Synthesis, characterization and application of novel composite nano-materials for the electrochemical detection of MRSA and related drugs.

Doctoral Degree. University of KwaZulu-Natal, Durban.This thesis reports the development of electroanalytical methods applicable for detection of MRSA and selected antibacterial drugs; vancomycin and ciprofloxacin. The detection techniques used to carryout all electrochemical measurements involved Ag/AgCl (3 M KCl)) as reference electrode, platinum wire as the counter electrode and the glassy carbon electrode (GCE) as the working electrode. Firstly, to carry out the detection of vancomycin as single shot detection assay comprising of poly acrylic acid modied copper tricarboxylic acid based metal organic framework was employed. Cyclic voltammetry was used to carry out the highly sensitive detection of vancomycin. Ciprofloxacin was detected by modification of the GCE by beta cyclodextrin modified silver nanoparticles (Ag-β-CD/GCE) and by modifying another electrode with poly(phenol red)/reduced graphene oxide (rGO/PPR/GCE). Differential pulse voltammetry (DPV) was used to carry out the detection of the drug in various analytes such as animal blood serum, urine and domestic waste water samples. Finally, the electrode was modified with copperbeta-cyclodextrin-graphene oxide composite conjugated with vancomycin to act as a thernostic tool for detection and treatment of MRSA bacterial strains. DPV and impedance spectroscopy was used to carry out optimization and further the detection of MRSA. The designed sensors provided good sensitivity and low LOD for detection of the respective analytes with good specificity. Thus, the present study demonstrates a promising and alternative approach for clinical analysis and quality control of vancomycin and ciprofloxacin

Machine Learning Framework: Competitive Intelligence and Key Drivers Identification of Market Share Trends Among Healthcare Facilities

The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.Comment: 7 Pages 5 figures 6 tables To appear in ICHA 202

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients

Introduction of an integrated interactive lecture in the early blocks of 1st year medical students: A pilot study

Background: In an integrated curriculum, topics are integrated at the system block level from semester 2 onward. However, in the basic biomedical block, opportunities to integrate also exist which is often overlooked. Aim: A study was conducted to determine the possibility and acceptability of a new pedagogical approach to aid integration of form and function along with the improvement of understanding. Materials and Design: The study was prospective and questionnaire-based, conducted in an upcoming medical college in Malaysia with 1st year medical students. Materials and Methods: An integrated lecture on the autonomic nervous system was planned for the 1st year medical students in their biomedical science block of the first semester by the lecturers from anatomy and physiology and interactive lecture for 1 h and 30 min was delivered. After the session, responses were taken on a structured questionnaire with 10 questions on the Likert scale and a test was conducted to check their understandability. SPSS version 19.1 was used to analyze the results and data were reported based on descriptive statistics and scores were compared by t-test. Results: About 84.2% of the students wanted more lectures of this kind whereas 15.8% disagreed. About 80% stated that proper integration increased their understandability, whereas 83% preferred this modality in comparison to didactic lectures. Conclusions: This pedagogical approach with careful planning can be extended to involve clinical departments thus reaching vertical integration. Including more such integrated interactive sessions will prove to be significant and an effective tool for teaching and learning

Role of water in cyclooxygenase catalysis and design of anti-inflammatory agents targeting two sites of the enzyme

Abstract While designing the anti-inflammatory agents targeting cyclooxygenase-2 (COX-2), we first identified a water loop around the heme playing critical role in the enzyme catalysis. The results of molecular dynamic studies supported by the strong hydrogen-bonding equilibria of the participating atoms, radical stabilization energies, the pKa of the H-donor/acceptor sites and the cyclooxygenase activity of pertinent muCOX-2 ravelled the working of the water–peptide channel for coordinating the flow of H·/electron between the heme and Y385. Based on the working of H·/electron transfer channel between the 12.5 Å distant radical generation and the radical disposal sites, a series of molecules was designed and synthesized. Among this category of compounds, an appreciably potent anti-inflammatory agent exhibiting IC50 0.06 μM against COX-2 and reversing the formalin induced analgesia and carageenan induced inflammation in mice by 90% was identified. Further it was revealed that, justifying its bidentate design, the compound targets water loop (heme bound site) and the arachidonic acid binding pockets of COX-2