ECG waveform generation from radar signals: A deep learning perspective.

Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy. Additionally, ECG monitoring usually requires highly trained professionals and specialized equipment, which increases the treatment's complexity and cost. In critical care scenarios, such as continuous monitoring of hospitalized patients, wearable sensors for collecting ECG data may be difficult to use. Although there are issues with ECG, it remains a valuable tool for diagnosing and monitoring cardiac disorders due to its non-invasive nature and the detailed information it provides about the heart. The goal of this study is to present an innovative method for generating continuous ECG waveforms from non-contact radar data by using Deep Learning. The method can eliminate the need for invasive or wearable biosensors and expensive equipment to collect ECGs. In this paper, we propose the MultiResLinkNet, a one-dimensional convolutional neural network (1D CNN) model for generating ECG signals from radar waveforms. With the help of a publicly accessible radar benchmark dataset, an end-to-end DL architecture is trained and assessed. There are six ports of raw radar data in this dataset, along with ground truth physiological signals collected from 30 participants in five distinct scenarios: Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. By using strong temporal and spectral measurements, we assessed our proposed framework's ability to convert ECG data from Radar signals in three distinct scenarios, namely Resting, Valsalva, and Apnea (RVA). ECG segmentation performed better by MultiResLinkNet than by state-of-the-art networks in both combined and individual cases. As a result of the simulations, the resting, valsalva, and RVA scenarios showed the highest average temporal values, respectively: 66.09523 ± 19.33, 60.13625 ± 21.92, and 61.86265 ± 21.37. In addition, it exhibited the highest spectral correlation values (82.4388 ± 18.42 (Resting), 77.05186 ± 23.26 (Valsalva), 74.65785 ± 23.17 (Apnea), and 79.96201 ± 20.82 (RVA)), along with minimal temporal and spectral errors in almost every case. The qualitative evaluation revealed strong similarities between generated and actual ECG waveforms. As a result of our method of forecasting ECG patterns from remote radar data, we can monitor high-risk patients, especially those undergoing surgery.

Systematic Alignment Analysis of Neural Transplant Cells in Electrospun Nanofibre Scaffolds.

Spinal cord injury is debilitating with functional loss often permanent due to a lack of neuro-regenerative or neuro-therapeutic strategies. A promising approach to enhance biological function is through implantation of tissue engineered constructs, to offer neural cell replacement and reconstruction of the functional neuro-architecture. A key goal is to achieve spatially targeted guidance of regenerating tissue across the lesion site to achieve an aligned tissue structure lost as a consequence of injury. Electrospun nanofibres mimic the nanoscale architecture of the spinal cord, can be readily aligned, functionalised with pro-regenerative molecules and incorporated into implantable matrices to provide topographical cues. Crucially, electrospun nanofibers are routinely manufactured at a scale required for clinical use. Although promising, few studies have tested whether electrospun nanofibres can guide targeted spatial growth of clinically relevant neural stem/precursor populations. The alignment fate of daughter cells (derived from the pre-aligned parent cells) has also received limited attention. Further, a standardised quantification methodology to correlate neural cell alignment with topographical cues is not available. We have adapted an image analysis technique to quantify nanofibre-induced alignment of neural cells. Using this method, we show that two key neural stem/precursor populations of clinical relevance (namely, neural stem cells (NSCs) and oligodendrocyte precursor cells), reproducibly orientate their growth to aligned, high-density electrospun nanofiber meshes, but not randomly distributed ones. Daughter populations derived from aligned NSCs (neurons and astrocytes) maintained their alignment following differentiation, but oligodendrocytes did not. Our data show that pre-aligned transplant populations can be used to generate complex, multicellular aligned-fibre constructs for neural implantation.

Life-threatening bupropion ingestion: is there a role for intravenous fat emulsion?

