LC-MS/MS-PRM Quantification of IgG Glycoforms Using Stable Isotope Labeled IgG1 Fc Glycopeptide Standard.

Targeted quantification of proteins is a standard methodology with broad utility, but targeted quantification of glycoproteins has not reached its full potential. The lack of optimized workflows and isotopically labeled standards limits the acceptance of glycoproteomics quantification. In this work, we introduce an efficient and streamlined chemoenzymatic synthesis of a library of isotopically labeled glycopeptides of IgG1 which we use for quantification in an energy optimized LC-MS/MS-PRM workflow. Incorporation of the stable isotope labeled N-acetylglucosamine enables an efficient monitoring of all major fragment ions of the glycopeptides generated under the soft higher-energy C-trap dissociation (HCD) conditions, which reduces the coefficients of variability (CVs) of the quantification to 0.7-2.8%. Our results document, for the first time, that the workflow using a combination of stable isotope labeled standards with intrascan normalization enables quantification of the glycopeptides by an electron transfer dissociation (ETD) workflow, as well as the HCD workflow, with the highest sensitivity compared to traditional workflows. This was exemplified by a rapid quantification (13 min) of IgG1 Fc glycoforms from COVID-19 patients.

MEASURING THE EFFECT OF THE NUMBER OF COVID-19 INFECTIONS ON THE NUMBER OF RECOVERIES USING PANEL DATA

The purpose of this study is to measure the effect of the number of infections with the Covid-19 virus on the number of recovery cases using the method Panel data. The real data was relied on for seven sections (Erbil, Dohuk, Karkh, Rusafa, Diwaniyah, Karbala, Najaf) for a period of six months from January to June 2021. It was concluded that the best model that represents the data is the Pooled regression model (PRM). As well as the presence of a significant effect between the number of injuries and the number of recovery cases. © 2022 DAV College. All rights reserved.

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

A fast and sensitive absolute quantification assay for the detection of SARS-CoV-2 peptides using parallel reaction monitoring mass spectrometry.

The on-going SARS-CoV-2 (COVID-19) pandemic has called for an urgent need for rapid and high-throughput methods for mass testing and early detection, prevention as well as surveillance of the disease. We investigated whether targeted parallel reaction monitoring (PRM) quantification using high resolution Orbitrap instruments can provide the sensitivity and speed required for a high-throughput method that could be used for clinical diagnosis. We developed a high-throughput and sensitive PRM-MS assay that enables absolute quantification of SARS-CoV-2 nucleocapsid peptides with short turn-around times by using isotopically labelled synthetic SARS-CoV-2 concatenated peptides. We established a fast and high-throughput S-trap-based sample preparation method and utilized it for testing 25 positive and 25 negative heat-inactivated clinical nasopharyngeal swab samples for SARS-CoV-2 detection. The method was able to differentiate between negative and some of the positive patients with high viral load. Moreover, based on the absolute quantification calculations, our data show that patients with Ct values as low as 17.8 correspond to NCAP protein amounts of around 7.5 pmol in swab samples. The present high-throughput method could potentially be utilized in specialized clinics as an alternative tool for detection of SARS-CoV-2 but will require enrichment of viral proteins in order to compete with RT-qPCR.

QoS Enhancement of Hospital e-Healthcare Management using IoT

Hospital e-healthcare management is one of the important and challenging application domains of Internet of Things (IoT). During the pandemic period of Covid-19, government advices the people through media, only come to the hospital if any urgency and to take the opportunities of e-service from the hospital to control the infection. Internet plays an important role during this crucial period. The important problems are the network problem and effective way to handle e-healthcare service. Efficient management of e-healthcare possible by using IoT based 5G mobile technology. The latest technology improves the quality of e-healthcare service and efficient management of the application. Healthcare management depends on patient's satisfaction, service quality and customer experience etc. In this paper we proposed a model on Patient Relationship Management (PRM) which improves the quality of e-healthcare facilities by using the new technologies like RFID, IoT and 5G. Comparisons are shown between 3G/4G ICT based system and 5G ICT-RFID-IoT enabled system. The PRM parameters cost, accuracy and satisfaction are taken into consideration and how these parameters significantly perform better in healthcare sector with the advent of newer technologies in is main the focus of the paper. © 2021 IEEE.

A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples.

