Clinicians' approach to predicting post-cardiac arrest outcomes for patients enrolled in a United States clinical trial.

PURPOSE: Perceived poor prognosis can lead to withdrawal of life-sustaining therapies (WLST) in patients who might otherwise recover. We characterized clinicians' approach to post-arrest prognostication in a multicenter clinical trial. METHODS: Semi-structured interviews were conducted with clinicians who treated a comatose post-cardiac arrest patient enrolled in the Influence of Cooling Duration on Efficacy in Cardiac Arrest Patients (ICECAP) trial (NCT04217551). Two authors independently analyzed each interview using inductive and deductive coding. The clinician reported how they arrived at a prognosis for the specific patient. We summarized the frequency with which clinicians reported using objective diagnostics to formulate their prognosis, and compared the reported approaches to established guidelines. Each respondent provided demographic information and described local neuroprognostication practices. RESULTS: We interviewed 30 clinicians at 19 US hospitals. Most claimed adherence to local hospital neuroprognostication protocols (n = 19). Prognostication led to WLST for perceived poor neurological prognosis in 15/30 patients, of whom most showed inconsistencies with guidelines or trial recommendations, respectively. In 10/15 WLST cases, clinicians reported relying on multimodal testing. A prevalent theme was the use of "clinical gestalt," defined as prognosticating based on a patient's overall appearance or a subjective impression in the absence of objective data. Many clinicians (21/30) reported using clinical gestalt for initial prognostication, with 9/21 expressing high confidence initially. CONCLUSION: Clinicians in our study state they follow neuroprognostication guidelines in general but often do not do so in actual practice. They reported clinical gestalt frequently informed early, highly confident prognostic judgments, and few objective tests changed initial impressions. Subjective prognostication may undermine well-designed trials.

Quantification of Active Visual Attention using RGB camera.

Active visual attention (AVA) is the cognitive ability that helps to focus on important visual information while responding to a stimulus and is important for human-behavior and psychophysiological research. Existing eye-trackers/camera-based methods are either expensive or impose privacy issues as face videos are recorded for analysis. Proposed approach using blink-rate variability (BRV), is inexpensive, easy to implement, efficient and handles privacy issues, making it amenable to real-time applications. Our solution uses laptop camera/webcams and a single blink feature, namely BRV. First, we estimated participant's head pose to check camera alignment and detect if he is looking at the screen. Next, subject-specific threshold is computed using eye aspect ratio (EAR) to detect blinks from which BRV signal is constructed. Only EAR values are saved, and participant's face video is NOT saved or transmitted. Finally, a novel AVA score is computed. Results shows that the proposed score is robust across participants, ambient light conditions and occlusions like spectacles.

An approach to a wrist wearable based Covid-19 prediction system to protect Health Care Professionals.

With healthcare professionals being the frontline warriors in battling the Covid pandemic, their risk of exposure to the virus is extremely high. We present our experience in building a system, aimed at monitoring the physiology of these professionals 24/7, with the hope of providing timely detection of infection and thereby better care. We discuss a machine learning approach and model using signals from a wrist wearable device to predict infection at a very early stage. In a double-blind test on a small group of patients, our model could successfully identify the infected versus non-infected cases with near 100% accuracy. We also discuss some of the challenges we faced, both technical and non-technical, to get this system off the ground as well as offer some suggestions that could help tackle these hurdles.

Surface Potential Simulation and Electrode Design for in-Ear EEG Measurement.

The need for everyday-real-time EEG sensing has resulted in the development of minimalistic and discreet wearable EEG measuring devices. A front runner in this race is in-ear worn device. However, the position of the ear as well as its restricted accessibility poses certain challenges in the design of such devices - from the type of materials used to the placement of electrodes. These choices are important as they will determine the quality of the EEG signal available and in turn the accuracy of the application. We therefore create a simulation model of the human ear, allowing us to understand the impact of these choices on our design of an In-Ear EEG wearable. We first study the signal acquisition characteristics of a proposed gold-plated electrode against two other state-of-the-art electrode materials for in-ear EEG data collection. We further validate this electrode choice by fabricating a personalized silicone-based earpiece and collecting in-situ EEG data. This work explores the properties of using gold plated electrodes in capturing in-ear EEG signals enabling unobtrusive collection of the brain physiology data in real world setting.

