Prevalence of impostor phenomenon and burnout in a Singapore health system.

Introduction: Impostor phenomenon (IP) is a set of feelings encountered by individuals of being incompetent, despite experiencing successes. IP affects not only individuals on a personal level, but also organisations where the leadership diversity decreases due to employees' self-doubt. We aim to investigate the prevalence of IP and burnout among employees in the National University Health System (NUHS). Methods: All permanently employed full-time NUHS employees aged 21 years and above were invited to participate in this self-administered cross-sectional study between April 2021 and August 2021. Mass emails with the embedded study link were sent every 2-3 weeks to the employees' corporate email accounts. Results: In our study, 61% of our study respondents reported having IP experiences and 97% reported having burnout. The associations of IP with ethnicity and age group were significant. Post hoc tests, however, showed that the association was statistically significant only in the 21-29 years age group. Conclusion: We found that there was no statistical significance between gender and the Maslach Burnout Inventory (MBI) profile types. However, we found that IP was significantly associated with individuals in the 21-29 years age group. This could be because younger individuals who just entered workforce may feel uncomfortable with their newfound independence and responsibility. Workplace support, such as workshops, and emotional support were found to be useful in helping individuals cope with IP. Future studies could be done post coronavirus disease 2019 (COVID-19) pandemic among healthcare workers to have a larger sample size to determine true IP and burnout prevalence.

Effect of COVID-19 vaccination on semen parameters: A systematic review and meta-analysis.

The aim of this study was to investigate the effect of coronavirus disease 2019 (COVID-19) vaccination on semen parameters through systematic review and meta-analysis. PubMed, EMBASE, Web of Science, and Cochrane Library were comprehensively searched by June 2022. Studies were considered eligible if they compared semen parameters before and after COVID-19 vaccination or between vaccinated and unvaccinated men, with no restrictions on vaccine types or doses. The effect size was calculated as mean difference (MD) with 95% confidence interval (CI) using a random-effects model. Subgroup and sensitivity analyses were conducted to assess the sources of heterogeneity measured by the I2 statistic, with publication bias evaluated by Egger's test. Twelve cohort studies involving 914 participants fulfilled the inclusion criteria. In a comparison of vaccinated versus unvaccinated group, the pooled data revealed no significant differences in semen volume (MD = 0.18 ml, 95% CI -0.02 to 0.38), sperm concentration (MD = 1.16 million/ml, 95% CI -1.34 to 3.66), total sperm motility (MD = -0.14%, 95% CI -2.84 to 2.56), progressive sperm motility (MD = -1.06%, 95% CI -2.88 to 0.77), total sperm count (MD = 5.92 million, 95% CI -10.22 to 22.05), total motile sperm count (MD = 2.18 million, 95% CI -1.28 to 5.63), total progressively motile sperm count (MD = -3.87 million, 95% CI -13.16 to 5.43), and sperm morphology (MD = 0.07%, 95% CI -0.84 to 0.97). The results also remained similar across messenger ribonucleic acid, viral-vector, and inactivated COVID-19 vaccines. Sensitivity analysis identified two individual studies that contributed to heterogeneity, while the effect size was not materially altered. No obvious publication bias was detected among included studies. Our finding suggested that COVID-19 vaccination had no detrimental impact on semen quality, which could be potentially helpful to reduce male vaccine hesitancy and increase vaccination coverage.

Characterization of oral and gut microbiome and plasma metabolomics in COVID-19 patients after 1-year follow-up.

BACKGROUND: Due to the outbreak and rapid spread of coronavirus disease 2019 (COVID-19), more than 160 million patients have become convalescents worldwide to date. Significant alterations have occurred in the gut and oral microbiome and metabonomics of patients with COVID-19. However, it is unknown whether their characteristics return to normal after the 1-year recovery. METHODS: We recruited 35 confirmed patients to provide specimens at discharge and one year later, as well as 160 healthy controls. A total of 497 samples were prospectively collected, including 219 tongue-coating, 129 stool and 149 plasma samples. Tongue-coating and stool samples were subjected to 16S rRNA sequencing, and plasma samples were subjected to untargeted metabolomics testing. RESULTS: The oral and gut microbiome and metabolomics characteristics of the 1-year convalescents were restored to a large extent but did not completely return to normal. In the recovery process, the microbial diversity gradually increased. Butyric acid-producing microbes and Bifidobacterium gradually increased, whereas lipopolysaccharide-producing microbes gradually decreased. In addition, sphingosine-1-phosphate, which is closely related to the inflammatory factor storm of COVID-19, increased significantly during the recovery process. Moreover, the predictive models established based on the microbiome and metabolites of patients at the time of discharge reached high efficacy in predicting their neutralizing antibody levels one year later. CONCLUSIONS: This study is the first to characterize the oral and gut microbiome and metabonomics in 1-year convalescents of COVID-19. The key microbiome and metabolites in the process of recovery were identified, and provided new treatment ideas for accelerating recovery. And the predictive models based on the microbiome and metabolomics afford new insights for predicting the recovery situation which benefited affected individuals and healthcare.

