Tag: pubmed

Turk Arch Pediatr. 2021 Jul 1;56(4):332-338. doi: 10.5152/TurkArchPediatr.2021.20068. eCollection 2021 Jul.

ABSTRACT

OBJECTIVE: Cardiac catheterization remains a major source of radiation exposure for patients with congenital heart disease. This study reports the magnitude of radiation with a 3.75 frame per second (fps) pulse fluoroscopy rate and compares the reduction with the previous 15 fps protocol during cardiac catheterization for pediatric and adult congenital heart disease.

MATERIAL AND METHODS: All diagnostic and interventional cardiac catheterization procedures from a single tertiary center were analyzed from January 1, 2014 to December 31, 2015, one year before and after implementing lower starting pulse fluoroscopy rates. The radiation dose was quantified as air kerma dose (mGy) and dose-area product (DAP; µGy/m²). Radiation exposure was analyzed for diagnostic and interventional procedures; the diagnostic group was subdivided into cyanotic and acyanotic patients, whereas the interventional group was subdivided according to the most common indications.

RESULTS: A total of 786 procedures were analyzed. The median fluoroscopy times and contrast amounts did not show a statistically significant difference between both periods (487 vs. 456 seconds and 42.5 vs. 45.3 cm³). The median air kerma for all procedures showed an 88% reduction after implementing lower pulse fluoroscopy rates (340-41 mGy). The doses were reduced significantly for diagnostic and interventional angiograms from 470 mGy and 162 mGy to 40 mGy and154 mGy. Among all patient groups, the most striking decrease was observed in the diagnostic procedures we use, of which fluoroscopy is more prominent than cine angiography.

CONCLUSION: We claim that novel radiation dose reduction protocols could be easily applied without increasing fluoroscopy time or losing image quality.

PMID:35005727 | PMC:PMC8655960 | DOI:10.5152/TurkArchPediatr.2021.20068

MedComm (2020). 2021 Dec 31;3(1):1-12. doi: 10.1002/mco2.109. eCollection 2022 Jan.

ABSTRACT

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sweeping the world, effective and affordable vaccines are in urgent need. A reliable system for the assessment of SARS-CoV-2 vaccines would boost the development of vaccines and reduce the research cost. We constructed a logistic regression model and analyzed the relationship between antibody (Ab) level and efficacy of different vaccine types. The relationship between assessment dates and Ab levels was depicted by plotting the mean of Ab levels evolved over time and a fitted cubic polynomial model. Anti-spike immunoglobulin G (IgG) could best estimate the vaccine efficacy (VE) (adjusted R ²= 0.731) and neutralizing Ab to live SARS-CoV-2 also explained a fine relationship (adjusted R ² = 0.577). Neutralizing Abs to live SARS-CoV-2 in inactivated virus vaccines reached a peak during days 40-60, and their receptor-binding domain (RBD)-IgG peaked during days 40-50. For messenger RNA (mRNA) and viral vector vaccines, their neutralizing Ab to live SARS-CoV-2 peaked later than day 40, and for RBD-IgG during days 30-50. For mRNA and viral vector vaccines, their peak time of Abs was later than that in inactivated virus vaccines. RBD-IgG peaked earlier than Ab to live SARS-CoV-2. Anti-spike IgG and Ab to live SARS-CoV-2 may be good immune markers for VE assessment.

PMID:35005708 | PMC:PMC8719528 | DOI:10.1002/mco2.109

Front Digit Health. 2021 Dec 23;3:761128. doi: 10.3389/fdgth.2021.761128. eCollection 2021.