SARS-CoV-2, a novel human coronavirus, has created a global disease burden infecting > 100 million humans in just over a year. RT-PCR is currently the predominant method of diagnosing this viral infection although a variety of tests to detect viral antigens have also been developed. In this study, we adopted a SISCAPA-based enrichment approach using anti-peptide antibodies generated against peptides from the nucleocapsid protein of SARS-CoV-2. We developed a targeted workflow in which nasopharyngeal swab samples were digested followed by enrichment of viral peptides using the anti-peptide antibodies and targeted parallel reaction monitoring (PRM) analysis using a high-resolution mass spectrometer. This workflow was applied to 41 RT-PCR-confirmed clinical SARS-CoV-2 positive nasopharyngeal swab samples and 30 negative samples. The workflow employed was highly specific as none of the target peptides were detected in negative samples. Further, the detected peptides showed a positive correlation with the viral loads as measured by RT-PCR Ct values. The SISCAPA-based platform described in the current study can serve as an alternative method for SARS-CoV-2 viral detection and can also be applied for detecting other microbial pathogens directly from clinical samples.

Application of spectral library prediction for parallel reaction monitoring of viral peptides.

A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here, we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Viral peptides were chosen as an example, because virology is an area where in silico library generation could significantly improve PRM assay design. With both libraries a total of 1174 precursors were identified. Notably, compared to the DDA-derived library, we could identify 101 more precursors by using the Prosit-derived library. Additionally, we show that Prosit can be applied to predict tandem mass spectra of synthetic viral peptides with different collision energies. Finally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab demonstrating that both libraries are suited for peptide identification by PRM. Summarized, Prosit-derived viral spectral libraries predicted in silico can be used for PRM data analysis, making DDA analysis for library generation partially redundant in the future.

Telehealth in pediatric rehabilitation medicine: A survey of clinician reported utilization and experience.

PURPOSE: Telehealth services have been touted to improve access to specialty pediatric care. COVID-19 accelerated the adoption of telehealth across many medical specialties. The purpose of this study was to examine telehealth utilization and satisfaction among pediatric physiatrists. METHODS: Using Google Forms, a voluntary survey was created and administered to pediatric physiatrists. The survey collected information on practice setting, telehealth utilization, provider satisfaction, perceived satisfaction of patients and families, and the anticipated role of telehealth in pediatric rehabilitation going forward. RESULTS: Seventy-eight respondents completed the survey. There was a significant reported increase in telehealth utilization since COVID-19 from 14.5% to 97.4%. Eighty-two percent of participants reported feeling comfortable utilizing telehealth, 77% felt confident in the quality of the care provided, and 91% believed patients were satisfied with telehealth visits. Responses indicate that telehealth is expected to play a role in future pediatric physiatry and interest in telehealth continuing medical education is prevalent. Most pediatric physiatrists plan to continue or expand telehealth offerings after COVID-19. CONCLUSION: Telehealth adoption has been expedited by COVID-19. Physician interest in and satisfaction with telehealth is high. Patient and family perceptions, outcomes of care, and barriers to implementation limiting program expansion deserve further study.

Telemedicine for pediatric physiatry: How social distancing can bring physicians and families closer together.

The coronavirus (COVID-19) pandemic triggered wide scale implementation of telemedicine in the United States. The government response, Coronavirus Aid, Relief, and Economic Security (CARES) Act, permitted loosening of existing restrictions on telemedicine enabling its rapid incorporation into the delivery of medical care for children and adults. Prior to COVID-19, few pediatric physiatrists had opportunities to access high fidelity telemedicine platforms to provide health care for patients with special needs, mobility impairments, developmental delays, neuromuscular disorders or other complex medical conditions. This literature review will explore how telemedicine can optimize health care delivery options for pediatric physiatrists in various inpatient and outpatient settings such as consultations, acute inpatient units, outpatient clinics and long-term care facilities. Detailed analysis of the current research in telemedicine applications as well as a critical review of the limitations and barriers for its use offers a plethora of opportunities for enhancement of continuity and coordination of care. Telemedicine may decrease healthcare disparities and increase access of care for children with special needs. Additional research is needed to assess the efficacy of telemedicine when addressing complex medical conditions in children.

Perspective on Proteomics for Virus Detection in Clinical Samples.

One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted.