Salivary Analysis for Evidence based Ayurvedic Diagnosis

Ayurveda is called Mother of all medical sciences. It's the oldest therapeutic and medicinal treatment invented in ancient India. Ayurveda or Ayurvedic treatment is bit different from modern medical science. It believes in Nadi Pariksha and many subjective parameters are included to start diagnosis of disease. Whereas modern medical science has different approach of disease diagnosis. It utilizes different tools and testing to diagnose a disease effectively. Saliva analysis is already accepted in modern medical as an important bio-substance, as we see in COVID-19, but not in ayurveda. This paper shows how salivary analysis can act as an evidential proof for diagnosing a disease, in the ayurvedic way. The salivary contents can be analyzed use various biosensors. One of these is Surface Enhanced Raman Spectroscopy (SERS) platform. It allows molecular detection in bio fluids like saliva, sweat, urine, etc. The saliva analysis using SERS technique will help to detect various trace level molecules which is likely to assist the Ayurvedic diagnosis more accurately and dependency on subjective parameters will reduce to evaluate patient's condition. © 2023 IEEE.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research.

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Identification of Circulating Biomarkers of COVID-19 Using MALDI-TOF Mass Spectrometry.

Matrix-assisted laser desorption/ionization source coupled with time-of-flight mass analyzer mass spectrometry (MALDI-TOF MS) is being widely used to obtain proteomic profiles for clinical purposes, as a fast, low-cost, robust, and efficient technique. Here we describe a method for biofluid analysis using MALDI-TOF MS for rapid acquisition of proteomic signatures of COVID-19 infected patients. By using solid-phase extraction, the method allows the analysis of biofluids in less than 15 min.

Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning.

SIGNIFICANCE: The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise and reagents may become less specific to the virus. AIM: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents. APPROACH: We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique-Raman spectroscopy-to detect changes in the molecular profile of saliva associated with COVID-19 infection. RESULTS: We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%. CONCLUSION: These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.

Using Simulation-Based Active Learning Strategies for Teaching Biofluids Concepts.

Biofluids comprises a core topical domain for modern biomedical engineering education. Like other biomedical topic areas, biofluids education must address highly interdisciplinary and applied topics. Concept/problem-based active learning approaches can provide effective avenues to teach such diverse and applied topics. However, with the heterogeneity within biofluids topics across cellular, physiological, and/or extra-organismal scales, it is important to develop active learning content that enables students to explore concepts with appropriate context. This challenge is further complicated by the need to administer such content remotely (due to the Covid-19 pandemic). Here, we outline our design process and implementation experience for simulation-based active learning modules for a newly developed physiological biofluids course. We share the overall design approach, with two example cases of simulation-based concept exploration: (a) arterial Windkessel effects and lumped parameter hemodynamic analysis; and (b) curvature-induced helical flow in human aorta illustrated using four-dimensional (4D) flow magnetic resonance imaging (MRI). Evidence from student survey ratings, student comments and feedback, and monitoring student performance for course deliverables indicate positive student response toward these modules, and efficacy of the modules in enabling student learning. Based on our design and implementation experience, we argue that simulation-based approaches can enable active learning of biofluids through remote and online learning modalities.

Discovery Proteomics for COVID-19: Where We Are Now.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible coronavirus responsible for the pandemic coronavirus disease 2019 (COVID-19), which has had a devastating impact on society. Here, we summarize proteomic research that has helped elucidate hallmark proteins associated with the disease with respect to both short- and long-term diagnosis and prognosis. Additionally, we review the highly variable humoral response associated with COVID-19 and the increased risk of autoimmunity.

Electrochemical sensing: A prognostic tool in the fight against COVID-19.

The COVID-19 pandemic has devastated the world, despite all efforts in infection control and treatment/vaccine development. Hospitals are currently overcrowded, with health statuses of patients often being hard to gauge. Therefore, methods for determining infection severity need to be developed so that high-risk patients can be prioritized, resources can be efficiently distributed, and fatalities can be prevented. Electrochemical prognostic biosensing of various biomarkers may hold promise in solving these problems as they are low-cost and provide timely results. Therefore, we have reviewed the literature and extracted the most promising biomarkers along with their most favourable electrochemical sensors. The biomarkers discussed in this paper are C-reactive protein (CRP), interleukins (ILs), tumour necrosis factor alpha (TNFα), interferons (IFNs), glutamate, breath pH, lymphocytes, platelets, neutrophils and D-dimer. Metabolic syndrome is also discussed as comorbidity for COVID-19 patients, as it increases infection severity and raises chances of becoming infected. Cannabinoids, especially cannabidiol (CBD), are discussed as a potential adjunct therapy for COVID-19 as their medicinal properties may be desirable in minimizing the neurodegenerative or severe inflammatory damage caused by severe COVID-19 infection. Currently, hospitals are struggling to provide adequate care; thus, point-of-care electrochemical sensor development needs to be prioritized to provide an approximate prognosis for hospital patients. During and following the immediate aftermath of the pandemic, electrochemical sensors can also be integrated into wearable and portable devices to help patients monitor recovery while returning to their daily lives. Beyond the COVID-19 pandemic, these sensors will also prove useful for monitoring inflammation-based diseases such as cancer and cardiovascular disease.

Maximizing Safety in the Conduct of Alzheimer's Disease Fluid Biomarker Research in the Era of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic led to an abrupt halt of many Alzheimer's disease (AD) research studies at sites spanning the world. This is especially true for studies requiring in-person contact, such as studies collecting biofluids. Since COVID-19 is likely to remain a threat for an extended period, the resumption of fluid biomarker studies requires the development and implementation of procedures that minimize the risk of in-person visits to participants, staff, and individuals handling the biofluid samples. Some issues to consider include structuring the visit workflow to minimize contacts and promote social distancing; screening and/or testing participants and staff for COVID-19; wearing masks and performing hand hygiene; and precautions for handling, storing, and analyzing biofluids. AD fluid biomarker research remains a vitally important public health priority and resuming studies requires appropriate safety procedures to protect research participants and staff.