ABSTRACT

Introduction: Understanding the extent and cause of high neonatal deaths rates in Sub-Saharan Africa is a challenge, especially in the presence of poor-quality and inaccurate data. The NeoTree digital data capture and quality improvement system has been live at Kamuzu Central Hospital, Neonatal Unit, Malawi, since April 2019. Objective: To describe patterns of admissions and outcomes in babies admitted to a Malawian neonatal unit over a 1-year period via a prototype data dashboard. Methods: Data were collected prospectively at the point of care, using the NeoTree app, which includes digital admission and outcome forms containing embedded clinical decision and management support and education in newborn care according to evidence-based guidelines. Data were exported and visualised using Microsoft Power BI. Descriptive and inferential analysis statistics were executed using R. Results: Data collected via NeoTree were 100% for all mandatory fields and, on average, 96% complete across all fields. Coverage of admissions, discharges, and deaths was 97, 99, and 91%, respectively, when compared with the ward logbook. A total of 2,732 neonates were admitted and 2,413 (88.3%) had an electronic outcome recorded: 1,899 (78.7%) were discharged alive, 12 (0.5%) were referred to another hospital, 10 (0.4%) absconded, and 492 (20%) babies died. The overall case fatality rate (CFR) was 204/1,000 admissions. Babies who were premature, low birth weight, out born, or hypothermic on admission, and had significantly higher CFR. Lead causes of death were prematurity with respiratory distress (n = 252, 51%), neonatal sepsis (n = 116, 23%), and neonatal encephalopathy (n = 80, 16%). The most common perceived modifiable factors in death were inadequate monitoring of vital signs and suboptimal management of sepsis. Two hundred and two (8.1%) neonates were HIV exposed, of whom a third [59 (29.2%)] did not receive prophylactic nevirapine, hence vulnerable to vertical infection. Conclusion: A digital data capture and quality improvement system was successfully deployed in a low resource neonatal unit with high (1 in 5) mortality rates providing and visualising reliable, timely, and complete data describing patterns, risk factors, and modifiable causes of newborn mortality. Key targets for quality improvement were identified. Future research will explore the impact of the NeoTree on quality of care and newborn survival.

PMID:35005696 | PMC:PMC8732863 | DOI:10.3389/fdgth.2021.761128

J Am Coll Emerg Physicians Open. 2021 Dec 29;2(6):e12619. doi: 10.1002/emp2.12619. eCollection 2021 Dec.

ABSTRACT

OBJECTIVE: Emergency clinicians face elevated rates of burnout that result in poor outcomes for clinicians, patients, and health systems. The objective of this single-arm pilot study was to evaluate the feasibility of a Transcendental Meditation (TM) intervention for emergency clinicians during the coronavirus disease 2019 (COVID-19) pandemic and to explore the potential effectiveness in improving burnout, sleep, and psychological health.

METHODS: Emergency clinicians (physicians, nurses, and physician-assistants) from 2 urban hospitals were recruited to participate in TM instruction (8 individual or group in-person and remote sessions) for 3 months. Session attendance was the primary feasibility outcome (prespecified as attending 6/8 sessions), and burnout was the primary clinical outcome. Participant-reported measures of feasibility and validated measures of burnout, depression, anxiety, sleep disturbance, and stress were collected at baseline and the 1-month and 3-month follow-ups. Descriptive statistics and linear mixed-effects models were used.

RESULTS: Of the 14 physicians (46%), 7 nurses (22%), and 10 physician-assistants (32%) who participated, 61% were female (n = 19/32). TM training and at-home meditation practice was feasible for clinicians as 90.6% (n = 29/32) attended 6/8 training sessions and 80.6% self-reported meditating at least once a day on average. Participants demonstrated significant reductions in burnout (P < .05; effect sizes, Cohen’s d = 0.43-0.45) and in symptoms of depression, anxiety, stress, and sleep disturbance (P values < .001; Cohen’s d = 0.70-0.87).

CONCLUSION: TM training was feasible for emergency clinicians during the COVID-19 pandemic and led to significant reductions in burnout and psychological symptoms. TM is a safe and effective meditation tool to improve clinicians’ well-being.

PMID:35005707 | PMC:PMC8716568 | DOI:10.1002/emp2.12619

Front Digit Health. 2021 Dec 23;3:637944. doi: 10.3389/fdgth.2021.637944. eCollection 2021.

ABSTRACT

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.

PMID:35005694 | PMC:PMC8734592 | DOI:10.3389/fdgth.2021.637944

Lancet Reg Health West Pac. 2022 Mar;20:100362. doi: 10.1016/j.lanwpc.2021.100362. Epub 2022 Jan 1.

