ABSTRACT
Purpose: At the beginning of the pandemic, kidneys from SARS-CoV-2 (COVID) RT-PCR positive donors were not utilized for transplantation, due to the risk of viral transmission. With the advent of the COVID vaccines, and improved monoclonal antibody therapy we transplanted organs from COVID positive donors irrespective of disease severity. Method(s): We performed six kidney transplants from COVID RT-PCR positive donors. Potential donors were screened for the date of the first positive COVID RTPCR. Only donors whose test had been positive at least 10 days prior to donation on a nasopharyngeal swab or bronchoalveolar lavage were accepted. A cycle threshold (ct)of >= 35 cycles was used as a cut off for accepting kidneys, when results were available prior to donation. Disease severity was not considered in donor evaluation. Recipient selection was performed based on willingness to give informed consent for the use of such kidneys, prior vaccination with at least 2 doses of the COVID vaccine and negative RT-PCRs in the month prior to transplantation. Result(s): We successfully transplanted 6 recipients from 5 donors. While one of the kidneys was recovered locally, the remainder were imported as non mandatory nationally shared organs. Four donors suffered from ARDS secondary to COVID pneumonia. Two donors were on ECMO at the time of donation. Two of the 5 donors were DCD recoveries with warm ischemic times times of 22 and 28 minutes. Co-infections in the donors included Candida glabrata, Enterococcus faecalis, and Burkholderia Cepacia for which appropriate prophylaxis was used in the recipients. All donors had positive nasopharyngeal RT-PCRs. Three had positive bronchioloalveolar lavage RT-PCRs. One donor was RT-PCR negative at the time of donation. Three recipients were sensitized with a PRA of 48%, 96%and 100%. The mean cold ischemic time was 25 hours. The mean KDPI was 51%. The delayed graft function rate was 33%. There was no primary nonfunction, rejection, death or graft loss after median follow-up of 87 (30-250days). The mean recipient GFR was 43ml/min. Dual kidney transplants were performed in two recipients. None of the recipients developed a COVID infection. 5/6 recipients received monoclonal antibodies (casirivimab and imdevimab) immediately after reperfusion. One patient did not receive casirivimab and imdevimab as it was not yet available in our region. All 6 patients received Thymoglobulin induction. Conclusion(s): With careful selection of immunized recipients, clinical assessment of transmission risk, and the preemptive use of monoclonal antibodies post exposure , SARS-Cov-2 positive donor kidneys can be safely utilized for single or dual kidney transplantation, without an increased risk of viral transmission, rejection or graft loss.
ABSTRACT
Objective: To explore the Neurology residency applicant experience during the 2020-2021 cycle. Background: Due to the SARS-CoV2 pandemic, the 2020-2021 residency interviews were entirely virtual. We obtained post-recruitment input from applicants about their experience across the following domains: 1. Overall experience with virtual interviewing and preference for future cycles. 2. Technical issues and preferred platform. 3. Use of different online sources of program information. Design/Methods: After the NRMP match, a link to an anonymous online survey (IRB approved) through Google Forms was emailed to the 100 applicants who interviewed at our program in 2020-2021. Results: 45 out of 100 applicants completed the survey. 80% felt that interviewing virtually did not affect their experience. 64% interviewed at more programs than they initially anticipated. If given the choice, 55% want a combination of in-person and virtual in the future, 15.9% want to resume fully in-person interviews, and 30% want to continue entirely virtual interviews. 97% preferred Zoom™ as the interview platform. 27% were interrupted 1-2 times during their interviews due to connectivity issues. In terms of online outreach, applicants found the following to be either very or somewhat helpful;website (97.8%), YouTube videos (62.2%), Twitter (46.7%), and GME track information (48.9%). Facebook and Reddit were less helpful (17.8% and 26.7% respectively). Conclusions: Based on our results, applicants were able to transition to the virtual process well. They favor a hybrid of in person and virtual interviews in the future, most preferring Zoom ™ as the interview platform. An updated program website was the top choice for their source of program information. Given the continuation of virtual recruitment in 2021-22, programs should focus on how to improve their online resources.
ABSTRACT
With the advent of Coronavirus Disease 2019 (COVID-19), the world encountered an unprecedented health crisis due to the severe acute respiratory syndrome (SARS) pathogen. This impacted all of the sectors but more critically the transportation sector which required a strategy in the light of mobility trends using transportation modes and regions. We analyse a mobility prediction model for smart transportation by considering key indicators including data selection, processing and, integration of transportation modes, and data point normalisation in regional mobility. A Machine Learning (ML) driven classification has been performed to predict transportation modes efficiency and variations using driving, walking and transit. Additionally, regional mobility by considering Asia, Europe, Africa, Australasia, Middle-East, and America has also been analysed. In this regard, six ML algorithms have been applied for the precise assessment of transportation modes and regions. The initial experimental results demonstrate that the majority of the world's travelling dynamics have been contrastively shaped with the accuracy of 91.21% and 84.5% using Support Vector Machine (SVM) and Random Forest (RT) for different transportation modes and regions. This study will pave a new direction for the assessment of transportation modes affected by the pandemic to optimize economic benefits for smart transportation.
ABSTRACT
Objective: To assess level of satisfaction among medical students and faculty members regarding online teaching during COVID-19 pandemic. Methodology: This descriptive cross-sectional study was conducted at Fazaia Medical College and PAF Hospital, Islamabad and included 250 participants including 220 students of 4th and final year MBBS and 30 faculty members of clinical sciences by quota sampling. Views of participants regarding online medical teaching during COVID-19 pandemic were recorded. Results: Out of 220 students, 151 were fully satisfied with online teaching while out of 30 faculty members, 16 were fully satisfied. Satisfaction with theoretical component of online teaching was found in 100% students and faculty members. Whereas, 100 % students and faculty members were totally unsatisfied with clinical component of online teaching. Conclusion: The theoretical component can be covered nicely with online teaching. However, clinical and bedside teaching cannot be delivered effectively.
ABSTRACT
With the advent of Coronavirus Disease 2019 (COVID-19) throughout the world, safe transportation becomes critical while maintaining reasonable social distancing that requires a strategy in the mobility of daily travelers. Crowded train carriages, stations, and platforms are highly susceptible to spreading the disease, especially when infected travelers intermix with healthy travelers. Travelers-profiling is one of the essential interventions that railway network professionals rely on managing the disease outbreak while providing safe commute to staff and the public. In this plethora, a Machine Learning (ML) driven intelligent approach is proposed to manage daily train travelers that are in the age-group 16-59 years and over 60 years (vulnerable age-group) with the recommendations of certain times and routes of traveling, designated train carriages, stations, platforms, and special services using the London Underground and Overground (LUO) Network. LUO dataset has been compared with various ML algorithms to classify different agegroup travelers where Support Vector Machine (SVM) mobility prediction classification achieves up to 86.43% and 81.96% in age-group 16-59 years and over 60 years. © 2020 IEEE.