Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.

Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due to subclinical manifestations of the disease at the early phase of onset. Machine learning systems can increase timely detection of shock onset by exploiting complex interactions among continuous physiological waveforms. We use a dataset consisting of high-resolution physiological waveforms from intensive care unit (ICU) of a tertiary hospital system. We investigate the use of mean arterial blood pressure (MAP), pulse arrival time (PAT), heart rate variability (HRV), and heart rate (HR) for the early prediction of shock onset. Using only five minutes of the aforementioned vital signals from 239 ICU patients, our developed models can accurately predict septic shock onset 6 to 36 hours prior to clinical recognition with area under the receiver operating characteristic (AUROC) of 0.84 and 0.8 respectively. This work lays foundations for a robust, efficient, accurate and early prediction of septic shock onset which may help clinicians in their decision-making processes. This study introduces machine learning models that provide fast and accurate predictions of septic shock onset times up to 36 hours in advance. BP, PAT and HR dynamics can independently predict septic shock onset with a look-back period of only 5 mins.

Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals.

The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.

Polyethylene oxide-polyacrylic acid-folic acid (PEO-PAAc) nanogel as a 99m Tc targeting receptor for cancer diagnostic imaging.

Nanoparticles are frequently used as targeting delivery systems for therapeutic and diagnostic radiopharmaceuticals. Polyethylene oxide-polyacrylic acid (PEO-PAAc) nanogel was prepared via γ-radiation-induced polymerization. Variable factors affecting nanoparticles size were investigated. The nanogel was radiolabeled with the imaging radioisotope 99m Tc and finally conjugated with folic acid to target folate receptor actively. PEO-PAAc-folic acid gel was characterized by dynamic light scattering (DLS) and atomic force microscopy (AFM). Biodistribution was studied in normal mice and solid tumor-bearing mice via intravenous and intratumor injections of the radiolabeled PEO-PAAc-folic acid nanogel. Results of biodistribution showed high selective uptake of the prepared complex in tumor muscle compared with normal muscle for both intravenous and intratumor injections. The T/NT ratio was found to be 6.186 and 294.5 for intravenous and intratumor injections, respectively. Consequently, 99m Tc-PEO-PAAc-folic acid complex could be a promising agent for cancer diagnostic imaging.

On the relationship between rutile/anatase ratio and the nature of defect states in sub-100 nm TiO2 nanostructures: experimental insights.

Black TiO2 is being widely investigated due to its superior optical activity and potential applications in photocatalytic hydrogen generation. Herein, the limitations of the hydrogenation process of TiO2 nanostructures are unraveled by exploiting the fundamental tradeoffs affecting the overall efficiency of the water splitting process. To control the nature and concentration of defect states, different reduction rates are applied to sub-100 nm TiO2 nanotubes, chosen primarily for their superiority over their long counterparts. X-Ray Photoelectron Spectroscopy disclosed changes in the stoichiometry of TiO2 with the reduction rate. UV-vis and Raman spectra showed that high reduction rates promote the formation of the rutile phase in TiO2, which is inactive towards water splitting. Furthermore, electrochemical analysis revealed that such high rates induce a higher concentration of localized electronic defect states that hinder the water splitting performance. Finally, incident photon-to-current conversion efficiency (IPCE) highlighted the optimum reduction rate that attains a relatively lower defect concentration as well as lower rutile content, thereby achieving the highest conversion efficiency.