ABSTRACT

BACKGROUND: In early 2020, non-pharmaceutical interventions (NPIs) were implemented in China to reduce and contain the coronavirus disease 2019 (COVID-19) transmission. These NPIs might have also reduced the incidence of hand, foot, and mouth disease (HFMD).

METHODS: The weekly numbers of HFMD cases and meteorological factors in 31 provincial capital cities and municipalities in mainland China were obtained from Chinese Center for Disease Control and Prevention (CCDC) and National Meteorological Information Center of China from 2016 to 2020. The NPI data were collected from local CDCs. The incidence rate ratios (IRRs) were calculated for the entire year of 2020, and for January-July 2020 and August-December 2020. The expected case numbers were estimated using seasonal autoregressive integrated moving average models. The relationships between kindergarten closures and incidence of HFMD were quantified using a generalized additive model. The estimated associations from all cities were pooled using a multivariate meta-regression model.

FINDINGS: Stringent NPIs were widely implemented for COVID-19 control from January to July 2020, and the IRRs for HFMD were less than 1 in all 31 cities, and less than 0·1 for 23 cities. Overall, the proportion of HFMD cases reduced by 52·9% (95% CI: 49·3-55·5%) after the implementation of kindergarten closures in 2020, and this effect was generally consistent across subgroups.

INTERPRETATION: The decrease in HFMD incidence was strongly associated with the NPIs for COVID-19. HFMD epidemic peaks were either absent or delayed, and the final epidemic size was reduced. Kindergarten closure is an intervention to prevent HFMD outbreaks.

FUNDING: This research was supported by the National Natural Science Foundation of China (81973102 & 81773487), Public Health Talents Training Program of Shanghai Municipality (GWV-10.2-XD21), the Shanghai New Three-year Action Plan for Public Health (GWV-10.1-XK16), the Major Project of Scientific and Technical Winter Olympics from National Key Research and Development Program of China (2021YFF0306000), 13th Five-Year National Science and Technology Major Project for Infectious Diseases (2018ZX10725-509) and Key projects of the PLA logistics Scientific research Program (BHJ17J013).

PMID:35005671 | PMC:PMC8720138 | DOI:10.1016/j.lanwpc.2021.100362

Cell Rep Methods. 2021 Nov 16;1(8):100120. doi: 10.1016/j.crmeth.2021.100120. Epub 2021 Nov 22.

ABSTRACT

To estimate a study design’s power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observed in single-cell data. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.

PMID:35005693 | PMC:PMC8740883 | DOI:10.1016/j.crmeth.2021.100120

Lancet Reg Health West Pac. 2022 Feb;19:100347. doi: 10.1016/j.lanwpc.2021.100347. Epub 2021 Dec 31.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has had widespread adverse collateral effects on health care delivery for non-COVID-19 disease conditions. Paediatric oncology care is reliant on prompt testing and diagnosis and on timely and coordinated multimodal treatment, all of which have been impacted by the pandemic. This study aimed to quantify the initial and enduring effects of the COVID-19 pandemic on the utilization of paediatric cancer care and to examine whether the pandemic differentially impacted specific demographic groups.

METHOD: We performed an interrupted time series analysis using negative binomial regression to estimate the change in the monthly admissions for paediatric cancer patients (Age 0-17) associated with the COVID-19 pandemic and subsequent lockdown policies. We obtained data from deidentified individual electronic medical records of paediatric cancer inpatients admitted between January 1, 2015 and May 31, 2021 to a tertiary hospital that provides general and specialized healthcare services to an estimated population of 8.4 million in Jining China. Relative risk (RR) estimates representing monthly admissions compared with expected admissions had the pandemic not occurred were derived. The number of inpatient admissions lost due to the pandemic were estimated.

