Identification of microbial community in the urban environment: The concordance between conventional culture and nanopore 16S rRNA sequencing

IntroductionMicrobes in the built environment have been implicated as a source of infectious diseases. Bacterial culture is the standard method for assessing the risk of exposure to pathogens in urban environments, but this method only accounts for <1% of the diversity of bacteria. Recently, full-length 16S rRNA gene analysis using nanopore sequencing has been applied for microbial evaluations, resulting in a rise in the development of long-read taxonomic tools for species-level classification. Regarding their comparative performance, there is, however, a lack of information.MethodsHere, we aim to analyze the concordance of the microbial community in the urban environment inferred by multiple taxonomic classifiers, including ARGpore2, Emu, Kraken2/Bracken and NanoCLUST, using our 16S-nanopore dataset generated by MegaBLAST, as well as assess their abilities to identify culturable species based on the conventional culture results.ResultsAccording to our results, NanoCLUST was preferred for 16S microbial profiling because it had a high concordance of dominant species and a similar microbial profile to MegaBLAST, whereas Kraken2/Bracken, which had similar clustering results as NanoCLUST, was also desirable. Second, for culturable species identification, Emu with the highest accuracy (81.2%) and F1 score (29%) for the detection of culturable species was suggested.DiscussionIn addition to generating datasets in complex communities for future benchmarking studies, our comprehensive evaluation of the taxonomic classifiers offers recommendations for ongoing microbial community research, particularly for complex communities using nanopore 16S rRNA sequencing

Efficacy, Usability, and Acceptability of a Chatbot for Promoting COVID-19 Vaccination in Unvaccinated or Booster-Hesitant Young Adults: Pre-Post Pilot Study

BackgroundCOVID-19 vaccines are highly effective in preventing severe disease and death but are underused. Interventions to address COVID-19 vaccine hesitancy are paramount to reducing the burden of COVID-19. ObjectiveWe aimed to evaluate the preliminary efficacy, usability, and acceptability of a chatbot for promoting COVID-19 vaccination and examine the factors associated with COVID-19 vaccine hesitancy. MethodsIn November 2021, we conducted a pre-post pilot study to evaluate “Vac Chat, Fact Check,” a web-based chatbot for promoting COVID-19 vaccination. We conducted a web-based survey (N=290) on COVID-19 vaccination at a university in Hong Kong. A subset of 46 participants who were either unvaccinated (n=22) or were vaccinated but hesitant to receive boosters (n=24) were selected and given access to the chatbot for a 7-day trial period. The chatbot provided information about COVID-19 vaccination (eg, efficacy and common side effects), debunked common myths about the vaccine, and included a decision aid for selecting vaccine platforms (inactivated and mRNA vaccines). The main efficacy outcome was changes in the COVID-19 Vaccine Hesitancy Scale (VHS) score (range 9-45) from preintervention (web-based survey) to postintervention (immediately posttrial). Other efficacy outcomes included changes in intention to vaccinate or receive boosters and willingness to encourage others to vaccinate on a scale from 1 (not at all) to 5 (very). Usability was assessed by the System Usability Scale (range 0-100). Linear regression was used to examine the factors associated with COVID-19 VHS scores in all survey respondents. ResultsThe mean (SD) age of all survey respondents was 21.4 (6.3) years, and 61% (177/290) of respondents were female. Higher eHealth literacy (B=–0.26; P<.001) and perceived danger of COVID-19 (B=–0.17; P=.009) were associated with lower COVID-19 vaccine hesitancy, adjusting for age, sex, chronic disease status, previous flu vaccination, and perceived susceptibility to COVID-19. The main efficacy outcome of COVID-19 VHS score significantly decreased from 28.6 (preintervention) to 24.5 (postintervention), with a mean difference of –4.2 (P<.001) and an effect size (Cohen d) of 0.94. The intention to vaccinate increased from 3.0 to 3.9 (P<.001) in unvaccinated participants, whereas the intention to receive boosters increased from 1.9 to 2.8 (P<.001) in booster-hesitant participants. Willingness to encourage others to vaccinate increased from 2.7 to 3.0 (P=.04). At postintervention, the median (IQR) System Usability Scale score was 72.5 (65-77.5), whereas the median (IQR) recommendation score was 7 (6-8) on a scale from 0 to 10. In a post hoc 4-month follow-up, 82% (18/22) of initially unvaccinated participants reported having received the COVID-19 vaccine, whereas 29% (7/24) of booster-hesitant participants received boosters. ConclusionsThis pilot study provided initial evidence to support the efficacy, usability, and acceptability of a chatbot for promoting COVID-19 vaccination in young adults who were unvaccinated or booster-hesitant

Table_1_Identification of microbial community in the urban environment: The concordance between conventional culture and nanopore 16S rRNA sequencing.xlsx

IntroductionMicrobes in the built environment have been implicated as a source of infectious diseases. Bacterial culture is the standard method for assessing the risk of exposure to pathogens in urban environments, but this method only accounts for MethodsHere, we aim to analyze the concordance of the microbial community in the urban environment inferred by multiple taxonomic classifiers, including ARGpore2, Emu, Kraken2/Bracken and NanoCLUST, using our 16S-nanopore dataset generated by MegaBLAST, as well as assess their abilities to identify culturable species based on the conventional culture results.ResultsAccording to our results, NanoCLUST was preferred for 16S microbial profiling because it had a high concordance of dominant species and a similar microbial profile to MegaBLAST, whereas Kraken2/Bracken, which had similar clustering results as NanoCLUST, was also desirable. Second, for culturable species identification, Emu with the highest accuracy (81.2%) and F1 score (29%) for the detection of culturable species was suggested.DiscussionIn addition to generating datasets in complex communities for future benchmarking studies, our comprehensive evaluation of the taxonomic classifiers offers recommendations for ongoing microbial community research, particularly for complex communities using nanopore 16S rRNA sequencing.</p

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting

ObjectivesTo develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.MethodsPatients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).ResultsA total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV.ConclusionTwo simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation

Mitochondrial diseases in Hong Kong: prevalence, clinical characteristics and genetic landscape

Abstract Objective To determine the prevalence of mitochondrial diseases (MD) in Hong Kong (HK) and to evaluate the clinical characteristics and genetic landscape of MD patients in the region. Methods This study retrospectively reviewed the phenotypic and molecular characteristics of MD patients from participating public hospitals in HK between January 1985 to October 2020. Molecularly and/or enzymatically confirmed MD cases of any age were recruited via the Clinical Analysis and Reporting System (CDARS) using relevant keywords and/or International Classification of Disease (ICD) codes under the HK Hospital Authority or through the personal recollection of treating clinicians among the investigators. Results A total of 119 MD patients were recruited and analyzed in the study. The point prevalence of MD in HK was 1.02 in 100,000 people (95% confidence interval 0.81–1.28 in 100,000). 110 patients had molecularly proven MD and the other nine were diagnosed by OXPHOS enzymology analysis or mitochondrial DNA depletion analysis with unknown molecular basis. Pathogenic variants in the mitochondrial genome (72 patients) were more prevalent than those in the nuclear genome (38 patients) in our cohort. The most commonly involved organ system at disease onset was the neurological system, in which developmental delay, seizures or epilepsy, and stroke-like episodes were the most frequently reported presentations. The mortality rate in our cohort was 37%. Conclusion This study is a territory-wide overview of the clinical and genetic characteristics of MD patients in a Chinese population, providing the first available prevalence rate of MD in Hong Kong. The findings of this study aim to facilitate future in-depth evaluation of MD and lay the foundation to establish a local MD registry