Inhibition of SARS-CoV-2 pseudovirus invasion by ACE2 protecting and Spike neutralizing peptides: An alternative approach to COVID19 prevention and therapy.

SARS-CoV-2 invades host cells mainly through the interaction of its spike-protein with host cell membrane ACE2. Various antibodies targeting S-protein have been developed to combat COVID-19 pandemic; however, the potential risk of antibody-dependent enhancement and novel spike mutants-induced neutralization loss or antibody resistance still remain. Alternative preventative agents or therapeutics are still urgently needed. In this study, we designed series of peptides with either ACE2 protecting or Spike-protein neutralizing activities. Molecular docking predicted that, among these peptides, ACE2 protecting peptide AYp28 and Spike-protein neutralizing peptide AYn1 showed strongest intermolecular interaction to ACE2 and Spike-protein, respectively, which were further confirmed by both cell- and non-cell-based in vitro assays. In addition, both peptides inhibited the invasion of pseudotype SARS-CoV-2 into HEK293T/hACE2 cells, either alone or in combination. Moreover, the intranasal administration of AYp28 could partially block pseudovirus invasion in hACE2 transgenic mice. Much more importantly, no significant toxicity was observed in peptides-treated cells. AYp28 showed no impacts on ACE2 function. Taken together, the data from our present study predicted promising preventative and therapeutic values of peptides against COVID-19, and may prove the concept that cocktail containing ACE2 protecting peptides and spike neutralizing peptides could serve as a safe and effective approach for SARS-CoV-2 prevention and therapy.

Deep Learning-Based COVID-19 Pneumonia Classification Using Chest CT Images: Model Generalizability.

Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19-positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We collected one dataset at UT Southwestern (UTSW) and three external datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran), and MosMedData (Russia). We divided the data into two classes: COVID-19-positive and COVID-19-negative patients. We trained nine identical DL-based classification models by using combinations of datasets with a 72% train, 8% validation, and 20% test data split. The models trained on a single dataset achieved accuracy/area under the receiver operating characteristic curve (AUC) values of 0.87/0.826 (UTSW), 0.97/0.988 (CC-CCCI), and 0.86/0.873 (COVID-CTset) when evaluated on their own dataset. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, the performance dropped close to an AUC of 0.5 (random guess) for all models when evaluated on a different dataset outside of its training datasets. Including MosMedData, which only contained positive labels, into the training datasets did not necessarily help the performance of other datasets. Multiple factors likely contributed to these results, such as patient demographics and differences in image acquisition or reconstruction, causing a data shift among different study cohorts.

Making a Traditional Spine Surgery Clinic Telemedicine-Ready in the "New Normal" of Coronavirus Disease 2019.

STUDY DESIGN: An original article describing a comprehensive methodology for making a traditional spine surgery clinic telemedicineready in terms of logistical considerations and workflow. PURPOSE: The aim of this study is to promote the use of telemedicine via videoconferencing to reduce human exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and reduce the risk of coronavirus disease 2019 (COVID-19) transmission at outpatient clinics. OVERVIEW OF LITERATURE: The COVID-19 pandemic is the biggest healthcare crisis in the 21st century. Until a vaccine is developed or herd immunity against SARS-CoV-2 is achieved, social distancing to avoid crowding is an important strategy to reduce disease transmission and resurgence. Telemedicine has already been applied in the field of orthopedics with encouraging results. METHODS: We reviewed the evidence behind telemedicine and described our clinical protocol, patient selection criteria, and workflow for telemedicine. We discussed a simple methodology to convert pre-existing traditional clinic resources into telemedicine tools, along with future challenges. RESULTS: Our methodology was successfully and easily applied in our clinical practice, with a streamlined workflow allowing our spine surgery service to implement telemedicine as a consultation modality in line with the national recommendations of social distancing. CONCLUSIONS: Telemedicine was well incorporated into our outpatient practice using the above workflow. We believe that the use of telemedicine via videoconferencing can become part of the new normal and a safe strategy for healthcare systems as both a medical and an economic countermeasure against COVID-19.