FINDINGS: The overall denominator for the paediatric population was 1 858 209 individuals in January 2015, which increased to 2 043 803 by May 2021. In total, there were 4 901 admissions for paediatric cancer during the study period, including 1 479 (30%) since February 2020 when the lockdown was implemented. A 33% reduction (95% CI: -43% to -22%) in admissions was observed in February 2020, with the largest relative reduction (-48%, 95% CI: -64% to -24%) among first-time admissions and admissions for patients from rural districts (-46%, 95% CI: -55% to -36%). Admissions quickly rebounded in March 2020 when many government-imposed mobility restrictions were lifted, and continued to resume gradually over time since April 2020, leading to a full recovery as of November 2020. However, the recovery for first-time admissions, and among female patients, younger patients (<5 years) and patients from rural districts was slower over time and incomplete (first-time admissions and rural patients) as of January 2021.

INTERPRETATION: The COVID-19 pandemic has had substantial impact on the timely utilization of paediatric oncology services in China, particularly in the early stage of the first wave. Importantly, some population groups were disproportionately affected and the recovery of admissions among those subgroups has been slow and incomplete, warranting targeted approaches to address potentially exacerbated gender and socio-economic inequalities in access to healthcare resources.

PMID:35005670 | PMC:PMC8718443 | DOI:10.1016/j.lanwpc.2021.100347

Curr Res Microb Sci. 2021 Dec 20;3:100093. doi: 10.1016/j.crmicr.2021.100093. eCollection 2022.

ABSTRACT

Analysis of Escherichia coli taxonomy has expanded into a species-complex with the identification of divergent cryptic clades. A key question is the evolutionary trajectory of these clades and their relationship to isolates of clinical or veterinary importance. Since they have some environmental association, we screened a collection of E. coli isolated from a long-term spring barley field trial for their presence. While most isolates clustered into the enteric-clade, four of them clustered into Clade-V, and one in Clade-IV. The Clade -V isolates shared >96% intra-clade average nucleotide sequence identity but <91% with other clades. Although pan-genomics analysis confirmed their taxonomy as Clade -V (E. marmotae), retrospective phylogroup PCR did not discriminate them correctly. Differences in metabolic and adherence gene alleles occurred in the Clade -V isolates compared to E. coli sensu scricto. They also encoded the bacteriophage phage-associated cyto-lethal distending toxin (CDT) and antimicrobial resistance (AMR) genes, including an ESBL, blaOXA-453. Thus, the isolate collection encompassed a genetic diversity, and included cryptic clade isolates that encode potential virulence factors. The analysis has determined the phylogenetic relationship of cryptic clade isolates with E. coli sensu scricto and indicates a potential for horizontal transfer of virulence factors.

PMID:35005658 | PMC:PMC8718834 | DOI:10.1016/j.crmicr.2021.100093

HGG Adv. 2022 Jan 1:100084. doi: 10.1016/j.xhgg.2021.100084. Online ahead of print.

ABSTRACT

COVID-19 vaccines are safe and highly effective, but some individuals experience unpleasant reactions to vaccination. As the majority of adults in the US have received a COVID-19 vaccine this year, there is an unprecedented opportunity to study the genetics of reactions to vaccination via surveys of individuals who are already part of genetic research studies. Here, we have queried 17,440 participants in the Helix DNA Discovery Project and Healthy Nevada Project about their reactions to COVID-19 vaccination. Our GWAS identifies an association between severe difficulties with daily routine after vaccination and HLA-A*03:01. This association was statistically significant only for those who received the Pfizer-BioNTech vaccine (BNT162b2; n=3,694; p=4.70E-11; OR=2.07 (95%CI 1.67-2.56)), and showed a smaller effect size in those who received the Moderna vaccine (mRNA-1273; n=3,610; p=0.005; OR=1.32 (95%CI 1.09-1.59)). In Pfizer-BioNTech recipients, HLA-A*03:01 was associated with a two-fold increase in risk of self-reported severe difficulties with daily routine following vaccination. The effect was consistent across ages, sexes, and whether the person had previously had a COVID-19 infection. The reactions experienced by HLA-A*03:01 carriers were driven by associations with chills, fever, fatigue, and in general feeling unwell.

PMID:35005651 | PMC:PMC8719913 | DOI:10.1016/j.xhgg.2021.100084