3D Printed Interdigitated Electrodes for Cardiac Biomarker Detection.

The identification of biomarkers has significant benefits for early disease diagnosis and treatment. Hence, there is an increasing demand for low-cost, disposable point-of-care diagnostic devices for rapid and specific biomarker detection, with good sensitivity and range. Interdigitated electrodes (IDEs) are among the most widely used transducers, especially in chemical and biological sensors, because of their high sensitivity, low cost, and straightforward manufacturing procedure. In this work, a simple 3D printed IDE structure has been developed for cardiac troponin I detection to indicate the risk of acute myocardial infarction (AMI). IDEs have been fabricated using 3D printing technique and the electrically conductive composite polylactic acid (PLA) filament being utilized for the fabrication of electrodes. The demonstrated cardiac troponin I sensor has a calculated quantification limit and detection limit of 0.233 ng ml-1 and 76.97 pg ml-1, respectively which are clinically significant ranges for AMI identification. Electrochemical analytical techniques, such as electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV), were carried out for the detection of analyte concentration. Furthermore, using this fabrication methodology IDEs can be fabricated for under US$ 0.4 which can be utilized to detect several other biomarkers.

Radical pair model for magnetic field effects on NMDA receptor activity.

The N-methyl-D-aspartate receptor is a prominent player in brain development and functioning. Perturbations to its functioning through external stimuli like magnetic fields can potentially affect the brain in numerous ways. Various studies have shown that magnetic fields of varying strengths affect these receptors. We propose that the radical pair mechanism, a quantum mechanical process, could explain some of these field effects. Radicals of the form [Formula: see text], where R is a protein residue that can be Serine or Tyrosine, are considered for this study. The variation in the singlet fractional yield of the radical pairs, as a function of magnetic field strength, is calculated to understand how the magnetic field affects the products of the radical pair reactions. Based on the results, the radical pair mechanism is a likely candidate for explaining the magnetic field effects observed on the receptor activity. The model predicts changes in the behaviour of the system as magnetic field strength is varied and also predicts certain isotope effects. The results further suggest that similar effects on radical pairs could be a plausible explanation for various magnetic field effects within the brain.

Chronic inflammation: Cross linking insights from Ayurvedic Sciences, a silver lining to systems biology and personalized medicine.

Precision in personalized medicine is a crucial subject that needs comprehensive discussion and scientific validation. Traditional healthcare approaches like the Ayurvedic Sciences are often contextually linked with personalized medicine. However, it is unfortunate that this knowledge concerning Ayurveda and personalized medicine is restricted to applying systems biology techniques to 'prakriti' the phenotypic expression and characterization detailed in the literature. There are other significant constructs besides prakruti that interest an Ayurvedic physician, which accounts for crafting precision in evidence-based medicinal practices. There is this influential model of Ayurvedic healthcare practice wherein the physician maps specific personalized characters in addition to prakruti to deduce the host responses to endogenous and exposome conditions. Subsequently, tailored protocols are administered that bring about holistic, personalized outcomes. The review aimed to determine the effective methods for integrating Systems Biology, Ayurvedic Sciences, and Personalized Medicine (precision medicinebased). Ayurveda adopts a holistic approach, considering multiple variables and their interconnections, while the modern reductionist approach focuses on understanding complex details of smaller parts through rigorous experimentation. Despite seeming extremes, ongoing research on lifestyle, gut health, and spiritual well-being highlights the evolving intersection between traditional Ayurvedic practices and modern science. The current focus is on developing the fundamental concept of Ayurveda Biology by incorporating Systems Biology techniques. Challenges in this integration include understanding diverse data types, bridging interdisciplinary knowledge gaps, and addressing technological limitations and ethical concerns. Overcoming these challenges will require interdisciplinary collaboration, innovative methodologies, substantial investment in technology, and cultural sensitivity to preserve Ayurveda's core principles while leveraging modern scientific advancements. The focus of discussions and debates on such collaborations should be breakthrough clinical models, such as chronic inflammation, which can be objectively related to specific stages of disease manifestations described in Ayurveda. Validating patient characteristics with systems biology approaches, particularly in shared pathologies like chronic inflammation, is crucial for bringing prediction and precision to personalized medicine.

Transforming Medicinal Oil into Advanced Gel: An Update on Advancements.

