Transition metal complexes of 5-bromosalicylidene-4-amino-3-mercapto-1,2,4-triazine-5-one: Synthesis, characterization, catalytic and antibacterial studies

Transition metal complexes of 5-bromosalicylidene-4-amino-3-mercapto-1,2,4-triazine-5-one with metal precursors, such as Cu(II), Ni(II), Co(II) and Pd(II), were synthesized and characterized by physico–chemical and spectroscopic techniques. All the complexes are of the ML type. Based on analytical, spectral data and magnetic moments, the Co(II) and Ni(II) complexes were assigned octahedral geometries, while the Cu (II) and Pd(II) complexes square planar. A study on the catalytic oxidation of benzyl alcohol, cyclohexanol, cinnamyl alcohol, 2-propanol and 2-methyl-1-propanol was performed with N-methylmorpholine-N-oxide (NMO) as co-oxidant. All the complexes and their parent organic moiety were screened for their biological activity on several pathogenic bacteria and were found to possess appreciable bactericidal properties

Measuring Nociception Under Anesthesia

Sixty thousand patients receive general anesthesia each day in the US alone. The problem of monitoring and managing nociception, the flow of information associated with harmful stimuli through the nervous system even when unconscious, in real time is critical during surgery. While there are measures to assess unconsciousness, immobility, and physiologic stability, objectively monitoring a patient’s nociceptive state remains challenging. Intraoperative management of nociception affects post-operative pain management and side effects such as delirium and post-operative cognitive dysfunction. This thesis focuses on monitoring nociceptive state by tracking autonomic nervous system (ANS) responses. The two autonomic markers are heart rate variability (HRV), the beat-to-beat variation in heart rate, and electrodermal activity (EDA), the measurable change in skin conductance due to sweat gland activity. Since traditional experimental models of pain such as thermal or electrical stimulation are not adequate representations of true surgical nociception, I collected continuous electrocardiogram (ECG), EDA, and ANI data during 70 surgeries at Massachusetts General Hospital (MGH) in an IRB-approved study. I annotated the occurrence of nociceptive stimuli and retrieved the times and doses of anesthetics from the electronic medical record. First, I developed a statistically rigorous framework to extract the valuable instantaneous information from EDA. I also developed a pipeline to preprocess and clean the operating room data. Then I used two frameworks, supervised classification models and state space models, to show that my physiological indices can track the occurrence of nociceptive stimulation to determine the degree of antinociception more accurately on a subject-by-subject basis than the ANI. In summary, I have: 1) constructed and validated quantitative multi-dimensional measures of intraoperative nociceptive state using HRV and EDA; and 2) compared these measures to the existing Analgesic Nociception Index (ANI) index for nociception monitoring using data collected during surgery. This work presents the first step towards truly integrated and physiology-based intraoperative management, and eventually closed-loop control of nociception under general anesthesia.Ph.D

A Point Process Characterization of Electrodermal Activity

Electrodermal activity (EDA) is a measure of sympathetic activity using skin conductance that has applications in research and in clinical medicine. However, current EDA analysis does not have physiologically-based statistical models that use stochastic structure to provide nuanced insight into autonomic dynamics. Therefore, in this study, we analyzed the data of two healthy volunteers under controlled propofol sedation. We identified a novel statistical model for EDA and used a point process framework to track instantaneous dynamics. Our results demonstrate for the first time that point process models rooted in physiology and built upon inherent statistical structure of EDA pulses have the potential to accurately track instantaneous dynamics in sympathetic tone

Multimodal vs Unimodal Estimation of Sympathetic-Driven Arousal States

Estimation of sympathetic-driven arousal state (SDAS) traditionally consists of computing frequency-based measures of heart rate variability. However, in the presence of confounds such as breathing frequency, these measures can incorrectly estimate the underlying SDAS. In this work, we present an example of such a case during a three-stage paced breathing task. Using a state space framework, we demonstrate that a unimodal model that relies solely on these frequency-based heart rate variability measures overestimates SDAS during the slowest breathing stage and underestimates it in subsequent stages. On the other hand, a multimodal model with both time and frequency domain heart rate variability observations as well as electrodermal activity information provides a more realistic estimate of SDAS throughout the task. This suggests that multimodal estimation of SDAS is more accurate and robust than unimodal estimation

Point process temporal structure characterizes electrodermal activity

Electrodermal activity (EDA) is a direct readout of the body's sympathetic nervous system measured as sweat-induced changes in the skin's electrical conductance. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Standardized EDA data analysis methods are readily available. However, none considers an established physiological feature of EDA. The sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process. An integrate-and-fire process modeled as a Gaussian random walk with drift diffusion yields an inverse Gaussian model as the interpulse interval distribution. Therefore, we chose an inverse Gaussian model as our principal probability model to characterize EDA interpulse interval distributions. To analyze deviations from the inverse Gaussian model, we considered a broader model set: the generalized inverse Gaussian distribution, which includes the inverse Gaussian and other diffusion and nondiffusion models; the lognormal distribution which has heavier tails (lower settling rates) than the inverse Gaussian; and the gamma and exponential probability distributions which have lighter tails (higher settling rates) than the inverse Gaussian. To assess the validity of these probability models we recorded and analyzed EDA measurements in 11 healthy volunteers during 1 h of quiet wakefulness. Each of the 11 time series was accurately described by an inverse Gaussian model measured by Kolmogorov-Smirnov measures. Our broader model set offered a useful framework to enhance further statistical descriptions of EDA. Our findings establish that a physiologically based inverse Gaussian probability model provides a parsimonious and accurate description of EDA.NIH (Award P01-GM118629

Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.</jats:p

Elementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity

Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skin’s electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models which represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaike’s Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA.</jats:p