Design of a Realtime Photoplethysmogram Signal Quality Checker for Wearables and Edge Computing.

Photoplethysmogram (PPG) signal is extensively used for deducing health parameters of patients in order to infer about physiological conditions of heart, blood pressure, respiratory patterns, and so on. Such analysis and estimations can be done accurately only on high quality PPG signals with very minimal artifacts. PPG signals collected from fitness grade and smart phone scenarios are prone to muscle artifacts and hence there is a need to assess the signal quality before using the signal. Although there are approaches available in the realm of machine learning and deep learning, they are computationally expensive and may not be suitable for a wearable or edge computing scenario. In this paper, we propose the design of a quality checker to check the quality of the signal that can be directly implemented on edge devices like smartwatch. The algorithm is tested on PPG data collected from wearable, ICU and medical grade devices. In the wearable scenario where the noise levels are very high, our algorithm has performed significantly better with a Fscore of over 0.92. Further we show that by applying the proposed quality checker, the accuracy of the computed heart rate from a smart phone PPG-application significantly improves.

Person and Stressor Independent Generic Model for Stress Detection Using GSR.

Stress detection is a widely researched topic and is important for overall well-being of an individual. Several approaches are used for prediction/classification of stress. Most of these approaches perform well for subject and activity specific scenarios as stress is highly subjective. So, it is difficult to create a generic model for stress prediction. Here, we have proposed an approach for creating a generic stress prediction model by utilizing knowledge from three different datasets. Proposed model has been validated using two open datasets as well as on a set of data collected in our lab. Results show that the proposed generic model performs well across studies conducted independently and hence can be used for monitoring stress in real life scenarios and to create mass-market stress prediction products.

A Novel Optimization Algorithm Leveraging a Three-Dimensional Approach of Periscopic, Pheromonic and Fractal Search.

This paper focuses on a new algorithm for solving optimization problems using the nature of food search behaviour of caterpillars. The paper describes how the periscopic, pheromonic and fractal search properties analogous to the caterpillars, can aid in designing a new optimization algorithm. The performance characteristics of the new method is compared using 26 standard test functions and the area under the curve of the fitness evaluations is used to validate and compare the proposed algorithms against existing related works. The proposed algorithm is found to be efficient when compared with the existing methods. The proposed algorithm is then tested on a real world problem to remove signal noise from eye gaze data, effectively.

A trial study of using DSST to evaluate cognitive impairment in older adults.

Non-invasive means of monitoring mild cognitive impairments (MCI) is recently gaining popularity. With the advent of easy to use physiological sensors, there have been an outburst of studies from the last decade which aim at detecting a target's mental health condition. However, not many studies present the experience or insights gained from carrying out such in-situ research work, particularly when working with older adults. Such insights could not only assist researchers in related areas when designing their study but also avoid potential pitfalls. Clinical trials were conducted by our organization in collaboration with the Geriatric Educational Research Institute, Singapore (GERI) and Singapore Management University (SMU) for detecting mild cognitive impairments in a geriatric population. Digitized versions of the standard pen & paper psychological tests were used along with gaze tracking technologies for MCI detection. Details of our user study and it's outcomes are discussed as well as a generic approach of digitizing any given psychological test battery is highlighted.

Affective response to tunes synthesized with musical pitch curves.

Tunes perceived as happy may help a user reach an affective state of positive valence. However, a user with negative valence may not be ready to listen to such a tune immediately. In this paper, we consider nudging a user from their current affective state to a target affective state in small steps. We propose a technique to generate a gradation of tunes between an initial-reference tune and a target-reference tune, to achieve the affect transition. The two-dimensional gradation is realized in time and in pitch, respectively, by varying the tempo and by the use of musical pitch curves, i.e. pitch transients or simply 'transients'. We exploit the duration and scaling of transients observed in South Indian music (Carnatic) to introduce transients into existing tunes. In our experiment, we have introduced the transients into Western music tunes. The results of perceptual evaluation show that the affective response to transients is likely to be higher at slow tempos than at fast tempos. Further, when felt, transient-tunes are twice as likely to be associated with positive valence than with negative valence, irrespective of tempo.