Intravenous fat emulsion (IFE) is emerging as a novel antidote in clinical toxicology. Its current usage is extending beyond local anaesthetic toxicity into management of severe toxicity from some lipophilic drugs. We present a 51-year-old woman with severe bupropion toxicity whose haemodynamic status transiently improved after IFE. Serum analysis demonstrated an increase in serum concentration of hydroxybupropion, an active metabolite of bupropion, after IFE administration, lending support to one of the proposed mechanisms of IFE. A 51-year-old woman presented to the emergency department with generalised tonic-clonic convulsions lasting approximately 30 sec., and a wide complex rhythm on her ECG that was suggestive of myocardial sodium channel blockade. Despite sodium bicarbonate therapy, the patient developed profound hypotension refractory to high-dose norepinephrine. IFE was administered with haemodynamic improvement over the course of 30 min., followed by a significant decrease in norepinephrine requirement. The patient had an episode of ventricular tachycardia 24 hr after presentation, and received a second infusion of IFE. Analysis of serum for a panel of myocardial sodium channel blocking drugs revealed that significant bupropion ingestion had occurred. Bupropion poisoning may produce life-threatening clinical effects, and IFE may be considered in cases of severe haemodynamic instability. Further studies would be instrumental in determining the optimal clinical situations for utilisation of IFE.

Regulation of pyruvate dehydrogenase kinase isoform 4 (PDK4) gene expression by glucocorticoids and insulin.

The pyruvate dehydrogenase complex (PDC) catalyzes the conversion of pyruvate to acetyl-CoA in mitochondria and is a key regulatory enzyme in the oxidation of glucose to acetyl-CoA. Phosphorylation of PDC by the pyruvate dehydrogenase kinases (PDK) inhibits its activity. The expression of the pyruvate dehydrogenase kinase 4 (PDK4) gene is increased in fasting and other conditions associated with the switch from the utilization of glucose to fatty acids as an energy source. Transcription of the PDK4 gene is elevated by glucocorticoids and inhibited by insulin. In this study, we have investigated the factors involved in the regulation of the PDK4 gene by these hormones. Glucocorticoids stimulate PDK4 through two glucocorticoid receptor (GR) binding sites located more than 6000 base pairs upstream of the transcriptional start site. Insulin inhibits the glucocorticoid induction in part by causing dissociation of the GR from the promoter. Previously, we found that the estrogen related receptor alpha (ERRalpha) stimulates the expression of PDK4. Here, we determined that one of the ERRalpha binding sites contributes to the insulin inhibition of PDK4. A binding site for the forkhead transcription factor (FoxO1) is adjacent to the ERRalpha binding sites. FoxO1 participates in the glucocorticoid induction of PDK4 and the regulation of this gene by insulin. Our data demonstrate that glucocorticoids and insulin each modulate PDK4 gene expression through complex hormone response units that contain multiple factors.

Estrogen-related receptors stimulate pyruvate dehydrogenase kinase isoform 4 gene expression.

The pyruvate dehydrogenase complex (PDC) catalyzes the conversion of pyruvate to acetyl-CoA in mitochondria and is a key regulatory enzyme in the oxidation of glucose to acetyl-CoA. Phosphorylation of PDC by the pyruvate dehydrogenase kinases (PDK2 and PDK4) inhibits PDC activity. Expression of the PDK genes is elevated in diabetes, leading to the decreased oxidation of pyruvate to acetyl-CoA. In these studies we have investigated the transcriptional regulation of the PDK4 gene by the estrogen-related receptors (ERRalpha and ERRgamma). The ERRs are orphan nuclear receptors whose physiological roles include the induction of fatty acid oxidation in heart and muscle. Previously, we found that the peroxisome proliferator-activated receptor gamma coactivator (PGC-1alpha) stimulates the expression of PDK4. Here we report that ERRalpha and ERRgamma stimulate the PDK4 gene in hepatoma cells, suggesting a novel role for ERRs in controlling pyruvate metabolism. In addition, both ERR isoforms recruit PGC-1alpha to the PDK4 promoter. Insulin, which decreases the expression of the PDK4 gene, inhibits the induction of PDK4 by ERRalpha and ERRgamma. The forkhead transcription factor (FoxO1) binds the PDK4 gene and contributes to the induction of PDK4 by ERRs and PGC-1alpha. Insulin suppresses PDK4 expression in part through the dissociation of FoxO1 and PGC-1alpha from the PDK4 promoter. Our data demonstrate a key role for the ERRs in the induction of hepatic PDK4 gene expression.