Outbreak of Mycoplasma pneumoniae at a military academy.

In 2019, an outbreak of Mycoplasma pneumoniae occurred at a military academy in China. The attack rate (10.08%,60/595) was significantly different among the units. High-intensity training and crowded environments to which cadets are exposed are the high risk factors for the outbreak of M. pneumoniae. In-time prevention and control measures effectively controlled the spread of the epidemic.

A novel hospital capacity versus clinical justification triage score (CCTS) for prioritization of spinal surgeries in the "new normal state" of the COVID-19 pandemic.

INTRODUCTION: During the Coronavirus disease 2019 outbreak, while healthcare systems and hospitals are diverting their resources to combat the pandemic, patients who require spinal surgeries continue to accumulate. The aim of this study is to describe a novel hospital capacity versus clinical justification triage score (CCTS) to prioritize patients who require surgery during the "new normal state" of the COVID-19 pandemic. METHODOLOGY: A consensus study using the Delphi technique was carried out among clinicians from the Orthopaedic Surgery, Neurosurgery, and Anaesthesia departments. Three rounds of consensus were carried out via survey and Webinar discussions. RESULTS: A 50-points score system consisting of 4 domains with 4 subdomains was formed. The CCTS were categorized into the hospital capacity, patient factors, disease severity, and surgery complexity domains. A score between 30 and 50 points indicated that the proposed operation should proceed without delay. A score of less than 20 indicates that the proposed operation should be postponed. A score between 20 and 29 indicates that the surgery falls within a grey area where further discussion should be undertaken to make a joint justification for approval of surgery. CONCLUSION: This study is a proof of concept for the novel CCTS scoring system to prioritize surgeries to meet the rapidly changing demands of the COVID-19 pandemic. It offers a simple and objective method to stratify patients who require surgery and allows these complex and difficult decisions to be unbiased and made transparently among surgeons and hospital administrators.

Considerations for Group Testing: A Practical Approach for the Clinical Laboratory.

Group testing, also known as pooled sample testing, was first proposed by Robert Dorfman in 1943. While sample pooling has been widely practiced in blood-banking, it is traditionally seen as anathema for clinical laboratories. However, the ongoing COVID-19 pandemic has re-ignited interest for group testing among clinical laboratories to mitigate supply shortages. We propose five criteria to assess the suitability of an analyte for pooled sample testing in general and outline a practical approach that a clinical laboratory may use to implement pooled testing for SARS-CoV-2 PCR testing. The five criteria we propose are: (1) the analyte concentrations in the diseased persons should be at least one order of magnitude (10 times) higher than in healthy persons; (2) sample dilution should not overly reduce clinical sensitivity; (3) the current prevalence must be sufficiently low for the number of samples pooled for the specific protocol; (4) there is no requirement for a fast turnaround time; and (5) there is an imperative need for resource rationing to maximise public health outcomes. The five key steps we suggest for a successful implementation are: (1) determination of when pooling takes place (pre-pre analytical, pre-analytical, analytical); (2) validation of the pooling protocol; (3) ensuring an adequate infrastructure and archival system; (4) configuration of the laboratory information system; and (5) staff training. While pool testing is not a panacea to overcome reagent shortage, it may allow broader access to testing but at the cost of reduction in sensitivity and increased turnaround time.

Protective effect of interleukin-36 receptor antagonist on liver injury induced by concanavalin A in mice.

OBJECTIVES: Interleukin-36 receptor antagonist (IL-36Ra) is a new member of the IL-1 family that exhibits anti-inflammatory activity in a variety of inflammatory and immune diseases. Our purpose was to determine the effect of IL-36Ra on liver injury in a mouse hepatitis model induced by concanavalin A (ConA). MATERIALS AND METHODS: Mice were treated with IL-36Ra DNA or pcDNA3.1 control plasmid using a hydrodynamic gene delivery approach. RESULTS: Our data reveal that treatment with IL-36Ra decreased liver inflammation and serum level of aminotransferases. Furthermore, IL-36Ra reduced ConA-induced pro-inflammatory cytokines (interferon-γ, tumor necrosis factor-α, and IL-17A) production when compared to control plasmid. CONCLUSION: Our results demonstrated that IL-36Ra is a critical protector against ConA-induced liver injury.