Single feature spatio-temporal architecture for EEG Based cognitive load assessment.

The study of electroencephalography (EEG) data for cognitive load analysis plays an important role in identification of stress-inducing tasks. This can be useful in applications such as optimal work allocation, increasing efficiency in the workplace and ensuring safety in difficult work environments. In order for such systems to be realistically deployable, easy acquisition and processing of the data on a wearable device is imperative. Current techniques primarily perform offline processing to analyse a multi-channel EEG to make a post facto assessment. This work focusses on building a new deep learning architecture that performs a single feature based spatio-temporal analysis of EEG data. This is achieved by creating a brain topographic map based on a single feature followed by spatio-temporal analysis using the developed network architecture. Data from two cognitive load experiments on the Physionet EEGMAT dataset were used to validate the performance. The network achieves an accuracy of 98.3% which is better than similar state-of-the-art approaches. Moreover, the proposed approach facilitates analysis of the spatial propagation of a signal, which is not possible through conventional EEG signal representations.

Cost Effective Real-time System for cognitive computing using Personalized Eye Blink Detection from Camera.

Eye blink is indicative of various mental states. Generally, vision based approaches are used for detecting eye blinks. However, performance of such approaches varies across participants. Standard eye tracker or eye glasses used for detecting blinks, are very costly. Here, we are proposing a personalized vision based eye blink detector system. Proposed approach is ubiquitous and unobtrusive in nature and can be implemented using standard webcams/mobile camera, making it deployable for real world scenarios. Our approach has been validated on a set of data collected from our lab and on an open data set. Results show that in both cases, our system performs well for various conditions like natural/artificial light, with or without spectacles. We achieved a Fscore of 0.98 for own collected data and 0.91 for open dataset, which outperform state of the art approaches.

Effect of Moisture Sorption on Free Volume and Relaxation of Spray Dried Dispersions: Relation to Drug Recrystallization.

The effect of vapor sorption on the free volume of drug-polymer spray-dried dispersions (SDDs) was investigated, along with the crystallization propensity of drug molecules in SDDs after exposure to humidity. Subsequently, the correlation of free volume change and relaxation time with drug recrystallization was examined. Four polymers, including polyvinylpyrrolidone, polyvinylpyrrolidone vinyl acetate copolymer, hydroxypropyl cellulose, and hydroxypropyl methylcellulose acetate succinate, and 2 drugs (indomethacin and ketoconazole) were selected for preparing SDDs. Free volume data of the exposed SDDs were obtained with positron annihilation lifetime spectroscopy, while the relaxation time was measured using a TA rheometer. Additionally, the crystallization propensity of active pharmaceutical ingredients (APIs) in the exposed SDDs was assessed using both polarized light microscopy and powder X-ray diffraction, followed by relating API crystallization inclination with expansion of holes and relaxation time. Finally, Cohen and Turnbull molecular transport model, along with its extensions by Vrentas and Duda, was qualitatively utilized for interpreting the recrystallization propensity of API molecules. In conclusion, API recrystallization is closely related to free volume change upon moisture sorption and relaxation time, but system dependent; overall, drug-hydroxypropyl methylcellulose acetate succinate SDDs appear physically stable against recrystallization due to less increase in free volume.

Structural Variation Detection by Proximity Ligation from Formalin-Fixed, Paraffin-Embedded Tumor Tissue.

The clinical management and therapy of many solid tumor malignancies depends on detection of medically actionable or diagnostically relevant genetic variation. However, a principal challenge for genetic assays from tumors is the fragmented and chemically damaged state of DNA in formalin-fixed, paraffin-embedded (FFPE) samples. From highly fragmented DNA and RNA there is no current technology for generating long-range DNA sequence data as is required to detect genomic structural variation or long-range genotype phasing. We have developed a high-throughput chromosome conformation capture approach for FFPE samples that we call Fix-C, which is similar in concept to Hi-C. Fix-C enables structural variation detection from archival FFPE samples. This method was applied to 15 clinical adenocarcinoma- and sarcoma-positive control specimens spanning a broad range of tumor purities. In this panel, Fix-C analysis achieves a 90% concordance rate with fluorescence in situ hybridization assays, the current clinical gold standard. In addition, novel structural variation undetected by other methods could be identified, and long-range chromatin configuration information recovered from these FFPE samples harboring highly degraded DNA. This powerful approach will enable detailed resolution of global genome rearrangement events during cancer progression from FFPE material and will inform the development of targeted molecular diagnostic assays for patient care.

A Sensor-enabled Digital Trier Social Stress Test in an Enterprise Context.

The Trier Social Stress Test (TSST) protocol is a widely accepted method of inducing social and/or cognitive stress in participants and studying its effects. Traditionally, this protocol is administered in laboratory or university settings, which are less formal than in offices. In this paper, we report the results of the analysis of multi-modal sensor data collected from employees of an enterprise who underwent the test. We briefly discuss the adaptations that enabled administering it digitally in a semi-automatic mode with minimal researcher/test-administrator intervention. In our setup, noninvasive sensor-signals, including the Galvanic Skin Response and Photoplethysmogram, were collected during and outside the stress-inducing tasks. We analyze the data collected from twenty participants and show that the State Trait Anxiety Inventory (STAI) score is needed in assessing the effect of the digital version of the TSST. A support vector machine classifier yielded an F1 score of 0.723 with the STAI score taken as ground truth. We also introduce the idea of ground truth based on the change in the STAI scores to reduce variation due to subjective interpretation, for which an F1 score of 0.847 was obtained.

Unraveling substituent effects on the glass transition temperatures of biorenewable polyesters.

Converting biomass-based feedstocks into polymers not only reduces our reliance on fossil fuels, but also furnishes multiple opportunities to design biorenewable polymers with targeted properties and functionalities. Here we report a series of high glass transition temperature (Tg up to 184 °C) polyesters derived from sugar-based furan derivatives as well as a joint experimental and theoretical study of substituent effects on their thermal properties. Surprisingly, we find that polymers with moderate steric hindrance exhibit the highest Tg values. Through a detailed Ramachandran-type analysis of the rotational flexibility of the polymer backbone, we find that additional steric hindrance does not necessarily increase chain stiffness in these polyesters. We attribute this interesting structure-property relationship to a complex interplay between methyl-induced steric strain and the concerted rotations along the polymer backbone. We believe that our findings provide key insight into the relationship between structure and thermal properties across a range of synthetic polymers.

Single-Cell mRNA-Seq Using the Fluidigm C1 System and Integrated Fluidics Circuits.

Single-cell mRNA-seq is a valuable tool to dissect expression profiles and to understand the regulatory network of genes. Microfluidics is well suited for single-cell analysis owing both to the small volume of the reaction chambers and easiness of automation. Here we describe the workflow of single-cell mRNA-seq using C1 IFC, which can isolate and process up to 96 cells. Both on-chip procedure (lysis, reverse transcription, and preamplification PCR) and off-chip sequencing library preparation protocols are described. The workflow generates full-length mRNA information, which is more valuable compared to 3' end counting method for many applications.

Emission analysis on the effect of nanoparticles on neat biodiesel in unmodified diesel engine.

Biodiesels derived from the mahua seeds are established as a promising alternative for the diesel fuel owing to its non-edible nature and improved properties. TiO2 nanoparticle in powder form is added to neat mahua oil biodiesel (BD100) to examine its effect on emission characteristics. TiO2 nanoparticle is chosen as an additive owing to its catalytic effect, higher surface energy, and larger surface to volume ratio. TiO2 nanoparticle with an average size of 60 nm was synthesized by sol-gel route. TiO2 nanoparticles are added with mahua biodiesel (BD100) at 100 and 200 ppm. Mahua oil biodiesel doped with 100 and 200 ppm of TiO2 nanoparticles are referred as BD100T100 and BD100T200. A constant speed diesel engine is employed for the experimental trail. Engine is fueled with diesel, BD100, BD100T100, and BD100T200, respectively. Experimental result confirmed that the modified fuels (BD100T200 and BD100T100) showed a significant reduction in all the emissions. Further, the addition of TiO2 nanoparticle (200 ppm) to mahua biodiesel gave respective reduction of 9.3, 5.8, 6.6, and 2.7% in carbon monoxide, hydrocarbon, nitrogen oxide, and smoke emissions when compared to neat mahua biodiesel.

Single-cell protein-mRNA correlation analysis enabled by multiplexed dual-analyte co-detection.

We have investigated the correlation between proteins and mRNAs in single cells employing an integrated workflow for dual-analyte co-detection. This is achieved by combining the oligo extension reaction (OER), which converts protein levels to DNA levels, with reverse transcription for mRNA detection. Unsupervised gene expression profiling analysis, including principal component analysis and hierarchical clustering, revealed different aspects of the protein-mRNA relationship. Violin plot analysis showed that some genes exhibited similar distribution patterns for proteins and mRNAs. We also demonstrate that cells can be separated into subpopulations based on their protein-mRNA expression profiles, and that different subpopulations have distinct correlation coefficient values. Our results demonstrated that integrated investigations of mRNA and protein levels in single cells allows comprehensive analysis not attainable at bulk levels.

Fast Self-Healing of Polyelectrolyte Multilayer Nanocoating and Restoration of Super Oxygen Barrier.

A self-healable gas barrier nanocoating, which is fabricated by alternate deposition of polyethyleneimine (PEI) and polyacrylic acid (PAA) polyelectrolytes, is demonstrated in this study. This multilayer film, with high elastic modulus, high glass transition temperature, and small free volume, has been shown to be a super oxygen gas barrier. An 8-bilayer PEI/PAA multilayer assembly (≈700 nm thick) exhibits an oxygen transmission rate (OTR) undetectable to commercial instrumentation (<0.005 cc (m-2 d-1 atm-1 )). The barrier property of PEI/PAA nanocoating is lost after a moderate amount of stretching due to its rigidity, which is then completely restored after high humidity exposure, therefore achieving a healing efficiency of 100%. The OTR of the multilayer nanocoating remains below the detection limit after ten stretching-healing cycles, which proves this healing process to be highly robust. The high oxygen barrier and self-healing behavior of this polymer multilayer nanocoating makes it ideal for packaging (food, electronics, and pharmaceutical) and gas separation applications.

Simple Method To Prepare Oligonucleotide-Conjugated Antibodies and Its Application in Multiplex Protein Detection in Single Cells.

The diversity of nucleic acid sequences enables genomics studies in a highly multiplexed format. Since multiplex protein detection is still a challenge, it would be useful to use genomics tools for this purpose. This can be accomplished by conjugating specific oligonucleotides to antibodies. Upon binding of the oligonucleotide-conjugated antibodies to their targets, the protein levels can be converted to oligonucleotide levels. In this report we describe a simple method for preparing oligonucleotide-conjugated antibodies and discuss this method's application in oligonucleotide extension reaction (OER) for multiplex protein detection. Conjugation is based on strain-promoted alkyne-azide cycloaddition (the Cu-free click reaction), in which the antibody is activated with a dibenzocyclooctyne (DBCO) moiety and subsequently linked covalently with an azide-modified oligonucleotide. In the functional test, the reaction conditions and purification processes were optimized to achieve maximum yield and best performance. The OER assay employs a pair of antibody binders (two antibodies, each conjugated with its own oligonucleotide) developed for each protein target. The two oligonucleotides contain unique six-base complementary regions at their 3' prime ends to allow annealing and extension by DNA synthesis enzymes to form a DNA template. Following preamplification, the DNA template is detected by qPCR. Distinct oligonucleotide sequences are assigned to different antibody binders to enable multiplex protein detection. When tested using recombinant proteins, some antibody binders, such as those specific to CSTB, MET, EpCAM, and CASP3, had dynamic ranges of 5-6 logs. The antibody binders were also used in a multiplexed format in OER assays, and the binders successfully detected their protein targets in cell lysates, and in single cells in combination with the C1 system. This click reaction-based antibody conjugation procedure is cost-effective, needs minimal hands-on time, and is well-suited for the development of affordable multiplex protein assays, which provides the potential to accelerate proteomics research.