The present study delves into the evolution of traditional Ayurvedic oil preparations through innovative strategies to develop advanced gel formulations, aiming at amplifying their therapeutic efficacy. Ayurvedic oils have a rich historical context in healing practices, yet their conversion into contemporary gel-based formulations represents a revolutionary approach to augment their medicinal potential. The primary objective of this transformation is to leverage scientific advancements and modern pharmaceutical techniques to enhance the application, absorption, and overall therapeutic impact of these traditional remedies. By encapsulating the essential constituents of Ayurvedic oils within gel matrices, these novel strategies endeavor to improve their stability, bioavailability, and targeted delivery mechanisms. This review highlights the fusion of traditional Ayurvedic wisdom with cutting-edge pharmaceutical technology, paving the way for more effective and accessible utilization of these revered remedies in modern healthcare.

Rodent models in polycystic ovarian Syndrome: Dissecting reproductive and metabolic phenotypes for therapeutic Advancements.

The most prevalent reason for female infertility is polycystic ovarian syndrome (PCOS) exhibiting two of three phenotypes including biochemical or clinical hyperandrogenism, anovulation and polycystic ovaries. Insulin resistance and obesity are common in PCOS-afflicted women. Androgens are thought to be the primary cause of PCOS causing symptoms including anovulation, follicles that resemble cysts, higher levels of the luteinizing hormone (LH), increased adiposity, and insulin resistance. However, due to the heterogeneity of PCOS, it is challenging to establish a single model that accurately mimics all the reproductive and metabolic phenotypes seen in PCOS patients. In this review, we aimed to investigate rodent models of PCOS and related phenotypes with or without direct hormonal treatments and to determine the underlying mechanisms to comprehend PCOS better. We summarized rodent models of PCOS that includes direct and indirect hormone intervention and discussed the aetiology of PCOS and related phenotypes produced in rodent models. We presented combined insights on multiple rodent models of PCOS and compared their reproductive and/or metabolic phenotypes. Our review indicates that there are various models for studying PCOS and one should select a model most suitable for their purpose. This review will be helpful for consideration of rodent models for PCOS which are not conventionally used to determine mechanisms at the molecular/cellular levels encouraging development of novel treatments and control methods for PCOS.

Association between baseline insulin resistance and hospital mortality in moderate-to-severe coronavirus disease 2019 patients without diabetes mellitus: An observational study.

Background: Insulin resistance is often implicated as a risk factor of cell-mediated immune dysfunction in sepsis patients and results in poor clinical outcome. However, it is unclear whether early insulin resistance is contributory to T-cell dysfunction and poor clinical outcome in coronavirus disease 2019 (COVID-19) patients. Methods: Adult patients with moderate-to-severe or critically ill COVID-19 infection were included in this study. Serum samples were collected at the time of diagnosis for fasting plasma glucose, serum insulin, serum cortisol, and serum glucagon measurements, and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) score was calculated. Results: One hundred and twenty-six subjects with a mean (standard deviation) age of 49.6 (16.3) years were recruited in this study, and 62.4% (78 of 125 patients) were male. HOMA-IR was a predictor of inhospital mortality with the area under the receiver operating characteristics curve (AUROC) (95% confidence interval [CI] of 0.61 [0.49-0.73]). With a cutoff value of 1.91, sensitivity was 75.5% and specificity was 45.2%. Higher serum insulin was associated with higher survival with AUROC (95% CI) of 0.65 (0.53-0.76), and the best cutoff was 7.15, with a sensitivity and specificity of 62.1% and 64.5%. Serum cortisol was also a predictor of inhospital mortality with an AUROC (95% CI) of 0.67 (0.56-0.77). Conclusion: An independent association between baseline serum cortisol and poor outcome in moderate-to-severe COVID-19 patients was observed. Hyperglycemia and HOMA-IR can also predict poor outcome in these patients with some accuracy.

Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

BACKGROUND: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data. METHODS: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm. The study was first interpreted by level III-trained echocardiography cardiologists as the gold standard for rhythm diagnosis based on ECG rhythm strip and imaging assessment, which was also verified with a 12-lead ECG around the time of the study. AP4 cines with three cardiac cycles were then extracted from these studies with the rhythm strip and Doppler information removed and introduced to the deep learning model ResNet(2+1)D with an 80:10:10 training-validation-test split ratio. RESULTS: 634 patient studies (1205 cines) were included. After training, the AI model achieved high accuracy on validation for detection of both AF and sinus rhythm (mean F1-score = 0.92; AUROC = 0.95). Performance was consistent on the test dataset (mean F1-score = 0.94, AUROC = 0.98) when using the cardiologist's assessment of the ECG rhythm strip as the gold standard, who had access to the full study and external ECG data, while the AI model did not. CONCLUSIONS: AF detection by AI on echocardiography without ECG appears accurate when compared to an echocardiography cardiologist's assessment of the ECG rhythm strip as the gold standard. This has potential clinical implications in point-of-care ultrasound and stroke risk stratification.

Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL) models. A total of 30,080 unique studies were included; 24,013 studies were used to train a convolutional neural network model to automatically assess, at end-diastole, LV internal diameter (LVID), interventricular septal wall thickness (IVS), posterior wall thickness (PWT), and LV mass. The model was trained to select end-diastolic frames with the largest LVID and to identify four landmarks, marking the dimensions of LVID, IVS, and PWT using manually labeled landmarks as reference. The model was validated with 3,014 echocardiographic cines and the accuracy of the model was evaluated with a test set of 3,053 echocardiographic cines. The model accurately measured LVID, IVS, PWT, and LV mass compared to study report values with a mean relative error of 5.40%, 11.73%, 12.76%, and 13.93%, respectively. The 𝑅2 of the model for the LVID, IVS, PWT, and the LV mass was 0.88, 0.63, 0.50, and 0.87, respectively. The novel DL model developed in this study was accurate for LV dimension assessment without the need to select end-diastolic frames manually. DL automated measurements of IVS and PWT were less accurate with greater wall thickness. Validation studies in larger and more diverse populations are ongoing.

Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance.

BACKGROUND: Machine learning (ML) algorithms can accurately estimate left ventricular ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of-care ultrasound (POCUS) is not well understood. OBJECTIVES: We evaluate the performance of an ML model for estimation of LVEF on cardiac POCUS compared with Level III echocardiographers' interpretation and formal echo reported LVEF. METHODS: Clinicians at a tertiary care heart failure clinic prospectively scanned 138 participants using hand-carried devices. Video data were analyzed offline by an ML model for LVEF. We compared the ML model's performance with Level III echocardiographers' interpretation and echo reported LVEF. RESULTS: There were 138 participants scanned, yielding 1257 videos. The ML model generated LVEF predictions on 341 videos. We observed a good intraclass correlation (ICC) between the ML model's predictions and the reference standards (ICC = 0.77-0.84). When comparing LVEF estimates for randomized single POCUS videos, the ICC between the ML model and Level III echocardiographers' estimates was 0.772, and it was 0.778 for videos where quantitative LVEF was feasible. When the Level III echocardiographer reviewed all POCUS videos for a participant, the ICC improved to 0.794 and 0.843 when only accounting for studies that could be segmented. The ML model's LVEF estimates also correlated well with LVEF derived from formal echocardiogram reports (ICC = 0.798). CONCLUSION: Our results suggest that clinician-driven cardiac POCUS produces ML model LVEF estimates that correlate well with expert interpretation and echo reported LVEF.

Genome-wide association study identifies human genetic variants associated with fatal outcome from Lassa fever.

Infection with Lassa virus (LASV) can cause Lassa fever, a haemorrhagic illness with an estimated fatality rate of 29.7%, but causes no or mild symptoms in many individuals. Here, to investigate whether human genetic variation underlies the heterogeneity of LASV infection, we carried out genome-wide association studies (GWAS) as well as seroprevalence surveys, human leukocyte antigen typing and high-throughput variant functional characterization assays. We analysed Lassa fever susceptibility and fatal outcomes in 533 cases of Lassa fever and 1,986 population controls recruited over a 7 year period in Nigeria and Sierra Leone. We detected genome-wide significant variant associations with Lassa fever fatal outcomes near GRM7 and LIF in the Nigerian cohort. We also show that a haplotype bearing signatures of positive selection and overlapping LARGE1, a required LASV entry factor, is associated with decreased risk of Lassa fever in the Nigerian cohort but not in the Sierra Leone cohort. Overall, we identified variants and genes that may impact the risk of severe Lassa fever, demonstrating how GWAS can provide insight into viral pathogenesis.