Algorithm for ADC-Based Serial Link

Channel impairments in high data rates make Analog-to-digital (ADC) serial link a very attractive choice in terms of bandwidth efficient modulation; however, power limitation of these receivers make the ADC front-end design rather challenging [3]. By replacing traditional symbol by-symbol digital equalizer with a maximum likelihood sequence estimator (MLSE) receiver, in ADC serial link, we can produce a more optimal equalizing architecture in terms of noise, and simplify the complexity of the design in the analog front-end [7]. MLSE architecture is implemented using the Viterbi algorithm, in Matlab, and the parameters for the analog front-end circuits were defined by plotting the bit error rate (BER) as a function of different SNRs. Comparing the BER between the traditionally used MMSE equalizer and MLSE receiver BER was found to be lower for same SNR. Although using the Viterbi algorithm to determine the original signal sequence may make MLSE computationally challenging, the simplicity of analog front-end and lower BER makes this an effective choice for high bandwidth transmission in a digital-heavy receiver

Risk of Nosocomial Transmission of Nipah Virus in a Bangladesh Hospital

We conducted a seroprevalence study and exposure survey of healthcare workers to assess the risk of nosocomial transmission of Nipah virus during an outbreak in Bangladesh in 2004. No evidence of recent Nipah virus infection was detected despite substantial exposures and minimal use of personal protective equipmen

Charge Transfer between Benzene-1, 3-diamidoethanethiol (BDET) and Metal Sulfides Affect Efficiency of Acid Mine Drainage Treatment

Acid mine drainage (AMD) from metal sulfides that result in heavy metal leaching into the environment is a prevalent problem. Coating natural pyrite (FeS2/SiO2) and galena (PbS) mineral surfaces with benzene-1,3-diamidoethanethiol (BDET) was found to be an effective method for preventing heavy metal leaching into aqueous media. The natural pyrite examined was found to have a significant amount of quartz in its matrix that influenced its ability to bind with BDET. X-ray photoelectron spectroscopy (XPS) core level shifts of the S 2p, N1s, Fe 2p and Pb 4f orbitals upon complexation revealed that greater BDET binding to these metal sulfides correlated with ligand-to-metal (LMCT) charge transfer. Improved binding of BDET to FeS2 over PbS was observed as the concentration of metal in the supernatant dramatically decreased. Stronger BDET binding to FeS2/SiO2 was attributed to LMCT from the open shell Fe absent in the corresponding metal in galena (Pb), which had a closed shell configuration. Comparison of N 1s spectra in control experiments show that SiO2 resulted in a reduced number of N 1s oxidation states, improving anti-leaching properties. Coverage of BDET complexed to FeS2/SiO2 was markedly greater than that for PbS, in agreement with inductive coupled plasma optical emission spectrometry (ICP-OES) data. Photoelectron spectroscopy data revealed that the electronic shell structure of the sulfide metal is a contributing factor in BDET\u27s ability to inhibit heavy metal leaching

Prevention of Sulfide Leaching through Covalent Coating

The use of benzene-1,3-diamidoethanethiol as a covalent surface coating for the prevention of metal leaching was demonstrated with several sulfide minerals including cinnabar (HgS), pyrite (FeS2), chalcopyrite (CuFeS2), covellite (CuS), galena (PbS), realgar (As4S4) and sphalerite (ZnS). The minerals were coated with sufficient H2BDT to bind the surface metals in a 1:1 ratio. Leaching at pH 1, 3 and 7 was then conducted on both treated and untreated minerals. ICP and CVAFS (for mercury) analyses revealed that the coated minerals showed a dramatic reduction in metal leaching as compared to uncoated control samples. X-ray photoelectron spectroscopy indicated the formation of covalent bonds between the sulphur of the ligand and the metals from the minerals

A Smartphone-Based Decision Support Tool for Predicting Patients at Risk of Chemotherapy-Induced Nausea and Vomiting: Retrospective Study on App Development Using Decision Tree Induction

BackgroundChemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. ObjectiveThis study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. MethodsData were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. ResultsThe decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. ConclusionsThis study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs

Utility of Features in a Natural-Language-Processing-Based Clinical De-Identification Model Using Radiology Reports for Advanced NSCLC Patients

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F1-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature

Utility of Features in a Natural-Language-Processing-Based Clinical De-Identification Model Using Radiology Reports for Advanced NSCLC Patients

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F1-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature